From 2570e11501c84d570803f34790d8a401310588eb Mon Sep 17 00:00:00 2001 From: "Tuan Anh Nguyen Dang (Tadashi_Cin)" Date: Mon, 26 Aug 2024 08:50:37 +0700 Subject: [PATCH] feat: merge develop (#123) * Support hybrid vector retrieval * Enable figures and table reading in Azure DI * Retrieve with multi-modal * Fix mixing up table * Add txt loader * Add Anthropic Chat * Raising error when retrieving help file * Allow same filename for different people if private is True * Allow declaring extra LLM vendors * Show chunks on the File page * Allow elasticsearch to get more docs * Fix Cohere response (#86) * Fix Cohere response * Remove Adobe pdfservice from dependency kotaemon doesn't rely more pdfservice for its core functionality, and pdfservice uses very out-dated dependency that causes conflict. --------- Co-authored-by: trducng * Add confidence score (#87) * Save question answering data as a log file * Save the original information besides the rewritten info * Export Cohere relevance score as confidence score * Fix style check * Upgrade the confidence score appearance (#90) * Highlight the relevance score * Round relevance score. Get key from config instead of env * Cohere return all scores * Display relevance score for image * Remove columns and rows in Excel loader which contains all NaN (#91) * remove columns and rows which contains all NaN * back to multiple joiner options * Fix style --------- Co-authored-by: linhnguyen-cinnamon Co-authored-by: trducng * Track retriever state * Bump llama-index version 0.10 * feat/save-azuredi-mhtml-to-markdown (#93) * feat/save-azuredi-mhtml-to-markdown * fix: replace os.path to pathlib change theflow.settings * refactor: base on pre-commit * chore: move the func of saving content markdown above removed_spans --------- Co-authored-by: jacky0218 * fix: losing first chunk (#94) * fix: losing first chunk. * fix: update the method of preventing losing chunks --------- Co-authored-by: jacky0218 * fix: adding the base64 image in markdown (#95) * feat: more chunk info on UI * fix: error when reindexing files * refactor: allow more information exception trace when using gpt4v * feat: add excel reader that treats each worksheet as a document * Persist loader information when indexing file * feat: allow hiding unneeded setting panels * feat: allow specific timezone when creating conversation * feat: add more confidence score (#96) * Allow a list of rerankers * Export llm reranking score instead of filter with boolean * Get logprobs from LLMs * Rename cohere reranking score * Call 2 rerankers at once * Run QA pipeline for each chunk to get qa_score * Display more relevance scores * Define another LLMScoring instead of editing the original one * Export logprobs instead of probs * Call LLMScoring * Get qa_score only in the final answer * feat: replace text length with token in file list * ui: show index name instead of id in the settings * feat(ai): restrict the vision temperature * fix(ui): remove the misleading message about non-retrieved evidences * feat(ui): show the reasoning name and description in the reasoning setting page * feat(ui): show version on the main windows * feat(ui): show default llm name in the setting page * fix(conf): append the result of doc in llm_scoring (#97) * fix: constraint maximum number of images * feat(ui): allow filter file by name in file list page * Fix exceeding token length error for OpenAI embeddings by chunking then averaging (#99) * Average embeddings in case the text exceeds max size * Add docstring * fix: Allow empty string when calling embedding * fix: update trulens LLM ranking score for retrieval confidence, improve citation (#98) * Round when displaying not by default * Add LLMTrulens reranking model * Use llmtrulensscoring in pipeline * fix: update UI display for trulen score --------- Co-authored-by: taprosoft * feat: add question decomposition & few-shot rewrite pipeline (#89) * Create few-shot query-rewriting. Run and display the result in info_panel * Fix style check * Put the functions to separate modules * Add zero-shot question decomposition * Fix fewshot rewriting * Add default few-shot examples * Fix decompose question * Fix importing rewriting pipelines * fix: update decompose logic in fullQA pipeline --------- Co-authored-by: taprosoft * fix: add encoding utf-8 when save temporal markdown in vectorIndex (#101) * fix: improve retrieval pipeline and relevant score display (#102) * fix: improve retrieval pipeline by extending first round top_k with multiplier * fix: minor fix * feat: improve UI default settings and add quick switch option for pipeline * fix: improve agent logics (#103) * fix: improve agent progres display * fix: update retrieval logic * fix: UI display * fix: less verbose debug log * feat: add warning message for low confidence * fix: LLM scoring enabled by default * fix: minor update logics * fix: hotfix image citation * feat: update docx loader for handle merged table cells + handle zip file upload (#104) * feat: update docx loader for handle merged table cells * feat: handle zip file * refactor: pre-commit * fix: escape text in download UI * feat: optimize vector store query db (#105) * feat: optimize vector store query db * feat: add file_id to chroma metadatas * feat: remove unnecessary logs and update migrate script * feat: iterate through file index * fix: remove unused code --------- Co-authored-by: taprosoft * fix: add openai embedidng exponential back-off * fix: update import download_loader * refactor: codespell * fix: update some default settings * fix: update installation instruction * fix: default chunk length in simple QA * feat: add share converstation feature and enable retrieval history (#108) * feat: add share converstation feature and enable retrieval history * fix: update share conversation UI --------- Co-authored-by: taprosoft * fix: allow exponential backoff for failed OCR call (#109) * fix: update default prompt when no retrieval is used * fix: create embedding for long image chunks * fix: add exception handling for additional table retriever * fix: clean conversation & file selection UI * fix: elastic search with empty doc_ids * feat: add thumbnail PDF reader for quick multimodal QA * feat: add thumbnail handling logic in indexing * fix: UI text update * fix: PDF thumb loader page number logic * feat: add quick indexing pipeline and update UI * feat: add conv name suggestion * fix: minor UI change * feat: citation in thread * fix: add conv name suggestion in regen * chore: add assets for usage doc * chore: update usage doc * feat: pdf viewer (#110) * feat: update pdfviewer * feat: update missing files * fix: update rendering logic of infor panel * fix: improve thumbnail retrieval logic * fix: update PDF evidence rendering logic * fix: remove pdfjs built dist * fix: reduce thumbnail evidence count * chore: update gitignore * fix: add js event on chat msg select * fix: update css for viewer * fix: add env var for PDFJS prebuilt * fix: move language setting to reasoning utils --------- Co-authored-by: phv2312 Co-authored-by: trducng * feat: graph rag (#116) * fix: reload server when add/delete index * fix: rework indexing pipeline to be able to disable vectorstore and splitter if needed * feat: add graphRAG index with plot view * fix: update requirement for graphRAG and lighten unnecessary packages * feat: add knowledge network index (#118) * feat: add Knowledge Network index * fix: update reader mode setting for knet * fix: update init knet * fix: update collection name to index pipeline * fix: missing req --------- Co-authored-by: jeff52415 * fix: update info panel return for graphrag * fix: retriever setting graphrag * feat: local llm settings (#122) * feat: expose context length as reasoning setting to better fit local models * fix: update context length setting for agents * fix: rework threadpool llm call * fix: fix improve indexing logic * fix: fix improve UI * feat: add lancedb * fix: improve lancedb logic * feat: add lancedb vectorstore * fix: lighten requirement * fix: improve lanceDB vs * fix: improve UI * fix: openai retry * fix: update reqs * fix: update launch command * feat: update Dockerfile * feat: add plot history * fix: update default config * fix: remove verbose print * fix: update default setting * fix: update gradio plot return * fix: default gradio tmp * fix: improve lancedb docstore * fix: fix question decompose pipeline * feat: add multimodal reader in UI * fix: udpate docs * fix: update default settings & docker build * fix: update app startup * chore: update documentation * chore: update README * chore: update README --------- Co-authored-by: trducng * chore: update README * chore: update README --------- Co-authored-by: trducng Co-authored-by: cin-ace Co-authored-by: Linh Nguyen <70562198+linhnguyen-cinnamon@users.noreply.github.com> Co-authored-by: linhnguyen-cinnamon Co-authored-by: cin-jacky <101088014+jacky0218@users.noreply.github.com> Co-authored-by: jacky0218 Co-authored-by: kan_cin Co-authored-by: phv2312 Co-authored-by: jeff52415 --- .dockerignore | 13 + .env | 25 +- .gitignore | 1 + Dockerfile | 37 + README.md | 206 +- app.py | 23 +- docs/about.md | 3 + docs/images/info-panel-scores.png | Bin 0 -> 558176 bytes docs/images/models.png | Bin 0 -> 139496 bytes docs/images/pdf-viewer-setup.png | Bin 0 -> 38947 bytes docs/images/preview-graph.png | Bin 0 -> 295424 bytes docs/images/preview.png | Bin 0 -> 579582 bytes docs/pages/app/index/file.md | 12 +- docs/usage.md | 30 +- flowsettings.py | 169 +- libs/kotaemon/kotaemon/agents/react/agent.py | 33 +- libs/kotaemon/kotaemon/agents/rewoo/agent.py | 39 +- libs/kotaemon/kotaemon/agents/tools/google.py | 2 +- libs/kotaemon/kotaemon/agents/tools/llm.py | 8 +- libs/kotaemon/kotaemon/base/schema.py | 7 +- .../kotaemon/contribs/promptui/ui/chat.py | 4 +- .../kotaemon/contribs/promptui/ui/pipeline.py | 4 +- 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| 4 +- libs/kotaemon/pyproject.toml | 19 +- libs/kotaemon/tests/test_reader.py | 2 +- libs/kotaemon/tests/test_splitter.py | 2 +- libs/ktem/.gitignore | 1 + libs/ktem/ktem/app.py | 18 +- libs/ktem/ktem/assets/__init__.py | 6 + libs/ktem/ktem/assets/css/main.css | 65 + libs/ktem/ktem/assets/icons/delete.svg | 1 + libs/ktem/ktem/assets/icons/new.svg | 1 + libs/ktem/ktem/assets/icons/rename.svg | 1 + libs/ktem/ktem/assets/icons/sidebar.svg | 1 + libs/ktem/ktem/assets/js/main.js | 57 +- libs/ktem/ktem/assets/js/pdf_viewer.js | 99 + libs/ktem/ktem/assets/md/about.md | 3 + libs/ktem/ktem/assets/md/usage.md | 2 +- libs/ktem/ktem/db/base_models.py | 8 +- libs/ktem/ktem/embeddings/manager.py | 2 +- libs/ktem/ktem/embeddings/ui.py | 12 +- libs/ktem/ktem/index/file/base.py | 10 +- libs/ktem/ktem/index/file/graph/__init__.py | 3 + .../ktem/ktem/index/file/graph/graph_index.py | 36 + libs/ktem/ktem/index/file/graph/pipelines.py | 359 + libs/ktem/ktem/index/file/graph/visualize.py | 102 + 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+++++++++++++++++ .../prompt_optimization/rewrite_question.py | 37 + .../suggest_conversation_name.py | 36 + libs/ktem/ktem/reasoning/react.py | 27 +- libs/ktem/ktem/reasoning/rewoo.py | 66 +- libs/ktem/ktem/reasoning/simple.py | 848 +- libs/ktem/ktem/settings.py | 4 + libs/ktem/ktem/utils/__init__.py | 3 + libs/ktem/ktem/utils/conversation.py | 20 + libs/ktem/ktem/utils/lang.py | 1 + libs/ktem/ktem/utils/render.py | 175 + libs/ktem/pyproject.toml | 2 +- scripts/migrate/__init__.py | 0 scripts/migrate/migrate_chroma_db.py | 191 + 121 files changed, 14748 insertions(+), 1063 deletions(-) create mode 100644 .dockerignore create mode 100644 Dockerfile create mode 100644 docs/images/info-panel-scores.png create mode 100644 docs/images/models.png create mode 100644 docs/images/pdf-viewer-setup.png create mode 100644 docs/images/preview-graph.png create mode 100644 docs/images/preview.png create mode 100644 libs/kotaemon/kotaemon/indices/rankings/llm_scoring.py create mode 100644 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mode 100644 libs/ktem/ktem/index/file/knet/knet_index.py create mode 100644 libs/ktem/ktem/index/file/knet/pipelines.py create mode 100644 libs/ktem/ktem/reasoning/prompt_optimization/__init__.py create mode 100644 libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py create mode 100644 libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py create mode 100644 libs/ktem/ktem/reasoning/prompt_optimization/rephrase_question_train.json create mode 100644 libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py create mode 100644 libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py create mode 100644 libs/ktem/ktem/utils/conversation.py create mode 100644 libs/ktem/ktem/utils/lang.py create mode 100644 scripts/migrate/__init__.py create mode 100644 scripts/migrate/migrate_chroma_db.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..0a57312 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,13 @@ +.github/ +.git/ +.mypy_cache/ +__pycache__/ +ktem_app_data/ +env/ +.pre-commit-config.yaml +.commitlintrc +.gitignore +.gitattributes +README.md +*.zip +*.sh diff --git a/.env b/.env index e033553..526100a 100644 --- a/.env +++ b/.env @@ -1,8 +1,8 @@ # settings for OpenAI OPENAI_API_BASE=https://api.openai.com/v1 -OPENAI_API_KEY= -OPENAI_CHAT_MODEL=gpt-3.5-turbo -OPENAI_EMBEDDINGS_MODEL=text-embedding-ada-002 +OPENAI_API_KEY=openai_key +OPENAI_CHAT_MODEL=gpt-4o +OPENAI_EMBEDDINGS_MODEL=text-embedding-3-small # settings for Azure OpenAI AZURE_OPENAI_ENDPOINT= @@ -15,4 +15,21 @@ AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=text-embedding-ada-002 COHERE_API_KEY= # settings for local models -LOCAL_MODEL= +LOCAL_MODEL=llama3.1:8b +LOCAL_MODEL_EMBEDDINGS=nomic-embed-text + +# settings for GraphRAG +GRAPHRAG_API_KEY=openai_key +GRAPHRAG_LLM_MODEL=gpt-4o-mini +GRAPHRAG_EMBEDDING_MODEL=text-embedding-3-small + +# settings for Azure DI +AZURE_DI_ENDPOINT= +AZURE_DI_CREDENTIAL= + +# settings for Adobe API +PDF_SERVICES_CLIENT_ID= +PDF_SERVICES_CLIENT_SECRET= + +# settings for PDF.js +PDFJS_VERSION_DIST="pdfjs-4.0.379-dist" diff --git a/.gitignore b/.gitignore index 789304e..ef2704a 100644 --- a/.gitignore +++ b/.gitignore @@ -471,3 +471,4 @@ doc_env/ # application data ktem_app_data/ +gradio_tmp/ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..497a0ad --- /dev/null +++ b/Dockerfile @@ -0,0 +1,37 @@ +# syntax=docker/dockerfile:1.0.0-experimental +FROM python:3.10-slim as base_image + +# for additional file parsers + +# tesseract-ocr \ +# tesseract-ocr-jpn \ +# libsm6 \ +# libxext6 \ +# ffmpeg \ + +RUN apt update -qqy \ + && apt install -y \ + ssh git \ + gcc g++ \ + poppler-utils \ + libpoppler-dev \ + && \ + apt-get clean && \ + apt-get autoremove + +ENV PYTHONDONTWRITEBYTECODE=1 +ENV PYTHONUNBUFFERED=1 +ENV PYTHONIOENCODING=UTF-8 + +WORKDIR /app + + +FROM base_image as dev + +COPY . /app +RUN --mount=type=ssh pip install -e "libs/kotaemon[all]" +RUN --mount=type=ssh pip install -e "libs/ktem" +RUN pip install graphrag future +RUN pip install "pdfservices-sdk@git+https://github.com/niallcm/pdfservices-python-sdk.git@bump-and-unfreeze-requirements" + +ENTRYPOINT ["gradio", "app.py"] diff --git a/README.md b/README.md index b7e124f..295875f 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ # kotaemon -An open-source tool for chatting with your documents. Built with both end users and +An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind. -https://github.com/Cinnamon/kotaemon/assets/25688648/815ecf68-3a02-4914-a0dd-3f8ec7e75cd9 +![Preview](docs/images/preview-graph.png) -[Source Code](https://github.com/Cinnamon/kotaemon) | -[Live Demo](https://huggingface.co/spaces/cin-model/kotaemon-public) +[Live Demo](https://huggingface.co/spaces/taprosoft/kotaemon) | +[Source Code](https://github.com/Cinnamon/kotaemon) [User Guide](https://cinnamon.github.io/kotaemon/) | [Developer Guide](https://cinnamon.github.io/kotaemon/development/) | @@ -14,20 +14,23 @@ https://github.com/Cinnamon/kotaemon/assets/25688648/815ecf68-3a02-4914-a0dd-3f8 [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-31013/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + +docker pull taprosoft/kotaemon:v1.0 [![built with Codeium](https://codeium.com/badges/main)](https://codeium.com) -This project would like to appeal to both end users who want to do QA on their -documents and developers who want to build their own QA pipeline. +## Introduction + +This project serves as a functional RAG UI for both end users who want to do QA on their +documents and developers who want to build their own RAG pipeline. - For end users: - - A local Question Answering UI for RAG-based QA. + - A clean & minimalistic UI for RAG-based QA. - Supports LLM API providers (OpenAI, AzureOpenAI, Cohere, etc) and local LLMs - (currently only GGUF format is supported via `llama-cpp-python`). - - Easy installation scripts, no environment setup required. + (via `ollama` and `llama-cpp-python`). + - Easy installation scripts. - For developers: - - A framework for building your own RAG-based QA pipeline. - - See your RAG pipeline in action with the provided UI (built with Gradio). - - Share your pipeline so that others can use it. + - A framework for building your own RAG-based document QA pipeline. + - Customize and see your RAG pipeline in action with the provided UI (built with Gradio). ```yml +----------------------------------------------------------------------------+ @@ -45,78 +48,128 @@ documents and developers who want to build their own QA pipeline. ``` This repository is under active development. Feedback, issues, and PRs are highly -appreciated. Your input is valuable as it helps us persuade our business guys to support -open source. +appreciated. + +## Key Features + +- **Host your own document QA (RAG) web-UI**. Support multi-user login, organize your files in private / public collections, collaborate and share your favorite chat with others. + +- **Organize your LLM & Embedding models**. Support both local LLMs & popular API providers (OpenAI, Azure, Ollama, Groq). + +- **Hybrid RAG pipeline**. Sane default RAG pipeline with hybrid (full-text & vector) retriever + re-ranking to ensure best retrieval quality. + +- **Multi-modal QA support**. Perform Question Answering on multiple documents with figures & tables support. Support multi-modal document parsing (selectable options on UI). + +- **Advance citations with document preview**. By default the system will provide detailed citations to ensure the correctness of LLM answers. View your citations (incl. relevant score) directly in the _in-browser PDF viewer_ with highlights. Warning when retrieval pipeline return low relevant articles. + +- **Support complex reasoning methods**. Use question decomposition to answer your complex / multi-hop question. Support agent-based reasoning with ReAct, ReWOO and other agents. + +- **Configurable settings UI**. You can adjust most important aspects of retrieval & generation process on the UI (incl. prompts). + +- **Extensible**. Being built on Gradio, you are free to customize / add any UI elements as you like. Also, we aim to support multiple strategies for document indexing & retrieval. `GraphRAG` indexing pipeline is provided as an example. + +![Preview](docs/images/preview.png) ## Installation ### For end users This document is intended for developers. If you just want to install and use the app as -it, please follow the [User Guide](https://cinnamon.github.io/kotaemon/). +it is, please follow the non-technical [User Guide](https://cinnamon.github.io/kotaemon/) (WIP). ### For developers -```shell -# Create a environment -python -m venv kotaemon-env +#### With Docker (recommended) -# Activate the environment -source kotaemon-env/bin/activate +- Use this command to launch the server -# Install the package -pip install git+https://github.com/Cinnamon/kotaemon.git +``` +docker run \ +-e GRADIO_SERVER_NAME=0.0.0.0 \ +-e GRADIO_SERVER_PORT=7860 \ +-p 7860:7860 -it --rm \ +taprosoft/kotaemon:v1.0 ``` -### For Contributors +Navigate to `http://localhost:7860/` to access the web UI. + +#### Without Docker + +- Clone and install required packages on a fresh python environment. ```shell -# Clone the repo -git clone git@github.com:Cinnamon/kotaemon.git +# optional (setup env) +conda create -n kotaemon python=3.10 +conda activate kotaemon -# Create a environment -python -m venv kotaemon-env - -# Activate the environment -source kotaemon-env/bin/activate +# clone this repo +git clone https://github.com/Cinnamon/kotaemon cd kotaemon -# Install the package in editable mode pip install -e "libs/kotaemon[all]" pip install -e "libs/ktem" -pip install -e "." - -# Setup pre-commit -pre-commit install ``` -## Creating your application +- View and edit your environment variables (API keys, end-points) in `.env`. -In order to create your own application, you need to prepare these files: +- (Optional) To enable in-browser PDF_JS viewer, download [PDF_JS_DIST](https://github.com/mozilla/pdf.js/releases/download/v4.0.379/pdfjs-4.0.379-dist.zip) and extract it to `libs/ktem/ktem/assets/prebuilt` + +pdf-setup + +- Start the web server: + +```shell +python app.py +``` + +The app will be automatically launched in your browser. + +Default username / password are: `admin` / `admin`. You can setup additional users directly on the UI. + +![Chat tab](https://raw.githubusercontent.com/Cinnamon/kotaemon/main/docs/images/chat-tab.png) + +## Customize your application + +By default, all application data are stored in `./ktem_app_data` folder. You can backup or copy this folder to move your installation to a new machine. + +For advance users or specific use-cases, you can customize those files: - `flowsettings.py` -- `app.py` -- `.env` (Optional) +- `.env` ### `flowsettings.py` This file contains the configuration of your application. You can use the example -[here](https://github.com/Cinnamon/kotaemon/blob/main/libs/ktem/flowsettings.py) as the +[here](flowsettings.py) as the starting point. -### `app.py` +
-This file is where you create your Gradio app object. This can be as simple as: +Notable settings -```python -from ktem.main import App +``` +# setup your preferred document store (with full-text search capabilities) +KH_DOCSTORE=(Elasticsearch | LanceDB | SimpleFileDocumentStore) -app = App() -demo = app.make() -demo.launch() +# setup your preferred vectorstore (for vector-based search) +KH_VECTORSTORE=(ChromaDB | LanceDB + +# Enable / disable multimodal QA +KH_REASONINGS_USE_MULTIMODAL=True + +# Setup your new reasoning pipeline or modify existing one. +KH_REASONINGS = [ + "ktem.reasoning.simple.FullQAPipeline", + "ktem.reasoning.simple.FullDecomposeQAPipeline", + "ktem.reasoning.react.ReactAgentPipeline", + "ktem.reasoning.rewoo.RewooAgentPipeline", +] +) ``` -### `.env` (Optional) +
+ +### `.env` This file provides another way to configure your models and credentials. @@ -159,18 +212,22 @@ AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=text-embedding-ada-002 #### Local models -- Pros: -- Privacy. Your documents will be stored and process locally. -- Choices. There are a wide range of LLMs in terms of size, domain, language to choose - from. -- Cost. It's free. -- Cons: -- Quality. Local models are much smaller and thus have lower generative quality than - paid APIs. -- Speed. Local models are deployed using your machine so the processing speed is - limited by your hardware. +##### Using ollama OpenAI compatible server -##### Find and download a LLM +Install [ollama](https://github.com/ollama/ollama) and start the application. + +Pull your model (e.g): + +``` +ollama pull llama3.1:8b +ollama pull nomic-embed-text +``` + +Set the model names on web UI and make it as default. + +![Models](docs/images/models.png) + +##### Using GGUF with llama-cpp-python You can search and download a LLM to be ran locally from the [Hugging Face Hub](https://huggingface.co/models). Currently, these model formats are supported: @@ -187,33 +244,26 @@ Here are some recommendations and their size in memory: - [Qwen1.5-1.8B-Chat-GGUF](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat-GGUF/resolve/main/qwen1_5-1_8b-chat-q8_0.gguf?download=true): around 2 GB -##### Enable local models +Add a new LlamaCpp model with the provided model name on the web uI. -To add a local model to the model pool, set the `LOCAL_MODEL` variable in the `.env` -file to the path of the model file. - -```shell -LOCAL_MODEL= -``` - -Here is how to get the full path of your model file: - -- On Windows 11: right click the file and select `Copy as Path`. -## Start your application +## Adding your own RAG pipeline -Simply run the following command: +#### Custom reasoning pipeline -```shell -python app.py -``` +First, check the default pipeline implementation in +[here](libs/ktem/ktem/reasoning/simple.py). You can make quick adjustment to how the default QA pipeline work. -The app will be automatically launched in your browser. +Next, if you feel comfortable adding new pipeline, add new `.py` implementation in `libs/ktem/ktem/reasoning/` and later include it in `flowssettings` to enable it on the UI. -![Chat tab](https://raw.githubusercontent.com/Cinnamon/kotaemon/main/docs/images/chat-tab.png) +#### Custom indexing pipeline -## Customize your application +Check sample implementation in `libs/ktem/ktem/index/file/graph` + +(more instruction WIP). + +## Developer guide Please refer to the [Developer Guide](https://cinnamon.github.io/kotaemon/development/) for more details. diff --git a/app.py b/app.py index 2ac7a1a..a432d17 100644 --- a/app.py +++ b/app.py @@ -1,5 +1,24 @@ -from ktem.main import App +import os + +from theflow.settings import settings as flowsettings + +KH_APP_DATA_DIR = getattr(flowsettings, "KH_APP_DATA_DIR", ".") +GRADIO_TEMP_DIR = os.getenv("GRADIO_TEMP_DIR", None) +# override GRADIO_TEMP_DIR if it's not set +if GRADIO_TEMP_DIR is None: + GRADIO_TEMP_DIR = os.path.join(KH_APP_DATA_DIR, "gradio_tmp") + os.environ["GRADIO_TEMP_DIR"] = GRADIO_TEMP_DIR + + +from ktem.main import App # noqa app = App() demo = app.make() -demo.queue().launch(favicon_path=app._favicon, inbrowser=True) +demo.queue().launch( + favicon_path=app._favicon, + inbrowser=True, + allowed_paths=[ + "libs/ktem/ktem/assets", + GRADIO_TEMP_DIR, + ], +) diff --git a/docs/about.md b/docs/about.md index 4792b87..fca0067 100644 --- a/docs/about.md +++ b/docs/about.md @@ -9,3 +9,6 @@ developers in mind. 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z?*47T&G)Z;m~(=aXe;=2_f7H3pMH&?`RQ~C-Wt*FMamU_r`*-hlI*WOY>4+>^!zn% z*uf2~{6D!1+@6xoO7cTKi(`5}E3=Kl5+a7)y)3iH!lgGc#xi(-fB^Pk_ z&&qeIetN>T{CRF_Mq1V+6i<)PgSS}4SWmr2wJt)d)JuI~{y1kd#!kxgx_KgmbdIT? zJ_l<26W31PQ(G-uPG&7XZhI8yf|zv%ZZ(1R5AZTX%##j>#pf_FEw$TSJ{l>Z*=%UF8;wXQ3GVM~ zpQwA5(~=IYOfBk=YW!e7?D%oax%yzFqLQxBK=av^lRojUwr zEMEO7isdeaOrCY9yCJ#Arh7+tK0Fw``7hiMYYF8(ietEYqUwy8aWMBj%PG?Y|KR-5 z@tJupjJp;_OfTlW+tx4tvj~n9-5C7d8*kX*R+ASO#=Zrgd6C6xRhz+A|9WQe4M~|C z4#Z&+i*Yz@E1zDJnSgU{-Th+wbcO*-IuWQugDAuIQ%z^qtB)O|M_19j*b7Z!C6cc Tm6~@lPhLIHG1jiqbdLHT`Z%{t literal 0 HcmV?d00001 diff --git a/docs/pages/app/index/file.md b/docs/pages/app/index/file.md index 2d479c5..e3bb344 100644 --- a/docs/pages/app/index/file.md +++ b/docs/pages/app/index/file.md @@ -107,9 +107,9 @@ string rather than a string. ## Software infrastructure -| Infra | Access | Schema | Ref | -| ---------------- | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- | -| SQL table Source | self.\_Source | - id (int): id of the source (auto)
- name (str): the name of the file
- path (str): the path of the file
- size (int): the file size in bytes
- text_length (int): the number of characters in the file (default 0)
- date_created (datetime): the time the file is created (auto) | This is SQLALchemy ORM class. Can consult | -| SQL table Index | self.\_Index | - id (int): id of the index entry (auto)
- source_id (int): the id of a file in the Source table
- target_id: the id of the segment in docstore or vector store
- relation_type (str): if the link is "document" or "vector" | This is SQLAlchemy ORM class | -| Vector store | self.\_VS | - self.\_VS.add: add the list of embeddings to the vector store (optionally associate metadata and ids)
- self.\_VS.delete: delete vector entries based on ids
- self.\_VS.query: get embeddings based on embeddings. | kotaemon > storages > vectorstores > BaseVectorStore | -| Doc store | self.\_DS | - self.\_DS.add: add the segments to document stores
- self.\_DS.get: get the segments based on id
- self.\_DS.get_all: get all segments
- self.\_DS.delete: delete segments based on id | kotaemon > storages > docstores > base > BaseDocumentStore | +| Infra | Access | Schema | Ref | +| ---------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------- | +| SQL table Source | self.\_Source | - id (int): id of the source (auto)
- name (str): the name of the file
- path (str): the path of the file
- size (int): the file size in bytes
- note (dict): allow extra optional information about the file
- date_created (datetime): the time the file is created (auto) | This is SQLALchemy ORM class. Can consult | +| SQL table Index | self.\_Index | - id (int): id of the index entry (auto)
- source_id (int): the id of a file in the Source table
- target_id: the id of the segment in docstore or vector store
- relation_type (str): if the link is "document" or "vector" | This is SQLAlchemy ORM class | +| Vector store | self.\_VS | - self.\_VS.add: add the list of embeddings to the vector store (optionally associate metadata and ids)
- self.\_VS.delete: delete vector entries based on ids
- self.\_VS.query: get embeddings based on embeddings. | kotaemon > storages > vectorstores > BaseVectorStore | +| Doc store | self.\_DS | - self.\_DS.add: add the segments to document stores
- self.\_DS.get: get the segments based on id
- self.\_DS.get_all: get all segments
- self.\_DS.delete: delete segments based on id | kotaemon > storages > docstores > base > BaseDocumentStore | diff --git a/docs/usage.md b/docs/usage.md index 348e414..f4e9f22 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -1,5 +1,3 @@ -# Basic Usage - ## 1. Add your AI models ![resources tab](https://raw.githubusercontent.com/Cinnamon/kotaemon/main/docs/images/resources-tab.png) @@ -63,12 +61,15 @@ AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=text-embedding-ada-002 # change to your deplo ### Local models -- Pros: +Pros: + - Privacy. Your documents will be stored and process locally. - Choices. There are a wide range of LLMs in terms of size, domain, language to choose from. - Cost. It's free. -- Cons: + +Cons: + - Quality. Local models are much smaller and thus have lower generative quality than paid APIs. - Speed. Local models are deployed using your machine so the processing speed is @@ -136,6 +137,21 @@ Now navigate back to the `Chat` tab. The chat tab is divided into 3 regions: files will be considered during chat. 2. Chat Panel - This is where you can chat with the chatbot. -3. Information panel - - Supporting information such as the retrieved evidence and reference will be - displayed here. +3. Information Panel + +![information panel](https://raw.githubusercontent.com/Cinnamon/kotaemon/develop/docs/images/info-panel-scores.png) + +- Supporting information such as the retrieved evidence and reference will be + displayed here. +- Direct citation for the answer produced by the LLM is highlighted. +- The confidence score of the answer and relevant scores of evidences are displayed to quickly assess the quality of the answer and retrieved content. + +- Meaning of the score displayed: + - **Answer confidence**: answer confidence level from the LLM model. + - **Relevance score**: overall relevant score between evidence and user question. + - **Vectorstore score**: relevant score from vector embedding similarity calculation (show `full-text search` if retrieved from full-text search DB). + - **LLM relevant score**: relevant score from LLM model (which judge relevancy between question and evidence using specific prompt). + - **Reranking score**: relevant score from Cohere [reranking model](https://cohere.com/rerank). + +Generally, the score quality is `LLM relevant score` > `Reranking score` > `Vectorscore`. +By default, overall relevance score is taken directly from LLM relevant score. Evidences are sorted based on their overall relevance score and whether they have citation or not. diff --git a/flowsettings.py b/flowsettings.py index 8d7f3cc..aa3489c 100644 --- a/flowsettings.py +++ b/flowsettings.py @@ -15,7 +15,7 @@ this_dir = Path(this_file).parent # change this if your app use a different name KH_PACKAGE_NAME = "kotaemon_app" -KH_APP_VERSION = os.environ.get("KH_APP_VERSION", None) +KH_APP_VERSION = config("KH_APP_VERSION", "local") if not KH_APP_VERSION: try: # Caution: This might produce the wrong version @@ -33,8 +33,21 @@ KH_APP_DATA_DIR.mkdir(parents=True, exist_ok=True) KH_USER_DATA_DIR = KH_APP_DATA_DIR / "user_data" KH_USER_DATA_DIR.mkdir(parents=True, exist_ok=True) -# doc directory -KH_DOC_DIR = this_dir / "docs" +# markdown output directory +KH_MARKDOWN_OUTPUT_DIR = KH_APP_DATA_DIR / "markdown_cache_dir" +KH_MARKDOWN_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + +# chunks output directory +KH_CHUNKS_OUTPUT_DIR = KH_APP_DATA_DIR / "chunks_cache_dir" +KH_CHUNKS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + +# zip output directory +KH_ZIP_OUTPUT_DIR = KH_APP_DATA_DIR / "zip_cache_dir" +KH_ZIP_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + +# zip input directory +KH_ZIP_INPUT_DIR = KH_APP_DATA_DIR / "zip_cache_dir_in" +KH_ZIP_INPUT_DIR.mkdir(parents=True, exist_ok=True) # HF models can be big, let's store them in the app data directory so that it's easier # for users to manage their storage. @@ -42,24 +55,30 @@ KH_DOC_DIR = this_dir / "docs" os.environ["HF_HOME"] = str(KH_APP_DATA_DIR / "huggingface") os.environ["HF_HUB_CACHE"] = str(KH_APP_DATA_DIR / "huggingface") -COHERE_API_KEY = config("COHERE_API_KEY", default="") +# doc directory +KH_DOC_DIR = this_dir / "docs" + KH_MODE = "dev" -KH_FEATURE_USER_MANAGEMENT = False +KH_FEATURE_USER_MANAGEMENT = True +KH_USER_CAN_SEE_PUBLIC = None KH_FEATURE_USER_MANAGEMENT_ADMIN = str( config("KH_FEATURE_USER_MANAGEMENT_ADMIN", default="admin") ) KH_FEATURE_USER_MANAGEMENT_PASSWORD = str( - config("KH_FEATURE_USER_MANAGEMENT_PASSWORD", default="XsdMbe8zKP8KdeE@") + config("KH_FEATURE_USER_MANAGEMENT_PASSWORD", default="admin") ) KH_ENABLE_ALEMBIC = False KH_DATABASE = f"sqlite:///{KH_USER_DATA_DIR / 'sql.db'}" KH_FILESTORAGE_PATH = str(KH_USER_DATA_DIR / "files") KH_DOCSTORE = { - "__type__": "kotaemon.storages.SimpleFileDocumentStore", + # "__type__": "kotaemon.storages.ElasticsearchDocumentStore", + # "__type__": "kotaemon.storages.SimpleFileDocumentStore", + "__type__": "kotaemon.storages.LanceDBDocumentStore", "path": str(KH_USER_DATA_DIR / "docstore"), } KH_VECTORSTORE = { + # "__type__": "kotaemon.storages.LanceDBVectorStore", "__type__": "kotaemon.storages.ChromaVectorStore", "path": str(KH_USER_DATA_DIR / "vectorstore"), } @@ -83,8 +102,6 @@ if config("AZURE_OPENAI_API_KEY", default="") and config( "timeout": 20, }, "default": False, - "accuracy": 5, - "cost": 5, } if config("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default=""): KH_EMBEDDINGS["azure"] = { @@ -110,71 +127,66 @@ if config("OPENAI_API_KEY", default=""): "base_url": config("OPENAI_API_BASE", default="") or "https://api.openai.com/v1", "api_key": config("OPENAI_API_KEY", default=""), - "model": config("OPENAI_CHAT_MODEL", default="") or "gpt-3.5-turbo", + "model": config("OPENAI_CHAT_MODEL", default="gpt-3.5-turbo"), + "timeout": 20, + }, + "default": True, + } + KH_EMBEDDINGS["openai"] = { + "spec": { + "__type__": "kotaemon.embeddings.OpenAIEmbeddings", + "base_url": config("OPENAI_API_BASE", default="https://api.openai.com/v1"), + "api_key": config("OPENAI_API_KEY", default=""), + "model": config( + "OPENAI_EMBEDDINGS_MODEL", default="text-embedding-ada-002" + ), "timeout": 10, - }, - "default": False, - } - if len(KH_EMBEDDINGS) < 1: - KH_EMBEDDINGS["openai"] = { - "spec": { - "__type__": "kotaemon.embeddings.OpenAIEmbeddings", - "base_url": config("OPENAI_API_BASE", default="") - or "https://api.openai.com/v1", - "api_key": config("OPENAI_API_KEY", default=""), - "model": config( - "OPENAI_EMBEDDINGS_MODEL", default="text-embedding-ada-002" - ) - or "text-embedding-ada-002", - "timeout": 10, - }, - "default": False, - } - -if config("LOCAL_MODEL", default=""): - KH_LLMS["local"] = { - "spec": { - "__type__": "kotaemon.llms.EndpointChatLLM", - "endpoint_url": "http://localhost:31415/v1/chat/completions", - }, - "default": False, - "cost": 0, - } - if len(KH_EMBEDDINGS) < 1: - KH_EMBEDDINGS["local"] = { - "spec": { - "__type__": "kotaemon.embeddings.EndpointEmbeddings", - "endpoint_url": "http://localhost:31415/v1/embeddings", - }, - "default": False, - "cost": 0, - } - -if len(KH_EMBEDDINGS) < 1: - KH_EMBEDDINGS["local-bge-base-en-v1.5"] = { - "spec": { - "__type__": "kotaemon.embeddings.FastEmbedEmbeddings", - "model_name": "BAAI/bge-base-en-v1.5", + "context_length": 8191, }, "default": True, } -KH_REASONINGS = ["ktem.reasoning.simple.FullQAPipeline"] +if config("LOCAL_MODEL", default=""): + KH_LLMS["ollama"] = { + "spec": { + "__type__": "kotaemon.llms.ChatOpenAI", + "base_url": "http://localhost:11434/v1/", + "model": config("LOCAL_MODEL", default="llama3.1:8b"), + }, + "default": False, + } + KH_EMBEDDINGS["ollama"] = { + "spec": { + "__type__": "kotaemon.embeddings.OpenAIEmbeddings", + "base_url": "http://localhost:11434/v1/", + "model": config("LOCAL_MODEL_EMBEDDINGS", default="nomic-embed-text"), + }, + "default": False, + } + + KH_EMBEDDINGS["local-bge-en"] = { + "spec": { + "__type__": "kotaemon.embeddings.FastEmbedEmbeddings", + "model_name": "BAAI/bge-base-en-v1.5", + }, + "default": False, + } + +KH_REASONINGS = [ + "ktem.reasoning.simple.FullQAPipeline", + "ktem.reasoning.simple.FullDecomposeQAPipeline", + "ktem.reasoning.react.ReactAgentPipeline", + "ktem.reasoning.rewoo.RewooAgentPipeline", +] +KH_REASONINGS_USE_MULTIMODAL = False KH_VLM_ENDPOINT = "{0}/openai/deployments/{1}/chat/completions?api-version={2}".format( config("AZURE_OPENAI_ENDPOINT", default=""), - config("OPENAI_VISION_DEPLOYMENT_NAME", default="gpt-4-vision"), + config("OPENAI_VISION_DEPLOYMENT_NAME", default="gpt-4o"), config("OPENAI_API_VERSION", default=""), ) -SETTINGS_APP = { - "lang": { - "name": "Language", - "value": "en", - "choices": [("English", "en"), ("Japanese", "ja")], - "component": "dropdown", - } -} +SETTINGS_APP: dict[str, dict] = {} SETTINGS_REASONING = { @@ -187,17 +199,42 @@ SETTINGS_REASONING = { "lang": { "name": "Language", "value": "en", - "choices": [("English", "en"), ("Japanese", "ja")], + "choices": [("English", "en"), ("Japanese", "ja"), ("Vietnamese", "vi")], "component": "dropdown", }, + "max_context_length": { + "name": "Max context length (LLM)", + "value": 32000, + "component": "number", + }, } -KH_INDEX_TYPES = ["ktem.index.file.FileIndex"] +KH_INDEX_TYPES = [ + "ktem.index.file.FileIndex", + "ktem.index.file.graph.GraphRAGIndex", +] KH_INDICES = [ { "name": "File", - "config": {}, + "config": { + "supported_file_types": ( + ".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, " + ".pptx, .csv, .html, .mhtml, .txt, .zip" + ), + "private": False, + }, "index_type": "ktem.index.file.FileIndex", }, + { + "name": "GraphRAG", + "config": { + "supported_file_types": ( + ".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, " + ".pptx, .csv, .html, .mhtml, .txt, .zip" + ), + "private": False, + }, + "index_type": "ktem.index.file.graph.GraphRAGIndex", + }, ] diff --git a/libs/kotaemon/kotaemon/agents/react/agent.py b/libs/kotaemon/kotaemon/agents/react/agent.py index dffcf6b..73d2d21 100644 --- a/libs/kotaemon/kotaemon/agents/react/agent.py +++ b/libs/kotaemon/kotaemon/agents/react/agent.py @@ -39,16 +39,11 @@ class ReactAgent(BaseAgent): ) max_iterations: int = 5 strict_decode: bool = False - trim_func: TokenSplitter = TokenSplitter.withx( - chunk_size=800, - chunk_overlap=0, - separator=" ", - tokenizer=partial( - tiktoken.encoding_for_model("gpt-3.5-turbo").encode, - allowed_special=set(), - disallowed_special="all", - ), + max_context_length: int = Param( + default=3000, + help="Max context length for each tool output.", ) + trim_func: TokenSplitter | None = None def _compose_plugin_description(self) -> str: """ @@ -149,14 +144,28 @@ class ReactAgent(BaseAgent): function_map[plugin.name] = plugin return function_map - def _trim(self, text: str) -> str: + def _trim(self, text: str | Document) -> str: """ Trim the text to the maximum token length. """ + evidence_trim_func = ( + self.trim_func + if self.trim_func + else TokenSplitter( + chunk_size=self.max_context_length, + chunk_overlap=0, + separator=" ", + tokenizer=partial( + tiktoken.encoding_for_model("gpt-3.5-turbo").encode, + allowed_special=set(), + disallowed_special="all", + ), + ) + ) if isinstance(text, str): - texts = self.trim_func([Document(text=text)]) + texts = evidence_trim_func([Document(text=text)]) elif isinstance(text, Document): - texts = self.trim_func([text]) + texts = evidence_trim_func([text]) else: raise ValueError("Invalid text type to trim") trim_text = texts[0].text diff --git a/libs/kotaemon/kotaemon/agents/rewoo/agent.py b/libs/kotaemon/kotaemon/agents/rewoo/agent.py index 3ec1950..076595b 100644 --- a/libs/kotaemon/kotaemon/agents/rewoo/agent.py +++ b/libs/kotaemon/kotaemon/agents/rewoo/agent.py @@ -39,16 +39,11 @@ class RewooAgent(BaseAgent): examples: dict[str, str | list[str]] = Param( default_callback=lambda _: {}, help="Examples to be used in the agent." ) - trim_func: TokenSplitter = TokenSplitter.withx( - chunk_size=3000, - chunk_overlap=0, - separator=" ", - tokenizer=partial( - tiktoken.encoding_for_model("gpt-3.5-turbo").encode, - allowed_special=set(), - disallowed_special="all", - ), + max_context_length: int = Param( + default=3000, + help="Max context length for each tool output.", ) + trim_func: TokenSplitter | None = None @Node.auto(depends_on=["planner_llm", "plugins", "prompt_template", "examples"]) def planner(self): @@ -248,8 +243,22 @@ class RewooAgent(BaseAgent): return p def _trim_evidence(self, evidence: str): + evidence_trim_func = ( + self.trim_func + if self.trim_func + else TokenSplitter( + chunk_size=self.max_context_length, + chunk_overlap=0, + separator=" ", + tokenizer=partial( + tiktoken.encoding_for_model("gpt-3.5-turbo").encode, + allowed_special=set(), + disallowed_special="all", + ), + ) + ) if evidence: - texts = self.trim_func([Document(text=evidence)]) + texts = evidence_trim_func([Document(text=evidence)]) evidence = texts[0].text logging.info(f"len (trimmed): {len(evidence)}") return evidence @@ -317,6 +326,14 @@ class RewooAgent(BaseAgent): ) print("Planner output:", planner_text_output) + # output planner to info panel + yield AgentOutput( + text="", + agent_type=self.agent_type, + status="thinking", + intermediate_steps=[{"planner_log": planner_text_output}], + ) + # Work worker_evidences, plugin_cost, plugin_token = self._get_worker_evidence( planner_evidences, evidence_level @@ -326,7 +343,9 @@ class RewooAgent(BaseAgent): worker_log += f"{plan}: {plans[plan]}\n" current_progress = f"{plan}: {plans[plan]}\n" for e in plan_to_es[plan]: + worker_log += f"#Action: {planner_evidences.get(e, None)}\n" worker_log += f"{e}: {worker_evidences[e]}\n" + current_progress += f"#Action: {planner_evidences.get(e, None)}\n" current_progress += f"{e}: {worker_evidences[e]}\n" yield AgentOutput( diff --git a/libs/kotaemon/kotaemon/agents/tools/google.py b/libs/kotaemon/kotaemon/agents/tools/google.py index 80009e2..98c5eaf 100644 --- a/libs/kotaemon/kotaemon/agents/tools/google.py +++ b/libs/kotaemon/kotaemon/agents/tools/google.py @@ -1,7 +1,7 @@ from typing import AnyStr, Optional, Type from urllib.error import HTTPError -from langchain.utilities import SerpAPIWrapper +from langchain_community.utilities import SerpAPIWrapper from pydantic import BaseModel, Field from .base import BaseTool diff --git a/libs/kotaemon/kotaemon/agents/tools/llm.py b/libs/kotaemon/kotaemon/agents/tools/llm.py index ae2dd2e..d7cf748 100644 --- a/libs/kotaemon/kotaemon/agents/tools/llm.py +++ b/libs/kotaemon/kotaemon/agents/tools/llm.py @@ -22,12 +22,16 @@ class LLMTool(BaseTool): ) llm: BaseLLM args_schema: Optional[Type[BaseModel]] = LLMArgs + dummy_mode: bool = True def _run_tool(self, query: AnyStr) -> str: output = None try: - response = self.llm(query) + if not self.dummy_mode: + response = self.llm(query) + else: + response = None except ValueError: raise ToolException("LLM Tool call failed") - output = response.text + output = response.text if response else "<->" return output diff --git a/libs/kotaemon/kotaemon/base/schema.py b/libs/kotaemon/kotaemon/base/schema.py index a153ed3..ea26032 100644 --- a/libs/kotaemon/kotaemon/base/schema.py +++ b/libs/kotaemon/kotaemon/base/schema.py @@ -5,8 +5,8 @@ from typing import TYPE_CHECKING, Any, Literal, Optional, TypeVar from langchain.schema.messages import AIMessage as LCAIMessage from langchain.schema.messages import HumanMessage as LCHumanMessage from langchain.schema.messages import SystemMessage as LCSystemMessage -from llama_index.bridge.pydantic import Field -from llama_index.schema import Document as BaseDocument +from llama_index.core.bridge.pydantic import Field +from llama_index.core.schema import Document as BaseDocument if TYPE_CHECKING: from haystack.schema import Document as HaystackDocument @@ -38,7 +38,7 @@ class Document(BaseDocument): content: Any = None source: Optional[str] = None - channel: Optional[Literal["chat", "info", "index", "debug"]] = None + channel: Optional[Literal["chat", "info", "index", "debug", "plot"]] = None def __init__(self, content: Optional[Any] = None, *args, **kwargs): if content is None: @@ -140,6 +140,7 @@ class LLMInterface(AIMessage): total_cost: float = 0 logits: list[list[float]] = Field(default_factory=list) messages: list[AIMessage] = Field(default_factory=list) + logprobs: list[float] = [] class ExtractorOutput(Document): diff --git a/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py b/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py index 78eecb5..b3ef356 100644 --- a/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py +++ b/libs/kotaemon/kotaemon/contribs/promptui/ui/chat.py @@ -133,9 +133,7 @@ def construct_chat_ui( label="Output file", show_label=True, height=100 ) export_btn = gr.Button("Export") - export_btn.click( - func_export_to_excel, inputs=None, outputs=exported_file - ) + export_btn.click(func_export_to_excel, inputs=[], outputs=exported_file) with gr.Row(): with gr.Column(): diff --git a/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py b/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py index 725893d..978a751 100644 --- a/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py +++ b/libs/kotaemon/kotaemon/contribs/promptui/ui/pipeline.py @@ -91,7 +91,7 @@ def construct_pipeline_ui( save_btn.click(func_save, inputs=params, outputs=history_dataframe) load_params_btn = gr.Button("Reload params") load_params_btn.click( - func_load_params, inputs=None, outputs=history_dataframe + func_load_params, inputs=[], outputs=history_dataframe ) history_dataframe.render() history_dataframe.select( @@ -103,7 +103,7 @@ def construct_pipeline_ui( export_btn = gr.Button( "Export (Result will be in Exported file next to Output)" ) - export_btn.click(func_export, inputs=None, outputs=exported_file) + export_btn.click(func_export, inputs=[], outputs=exported_file) with gr.Row(): with gr.Column(): if params: diff --git a/libs/kotaemon/kotaemon/embeddings/openai.py b/libs/kotaemon/kotaemon/embeddings/openai.py index 74655dc..604540d 100644 --- a/libs/kotaemon/kotaemon/embeddings/openai.py +++ b/libs/kotaemon/kotaemon/embeddings/openai.py @@ -1,5 +1,15 @@ +from itertools import islice from typing import Optional +import numpy as np +import openai +import tiktoken +from tenacity import ( + retry, + retry_if_not_exception_type, + stop_after_attempt, + wait_random_exponential, +) from theflow.utils.modules import import_dotted_string from kotaemon.base import Param @@ -7,6 +17,24 @@ from kotaemon.base import Param from .base import BaseEmbeddings, Document, DocumentWithEmbedding +def split_text_by_chunk_size(text: str, chunk_size: int) -> list[list[int]]: + """Split the text into chunks of a given size + + Args: + text: text to split + chunk_size: size of each chunk + + Returns: + list of chunks (as tokens) + """ + encoding = tiktoken.get_encoding("cl100k_base") + tokens = iter(encoding.encode(text)) + result = [] + while chunk := list(islice(tokens, chunk_size)): + result.append(chunk) + return result + + class BaseOpenAIEmbeddings(BaseEmbeddings): """Base interface for OpenAI embedding model, using the openai library. @@ -32,6 +60,9 @@ class BaseOpenAIEmbeddings(BaseEmbeddings): "Only supported in `text-embedding-3` and later models." ), ) + context_length: Optional[int] = Param( + None, help="The maximum context length of the embedding model" + ) @Param.auto(depends_on=["max_retries"]) def max_retries_(self): @@ -56,16 +87,42 @@ class BaseOpenAIEmbeddings(BaseEmbeddings): def invoke( self, text: str | list[str] | Document | list[Document], *args, **kwargs ) -> list[DocumentWithEmbedding]: - input_ = self.prepare_input(text) + input_doc = self.prepare_input(text) client = self.prepare_client(async_version=False) - resp = self.openai_response( - client, input=[_.text if _.text else " " for _ in input_], **kwargs - ).dict() - output_ = sorted(resp["data"], key=lambda x: x["index"]) - return [ - DocumentWithEmbedding(embedding=o["embedding"], content=i) - for i, o in zip(input_, output_) - ] + + input_: list[str | list[int]] = [] + splitted_indices = {} + for idx, text in enumerate(input_doc): + if self.context_length: + chunks = split_text_by_chunk_size(text.text or " ", self.context_length) + splitted_indices[idx] = (len(input_), len(input_) + len(chunks)) + input_.extend(chunks) + else: + splitted_indices[idx] = (len(input_), len(input_) + 1) + input_.append(text.text) + + resp = self.openai_response(client, input=input_, **kwargs).dict() + output_ = list(sorted(resp["data"], key=lambda x: x["index"])) + + output = [] + for idx, doc in enumerate(input_doc): + embs = output_[splitted_indices[idx][0] : splitted_indices[idx][1]] + if len(embs) == 1: + output.append( + DocumentWithEmbedding(embedding=embs[0]["embedding"], content=doc) + ) + continue + + chunk_lens = [ + len(_) + for _ in input_[splitted_indices[idx][0] : splitted_indices[idx][1]] + ] + vs: list[list[float]] = [_["embedding"] for _ in embs] + emb = np.average(vs, axis=0, weights=chunk_lens) + emb = emb / np.linalg.norm(emb) + output.append(DocumentWithEmbedding(embedding=emb.tolist(), content=doc)) + + return output async def ainvoke( self, text: str | list[str] | Document | list[Document], *args, **kwargs @@ -118,6 +175,13 @@ class OpenAIEmbeddings(BaseOpenAIEmbeddings): return OpenAI(**params) + @retry( + retry=retry_if_not_exception_type( + (openai.NotFoundError, openai.BadRequestError) + ), + wait=wait_random_exponential(min=1, max=40), + stop=stop_after_attempt(6), + ) def openai_response(self, client, **kwargs): """Get the openai response""" params: dict = { @@ -174,6 +238,13 @@ class AzureOpenAIEmbeddings(BaseOpenAIEmbeddings): return AzureOpenAI(**params) + @retry( + retry=retry_if_not_exception_type( + (openai.NotFoundError, openai.BadRequestError) + ), + wait=wait_random_exponential(min=1, max=40), + stop=stop_after_attempt(6), + ) def openai_response(self, client, **kwargs): """Get the openai response""" params: dict = { diff --git a/libs/kotaemon/kotaemon/indices/base.py b/libs/kotaemon/kotaemon/indices/base.py index 938be66..a20ca6b 100644 --- a/libs/kotaemon/kotaemon/indices/base.py +++ b/libs/kotaemon/kotaemon/indices/base.py @@ -3,7 +3,7 @@ from __future__ import annotations from abc import abstractmethod from typing import Any, Type -from llama_index.node_parser.interface import NodeParser +from llama_index.core.node_parser.interface import NodeParser from kotaemon.base import BaseComponent, Document, RetrievedDocument @@ -32,7 +32,7 @@ class LlamaIndexDocTransformerMixin: Example: class TokenSplitter(LlamaIndexMixin, BaseSplitter): def _get_li_class(self): - from llama_index.text_splitter import TokenTextSplitter + from llama_index.core.text_splitter import TokenTextSplitter return TokenTextSplitter To use this mixin, please: diff --git a/libs/kotaemon/kotaemon/indices/extractors/doc_parsers.py b/libs/kotaemon/kotaemon/indices/extractors/doc_parsers.py index 7dad528..a57743b 100644 --- a/libs/kotaemon/kotaemon/indices/extractors/doc_parsers.py +++ b/libs/kotaemon/kotaemon/indices/extractors/doc_parsers.py @@ -15,7 +15,7 @@ class TitleExtractor(LlamaIndexDocTransformerMixin, BaseDocParser): super().__init__(llm=llm, nodes=nodes, **params) def _get_li_class(self): - from llama_index.extractors import TitleExtractor + from llama_index.core.extractors import TitleExtractor return TitleExtractor @@ -30,6 +30,6 @@ class SummaryExtractor(LlamaIndexDocTransformerMixin, BaseDocParser): super().__init__(llm=llm, summaries=summaries, **params) def _get_li_class(self): - from llama_index.extractors import SummaryExtractor + from llama_index.core.extractors import SummaryExtractor return SummaryExtractor diff --git a/libs/kotaemon/kotaemon/indices/ingests/files.py b/libs/kotaemon/kotaemon/indices/ingests/files.py index 3eb5361..7da3713 100644 --- a/libs/kotaemon/kotaemon/indices/ingests/files.py +++ b/libs/kotaemon/kotaemon/indices/ingests/files.py @@ -1,27 +1,42 @@ from pathlib import Path from typing import Type -from llama_index.readers import PDFReader -from llama_index.readers.base import BaseReader +from decouple import config +from llama_index.core.readers.base import BaseReader +from theflow.settings import settings as flowsettings from kotaemon.base import BaseComponent, Document, Param from kotaemon.indices.extractors import BaseDocParser from kotaemon.indices.splitters import BaseSplitter, TokenSplitter from kotaemon.loaders import ( AdobeReader, + AzureAIDocumentIntelligenceLoader, DirectoryReader, HtmlReader, MathpixPDFReader, MhtmlReader, OCRReader, PandasExcelReader, + PDFThumbnailReader, UnstructuredReader, ) unstructured = UnstructuredReader() +adobe_reader = AdobeReader() +azure_reader = AzureAIDocumentIntelligenceLoader( + endpoint=str(config("AZURE_DI_ENDPOINT", default="")), + credential=str(config("AZURE_DI_CREDENTIAL", default="")), + cache_dir=getattr(flowsettings, "KH_MARKDOWN_OUTPUT_DIR", None), +) +adobe_reader.vlm_endpoint = azure_reader.vlm_endpoint = getattr( + flowsettings, "KH_VLM_ENDPOINT", "" +) + + KH_DEFAULT_FILE_EXTRACTORS: dict[str, BaseReader] = { ".xlsx": PandasExcelReader(), ".docx": unstructured, + ".pptx": unstructured, ".xls": unstructured, ".doc": unstructured, ".html": HtmlReader(), @@ -31,7 +46,7 @@ KH_DEFAULT_FILE_EXTRACTORS: dict[str, BaseReader] = { ".jpg": unstructured, ".tiff": unstructured, ".tif": unstructured, - ".pdf": PDFReader(), + ".pdf": PDFThumbnailReader(), } diff --git a/libs/kotaemon/kotaemon/indices/qa/citation.py b/libs/kotaemon/kotaemon/indices/qa/citation.py index bf8def0..30eceaa 100644 --- a/libs/kotaemon/kotaemon/indices/qa/citation.py +++ b/libs/kotaemon/kotaemon/indices/qa/citation.py @@ -103,7 +103,9 @@ class CitationPipeline(BaseComponent): print("CitationPipeline: invoking LLM") llm_output = self.get_from_path("llm").invoke(messages, **llm_kwargs) print("CitationPipeline: finish invoking LLM") - if not llm_output.messages: + if not llm_output.messages or not llm_output.additional_kwargs.get( + "tool_calls" + ): return None function_output = llm_output.additional_kwargs["tool_calls"][0]["function"][ "arguments" diff --git a/libs/kotaemon/kotaemon/indices/rankings/__init__.py b/libs/kotaemon/kotaemon/indices/rankings/__init__.py index ccd99bb..84b8765 100644 --- a/libs/kotaemon/kotaemon/indices/rankings/__init__.py +++ b/libs/kotaemon/kotaemon/indices/rankings/__init__.py @@ -1,5 +1,13 @@ from .base import BaseReranking from .cohere import CohereReranking from .llm import LLMReranking +from .llm_scoring import LLMScoring +from .llm_trulens import LLMTrulensScoring -__all__ = ["CohereReranking", "LLMReranking", "BaseReranking"] +__all__ = [ + "CohereReranking", + "LLMReranking", + "LLMScoring", + "BaseReranking", + "LLMTrulensScoring", +] diff --git a/libs/kotaemon/kotaemon/indices/rankings/cohere.py b/libs/kotaemon/kotaemon/indices/rankings/cohere.py index d102efd..e759d6c 100644 --- a/libs/kotaemon/kotaemon/indices/rankings/cohere.py +++ b/libs/kotaemon/kotaemon/indices/rankings/cohere.py @@ -1,6 +1,6 @@ from __future__ import annotations -import os +from decouple import config from kotaemon.base import Document @@ -9,8 +9,7 @@ from .base import BaseReranking class CohereReranking(BaseReranking): model_name: str = "rerank-multilingual-v2.0" - cohere_api_key: str = os.environ.get("COHERE_API_KEY", "") - top_k: int = 1 + cohere_api_key: str = config("COHERE_API_KEY", "") def run(self, documents: list[Document], query: str) -> list[Document]: """Use Cohere Reranker model to re-order documents @@ -22,6 +21,10 @@ class CohereReranking(BaseReranking): "Please install Cohere " "`pip install cohere` to use Cohere Reranking" ) + if not self.cohere_api_key: + print("Cohere API key not found. Skipping reranking.") + return documents + cohere_client = cohere.Client(self.cohere_api_key) compressed_docs: list[Document] = [] @@ -29,12 +32,13 @@ class CohereReranking(BaseReranking): return compressed_docs _docs = [d.content for d in documents] - results = cohere_client.rerank( - model=self.model_name, query=query, documents=_docs, top_n=self.top_k + response = cohere_client.rerank( + model=self.model_name, query=query, documents=_docs ) - for r in results: + print("Cohere score", [r.relevance_score for r in response.results]) + for r in response.results: doc = documents[r.index] - doc.metadata["relevance_score"] = r.relevance_score + doc.metadata["cohere_reranking_score"] = r.relevance_score compressed_docs.append(doc) return compressed_docs diff --git a/libs/kotaemon/kotaemon/indices/rankings/llm_scoring.py b/libs/kotaemon/kotaemon/indices/rankings/llm_scoring.py new file mode 100644 index 0000000..b4f5105 --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/rankings/llm_scoring.py @@ -0,0 +1,54 @@ +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor + +import numpy as np +from langchain.output_parsers.boolean import BooleanOutputParser + +from kotaemon.base import Document + +from .llm import LLMReranking + + +class LLMScoring(LLMReranking): + def run( + self, + documents: list[Document], + query: str, + ) -> list[Document]: + """Filter down documents based on their relevance to the query.""" + filtered_docs: list[Document] = [] + output_parser = BooleanOutputParser() + + if self.concurrent: + with ThreadPoolExecutor() as executor: + futures = [] + for doc in documents: + _prompt = self.prompt_template.populate( + question=query, context=doc.get_content() + ) + futures.append(executor.submit(lambda: self.llm(_prompt))) + + results = [future.result() for future in futures] + else: + results = [] + for doc in documents: + _prompt = self.prompt_template.populate( + question=query, context=doc.get_content() + ) + results.append(self.llm(_prompt)) + + for result, doc in zip(results, documents): + score = np.exp(np.average(result.logprobs)) + include_doc = output_parser.parse(result.text) + if include_doc: + doc.metadata["llm_reranking_score"] = score + else: + doc.metadata["llm_reranking_score"] = 1 - score + filtered_docs.append(doc) + + # prevent returning empty result + if len(filtered_docs) == 0: + filtered_docs = documents[: self.top_k] + + return filtered_docs diff --git a/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py b/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py new file mode 100644 index 0000000..046998f --- /dev/null +++ b/libs/kotaemon/kotaemon/indices/rankings/llm_trulens.py @@ -0,0 +1,182 @@ +from __future__ import annotations + +import re +from concurrent.futures import ThreadPoolExecutor +from functools import partial + +import tiktoken + +from kotaemon.base import Document, HumanMessage, SystemMessage +from kotaemon.indices.splitters import TokenSplitter +from kotaemon.llms import BaseLLM, PromptTemplate + +from .llm import LLMReranking + +SYSTEM_PROMPT_TEMPLATE = PromptTemplate( + """You are a RELEVANCE grader; providing the relevance of the given CONTEXT to the given QUESTION. + Respond only as a number from 0 to 10 where 0 is the least relevant and 10 is the most relevant. + + A few additional scoring guidelines: + + - Long CONTEXTS should score equally well as short CONTEXTS. + + - RELEVANCE score should increase as the CONTEXTS provides more RELEVANT context to the QUESTION. + + - RELEVANCE score should increase as the CONTEXTS provides RELEVANT context to more parts of the QUESTION. + + - CONTEXT that is RELEVANT to some of the QUESTION should score of 2, 3 or 4. Higher score indicates more RELEVANCE. + + - CONTEXT that is RELEVANT to most of the QUESTION should get a score of 5, 6, 7 or 8. Higher score indicates more RELEVANCE. + + - CONTEXT that is RELEVANT to the entire QUESTION should get a score of 9 or 10. Higher score indicates more RELEVANCE. + + - CONTEXT must be relevant and helpful for answering the entire QUESTION to get a score of 10. + + - Never elaborate.""" # noqa: E501 +) + +USER_PROMPT_TEMPLATE = PromptTemplate( + """QUESTION: {question} + + CONTEXT: {context} + + RELEVANCE: """ +) # noqa + +PATTERN_INTEGER: re.Pattern = re.compile(r"([+-]?[1-9][0-9]*|0)") +"""Regex that matches integers.""" + +MAX_CONTEXT_LEN = 7500 + + +def validate_rating(rating) -> int: + """Validate a rating is between 0 and 10.""" + + if not 0 <= rating <= 10: + raise ValueError("Rating must be between 0 and 10") + + return rating + + +def re_0_10_rating(s: str) -> int: + """Extract a 0-10 rating from a string. + + If the string does not match an integer or matches an integer outside the + 0-10 range, raises an error instead. If multiple numbers are found within + the expected 0-10 range, the smallest is returned. + + Args: + s: String to extract rating from. + + Returns: + int: Extracted rating. + + Raises: + ParseError: If no integers between 0 and 10 are found in the string. + """ + + matches = PATTERN_INTEGER.findall(s) + if not matches: + raise AssertionError + + vals = set() + for match in matches: + try: + vals.add(validate_rating(int(match))) + except ValueError: + pass + + if not vals: + raise AssertionError + + # Min to handle cases like "The rating is 8 out of 10." + return min(vals) + + +class LLMTrulensScoring(LLMReranking): + llm: BaseLLM + system_prompt_template: PromptTemplate = SYSTEM_PROMPT_TEMPLATE + user_prompt_template: PromptTemplate = USER_PROMPT_TEMPLATE + concurrent: bool = True + normalize: float = 10 + trim_func: TokenSplitter = TokenSplitter.withx( + chunk_size=MAX_CONTEXT_LEN, + chunk_overlap=0, + separator=" ", + tokenizer=partial( + tiktoken.encoding_for_model("gpt-3.5-turbo").encode, + allowed_special=set(), + disallowed_special="all", + ), + ) + + def run( + self, + documents: list[Document], + query: str, + ) -> list[Document]: + """Filter down documents based on their relevance to the query.""" + filtered_docs = [] + + documents = sorted(documents, key=lambda doc: doc.get_content()) + if self.concurrent: + with ThreadPoolExecutor() as executor: + futures = [] + for doc in documents: + chunked_doc_content = self.trim_func( + [ + Document(content=doc.get_content()) + # skip metadata which cause troubles + ] + )[0].text + + messages = [] + messages.append( + SystemMessage(self.system_prompt_template.populate()) + ) + messages.append( + HumanMessage( + self.user_prompt_template.populate( + question=query, context=chunked_doc_content + ) + ) + ) + + def llm_call(): + return self.llm(messages).text + + futures.append(executor.submit(llm_call)) + + results = [future.result() for future in futures] + else: + results = [] + for doc in documents: + messages = [] + messages.append(SystemMessage(self.system_prompt_template.populate())) + messages.append( + SystemMessage( + self.user_prompt_template.populate( + question=query, context=doc.get_content() + ) + ) + ) + results.append(self.llm(messages).text) + + # use Boolean parser to extract relevancy output from LLM + results = [ + (r_idx, float(re_0_10_rating(result)) / self.normalize) + for r_idx, result in enumerate(results) + ] + results.sort(key=lambda x: x[1], reverse=True) + + for r_idx, score in results: + doc = documents[r_idx] + doc.metadata["llm_trulens_score"] = score + filtered_docs.append(doc) + + print( + "LLM rerank scores", + [doc.metadata["llm_trulens_score"] for doc in filtered_docs], + ) + + return filtered_docs diff --git a/libs/kotaemon/kotaemon/indices/splitters/__init__.py b/libs/kotaemon/kotaemon/indices/splitters/__init__.py index 16a31fe..d9eb65f 100644 --- a/libs/kotaemon/kotaemon/indices/splitters/__init__.py +++ b/libs/kotaemon/kotaemon/indices/splitters/__init__.py @@ -23,7 +23,7 @@ class TokenSplitter(LlamaIndexDocTransformerMixin, BaseSplitter): ) def _get_li_class(self): - from llama_index.text_splitter import TokenTextSplitter + from llama_index.core.text_splitter import TokenTextSplitter return TokenTextSplitter @@ -44,6 +44,6 @@ class SentenceWindowSplitter(LlamaIndexDocTransformerMixin, BaseSplitter): ) def _get_li_class(self): - from llama_index.node_parser import SentenceWindowNodeParser + from llama_index.core.node_parser import SentenceWindowNodeParser return SentenceWindowNodeParser diff --git a/libs/kotaemon/kotaemon/indices/vectorindex.py b/libs/kotaemon/kotaemon/indices/vectorindex.py index 8902528..e2984c7 100644 --- a/libs/kotaemon/kotaemon/indices/vectorindex.py +++ b/libs/kotaemon/kotaemon/indices/vectorindex.py @@ -1,14 +1,18 @@ from __future__ import annotations +import threading import uuid +from pathlib import Path from typing import Optional, Sequence, cast +from theflow.settings import settings as flowsettings + from kotaemon.base import BaseComponent, Document, RetrievedDocument from kotaemon.embeddings import BaseEmbeddings from kotaemon.storages import BaseDocumentStore, BaseVectorStore from .base import BaseIndexing, BaseRetrieval -from .rankings import BaseReranking +from .rankings import BaseReranking, LLMReranking VECTOR_STORE_FNAME = "vectorstore" DOC_STORE_FNAME = "docstore" @@ -23,9 +27,11 @@ class VectorIndexing(BaseIndexing): - List of texts """ + cache_dir: Optional[str] = getattr(flowsettings, "KH_CHUNKS_OUTPUT_DIR", None) vector_store: BaseVectorStore doc_store: Optional[BaseDocumentStore] = None embedding: BaseEmbeddings + count_: int = 0 def to_retrieval_pipeline(self, *args, **kwargs): """Convert the indexing pipeline to a retrieval pipeline""" @@ -44,6 +50,52 @@ class VectorIndexing(BaseIndexing): qa_pipeline=CitationQAPipeline(**kwargs), ) + def write_chunk_to_file(self, docs: list[Document]): + # save the chunks content into markdown format + if self.cache_dir: + file_name = Path(docs[0].metadata["file_name"]) + for i in range(len(docs)): + markdown_content = "" + if "page_label" in docs[i].metadata: + page_label = str(docs[i].metadata["page_label"]) + markdown_content += f"Page label: {page_label}" + if "file_name" in docs[i].metadata: + filename = docs[i].metadata["file_name"] + markdown_content += f"\nFile name: {filename}" + if "section" in docs[i].metadata: + section = docs[i].metadata["section"] + markdown_content += f"\nSection: {section}" + if "type" in docs[i].metadata: + if docs[i].metadata["type"] == "image": + image_origin = docs[i].metadata["image_origin"] + image_origin = f'

' + markdown_content += f"\nImage origin: {image_origin}" + if docs[i].text: + markdown_content += f"\ntext:\n{docs[i].text}" + + with open( + Path(self.cache_dir) / f"{file_name.stem}_{self.count_+i}.md", + "w", + encoding="utf-8", + ) as f: + f.write(markdown_content) + + def add_to_docstore(self, docs: list[Document]): + if self.doc_store: + print("Adding documents to doc store") + self.doc_store.add(docs) + + def add_to_vectorstore(self, docs: list[Document]): + # in case we want to skip embedding + if self.vector_store: + print(f"Getting embeddings for {len(docs)} nodes") + embeddings = self.embedding(docs) + print("Adding embeddings to vector store") + self.vector_store.add( + embeddings=embeddings, + ids=[t.doc_id for t in docs], + ) + def run(self, text: str | list[str] | Document | list[Document]): input_: list[Document] = [] if not isinstance(text, list): @@ -59,16 +111,10 @@ class VectorIndexing(BaseIndexing): f"Invalid input type {type(item)}, should be str or Document" ) - print(f"Getting embeddings for {len(input_)} nodes") - embeddings = self.embedding(input_) - print("Adding embeddings to vector store") - self.vector_store.add( - embeddings=embeddings, - ids=[t.doc_id for t in input_], - ) - if self.doc_store: - print("Adding documents to doc store") - self.doc_store.add(input_) + self.add_to_vectorstore(input_) + self.add_to_docstore(input_) + self.write_chunk_to_file(input_) + self.count_ += len(input_) class VectorRetrieval(BaseRetrieval): @@ -78,7 +124,16 @@ class VectorRetrieval(BaseRetrieval): doc_store: Optional[BaseDocumentStore] = None embedding: BaseEmbeddings rerankers: Sequence[BaseReranking] = [] - top_k: int = 1 + top_k: int = 5 + first_round_top_k_mult: int = 10 + retrieval_mode: str = "hybrid" # vector, text, hybrid + + def _filter_docs( + self, documents: list[RetrievedDocument], top_k: int | None = None + ): + if top_k: + documents = documents[:top_k] + return documents def run( self, text: str | Document, top_k: Optional[int] = None, **kwargs @@ -95,24 +150,155 @@ class VectorRetrieval(BaseRetrieval): if top_k is None: top_k = self.top_k + do_extend = kwargs.pop("do_extend", False) + thumbnail_count = kwargs.pop("thumbnail_count", 3) + + if do_extend: + top_k_first_round = top_k * self.first_round_top_k_mult + else: + top_k_first_round = top_k + if self.doc_store is None: raise ValueError( "doc_store is not provided. Please provide a doc_store to " "retrieve the documents" ) - emb: list[float] = self.embedding(text)[0].embedding - _, scores, ids = self.vector_store.query(embedding=emb, top_k=top_k, **kwargs) - docs = self.doc_store.get(ids) - result = [ - RetrievedDocument(**doc.to_dict(), score=score) - for doc, score in zip(docs, scores) - ] + result: list[RetrievedDocument] = [] + # TODO: should declare scope directly in the run params + scope = kwargs.pop("scope", None) + emb: list[float] + + if self.retrieval_mode == "vector": + emb = self.embedding(text)[0].embedding + _, scores, ids = self.vector_store.query( + embedding=emb, top_k=top_k_first_round, **kwargs + ) + docs = self.doc_store.get(ids) + result = [ + RetrievedDocument(**doc.to_dict(), score=score) + for doc, score in zip(docs, scores) + ] + elif self.retrieval_mode == "text": + query = text.text if isinstance(text, Document) else text + docs = self.doc_store.query(query, top_k=top_k_first_round, doc_ids=scope) + result = [RetrievedDocument(**doc.to_dict(), score=-1.0) for doc in docs] + elif self.retrieval_mode == "hybrid": + # similarity search section + emb = self.embedding(text)[0].embedding + vs_docs: list[RetrievedDocument] = [] + vs_ids: list[str] = [] + vs_scores: list[float] = [] + + def query_vectorstore(): + nonlocal vs_docs + nonlocal vs_scores + nonlocal vs_ids + + assert self.doc_store is not None + _, vs_scores, vs_ids = self.vector_store.query( + embedding=emb, top_k=top_k_first_round, **kwargs + ) + if vs_ids: + vs_docs = self.doc_store.get(vs_ids) + + # full-text search section + ds_docs: list[RetrievedDocument] = [] + + def query_docstore(): + nonlocal ds_docs + + assert self.doc_store is not None + query = text.text if isinstance(text, Document) else text + ds_docs = self.doc_store.query( + query, top_k=top_k_first_round, doc_ids=scope + ) + + vs_query_thread = threading.Thread(target=query_vectorstore) + ds_query_thread = threading.Thread(target=query_docstore) + + vs_query_thread.start() + ds_query_thread.start() + + vs_query_thread.join() + ds_query_thread.join() + + result = [ + RetrievedDocument(**doc.to_dict(), score=-1.0) + for doc in ds_docs + if doc not in vs_ids + ] + result += [ + RetrievedDocument(**doc.to_dict(), score=score) + for doc, score in zip(vs_docs, vs_scores) + ] + print(f"Got {len(vs_docs)} from vectorstore") + print(f"Got {len(ds_docs)} from docstore") + # use additional reranker to re-order the document list - if self.rerankers: + if self.rerankers and text: for reranker in self.rerankers: + # if reranker is LLMReranking, limit the document with top_k items only + if isinstance(reranker, LLMReranking): + result = self._filter_docs(result, top_k=top_k) result = reranker(documents=result, query=text) + result = self._filter_docs(result, top_k=top_k) + print(f"Got raw {len(result)} retrieved documents") + + # add page thumbnails to the result if exists + thumbnail_doc_ids: set[str] = set() + # we should copy the text from retrieved text chunk + # to the thumbnail to get relevant LLM score correctly + text_thumbnail_docs: dict[str, RetrievedDocument] = {} + + non_thumbnail_docs = [] + raw_thumbnail_docs = [] + for doc in result: + if doc.metadata.get("type") == "thumbnail": + # change type to image to display on UI + doc.metadata["type"] = "image" + raw_thumbnail_docs.append(doc) + continue + if ( + "thumbnail_doc_id" in doc.metadata + and len(thumbnail_doc_ids) < thumbnail_count + ): + thumbnail_id = doc.metadata["thumbnail_doc_id"] + thumbnail_doc_ids.add(thumbnail_id) + text_thumbnail_docs[thumbnail_id] = doc + else: + non_thumbnail_docs.append(doc) + + linked_thumbnail_docs = self.doc_store.get(list(thumbnail_doc_ids)) + print( + "thumbnail docs", + len(linked_thumbnail_docs), + "non-thumbnail docs", + len(non_thumbnail_docs), + "raw-thumbnail docs", + len(raw_thumbnail_docs), + ) + additional_docs = [] + + for thumbnail_doc in linked_thumbnail_docs: + text_doc = text_thumbnail_docs[thumbnail_doc.doc_id] + doc_dict = thumbnail_doc.to_dict() + doc_dict["_id"] = text_doc.doc_id + doc_dict["content"] = text_doc.content + doc_dict["metadata"]["type"] = "image" + for key in text_doc.metadata: + if key not in doc_dict["metadata"]: + doc_dict["metadata"][key] = text_doc.metadata[key] + + additional_docs.append(RetrievedDocument(**doc_dict, score=text_doc.score)) + + result = additional_docs + non_thumbnail_docs + + if not result: + # return output from raw retrieved thumbnails + result = self._filter_docs(raw_thumbnail_docs, top_k=thumbnail_count) + return result diff --git a/libs/kotaemon/kotaemon/llms/__init__.py b/libs/kotaemon/kotaemon/llms/__init__.py index 266e391..6494fc9 100644 --- a/libs/kotaemon/kotaemon/llms/__init__.py +++ b/libs/kotaemon/kotaemon/llms/__init__.py @@ -7,6 +7,7 @@ from .chats import ( ChatLLM, ChatOpenAI, EndpointChatLLM, + LCAnthropicChat, LCAzureChatOpenAI, LCChatOpenAI, LlamaCppChat, @@ -27,6 +28,7 @@ __all__ = [ "SystemMessage", "AzureChatOpenAI", "ChatOpenAI", + "LCAnthropicChat", "LCAzureChatOpenAI", "LCChatOpenAI", "LlamaCppChat", diff --git a/libs/kotaemon/kotaemon/llms/chats/__init__.py b/libs/kotaemon/kotaemon/llms/chats/__init__.py index 7fc1c40..6e3d3d5 100644 --- a/libs/kotaemon/kotaemon/llms/chats/__init__.py +++ b/libs/kotaemon/kotaemon/llms/chats/__init__.py @@ -1,6 +1,11 @@ from .base import ChatLLM from .endpoint_based import EndpointChatLLM -from .langchain_based import LCAzureChatOpenAI, LCChatMixin, LCChatOpenAI +from .langchain_based import ( + LCAnthropicChat, + LCAzureChatOpenAI, + LCChatMixin, + LCChatOpenAI, +) from .llamacpp import LlamaCppChat from .openai import AzureChatOpenAI, ChatOpenAI @@ -10,6 +15,7 @@ __all__ = [ "ChatLLM", "EndpointChatLLM", "ChatOpenAI", + "LCAnthropicChat", "LCChatOpenAI", "LCAzureChatOpenAI", "LCChatMixin", diff --git a/libs/kotaemon/kotaemon/llms/chats/langchain_based.py b/libs/kotaemon/kotaemon/llms/chats/langchain_based.py index fca78dc..077c3f8 100644 --- a/libs/kotaemon/kotaemon/llms/chats/langchain_based.py +++ b/libs/kotaemon/kotaemon/llms/chats/langchain_based.py @@ -221,3 +221,27 @@ class LCAzureChatOpenAI(LCChatMixin, ChatLLM): # type: ignore from langchain.chat_models import AzureChatOpenAI return AzureChatOpenAI + + +class LCAnthropicChat(LCChatMixin, ChatLLM): # type: ignore + def __init__( + self, + api_key: str | None = None, + model_name: str | None = None, + temperature: float = 0.7, + **params, + ): + super().__init__( + api_key=api_key, + model_name=model_name, + temperature=temperature, + **params, + ) + + def _get_lc_class(self): + try: + from langchain_anthropic import ChatAnthropic + except ImportError: + raise ImportError("Please install langchain-anthropic") + + return ChatAnthropic diff --git a/libs/kotaemon/kotaemon/llms/chats/openai.py b/libs/kotaemon/kotaemon/llms/chats/openai.py index b12567d..6a605f6 100644 --- a/libs/kotaemon/kotaemon/llms/chats/openai.py +++ b/libs/kotaemon/kotaemon/llms/chats/openai.py @@ -159,6 +159,15 @@ class BaseChatOpenAI(ChatLLM): additional_kwargs["tool_calls"] = resp["choices"][0]["message"][ "tool_calls" ] + + if resp["choices"][0].get("logprobs") is None: + logprobs = [] + else: + all_logprobs = resp["choices"][0]["logprobs"].get("content") + logprobs = ( + [logprob["logprob"] for logprob in all_logprobs] if all_logprobs else [] + ) + output = LLMInterface( candidates=[(_["message"]["content"] or "") for _ in resp["choices"]], content=resp["choices"][0]["message"]["content"] or "", @@ -170,6 +179,7 @@ class BaseChatOpenAI(ChatLLM): AIMessage(content=(_["message"]["content"]) or "") for _ in resp["choices"] ], + logprobs=logprobs, ) return output @@ -216,11 +226,24 @@ class BaseChatOpenAI(ChatLLM): client, messages=input_messages, stream=True, **kwargs ) - for chunk in resp: - if not chunk.choices: + for c in resp: + chunk = c.dict() + if not chunk["choices"]: continue - if chunk.choices[0].delta.content is not None: - yield LLMInterface(content=chunk.choices[0].delta.content) + if chunk["choices"][0]["delta"]["content"] is not None: + if chunk["choices"][0].get("logprobs") is None: + logprobs = [] + else: + logprobs = [ + logprob["logprob"] + for logprob in chunk["choices"][0]["logprobs"].get( + "content", [] + ) + ] + + yield LLMInterface( + content=chunk["choices"][0]["delta"]["content"], logprobs=logprobs + ) async def astream( self, messages: str | BaseMessage | list[BaseMessage], *args, **kwargs diff --git a/libs/kotaemon/kotaemon/loaders/__init__.py b/libs/kotaemon/kotaemon/loaders/__init__.py index 6ccdbda..624a76b 100644 --- a/libs/kotaemon/kotaemon/loaders/__init__.py +++ b/libs/kotaemon/kotaemon/loaders/__init__.py @@ -3,10 +3,12 @@ from .azureai_document_intelligence_loader import AzureAIDocumentIntelligenceLoa from .base import AutoReader, BaseReader from .composite_loader import DirectoryReader from .docx_loader import DocxReader -from .excel_loader import PandasExcelReader +from .excel_loader import ExcelReader, PandasExcelReader from .html_loader import HtmlReader, MhtmlReader from .mathpix_loader import MathpixPDFReader from .ocr_loader import ImageReader, OCRReader +from .pdf_loader import PDFThumbnailReader +from .txt_loader import TxtReader from .unstructured_loader import UnstructuredReader __all__ = [ @@ -14,6 +16,7 @@ __all__ = [ "AzureAIDocumentIntelligenceLoader", "BaseReader", "PandasExcelReader", + "ExcelReader", "MathpixPDFReader", "ImageReader", "OCRReader", @@ -23,4 +26,6 @@ __all__ = [ "HtmlReader", "MhtmlReader", "AdobeReader", + "TxtReader", + "PDFThumbnailReader", ] diff --git a/libs/kotaemon/kotaemon/loaders/adobe_loader.py b/libs/kotaemon/kotaemon/loaders/adobe_loader.py index 09a802c..f1b0379 100644 --- a/libs/kotaemon/kotaemon/loaders/adobe_loader.py +++ b/libs/kotaemon/kotaemon/loaders/adobe_loader.py @@ -6,7 +6,7 @@ from pathlib import Path from typing import Any, Dict, List, Optional from decouple import config -from llama_index.readers.base import BaseReader +from llama_index.core.readers.base import BaseReader from kotaemon.base import Document @@ -154,7 +154,7 @@ class AdobeReader(BaseReader): for page_number, table_content, table_caption in tables: documents.append( Document( - text=table_caption, + text=table_content, metadata={ "table_origin": table_content, "type": "table", diff --git a/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py b/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py index 7e4c516..1e1d902 100644 --- a/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py +++ b/libs/kotaemon/kotaemon/loaders/azureai_document_intelligence_loader.py @@ -1,10 +1,56 @@ +import base64 import os +from io import BytesIO from pathlib import Path from typing import Optional +from PIL import Image + from kotaemon.base import Document, Param from .base import BaseReader +from .utils.adobe import generate_single_figure_caption + + +def crop_image(file_path: Path, bbox: list[float], page_number: int = 0) -> Image.Image: + """Crop the image based on the bounding box + + Args: + file_path (Path): path to the image file + bbox (list[float]): bounding box of the image (in percentage [x0, y0, x1, y1]) + page_number (int, optional): page number of the image. Defaults to 0. + + Returns: + Image.Image: cropped image + """ + left, upper, right, lower = bbox + + img: Image.Image + suffix = file_path.suffix.lower() + if suffix == ".pdf": + try: + import fitz + except ImportError: + raise ImportError("Please install PyMuPDF: 'pip install PyMuPDF'") + + doc = fitz.open(file_path) + page = doc.load_page(page_number) + pm = page.get_pixmap(dpi=150) + img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples) + elif suffix in [".tif", ".tiff"]: + img = Image.open(file_path) + img.seek(page_number) + else: + img = Image.open(file_path) + + return img.crop( + ( + int(left * img.width), + int(upper * img.height), + int(right * img.width), + int(lower * img.height), + ) + ) class AzureAIDocumentIntelligenceLoader(BaseReader): @@ -14,7 +60,7 @@ class AzureAIDocumentIntelligenceLoader(BaseReader): heif, docx, xlsx, pptx and html. """ - _dependencies = ["azure-ai-documentintelligence"] + _dependencies = ["azure-ai-documentintelligence", "PyMuPDF", "Pillow"] endpoint: str = Param( os.environ.get("AZUREAI_DOCUMENT_INTELLIGENT_ENDPOINT", None), @@ -34,6 +80,29 @@ class AzureAIDocumentIntelligenceLoader(BaseReader): "#model-analysis-features)" ), ) + output_content_format: str = Param( + "markdown", + help="Output content format. Can be 'markdown' or 'text'.Default is markdown", + ) + vlm_endpoint: str = Param( + help=( + "Default VLM endpoint for figure captioning. If not provided, will not " + "caption the figures" + ) + ) + figure_friendly_filetypes: list[str] = Param( + [".pdf", ".jpeg", ".jpg", ".png", ".bmp", ".tiff", ".heif", ".tif"], + help=( + "File types that we can reliably open and extract figures. " + "For files like .docx or .html, the visual layout may be different " + "when viewed from different tools, hence we cannot use Azure DI " + "location to extract figures." + ), + ) + cache_dir: str = Param( + None, + help="Directory to cache the downloaded files. Default is None", + ) @Param.auto(depends_on=["endpoint", "credential"]) def client_(self): @@ -55,14 +124,114 @@ class AzureAIDocumentIntelligenceLoader(BaseReader): def load_data( self, file_path: Path, extra_info: Optional[dict] = None, **kwargs ) -> list[Document]: + """Extract the input file, allowing multi-modal extraction""" metadata = extra_info or {} + file_name = Path(file_path) with open(file_path, "rb") as fi: poller = self.client_.begin_analyze_document( self.model, analyze_request=fi, content_type="application/octet-stream", - output_content_format="markdown", + output_content_format=self.output_content_format, ) result = poller.result() - return [Document(content=result.content, metadata=metadata)] + # the total text content of the document in `output_content_format` format + text_content = result.content + removed_spans: list[dict] = [] + + # extract the figures + figures = [] + for figure_desc in result.get("figures", []): + if not self.vlm_endpoint: + continue + if file_path.suffix.lower() not in self.figure_friendly_filetypes: + continue + + # read & crop the image + page_number = figure_desc["boundingRegions"][0]["pageNumber"] + page_width = result.pages[page_number - 1]["width"] + page_height = result.pages[page_number - 1]["height"] + polygon = figure_desc["boundingRegions"][0]["polygon"] + xs = [polygon[i] for i in range(0, len(polygon), 2)] + ys = [polygon[i] for i in range(1, len(polygon), 2)] + bbox = [ + min(xs) / page_width, + min(ys) / page_height, + max(xs) / page_width, + max(ys) / page_height, + ] + img = crop_image(file_path, bbox, page_number - 1) + + # convert the image into base64 + img_bytes = BytesIO() + img.save(img_bytes, format="PNG") + img_base64 = base64.b64encode(img_bytes.getvalue()).decode("utf-8") + img_base64 = f"data:image/png;base64,{img_base64}" + + # caption the image + caption = generate_single_figure_caption( + figure=img_base64, vlm_endpoint=self.vlm_endpoint + ) + + # store the image into document + figure_metadata = { + "image_origin": img_base64, + "type": "image", + "page_label": page_number, + } + figure_metadata.update(metadata) + + figures.append( + Document( + text=caption, + metadata=figure_metadata, + ) + ) + removed_spans += figure_desc["spans"] + + # extract the tables + tables = [] + for table_desc in result.get("tables", []): + if not table_desc["spans"]: + continue + + # convert the tables into markdown format + boundingRegions = table_desc["boundingRegions"] + if boundingRegions: + page_number = boundingRegions[0]["pageNumber"] + else: + page_number = 1 + + # store the tables into document + offset = table_desc["spans"][0]["offset"] + length = table_desc["spans"][0]["length"] + table_metadata = { + "type": "table", + "page_label": page_number, + "table_origin": text_content[offset : offset + length], + } + table_metadata.update(metadata) + + tables.append( + Document( + text=text_content[offset : offset + length], + metadata=table_metadata, + ) + ) + removed_spans += table_desc["spans"] + # save the text content into markdown format + if self.cache_dir is not None: + with open( + Path(self.cache_dir) / f"{file_name.stem}.md", "w", encoding="utf-8" + ) as f: + f.write(text_content) + + removed_spans = sorted(removed_spans, key=lambda x: x["offset"], reverse=True) + for span in removed_spans: + text_content = ( + text_content[: span["offset"]] + + text_content[span["offset"] + span["length"] :] + ) + + return [Document(content=text_content, metadata=metadata)] + figures + tables diff --git a/libs/kotaemon/kotaemon/loaders/base.py b/libs/kotaemon/kotaemon/loaders/base.py index 52bef49..aebf919 100644 --- a/libs/kotaemon/kotaemon/loaders/base.py +++ b/libs/kotaemon/kotaemon/loaders/base.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, Any, List, Type, Union from kotaemon.base import BaseComponent, Document if TYPE_CHECKING: - from llama_index.readers.base import BaseReader as LIBaseReader + from llama_index.core.readers.base import BaseReader as LIBaseReader class BaseReader(BaseComponent): @@ -20,7 +20,7 @@ class AutoReader(BaseReader): """Init reader using string identifier or class name from llama-hub""" if isinstance(reader_type, str): - from llama_index import download_loader + from llama_index.core import download_loader self._reader = download_loader(reader_type)() else: diff --git a/libs/kotaemon/kotaemon/loaders/composite_loader.py b/libs/kotaemon/kotaemon/loaders/composite_loader.py index 9d35e26..ddf71fa 100644 --- a/libs/kotaemon/kotaemon/loaders/composite_loader.py +++ b/libs/kotaemon/kotaemon/loaders/composite_loader.py @@ -1,6 +1,6 @@ from typing import Callable, List, Optional, Type -from llama_index.readers.base import BaseReader as LIBaseReader +from llama_index.core.readers.base import BaseReader as LIBaseReader from .base import BaseReader, LIReaderMixin @@ -48,6 +48,6 @@ class DirectoryReader(LIReaderMixin, BaseReader): file_metadata: Optional[Callable[[str], dict]] = None def _get_wrapped_class(self) -> Type["LIBaseReader"]: - from llama_index import SimpleDirectoryReader + from llama_index.core import SimpleDirectoryReader return SimpleDirectoryReader diff --git a/libs/kotaemon/kotaemon/loaders/docx_loader.py b/libs/kotaemon/kotaemon/loaders/docx_loader.py index dcec539..463a8fa 100644 --- a/libs/kotaemon/kotaemon/loaders/docx_loader.py +++ b/libs/kotaemon/kotaemon/loaders/docx_loader.py @@ -3,7 +3,7 @@ from pathlib import Path from typing import List, Optional import pandas as pd -from llama_index.readers.base import BaseReader +from llama_index.core.readers.base import BaseReader from kotaemon.base import Document @@ -27,6 +27,21 @@ class DocxReader(BaseReader): "Please install it using `pip install python-docx`" ) + def _load_single_table(self, table) -> List[List[str]]: + """Extract content from tables. Return a list of columns: list[str] + Some merged cells will share duplicated content. + """ + n_row = len(table.rows) + n_col = len(table.columns) + + arrays = [["" for _ in range(n_row)] for _ in range(n_col)] + + for i, row in enumerate(table.rows): + for j, cell in enumerate(row.cells): + arrays[j][i] = cell.text + + return arrays + def load_data( self, file_path: Path, extra_info: Optional[dict] = None, **kwargs ) -> List[Document]: @@ -50,13 +65,9 @@ class DocxReader(BaseReader): tables = [] for t in doc.tables: - arrays = [ - [ - unicodedata.normalize("NFKC", t.cell(i, j).text) - for i in range(len(t.rows)) - ] - for j in range(len(t.columns)) - ] + # return list of columns: list of string + arrays = self._load_single_table(t) + tables.append(pd.DataFrame({a[0]: a[1:] for a in arrays})) extra_info = extra_info or {} diff --git a/libs/kotaemon/kotaemon/loaders/excel_loader.py b/libs/kotaemon/kotaemon/loaders/excel_loader.py index d903aea..67d0043 100644 --- a/libs/kotaemon/kotaemon/loaders/excel_loader.py +++ b/libs/kotaemon/kotaemon/loaders/excel_loader.py @@ -6,7 +6,7 @@ Pandas parser for .xlsx files. from pathlib import Path from typing import Any, List, Optional, Union -from llama_index.readers.base import BaseReader +from llama_index.core.readers.base import BaseReader from kotaemon.base import Document @@ -82,6 +82,9 @@ class PandasExcelReader(BaseReader): sheet = [] if include_sheetname: sheet.append([key]) + dfs[key] = dfs[key].dropna(axis=0, how="all") + dfs[key] = dfs[key].dropna(axis=0, how="all") + dfs[key].fillna("", inplace=True) sheet.extend(dfs[key].values.astype(str).tolist()) df_sheets.append(sheet) @@ -99,3 +102,91 @@ class PandasExcelReader(BaseReader): ] return output + + +class ExcelReader(BaseReader): + r"""Spreadsheet exporter respecting multiple worksheets + + Parses CSVs using the separator detection from Pandas `read_csv` function. + If special parameters are required, use the `pandas_config` dict. + + Args: + + pandas_config (dict): Options for the `pandas.read_excel` function call. + Refer to https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html + for more information. Set to empty dict by default, + this means defaults will be used. + + """ + + def __init__( + self, + *args: Any, + pandas_config: Optional[dict] = None, + row_joiner: str = "\n", + col_joiner: str = " ", + **kwargs: Any, + ) -> None: + """Init params.""" + super().__init__(*args, **kwargs) + self._pandas_config = pandas_config or {} + self._row_joiner = row_joiner if row_joiner else "\n" + self._col_joiner = col_joiner if col_joiner else " " + + def load_data( + self, + file: Path, + include_sheetname: bool = True, + sheet_name: Optional[Union[str, int, list]] = None, + extra_info: Optional[dict] = None, + **kwargs, + ) -> List[Document]: + """Parse file and extract values from a specific column. + + Args: + file (Path): The path to the Excel file to read. + include_sheetname (bool): Whether to include the sheet name in the output. + sheet_name (Union[str, int, None]): The specific sheet to read from, + default is None which reads all sheets. + + Returns: + List[Document]: A list of`Document objects containing the + values from the specified column in the Excel file. + """ + + try: + import pandas as pd + except ImportError: + raise ImportError( + "install pandas using `pip3 install pandas` to use this loader" + ) + + if sheet_name is not None: + sheet_name = ( + [sheet_name] if not isinstance(sheet_name, list) else sheet_name + ) + + # clean up input + file = Path(file) + extra_info = extra_info or {} + + dfs = pd.read_excel(file, sheet_name=sheet_name, **self._pandas_config) + sheet_names = dfs.keys() + output = [] + + for idx, key in enumerate(sheet_names): + dfs[key] = dfs[key].dropna(axis=0, how="all") + dfs[key] = dfs[key].dropna(axis=0, how="all") + dfs[key] = dfs[key].astype("object") + dfs[key].fillna("", inplace=True) + + rows = dfs[key].values.astype(str).tolist() + content = self._row_joiner.join( + self._col_joiner.join(row).strip() for row in rows + ).strip() + if include_sheetname: + content = f"(Sheet {key} of file {file.name})\n{content}" + metadata = {"page_label": idx + 1, "sheet_name": key, **extra_info} + output.append(Document(text=content, metadata=metadata)) + + return output diff --git a/libs/kotaemon/kotaemon/loaders/html_loader.py b/libs/kotaemon/kotaemon/loaders/html_loader.py index c939c8a..555efe5 100644 --- a/libs/kotaemon/kotaemon/loaders/html_loader.py +++ b/libs/kotaemon/kotaemon/loaders/html_loader.py @@ -2,7 +2,8 @@ import email from pathlib import Path from typing import Optional -from llama_index.readers.base import BaseReader +from llama_index.core.readers.base import BaseReader +from theflow.settings import settings as flowsettings from kotaemon.base import Document @@ -78,6 +79,9 @@ class MhtmlReader(BaseReader): def __init__( self, + cache_dir: Optional[str] = getattr( + flowsettings, "KH_MARKDOWN_OUTPUT_DIR", None + ), open_encoding: Optional[str] = None, bs_kwargs: Optional[dict] = None, get_text_separator: str = "", @@ -86,6 +90,7 @@ class MhtmlReader(BaseReader): to pass to the BeautifulSoup object. Args: + cache_dir: Path for markdwon format. file_path: Path to file to load. open_encoding: The encoding to use when opening the file. bs_kwargs: Any kwargs to pass to the BeautifulSoup object. @@ -100,6 +105,7 @@ class MhtmlReader(BaseReader): "`pip install beautifulsoup4`" ) + self.cache_dir = cache_dir self.open_encoding = open_encoding if bs_kwargs is None: bs_kwargs = {"features": "lxml"} @@ -116,6 +122,7 @@ class MhtmlReader(BaseReader): extra_info = extra_info or {} metadata: dict = extra_info page = [] + file_name = Path(file_path) with open(file_path, "r", encoding=self.open_encoding) as f: message = email.message_from_string(f.read()) parts = message.get_payload() @@ -144,5 +151,11 @@ class MhtmlReader(BaseReader): text = "\n\n".join(lines) if text: page.append(text) + # save the page into markdown format + print(self.cache_dir) + if self.cache_dir is not None: + print(Path(self.cache_dir) / f"{file_name.stem}.md") + with open(Path(self.cache_dir) / f"{file_name.stem}.md", "w") as f: + f.write(page[0]) return [Document(text="\n\n".join(page), metadata=metadata)] diff --git a/libs/kotaemon/kotaemon/loaders/mathpix_loader.py b/libs/kotaemon/kotaemon/loaders/mathpix_loader.py index 19f1001..d07b069 100644 --- a/libs/kotaemon/kotaemon/loaders/mathpix_loader.py +++ b/libs/kotaemon/kotaemon/loaders/mathpix_loader.py @@ -6,7 +6,7 @@ from typing import Any, Dict, List, Optional import requests from langchain.utils import get_from_dict_or_env -from llama_index.readers.base import BaseReader +from llama_index.core.readers.base import BaseReader from kotaemon.base import Document diff --git a/libs/kotaemon/kotaemon/loaders/ocr_loader.py b/libs/kotaemon/kotaemon/loaders/ocr_loader.py index bb1ac5d..4e009de 100644 --- a/libs/kotaemon/kotaemon/loaders/ocr_loader.py +++ b/libs/kotaemon/kotaemon/loaders/ocr_loader.py @@ -5,8 +5,8 @@ from typing import List, Optional from uuid import uuid4 import requests -from llama_index.readers.base import BaseReader -from tenacity import after_log, retry, stop_after_attempt, wait_fixed, wait_random +from llama_index.core.readers.base import BaseReader +from tenacity import after_log, retry, stop_after_attempt, wait_exponential from kotaemon.base import Document @@ -19,13 +19,16 @@ DEFAULT_OCR_ENDPOINT = "http://127.0.0.1:8000/v2/ai/infer/" @retry( - stop=stop_after_attempt(3), - wait=wait_fixed(5) + wait_random(0, 2), - after=after_log(logger, logging.DEBUG), + stop=stop_after_attempt(6), + wait=wait_exponential(multiplier=20, exp_base=2, min=1, max=1000), + after=after_log(logger, logging.WARNING), ) -def tenacious_api_post(url, **kwargs): - resp = requests.post(url=url, **kwargs) - resp.raise_for_status() +def tenacious_api_post(url, file_path, table_only, **kwargs): + with file_path.open("rb") as content: + files = {"input": content} + data = {"job_id": uuid4(), "table_only": table_only} + resp = requests.post(url=url, files=files, data=data, **kwargs) + resp.raise_for_status() return resp @@ -71,18 +74,16 @@ class OCRReader(BaseReader): """ file_path = Path(file_path).resolve() - with file_path.open("rb") as content: - files = {"input": content} - data = {"job_id": uuid4(), "table_only": not self.use_ocr} - - # call the API from FullOCR endpoint - if "response_content" in kwargs: - # overriding response content if specified - ocr_results = kwargs["response_content"] - else: - # call original API - resp = tenacious_api_post(url=self.ocr_endpoint, files=files, data=data) - ocr_results = resp.json()["result"] + # call the API from FullOCR endpoint + if "response_content" in kwargs: + # overriding response content if specified + ocr_results = kwargs["response_content"] + else: + # call original API + resp = tenacious_api_post( + url=self.ocr_endpoint, file_path=file_path, table_only=not self.use_ocr + ) + ocr_results = resp.json()["result"] debug_path = kwargs.pop("debug_path", None) artifact_path = kwargs.pop("artifact_path", None) @@ -168,18 +169,16 @@ class ImageReader(BaseReader): """ file_path = Path(file_path).resolve() - with file_path.open("rb") as content: - files = {"input": content} - data = {"job_id": uuid4(), "table_only": False} - - # call the API from FullOCR endpoint - if "response_content" in kwargs: - # overriding response content if specified - ocr_results = kwargs["response_content"] - else: - # call original API - resp = tenacious_api_post(url=self.ocr_endpoint, files=files, data=data) - ocr_results = resp.json()["result"] + # call the API from FullOCR endpoint + if "response_content" in kwargs: + # overriding response content if specified + ocr_results = kwargs["response_content"] + else: + # call original API + resp = tenacious_api_post( + url=self.ocr_endpoint, file_path=file_path, table_only=False + ) + ocr_results = resp.json()["result"] extra_info = extra_info or {} result = [] diff --git a/libs/kotaemon/kotaemon/loaders/pdf_loader.py b/libs/kotaemon/kotaemon/loaders/pdf_loader.py new file mode 100644 index 0000000..ecba89d --- /dev/null +++ b/libs/kotaemon/kotaemon/loaders/pdf_loader.py @@ -0,0 +1,114 @@ +import base64 +from io import BytesIO +from pathlib import Path +from typing import Dict, List, Optional + +from fsspec import AbstractFileSystem +from llama_index.readers.file import PDFReader +from PIL import Image + +from kotaemon.base import Document + + +def get_page_thumbnails( + file_path: Path, pages: list[int], dpi: int = 80 +) -> List[Image.Image]: + """Get image thumbnails of the pages in the PDF file. + + Args: + file_path (Path): path to the image file + page_number (list[int]): list of page numbers to extract + + Returns: + list[Image.Image]: list of page thumbnails + """ + + img: Image.Image + suffix = file_path.suffix.lower() + assert suffix == ".pdf", "This function only supports PDF files." + try: + import fitz + except ImportError: + raise ImportError("Please install PyMuPDF: 'pip install PyMuPDF'") + + doc = fitz.open(file_path) + + output_imgs = [] + for page_number in pages: + page = doc.load_page(page_number) + pm = page.get_pixmap(dpi=dpi) + img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples) + output_imgs.append(convert_image_to_base64(img)) + + return output_imgs + + +def convert_image_to_base64(img: Image.Image) -> str: + # convert the image into base64 + img_bytes = BytesIO() + img.save(img_bytes, format="PNG") + img_base64 = base64.b64encode(img_bytes.getvalue()).decode("utf-8") + img_base64 = f"data:image/png;base64,{img_base64}" + + return img_base64 + + +class PDFThumbnailReader(PDFReader): + """PDF parser with thumbnail for each page.""" + + def __init__(self) -> None: + """ + Initialize PDFReader. + """ + super().__init__(return_full_document=False) + + def load_data( + self, + file: Path, + extra_info: Optional[Dict] = None, + fs: Optional[AbstractFileSystem] = None, + ) -> List[Document]: + """Parse file.""" + documents = super().load_data(file, extra_info, fs) + + page_numbers_str = [] + filtered_docs = [] + is_int_page_number: dict[str, bool] = {} + + for doc in documents: + if "page_label" in doc.metadata: + page_num_str = doc.metadata["page_label"] + page_numbers_str.append(page_num_str) + try: + _ = int(page_num_str) + is_int_page_number[page_num_str] = True + filtered_docs.append(doc) + except ValueError: + is_int_page_number[page_num_str] = False + continue + + documents = filtered_docs + page_numbers = list(range(len(page_numbers_str))) + + print("Page numbers:", len(page_numbers)) + page_thumbnails = get_page_thumbnails(file, page_numbers) + + documents.extend( + [ + Document( + text="Page thumbnail", + metadata={ + "image_origin": page_thumbnail, + "type": "thumbnail", + "page_label": page_number, + **(extra_info if extra_info is not None else {}), + }, + ) + for (page_thumbnail, page_number) in zip( + page_thumbnails, page_numbers_str + ) + if is_int_page_number[page_number] + ] + ) + + return documents diff --git a/libs/kotaemon/kotaemon/loaders/txt_loader.py b/libs/kotaemon/kotaemon/loaders/txt_loader.py new file mode 100644 index 0000000..6484029 --- /dev/null +++ b/libs/kotaemon/kotaemon/loaders/txt_loader.py @@ -0,0 +1,22 @@ +from pathlib import Path +from typing import Optional + +from kotaemon.base import Document + +from .base import BaseReader + + +class TxtReader(BaseReader): + def run( + self, file_path: str | Path, extra_info: Optional[dict] = None, **kwargs + ) -> list[Document]: + return self.load_data(Path(file_path), extra_info=extra_info, **kwargs) + + def load_data( + self, file_path: Path, extra_info: Optional[dict] = None, **kwargs + ) -> list[Document]: + with open(file_path, "r") as f: + text = f.read() + + metadata = extra_info or {} + return [Document(text=text, metadata=metadata)] diff --git a/libs/kotaemon/kotaemon/loaders/unstructured_loader.py b/libs/kotaemon/kotaemon/loaders/unstructured_loader.py index 82f3255..e8f8e30 100644 --- a/libs/kotaemon/kotaemon/loaders/unstructured_loader.py +++ b/libs/kotaemon/kotaemon/loaders/unstructured_loader.py @@ -12,7 +12,7 @@ pip install xlrd from pathlib import Path from typing import Any, Dict, List, Optional -from llama_index.readers.base import BaseReader +from llama_index.core.readers.base import BaseReader from kotaemon.base import Document diff --git a/libs/kotaemon/kotaemon/loaders/utils/gpt4v.py b/libs/kotaemon/kotaemon/loaders/utils/gpt4v.py index 1e219d6..e43059c 100644 --- a/libs/kotaemon/kotaemon/loaders/utils/gpt4v.py +++ b/libs/kotaemon/kotaemon/loaders/utils/gpt4v.py @@ -1,12 +1,19 @@ import json +import logging from typing import Any, List import requests from decouple import config +logger = logging.getLogger(__name__) + def generate_gpt4v( - endpoint: str, images: str | List[str], prompt: str, max_tokens: int = 512 + endpoint: str, + images: str | List[str], + prompt: str, + max_tokens: int = 512, + max_images: int = 10, ) -> str: # OpenAI API Key api_key = config("AZURE_OPENAI_API_KEY", default="") @@ -27,24 +34,36 @@ def generate_gpt4v( "type": "image_url", "image_url": {"url": image}, } - for image in images + for image in images[:max_images] ], } ], "max_tokens": max_tokens, + "temperature": 0, } + if len(images) > max_images: + print(f"Truncated to {max_images} images (original {len(images)} images") + + response = requests.post(endpoint, headers=headers, json=payload) + try: - response = requests.post(endpoint, headers=headers, json=payload) - output = response.json() - output = output["choices"][0]["message"]["content"] - except Exception: - output = "" + response.raise_for_status() + except Exception as e: + logger.exception(f"Error generating gpt4v: {response.text}; error {e}") + return "" + + output = response.json() + output = output["choices"][0]["message"]["content"] return output def stream_gpt4v( - endpoint: str, images: str | List[str], prompt: str, max_tokens: int = 512 + endpoint: str, + images: str | List[str], + prompt: str, + max_tokens: int = 512, + max_images: int = 10, ) -> Any: # OpenAI API Key api_key = config("AZURE_OPENAI_API_KEY", default="") @@ -65,17 +84,22 @@ def stream_gpt4v( "type": "image_url", "image_url": {"url": image}, } - for image in images + for image in images[:max_images] ], } ], "max_tokens": max_tokens, "stream": True, + "logprobs": True, + "temperature": 0, } + if len(images) > max_images: + print(f"Truncated to {max_images} images (original {len(images)} images") try: response = requests.post(endpoint, headers=headers, json=payload, stream=True) assert response.status_code == 200, str(response.content) output = "" + logprobs = [] for line in response.iter_lines(): if line: if line.startswith(b"\xef\xbb\xbf"): @@ -89,8 +113,23 @@ def stream_gpt4v( except Exception: break if len(line["choices"]): + if line["choices"][0].get("logprobs") is None: + _logprobs = [] + else: + _logprobs = [ + logprob["logprob"] + for logprob in line["choices"][0]["logprobs"].get( + "content", [] + ) + ] + output += line["choices"][0]["delta"].get("content", "") - yield line["choices"][0]["delta"].get("content", "") - except Exception: + logprobs += _logprobs + yield line["choices"][0]["delta"].get("content", ""), _logprobs + + except Exception as e: + logger.error(f"Error streaming gpt4v {e}") + logprobs = [] output = "" - return output + + return output, logprobs diff --git a/libs/kotaemon/kotaemon/storages/__init__.py b/libs/kotaemon/kotaemon/storages/__init__.py index 63cc6f6..d5f5c94 100644 --- a/libs/kotaemon/kotaemon/storages/__init__.py +++ b/libs/kotaemon/kotaemon/storages/__init__.py @@ -2,12 +2,14 @@ from .docstores import ( BaseDocumentStore, ElasticsearchDocumentStore, InMemoryDocumentStore, + LanceDBDocumentStore, SimpleFileDocumentStore, ) from .vectorstores import ( BaseVectorStore, ChromaVectorStore, InMemoryVectorStore, + LanceDBVectorStore, SimpleFileVectorStore, ) @@ -17,9 +19,11 @@ __all__ = [ "InMemoryDocumentStore", "ElasticsearchDocumentStore", "SimpleFileDocumentStore", + "LanceDBDocumentStore", # Vector stores "BaseVectorStore", "ChromaVectorStore", "InMemoryVectorStore", "SimpleFileVectorStore", + "LanceDBVectorStore", ] diff --git a/libs/kotaemon/kotaemon/storages/docstores/__init__.py b/libs/kotaemon/kotaemon/storages/docstores/__init__.py index 8c1da98..8bd0788 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/__init__.py +++ b/libs/kotaemon/kotaemon/storages/docstores/__init__.py @@ -1,6 +1,7 @@ from .base import BaseDocumentStore from .elasticsearch import ElasticsearchDocumentStore from .in_memory import InMemoryDocumentStore +from .lancedb import LanceDBDocumentStore from .simple_file import SimpleFileDocumentStore __all__ = [ @@ -8,4 +9,5 @@ __all__ = [ "InMemoryDocumentStore", "ElasticsearchDocumentStore", "SimpleFileDocumentStore", + "LanceDBDocumentStore", ] diff --git a/libs/kotaemon/kotaemon/storages/docstores/base.py b/libs/kotaemon/kotaemon/storages/docstores/base.py index 243584b..4b6f397 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/base.py +++ b/libs/kotaemon/kotaemon/storages/docstores/base.py @@ -41,6 +41,13 @@ class BaseDocumentStore(ABC): """Count number of documents""" ... + @abstractmethod + def query( + self, query: str, top_k: int = 10, doc_ids: Optional[list] = None + ) -> List[Document]: + """Search document store using search query""" + ... + @abstractmethod def delete(self, ids: Union[List[str], str]): """Delete document by id""" diff --git a/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py b/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py index b58b211..27045cf 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py +++ b/libs/kotaemon/kotaemon/storages/docstores/elasticsearch.py @@ -92,7 +92,10 @@ class ElasticsearchDocumentStore(BaseDocumentStore): "_id": doc_id, } requests.append(request) - self.es_bulk(self.client, requests) + + success, failed = self.es_bulk(self.client, requests) + print("Added/Updated documents to index", success) + print("Failed documents to index", failed) if refresh_indices: self.client.indices.refresh(index=self.index_name) @@ -131,16 +134,17 @@ class ElasticsearchDocumentStore(BaseDocumentStore): Returns: List[Document]: List of result documents """ - query_dict: dict = {"query": {"match": {"content": query}}, "size": top_k} - if doc_ids: - query_dict["query"]["match"]["_id"] = {"values": doc_ids} + query_dict: dict = {"match": {"content": query}} + if doc_ids is not None: + query_dict = {"bool": {"must": [query_dict, {"terms": {"_id": doc_ids}}]}} + query_dict = {"query": query_dict, "size": top_k} return self.query_raw(query_dict) def get(self, ids: Union[List[str], str]) -> List[Document]: """Get document by id""" if not isinstance(ids, list): ids = [ids] - query_dict = {"query": {"terms": {"_id": ids}}} + query_dict = {"query": {"terms": {"_id": ids}}, "size": 10000} return self.query_raw(query_dict) def count(self) -> int: diff --git a/libs/kotaemon/kotaemon/storages/docstores/in_memory.py b/libs/kotaemon/kotaemon/storages/docstores/in_memory.py index 3e2ee01..0df7a3e 100644 --- a/libs/kotaemon/kotaemon/storages/docstores/in_memory.py +++ b/libs/kotaemon/kotaemon/storages/docstores/in_memory.py @@ -81,6 +81,12 @@ class InMemoryDocumentStore(BaseDocumentStore): # Also, for portability, use SQLAlchemy for document store. self._store = {key: Document.from_dict(value) for key, value in store.items()} + def query( + self, query: str, top_k: int = 10, doc_ids: Optional[list] = None + ) -> List[Document]: + """Perform full-text search on document store""" + return [] + def __persist_flow__(self): return {} diff --git a/libs/kotaemon/kotaemon/storages/docstores/lancedb.py b/libs/kotaemon/kotaemon/storages/docstores/lancedb.py new file mode 100644 index 0000000..a0f69e3 --- /dev/null +++ b/libs/kotaemon/kotaemon/storages/docstores/lancedb.py @@ -0,0 +1,153 @@ +import json +from typing import List, Optional, Union + +from kotaemon.base import Document + +from .base import BaseDocumentStore + +MAX_DOCS_TO_GET = 10**4 + + +class LanceDBDocumentStore(BaseDocumentStore): + """LancdDB document store which support full-text search query""" + + def __init__(self, path: str = "lancedb", collection_name: str = "docstore"): + try: + import lancedb + except ImportError: + raise ImportError( + "Please install lancedb: 'pip install lancedb tanvity-py'" + ) + + self.db_uri = path + self.collection_name = collection_name + self.db_connection = lancedb.connect(self.db_uri) # type: ignore + + def add( + self, + docs: Union[Document, List[Document]], + ids: Optional[Union[List[str], str]] = None, + refresh_indices: bool = True, + **kwargs, + ): + """Load documents into lancedb storage.""" + doc_ids = ids if ids else [doc.doc_id for doc in docs] + data: list[dict[str, str]] | None = [ + { + "id": doc_id, + "text": doc.text, + "attributes": json.dumps(doc.metadata), + } + for doc_id, doc in zip(doc_ids, docs) + ] + + if self.collection_name not in self.db_connection.table_names(): + if data: + document_collection = self.db_connection.create_table( + self.collection_name, data=data, mode="overwrite" + ) + else: + # add data to existing table + document_collection = self.db_connection.open_table(self.collection_name) + if data: + document_collection.add(data) + + if refresh_indices: + document_collection.create_fts_index( + "text", + tokenizer_name="en_stem", + replace=True, + ) + + def query( + self, query: str, top_k: int = 10, doc_ids: Optional[list] = None + ) -> List[Document]: + if doc_ids: + id_filter = ", ".join([f"'{_id}'" for _id in doc_ids]) + query_filter = f"id in ({id_filter})" + else: + query_filter = None + try: + document_collection = self.db_connection.open_table(self.collection_name) + if query_filter: + docs = ( + document_collection.search(query, query_type="fts") + .where(query_filter, prefilter=True) + .limit(top_k) + .to_list() + ) + else: + docs = ( + document_collection.search(query, query_type="fts") + .limit(top_k) + .to_list() + ) + except (ValueError, FileNotFoundError): + docs = [] + return [ + Document( + id_=doc["id"], + text=doc["text"] if doc["text"] else "", + metadata=json.loads(doc["attributes"]), + ) + for doc in docs + ] + + def get(self, ids: Union[List[str], str]) -> List[Document]: + """Get document by id""" + if not isinstance(ids, list): + ids = [ids] + + id_filter = ", ".join([f"'{_id}'" for _id in ids]) + try: + document_collection = self.db_connection.open_table(self.collection_name) + query_filter = f"id in ({id_filter})" + docs = ( + document_collection.search() + .where(query_filter) + .limit(MAX_DOCS_TO_GET) + .to_list() + ) + except (ValueError, FileNotFoundError): + docs = [] + return [ + Document( + id_=doc["id"], + text=doc["text"] if doc["text"] else "", + metadata=json.loads(doc["attributes"]), + ) + for doc in docs + ] + + def delete(self, ids: Union[List[str], str], refresh_indices: bool = True): + """Delete document by id""" + if not isinstance(ids, list): + ids = [ids] + + document_collection = self.db_connection.open_table(self.collection_name) + id_filter = ", ".join([f"'{_id}'" for _id in ids]) + query_filter = f"id in ({id_filter})" + document_collection.delete(query_filter) + + if refresh_indices: + document_collection.create_fts_index( + "text", + tokenizer_name="en_stem", + replace=True, + ) + + def drop(self): + """Drop the document store""" + self.db_connection.drop_table(self.collection_name) + + def count(self) -> int: + raise NotImplementedError + + def get_all(self) -> List[Document]: + raise NotImplementedError + + def __persist_flow__(self): + return { + "db_uri": self.db_uri, + "collection_name": self.collection_name, + } diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py b/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py index 1606393..befc1e8 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/__init__.py @@ -1,6 +1,7 @@ from .base import BaseVectorStore from .chroma import ChromaVectorStore from .in_memory import InMemoryVectorStore +from .lancedb import LanceDBVectorStore from .simple_file import SimpleFileVectorStore __all__ = [ @@ -8,4 +9,5 @@ __all__ = [ "ChromaVectorStore", "InMemoryVectorStore", "SimpleFileVectorStore", + "LanceDBVectorStore", ] diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/base.py b/libs/kotaemon/kotaemon/storages/vectorstores/base.py index 0e762a9..e6f2518 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/base.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/base.py @@ -3,10 +3,10 @@ from __future__ import annotations from abc import ABC, abstractmethod from typing import Any, Optional -from llama_index.schema import NodeRelationship, RelatedNodeInfo -from llama_index.vector_stores.types import BasePydanticVectorStore -from llama_index.vector_stores.types import VectorStore as LIVectorStore -from llama_index.vector_stores.types import VectorStoreQuery +from llama_index.core.schema import NodeRelationship, RelatedNodeInfo +from llama_index.core.vector_stores.types import BasePydanticVectorStore +from llama_index.core.vector_stores.types import VectorStore as LIVectorStore +from llama_index.core.vector_stores.types import VectorStoreQuery from kotaemon.base import DocumentWithEmbedding diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py b/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py index a8ba7e1..9e01766 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/in_memory.py @@ -2,8 +2,8 @@ from typing import Any, Optional, Type import fsspec -from llama_index.vector_stores import SimpleVectorStore as LISimpleVectorStore -from llama_index.vector_stores.simple import SimpleVectorStoreData +from llama_index.core.vector_stores import SimpleVectorStore as LISimpleVectorStore +from llama_index.core.vector_stores.simple import SimpleVectorStoreData from .base import LlamaIndexVectorStore diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py b/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py new file mode 100644 index 0000000..e0fa4c0 --- /dev/null +++ b/libs/kotaemon/kotaemon/storages/vectorstores/lancedb.py @@ -0,0 +1,87 @@ +from typing import Any, List, Type, cast + +from llama_index.core.vector_stores.types import MetadataFilters +from llama_index.vector_stores.lancedb import LanceDBVectorStore as LILanceDBVectorStore +from llama_index.vector_stores.lancedb import base as base_lancedb + +from .base import LlamaIndexVectorStore + +# custom monkey patch for LanceDB +original_to_lance_filter = base_lancedb._to_lance_filter + + +def custom_to_lance_filter( + standard_filters: MetadataFilters, metadata_keys: list +) -> Any: + for filter in standard_filters.filters: + if isinstance(filter.value, list): + # quote string values if filter are list of strings + if filter.value and isinstance(filter.value[0], str): + filter.value = [f"'{v}'" for v in filter.value] + + return original_to_lance_filter(standard_filters, metadata_keys) + + +# skip table existence check +LILanceDBVectorStore._table_exists = lambda _: False +base_lancedb._to_lance_filter = custom_to_lance_filter + + +class LanceDBVectorStore(LlamaIndexVectorStore): + _li_class: Type[LILanceDBVectorStore] = LILanceDBVectorStore + + def __init__( + self, + path: str = "./lancedb", + collection_name: str = "default", + **kwargs: Any, + ): + self._path = path + self._collection_name = collection_name + + try: + import lancedb + except ImportError: + raise ImportError( + "Please install lancedb: 'pip install lancedb tanvity-py'" + ) + + db_connection = lancedb.connect(path) # type: ignore + try: + table = db_connection.open_table(collection_name) + except FileNotFoundError: + table = None + + self._kwargs = kwargs + + # pass through for nice IDE support + super().__init__( + uri=path, + table_name=collection_name, + table=table, + **kwargs, + ) + self._client = cast(LILanceDBVectorStore, self._client) + self._client._metadata_keys = ["file_id"] + + def delete(self, ids: List[str], **kwargs): + """Delete vector embeddings from vector stores + + Args: + ids: List of ids of the embeddings to be deleted + kwargs: meant for vectorstore-specific parameters + """ + self._client.delete_nodes(ids) + + def drop(self): + """Delete entire collection from vector stores""" + self._client.client.drop_table(self.collection_name) + + def count(self) -> int: + raise NotImplementedError + + def __persist_flow__(self): + return { + "path": self._path, + "collection_name": self._collection_name, + } diff --git a/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py b/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py index 043c66e..b96b67c 100644 --- a/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py +++ b/libs/kotaemon/kotaemon/storages/vectorstores/simple_file.py @@ -3,8 +3,8 @@ from pathlib import Path from typing import Any, Optional, Type import fsspec -from llama_index.vector_stores import SimpleVectorStore as LISimpleVectorStore -from llama_index.vector_stores.simple import SimpleVectorStoreData +from llama_index.core.vector_stores import SimpleVectorStore as LISimpleVectorStore +from llama_index.core.vector_stores.simple import SimpleVectorStoreData from kotaemon.base import DocumentWithEmbedding diff --git a/libs/kotaemon/pyproject.toml b/libs/kotaemon/pyproject.toml index 101b91b..f1e1f83 100644 --- a/libs/kotaemon/pyproject.toml +++ b/libs/kotaemon/pyproject.toml @@ -26,9 +26,11 @@ dependencies = [ "langchain-openai>=0.1.4,<0.2.0", "openai>=1.23.6,<2", "theflow>=0.8.6,<0.9.0", - "llama-index==0.9.48", + "llama-index>=0.10.40,<0.11.0", + "llama-index-vector-stores-chroma>=0.1.9", + "llama-index-vector-stores-lancedb", "llama-hub>=0.0.79,<0.1.0", - "gradio>=4.26.0,<5", + "gradio>=4.31.0,<4.40", "openpyxl>=3.1.2,<3.2", "cookiecutter>=2.6.0,<2.7", "click>=8.1.7,<9", @@ -36,13 +38,9 @@ dependencies = [ "trogon>=0.5.0,<0.6", "tenacity>=8.2.3,<8.3", "python-dotenv>=1.0.1,<1.1", - "chromadb>=0.4.21,<0.5", - "unstructured==0.13.4", "pypdf>=4.2.0,<4.3", + "PyMuPDF>=1.23", "html2text==2024.2.26", - "fastembed==0.2.6", - "llama-cpp-python>=0.2.72,<0.3", - "azure-ai-documentintelligence", "cohere>=5.3.2,<5.4", ] readme = "README.md" @@ -63,11 +61,12 @@ adv = [ "duckduckgo-search>=6.1.0,<6.2", "googlesearch-python>=1.2.4,<1.3", "python-docx>=1.1.0,<1.2", - "unstructured[pdf]==0.13.4", - "sentence_transformers==2.7.0", "elasticsearch>=8.13.0,<8.14", - "pdfservices-sdk @ git+https://github.com/niallcm/pdfservices-python-sdk.git@bump-and-unfreeze-requirements", "beautifulsoup4>=4.12.3,<4.13", + "plotly", + "tabulate", + "fast_langdetect", + "azure-ai-documentintelligence", ] dev = [ "ipython", diff --git a/libs/kotaemon/tests/test_reader.py b/libs/kotaemon/tests/test_reader.py index 0cdfa51..24c2dc0 100644 --- a/libs/kotaemon/tests/test_reader.py +++ b/libs/kotaemon/tests/test_reader.py @@ -2,7 +2,7 @@ from pathlib import Path from unittest.mock import patch from langchain.schema import Document as LangchainDocument -from llama_index.node_parser import SimpleNodeParser +from llama_index.core.node_parser import SimpleNodeParser from kotaemon.base import Document from kotaemon.loaders import ( diff --git a/libs/kotaemon/tests/test_splitter.py b/libs/kotaemon/tests/test_splitter.py index 71e63ee..75e9bc3 100644 --- a/libs/kotaemon/tests/test_splitter.py +++ b/libs/kotaemon/tests/test_splitter.py @@ -1,4 +1,4 @@ -from llama_index.schema import NodeRelationship +from llama_index.core.schema import NodeRelationship from kotaemon.base import Document from kotaemon.indices.splitters import TokenSplitter diff --git a/libs/ktem/.gitignore b/libs/ktem/.gitignore index a3659c1..e95c0eb 100644 --- a/libs/ktem/.gitignore +++ b/libs/ktem/.gitignore @@ -1,2 +1,3 @@ 14-1_抜粋-1.pdf _example_.db +ktem/assets/prebuilt/ diff --git a/libs/ktem/ktem/app.py b/libs/ktem/ktem/app.py index 357b0d9..f4a4237 100644 --- a/libs/ktem/ktem/app.py +++ b/libs/ktem/ktem/app.py @@ -4,6 +4,7 @@ from typing import Optional import gradio as gr import pluggy from ktem import extension_protocol +from ktem.assets import PDFJS_PREBUILT_DIR from ktem.components import reasonings from ktem.exceptions import HookAlreadyDeclared, HookNotDeclared from ktem.index import IndexManager @@ -36,6 +37,7 @@ class BaseApp: def __init__(self): self.dev_mode = getattr(settings, "KH_MODE", "") == "dev" self.app_name = getattr(settings, "KH_APP_NAME", "Kotaemon") + self.app_version = getattr(settings, "KH_APP_VERSION", "") self.f_user_management = getattr(settings, "KH_FEATURE_USER_MANAGEMENT", False) self._theme = gr.Theme.from_hub("lone17/kotaemon") @@ -44,6 +46,13 @@ class BaseApp: self._css = fi.read() with (dir_assets / "js" / "main.js").open() as fi: self._js = fi.read() + self._js = self._js.replace("KH_APP_VERSION", self.app_version) + with (dir_assets / "js" / "pdf_viewer.js").open() as fi: + self._pdf_view_js = fi.read() + self._pdf_view_js = self._pdf_view_js.replace( + "PDFJS_PREBUILT_DIR", str(PDFJS_PREBUILT_DIR) + ) + self._favicon = str(dir_assets / "img" / "favicon.svg") self.default_settings = SettingGroup( @@ -156,11 +165,17 @@ class BaseApp: """Called when the app is created""" def make(self): + external_js = """ + + """ + with gr.Blocks( theme=self._theme, css=self._css, title=self.app_name, analytics_enabled=False, + js=self._js, + head=external_js, ) as demo: self.app = demo self.settings_state.render() @@ -173,6 +188,8 @@ class BaseApp: self.register_events() self.on_app_created() + demo.load(None, None, None, js=self._pdf_view_js) + return demo def declare_public_events(self): @@ -200,7 +217,6 @@ class BaseApp: def on_app_created(self): """Execute on app created callbacks""" - self.app.load(lambda: None, None, None, js=f"() => {{{self._js}}}") self._on_app_created() for value in self.__dict__.values(): if isinstance(value, BasePage): diff --git a/libs/ktem/ktem/assets/__init__.py b/libs/ktem/ktem/assets/__init__.py new file mode 100644 index 0000000..8c0df2a --- /dev/null +++ b/libs/ktem/ktem/assets/__init__.py @@ -0,0 +1,6 @@ +from pathlib import Path + +from decouple import config + +PDFJS_VERSION_DIST: str = config("PDFJS_VERSION_DIST", "pdfjs-4.0.379-dist") +PDFJS_PREBUILT_DIR: Path = Path(__file__).parent / "prebuilt" / PDFJS_VERSION_DIST diff --git a/libs/ktem/ktem/assets/css/main.css b/libs/ktem/ktem/assets/css/main.css index 36fc7ab..da83e83 100644 --- a/libs/ktem/ktem/assets/css/main.css +++ b/libs/ktem/ktem/assets/css/main.css @@ -147,6 +147,16 @@ mark { max-height: 42px; } +/* Hide sort buttons at gr.DataFrame */ +.sort-button { + display: none !important; +} + +/* Show sort button only in File list*/ +#file_list_view .sort-button { + display: block !important; +} + .scrollable { overflow-y: auto; } @@ -158,3 +168,58 @@ mark { .unset-overflow { overflow: unset !important; } + +/*body {*/ +/* margin: 0;*/ +/* font-family: Arial, sans-serif;*/ +/*}*/ + +pdfjs-viewer-element { + height: 100vh; + height: 100dvh; +} + +/* Modal styles */ + +.modal { + display: none; + position: relative; + z-index: 1; + left: 0; + top: 0; + width: 100%; + height: 100%; + overflow: auto; + background-color: rgb(0, 0, 0); + background-color: rgba(0, 0, 0, 0.4); +} + +.modal-header { + padding: 0px 10px +} + +.modal-content { + background-color: #fefefe; + height: 110%; + display: flex; + flex-direction: column; +} + +.close { + color: #aaa; + align-self: flex-end; + font-size: 28px; + font-weight: bold; +} + +.close:hover, +.close:focus { + color: black; + text-decoration: none; + cursor: pointer; +} + +.modal-body { + flex: 1; + overflow: auto; +} diff --git a/libs/ktem/ktem/assets/icons/delete.svg b/libs/ktem/ktem/assets/icons/delete.svg new file mode 100644 index 0000000..66b72c2 --- /dev/null +++ b/libs/ktem/ktem/assets/icons/delete.svg @@ -0,0 +1 @@ + diff --git a/libs/ktem/ktem/assets/icons/new.svg b/libs/ktem/ktem/assets/icons/new.svg new file mode 100644 index 0000000..bd3deb7 --- /dev/null +++ b/libs/ktem/ktem/assets/icons/new.svg @@ -0,0 +1 @@ + diff --git a/libs/ktem/ktem/assets/icons/rename.svg b/libs/ktem/ktem/assets/icons/rename.svg new file mode 100644 index 0000000..200a94b --- /dev/null +++ b/libs/ktem/ktem/assets/icons/rename.svg @@ -0,0 +1 @@ + diff --git a/libs/ktem/ktem/assets/icons/sidebar.svg b/libs/ktem/ktem/assets/icons/sidebar.svg new file mode 100644 index 0000000..41363a2 --- /dev/null +++ b/libs/ktem/ktem/assets/icons/sidebar.svg @@ -0,0 +1 @@ + diff --git a/libs/ktem/ktem/assets/js/main.js b/libs/ktem/ktem/assets/js/main.js index 9ce6933..a441b8a 100644 --- a/libs/ktem/ktem/assets/js/main.js +++ b/libs/ktem/ktem/assets/js/main.js @@ -1,30 +1,37 @@ -let main_parent = document.getElementById("chat-tab").parentNode; +function run() { + let main_parent = document.getElementById("chat-tab").parentNode; -main_parent.childNodes[0].classList.add("header-bar"); -main_parent.style = "padding: 0; margin: 0"; -main_parent.parentNode.style = "gap: 0"; -main_parent.parentNode.parentNode.style = "padding: 0"; + main_parent.childNodes[0].classList.add("header-bar"); + main_parent.style = "padding: 0; margin: 0"; + main_parent.parentNode.style = "gap: 0"; + main_parent.parentNode.parentNode.style = "padding: 0"; + const version_node = document.createElement("p"); + version_node.innerHTML = "version: KH_APP_VERSION"; + version_node.style = "position: fixed; top: 10px; right: 10px;"; + main_parent.appendChild(version_node); -// clpse -globalThis.clpseFn = (id) => { - var obj = document.getElementById('clpse-btn-' + id); - obj.classList.toggle("clpse-active"); - var content = obj.nextElementSibling; - if (content.style.display === "none") { - content.style.display = "block"; - } else { - content.style.display = "none"; + // clpse + globalThis.clpseFn = (id) => { + var obj = document.getElementById('clpse-btn-' + id); + obj.classList.toggle("clpse-active"); + var content = obj.nextElementSibling; + if (content.style.display === "none") { + content.style.display = "block"; + } else { + content.style.display = "none"; + } + } + + // store info in local storage + globalThis.setStorage = (key, value) => { + localStorage.setItem(key, value) + } + globalThis.getStorage = (key, value) => { + item = localStorage.getItem(key); + return item ? item : value; + } + globalThis.removeFromStorage = (key) => { + localStorage.removeItem(key) } } - -// store info in local storage -globalThis.setStorage = (key, value) => { - localStorage.setItem(key, JSON.stringify(value)) -} -globalThis.getStorage = (key, value) => { - return JSON.parse(localStorage.getItem(key)) -} -globalThis.removeFromStorage = (key) => { - localStorage.removeItem(key) -} diff --git a/libs/ktem/ktem/assets/js/pdf_viewer.js b/libs/ktem/ktem/assets/js/pdf_viewer.js new file mode 100644 index 0000000..10b32d9 --- /dev/null +++ b/libs/ktem/ktem/assets/js/pdf_viewer.js @@ -0,0 +1,99 @@ +function onBlockLoad () { + var infor_panel_scroll_pos = 0; + globalThis.createModal = () => { + // Create modal for the 1st time if it does not exist + var modal = document.getElementById("pdf-modal"); + var old_position = null; + var old_width = null; + var old_left = null; + var expanded = false; + + modal.id = "pdf-modal"; + modal.className = "modal"; + modal.innerHTML = ` +
+
+ × + ⛶ +
+
+ + +
+
+ `; + + modal.querySelector("#modal-close").onclick = function() { + modal.style.display = "none"; + var info_panel = document.getElementById("html-info-panel"); + if (info_panel) { + info_panel.style.display = "block"; + } + var scrollableDiv = document.getElementById("chat-info-panel"); + scrollableDiv.scrollTop = infor_panel_scroll_pos; + }; + + modal.querySelector("#modal-expand").onclick = function () { + expanded = !expanded; + if (expanded) { + old_position = modal.style.position; + old_left = modal.style.left; + old_width = modal.style.width; + + modal.style.position = "fixed"; + modal.style.width = "70%"; + modal.style.left = "15%"; + } else { + modal.style.position = old_position; + modal.style.width = old_width; + modal.style.left = old_left; + } + }; + } + + // Function to open modal and display PDF + globalThis.openModal = (event) => { + event.preventDefault(); + var target = event.currentTarget; + var src = target.getAttribute("data-src"); + var page = target.getAttribute("data-page"); + var search = target.getAttribute("data-search"); + var phrase = target.getAttribute("data-phrase"); + + var pdfViewer = document.getElementById("pdf-viewer"); + + current_src = pdfViewer.getAttribute("src"); + if (current_src != src) { + pdfViewer.setAttribute("src", src); + } + pdfViewer.setAttribute("phrase", phrase); + pdfViewer.setAttribute("search", search); + pdfViewer.setAttribute("page", page); + + var scrollableDiv = document.getElementById("chat-info-panel"); + infor_panel_scroll_pos = scrollableDiv.scrollTop; + + var modal = document.getElementById("pdf-modal") + modal.style.display = "block"; + var info_panel = document.getElementById("html-info-panel"); + if (info_panel) { + info_panel.style.display = "none"; + } + scrollableDiv.scrollTop = 0; + } + + globalThis.assignPdfOnclickEvent = () => { + // Get all links and attach click event + var links = document.getElementsByClassName("pdf-link"); + for (var i = 0; i < links.length; i++) { + links[i].onclick = openModal; + } + } + + var created_modal = document.getElementById("pdf-viewer"); + if (!created_modal) { + createModal(); + console.log("Created modal") + } + +} diff --git a/libs/ktem/ktem/assets/md/about.md b/libs/ktem/ktem/assets/md/about.md index 76f86df..8c69d96 100644 --- a/libs/ktem/ktem/assets/md/about.md +++ b/libs/ktem/ktem/assets/md/about.md @@ -8,3 +8,6 @@ An open-source tool for you to chat with your documents. [User Guide](https://cinnamon.github.io/kotaemon/) | [Developer Guide](https://cinnamon.github.io/kotaemon/development/) | [Feedback](https://github.com/Cinnamon/kotaemon/issues) + +[Dark Mode](?__theme=dark) +[Night Mode](?__theme=light) diff --git a/libs/ktem/ktem/assets/md/usage.md b/libs/ktem/ktem/assets/md/usage.md index e976f8c..58c0cdb 100644 --- a/libs/ktem/ktem/assets/md/usage.md +++ b/libs/ktem/ktem/assets/md/usage.md @@ -136,6 +136,6 @@ Now navigate back to the `Chat` tab. The chat tab is divided into 3 regions: files will be considered during chat. 2. Chat Panel - This is where you can chat with the chatbot. -3. Information panel +3. Information Panel - Supporting information such as the retrieved evidence and reference will be displayed here. diff --git a/libs/ktem/ktem/db/base_models.py b/libs/ktem/ktem/db/base_models.py index 36349fb..1379caf 100644 --- a/libs/ktem/ktem/db/base_models.py +++ b/libs/ktem/ktem/db/base_models.py @@ -1,9 +1,11 @@ import datetime import uuid from typing import Optional +from zoneinfo import ZoneInfo from sqlalchemy import JSON, Column from sqlmodel import Field, SQLModel +from theflow.settings import settings as flowsettings class BaseConversation(SQLModel): @@ -24,10 +26,14 @@ class BaseConversation(SQLModel): default_factory=lambda: uuid.uuid4().hex, primary_key=True, index=True ) name: str = Field( - default_factory=lambda: datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S") + default_factory=lambda: datetime.datetime.now( + ZoneInfo(getattr(flowsettings, "TIME_ZONE", "UTC")) + ).strftime("%Y-%m-%d %H:%M:%S") ) user: int = Field(default=0) # For now we only have one user + is_public: bool = Field(default=False) + # contains messages + current files data_source: dict = Field(default={}, sa_column=Column(JSON)) diff --git a/libs/ktem/ktem/embeddings/manager.py b/libs/ktem/ktem/embeddings/manager.py index dde04f4..4948aeb 100644 --- a/libs/ktem/ktem/embeddings/manager.py +++ b/libs/ktem/ktem/embeddings/manager.py @@ -36,7 +36,7 @@ class EmbeddingManager: def load(self): """Load the model pool from database""" - self._models, self._info, self._defaut = {}, {}, "" + self._models, self._info, self._default = {}, {}, "" with Session(engine) as sess: stmt = select(EmbeddingTable) items = sess.execute(stmt) diff --git a/libs/ktem/ktem/embeddings/ui.py b/libs/ktem/ktem/embeddings/ui.py index f8db9f1..1b2e549 100644 --- a/libs/ktem/ktem/embeddings/ui.py +++ b/libs/ktem/ktem/embeddings/ui.py @@ -115,7 +115,7 @@ class EmbeddingManagement(BasePage): """Called when the app is created""" self._app.app.load( self.list_embeddings, - inputs=None, + inputs=[], outputs=[self.emb_list], ) self._app.app.load( @@ -144,7 +144,7 @@ class EmbeddingManagement(BasePage): self.create_emb, inputs=[self.name, self.emb_choices, self.spec, self.default], outputs=None, - ).success(self.list_embeddings, inputs=None, outputs=[self.emb_list]).success( + ).success(self.list_embeddings, inputs=[], outputs=[self.emb_list]).success( lambda: ("", None, "", False, self.spec_desc_default), outputs=[ self.name, @@ -179,7 +179,7 @@ class EmbeddingManagement(BasePage): ) self.btn_delete.click( self.on_btn_delete_click, - inputs=None, + inputs=[], outputs=[self.btn_delete, self.btn_delete_yes, self.btn_delete_no], show_progress="hidden", ) @@ -190,7 +190,7 @@ class EmbeddingManagement(BasePage): show_progress="hidden", ).then( self.list_embeddings, - inputs=None, + inputs=[], outputs=[self.emb_list], ) self.btn_delete_no.click( @@ -199,7 +199,7 @@ class EmbeddingManagement(BasePage): gr.update(visible=False), gr.update(visible=False), ), - inputs=None, + inputs=[], outputs=[self.btn_delete, self.btn_delete_yes, self.btn_delete_no], show_progress="hidden", ) @@ -213,7 +213,7 @@ class EmbeddingManagement(BasePage): show_progress="hidden", ).then( self.list_embeddings, - inputs=None, + inputs=[], outputs=[self.emb_list], ) self.btn_close.click( diff --git a/libs/ktem/ktem/index/file/base.py b/libs/ktem/ktem/index/file/base.py index a489a8e..427a396 100644 --- a/libs/ktem/ktem/index/file/base.py +++ b/libs/ktem/ktem/index/file/base.py @@ -54,6 +54,7 @@ class BaseFileIndexIndexing(BaseComponent): DS = Param(help="The DocStore") FSPath = Param(help="The file storage path") user_id = Param(help="The user id") + private = Param(False, help="Whether this is private index") def run( self, file_paths: str | Path | list[str | Path], *args, **kwargs @@ -73,7 +74,9 @@ class BaseFileIndexIndexing(BaseComponent): def stream( self, file_paths: str | Path | list[str | Path], *args, **kwargs - ) -> Generator[Document, None, tuple[list[str | None], list[str | None]]]: + ) -> Generator[ + Document, None, tuple[list[str | None], list[str | None], list[Document]] + ]: """Stream the indexing pipeline Args: @@ -87,6 +90,7 @@ class BaseFileIndexIndexing(BaseComponent): None if the indexing failed for that file path) - the error messages (each error message corresponds to an input file path, or None if the indexing was successful for that file path) + - the indexed documents in form of list[Documents] """ raise NotImplementedError @@ -149,3 +153,7 @@ class BaseFileIndexIndexing(BaseComponent): msg: the message to log """ print(msg) + + def rebuild_index(self): + """Rebuild the index""" + raise NotImplementedError diff --git a/libs/ktem/ktem/index/file/graph/__init__.py b/libs/ktem/ktem/index/file/graph/__init__.py new file mode 100644 index 0000000..194be6f --- /dev/null +++ b/libs/ktem/ktem/index/file/graph/__init__.py @@ -0,0 +1,3 @@ +from .graph_index import GraphRAGIndex + +__all__ = ["GraphRAGIndex"] diff --git a/libs/ktem/ktem/index/file/graph/graph_index.py b/libs/ktem/ktem/index/file/graph/graph_index.py new file mode 100644 index 0000000..797fd3e --- /dev/null +++ b/libs/ktem/ktem/index/file/graph/graph_index.py @@ -0,0 +1,36 @@ +from typing import Any + +from ktem.index.file import FileIndex + +from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever +from .pipelines import GraphRAGIndexingPipeline, GraphRAGRetrieverPipeline + + +class GraphRAGIndex(FileIndex): + def _setup_indexing_cls(self): + self._indexing_pipeline_cls = GraphRAGIndexingPipeline + + def _setup_retriever_cls(self): + self._retriever_pipeline_cls = [GraphRAGRetrieverPipeline] + + def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: + """Define the interface of the indexing pipeline""" + + obj = super().get_indexing_pipeline(settings, user_id) + # disable vectorstore for this kind of Index + obj.VS = None + + return obj + + def get_retriever_pipelines( + self, settings: dict, user_id: int, selected: Any = None + ) -> list["BaseFileIndexRetriever"]: + _, file_ids, _ = selected + retrievers = [ + GraphRAGRetrieverPipeline( + file_ids=file_ids, + Index=self._resources["Index"], + ) + ] + + return retrievers diff --git a/libs/ktem/ktem/index/file/graph/pipelines.py b/libs/ktem/ktem/index/file/graph/pipelines.py new file mode 100644 index 0000000..3016753 --- /dev/null +++ b/libs/ktem/ktem/index/file/graph/pipelines.py @@ -0,0 +1,359 @@ +import os +import subprocess +from pathlib import Path +from shutil import rmtree +from typing import Generator +from uuid import uuid4 + +import pandas as pd +import tiktoken +from ktem.db.models import engine +from sqlalchemy.orm import Session +from theflow.settings import settings + +from kotaemon.base import Document, Param, RetrievedDocument + +from ..pipelines import BaseFileIndexRetriever, IndexDocumentPipeline, IndexPipeline +from .visualize import create_knowledge_graph, visualize_graph + +try: + from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey + from graphrag.query.indexer_adapters import ( + read_indexer_entities, + read_indexer_relationships, + read_indexer_reports, + read_indexer_text_units, + ) + from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings + from graphrag.query.llm.oai.embedding import OpenAIEmbedding + from graphrag.query.llm.oai.typing import OpenaiApiType + from graphrag.query.structured_search.local_search.mixed_context import ( + LocalSearchMixedContext, + ) + from graphrag.vector_stores.lancedb import LanceDBVectorStore +except ImportError: + print( + ( + "GraphRAG dependencies not installed. " + "GraphRAG retriever pipeline will not work properly." + ) + ) + + +filestorage_path = Path(settings.KH_FILESTORAGE_PATH) / "graphrag" +filestorage_path.mkdir(parents=True, exist_ok=True) + + +def prepare_graph_index_path(graph_id: str): + root_path = Path(filestorage_path) / graph_id + input_path = root_path / "input" + + return root_path, input_path + + +class GraphRAGIndexingPipeline(IndexDocumentPipeline): + """GraphRAG specific indexing pipeline""" + + def route(self, file_path: Path) -> IndexPipeline: + """Simply disable the splitter (chunking) for this pipeline""" + pipeline = super().route(file_path) + pipeline.splitter = None + + return pipeline + + def store_file_id_with_graph_id(self, file_ids: list[str | None]): + # create new graph_id and assign them to doc_id in self.Index + # record in the index + graph_id = str(uuid4()) + with Session(engine) as session: + nodes = [] + for file_id in file_ids: + if not file_id: + continue + nodes.append( + self.Index( + source_id=file_id, + target_id=graph_id, + relation_type="graph", + ) + ) + + session.add_all(nodes) + session.commit() + + return graph_id + + def write_docs_to_files(self, graph_id: str, docs: list[Document]): + root_path, input_path = prepare_graph_index_path(graph_id) + input_path.mkdir(parents=True, exist_ok=True) + + for doc in docs: + if doc.metadata.get("type", "text") == "text": + with open(input_path / f"{doc.doc_id}.txt", "w") as f: + f.write(doc.text) + + return root_path + + def call_graphrag_index(self, input_path: str): + # Construct the command + command = [ + "python", + "-m", + "graphrag.index", + "--root", + input_path, + "--reporter", + "rich", + "--init", + ] + + # Run the command + yield Document( + channel="debug", + text="[GraphRAG] Creating index... This can take a long time.", + ) + result = subprocess.run(command, capture_output=True, text=True) + print(result.stdout) + command = command[:-1] + + # Run the command and stream stdout + with subprocess.Popen(command, stdout=subprocess.PIPE, text=True) as process: + if process.stdout: + for line in process.stdout: + yield Document(channel="debug", text=line) + + def stream( + self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs + ) -> Generator[ + Document, None, tuple[list[str | None], list[str | None], list[Document]] + ]: + file_ids, errors, all_docs = yield from super().stream( + file_paths, reindex=reindex, **kwargs + ) + + # assign graph_id to file_ids + graph_id = self.store_file_id_with_graph_id(file_ids) + # call GraphRAG index with docs and graph_id + graph_index_path = self.write_docs_to_files(graph_id, all_docs) + yield from self.call_graphrag_index(graph_index_path) + + return file_ids, errors, all_docs + + +class GraphRAGRetrieverPipeline(BaseFileIndexRetriever): + """GraphRAG specific retriever pipeline""" + + Index = Param(help="The SQLAlchemy Index table") + file_ids: list[str] = [] + + @classmethod + def get_user_settings(cls) -> dict: + return { + "search_type": { + "name": "Search type", + "value": "local", + "choices": ["local", "global"], + "component": "dropdown", + "info": "Whether to use local or global search in the graph.", + } + } + + def _build_graph_search(self): + assert ( + len(self.file_ids) <= 1 + ), "GraphRAG retriever only supports one file_id at a time" + + file_id = self.file_ids[0] + # retrieve the graph_id from the index + with Session(engine) as session: + graph_id = ( + session.query(self.Index.target_id) + .filter(self.Index.source_id == file_id) + .filter(self.Index.relation_type == "graph") + .first() + ) + graph_id = graph_id[0] if graph_id else None + assert graph_id, f"GraphRAG index not found for file_id: {file_id}" + + root_path, _ = prepare_graph_index_path(graph_id) + output_path = root_path / "output" + child_paths = sorted( + list(output_path.iterdir()), key=lambda x: x.stem, reverse=True + ) + + # get the latest child path + assert child_paths, "GraphRAG index output not found" + latest_child_path = Path(child_paths[0]) / "artifacts" + + INPUT_DIR = latest_child_path + LANCEDB_URI = str(INPUT_DIR / "lancedb") + COMMUNITY_REPORT_TABLE = "create_final_community_reports" + ENTITY_TABLE = "create_final_nodes" + ENTITY_EMBEDDING_TABLE = "create_final_entities" + RELATIONSHIP_TABLE = "create_final_relationships" + TEXT_UNIT_TABLE = "create_final_text_units" + COMMUNITY_LEVEL = 2 + + # read nodes table to get community and degree data + entity_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_TABLE}.parquet") + entity_embedding_df = pd.read_parquet( + f"{INPUT_DIR}/{ENTITY_EMBEDDING_TABLE}.parquet" + ) + entities = read_indexer_entities( + entity_df, entity_embedding_df, COMMUNITY_LEVEL + ) + + # load description embeddings to an in-memory lancedb vectorstore + # to connect to a remote db, specify url and port values. + description_embedding_store = LanceDBVectorStore( + collection_name="entity_description_embeddings", + ) + description_embedding_store.connect(db_uri=LANCEDB_URI) + if Path(LANCEDB_URI).is_dir(): + rmtree(LANCEDB_URI) + _ = store_entity_semantic_embeddings( + entities=entities, vectorstore=description_embedding_store + ) + print(f"Entity count: {len(entity_df)}") + + # Read relationships + relationship_df = pd.read_parquet(f"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet") + relationships = read_indexer_relationships(relationship_df) + + # Read community reports + report_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet") + reports = read_indexer_reports(report_df, entity_df, COMMUNITY_LEVEL) + + # Read text units + text_unit_df = pd.read_parquet(f"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet") + text_units = read_indexer_text_units(text_unit_df) + + embedding_model = os.getenv("GRAPHRAG_EMBEDDING_MODEL") + text_embedder = OpenAIEmbedding( + api_key=os.getenv("OPENAI_API_KEY"), + api_base=None, + api_type=OpenaiApiType.OpenAI, + model=embedding_model, + deployment_name=embedding_model, + max_retries=20, + ) + token_encoder = tiktoken.get_encoding("cl100k_base") + + context_builder = LocalSearchMixedContext( + community_reports=reports, + text_units=text_units, + entities=entities, + relationships=relationships, + covariates=None, + entity_text_embeddings=description_embedding_store, + embedding_vectorstore_key=EntityVectorStoreKey.ID, + # if the vectorstore uses entity title as ids, + # set this to EntityVectorStoreKey.TITLE + text_embedder=text_embedder, + token_encoder=token_encoder, + ) + return context_builder + + def _to_document(self, header: str, context_text: str) -> RetrievedDocument: + return RetrievedDocument( + text=context_text, + metadata={ + "file_name": header, + "type": "table", + "llm_trulens_score": 1.0, + }, + score=1.0, + ) + + def format_context_records(self, context_records) -> list[RetrievedDocument]: + entities = context_records.get("entities", []) + relationships = context_records.get("relationships", []) + reports = context_records.get("reports", []) + sources = context_records.get("sources", []) + + docs = [] + + context: str = "" + + header = "Entities\n" + context = entities[["entity", "description"]].to_markdown(index=False) + docs.append(self._to_document(header, context)) + + header = "\nRelationships\n" + context = relationships[["source", "target", "description"]].to_markdown( + index=False + ) + docs.append(self._to_document(header, context)) + + header = "\nReports\n" + context = "" + for idx, row in reports.iterrows(): + title, content = row["title"], row["content"] + context += f"\n\n
Report {title}
\n" + context += content + docs.append(self._to_document(header, context)) + + header = "\nSources\n" + context = "" + for idx, row in sources.iterrows(): + title, content = row["id"], row["text"] + context += f"\n\n
Source #{title}
\n" + context += content + docs.append(self._to_document(header, context)) + + return docs + + def plot_graph(self, context_records): + relationships = context_records.get("relationships", []) + G = create_knowledge_graph(relationships) + plot = visualize_graph(G) + return plot + + def generate_relevant_scores(self, text, documents: list[RetrievedDocument]): + return documents + + def run( + self, + text: str, + ) -> list[RetrievedDocument]: + if not self.file_ids: + return [] + context_builder = self._build_graph_search() + + local_context_params = { + "text_unit_prop": 0.5, + "community_prop": 0.1, + "conversation_history_max_turns": 5, + "conversation_history_user_turns_only": True, + "top_k_mapped_entities": 10, + "top_k_relationships": 10, + "include_entity_rank": False, + "include_relationship_weight": False, + "include_community_rank": False, + "return_candidate_context": False, + "embedding_vectorstore_key": EntityVectorStoreKey.ID, + # set this to EntityVectorStoreKey.TITLE i + # f the vectorstore uses entity title as ids + "max_tokens": 12_000, + # change this based on the token limit you have on your model + # (if you are using a model with 8k limit, a good setting could be 5000) + } + + context_text, context_records = context_builder.build_context( + query=text, + conversation_history=None, + **local_context_params, + ) + documents = self.format_context_records(context_records) + plot = self.plot_graph(context_records) + + return documents + [ + RetrievedDocument( + text="", + metadata={ + "file_name": "GraphRAG", + "type": "plot", + "data": plot, + }, + ), + ] diff --git a/libs/ktem/ktem/index/file/graph/visualize.py b/libs/ktem/ktem/index/file/graph/visualize.py new file mode 100644 index 0000000..722dab7 --- /dev/null +++ b/libs/ktem/ktem/index/file/graph/visualize.py @@ -0,0 +1,102 @@ +import networkx as nx +import plotly.graph_objects as go +from plotly.io import to_json + + +def create_knowledge_graph(df): + """ + create nx Graph from DataFrame relations data + """ + G = nx.Graph() + for _, row in df.iterrows(): + source = row["source"] + target = row["target"] + attributes = {k: v for k, v in row.items() if k not in ["source", "target"]} + G.add_edge(source, target, **attributes) + + return G + + +def visualize_graph(G): + pos = nx.spring_layout(G, dim=2) + + edge_x = [] + edge_y = [] + edge_texts = nx.get_edge_attributes(G, "description") + to_display_edge_texts = [] + for edge in G.edges(): + x0, y0 = pos[edge[0]] + x1, y1 = pos[edge[1]] + edge_x.append(x0) + edge_x.append(x1) + edge_x.append(None) + edge_y.append(y0) + edge_y.append(y1) + edge_y.append(None) + to_display_edge_texts.append(edge_texts[edge]) + + edge_trace = go.Scatter( + x=edge_x, + y=edge_y, + text=to_display_edge_texts, + line=dict(width=0.5, color="#888"), + hoverinfo="text", + mode="lines", + ) + + node_x = [] + node_y = [] + for node in G.nodes(): + x, y = pos[node] + node_x.append(x) + node_y.append(y) + + node_adjacencies = [] + node_text = [] + node_size = [] + for node_id, adjacencies in enumerate(G.adjacency()): + degree = len(adjacencies[1]) + node_adjacencies.append(degree) + node_text.append(adjacencies[0]) + node_size.append(15 if degree < 5 else (30 if degree < 10 else 60)) + + node_trace = go.Scatter( + x=node_x, + y=node_y, + textfont=dict( + family="Courier New, monospace", + size=10, # Set the font size here + ), + textposition="top center", + mode="markers+text", + hoverinfo="text", + text=node_text, + marker=dict( + showscale=True, + # colorscale options + size=node_size, + colorscale="YlGnBu", + reversescale=True, + color=node_adjacencies, + colorbar=dict( + thickness=5, + xanchor="left", + titleside="right", + ), + line_width=2, + ), + ) + + fig = go.Figure( + data=[edge_trace, node_trace], + layout=go.Layout( + showlegend=False, + hovermode="closest", + margin=dict(b=20, l=5, r=5, t=40), + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + ), + ) + fig.update_layout(autosize=True) + + return to_json(fig) diff --git a/libs/ktem/ktem/index/file/index.py b/libs/ktem/ktem/index/file/index.py index e3d4405..e94a8a7 100644 --- a/libs/ktem/ktem/index/file/index.py +++ b/libs/ktem/ktem/index/file/index.py @@ -4,8 +4,9 @@ from typing import Any, Optional, Type from ktem.components import filestorage_path, get_docstore, get_vectorstore from ktem.db.engine import engine from ktem.index.base import BaseIndex -from sqlalchemy import Column, DateTime, Integer, String +from sqlalchemy import JSON, Column, DateTime, Integer, String, UniqueConstraint from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.ext.mutable import MutableDict from sqlalchemy.sql import func from theflow.settings import settings as flowsettings from theflow.utils.modules import import_dotted_string @@ -52,27 +53,60 @@ class FileIndex(BaseIndex): - File storage path """ Base = declarative_base() - Source = type( - "Source", - (Base,), - { - "__tablename__": f"index__{self.id}__source", - "id": Column( - String, - primary_key=True, - default=lambda: str(uuid.uuid4()), - unique=True, - ), - "name": Column(String, unique=True), - "path": Column(String), - "size": Column(Integer, default=0), - "text_length": Column(Integer, default=0), - "date_created": Column( - DateTime(timezone=True), server_default=func.now() - ), - "user": Column(Integer, default=1), - }, - ) + + if self.config.get("private", False): + Source = type( + "Source", + (Base,), + { + "__tablename__": f"index__{self.id}__source", + "__table_args__": ( + UniqueConstraint("name", "user", name="_name_user_uc"), + ), + "id": Column( + String, + primary_key=True, + default=lambda: str(uuid.uuid4()), + unique=True, + ), + "name": Column(String), + "path": Column(String), + "size": Column(Integer, default=0), + "date_created": Column( + DateTime(timezone=True), server_default=func.now() + ), + "user": Column(Integer, default=1), + "note": Column( + MutableDict.as_mutable(JSON), # type: ignore + default={}, + ), + }, + ) + else: + Source = type( + "Source", + (Base,), + { + "__tablename__": f"index__{self.id}__source", + "id": Column( + String, + primary_key=True, + default=lambda: str(uuid.uuid4()), + unique=True, + ), + "name": Column(String, unique=True), + "path": Column(String), + "size": Column(Integer, default=0), + "date_created": Column( + DateTime(timezone=True), server_default=func.now() + ), + "user": Column(Integer, default=1), + "note": Column( + MutableDict.as_mutable(JSON), # type: ignore + default={}, + ), + }, + ) Index = type( "IndexTable", (Base,), @@ -85,6 +119,7 @@ class FileIndex(BaseIndex): "user": Column(Integer, default=1), }, ) + self._vs: BaseVectorStore = get_vectorstore(f"index_{self.id}") self._docstore: BaseDocumentStore = get_docstore(f"index_{self.id}") self._fs_path = filestorage_path / f"index_{self.id}" @@ -358,8 +393,6 @@ class FileIndex(BaseIndex): for key, value in settings.items(): if key.startswith(prefix): stripped_settings[key[len(prefix) :]] = value - else: - stripped_settings[key] = value obj = self._indexing_pipeline_cls.get_pipeline(stripped_settings, self.config) obj.Source = self._resources["Source"] @@ -368,6 +401,7 @@ class FileIndex(BaseIndex): obj.DS = self._docstore obj.FSPath = self._fs_path obj.user_id = user_id + obj.private = self.config.get("private", False) return obj @@ -380,8 +414,6 @@ class FileIndex(BaseIndex): for key, value in settings.items(): if key.startswith(prefix): stripped_settings[key[len(prefix) :]] = value - else: - stripped_settings[key] = value # transform selected id selected_ids: Optional[list[str]] = self._selector_ui.get_selected_ids(selected) diff --git a/libs/ktem/ktem/index/file/knet/__init__.py b/libs/ktem/ktem/index/file/knet/__init__.py new file mode 100644 index 0000000..1ca8ef4 --- /dev/null +++ b/libs/ktem/ktem/index/file/knet/__init__.py @@ -0,0 +1,3 @@ +from .knet_index import KnowledgeNetworkFileIndex + +__all__ = ["KnowledgeNetworkFileIndex"] diff --git a/libs/ktem/ktem/index/file/knet/knet_index.py b/libs/ktem/ktem/index/file/knet/knet_index.py new file mode 100644 index 0000000..f98e7a0 --- /dev/null +++ b/libs/ktem/ktem/index/file/knet/knet_index.py @@ -0,0 +1,47 @@ +from typing import Any + +from ktem.index.file import FileIndex + +from ..base import BaseFileIndexIndexing, BaseFileIndexRetriever +from .pipelines import KnetIndexingPipeline, KnetRetrievalPipeline + + +class KnowledgeNetworkFileIndex(FileIndex): + @classmethod + def get_admin_settings(cls): + admin_settings = super().get_admin_settings() + + # remove embedding from admin settings + # as we don't need it + admin_settings.pop("embedding") + return admin_settings + + def _setup_indexing_cls(self): + self._indexing_pipeline_cls = KnetIndexingPipeline + + def _setup_retriever_cls(self): + self._retriever_pipeline_cls = [KnetRetrievalPipeline] + + def get_indexing_pipeline(self, settings, user_id) -> BaseFileIndexIndexing: + """Define the interface of the indexing pipeline""" + + obj = super().get_indexing_pipeline(settings, user_id) + # disable vectorstore for this kind of Index + # also set the collection_name for API call + obj.VS = None + obj.collection_name = f"kh_index_{self.id}" + + return obj + + def get_retriever_pipelines( + self, settings: dict, user_id: int, selected: Any = None + ) -> list["BaseFileIndexRetriever"]: + retrievers = super().get_retriever_pipelines(settings, user_id, selected) + + for obj in retrievers: + # disable vectorstore for this kind of Index + # also set the collection_name for API call + obj.VS = None + obj.collection_name = f"kh_index_{self.id}" + + return retrievers diff --git a/libs/ktem/ktem/index/file/knet/pipelines.py b/libs/ktem/ktem/index/file/knet/pipelines.py new file mode 100644 index 0000000..9741e5a --- /dev/null +++ b/libs/ktem/ktem/index/file/knet/pipelines.py @@ -0,0 +1,169 @@ +import base64 +import json +import os +from pathlib import Path +from typing import Optional, Sequence + +import requests +import yaml + +from kotaemon.base import RetrievedDocument +from kotaemon.indices.rankings import BaseReranking, LLMReranking, LLMTrulensScoring + +from ..pipelines import BaseFileIndexRetriever, IndexDocumentPipeline, IndexPipeline + + +class KnetIndexingPipeline(IndexDocumentPipeline): + """Knowledge Network specific indexing pipeline""" + + # collection name for external indexing call + collection_name: str = "default" + + @classmethod + def get_user_settings(cls): + return { + "reader_mode": { + "name": "Index parser", + "value": "knowledge_network", + "choices": [ + ("Default (KN)", "knowledge_network"), + ], + "component": "dropdown", + }, + } + + def route(self, file_path: Path) -> IndexPipeline: + """Simply disable the splitter (chunking) for this pipeline""" + pipeline = super().route(file_path) + pipeline.splitter = None + # assign IndexPipeline collection name to parse to loader + pipeline.collection_name = self.collection_name + + return pipeline + + +class KnetRetrievalPipeline(BaseFileIndexRetriever): + DEFAULT_KNET_ENDPOINT: str = "http://127.0.0.1:8081/retrieve" + + collection_name: str = "default" + rerankers: Sequence[BaseReranking] = [LLMReranking.withx()] + + def encode_image_base64(self, image_path: str | Path) -> bytes | str: + """Convert image to base64""" + img_base64 = "data:image/png;base64,{}" + with open(image_path, "rb") as image_file: + return img_base64.format( + base64.b64encode(image_file.read()).decode("utf-8") + ) + + def run( + self, + text: str, + doc_ids: Optional[list[str]] = None, + *args, + **kwargs, + ) -> list[RetrievedDocument]: + """Retrieve document excerpts similar to the text + + Args: + text: the text to retrieve similar documents + doc_ids: list of document ids to constraint the retrieval + """ + print("searching in doc_ids", doc_ids) + if not doc_ids: + return [] + + docs: list[RetrievedDocument] = [] + params = { + "query": text, + "collection": self.collection_name, + "meta_filters": {"doc_name": doc_ids}, + } + params["meta_filters"] = json.dumps(params["meta_filters"]) + response = requests.get(self.DEFAULT_KNET_ENDPOINT, params=params) + metadata_translation = { + "TABLE": "table", + "FIGURE": "image", + } + + if response.status_code == 200: + # Load YAML content from the response content + chunks = yaml.safe_load(response.content) + for chunk in chunks: + metadata = chunk["node"]["metadata"] + metadata["type"] = metadata_translation.get( + metadata.pop("content_type", ""), "" + ) + metadata["file_name"] = metadata.pop("company_name", "") + + # load image from returned path + image_path = metadata.get("image_path", "") + if image_path and os.path.isfile(image_path): + base64_im = self.encode_image_base64(image_path) + # explicitly set document type + metadata["type"] = "image" + metadata["image_origin"] = base64_im + + docs.append( + RetrievedDocument(text=chunk["node"]["text"], metadata=metadata) + ) + else: + raise IOError(f"{response.status_code}: {response.text}") + + for reranker in self.rerankers: + docs = reranker(documents=docs, query=text) + + return docs + + @classmethod + def get_user_settings(cls) -> dict: + from ktem.llms.manager import llms + + try: + reranking_llm = llms.get_default_name() + reranking_llm_choices = list(llms.options().keys()) + except Exception: + reranking_llm = None + reranking_llm_choices = [] + + return { + "reranking_llm": { + "name": "LLM for scoring", + "value": reranking_llm, + "component": "dropdown", + "choices": reranking_llm_choices, + "special_type": "llm", + }, + "retrieval_mode": { + "name": "Retrieval mode", + "value": "hybrid", + "choices": ["vector", "text", "hybrid"], + "component": "dropdown", + }, + } + + @classmethod + def get_pipeline(cls, user_settings, index_settings, selected): + """Get retriever objects associated with the index + + Args: + settings: the settings of the app + kwargs: other arguments + """ + from ktem.llms.manager import llms + + retriever = cls( + rerankers=[LLMTrulensScoring()], + ) + + # hacky way to input doc_ids to retriever.run() call (through theflow) + kwargs = {".doc_ids": selected} + retriever.set_run(kwargs, temp=False) + + for reranker in retriever.rerankers: + if isinstance(reranker, LLMReranking): + reranker.llm = llms.get( + user_settings["reranking_llm"], llms.get_default() + ) + + return retriever diff --git a/libs/ktem/ktem/index/file/pipelines.py b/libs/ktem/ktem/index/file/pipelines.py index 450bb3a..d664e7f 100644 --- a/libs/ktem/ktem/index/file/pipelines.py +++ b/libs/ktem/ktem/index/file/pipelines.py @@ -2,25 +2,29 @@ from __future__ import annotations import logging import shutil +import threading +import time import warnings from collections import defaultdict +from copy import deepcopy from functools import lru_cache from hashlib import sha256 from pathlib import Path -from typing import Generator, Optional +from typing import Generator, Optional, Sequence +import tiktoken from ktem.db.models import engine from ktem.embeddings.manager import embedding_models_manager from ktem.llms.manager import llms -from llama_index.readers.base import BaseReader -from llama_index.readers.file.base import default_file_metadata_func -from llama_index.vector_stores import ( +from llama_index.core.readers.base import BaseReader +from llama_index.core.readers.file.base import default_file_metadata_func +from llama_index.core.vector_stores import ( FilterCondition, FilterOperator, MetadataFilter, MetadataFilters, ) -from llama_index.vector_stores.types import VectorStoreQueryMode +from llama_index.core.vector_stores.types import VectorStoreQueryMode from sqlalchemy import delete, select from sqlalchemy.orm import Session from theflow.settings import settings @@ -29,8 +33,18 @@ from theflow.utils.modules import import_dotted_string from kotaemon.base import BaseComponent, Document, Node, Param, RetrievedDocument from kotaemon.embeddings import BaseEmbeddings from kotaemon.indices import VectorIndexing, VectorRetrieval -from kotaemon.indices.ingests.files import KH_DEFAULT_FILE_EXTRACTORS -from kotaemon.indices.rankings import BaseReranking, LLMReranking +from kotaemon.indices.ingests.files import ( + KH_DEFAULT_FILE_EXTRACTORS, + adobe_reader, + azure_reader, + unstructured, +) +from kotaemon.indices.rankings import ( + BaseReranking, + CohereReranking, + LLMReranking, + LLMTrulensScoring, +) from kotaemon.indices.splitters import BaseSplitter, TokenSplitter from .base import BaseFileIndexIndexing, BaseFileIndexRetriever @@ -60,6 +74,9 @@ def dev_settings(): return file_extractors, chunk_size, chunk_overlap +_default_token_func = tiktoken.encoding_for_model("gpt-3.5-turbo").encode + + class DocumentRetrievalPipeline(BaseFileIndexRetriever): """Retrieve relevant document @@ -75,10 +92,13 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): """ embedding: BaseEmbeddings - reranker: BaseReranking = LLMReranking.withx() + rerankers: Sequence[BaseReranking] = [] + # use LLM to create relevant scores for displaying on UI + llm_scorer: LLMReranking | None = LLMReranking.withx() get_extra_table: bool = False mmr: bool = False top_k: int = 5 + retrieval_mode: str = "hybrid" @Node.auto(depends_on=["embedding", "VS", "DS"]) def vector_retrieval(self) -> VectorRetrieval: @@ -86,6 +106,8 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): embedding=self.embedding, vector_store=self.VS, doc_store=self.DS, + retrieval_mode=self.retrieval_mode, # type: ignore + rerankers=self.rerankers, ) def run( @@ -101,27 +123,30 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): text: the text to retrieve similar documents doc_ids: list of document ids to constraint the retrieval """ + print("searching in doc_ids", doc_ids) if not doc_ids: logger.info(f"Skip retrieval because of no selected files: {self}") return [] - retrieval_kwargs = {} + retrieval_kwargs: dict = {} with Session(engine) as session: stmt = select(self.Index).where( - self.Index.relation_type == "vector", + self.Index.relation_type == "document", self.Index.source_id.in_(doc_ids), ) results = session.execute(stmt) - vs_ids = [r[0].target_id for r in results.all()] + chunk_ids = [r[0].target_id for r in results.all()] + # do first round top_k extension + retrieval_kwargs["do_extend"] = True + retrieval_kwargs["scope"] = chunk_ids retrieval_kwargs["filters"] = MetadataFilters( filters=[ MetadataFilter( - key="doc_id", - value=vs_id, - operator=FilterOperator.EQ, + key="file_id", + value=doc_ids, + operator=FilterOperator.IN, ) - for vs_id in vs_ids ], condition=FilterCondition.OR, ) @@ -132,9 +157,10 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): retrieval_kwargs["mmr_threshold"] = 0.5 # rerank + s_time = time.time() + print(f"retrieval_kwargs: {retrieval_kwargs.keys()}") docs = self.vector_retrieval(text=text, top_k=self.top_k, **retrieval_kwargs) - if docs and self.get_from_path("reranker"): - docs = self.reranker(docs, query=text) + print("retrieval step took", time.time() - s_time) if not self.get_extra_table: return docs @@ -157,17 +183,30 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): for fn, pls in table_pages.items() ] if queries: - extra_docs = self.vector_retrieval( - text="", - top_k=50, - where=queries[0] if len(queries) == 1 else {"$or": queries}, - ) - for doc in extra_docs: - if doc.doc_id not in retrieved_id: - docs.append(doc) + try: + extra_docs = self.vector_retrieval( + text="", + top_k=50, + where=queries[0] if len(queries) == 1 else {"$or": queries}, + ) + for doc in extra_docs: + if doc.doc_id not in retrieved_id: + docs.append(doc) + except Exception: + print("Error retrieving additional tables") return docs + def generate_relevant_scores( + self, query: str, documents: list[RetrievedDocument] + ) -> list[RetrievedDocument]: + docs = ( + documents + if not self.llm_scorer + else self.llm_scorer(documents=documents, query=query) + ) + return docs + @classmethod def get_user_settings(cls) -> dict: from ktem.llms.manager import llms @@ -182,43 +221,44 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): return { "reranking_llm": { - "name": "LLM for reranking", + "name": "LLM for relevant scoring", "value": reranking_llm, "component": "dropdown", "choices": reranking_llm_choices, - }, - "separate_embedding": { - "name": "Use separate embedding", - "value": False, - "choices": [("Yes", True), ("No", False)], - "component": "dropdown", + "special_type": "llm", }, "num_retrieval": { "name": "Number of document chunks to retrieve", - "value": 3, + "value": 10, "component": "number", }, "retrieval_mode": { "name": "Retrieval mode", - "value": "vector", + "value": "hybrid", "choices": ["vector", "text", "hybrid"], "component": "dropdown", }, "prioritize_table": { "name": "Prioritize table", - "value": True, + "value": False, "choices": [True, False], "component": "checkbox", }, "mmr": { "name": "Use MMR", - "value": True, + "value": False, "choices": [True, False], "component": "checkbox", }, "use_reranking": { "name": "Use reranking", - "value": False, + "value": True, + "choices": [True, False], + "component": "checkbox", + }, + "use_llm_reranking": { + "name": "Use LLM relevant scoring", + "value": True, "choices": [True, False], "component": "checkbox", }, @@ -232,6 +272,8 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): settings: the settings of the app kwargs: other arguments """ + use_llm_reranking = user_settings.get("use_llm_reranking", False) + retriever = cls( get_extra_table=user_settings["prioritize_table"], top_k=user_settings["num_retrieval"], @@ -241,16 +283,26 @@ class DocumentRetrievalPipeline(BaseFileIndexRetriever): "embedding", embedding_models_manager.get_default_name() ) ], + retrieval_mode=user_settings["retrieval_mode"], + llm_scorer=(LLMTrulensScoring() if use_llm_reranking else None), + rerankers=[CohereReranking()], ) if not user_settings["use_reranking"]: - retriever.reranker = None # type: ignore - else: - retriever.reranker.llm = llms.get( + retriever.rerankers = [] # type: ignore + + for reranker in retriever.rerankers: + if isinstance(reranker, LLMReranking): + reranker.llm = llms.get( + user_settings["reranking_llm"], llms.get_default() + ) + + if retriever.llm_scorer: + retriever.llm_scorer.llm = llms.get( user_settings["reranking_llm"], llms.get_default() ) kwargs = {".doc_ids": selected} - retriever.set_run(kwargs, temp=True) + retriever.set_run(kwargs, temp=False) return retriever @@ -258,8 +310,8 @@ class IndexPipeline(BaseComponent): """Index a single file""" loader: BaseReader - splitter: BaseSplitter - chunk_batch_size: int = 50 + splitter: BaseSplitter | None + chunk_batch_size: int = 200 Source = Param(help="The SQLAlchemy Source table") Index = Param(help="The SQLAlchemy Index table") @@ -267,6 +319,9 @@ class IndexPipeline(BaseComponent): DS = Param(help="The DocStore") FSPath = Param(help="The file storage path") user_id = Param(help="The user id") + collection_name: str = "default" + private: bool = False + run_embedding_in_thread: bool = False embedding: BaseEmbeddings @Node.auto(depends_on=["Source", "Index", "embedding"]) @@ -276,31 +331,81 @@ class IndexPipeline(BaseComponent): ) def handle_docs(self, docs, file_id, file_name) -> Generator[Document, None, int]: + s_time = time.time() + text_docs = [] + non_text_docs = [] + thumbnail_docs = [] + + for doc in docs: + doc_type = doc.metadata.get("type", "text") + if doc_type == "text": + text_docs.append(doc) + elif doc_type == "thumbnail": + thumbnail_docs.append(doc) + else: + non_text_docs.append(doc) + + print(f"Got {len(thumbnail_docs)} page thumbnails") + page_label_to_thumbnail = { + doc.metadata["page_label"]: doc.doc_id for doc in thumbnail_docs + } + + if self.splitter: + all_chunks = self.splitter(text_docs) + else: + all_chunks = text_docs + + # add the thumbnails doc_id to the chunks + for chunk in all_chunks: + page_label = chunk.metadata.get("page_label", None) + if page_label and page_label in page_label_to_thumbnail: + chunk.metadata["thumbnail_doc_id"] = page_label_to_thumbnail[page_label] + + to_index_chunks = all_chunks + non_text_docs + thumbnail_docs + + # add to doc store chunks = [] n_chunks = 0 - for cidx, chunk in enumerate(self.splitter(docs)): - chunks.append(chunk) - if cidx % self.chunk_batch_size == 0: - self.handle_chunks(chunks, file_id) - n_chunks += len(chunks) - chunks = [] - yield Document( - f" => [{file_name}] Processed {n_chunks} chunks", channel="debug" - ) - - if chunks: - self.handle_chunks(chunks, file_id) + chunk_size = self.chunk_batch_size * 4 + for start_idx in range(0, len(to_index_chunks), chunk_size): + chunks = to_index_chunks[start_idx : start_idx + chunk_size] + self.handle_chunks_docstore(chunks, file_id) n_chunks += len(chunks) yield Document( - f" => [{file_name}] Processed {n_chunks} chunks", channel="debug" + f" => [{file_name}] Processed {n_chunks} chunks", + channel="debug", ) + def insert_chunks_to_vectorstore(): + chunks = [] + n_chunks = 0 + chunk_size = self.chunk_batch_size + for start_idx in range(0, len(to_index_chunks), chunk_size): + chunks = to_index_chunks[start_idx : start_idx + chunk_size] + self.handle_chunks_vectorstore(chunks, file_id) + n_chunks += len(chunks) + if self.VS: + yield Document( + f" => [{file_name}] Created embedding for {n_chunks} chunks", + channel="debug", + ) + + # run vector indexing in thread if specified + if self.run_embedding_in_thread: + print("Running embedding in thread") + threading.Thread( + target=lambda: list(insert_chunks_to_vectorstore()) + ).start() + else: + yield from insert_chunks_to_vectorstore() + + print("indexing step took", time.time() - s_time) return n_chunks - def handle_chunks(self, chunks, file_id): + def handle_chunks_docstore(self, chunks, file_id): """Run chunks""" # run embedding, add to both vector store and doc store - self.vector_indexing(chunks) + self.vector_indexing.add_to_docstore(chunks) # record in the index with Session(engine) as session: @@ -313,16 +418,30 @@ class IndexPipeline(BaseComponent): relation_type="document", ) ) - nodes.append( - self.Index( - source_id=file_id, - target_id=chunk.doc_id, - relation_type="vector", - ) - ) session.add_all(nodes) session.commit() + def handle_chunks_vectorstore(self, chunks, file_id): + """Run chunks""" + # run embedding, add to both vector store and doc store + self.vector_indexing.add_to_vectorstore(chunks) + self.vector_indexing.write_chunk_to_file(chunks) + + if self.VS: + # record in the index + with Session(engine) as session: + nodes = [] + for chunk in chunks: + nodes.append( + self.Index( + source_id=file_id, + target_id=chunk.doc_id, + relation_type="vector", + ) + ) + session.add_all(nodes) + session.commit() + def get_id_if_exists(self, file_path: Path) -> Optional[str]: """Check if the file is already indexed @@ -332,8 +451,16 @@ class IndexPipeline(BaseComponent): Returns: the file id if the file is indexed, otherwise None """ + if self.private: + cond: tuple = ( + self.Source.name == file_path.name, + self.Source.user == self.user_id, + ) + else: + cond = (self.Source.name == file_path.name,) + with Session(engine) as session: - stmt = select(self.Source).where(self.Source.name == file_path.name) + stmt = select(self.Source).where(*cond) item = session.execute(stmt).first() if item: return item[0].id @@ -369,20 +496,36 @@ class IndexPipeline(BaseComponent): def finish(self, file_id: str, file_path: Path) -> str: """Finish the indexing""" with Session(engine) as session: - stmt = select(self.Index.target_id).where(self.Index.source_id == file_id) - doc_ids = [_[0] for _ in session.execute(stmt)] - if doc_ids: + stmt = select(self.Source).where(self.Source.id == file_id) + result = session.execute(stmt).first() + if not result: + return file_id + + item = result[0] + + # populate the number of tokens + doc_ids_stmt = select(self.Index.target_id).where( + self.Index.source_id == file_id, + self.Index.relation_type == "document", + ) + doc_ids = [_[0] for _ in session.execute(doc_ids_stmt)] + token_func = self.get_token_func() + if doc_ids and token_func: docs = self.DS.get(doc_ids) - stmt = select(self.Source).where(self.Source.id == file_id) - result = session.execute(stmt).first() - if result: - item = result[0] - item.text_length = sum([len(doc.text) for doc in docs]) - session.add(item) - session.commit() + item.note["tokens"] = sum([len(token_func(doc.text)) for doc in docs]) + + # populate the note + item.note["loader"] = self.get_from_path("loader").__class__.__name__ + + session.add(item) + session.commit() return file_id + def get_token_func(self): + """Get the token function for calculating the number of tokens""" + return _default_token_func + def delete_file(self, file_id: str): """Delete a file from the db, including its chunks in docstore and vectorstore @@ -398,44 +541,24 @@ class IndexPipeline(BaseComponent): for each in index: if each[0].relation_type == "vector": vs_ids.append(each[0].target_id) - else: + elif each[0].relation_type == "document": ds_ids.append(each[0].target_id) session.delete(each[0]) session.commit() - self.VS.delete(vs_ids) - self.DS.delete(ds_ids) - def run(self, file_path: str | Path, reindex: bool, **kwargs) -> str: - """Index the file and return the file id""" - # check for duplication - file_path = Path(file_path).resolve() - file_id = self.get_id_if_exists(file_path) - if file_id is not None: - if not reindex: - raise ValueError( - f"File {file_path.name} already indexed. Please rerun with " - "reindex=True to force reindexing." - ) - else: - # remove the existing records - self.delete_file(file_id) - file_id = self.store_file(file_path) - else: - # add record to db - file_id = self.store_file(file_path) + if vs_ids and self.VS: + self.VS.delete(vs_ids) + if ds_ids: + self.DS.delete(ds_ids) - # extract the file - extra_info = default_file_metadata_func(str(file_path)) - docs = self.loader.load_data(file_path, extra_info=extra_info) - for _ in self.handle_docs(docs, file_id, file_path.name): - continue - self.finish(file_id, file_path) - - return file_id + def run( + self, file_path: str | Path, reindex: bool, **kwargs + ) -> tuple[str, list[Document]]: + raise NotImplementedError def stream( self, file_path: str | Path, reindex: bool, **kwargs - ) -> Generator[Document, None, str]: + ) -> Generator[Document, None, tuple[str, list[Document]]]: # check for duplication file_path = Path(file_path).resolve() file_id = self.get_id_if_exists(file_path) @@ -456,6 +579,9 @@ class IndexPipeline(BaseComponent): # extract the file extra_info = default_file_metadata_func(str(file_path)) + extra_info["file_id"] = file_id + extra_info["collection_name"] = self.collection_name + yield Document(f" => Converting {file_path.name} to text", channel="debug") docs = self.loader.load_data(file_path, extra_info=extra_info) yield Document(f" => Converted {file_path.name} to text", channel="debug") @@ -464,7 +590,7 @@ class IndexPipeline(BaseComponent): self.finish(file_id, file_path) yield Document(f" => Finished indexing {file_path.name}", channel="debug") - return file_id + return file_id, docs class IndexDocumentPipeline(BaseFileIndexIndexing): @@ -479,16 +605,54 @@ class IndexDocumentPipeline(BaseFileIndexIndexing): decide which pipeline should be used. """ + reader_mode: str = Param("default", help="The reader mode") embedding: BaseEmbeddings + run_embedding_in_thread: bool = False + + @Param.auto(depends_on="reader_mode") + def readers(self): + readers = deepcopy(KH_DEFAULT_FILE_EXTRACTORS) + print("reader_mode", self.reader_mode) + if self.reader_mode == "adobe": + readers[".pdf"] = adobe_reader + elif self.reader_mode == "azure-di": + readers[".pdf"] = azure_reader + + dev_readers, _, _ = dev_settings() + readers.update(dev_readers) + + return readers + + @classmethod + def get_user_settings(cls): + return { + "reader_mode": { + "name": "File loader", + "value": "default", + "choices": [ + ("Default (open-source)", "default"), + ("Adobe API (figure+table extraction)", "adobe"), + ( + "Azure AI Document Intelligence (figure+table extraction)", + "azure-di", + ), + ], + "component": "dropdown", + }, + } @classmethod def get_pipeline(cls, user_settings, index_settings) -> BaseFileIndexIndexing: + use_quick_index_mode = user_settings.get("quick_index_mode", False) + print("use_quick_index_mode", use_quick_index_mode) obj = cls( embedding=embedding_models_manager[ index_settings.get( "embedding", embedding_models_manager.get_default_name() ) - ] + ], + run_embedding_in_thread=use_quick_index_mode, + reader_mode=user_settings.get("reader_mode", "default"), ) return obj @@ -497,16 +661,17 @@ class IndexDocumentPipeline(BaseFileIndexIndexing): Can subclass this method for a more elaborate pipeline routing strategy. """ - readers, chunk_size, chunk_overlap = dev_settings() + _, chunk_size, chunk_overlap = dev_settings() - ext = file_path.suffix - reader = readers.get(ext, KH_DEFAULT_FILE_EXTRACTORS.get(ext, None)) + ext = file_path.suffix.lower() + reader = self.readers.get(ext, unstructured) if reader is None: raise NotImplementedError( f"No supported pipeline to index {file_path.name}. Please specify " "the suitable pipeline for this file type in the settings." ) + print("Using reader", reader) pipeline: IndexPipeline = IndexPipeline( loader=reader, splitter=TokenSplitter( @@ -515,50 +680,37 @@ class IndexDocumentPipeline(BaseFileIndexIndexing): separator="\n\n", backup_separators=["\n", ".", "\u200B"], ), + run_embedding_in_thread=self.run_embedding_in_thread, Source=self.Source, Index=self.Index, VS=self.VS, DS=self.DS, FSPath=self.FSPath, user_id=self.user_id, + private=self.private, embedding=self.embedding, ) return pipeline def run( - self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs + self, file_paths: str | Path | list[str | Path], *args, **kwargs ) -> tuple[list[str | None], list[str | None]]: - """Return a list of indexed file ids, and a list of errors""" - if not isinstance(file_paths, list): - file_paths = [file_paths] - - file_ids: list[str | None] = [] - errors: list[str | None] = [] - for file_path in file_paths: - file_path = Path(file_path) - - try: - pipeline = self.route(file_path) - file_id = pipeline.run(file_path, reindex=reindex, **kwargs) - file_ids.append(file_id) - errors.append(None) - except Exception as e: - logger.error(e) - file_ids.append(None) - errors.append(str(e)) - - return file_ids, errors + raise NotImplementedError def stream( self, file_paths: str | Path | list[str | Path], reindex: bool = False, **kwargs - ) -> Generator[Document, None, tuple[list[str | None], list[str | None]]]: + ) -> Generator[ + Document, None, tuple[list[str | None], list[str | None], list[Document]] + ]: """Return a list of indexed file ids, and a list of errors""" if not isinstance(file_paths, list): file_paths = [file_paths] file_ids: list[str | None] = [] errors: list[str | None] = [] + all_docs = [] + n_files = len(file_paths) for idx, file_path in enumerate(file_paths): file_path = Path(file_path) @@ -569,9 +721,10 @@ class IndexDocumentPipeline(BaseFileIndexIndexing): try: pipeline = self.route(file_path) - file_id = yield from pipeline.stream( + file_id, docs = yield from pipeline.stream( file_path, reindex=reindex, **kwargs ) + all_docs.extend(docs) file_ids.append(file_id) errors.append(None) yield Document( @@ -579,7 +732,7 @@ class IndexDocumentPipeline(BaseFileIndexIndexing): channel="index", ) except Exception as e: - logger.error(e) + logger.exception(e) file_ids.append(None) errors.append(str(e)) yield Document( @@ -591,4 +744,4 @@ class IndexDocumentPipeline(BaseFileIndexIndexing): channel="index", ) - return file_ids, errors + return file_ids, errors, all_docs diff --git a/libs/ktem/ktem/index/file/ui.py b/libs/ktem/ktem/index/file/ui.py index d46e072..a639c43 100644 --- a/libs/ktem/ktem/index/file/ui.py +++ b/libs/ktem/ktem/index/file/ui.py @@ -1,5 +1,9 @@ +import html import os +import shutil import tempfile +import zipfile +from copy import deepcopy from pathlib import Path from typing import Generator @@ -9,8 +13,12 @@ from gradio.data_classes import FileData from gradio.utils import NamedString from ktem.app import BasePage from ktem.db.engine import engine +from ktem.utils.render import Render from sqlalchemy import select from sqlalchemy.orm import Session +from theflow.settings import settings as flowsettings + +DOWNLOAD_MESSAGE = "Press again to download" class File(gr.File): @@ -143,28 +151,57 @@ class FileIndexPage(BasePage): ) gr.Markdown("## File List") + self.filter = gr.Textbox( + value="", + label="Filter by name:", + info=( + "(1) Case-insensitive. " + "(2) Search with empty string to show all files." + ), + ) self.file_list_state = gr.State(value=None) self.file_list = gr.DataFrame( - headers=["id", "name", "size", "text_length", "date_created"], + headers=[ + "id", + "name", + "size", + "tokens", + "loader", + "date_created", + ], + column_widths=["0%", "50%", "8%", "7%", "15%", "20%"], interactive=False, + wrap=False, + elem_id="file_list_view", ) + with gr.Row(): + self.deselect_button = gr.Button( + "Close", + visible=False, + ) + self.delete_button = gr.Button( + "Delete", + variant="stop", + visible=False, + ) + with gr.Row(): + self.is_zipped_state = gr.State(value=False) + self.download_all_button = gr.DownloadButton( + "Download all files", + visible=True, + ) + self.download_single_button = gr.DownloadButton( + "Download file", + visible=False, + ) + with gr.Row() as self.selection_info: self.selected_file_id = gr.State(value=None) with gr.Column(scale=2): self.selected_panel = gr.Markdown(self.selected_panel_false) - self.deselect_button = gr.Button( - "Deselect", - visible=False, - elem_classes=["right-button"], - ) - self.delete_button = gr.Button( - "Delete", - variant="stop", - visible=False, - elem_classes=["right-button"], - ) + self.chunks = gr.HTML(visible=False) def on_subscribe_public_events(self): """Subscribe to the declared public event of the app""" @@ -189,12 +226,58 @@ class FileIndexPage(BasePage): ) def file_selected(self, file_id): + chunks = [] + if file_id is not None: + # get the chunks + + Index = self._index._resources["Index"] + with Session(engine) as session: + matches = session.execute( + select(Index).where( + Index.source_id == file_id, + Index.relation_type == "document", + ) + ) + doc_ids = [doc.target_id for (doc,) in matches] + docs = self._index._docstore.get(doc_ids) + docs = sorted( + docs, key=lambda x: x.metadata.get("page_label", float("inf")) + ) + + for idx, doc in enumerate(docs): + title = html.escape( + f"{doc.text[:50]}..." if len(doc.text) > 50 else doc.text + ) + doc_type = doc.metadata.get("type", "text") + content = "" + if doc_type == "text": + content = html.escape(doc.text) + elif doc_type == "table": + content = Render.table(doc.text) + elif doc_type == "image": + content = Render.image( + url=doc.metadata.get("image_origin", ""), text=doc.text + ) + + header_prefix = f"[{idx+1}/{len(docs)}]" + if doc.metadata.get("page_label"): + header_prefix += f" [Page {doc.metadata['page_label']}]" + + chunks.append( + Render.collapsible( + header=f"{header_prefix} {title}", + content=content, + ) + ) return ( + gr.update(value="".join(chunks), visible=file_id is not None), + gr.update(visible=file_id is not None), gr.update(visible=file_id is not None), gr.update(visible=file_id is not None), ) def delete_event(self, file_id): + file_name = "" with Session(engine) as session: source = session.execute( select(self._index._resources["Source"]).where( @@ -202,6 +285,7 @@ class FileIndexPage(BasePage): ) ).first() if source: + file_name = source[0].name session.delete(source[0]) vs_ids, ds_ids = [], [] @@ -213,15 +297,16 @@ class FileIndexPage(BasePage): for each in index: if each[0].relation_type == "vector": vs_ids.append(each[0].target_id) - else: + elif each[0].relation_type == "document": ds_ids.append(each[0].target_id) session.delete(each[0]) session.commit() - self._index._vs.delete(vs_ids) + if vs_ids: + self._index._vs.delete(vs_ids) self._index._docstore.delete(ds_ids) - gr.Info(f"File {file_id} has been deleted") + gr.Info(f"File {file_name} has been deleted") return None, self.selected_panel_false @@ -231,6 +316,57 @@ class FileIndexPage(BasePage): gr.update(visible=False), ) + def download_single_file(self, is_zipped_state, file_id): + with Session(engine) as session: + source = session.execute( + select(self._index._resources["Source"]).where( + self._index._resources["Source"].id == file_id + ) + ).first() + if source: + target_file_name = Path(source[0].name) + zip_files = [] + for file_name in os.listdir(flowsettings.KH_CHUNKS_OUTPUT_DIR): + if target_file_name.stem in file_name: + zip_files.append( + os.path.join(flowsettings.KH_CHUNKS_OUTPUT_DIR, file_name) + ) + for file_name in os.listdir(flowsettings.KH_MARKDOWN_OUTPUT_DIR): + if target_file_name.stem in file_name: + zip_files.append( + os.path.join(flowsettings.KH_MARKDOWN_OUTPUT_DIR, file_name) + ) + zip_file_path = os.path.join( + flowsettings.KH_ZIP_OUTPUT_DIR, target_file_name.stem + ) + with zipfile.ZipFile(f"{zip_file_path}.zip", "w") as zipMe: + for file in zip_files: + zipMe.write(file, arcname=os.path.basename(file)) + + if is_zipped_state: + new_button = gr.DownloadButton(label="Download", value=None) + else: + new_button = gr.DownloadButton( + label=DOWNLOAD_MESSAGE, value=f"{zip_file_path}.zip" + ) + + return not is_zipped_state, new_button + + def download_all_files(self): + zip_files = [] + for file_name in os.listdir(flowsettings.KH_CHUNKS_OUTPUT_DIR): + zip_files.append(os.path.join(flowsettings.KH_CHUNKS_OUTPUT_DIR, file_name)) + for file_name in os.listdir(flowsettings.KH_MARKDOWN_OUTPUT_DIR): + zip_files.append( + os.path.join(flowsettings.KH_MARKDOWN_OUTPUT_DIR, file_name) + ) + zip_file_path = os.path.join(flowsettings.KH_ZIP_OUTPUT_DIR, "all") + with zipfile.ZipFile(f"{zip_file_path}.zip", "w") as zipMe: + for file in zip_files: + arcname = Path(file) + zipMe.write(file, arcname=arcname.name) + return gr.DownloadButton(label=DOWNLOAD_MESSAGE, value=f"{zip_file_path}.zip") + def on_register_events(self): """Register all events to the app""" onDeleted = ( @@ -241,35 +377,61 @@ class FileIndexPage(BasePage): ) .then( fn=lambda: (None, self.selected_panel_false), - inputs=None, + inputs=[], outputs=[self.selected_file_id, self.selected_panel], show_progress="hidden", ) .then( fn=self.list_file, - inputs=[self._app.user_id], + inputs=[self._app.user_id, self.filter], outputs=[self.file_list_state, self.file_list], ) + .then( + fn=self.file_selected, + inputs=[self.selected_file_id], + outputs=[ + self.chunks, + self.deselect_button, + self.delete_button, + self.download_single_button, + ], + show_progress="hidden", + ) ) for event in self._app.get_event(f"onFileIndex{self._index.id}Changed"): onDeleted = onDeleted.then(**event) self.deselect_button.click( fn=lambda: (None, self.selected_panel_false), - inputs=None, + inputs=[], outputs=[self.selected_file_id, self.selected_panel], show_progress="hidden", - ) - self.selected_panel.change( + ).then( fn=self.file_selected, inputs=[self.selected_file_id], outputs=[ + self.chunks, self.deselect_button, self.delete_button, + self.download_single_button, ], show_progress="hidden", ) + self.download_all_button.click( + fn=self.download_all_files, + inputs=[], + outputs=self.download_all_button, + show_progress="hidden", + ) + + self.download_single_button.click( + fn=self.download_single_file, + inputs=[self.is_zipped_state, self.selected_file_id], + outputs=[self.is_zipped_state, self.download_single_button], + show_progress="hidden", + ) + onUploaded = self.upload_button.click( fn=lambda: gr.update(visible=True), outputs=[self.upload_progress_panel], @@ -285,9 +447,63 @@ class FileIndexPage(BasePage): concurrency_limit=20, ) + try: + # quick file upload event registration of first Index only + if self._index.id == 1: + self.quick_upload_state = gr.State(value=[]) + print("Setting up quick upload event") + quickUploadedEvent = ( + self._app.chat_page.quick_file_upload.upload( + fn=lambda: gr.update( + value="Please wait for the indexing process " + "to complete before adding your question." + ), + outputs=self._app.chat_page.quick_file_upload_status, + ) + .then( + fn=self.index_fn_with_default_loaders, + inputs=[ + self._app.chat_page.quick_file_upload, + gr.State(value=False), + self._app.settings_state, + self._app.user_id, + ], + outputs=self.quick_upload_state, + ) + .success( + fn=lambda: [ + gr.update(value=None), + gr.update(value="select"), + ], + outputs=[ + self._app.chat_page.quick_file_upload, + self._app.chat_page._indices_input[0], + ], + ) + ) + for event in self._app.get_event(f"onFileIndex{self._index.id}Changed"): + quickUploadedEvent = quickUploadedEvent.then(**event) + + quickUploadedEvent.success( + fn=lambda x: x, + inputs=self.quick_upload_state, + outputs=self._app.chat_page._indices_input[1], + ).then( + fn=lambda: gr.update(value="Indexing completed."), + outputs=self._app.chat_page.quick_file_upload_status, + ).then( + fn=self.list_file, + inputs=[self._app.user_id, self.filter], + outputs=[self.file_list_state, self.file_list], + concurrency_limit=20, + ) + + except Exception as e: + print(e) + uploadedEvent = onUploaded.then( fn=self.list_file, - inputs=[self._app.user_id], + inputs=[self._app.user_id, self.filter], outputs=[self.file_list_state, self.file_list], concurrency_limit=20, ) @@ -309,16 +525,64 @@ class FileIndexPage(BasePage): inputs=[self.file_list], outputs=[self.selected_file_id, self.selected_panel], show_progress="hidden", + ).then( + fn=self.file_selected, + inputs=[self.selected_file_id], + outputs=[ + self.chunks, + self.deselect_button, + self.delete_button, + self.download_single_button, + ], + show_progress="hidden", + ) + + self.filter.submit( + fn=self.list_file, + inputs=[self._app.user_id, self.filter], + outputs=[self.file_list_state, self.file_list], + show_progress="hidden", ) def _on_app_created(self): """Called when the app is created""" self._app.app.load( self.list_file, - inputs=[self._app.user_id], + inputs=[self._app.user_id, self.filter], outputs=[self.file_list_state, self.file_list], ) + def _may_extract_zip(self, files, zip_dir: str): + """Handle zip files""" + zip_files = [file for file in files if file.endswith(".zip")] + remaining_files = [file for file in files if not file.endswith("zip")] + + # Clean-up before unzip to remove old files + shutil.rmtree(zip_dir, ignore_errors=True) + + for zip_file in zip_files: + # Prepare new zip output dir, separated for each files + basename = os.path.splitext(os.path.basename(zip_file))[0] + zip_out_dir = os.path.join(zip_dir, basename) + os.makedirs(zip_out_dir, exist_ok=True) + with zipfile.ZipFile(zip_file, "r") as zip_ref: + zip_ref.extractall(zip_out_dir) + + n_zip_file = 0 + for root, dirs, files in os.walk(zip_dir): + for file in files: + ext = os.path.splitext(file)[1] + + # only allow supported file-types ( not zip ) + if ext not in [".zip"] and ext in self._supported_file_types: + remaining_files += [os.path.join(root, file)] + n_zip_file += 1 + + if n_zip_file > 0: + print(f"Update zip files: {n_zip_file}") + + return remaining_files + def index_fn( self, files, reindex: bool, settings, user_id ) -> Generator[tuple[str, str], None, None]: @@ -335,6 +599,8 @@ class FileIndexPage(BasePage): yield "", "" return + files = self._may_extract_zip(files, flowsettings.KH_ZIP_INPUT_DIR) + errors = self.validate(files) if errors: gr.Warning(", ".join(errors)) @@ -366,19 +632,61 @@ class FileIndexPage(BasePage): debugs.append(response.text) yield "\n".join(outputs), "\n".join(debugs) except StopIteration as e: - result, errors = e.value + results, index_errors, docs = e.value except Exception as e: debugs.append(f"Error: {e}") yield "\n".join(outputs), "\n".join(debugs) return - n_successes = len([_ for _ in result if _]) + n_successes = len([_ for _ in results if _]) if n_successes: gr.Info(f"Successfully index {n_successes} files") n_errors = len([_ for _ in errors if _]) if n_errors: gr.Warning(f"Have errors for {n_errors} files") + return results + + def index_fn_with_default_loaders( + self, files, reindex: bool, settings, user_id + ) -> list["str"]: + """Function for quick upload with default loaders + + Args: + files: the list of files to be uploaded + reindex: whether to reindex the files + selected_files: the list of files already selected + settings: the settings of the app + """ + print("Overriding with default loaders") + exist_ids = [] + to_process_files = [] + for str_file_path in files: + file_path = Path(str(str_file_path)) + exist_id = ( + self._index.get_indexing_pipeline(settings, user_id) + .route(file_path) + .get_id_if_exists(file_path) + ) + if exist_id: + exist_ids.append(exist_id) + else: + to_process_files.append(str_file_path) + + returned_ids = [] + settings = deepcopy(settings) + settings[f"index.options.{self._index.id}.reader_mode"] = "default" + settings[f"index.options.{self._index.id}.quick_index_mode"] = True + if to_process_files: + _iter = self.index_fn(to_process_files, reindex, settings, user_id) + try: + while next(_iter): + pass + except StopIteration as e: + returned_ids = e.value + + return exist_ids + returned_ids + def index_files_from_dir( self, folder_path, reindex, settings, user_id ) -> Generator[tuple[str, str], None, None]: @@ -452,7 +760,19 @@ class FileIndexPage(BasePage): yield from self.index_fn(files, reindex, settings, user_id) - def list_file(self, user_id): + def format_size_human_readable(self, num: float | str, suffix="B"): + try: + num = float(num) + except ValueError: + return num + + for unit in ("", "K", "M", "G", "T", "P", "E", "Z"): + if abs(num) < 1024.0: + return f"{num:3.0f}{unit}{suffix}" + num /= 1024.0 + return f"{num:.0f}Yi{suffix}" + + def list_file(self, user_id, name_pattern=""): if user_id is None: # not signed in return [], pd.DataFrame.from_records( @@ -461,7 +781,8 @@ class FileIndexPage(BasePage): "id": "-", "name": "-", "size": "-", - "text_length": "-", + "tokens": "-", + "loader": "-", "date_created": "-", } ] @@ -472,12 +793,17 @@ class FileIndexPage(BasePage): statement = select(Source) if self._index.config.get("private", False): statement = statement.where(Source.user == user_id) + if name_pattern: + statement = statement.where(Source.name.ilike(f"%{name_pattern}%")) results = [ { "id": each[0].id, "name": each[0].name, - "size": each[0].size, - "text_length": each[0].text_length, + "size": self.format_size_human_readable(each[0].size), + "tokens": self.format_size_human_readable( + each[0].note.get("tokens", "-"), suffix="" + ), + "loader": each[0].note.get("loader", "-"), "date_created": each[0].date_created.strftime("%Y-%m-%d %H:%M:%S"), } for each in session.execute(statement).all() @@ -492,12 +818,14 @@ class FileIndexPage(BasePage): "id": "-", "name": "-", "size": "-", - "text_length": "-", + "tokens": "-", + "loader": "-", "date_created": "-", } ] ) + print(f"{len(results)=}, {len(file_list)=}") return results, file_list def interact_file_list(self, list_files, ev: gr.SelectData): @@ -561,9 +889,8 @@ class FileSelector(BasePage): self.mode = gr.Radio( value=default_mode, choices=[ - ("Disabled", "disabled"), ("Search All", "all"), - ("Select", "select"), + ("Search In File(s)", "select"), ], container=False, ) diff --git a/libs/ktem/ktem/index/manager.py b/libs/ktem/ktem/index/manager.py index cfa6381..91e4270 100644 --- a/libs/ktem/ktem/index/manager.py +++ b/libs/ktem/ktem/index/manager.py @@ -123,8 +123,11 @@ class IndexManager: ) try: - # clean up - index.on_delete() + try: + # clean up + index.on_delete() + except Exception as e: + print(f"Error while deleting index {index.name}: {e}") # remove from database with Session(engine) as sess: diff --git a/libs/ktem/ktem/index/ui.py b/libs/ktem/ktem/index/ui.py index 9d6b7b2..47f7d9a 100644 --- a/libs/ktem/ktem/index/ui.py +++ b/libs/ktem/ktem/index/ui.py @@ -7,6 +7,21 @@ from ktem.utils.file import YAMLNoDateSafeLoader from .manager import IndexManager +# UGLY way to restart gradio server by updating atime +def update_current_module_atime(): + import os + import time + + # Define the file path + file_path = __file__ + print("Updating atime for", file_path) + + # Get the current time + current_time = time.time() + # Set the modified time (and access time) to the current time + os.utime(file_path, (current_time, current_time)) + + def format_description(cls): user_settings = cls.get_admin_settings() params_lines = ["| Name | Default | Description |", "| --- | --- | --- |"] @@ -29,7 +44,7 @@ class IndexManagement(BasePage): def on_building_ui(self): with gr.Tab(label="View"): self.index_list = gr.DataFrame( - headers=["ID", "Name", "Index Type"], + headers=["id", "name", "index type"], interactive=False, ) @@ -95,7 +110,7 @@ class IndexManagement(BasePage): """Called when the app is created""" self._app.app.load( self.list_indices, - inputs=None, + inputs=[], outputs=[self.index_list], ) self._app.app.load( @@ -117,7 +132,7 @@ class IndexManagement(BasePage): self.create_index, inputs=[self.name, self.index_type, self.spec], outputs=None, - ).success(self.list_indices, inputs=None, outputs=[self.index_list]).success( + ).success(self.list_indices, inputs=[], outputs=[self.index_list]).success( lambda: ("", None, "", self.spec_desc_default), outputs=[ self.name, @@ -125,6 +140,8 @@ class IndexManagement(BasePage): self.spec, self.spec_desc, ], + ).success( + update_current_module_atime ) self.index_list.select( self.select_index, @@ -152,7 +169,7 @@ class IndexManagement(BasePage): gr.update(visible=False), gr.update(visible=True), ), - inputs=None, + inputs=[], outputs=[ self.btn_edit_save, self.btn_delete, @@ -166,10 +183,8 @@ class IndexManagement(BasePage): inputs=[self.selected_index_id], outputs=[self.selected_index_id], show_progress="hidden", - ).then( - self.list_indices, - inputs=None, - outputs=[self.index_list], + ).then(self.list_indices, inputs=[], outputs=[self.index_list],).success( + update_current_module_atime ) self.btn_delete_no.click( lambda: ( @@ -178,7 +193,7 @@ class IndexManagement(BasePage): gr.update(visible=True), gr.update(visible=False), ), - inputs=None, + inputs=[], outputs=[ self.btn_edit_save, self.btn_delete, @@ -197,7 +212,7 @@ class IndexManagement(BasePage): show_progress="hidden", ).then( self.list_indices, - inputs=None, + inputs=[], outputs=[self.index_list], ) self.btn_close.click( @@ -245,16 +260,16 @@ class IndexManagement(BasePage): items = [] for item in self.manager.indices: record = {} - record["ID"] = item.id - record["Name"] = item.name - record["Index Type"] = item.__class__.__name__ + record["id"] = item.id + record["name"] = item.name + record["index type"] = item.__class__.__name__ items.append(record) if items: indices_list = pd.DataFrame.from_records(items) else: indices_list = pd.DataFrame.from_records( - [{"ID": "-", "Name": "-", "Index Type": "-"}] + [{"id": "-", "name": "-", "index type": "-"}] ) return indices_list @@ -268,7 +283,7 @@ class IndexManagement(BasePage): if not ev.selected: return -1 - return int(index_list["ID"][ev.index[0]]) + return int(index_list["id"][ev.index[0]]) def on_selected_index_change(self, selected_index_id: int): """Show the relevant index as user selects it on the UI diff --git a/libs/ktem/ktem/llms/manager.py b/libs/ktem/ktem/llms/manager.py index 6baa759..f4b204f 100644 --- a/libs/ktem/ktem/llms/manager.py +++ b/libs/ktem/ktem/llms/manager.py @@ -3,7 +3,7 @@ from typing import Optional, Type, overload from sqlalchemy import select from sqlalchemy.orm import Session from theflow.settings import settings as flowsettings -from theflow.utils.modules import deserialize +from theflow.utils.modules import deserialize, import_dotted_string from kotaemon.llms import ChatLLM @@ -38,7 +38,7 @@ class LLMManager: def load(self): """Load the model pool from database""" - self._models, self._info, self._defaut = {}, {}, "" + self._models, self._info, self._default = {}, {}, "" with Session(engine) as session: stmt = select(LLMTable) items = session.execute(stmt) @@ -54,14 +54,12 @@ class LLMManager: self._default = item.name def load_vendors(self): - from kotaemon.llms import ( - AzureChatOpenAI, - ChatOpenAI, - EndpointChatLLM, - LlamaCppChat, - ) + from kotaemon.llms import AzureChatOpenAI, ChatOpenAI, LlamaCppChat - self._vendors = [ChatOpenAI, AzureChatOpenAI, LlamaCppChat, EndpointChatLLM] + self._vendors = [ChatOpenAI, AzureChatOpenAI, LlamaCppChat] + + for extra_vendor in getattr(flowsettings, "KH_LLM_EXTRA_VENDORS", []): + self._vendors.append(import_dotted_string(extra_vendor, safe=False)) def __getitem__(self, key: str) -> ChatLLM: """Get model by name""" diff --git a/libs/ktem/ktem/llms/ui.py b/libs/ktem/ktem/llms/ui.py index 8b6f4ee..116982e 100644 --- a/libs/ktem/ktem/llms/ui.py +++ b/libs/ktem/ktem/llms/ui.py @@ -112,7 +112,7 @@ class LLMManagement(BasePage): """Called when the app is created""" self._app.app.load( self.list_llms, - inputs=None, + inputs=[], outputs=[self.llm_list], ) self._app.app.load( @@ -140,8 +140,8 @@ class LLMManagement(BasePage): self.btn_new.click( self.create_llm, inputs=[self.name, self.llm_choices, self.spec, self.default], - outputs=None, - ).success(self.list_llms, inputs=None, outputs=[self.llm_list]).success( + outputs=[], + ).success(self.list_llms, inputs=[], outputs=[self.llm_list]).success( lambda: ("", None, "", False, self.spec_desc_default), outputs=[ self.name, @@ -176,7 +176,7 @@ class LLMManagement(BasePage): ) self.btn_delete.click( self.on_btn_delete_click, - inputs=None, + inputs=[], outputs=[self.btn_delete, self.btn_delete_yes, self.btn_delete_no], show_progress="hidden", ) @@ -187,7 +187,7 @@ class LLMManagement(BasePage): show_progress="hidden", ).then( self.list_llms, - inputs=None, + inputs=[], outputs=[self.llm_list], ) self.btn_delete_no.click( @@ -196,7 +196,7 @@ class LLMManagement(BasePage): gr.update(visible=False), gr.update(visible=False), ), - inputs=None, + inputs=[], outputs=[self.btn_delete, self.btn_delete_yes, self.btn_delete_no], show_progress="hidden", ) @@ -210,7 +210,7 @@ class LLMManagement(BasePage): show_progress="hidden", ).then( self.list_llms, - inputs=None, + inputs=[], outputs=[self.llm_list], ) self.btn_close.click( diff --git a/libs/ktem/ktem/main.py b/libs/ktem/ktem/main.py index 6182e39..ba305e6 100644 --- a/libs/ktem/ktem/main.py +++ b/libs/ktem/ktem/main.py @@ -44,7 +44,7 @@ class App(BaseApp): if len(self.index_manager.indices) == 1: for index in self.index_manager.indices: with gr.Tab( - f"{index.name} Index", + f"{index.name}", elem_id="indices-tab", elem_classes=[ "fill-main-area-height", @@ -58,7 +58,7 @@ class App(BaseApp): setattr(self, f"_index_{index.id}", page) elif len(self.index_manager.indices) > 1: with gr.Tab( - "Indices", + "Files", elem_id="indices-tab", elem_classes=["fill-main-area-height", "scrollable", "indices-tab"], id="indices-tab", @@ -66,7 +66,7 @@ class App(BaseApp): ) as self._tabs["indices-tab"]: for index in self.index_manager.indices: with gr.Tab( - f"{index.name}", + f"{index.name} Collection", elem_id=f"{index.id}-tab", ) as self._tabs[f"{index.id}-tab"]: page = index.get_index_page_ui() diff --git a/libs/ktem/ktem/pages/chat/__init__.py b/libs/ktem/ktem/pages/chat/__init__.py index d9826e0..a21c5f2 100644 --- a/libs/ktem/ktem/pages/chat/__init__.py +++ b/libs/ktem/ktem/pages/chat/__init__.py @@ -1,15 +1,25 @@ import asyncio +import csv from copy import deepcopy +from datetime import datetime +from pathlib import Path from typing import Optional import gradio as gr +from filelock import FileLock from ktem.app import BasePage from ktem.components import reasonings from ktem.db.models import Conversation, engine +from ktem.index.file.ui import File +from ktem.reasoning.prompt_optimization.suggest_conversation_name import ( + SuggestConvNamePipeline, +) +from plotly.io import from_json from sqlmodel import Session, select from theflow.settings import settings as flowsettings from kotaemon.base import Document +from kotaemon.indices.ingests.files import KH_DEFAULT_FILE_EXTRACTORS from .chat_panel import ChatPanel from .chat_suggestion import ChatSuggestion @@ -17,23 +27,49 @@ from .common import STATE from .control import ConversationControl from .report import ReportIssue +DEFAULT_SETTING = "(default)" +INFO_PANEL_SCALES = {True: 8, False: 4} + + +pdfview_js = """ +function() { + // Get all links and attach click event + var links = document.getElementsByClassName("pdf-link"); + for (var i = 0; i < links.length; i++) { + links[i].onclick = openModal; + } +} +""" + class ChatPage(BasePage): def __init__(self, app): self._app = app self._indices_input = [] + self.on_building_ui() + self._reasoning_type = gr.State(value=None) + self._llm_type = gr.State(value=None) + self._conversation_renamed = gr.State(value=False) + self.info_panel_expanded = gr.State(value=True) def on_building_ui(self): with gr.Row(): - self.chat_state = gr.State(STATE) - with gr.Column(scale=1, elem_id="conv-settings-panel"): + self.state_chat = gr.State(STATE) + self.state_retrieval_history = gr.State([]) + self.state_chat_history = gr.State([]) + self.state_plot_history = gr.State([]) + self.state_settings = gr.State({}) + self.state_info_panel = gr.State("") + self.state_plot_panel = gr.State(None) + + with gr.Column(scale=1, elem_id="conv-settings-panel") as self.conv_column: self.chat_control = ConversationControl(self._app) if getattr(flowsettings, "KH_FEATURE_CHAT_SUGGESTION", False): self.chat_suggestion = ChatSuggestion(self._app) - for index in self._app.index_manager.indices: + for index_id, index in enumerate(self._app.index_manager.indices): index.selector = None index_ui = index.get_selector_component_ui() if not index_ui: @@ -41,7 +77,9 @@ class ChatPage(BasePage): continue index_ui.unrender() # need to rerender later within Accordion - with gr.Accordion(label=f"{index.name} Index", open=True): + with gr.Accordion( + label=f"{index.name} Collection", open=index_id < 1 + ): index_ui.render() gr_index = index_ui.as_gradio_component() if gr_index: @@ -60,14 +98,66 @@ class ChatPage(BasePage): self._indices_input.append(gr_index) setattr(self, f"_index_{index.id}", index_ui) + if len(self._app.index_manager.indices) > 0: + with gr.Accordion(label="Quick Upload") as _: + self.quick_file_upload = File( + file_types=list(KH_DEFAULT_FILE_EXTRACTORS.keys()), + file_count="multiple", + container=True, + show_label=False, + ) + self.quick_file_upload_status = gr.Markdown() + self.report_issue = ReportIssue(self._app) with gr.Column(scale=6, elem_id="chat-area"): self.chat_panel = ChatPanel(self._app) - with gr.Column(scale=3, elem_id="chat-info-panel"): + with gr.Row(): + with gr.Accordion(label="Chat settings", open=False): + # a quick switch for reasoning type option + with gr.Row(): + gr.HTML("Reasoning method") + gr.HTML("Model") + + with gr.Row(): + reasoning_type_values = [ + (DEFAULT_SETTING, DEFAULT_SETTING) + ] + self._app.default_settings.reasoning.settings[ + "use" + ].choices + self.reasoning_types = gr.Dropdown( + choices=reasoning_type_values, + value=DEFAULT_SETTING, + container=False, + show_label=False, + ) + self.model_types = gr.Dropdown( + choices=self._app.default_settings.reasoning.options[ + "simple" + ] + .settings["llm"] + .choices, + value="", + container=False, + show_label=False, + ) + + with gr.Column( + scale=INFO_PANEL_SCALES[False], elem_id="chat-info-panel" + ) as self.info_column: with gr.Accordion(label="Information panel", open=True): - self.info_panel = gr.HTML() + self.modal = gr.HTML("
") + self.plot_panel = gr.Plot(visible=False) + self.info_panel = gr.HTML(elem_id="html-info-panel") + + def _json_to_plot(self, json_dict: dict | None): + if json_dict: + plot = from_json(json_dict) + plot = gr.update(visible=True, value=plot) + else: + plot = gr.update(visible=False) + return plot def on_register_events(self): gr.on( @@ -98,27 +188,75 @@ class ChatPage(BasePage): self.chat_control.conversation_id, self.chat_panel.chatbot, self._app.settings_state, - self.chat_state, + self._reasoning_type, + self._llm_type, + self.state_chat, self._app.user_id, ] + self._indices_input, outputs=[ self.chat_panel.chatbot, self.info_panel, - self.chat_state, + self.plot_panel, + self.state_plot_panel, + self.state_chat, ], concurrency_limit=20, show_progress="minimal", + ).success( + fn=self.backup_original_info, + inputs=[ + self.chat_panel.chatbot, + self._app.settings_state, + self.info_panel, + self.state_chat_history, + ], + outputs=[ + self.state_chat_history, + self.state_settings, + self.state_info_panel, + ], ).then( - fn=self.update_data_source, + fn=self.persist_data_source, inputs=[ self.chat_control.conversation_id, + self._app.user_id, + self.info_panel, + self.state_plot_panel, + self.state_retrieval_history, + self.state_plot_history, self.chat_panel.chatbot, - self.chat_state, + self.state_chat, ] + self._indices_input, - outputs=None, + outputs=[ + self.state_retrieval_history, + self.state_plot_history, + ], concurrency_limit=20, + ).success( + fn=self.check_and_suggest_name_conv, + inputs=self.chat_panel.chatbot, + outputs=[ + self.chat_control.conversation_rn, + self._conversation_renamed, + ], + ).success( + self.chat_control.rename_conv, + inputs=[ + self.chat_control.conversation_id, + self.chat_control.conversation_rn, + self._conversation_renamed, + self._app.user_id, + ], + outputs=[ + self.chat_control.conversation, + self.chat_control.conversation, + self.chat_control.conversation_rn, + ], + show_progress="hidden", + ).then( + fn=None, inputs=None, outputs=None, js=pdfview_js ) self.chat_panel.regen_btn.click( @@ -127,33 +265,90 @@ class ChatPage(BasePage): self.chat_control.conversation_id, self.chat_panel.chatbot, self._app.settings_state, - self.chat_state, + self._reasoning_type, + self._llm_type, + self.state_chat, self._app.user_id, ] + self._indices_input, outputs=[ self.chat_panel.chatbot, self.info_panel, - self.chat_state, + self.plot_panel, + self.state_plot_panel, + self.state_chat, ], concurrency_limit=20, show_progress="minimal", ).then( - fn=self.update_data_source, + fn=self.persist_data_source, inputs=[ self.chat_control.conversation_id, + self._app.user_id, + self.info_panel, + self.state_plot_panel, + self.state_retrieval_history, + self.state_plot_history, self.chat_panel.chatbot, - self.chat_state, + self.state_chat, ] + self._indices_input, - outputs=None, + outputs=[ + self.state_retrieval_history, + self.state_plot_history, + ], concurrency_limit=20, + ).success( + fn=self.check_and_suggest_name_conv, + inputs=self.chat_panel.chatbot, + outputs=[ + self.chat_control.conversation_rn, + self._conversation_renamed, + ], + ).success( + self.chat_control.rename_conv, + inputs=[ + self.chat_control.conversation_id, + self.chat_control.conversation_rn, + self._conversation_renamed, + self._app.user_id, + ], + outputs=[ + self.chat_control.conversation, + self.chat_control.conversation, + self.chat_control.conversation_rn, + ], + show_progress="hidden", + ).then( + fn=None, inputs=None, outputs=None, js=pdfview_js + ) + + self.chat_control.btn_info_expand.click( + fn=lambda is_expanded: ( + gr.update(scale=INFO_PANEL_SCALES[is_expanded]), + not is_expanded, + ), + inputs=self.info_panel_expanded, + outputs=[self.info_column, self.info_panel_expanded], ) self.chat_panel.chatbot.like( fn=self.is_liked, inputs=[self.chat_control.conversation_id], outputs=None, + ).success( + self.save_log, + inputs=[ + self.chat_control.conversation_id, + self.chat_panel.chatbot, + self._app.settings_state, + self.info_panel, + self.state_chat_history, + self.state_settings, + self.state_info_panel, + gr.State(getattr(flowsettings, "KH_APP_DATA_DIR", "logs")), + ], + outputs=None, ) self.chat_control.btn_new.click( @@ -163,17 +358,25 @@ class ChatPage(BasePage): show_progress="hidden", ).then( self.chat_control.select_conv, - inputs=[self.chat_control.conversation], + inputs=[self.chat_control.conversation, self._app.user_id], outputs=[ self.chat_control.conversation_id, self.chat_control.conversation, self.chat_control.conversation_rn, self.chat_panel.chatbot, self.info_panel, - self.chat_state, + self.state_plot_panel, + self.state_retrieval_history, + self.state_plot_history, + self.chat_control.cb_is_public, + self.state_chat, ] + self._indices_input, show_progress="hidden", + ).then( + fn=self._json_to_plot, + inputs=self.state_plot_panel, + outputs=self.plot_panel, ) self.chat_control.btn_del.click( @@ -188,17 +391,25 @@ class ChatPage(BasePage): show_progress="hidden", ).then( self.chat_control.select_conv, - inputs=[self.chat_control.conversation], + inputs=[self.chat_control.conversation, self._app.user_id], outputs=[ self.chat_control.conversation_id, self.chat_control.conversation, self.chat_control.conversation_rn, self.chat_panel.chatbot, self.info_panel, - self.chat_state, + self.state_plot_panel, + self.state_retrieval_history, + self.state_plot_history, + self.chat_control.cb_is_public, + self.state_chat, ] + self._indices_input, show_progress="hidden", + ).then( + fn=self._json_to_plot, + inputs=self.state_plot_panel, + outputs=self.plot_panel, ).then( lambda: self.toggle_delete(""), outputs=[self.chat_control._new_delete, self.chat_control._delete_confirm], @@ -207,33 +418,80 @@ class ChatPage(BasePage): lambda: self.toggle_delete(""), outputs=[self.chat_control._new_delete, self.chat_control._delete_confirm], ) - self.chat_control.conversation_rn_btn.click( + self.chat_control.btn_conversation_rn.click( + lambda: gr.update(visible=True), + outputs=[ + self.chat_control.conversation_rn, + ], + ) + self.chat_control.conversation_rn.submit( self.chat_control.rename_conv, inputs=[ self.chat_control.conversation_id, self.chat_control.conversation_rn, + gr.State(value=True), self._app.user_id, ], - outputs=[self.chat_control.conversation, self.chat_control.conversation], + outputs=[ + self.chat_control.conversation, + self.chat_control.conversation, + self.chat_control.conversation_rn, + ], show_progress="hidden", ) self.chat_control.conversation.select( self.chat_control.select_conv, - inputs=[self.chat_control.conversation], + inputs=[self.chat_control.conversation, self._app.user_id], outputs=[ self.chat_control.conversation_id, self.chat_control.conversation, self.chat_control.conversation_rn, self.chat_panel.chatbot, self.info_panel, - self.chat_state, + self.state_plot_panel, + self.state_retrieval_history, + self.state_plot_history, + self.chat_control.cb_is_public, + self.state_chat, ] + self._indices_input, show_progress="hidden", + ).then( + fn=self._json_to_plot, + inputs=self.state_plot_panel, + outputs=self.plot_panel, ).then( lambda: self.toggle_delete(""), outputs=[self.chat_control._new_delete, self.chat_control._delete_confirm], + ).then( + fn=None, inputs=None, outputs=None, js=pdfview_js + ) + + # evidence display on message selection + self.chat_panel.chatbot.select( + self.message_selected, + inputs=[ + self.state_retrieval_history, + self.state_plot_history, + ], + outputs=[ + self.info_panel, + self.state_plot_panel, + ], + ).then( + fn=self._json_to_plot, + inputs=self.state_plot_panel, + outputs=self.plot_panel, + ).then( + fn=None, inputs=None, outputs=None, js=pdfview_js + ) + + self.chat_control.cb_is_public.change( + self.on_set_public_conversation, + inputs=[self.chat_control.cb_is_public, self.chat_control.conversation], + outputs=None, + show_progress="hidden", ) self.report_issue.report_btn.click( @@ -247,11 +505,26 @@ class ChatPage(BasePage): self._app.settings_state, self._app.user_id, self.info_panel, - self.chat_state, + self.state_chat, ] + self._indices_input, outputs=None, ) + self.reasoning_types.change( + self.reasoning_changed, + inputs=[self.reasoning_types], + outputs=[self._reasoning_type], + ) + self.model_types.change( + lambda x: x, + inputs=[self.model_types], + outputs=[self._llm_type], + ) + self.chat_control.conversation_id.change( + lambda: gr.update(visible=False), + outputs=self.plot_panel, + ) + if getattr(flowsettings, "KH_FEATURE_CHAT_SUGGESTION", False): self.chat_suggestion.example.select( self.chat_suggestion.select_example, @@ -291,6 +564,28 @@ class ChatPage(BasePage): else: return gr.update(visible=True), gr.update(visible=False) + def on_set_public_conversation(self, is_public, convo_id): + if not convo_id: + gr.Warning("No conversation selected") + return + + with Session(engine) as session: + statement = select(Conversation).where(Conversation.id == convo_id) + + result = session.exec(statement).one() + name = result.name + + if result.is_public != is_public: + # Only trigger updating when user + # select different value from the current + result.is_public = is_public + session.add(result) + session.commit() + + gr.Info( + f"Conversation: {name} is {'public' if is_public else 'private'}." + ) + def on_subscribe_public_events(self): if self._app.f_user_management: self._app.subscribe_event( @@ -306,25 +601,53 @@ class ChatPage(BasePage): self._app.subscribe_event( name="onSignOut", definition={ - "fn": lambda: self.chat_control.select_conv(""), + "fn": lambda: self.chat_control.select_conv("", None), "outputs": [ self.chat_control.conversation_id, self.chat_control.conversation, self.chat_control.conversation_rn, self.chat_panel.chatbot, self.info_panel, + self.state_plot_panel, + self.state_retrieval_history, + self.state_plot_history, + self.chat_control.cb_is_public, ] + self._indices_input, "show_progress": "hidden", }, ) - def update_data_source(self, convo_id, messages, state, *selecteds): + def persist_data_source( + self, + convo_id, + user_id, + retrieval_msg, + plot_data, + retrival_history, + plot_history, + messages, + state, + *selecteds, + ): """Update the data source""" if not convo_id: gr.Warning("No conversation selected") return + # if not regen, then append the new message + if not state["app"].get("regen", False): + retrival_history = retrival_history + [retrieval_msg] + plot_history = plot_history + [plot_data] + else: + if retrival_history: + print("Updating retrieval history (regen=True)") + retrival_history[-1] = retrieval_msg + plot_history[-1] = plot_data + + # reset regen state + state["app"]["regen"] = False + selecteds_ = {} for index in self._app.index_manager.indices: if index.selector is None: @@ -339,15 +662,29 @@ class ChatPage(BasePage): result = session.exec(statement).one() data_source = result.data_source + old_selecteds = data_source.get("selected", {}) + is_owner = result.user == user_id + + # Write down to db result.data_source = { - "selected": selecteds_, + "selected": selecteds_ if is_owner else old_selecteds, "messages": messages, + "retrieval_messages": retrival_history, + "plot_history": plot_history, "state": state, "likes": deepcopy(data_source.get("likes", [])), } session.add(result) session.commit() + return retrival_history, plot_history + + def reasoning_changed(self, reasoning_type): + if reasoning_type != DEFAULT_SETTING: + # override app settings state (temporary) + gr.Info("Reasoning type changed to `{}`".format(reasoning_type)) + return reasoning_type + def is_liked(self, convo_id, liked: gr.LikeData): with Session(engine) as session: statement = select(Conversation).where(Conversation.id == convo_id) @@ -362,7 +699,19 @@ class ChatPage(BasePage): session.add(result) session.commit() - def create_pipeline(self, settings: dict, state: dict, user_id: int, *selecteds): + def message_selected(self, retrieval_history, plot_history, msg: gr.SelectData): + index = msg.index[0] + return retrieval_history[index], plot_history[index] + + def create_pipeline( + self, + settings: dict, + session_reasoning_type: str, + session_llm: str, + state: dict, + user_id: int, + *selecteds, + ): """Create the pipeline from settings Args: @@ -374,10 +723,23 @@ class ChatPage(BasePage): Returns: - the pipeline objects """ - reasoning_mode = settings["reasoning.use"] + # override reasoning_mode by temporary chat page state + print("Session reasoning type", session_reasoning_type) + print("Session LLM", session_llm) + reasoning_mode = ( + settings["reasoning.use"] + if session_reasoning_type in (DEFAULT_SETTING, None) + else session_reasoning_type + ) reasoning_cls = reasonings[reasoning_mode] + print("Reasoning class", reasoning_cls) reasoning_id = reasoning_cls.get_info()["id"] + settings = deepcopy(settings) + llm_setting_key = f"reasoning.options.{reasoning_id}.llm" + if llm_setting_key in settings and session_llm not in (DEFAULT_SETTING, None): + settings[llm_setting_key] = session_llm + # get retrievers retrievers = [] for index in self._app.index_manager.indices: @@ -403,7 +765,15 @@ class ChatPage(BasePage): return pipeline, reasoning_state def chat_fn( - self, conversation_id, chat_history, settings, state, user_id, *selecteds + self, + conversation_id, + chat_history, + settings, + reasoning_type, + llm_type, + state, + user_id, + *selecteds, ): """Chat function""" chat_input = chat_history[-1][0] @@ -413,18 +783,23 @@ class ChatPage(BasePage): # construct the pipeline pipeline, reasoning_state = self.create_pipeline( - settings, state, user_id, *selecteds + settings, reasoning_type, llm_type, state, user_id, *selecteds ) + print("Reasoning state", reasoning_state) pipeline.set_output_queue(queue) - text, refs = "", "" + text, refs, plot, plot_gr = "", "", None, gr.update(visible=False) msg_placeholder = getattr( flowsettings, "KH_CHAT_MSG_PLACEHOLDER", "Thinking ..." ) print(msg_placeholder) - yield chat_history + [(chat_input, text or msg_placeholder)], refs, state - - len_ref = -1 # for logging purpose + yield ( + chat_history + [(chat_input, text or msg_placeholder)], + refs, + plot_gr, + plot, + state, + ) for response in pipeline.stream(chat_input, conversation_id, chat_history): @@ -446,22 +821,42 @@ class ChatPage(BasePage): else: refs += response.content - if len(refs) > len_ref: - print(f"Len refs: {len(refs)}") - len_ref = len(refs) + if response.channel == "plot": + plot = response.content + plot_gr = self._json_to_plot(plot) state[pipeline.get_info()["id"]] = reasoning_state["pipeline"] - yield chat_history + [(chat_input, text or msg_placeholder)], refs, state + yield ( + chat_history + [(chat_input, text or msg_placeholder)], + refs, + plot_gr, + plot, + state, + ) if not text: empty_msg = getattr( flowsettings, "KH_CHAT_EMPTY_MSG_PLACEHOLDER", "(Sorry, I don't know)" ) print(f"Generate nothing: {empty_msg}") - yield chat_history + [(chat_input, text or empty_msg)], refs, state + yield ( + chat_history + [(chat_input, text or empty_msg)], + refs, + plot_gr, + plot, + state, + ) def regen_fn( - self, conversation_id, chat_history, settings, state, user_id, *selecteds + self, + conversation_id, + chat_history, + settings, + reasoning_type, + llm_type, + state, + user_id, + *selecteds, ): """Regen function""" if not chat_history: @@ -470,11 +865,119 @@ class ChatPage(BasePage): return state["app"]["regen"] = True - for chat, refs, state in self.chat_fn( - conversation_id, chat_history, settings, state, user_id, *selecteds - ): - new_state = deepcopy(state) - new_state["app"]["regen"] = False - yield chat, refs, new_state + yield from self.chat_fn( + conversation_id, + chat_history, + settings, + reasoning_type, + llm_type, + state, + user_id, + *selecteds, + ) - state["app"]["regen"] = False + def check_and_suggest_name_conv(self, chat_history): + suggest_pipeline = SuggestConvNamePipeline() + new_name = gr.update() + renamed = False + + # check if this is a newly created conversation + if len(chat_history) == 1: + suggested_name = suggest_pipeline(chat_history).text[:40] + new_name = gr.update(value=suggested_name) + renamed = True + + return new_name, renamed + + def backup_original_info( + self, chat_history, settings, info_pannel, original_chat_history + ): + original_chat_history.append(chat_history[-1]) + return original_chat_history, settings, info_pannel + + def save_log( + self, + conversation_id, + chat_history, + settings, + info_panel, + original_chat_history, + original_settings, + original_info_panel, + log_dir, + ): + if not Path(log_dir).exists(): + Path(log_dir).mkdir(parents=True) + + lock = FileLock(Path(log_dir) / ".lock") + # get current date + today = datetime.now() + formatted_date = today.strftime("%d%m%Y_%H") + + with Session(engine) as session: + statement = select(Conversation).where(Conversation.id == conversation_id) + result = session.exec(statement).one() + + data_source = deepcopy(result.data_source) + likes = data_source.get("likes", []) + if not likes: + return + + feedback = likes[-1][-1] + message_index = likes[-1][0] + + current_message = chat_history[message_index[0]] + original_message = original_chat_history[message_index[0]] + is_original = all( + [ + current_item == original_item + for current_item, original_item in zip( + current_message, original_message + ) + ] + ) + + dataframe = [ + [ + conversation_id, + message_index, + current_message[0], + current_message[1], + chat_history, + settings, + info_panel, + feedback, + is_original, + original_message[1], + original_chat_history, + original_settings, + original_info_panel, + ] + ] + + with lock: + log_file = Path(log_dir) / f"{formatted_date}_log.csv" + is_log_file_exist = log_file.is_file() + with open(log_file, "a") as f: + writer = csv.writer(f) + # write headers + if not is_log_file_exist: + writer.writerow( + [ + "Conversation ID", + "Message ID", + "Question", + "Answer", + "Chat History", + "Settings", + "Evidences", + "Feedback", + "Original/ Rewritten", + "Original Answer", + "Original Chat History", + "Original Settings", + "Original Evidences", + ] + ) + + writer.writerows(dataframe) diff --git a/libs/ktem/ktem/pages/chat/control.py b/libs/ktem/ktem/pages/chat/control.py index 5e369b5..6fd47eb 100644 --- a/libs/ktem/ktem/pages/chat/control.py +++ b/libs/ktem/ktem/pages/chat/control.py @@ -1,13 +1,20 @@ import logging +import os import gradio as gr from ktem.app import BasePage -from ktem.db.models import Conversation, engine -from sqlmodel import Session, select +from ktem.db.models import Conversation, User, engine +from sqlmodel import Session, or_, select +import flowsettings + +from ...utils.conversation import sync_retrieval_n_message from .common import STATE logger = logging.getLogger(__name__) +ASSETS_DIR = "assets/icons" +if not os.path.isdir(ASSETS_DIR): + ASSETS_DIR = "libs/ktem/ktem/assets/icons" def is_conv_name_valid(name): @@ -35,14 +42,47 @@ class ConversationControl(BasePage): label="Chat sessions", choices=[], container=False, - filterable=False, + filterable=True, interactive=True, elem_classes=["unset-overflow"], ) with gr.Row() as self._new_delete: - self.btn_new = gr.Button(value="New", min_width=10, variant="primary") - self.btn_del = gr.Button(value="Delete", min_width=10, variant="stop") + self.btn_new = gr.Button( + value="", + icon=f"{ASSETS_DIR}/new.svg", + min_width=2, + scale=1, + size="sm", + elem_classes=["no-background", "body-text-color"], + ) + self.btn_del = gr.Button( + value="", + icon=f"{ASSETS_DIR}/delete.svg", + min_width=2, + scale=1, + size="sm", + elem_classes=["no-background", "body-text-color"], + ) + self.btn_conversation_rn = gr.Button( + value="", + icon=f"{ASSETS_DIR}/rename.svg", + min_width=2, + scale=1, + size="sm", + elem_classes=["no-background", "body-text-color"], + ) + self.btn_info_expand = gr.Button( + value="", + icon=f"{ASSETS_DIR}/sidebar.svg", + min_width=2, + scale=1, + size="sm", + elem_classes=["no-background", "body-text-color"], + ) + self.cb_is_public = gr.Checkbox( + value=False, label="Shared", min_width=10, scale=4 + ) with gr.Row(visible=False) as self._delete_confirm: self.btn_del_conf = gr.Button( @@ -54,28 +94,60 @@ class ConversationControl(BasePage): with gr.Row(): self.conversation_rn = gr.Text( + label="(Enter) to save", placeholder="Conversation name", - container=False, + container=True, scale=5, min_width=10, interactive=True, - ) - self.conversation_rn_btn = gr.Button( - value="Rename", - scale=1, - min_width=10, - elem_classes=["no-background", "body-text-color", "bold-text"], + visible=False, ) def load_chat_history(self, user_id): """Reload chat history""" + + # In case user are admin. They can also watch the + # public conversations + can_see_public: bool = False + with Session(engine) as session: + statement = select(User).where(User.id == user_id) + result = session.exec(statement).one_or_none() + + if result is not None: + if flowsettings.KH_USER_CAN_SEE_PUBLIC: + can_see_public = ( + result.username == flowsettings.KH_USER_CAN_SEE_PUBLIC + ) + else: + can_see_public = True + + print(f"User-id: {user_id}, can see public conversations: {can_see_public}") + options = [] with Session(engine) as session: - statement = ( - select(Conversation) - .where(Conversation.user == user_id) - .order_by(Conversation.date_created.desc()) # type: ignore - ) + # Define condition based on admin-role: + # - can_see: can see their conversations & public files + # - can_not_see: only see their conversations + if can_see_public: + statement = ( + select(Conversation) + .where( + or_( + Conversation.user == user_id, + Conversation.is_public, + ) + ) + .order_by( + Conversation.is_public.desc(), Conversation.date_created.desc() + ) # type: ignore + ) + else: + statement = ( + select(Conversation) + .where(Conversation.user == user_id) + .order_by(Conversation.date_created.desc()) # type: ignore + ) + results = session.exec(statement).all() for result in results: options.append((result.name, result.id)) @@ -129,7 +201,7 @@ class ConversationControl(BasePage): else: return None, gr.update(value=None, choices=[]) - def select_conv(self, conversation_id): + def select_conv(self, conversation_id, user_id): """Select the conversation""" with Session(engine) as session: statement = select(Conversation).where(Conversation.id == conversation_id) @@ -137,18 +209,46 @@ class ConversationControl(BasePage): result = session.exec(statement).one() id_ = result.id name = result.name - selected = result.data_source.get("selected", {}) + is_conv_public = result.is_public + + # disable file selection ids state if + # not the owner of the conversation + if user_id == result.user: + selected = result.data_source.get("selected", {}) + else: + selected = {} + chats = result.data_source.get("messages", []) - info_panel = "" + + retrieval_history: list[str] = result.data_source.get( + "retrieval_messages", [] + ) + plot_history: list[dict] = result.data_source.get("plot_history", []) + + # On initialization + # Ensure len of retrieval and messages are equal + retrieval_history = sync_retrieval_n_message(chats, retrieval_history) + + info_panel = ( + retrieval_history[-1] + if retrieval_history + else "
No evidence found.
" + ) + plot_data = plot_history[-1] if plot_history else None state = result.data_source.get("state", STATE) + except Exception as e: logger.warning(e) id_ = "" name = "" selected = {} chats = [] + retrieval_history = [] + plot_history = [] info_panel = "" + plot_data = None state = STATE + is_conv_public = False indices = [] for index in self._app.index_manager.indices: @@ -160,10 +260,29 @@ class ConversationControl(BasePage): if isinstance(index.selector, tuple): indices.extend(selected.get(str(index.id), index.default_selector)) - return id_, id_, name, chats, info_panel, state, *indices + return ( + id_, + id_, + name, + chats, + info_panel, + plot_data, + retrieval_history, + plot_history, + is_conv_public, + state, + *indices, + ) - def rename_conv(self, conversation_id, new_name, user_id): + def rename_conv(self, conversation_id, new_name, is_renamed, user_id): """Rename the conversation""" + if not is_renamed: + return ( + gr.update(), + conversation_id, + gr.update(visible=False), + ) + if user_id is None: gr.Warning("Please sign in first (Settings → User Settings)") return gr.update(), "" @@ -185,7 +304,12 @@ class ConversationControl(BasePage): session.commit() history = self.load_chat_history(user_id) - return gr.update(choices=history), conversation_id + gr.Info("Conversation renamed.") + return ( + gr.update(choices=history), + conversation_id, + gr.update(visible=False), + ) def _on_app_created(self): """Reload the conversation once the app is created""" diff --git a/libs/ktem/ktem/pages/chat/report.py b/libs/ktem/ktem/pages/chat/report.py index 7a4fe6a..f404743 100644 --- a/libs/ktem/ktem/pages/chat/report.py +++ b/libs/ktem/ktem/pages/chat/report.py @@ -12,7 +12,7 @@ class ReportIssue(BasePage): self.on_building_ui() def on_building_ui(self): - with gr.Accordion(label="Report", open=False): + with gr.Accordion(label="Feedback", open=False): self.correctness = gr.Radio( choices=[ ("The answer is correct", "correct"), diff --git a/libs/ktem/ktem/pages/help.py b/libs/ktem/ktem/pages/help.py index fd750ef..e3438d1 100644 --- a/libs/ktem/ktem/pages/help.py +++ b/libs/ktem/ktem/pages/help.py @@ -9,6 +9,7 @@ from theflow.settings import settings def get_remote_doc(url: str) -> str: try: res = requests.get(url) + res.raise_for_status() return res.text except Exception as e: print(f"Failed to fetch document from {url}: {e}") diff --git a/libs/ktem/ktem/pages/login.py b/libs/ktem/ktem/pages/login.py index 50f505f..9dc4839 100644 --- a/libs/ktem/ktem/pages/login.py +++ b/libs/ktem/ktem/pages/login.py @@ -7,9 +7,9 @@ from sqlmodel import Session, select fetch_creds = """ function() { - const username = getStorage('username') - const password = getStorage('password') - return [username, password]; + const username = getStorage('username', '') + const password = getStorage('password', '') + return [username, password, null]; } """ diff --git a/libs/ktem/ktem/pages/resources/__init__.py b/libs/ktem/ktem/pages/resources/__init__.py index c423d3f..aa606c9 100644 --- a/libs/ktem/ktem/pages/resources/__init__.py +++ b/libs/ktem/ktem/pages/resources/__init__.py @@ -15,18 +15,18 @@ class ResourcesTab(BasePage): self.on_building_ui() def on_building_ui(self): - if self._app.f_user_management: - with gr.Tab("User Management", visible=False) as self.user_management_tab: - self.user_management = UserManagement(self._app) + with gr.Tab("Index Collections") as self.index_management_tab: + self.index_management = IndexManagement(self._app) with gr.Tab("LLMs") as self.llm_management_tab: self.llm_management = LLMManagement(self._app) - with gr.Tab("Embedding Models") as self.emb_management_tab: + with gr.Tab("Embeddings") as self.emb_management_tab: self.emb_management = EmbeddingManagement(self._app) - with gr.Tab("Index Management") as self.index_management_tab: - self.index_management = IndexManagement(self._app) + if self._app.f_user_management: + with gr.Tab("Users", visible=False) as self.user_management_tab: + self.user_management = UserManagement(self._app) def on_subscribe_public_events(self): if self._app.f_user_management: diff --git a/libs/ktem/ktem/pages/resources/user.py b/libs/ktem/ktem/pages/resources/user.py index 2b65075..106c268 100644 --- a/libs/ktem/ktem/pages/resources/user.py +++ b/libs/ktem/ktem/pages/resources/user.py @@ -94,6 +94,28 @@ def validate_password(pwd, pwd_cnf): return "" +def create_user(usn, pwd) -> bool: + with Session(engine) as session: + statement = select(User).where(User.username_lower == usn.lower()) + result = session.exec(statement).all() + if result: + print(f'User "{usn}" already exists') + return False + + else: + hashed_password = hashlib.sha256(pwd.encode()).hexdigest() + user = User( + username=usn, + username_lower=usn.lower(), + password=hashed_password, + admin=True, + ) + session.add(user) + session.commit() + + return True + + class UserManagement(BasePage): def __init__(self, app): self._app = app @@ -105,23 +127,9 @@ class UserManagement(BasePage): usn = flowsettings.KH_FEATURE_USER_MANAGEMENT_ADMIN pwd = flowsettings.KH_FEATURE_USER_MANAGEMENT_PASSWORD - with Session(engine) as session: - statement = select(User).where(User.username_lower == usn.lower()) - result = session.exec(statement).all() - if result: - print(f'User "{usn}" already exists') - - else: - hashed_password = hashlib.sha256(pwd.encode()).hexdigest() - user = User( - username=usn, - username_lower=usn.lower(), - password=hashed_password, - admin=True, - ) - session.add(user) - session.commit() - gr.Info(f'User "{usn}" created successfully') + is_created = create_user(usn, pwd) + if is_created: + gr.Info(f'User "{usn}" created successfully') def on_building_ui(self): with gr.Tab(label="User list"): @@ -224,7 +232,7 @@ class UserManagement(BasePage): gr.update(visible=False), gr.update(visible=False), ), - inputs=None, + inputs=[], outputs=[self.btn_delete, self.btn_delete_yes, self.btn_delete_no], show_progress="hidden", ) diff --git a/libs/ktem/ktem/pages/settings.py b/libs/ktem/ktem/pages/settings.py index 23bbfac..498282f 100644 --- a/libs/ktem/ktem/pages/settings.py +++ b/libs/ktem/ktem/pages/settings.py @@ -2,13 +2,15 @@ import hashlib import gradio as gr from ktem.app import BasePage +from ktem.components import reasonings from ktem.db.models import Settings, User, engine from sqlmodel import Session, select signout_js = """ -function() { +function(u, c, pw, pwc) { removeFromStorage('username'); removeFromStorage('password'); + return [u, c, pw, pwc]; } """ @@ -72,6 +74,10 @@ class SettingsPage(BasePage): self._components = {} self._reasoning_mode = {} + # store llms and embeddings components + self._llms = [] + self._embeddings = [] + # render application page if there are application settings self._render_app_tab = False if self._default_settings.application.settings: @@ -101,14 +107,13 @@ class SettingsPage(BasePage): def on_building_ui(self): if self._app.f_user_management: - with gr.Tab("Users"): + with gr.Tab("User settings"): self.user_tab() - with gr.Tab("General"): - self.app_tab() - with gr.Tab("Document Indices"): - self.index_tab() - with gr.Tab("Reasoning Pipelines"): - self.reasoning_tab() + + self.app_tab() + self.index_tab() + self.reasoning_tab() + self.setting_save_btn = gr.Button( "Save changes", variant="primary", scale=1, elem_classes=["right-button"] ) @@ -192,7 +197,7 @@ class SettingsPage(BasePage): ) onSignOutClick = self.signout.click( lambda: (None, "Current user: ___", "", ""), - inputs=None, + inputs=[], outputs=[ self._user_id, self.current_name, @@ -248,10 +253,14 @@ class SettingsPage(BasePage): return "", "" def app_tab(self): - with gr.Tab("General application settings", visible=self._render_app_tab): + with gr.Tab("General", visible=self._render_app_tab): for n, si in self._default_settings.application.settings.items(): obj = render_setting_item(si, si.value) self._components[f"application.{n}"] = obj + if si.special_type == "llm": + self._llms.append(obj) + if si.special_type == "embedding": + self._embeddings.append(obj) def index_tab(self): # TODO: double check if we need general @@ -260,12 +269,18 @@ class SettingsPage(BasePage): # obj = render_setting_item(si, si.value) # self._components[f"index.{n}"] = obj - with gr.Tab("Index settings", visible=self._render_index_tab): + id2name = {k: v.name for k, v in self._app.index_manager.info().items()} + with gr.Tab("Retrieval settings", visible=self._render_index_tab): for pn, sig in self._default_settings.index.options.items(): - with gr.Tab(f"Index {pn}"): + name = "{} Collection".format(id2name.get(pn, f"")) + with gr.Tab(name): for n, si in sig.settings.items(): obj = render_setting_item(si, si.value) self._components[f"index.options.{pn}.{n}"] = obj + if si.special_type == "llm": + self._llms.append(obj) + if si.special_type == "embedding": + self._embeddings.append(obj) def reasoning_tab(self): with gr.Tab("Reasoning settings", visible=self._render_reasoning_tab): @@ -275,6 +290,10 @@ class SettingsPage(BasePage): continue obj = render_setting_item(si, si.value) self._components[f"reasoning.{n}"] = obj + if si.special_type == "llm": + self._llms.append(obj) + if si.special_type == "embedding": + self._embeddings.append(obj) gr.Markdown("### Reasoning-specific settings") self._components["reasoning.use"] = render_setting_item( @@ -289,10 +308,19 @@ class SettingsPage(BasePage): visible=idx == 0, elem_id=pn, ) as self._reasoning_mode[pn]: - gr.Markdown("**Name**: Description") + reasoning = reasonings.get(pn, None) + if reasoning is None: + gr.Markdown("**Name**: Description") + else: + info = reasoning.get_info() + gr.Markdown(f"**{info['name']}**: {info['description']}") for n, si in sig.settings.items(): obj = render_setting_item(si, si.value) self._components[f"reasoning.options.{pn}.{n}"] = obj + if si.special_type == "llm": + self._llms.append(obj) + if si.special_type == "embedding": + self._embeddings.append(obj) def change_reasoning_mode(self, value): output = [] @@ -360,3 +388,38 @@ class SettingsPage(BasePage): outputs=[self._settings_state] + self.components(), show_progress="hidden", ) + + def update_llms(): + from ktem.llms.manager import llms + + if llms._default: + llm_choices = [(f"{llms._default} (default)", "")] + else: + llm_choices = [("(random)", "")] + llm_choices += [(_, _) for _ in llms.options().keys()] + return gr.update(choices=llm_choices) + + def update_embeddings(): + from ktem.embeddings.manager import embedding_models_manager + + if embedding_models_manager._default: + emb_choices = [(f"{embedding_models_manager._default} (default)", "")] + else: + emb_choices = [("(random)", "")] + emb_choices += [(_, _) for _ in embedding_models_manager.options().keys()] + return gr.update(choices=emb_choices) + + for llm in self._llms: + self._app.app.load( + update_llms, + inputs=[], + outputs=[llm], + show_progress="hidden", + ) + for emb in self._embeddings: + self._app.app.load( + update_embeddings, + inputs=[], + outputs=[emb], + show_progress="hidden", + ) diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/__init__.py b/libs/ktem/ktem/reasoning/prompt_optimization/__init__.py new file mode 100644 index 0000000..29d8bf9 --- /dev/null +++ b/libs/ktem/ktem/reasoning/prompt_optimization/__init__.py @@ -0,0 +1,9 @@ +from .decompose_question import DecomposeQuestionPipeline +from .fewshot_rewrite_question import FewshotRewriteQuestionPipeline +from .rewrite_question import RewriteQuestionPipeline + +__all__ = [ + "DecomposeQuestionPipeline", + "FewshotRewriteQuestionPipeline", + "RewriteQuestionPipeline", +] diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py b/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py new file mode 100644 index 0000000..7fdc473 --- /dev/null +++ b/libs/ktem/ktem/reasoning/prompt_optimization/decompose_question.py @@ -0,0 +1,79 @@ +import logging + +from ktem.llms.manager import llms +from ktem.reasoning.prompt_optimization.rewrite_question import RewriteQuestionPipeline +from pydantic import BaseModel, Field + +from kotaemon.base import Document, HumanMessage, Node, SystemMessage +from kotaemon.llms import ChatLLM + +logger = logging.getLogger(__name__) + + +class SubQuery(BaseModel): + """Search over a database of insurance rulebooks or financial reports""" + + sub_query: str = Field( + ..., + description="A very specific query against the database.", + ) + + +class DecomposeQuestionPipeline(RewriteQuestionPipeline): + """Decompose user complex question into multiple sub-questions + + Args: + llm: the language model to rewrite question + lang: the language of the answer. Currently support English and Japanese + """ + + llm: ChatLLM = Node( + default_callback=lambda _: llms.get("openai-gpt4-turbo", llms.get_default()) + ) + DECOMPOSE_SYSTEM_PROMPT_TEMPLATE = ( + "You are an expert at converting user complex questions into sub questions. " + "Perform query decomposition using provided function_call. " + "Given a user question, break it down into the most specific sub" + " questions you can (at most 3) " + "which will help you answer the original question. " + "Each sub question should be about a single concept/fact/idea. " + "If there are acronyms or words you are not familiar with, " + "do not try to rephrase them." + ) + prompt_template: str = DECOMPOSE_SYSTEM_PROMPT_TEMPLATE + + def create_prompt(self, question): + schema = SubQuery.model_json_schema() + function = { + "name": schema["title"], + "description": schema["description"], + "parameters": schema, + } + llm_kwargs = { + "tools": [{"type": "function", "function": function}], + "tool_choice": "auto", + } + + messages = [ + SystemMessage(content=self.prompt_template), + HumanMessage(content=question), + ] + + return messages, llm_kwargs + + def run(self, question: str) -> list: # type: ignore + messages, llm_kwargs = self.create_prompt(question) + result = self.llm(messages, **llm_kwargs) + tool_calls = result.additional_kwargs.get("tool_calls", None) + sub_queries = [] + if tool_calls: + for tool_call in tool_calls: + sub_queries.append( + Document( + content=SubQuery.parse_raw( + tool_call["function"]["arguments"] + ).sub_query + ) + ) + + return sub_queries diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py b/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py new file mode 100644 index 0000000..3c3b719 --- /dev/null +++ b/libs/ktem/ktem/reasoning/prompt_optimization/fewshot_rewrite_question.py @@ -0,0 +1,100 @@ +import json +import uuid +from pathlib import Path + +from ktem.components import get_docstore, get_vectorstore +from ktem.llms.manager import llms +from ktem.reasoning.prompt_optimization.rewrite_question import ( + DEFAULT_REWRITE_PROMPT, + RewriteQuestionPipeline, +) +from theflow.settings import settings as flowsettings + +from kotaemon.base import AIMessage, Document, HumanMessage, Node, SystemMessage +from kotaemon.embeddings import BaseEmbeddings +from kotaemon.llms import ChatLLM +from kotaemon.storages import BaseDocumentStore, BaseVectorStore + + +class FewshotRewriteQuestionPipeline(RewriteQuestionPipeline): + """Rewrite user question + + Args: + llm: the language model to rewrite question + rewrite_template: the prompt template for llm to paraphrase a text input + lang: the language of the answer. Currently support English and Japanese + embedding: the embedding model to encode the question + vector_store: the vector store to store the encoded question + doc_store: the document store to store the original question + k: the number of examples to retrieve for rewriting + """ + + llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) + rewrite_template: str = DEFAULT_REWRITE_PROMPT + lang: str = "English" + embedding: BaseEmbeddings + vector_store: BaseVectorStore + doc_store: BaseDocumentStore + k: int = getattr(flowsettings, "N_PROMPT_OPT_EXAMPLES", 3) + + def add_documents(self, examples, batch_size: int = 50): + print("Adding fewshot examples for rewriting") + documents = [] + for example in examples: + doc = Document( + text=example["input"], id_=str(uuid.uuid4()), metadata=example + ) + documents.append(doc) + + for i in range(0, len(documents), batch_size): + embeddings = self.embedding(documents[i : i + batch_size]) + ids = [t.doc_id for t in documents[i : i + batch_size]] + self.vector_store.add( + embeddings=embeddings, + ids=ids, + ) + self.doc_store.add(documents[i : i + batch_size]) + + @classmethod + def get_pipeline( + cls, + embedding, + example_path=Path(__file__).parent / "rephrase_question_train.json", + collection_name: str = "fewshot_rewrite_examples", + ): + vector_store = get_vectorstore(collection_name) + doc_store = get_docstore(collection_name) + + pipeline = cls( + embedding=embedding, vector_store=vector_store, doc_store=doc_store + ) + if doc_store.count(): + return pipeline + + examples = json.load(open(example_path, "r")) + pipeline.add_documents(examples) + + return pipeline + + def run(self, question: str) -> Document: # type: ignore + emb = self.embedding(question)[0].embedding + _, _, ids = self.vector_store.query(embedding=emb, top_k=self.k) + examples = self.doc_store.get(ids) + messages = [SystemMessage(content="You are a helpful assistant")] + for example in examples: + messages.append( + HumanMessage( + content=self.rewrite_template.format( + question=example.metadata["input"], lang=self.lang + ) + ) + ) + messages.append(AIMessage(content=example.metadata["output"])) + messages.append( + HumanMessage( + content=self.rewrite_template.format(question=question, lang=self.lang) + ) + ) + + result = self.llm(messages) + return result diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/rephrase_question_train.json b/libs/ktem/ktem/reasoning/prompt_optimization/rephrase_question_train.json new file mode 100644 index 0000000..fbe6b31 --- /dev/null +++ b/libs/ktem/ktem/reasoning/prompt_optimization/rephrase_question_train.json @@ -0,0 +1,9090 @@ +[ + { + "input": "What was the percentage increase in General and Administrative expenses in the year 2018 compared to the previous year?", + "output": "What was the increase in the General and administrative in 2018?" + }, + { + "input": "What was the specific percentage increase in the General and Administrative expenses during the year 2018?", + "output": "What was the increase in the General and administrative in 2018?" + }, + { + "input": "What was the decrease in the Other net expense in 2019?", + "output": "What was the specific amount of the decrease in the Other net expense in the financial year of 2019?" + }, + { + "input": "What was the decrease in the Other net expense in 2019?", + "output": "What was the specific decrease amount experienced in the Other net expense during the year 2019?" + }, + { + "input": "What was the increase in interest income?", + "output": "What was the exact amount by which the interest income increased?" + }, + { + "input": "What was the increase in interest income?", + "output": "What was the exact amount of the increase in interest income?" + }, + { + "input": "What was the average Other expense, net for 2018 and 2019?", + "output": "What was the average net Other expense for the years 2018 and 2019 combined?" + }, + { + "input": "In which year was Other expense, net less than 5,000 thousands?", + "output": "In what particular year did the amount of Other expense, net fall below 5,000 thousands?" + }, + { + "input": "What were the income tax expenses in the years 2019, 2018, and 2017, respectively?", + "output": "What was the income tax expense in 2019,2018 and 2017 respectively?" + }, + { + "input": "\"What were the income tax expenses for the years 2019, 2018, and 2017?\"", + "output": "What was the income tax expense in 2019,2018 and 2017 respectively?" + }, + { + "input": "How much money did the company have in unremitted earnings for the year 2019?", + "output": "What was the company's unremitted earnings in 2019?" + }, + { + "input": "How much unremitted earnings did the company have in 2019, without releasing or distributing them?", + "output": "What was the company's unremitted earnings in 2019?" + }, + { + "input": "Which year witnessed interest and penalties that amounted to less than 20 thousand?", + "output": "In which year was Interest and penalties less than 20 thousand?" + }, + { + "input": "What is the net carrying amount in 2019?", + "output": "What is the net carrying amount for the year 2019? 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the total amount spent on depreciation in the year 2019?", + "output": "What was the depreciation cost in 2019?" + }, + { + "input": "What was the change in depreciation between 2018 and 2019?", + "output": "What was the difference in the amount of depreciation incurred in 2018 compared to 2019?" + }, + { + "input": "What was the gross amount of accumulated depreciation at the beginning of 2017 and how did it change by the beginning of 2018?", + "output": "What was the change in Gross amount of accumulated depreciation at beginning between 2017 and 2018?" + }, + { + "input": "Which specific years have the statute of limitations expired on certain unrecognized tax benefits?", + "output": "Which years did the statute of limitations on certain unrecognized tax benefits lapse?" + }, + { + "input": "In 2019, what was the total value of receivables that had not yet been invoiced to customers?", + "output": "What were the unbilled receivables in 2019?" + }, + { + "input": "What was the change 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"output": "What was the percentage change in the net interest expense between 2018 and 2019?" + }, + { + "input": "What percentage increase or decrease occurred in the net interest expense from 2018 to 2019?", + "output": "What was the percentage change in the net interest expense between 2018 and 2019?" + }, + { + "input": "What was the change in the Returns reserves between 2018 and 2019?", + "output": "What was the difference in the amount allocated to Returns reserves between the fiscal years 2018 and 2019?" + }, + { + "input": "How much did the Additions for tax positions change from 2018 to 2019 in the current year?", + "output": "What was the change in the Additions for tax positions of the current year between 2018 and 2019?" + }, + { + "input": "What was the reflection of Pension and other post-retirement benefits?", + "output": "What did Pension and other post-retirement benefits reflect?" + }, + { + "input": "What were the interest obligations that had payments due more than 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"output": "What was the balance in 2017 for Software Solutions?" + }, + { + "input": "What was the specific balance amount, in USD, for Software Solutions in the year 2017?", + "output": "What was the balance in 2017 for Software Solutions?" + }, + { + "input": "What was the specific amount of federal income tax expense incurred in the fiscal year of 2019?", + "output": "What was the current federal income tax expense in 2019?" + }, + { + "input": "Which years does the table provide information for depreciation and amortization by segment?", + "output": "For which specific periods does the table offer data regarding depreciation and amortization broken down by segment?" + }, + { + "input": "What was the value of the total assets before the implementation of ASC 606 revenue recognition standards?", + "output": "What were the total assets without the adoption of ASC 606?" + }, + { + "input": "What was the sum of all assets before the implementation of ASC 606?", + "output": "What were 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Please provide a detailed explanation comparing the two." + }, + { + "input": "What was the percentage change in the Net earnings margin between 2017 and 2019?", + "output": "What was the percentage difference in the Net earnings margin from 2017 to 2019?" + }, + { + "input": "What was the percentage change in the Net earnings margin between 2017 and 2019?", + "output": "What was the exact percentage difference in the net earnings margin from 2017 to 2019?" + }, + { + "input": "What was the percentage change in revenues between 2018 and 2019?", + "output": "What was the percentage increase or decrease in revenues from 2018 to 2019?" + }, + { + "input": "What was the percentage change in revenues between 2018 and 2019?", + "output": "What was the percentage difference in the amount of money earned in revenue from 2018 to 2019?" + }, + { + "input": "What were the specific prepaid expenses incurred during the year 2018?", + "output": "What were the prepaid expenses in 2018?" + }, + { + "input": "What were the specific categories of Other current assets in the financial statements for the year 2019?", + "output": "What were the Other current assets in 2019?" + }, + { + "input": "What was the change in contract assets between 2018 and 2019?", + "output": "What was the difference in the amount of contract assets from 2018 to 2019?" + }, + { + "input": "How many years did prepaid expenses exceed $40.0 million?", + "output": "For how many years was the amount of prepaid expenses higher than $40.0 million?" + }, + { + "input": "How many years did the operating margin exceed 20.0%?", + "output": "For how many years was the operating margin greater than 20.0%?" + }, + { + "input": "What was the amount of Corporate Services in 2018?", + "output": "What was the specific amount of funds allocated to Corporate Services during the year 2018?" + }, + { + "input": "What was the amount of Corporate Services in 2018?", + "output": "How much did Corporate Services amount to in the year 2018?" + }, + { + "input": "What specific factors or elements were taken into consideration when calculating the debt?", + "output": "What did the calculation for Debt include?" + }, + { + "input": "What was the difference between Total Debt and Total Interest on Debt?", + "output": "What is the distinction between the total amount of debt and the total interest incurred on that debt?" + }, + { + "input": "How many contracts with a value exceeding $100 million were made during the period of 2021-2022?", + "output": "For the period 2021-2022, how many contractual obligations exceeded $100 million?" + }, + { + "input": "What is the dollar difference between the actual cost and the estimated cost for software solutions?", + "output": "What was the dollar variance for software solutions?" + }, + { + "input": "What was the specific revenue generated from the field of Data and Analytics during the year 2017?", + "output": "What was revenue from Data and Analytics in 2017?" + }, + { + "input": "For how many consecutive years has revenue from Data and Analytics surpassed $150 million?", + "output": "How many years did revenue from Data and Analytics exceed $150 million?" + }, + { + "input": "What was the average amount of money earned between the years 2017 and 2018?", + "output": "What was the average total revenue between 2017 and 2018?" + }, + { + "input": "What was the average revenue from Corporate and Other between 2017 and 2018?", + "output": "What was the average revenue generated from the Corporate and Other category during the two-year period encompassing 2017 and 2018?" + }, + { + "input": "What was the change in purchased software between 2018 and 2019?", + "output": "What was the difference in the amount of software that was bought between the years 2018 and 2019?" + }, + { + "input": "For how many years has the internally developed software generated revenue exceeding $800 million?", + "output": "How many years did internally developed software exceed $800 million?" + }, + { + "input": "For how long did internally developed software generate revenue exceeding $800 million?", + "output": "How many years did internally developed software exceed $800 million?" + }, + { + "input": "What was the percentage change in net computer software between 2018 and 2019?", + "output": "What was the percentage increase or decrease in net computer software from 2018 to 2019?" + }, + { + "input": "How many years did Percentage of gross lease receivables exceed 2.0%?", + "output": "For how many years was the percentage of gross lease receivables above 2.0%?" + }, + { + "input": "How many years did Percentage of gross lease receivables exceed 2.0%?", + "output": "For how long has the percentage of gross lease receivables been above 2.0%?" + }, + { + "input": "What was the percentage change in Allowance for doubtful accounts between 2018 and 2019?", + "output": "What is the percentage difference in the allowance for doubtful accounts from 2018 to 2019?" 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"output": "What was the change in Provisions for warranties issued between 2017 and 2018?" + }, + { + "input": "For which specific years does the table offer data on cash and cash equivalents as well as investments?", + "output": "Which years does the table provide information for cash and cash equivalents and investments?" + }, + { + "input": "What was the change in the value of available-for-sale debt investments, indicating whether there was an increase or decrease?", + "output": "What was the increase (decrease) in available-for-sale debt investments?" + }, + { + "input": "In 2019, how did the distinction between cash and cash equivalents and available-for-sale debt investments manifest and how were they different from each other?", + "output": "What was the difference between cash and cash equivalents and Available-for-sale debt investments in 2019?" + }, + { + "input": "What differentiates cash and cash equivalents from available-for-sale debt investments in the financial records 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{ + "input": "What is the percentage difference in the Loss from continuing operations before income tax between the years 2018 and 2019?", + "output": "What is the percentage change in the Loss from continuing operations before income tax from 2018 to 2019?" + }, + { + "input": "What is the percentage change in the income tax benefit from 2018 to 2019?", + "output": "What is the percentage difference in the income tax benefit received between 2018 and 2019?" + }, + { + "input": "In which year did the net loss for the year exceed the net loss of any other year?", + "output": "In which year is there a greater net loss for the year?" + }, + { + "input": "What does the net deferred tax liabilities include?", + "output": "What is included in the calculation of net deferred tax liabilities?" + }, + { + "input": "What is the exact percentage change in the net deferred tax liabilities from 2018 to 2019?", + "output": "What is the percentage change in the net deferred tax liabilities from 2018 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"input": "For how many years did the repurchase of Class A common stock surpass a cumulative value of $10,000 million?", + "output": "How many years did Class A common stock repurchased exceed $10,000 million?" + }, + { + "input": "For how many years did the repurchase of Class A common stock amount to more than $10 billion?", + "output": "How many years did Class A common stock repurchased exceed $10,000 million?" + }, + { + "input": "For which years does the table provide details regarding the exclusion of weighted-average common share equivalents of Class A common stock from the calculations of diluted net income per share?", + "output": "Which years does the table provide information for the weighted-average common share equivalents of Class A common stock that were excluded from the diluted net income per share calculations?" + }, + { + "input": "What were the employee stock options for the year 2019?", + "output": "What was the Employee stock options in 2019?" + }, + { + "input": 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total amount of expenses that accumulated over time between the years 2019 and 2020?", + "output": "What was the change in the total accrued expenses between 2019 and 2020?" + }, + { + "input": "What is the difference in the total amount of expenses that accumulated over time between 2019 and 2020?", + "output": "What was the change in the total accrued expenses between 2019 and 2020?" + }, + { + "input": "In 2019, what were the specific support and administrative costs incurred by Dell's subsidiary?", + "output": "What was the Dell subsidiary support and administrative costs in 2019?" + }, + { + "input": "What was the change in Purchases and leases of products and purchases of services between 2018 and 2019?", + "output": "What was the difference in the amount spent on purchasing and leasing products as well as purchasing services from 2018 to 2019?" + }, + { + "input": "What was the change in Purchases and leases of products and purchases of services between 2018 and 2019?", + "output": "What was the difference in the amount spent on purchasing and leasing products and purchasing services from 2018 to 2019?" + }, + { + "input": "For how many consecutive years did Dell subsidiary incur support and administrative costs exceeding $200 million?", + "output": "How many years did Dell subsidiary support and administrative costs exceed $200 million?" + }, + { + "input": "What was the percentage change in the Dell subsidiary support and administrative costs between 2019 and 2020?", + "output": "What percentage increase or decrease was observed in the support and administrative costs of Dell's subsidiary from 2019 to 2020?" + }, + { + "input": "What was the significance or meaning of the aggregate intrinsic value?", + "output": "What did the aggregate intrinsic value represent?" + }, + { + "input": "What differentiates outstanding options that are both exercisable and vested as well as expected to vest?", + "output": "What was the difference between outstanding 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the percentage difference in the Total income tax provision from 2018 to 2019?" + }, + { + "input": "What was the specific monetary value of Restricted cash categorized under other current assets in the financial records for the year 2019?", + "output": "What was the amount of Restricted cash within other current assets in 2019?" + }, + { + "input": "What were the specific additions made to tax positions related to the current year of 2020 in the field of taxation?", + "output": "What were the additions to tax positions related to current year in 2020?" + }, + { + "input": "What categories are encompassed within the scope of purchases and leases of products, and what does this also include in terms of purchases?", + "output": "What did Purchases and leases of products and purchases of services include?" + }, + { + "input": "What was the change in Purchases and leases of products and purchases of services between 2018 and 2019?", + "output": "What was the year-on-year difference in the 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due to related parties include?" + }, + { + "input": "For how long has the current outstanding amount owed by related parties been over $1,000 million?", + "output": "How many years did current amount due from related parties exceed $1,000 million?" + }, + { + "input": "For how long has the present outstanding balance owed by affiliated organizations been over $1,000 million?", + "output": "How many years did current amount due from related parties exceed $1,000 million?" + }, + { + "input": "What was the percentage increase or decrease in the net current amount due from related parties from 2019 to 2020?", + "output": "What was the percentage change in the net current amount due from related parities between 2019 and 2020?" + }, + { + "input": "What was the change in Additions to intangible assets between 2019 and 2020?", + "output": "What was the difference in the amount of intangible assets added between the years 2019 and 2020?" + }, + { + "input": "What is the total amount of 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"output": "What does the line item \"Others\" in the table encompass?" + }, + { + "input": "What is the Singtel's largest joint venture in terms of the proportion of the Group's ownership?", + "output": "Which is the largest joint venture of Singtel, in terms of the proportion of Group's ownership?" + }, + { + "input": "Do the non-audit services offered by KPMG LLP have an impact on their independence in conducting audits?", + "output": "Does the non-audit services provided by KPMG LLP affect their independence?" + }, + { + "input": "What is included in the compensation for key management personnel?", + "output": "What does key management personnel compensation comprise?" + }, + { + "input": "What is included in the compensation package for key management personnel?", + "output": "What does key management personnel compensation comprise?" + }, + { + "input": "What are the components included in directors' remuneration?", + "output": "What does directors' remuneration comprise of?" + }, 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"How many factors are involved in calculating the balance for subsidiaries?" + }, + { + "input": "\"What is typically included in the category of selling and administrative costs?\"", + "output": "What does selling and administrative costs include?" + }, + { + "input": "How many distinct categories of operating expenses exist?", + "output": "How many different type of operating expenses are there?" + }, + { + "input": "What is the mean value of the three highest subcategories within operating expenses for the year 2019?", + "output": "What is the average of the top 3 operating expenses subcategories in 2019?" + }, + { + "input": "What is the average value of the three highest subcategories of operating expenses in the year 2019?", + "output": "What is the average of the top 3 operating expenses subcategories in 2019?" + }, + { + "input": "What are the projected maximum future payouts for Barry Litwin and Thomas Clark under the NEO plan of 2019?", + "output": "What is the maximum estimated future payouts under the 2019 NEO plan for Barry Litwin and Thomas Clark?" + }, + { + "input": "What is the maximum estimated future payout amount under the 2019 NEO plan specifically designated for Barry Litwin and Thomas Clark?", + "output": "What is the maximum estimated future payouts under the 2019 NEO plan for Barry Litwin and Thomas Clark?" + }, + { + "input": "What are the projected highest expected future payouts under the 2019 NEO plan for both Manoj Shetty and Lawrence Reinhold?", + "output": "What is the maximum estimated future payouts under the 2019 NEO plan for Manoj Shetty and Lawrence Reinhold?" + }, + { + "input": "What are the audit fees incurred by the company in 2018 and 2019?", + "output": "What were the audit fees that the company had to pay in the years 2018 and 2019?" + }, + { + "input": "What is the percentage difference in the total amount of fees paid by the company for all other expenses between the years 2018 and 2019?", + "output": "What is the 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"input": "What was the specific amount of long-term debt, after deducting any reductions or write-offs, recorded on the financial statements for the year 2018?", + "output": "What was the Net carrying amount of long-term debt in 2018?" + }, + { + "input": "What is the mean value of the unamortized debt discount for the fiscal years 2018 and 2019?", + "output": "What is the average Unamortized debt discount for 2018 and 2019?" + }, + { + "input": "When was the year when the net carrying amount of long-term debt was less than 210,000 thousands?", + "output": "In which year was Net carrying amount of long-term debt less than 210,000 thousands?" + }, + { + "input": "What is the average working capital for the period from December 31, 2018 to December 31, 2019?", + "output": "What is the average Working capital for December 31, 2019 to 2018?" + }, + { + "input": "What is the average Cash and cash equivalents for December 31, 2019 to 2018?", + "output": "What is the average amount of Cash 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"Which specific years can be found in the table that offer additional details pertaining to the stock options of the company?", + "output": "Which years does the table provide Additional information related to the company's stock options?" + }, + { + "input": "What was the specific intrinsic value associated with exercises in the year 2019?", + "output": "What was the intrinsic value of exercises in 2019?" + }, + { + "input": "What were the Proceeds received from exercises in 2018?", + "output": "What was the total amount of money received from exercises held in 2018?" + }, + { + "input": "What were the Proceeds received from exercises in 2018?", + "output": "What was the total amount of money received from exercises in the year 2018?" + }, + { + "input": "What was the gross income in 2017, prior to deducting income taxes?", + "output": "What was the total income before income taxes in 2017?" + }, + { + "input": "What was the change in foreign income before income taxes between 2017 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Please provide the change in figures for better understanding." + }, + { + "input": "What was the specific difference in foreign income, without considering income taxes, from 2018 to 2019?", + "output": "What was the change in foreign income before income taxes between 2018 and 2019?" + }, + { + "input": "What were the short-term investment options available in the year 2018?", + "output": "What were the short-term investments in 2018?" + }, + { + "input": "What were the types of short-term investments that were prevalent in 2018?", + "output": "What were the short-term investments in 2018?" + }, + { + "input": "What years does the table provide information for total assets?", + "output": "For which time period does the table provide data on total assets?" + }, + { + "input": "What years does the table provide information for total assets?", + "output": "For which years does the table present data regarding the total assets?" + }, + { + "input": "What was the total debt in 2015?", + "output": "What amount of debt, in terms of total outstanding obligations, was recorded in the year 2015?" + }, + { + "input": "What was the change in working capital between 2015 and 2016?", + "output": "What was the difference in the amount of working capital between 2015 and 2016?" + }, + { + "input": "By what percentage did the Total stockholders' equity change from 2018 to 2019?", + "output": "What was the percentage change in Total stockholders' equity between 2018 and 2019?" + }, + { + "input": "Which years does the table provide information for the funded status of the company's postretirement health care and other defined benefit plans?", + "output": "For which specific years does the provided table present data relating to the funded status of the company's postretirement health care and other defined benefit plans?" + }, + { + "input": "What was the specific amount of fair value attributed to the plan assets during the year 2019?", + "output": "What was the fair value of plan assets in 2019?" + }, + { + "input": "What was the fair value measurements using Level 2 for Corporate Bonds?", + "output": "What are the fair value measurements using Level 2 for Corporate Bonds?" + }, + { + "input": "What was the combined value of cash, cash equivalents, and short-term investments?", + "output": "What were the total cash, cash equivalents and short-term investments?" + }, + { + "input": "What was the sum of all cash, cash equivalents, and short-term investments?", + "output": "What were the total cash, cash equivalents and short-term investments?" + }, + { + "input": "What was the difference in the fair value for U.S. Treasury and government debt securities between Level 1 and Level 2?", + "output": "What was the discrepancy in the fair value of U.S. Treasury and government debt securities when classified as Level 1 and Level 2 assets?" + }, + { + "input": "What was the fair value of Level 2 Total cash, cash equivalents and short-term investments as a percentage 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outstanding based on?" + }, + { + "input": "What was the Days sales outstanding for three months ended april 2019?", + "output": "\"What was the specific value of Days Sales Outstanding (DSO) specifically for the three-month period ending in April 2019?\"" + }, + { + "input": "What was the change in day sales outstanding between 2018 and 2019?", + "output": "What was the difference in the number of days it took to collect sales revenue between the years 2018 and 2019?" + }, + { + "input": "What was the change in day sales outstanding between 2018 and 2019?", + "output": "What was the difference in the number of days it took to collect sales revenue between 2018 and 2019?" + }, + { + "input": "For how long was the days inventory outstanding higher than 20 days ?", + "output": "How many years did days inventory outstanding exceed 20 days?" + }, + { + "input": "What was the total amount spent on purchases in 2018?", + "output": "What was the Aggregate purchase price in 2018?" + }, + { + "input": "What was the difference in the total purchase price for all items combined in 2018 and 2019?", + "output": "What was the change in the Aggregate purchase price between 2018 and 2019?" + }, + { + "input": "What was the amount of finished goods in 2019?", + "output": "What was the specific quantity of fully completed and ready-for-sale products in the year 2019?" + }, + { + "input": "What was the exact quantity of inventories during the year 2018?", + "output": "What was the amount of inventories in 2018?" + }, + { + "input": "What was the change in finished goods between 2018 and 2019?", + "output": "How did the amount of finished goods change from 2018 to 2019?" + }, + { + "input": "What were the additions made to tax positions related to the current year in 2019, and how do they impact the overall financial situation?", + "output": "What were the Additions based on tax positions related to the current year in 2019?" + }, + { + "input": "What was the change in the Balance at beginning of period between 2017 and 2018?", + "output": "What was the difference in the starting balance from the beginning of the period in 2017 compared to 2018?" + }, + { + "input": "What is the overall difference in the total amount of Additions for tax positions of prior years from 2017 to 2019?", + "output": "What was the total change in Additions for tax positions of prior years between 2017 and 2019?" + }, + { + "input": "What was the difference in the total expenses recorded between 2018 and 2019?", + "output": "What was the change in expense accrued during the period between 2018 and 2019?" + }, + { + "input": "What was the difference in the amount of expenses recorded from 2018 to 2019?", + "output": "What was the change in expense accrued during the period between 2018 and 2019?" + }, + { + "input": "What was the percentage change in the Warranty costs incurred between 2018 and 2019?", + "output": "What was the difference in warranty costs incurred between 2018 and 2019, 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This information is specifically related to geographic areas, and is based on the physical location of the assets." + }, + { + "input": "What was the total combined value of property and equipment in the United States during the year 2019?", + "output": "What was the amount of property and equipment in U.S. in 2019?" + }, + { + "input": "How many years did International property and equipment exceed $150 million?", + "output": "For how many consecutive years has the value of International property and equipment been greater than $150 million?" + }, + { + "input": "What was the financed unearned services revenue in 2019?", + "output": "\"What was the amount of unearned revenue from financed services in 2019?\"" + }, + { + "input": "What was the financed unearned services revenue in 2019?", + "output": "What was the specific amount of unearned services revenue that was financed during the year 2019?" + }, + { + "input": "For how many years has the amount of financed unearned services revenue been greater than $100 million?", + "output": "How many years did financed unearned services revenue exceed $100 million?" + }, + { + "input": "For how many consecutive years has the unearned services revenue in financed exceeded $100 million?", + "output": "How many years did financed unearned services revenue exceed $100 million?" + }, + { + "input": "What was the change in deferred services revenue between 2018 and 2019?", + "output": "What was the difference in the amount of deferred services revenue recorded in 2018 and 2019?" + }, + { + "input": "What was the sum of the net revenues from the last two quarters?", + "output": "What is the total net revenue for the most recent two quarters?" + }, + { + "input": "What was the change in the Dividends per share declared between 2018 and 2019?", + "output": "What was the difference in the amount of Dividends per share declared between the years 2018 and 2019?" + }, + { + "input": "What was the change in the Dividends per share declared between 2018 and 2019?", + "output": "What was the difference in the amount of dividends per share declared for the years 2018 and 2019?" + }, + { + "input": "What was the change in the Dividend payments allocated to retained earnings (accumulated deficit) between 2017 and 2018?", + "output": "What was the difference in the amounts of dividends allocated to retained earnings (accumulated deficit) from 2017 to 2018?" + }, + { + "input": "What was the change in the Dividend payments allocated to retained earnings (accumulated deficit) between 2017 and 2018?", + "output": "What was the variation in the amount of dividends transferred to retained earnings (accumulated deficit) from 2017 to 2018?" + }, + { + "input": "What factors contributed to the rise in the cash inflows generated from day-to-day business operations during the year 2019?", + "output": "What caused the increase in the cash flow from operating activities in 2019?" + }, + { + "input": "What is the percentage change in the Free Cash Flow, considering both an increase and decrease in value?", + "output": "What is the increase / (decrease) in the Free Cash Flow?" + }, + { + "input": "What is the percentage change in Free Cash Flow?", + "output": "What is the increase / (decrease) in the Free Cash Flow?" + }, + { + "input": "What are the definitions of underlying operating profit and underlying operating margin, and how do they differ from each other?", + "output": "What is the definition of the Underlying operating profit and underlying operating margin?" + }, + { + "input": "What does the underlying effective tax rate measure reflects?", + "output": "What is the definition and significance of the underlying effective tax rate and what does it reflect in terms of a company's financial situation or performance?" + }, + { + "input": "What does the underlying effective tax rate measure reflects?", + "output": "What is the meaning of the underlying effective tax rate measure and what does it reflect?" + }, + { + "input": "What is the methodology used to calculate the effective tax rate?", + "output": "How is the underlying effective tax rate calculated?" + }, + { + "input": "What is the exact difference in the underlying effective tax rate between 2018 and 2019?", + "output": "What is the Underlying effective tax rate change from 2018 to 2019?" + }, + { + "input": "What is the percentage change in the underlying effective tax rate between 2018 and 2019?", + "output": "What is the Underlying effective tax rate change from 2018 to 2019?" + }, + { + "input": "By what percentage did the Biogas production increase from the year 2018 to 2019?", + "output": "What is the increase in the Biogas from 2018 to 2019?" + }, + { + "input": "What is the average total energy?", + "output": "What is the average amount of energy, when considering all types of energy combined?" + }, + { + "input": "\"What is the percentage change in the price of Fuel Oils from 2018 to 2019? 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Please provide the average lease obligations separately for both time periods.\"", + "output": "What is the average lease obligation for the period Less than 1 Year and 1-3 Years?" + }, + { + "input": "What does Purchase obligations incorporate?", + "output": "What is included in purchase obligations?" + }, + { + "input": "What is the criteria for an employee to receive additional bonuses?", + "output": "What are the specific requirements or factors that determine if an employee is eligible to receive extra bonuses in addition to their regular compensation?" + }, + { + "input": "What is the difference in Time Deposits: Non-Trading Purpose between Carrying Amount and Fair Amount?", + "output": "What is the distinction between the carrying amount and fair amount of Time Deposits used for non-trading purposes?" + }, + { + "input": "What is the difference between Bonds: Non-Trading Purpose Carrying Amount and Fair Amount?", + "output": "What distinguishes Bonds: Non-Trading Purpose Carrying Amount from Fair Amount in terms of their meaning, significance, and financial implications?" + }, + { + "input": "What is the average quantity of supplies and spare parts in general?", + "output": "What was the average Supplies and spare parts?" + }, + { + "input": "What was the average quantity of supplies and spare parts used?", + "output": "What was the average Supplies and spare parts?" + }, + { + "input": "What was the percentage increase / (decrease) in the Finished goods from 2018 to 2019?", + "output": "What was the percentage change in the amount of Finished goods from the year 2018 to 2019, indicating whether it increased or decreased?" + }, + { + "input": "What was the percentage increase / (decrease) in the Finished goods from 2018 to 2019?", + "output": "What was the percentage change in the value of Finished goods from 2018 to 2019, indicating whether it increased or decreased?" + }, + { + "input": "What is the percentage change in the value of financial assets at fair value through profit or loss between 2018 and 2019?", + "output": "What is the increase / (decrease) in the Financial assets at fair value through profit or loss from 2018 to 2019?" + }, + { + "input": "What is the increase / (decrease) in the Short-term loans from 2018 to 2019?", + "output": "What was the percentage change in Short-term loans from 2018 to 2019?" + }, + { + "input": "What is the increase / (decrease) in the Short-term loans from 2018 to 2019?", + "output": "What was the percentage change in Short-term loans between 2018 and 2019?" + }, + { + "input": "What measures does the company implement to effectively manage, maintain, or adapt its capital structure in response to changes in the business environment?", + "output": "What steps does the company take To maintain or adjust the capital structure?" + }, + { + "input": "What is the change in the total liabilities, expressed as a percentage, between 2018 and 2019?", + "output": "What is the increase / (decrease) in the Total liabilities from 2018 to 2019?" + }, + { + "input": "What is the percentage change in Total Capital from 2018 to 2019?", + "output": "What is the percentage increase / (decrease) of Total Capital from 2018 to 2019?" + }, + { + "input": "How do non-GAAP measures assist investors in making informed investment decisions and assessing a company's financial performance?", + "output": "How do these non-GAAP measures aid investors?" + }, + { + "input": "How much would diluted earnings per share for fiscal year 2018 be without the net charge (benefit) related to the enactment of the TCJA?", + "output": "What would be the diluted earnings per share for fiscal year 2018 if we exclude the net charge (benefit) associated with the enactment of the Tax Cuts and Jobs Act (TCJA)?" + }, + { + "input": "How much would diluted earnings per share for fiscal year 2018 be without the net charge (benefit) related to the enactment of the TCJA?", + "output": "What would be the diluted earnings per share for fiscal year 2018, excluding the impact of the net charge (benefit) resulting from the implementation of the Tax Cuts and Jobs Act (TCJA)?" + }, + { + "input": "What was the average revenue over the 3 year period from 2017 to 2019?", + "output": "What was the average revenue from 2017 to 2019, calculated over the span of three years?" + }, + { + "input": "What factors contributed to the change in the federal statutory rate during fiscal year 2017?", + "output": "Why did the federal statutory rate in fiscal year 2017? " + }, + { + "input": "How many items accounted for the difference between income taxes computed at the US federal statutory rate and the company's effective rate?", + "output": "How many specific items contributed to the variance between the income taxes calculated using the official US federal statutory rate and the actual rate paid by the company?" + }, + { + "input": "What was the average federal statutory rate over the 3 year period from 2017 to 2019? ", + "output": "What was the average federal statutory rate from 2017 to 2019?" + }, + { + "input": "What was the average effective rate for the years 2017, 2018, and 2019?", + "output": "What was the average effective rate over the 3 year period from 2017 to 2019? " + }, + { + "input": "How much were the top 3 components of property and equipment as a % of the total at cost, property and equipment for 2019?", + "output": "What were the percentages for the top 3 components of property and equipment in relation to the total at cost, property, and equipment in 2019?" + }, + { + "input": "What percentage of the overall revenue in 2019 was not generated by the commercial cloud revenue?", + "output": "How much of the total revenue in 2019 did not come from commercial cloud revenue?" + }, + { + "input": "Which were the bottom 2 revenue items for 2017?", + "output": "\"What were the two lowest revenue-generating items in 2017?\"" + }, + { + "input": "What was the average EPS (earnings per share) for the years 2017, 2018, and 2019?", + "output": "What was the average basic earnings per share over the 3 year period from 2017 to 2019?" + }, + { + "input": "\"Which specific locations does the company have both operating leases and finance leases in place?\"", + "output": "Which places does the company have operating and finance leases?" + }, + { + "input": "What is the topic or content covered in note 15?", + "output": "What is note 15 about?" + }, + { + "input": "\"What is the ascending order of the years when sorted by their total cost of finance leases?\"", + "output": "What are the years sorted by total finance lease cost, in ascending order?" + }, + { + "input": "How many different items are included when calculating operating income (loss)?", + "output": "How many items are there for operating income (loss)?" + }, + { + "input": "How many different items are included in the calculation of operating income (loss)?", + "output": "How many items are there for operating income (loss)?" + }, + { + "input": "How is the allocation of amortization and depreciation determined and distributed?", + "output": "How is the portion of amortization and depreciation allocated?" + }, + { + "input": "How is the allocation of the portion of amortization and depreciation determined and distributed?", + "output": "How is the portion of amortization and depreciation allocated?" + }, + { + "input": "How many countries have Long-lived assets classified based on the controlling statutory company's location, excluding financial instruments and tax assets? 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" + }, + { + "input": "As of December 31, 2018, how many shares were granted and still remain outstanding?", + "output": "What is the number of outstanding shares granted as of December 31, 2018? " + }, + { + "input": "What was the change in the number of outstanding shares between December 31, 2018, and December 31, 2019?", + "output": "What is the difference in outstanding shares as of December 31, 2018 and 2019?" + }, + { + "input": "What data is shown in the table?", + "output": "\"What specific information does the table display?\"" + }, + { + "input": "What is the units for the data shown in the table?", + "output": "What units are being used to represent the information presented in the table?" + }, + { + "input": "What is the total revenue earned by the company between 2017 to 2019?", + "output": "What is the cumulative revenue generated by the company from 2017 to 2019?" + }, + { + "input": "What is the exact amount of revenue generated in the EMEA region from 2017 to 2019?", + "output": "What is the total revenue earned in EMEA between 2017 to 2019?" + }, + { + "input": "What is the change in total revenue between 2018 to 2019?", + "output": "What is the difference in total revenue from 2018 compared to 2019, and how can this information assist in better understanding the financial performance of the company during this period?" + }, + { + "input": "What is the sum of all non-current liabilities excluding total other non-current liabilities as of 31 December 2019?", + "output": "What is the total other non-current liabilities as at 31 December 2019?" + }, + { + "input": "What is the aggregate amount of all non-current liabilities, excluding any current liabilities, as of December 31, 2019?", + "output": "What is the total other non-current liabilities as at 31 December 2019?" + }, + { + "input": "What is the depreciation expense on property and equipment as at December 31, 2018?", + "output": "What is the specific amount of the depreciation expense recorded for property and equipment on the financial statement as at the closing date of December 31, 2018?" + }, + { + "input": "What is the depreciation expense on property and equipment as at December 31, 2018?", + "output": "What was the amount of depreciation expense booked for property and equipment up until December 31st, 2018?" + }, + { + "input": "What is the depreciation expense on property and equipment as at December 31, 2019?", + "output": "As of December 31, 2019, what is the recorded depreciation expense amount for property and equipment?" + }, + { + "input": "What is the depreciation expense on property and equipment as at December 31, 2019?", + "output": "What is the specific amount of depreciation expense recorded for property and equipment as on December 31, 2019?" + }, + { + "input": "What is the total depreciation expense on property and equipment from 2017 to 2019?", + "output": "From 2017 to 2019, what was the cumulative amount of depreciation expense incurred on property and equipment?" + }, + { + "input": "What is the total depreciation expense on property and equipment from 2017 to 2019?", + "output": "What is the cumulative depreciation expense on property and equipment for the years 2017, 2018, and 2019?" + }, + { + "input": "What is the allowance for doubtful accounts as at December 31, 2018?", + "output": "What is the amount set aside for doubtful accounts on the balance sheet as of December 31, 2018?" + }, + { + "input": "What are some examples of financial instruments that can subject the company to credit risk?", + "output": "What financial instruments expose companies to credit risk? Can you provide some examples of such instruments?" + }, + { + "input": "What are some examples of financial instruments that can subject the company to credit risk?", + "output": "What financial instruments can expose a company to credit risk? Can you provide some examples?" + }, + { + "input": "How does the company effectively manage and reduce its credit risk associated with accounts receivable?", + "output": "How does the company mitigate its credit risk pertaining to accounts receivable?" + }, + { + "input": "How does the company identify and determine its important or major customers in its business operations?", + "output": "How does the company determine its significant customers?" + }, + { + "input": "How does the company identify and assess its significant customers for business purposes?", + "output": "How does the company determine its significant customers?" + }, + { + "input": "What was the monetary value of the company's losses within its own country during the year 2019?", + "output": "What is the company's domestic loss in 2019?" + }, + { + "input": "\"What was the total financial loss incurred by the company within its own country during the year 2019?\"", + "output": "What is the company's domestic loss in 2019?" + }, + { + "input": "What is the amount of money that the company lost within its own country during the year 2017?", + "output": "What is the company's domestic loss in 2017?" + }, + { + "input": "\"What was the fiscal year 2017 domestic financial loss incurred by the company?\"", + "output": "What is the company's domestic loss in 2017?" + }, + { + "input": "What is the company's total loss before income taxes between 2017 to 2019?", + "output": "What is the cumulative pre-tax loss of the company from 2017 to 2019?" + }, + { + "input": "What is the company's change in foreign income between 2018 and 2019?", + "output": "What was the percentage change in the company's foreign income from 2018 to 2019?" + }, + { + "input": "What is the cumulative amount of loss incurred by the company domestically during the period from 2017 to 2019?", + "output": "What is the company's total domestic loss between 2017 to 2019?" + }, + { + "input": "What is the aggregate amount of losses incurred by the company within its domestic markets during the period from 2017 to 2019?", + "output": "What is the company's total domestic loss between 2017 to 2019?" + }, + { + "input": "What is the percentage change in total deferred revenue 2018 and 2019?", + "output": "What is the percentage difference in the total deferred revenue between 2018 and 2019?" + }, + { + "input": "What is the percentage change in total deferred revenue 2018 and 2019?", + "output": "What is the percentage difference in the total amount of deferred revenue between the years 2018 and 2019?" + }, + { + "input": "What is the proportion of the cost of revenue and research and development expenses as a percentage of the total restructuring expense?", + "output": "What is the ratio of the combined cost of revenue and research and development expenses to the total restructuring expense, expressed as a percentage?" + }, + { + "input": "What is the total legal fees and facilities expenses?", + "output": "What are the combined expenses for legal fees and facilities?" + }, + { + "input": "What is the total legal fees and facilities expenses?", + "output": "What is the cumulative sum of the expenses incurred in legal fees and facilities?" + }, + { + "input": "What is the total increases related to tax positions taken during current year in 2018 and 2019?", + "output": "What is the cumulative increase in tax positions taken during the current year across 2018 and 2019?" + }, + { + "input": "What is the total increases related to tax positions taken during current year in 2018 and 2019?", + "output": "What is the combined amount of increments in relation to tax positions that were assumed during the present year in both 2018 and 2019?" + }, + { + "input": "As of 31st December 2018, what is the sum of all liabilities that have accumulated or been incurred by the company up to that date?", + "output": "What is the total accrued liabilities as at 31 December 2018?" + }, + { + "input": "What are the units used to measure the values in the table?", + "output": "What is the units that the values in the table are measured in?" + }, + { + "input": "What is the percentage increase or decrease in the total amount of accrued liabilities from 2018 to 2019?", + "output": "What is the percentage change in total accrued liabilities between 2018 and 2019?" + }, + { + "input": "What is the percentage increase or decrease in the total amount of liabilities that have accumulated between the years 2018 and 2019?", + "output": "What is the percentage change in total accrued liabilities between 2018 and 2019?" + }, + { + "input": "What is the total amount of long-lived asset in Japan in both 2018 and 2019?", + "output": "What is the total value of long-lived assets in Japan for the years 2018 and 2019 combined?" + }, + { + "input": "What were the capitalized costs of software development in 2019?", + "output": "What was the Capitalized software development costs in 2019?" + }, + { + "input": "What 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Please provide all relevant information regarding the years for accurate response." + }, + { + "input": "In which specific year did the fair value of interest rate swaps experience an increase compared to other years?", + "output": "In which year was the fair value of Interest rate swaps higher?" + }, + { + "input": "When was the fair value of interest rate swaps higher: in which specific year did the fair value of interest rate swaps surpass that of other years?", + "output": "In which year was the fair value of Interest rate swaps higher?" + }, + { + "input": "In which year did the company record the highest amount of expenses pertaining to share-based compensation?", + "output": "In which year was the total expense recognized in respect of share-based compensation the highest?" + }, + { + "input": "What was the highest year in terms of total recognized expense for share-based compensation?", + "output": "In which year was the total expense recognized in respect of share-based 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"input": "What were the pre-tax domestic and foreign earnings in 2017?", + "output": "What are the respective domestic and foreign income before income taxes in 2017?" + }, + { + "input": "\"What were the pre-tax incomes in 2017, separated by domestic and foreign sources?\"", + "output": "What are the respective domestic and foreign income before income taxes in 2017?" + }, + { + "input": "\"What were the total losses incurred in the domestic market and international markets in 2019, excluding any income tax benefits received?\"", + "output": "What are the respective domestic and foreign losses before income tax benefits in 2019?" + }, + { + "input": "What is the sum of all outstanding receivables that are yet to be invoiced, and are expected to be collected within a period of two years?", + "output": "What is the total unbilled receivables due within 2 years?" + }, + { + "input": "What is the sum of all outstanding amounts receivable that have not been invoiced yet, and are expected 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"input": "What was the percentage difference in METRO AG's performance in 2019 compared to 2018?", + "output": "What was the percentage change in METRO AG in 2019 from 2018?" + }, + { + "input": "What was the percentage difference in the performance of METRO AG in 2019 compared to 2018?", + "output": "What was the percentage change in METRO AG in 2019 from 2018?" + }, + { + "input": "In which year was the amount of total assets larger?", + "output": "\"When comparing the amount of total assets, which year had a larger value?\"" + }, + { + "input": "What was the percentage change in total assets in FY2019 from FY2018?", + "output": "What was the percentage increase or decrease in the total assets for the fiscal year 2019 compared to the total assets for the fiscal year 2018?" + }, + { + "input": "What is the source of revenue that compensates for the expenses incurred in providing logistics services?", + "output": "What is offset by income from logistics services?" + }, + { + "input": 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"output": "What was the increase / (decrease) in Interest on borrowings from 2018 to 2019?" + }, + { + "input": "What was the percentage change in Interest on borrowings from 2018 to 2019?", + "output": "What was the increase / (decrease) in Interest on borrowings from 2018 to 2019?" + }, + { + "input": "What was the increase / (decrease) in the Finance costs before interest on lease liabilities from 2018 to 2019?", + "output": "What was the change in the Finance costs before interest on lease liabilities from 2018 to 2019?" + }, + { + "input": "What was the percentage change in Adjusted EBITDA between 2018 and 2019?", + "output": "What was the increase / (decrease) in Adjusted EBITDA from 2018 to 2019?" + }, + { + "input": "What was the average Depreciation and amortization?", + "output": "What was the average amount of depreciation and amortization expenses incurred over a specific period?" + }, + { + "input": "What factors contributed to the overall rise in both depreciation and 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Please state whether there was an increase or decrease.", + "output": "What is the increase/ (decrease) in Interest on borrowings from 2018 to 2019?" + }, + { + "input": "What is the purpose of utilizing valuation-related analysis and making capital structure-related decisions?", + "output": "What is used for to conduct valuation-related analysis and make capital structure-related decisions?" + }, + { + "input": "What items are considered when calculating the adjusted net debt?", + "output": "What is included in the adjusted net debt?" + }, + { + "input": "In which context or industry is the valuation of debt derivatives without incorporating credit risk used as an evaluation tool?", + "output": "Where is debt derivatives valued without adjustment for credit risk used for evaluation?" + }, + { + "input": "What is the net change in long-term debt from December 31, 2018, to December 31, 2019?", + "output": "What is the increase/ (decrease) in Long-term debt from December 31, 2018 to December 31, 2019?" + }, + { + "input": "What is the change in the amount of Long-term debt from December 31, 2018 to December 31, 2019?", + "output": "What is the increase/ (decrease) in Long-term debt from December 31, 2018 to December 31, 2019?" + }, + { + "input": "What is the change in the value of net debt derivative assets without credit risk adjustment from December 31, 2018 to December 31, 2019?", + "output": "What is the increase/ (decrease) in Net debt derivative assets valued without any adjustment for credit risk from December 31, 2018 to December 31, 2019?" + }, + { + "input": "What is the method of measuring accounts receivable and what factors are considered in the measurement process?", + "output": "How is accounts receivable measured?" + }, + { + "input": "What is the percentage change in the amount of customer accounts receivable from 2018 to 2019?", + "output": "What is the increase/ (decrease) in Customer accounts receivable from 2018 to 2019?" + }, + { + "input": "\"What is the method employed to settle the principal amount of Convertible Notes in cash upon conversion, and how does it factor into the computation of diluted earnings?\"", + "output": "Which method is used for to settle the principal of the Convertible Notes in cash on conversion and calculates diluted earnings?" + }, + { + "input": "What are the consequences when a loss is incurred that can be attributed to shareholders?", + "output": "What happens if a loss attributable to shareholders has been incurred?" + }, + { + "input": "What was the Convertible Notes that had an anti-dilutive effect on the calculation of diluted earnings per common share in the year end 2019, 2018 and 2017 respectively?", + "output": "What were the Convertible Notes that impacted diluted earnings per common share calculation in 2019, 2018, and 2017, specifically in terms of their anti-dilutive effects?" + }, + { + "input": "What is the change in Loss per common share - basic and diluted from 2018 to 2019?", + "output": "What is the difference in the loss per common share - basic and diluted between 2018 and 2019?" + }, + { + "input": "What is the change in Loss per common share - basic and diluted from 2018 to 2019?", + "output": "What is the difference in basic and diluted Loss per common share between 2018 and 2019?" + }, + { + "input": "How are the recognition processes for realized and unrealized losses different?", + "output": "How are the realized and unrealized losses recognized?" + }, + { + "input": "How are both realized and unrealized losses recognized? 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Please provide the Fair value figures for both years separately.", + "output": "What was the Fair value at the beginning of the year for 2019 and 2018 respectively?" + }, + { + "input": "In which year was Fair value at the beginning of the year less than 15,000 thousands?", + "output": "In which year did the fair value at the beginning of the year fall below 15,000,000 (15 million) dollars?" + }, + { + "input": "\"What items are typically included in the calculation of current income tax expense, and what factors can affect this expense?\"", + "output": "What is included in current income tax expense?" + }, + { + "input": "What components are typically encompassed in the calculation of contemporary income tax expense?", + "output": "What is included in current income tax expense?" + }, + { + "input": "What is the change in Current income tax expense from December 31, 2019 to December 31, 2018?", + "output": "How much has the Current income tax expense changed from December 31, 2019, to December 31, 2018?" + }, + { + "input": "What is the change in Current income tax expense from December 31, 2019 to December 31, 2018?", + "output": "What was the difference in Current income tax expense between December 31, 2019 and December 31, 2018?" + }, + { + "input": "\"Between two years, which one exhibits a higher expense for current income tax?\"", + "output": "Which year has higher current income tax expense?" + }, + { + "input": "What were the Lease and Non-Lease commitments in the year 2021, and can you provide specific details about each commitment?", + "output": "What was the Lease and Non-Lease commitment in 2021 respectively?" + }, + { + "input": "What was the amount of lease commitment for the fiscal year 2022?", + "output": "What was the Lease commitment in 2022?" + }, + { + "input": "What was the total value of lease commitments in the year 2022?", + "output": "What was the Lease commitment in 2022?" + }, + { + "input": "In which year was the Lease commitment less than 10,000 thousands?", + "output": "In which specific year did the Lease commitment amount to less than 10,000 thousand units?" + }, + { + "input": "In which year was the Lease commitment less than 10,000 thousands?", + "output": "In which specific year did the Lease commitment amount fall below 10,000 thousands? (For better answering, please state the year and the unit of the Lease commitment.)" + }, + { + "input": "What is the difference in the lease commitment amount between 2020 and 2021?", + "output": "What is the change in the Lease commitment from 2020 to 2021?" + }, + { + "input": "What is the average duration of lease agreements from 2020 to 2022?", + "output": "What is the average Lease Commitment from 2020 to 2022?" + }, + { + "input": "What is the average duration of lease commitments between 2020 and 2022?", + "output": "What is the average Lease Commitment from 2020 to 2022?" + }, + { + "input": "How much was the equity loss for the year ended December 31, 2019?", + "output": "What was the amount of equity lost during the period from January 1 to December 31, 2019?" + }, + { + "input": "How much was the equity loss for the year ended December 31, 2019?", + "output": "What was the total amount of equity loss recorded for the period of the year ending December 31, 2019?" + }, + { + "input": "What is the difference in the combined amount of cash and restricted cash on December 31, 2019, compared to December 31, 2018?", + "output": "What is the change in Cash and restricted cash from December 31, 2019 to December 31, 2018?" + }, + { + "input": "What was the increase or decrease in the category of Other assets – current on the company's balance sheet from December 31, 2019 to December 31, 2018?", + "output": "What is the change in Other assets – current from December 31, 2019 to December 31, 2018?" + }, + { + "input": "What were the values of the other current assets in the years 2019 and 2018?", + "output": "What was the other assets - current in 2019 and 2018 respectively?" + }, + { + "input": "What were the respective amounts of non-current liabilities in 2019 and 2018 aside from the liabilities mentioned earlier?", + "output": "What was the other liabilities - non current in 2019 and 2018 respectively?" + }, + { + "input": "What were the amounts of non-current liabilities in 2019 and 2018, respectively, excluding any other liabilities that may be present on the financial statements?", + "output": "What was the other liabilities - non current in 2019 and 2018 respectively?" + }, + { + "input": "What is the difference in the number of Consolidated LNG carriers between 2020 and 2021?", + "output": "What is the change in Consolidated LNG carriers from 2020 to 2021?" + }, + { + "input": "What is the net difference in the number of Consolidated LNG carriers between 2020 and 2021?", + "output": "What is the change in Consolidated LNG carriers from 2020 to 2021?" + }, + { + "input": "What is the annual growth or decline in the number of Consolidated LNG carriers from 2021 to 2022?", + "output": "What is the change in Consolidated LNG carriers from 2021 to 2022?" + }, + { + "input": "What is the average number of Consolidated LNG carriers for the period from 2020 to 2021?", + "output": "What is the average Consolidated LNG carriers, for the year 2020 to 2021?" + }, + { + "input": "What is the average number of Consolidated LNG carriers between the years 2020 and 2021?", + "output": "What is the average Consolidated LNG carriers, for the year 2020 to 2021?" + }, + { + "input": "What is the change in Realized (losses) gains relating to Interest rate swap agreements from Year Ended December 31, 2019 to December 31, 2018?", + "output": "What was the difference in Realized (losses) gains associated with Interest rate swap agreements between December 31, 2019 and December 31, 2018?" + }, + { + "input": "What is the difference in Realized losses and gains from Forward freight agreements between Year Ended December 31, 2019 and December 31, 2018?", + "output": "What is the change in Realized (losses) gains relating to Forward freight agreements from Year Ended December 31, 2019 to December 31, 2018?" + }, + { + "input": "What was the difference in revenue between 2019 and 2018?", + "output": "What is the change in Revenues from, 2019 to 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outstanding number of vested RSUs in 2019?", + "output": "What is the final count of vested Restricted Stock Units (RSUs) at the conclusion of 2019?" + }, + { + "input": "What is the ending outstanding number of vested RSUs in 2019?", + "output": "How many RSUs were left as outstanding in the year 2019 once they had vested?" + }, + { + "input": "What was the exact amount of depreciation expense recorded in the financial statements for the year 2018?", + "output": "What was the depreciation expense in 2018?" + }, + { + "input": "What are the accepted accounting methods for handling and recording disposed assets?", + "output": "What are the accounting treatments for disposed assets?" + }, + { + "input": "What are the accounting procedures to be followed specifically for assets that have been disposed of?", + "output": "What are the accounting treatments for disposed assets?" + }, + { + "input": "What is the net total for property, plant and equipment in 2019?", + "output": "What is the total value of property, plant, and equipment after deducting accumulated depreciation in 2019?" + }, + { + "input": "What is the net total for property, plant and equipment in 2019?", + "output": "What is the total value of property, plant, and equipment after deducting accumulated depreciation and any impairment losses, as reported in the financial statements for the year 2019?" + }, + { + "input": "What is the percentage decrease in the net value of Property, Plant, and Equipment between 2018 and 2019?", + "output": "What is the percentage decrease in Net Total Property, Plant and Equipment from 2018 to 2019?" + }, + { + "input": "How much was the increase in Federal R&D tax credits from 2018 to 2019?", + "output": "How much did R&D tax credits from the Federal increased from 2018 to 2019?" + }, + { + "input": "What was the percentage increase in R&D tax credits provided by the Federal government from 2018 to 2019?", + "output": "How much did R&D tax credits from the Federal 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between 2018 and 2019?", + "output": "What was the difference in Net cash provided by (used in) operating activities from continuing operations between the years 2018 and 2019?" + }, + { + "input": "What was the difference in the amount of Net cash provided by (used in) operating activities between the years 2018 and 2019?", + "output": "What was the change in Net cash provided by (used in) operating activities between 2018 and 2019?" + }, + { + "input": "What was the percentage change in Income (loss) from discontinued operations, net of income taxes between 2018 and 2019?", + "output": "What was the percentage change in net income (loss) from discontinued operations, after accounting for income taxes, between the fiscal years 2018 and 2019?" + }, + { + "input": "Which periods does the company's effective tax rates differ from the U.S. federal statutory rate?", + "output": "\"During which specific time periods does the effective tax rate of the company deviate from the U.S. federal 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"What was the difference in the fair value of cash paid for acquisition between Trek and Electrostatic Product Line?" + }, + { + "input": "What was the diluted earnings per share (EPS) specifically for Continuing Operations for the quarter ending in September?", + "output": "What was the diluted earnings per share of Continuing Operations in Quarter Ended September?" + }, + { + "input": "What was the diluted earnings per share of Continuing Operations in the quarter ending in September?", + "output": "What was the diluted earnings per share of Continuing Operations in Quarter Ended September?" + }, + { + "input": "What was the total Income (loss) from discontinued operations, net of income taxes in 2019?", + "output": "In 2019, what was the net income (loss) from discontinued operations, adjusted for income taxes?" + }, + { + "input": "What was the difference in total operating expense between the fiscal years 2018 and 2019?", + "output": "What was the change in total operating expense 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"What was the accumulated amortization of customer relationships in 2018?", + "output": "\"What was the total amount of accumulated amortization recorded for customer relationships during the year 2018?\"" + }, + { + "input": "What were the top two segments with the highest net carrying amounts in the year 2018?", + "output": "What was the two highest net carrying amounts segments in 2018?" + }, + { + "input": "What were the two segments with the highest net carrying amounts in 2018?", + "output": "What was the two highest net carrying amounts segments in 2018?" + }, + { + "input": "How much did the service cost in 2019 and can you provide more details about the specific service being referred to?", + "output": "What was service cost in 2019?" + }, + { + "input": "What was the total amount spent on interest expenses in the year 2018?", + "output": "What was interest cost in 2018?" + }, + { + "input": "What was the expected rate of return on plan assets for the year 2017?", + "output": "What was the Expected return on plan assets in 2017?" + }, + { + "input": "What was the anticipated return on assets for the plan in 2017?", + "output": "What was the Expected return on plan assets in 2017?" + }, + { + "input": "What was the change in Amortization of actuarial gains and losses between 2017 and 2018?", + "output": "What was the difference in the amount of amortization of actuarial gains and losses recorded between the years 2017 and 2018?" + }, + { + "input": "What was the change in Amortization of actuarial gains and losses between 2017 and 2018?", + "output": "What was the difference in the amount of Amortization of actuarial gains and losses between the fiscal years 2017 and 2018?" + }, + { + "input": "What were the specific expenses incurred for severance payments and any associated costs during the year 2019?", + "output": "What were the severance and related charges in 2019?" + }, + { + "input": "What was the amount of severance and associated expenses recorded 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How can this information be used to analyze the financial performance in these two years?" + }, + { + "input": "What resulted in a higher operating income?", + "output": "What factor led to an increase in operating income?" + }, + { + "input": "How many finished products were produced in the year 2019?", + "output": "What is the amount of finished products in 2019?" + }, + { + "input": "What was the total quantity of completed products manufactured in the year 2019?", + "output": "What is the amount of finished products in 2019?" + }, + { + "input": "In which year does the value of finished products surpass the value of finished products in other years, resulting in a higher overall value?", + "output": "In which year is the value of finished products higher?" + }, + { + "input": "What are the differences in the types of materials and purchased parts between 2018 and 2019?", + "output": "What is the change in materials and purchased parts from 2018 to 2019?" + }, + { + "input": "\"What 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obligation at September 30, which year has the largest rate of compensation increase?" + }, + { + "input": "What was the change in the discount rate between 2018 and 2019 for determining the benefit obligation at September 30?", + "output": "For the weighted-average assumptions used to determine benefit obligation at September 30, what is the change in the discount rate in 2019 from 2018?" + }, + { + "input": "What is the weighted-average discount rate used to calculate the benefit obligation as of September 30 for the years 2017, 2018, and 2019?", + "output": "For the weighted-average assumptions used to determine benefit obligation at September 30, what is the average discount rate across 2017, 2018 and 2019?" + }, + { + "input": "In what specific year did the rate differential for foreign exchange exceed 10.0?", + "output": "In which year was Foreign rate differential greater than 10.0?" + }, + { + "input": "What was the average amount of state taxes, excluding federal benefits, for 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"What was the average amount of money lost from 2017 to 2019?", + "output": "What was the average Net loss for 2017 to 2019?" + }, + { + "input": "What is the change in the Weighted average common shares outstanding-basic from 2018 to 2019?", + "output": "What is the difference in the number of weighted average common shares outstanding-basic between 2018 and 2019?" + }, + { + "input": "What was the depreciation and amortization expense related to property and equipment for 2019?", + "output": "What was the specific amount of depreciation and amortization expense attributed to property and equipment for the fiscal year 2019?" + }, + { + "input": "\"What was the precise monetary worth or estimated value, expressed in the currency of the specific country or region, of the category of Machinery and Equipment during the calendar year of 2019?\"", + "output": "What was the value of Machinery and equipment in 2019?" + }, + { + "input": "What was the exact monetary value of Machinery and 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"In which year was Telecom less than 200,000 thousand?" + }, + { + "input": "What was the exact percentage decrease of the revenue in 2019 compared to the previous year?", + "output": "What was the decrease in the revenue in 2019?" + }, + { + "input": "What was the specific percentage decrease in revenue for the fiscal year of 2019?", + "output": "What was the decrease in the revenue in 2019?" + }, + { + "input": "What is the average value of the Telecom industry for the fiscal years 2017 and 2018 combined?", + "output": "What is the average Telecom value for fiscal year 2017 and 2018?" + }, + { + "input": "What was the average value of the telecommunications industry for the fiscal years 2017 and 2018?", + "output": "What is the average Telecom value for fiscal year 2017 and 2018?" + }, + { + "input": "In which year was Telecom value less than 300,000 thousand?", + "output": "In what specific year did the value of Telecom drop below 300,000 thousand?" + }, + { + "input": "What were 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"What were the equity awards held by named executives during 2019?" + }, + { + "input": "What is the significance of the number of shares obtained upon vesting?", + "output": "What does the number of shares acquired on vesting represent?" + }, + { + "input": "Where was the reported combined net loss of $82 million disclosed or published?", + "output": "Where was the combined net loss of $82 million reported in?" + }, + { + "input": "What was the percentage difference in the Adjusted EBITDA margin from 2017 to 2019?", + "output": "What is the total change in the Adjusted EBITDA margin between 2019 and 2017?" + }, + { + "input": "What specific expenses are included in the integration and transformation costs that have an impact on the Adjusted EBITDA?", + "output": "What does the integration and transformation costs impacting Adjusted EBITDA include?" + }, + { + "input": "What is the change in total special items impacting adjusted EBITDA between 2018 and 2019?", + "output": "What was 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"output": "Which rank of officer within a specific profession or organization is entitled to the longest period of protection?" + }, + { + "input": "Which type of officer has the longest protected period?", + "output": "Which type of officer is granted the longest period of protection against any form of termination or dismissal?" + }, + { + "input": "What was the gross debt amount in the year 2019?", + "output": "What is the gross debt in 2019?" + }, + { + "input": "What was the difference in the total amount of debt in 2019 compared to 2018?", + "output": "What is the change in gross debt in 2019 from 2018?" + }, + { + "input": "What is the percentage change in gross debt in 2019 from 2018?", + "output": "What was the percentage increase/decrease in the gross debt from 2018 to 2019?" + }, + { + "input": "What is the percentage change in gross debt in 2019 from 2018?", + "output": "What is the percentage difference in the amount of gross debt from 2018 to 2019?" + }, + { + "input": 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"output": "What is the Depreciation and amortization for 2019?" + }, + { + "input": "What is the total adjusted EBITDA for each segment in 2019?", + "output": "What is the total segment adjusted EBITDA in 2019?" + }, + { + "input": "What is the total amount of income tax expense (benefit) recorded in the financial statements for the years 2018 and 2019?", + "output": "What is the sum of income tax expense (benefit) in 2018 and 2019?" + }, + { + "input": "What is the percentage change in income tax expense (benefit) in 2019 from 2018?", + "output": "What is the percentage difference in income tax expense (benefit) between 2018 and 2019?" + }, + { + "input": "What was the total amount of interest expenses incurred in the year 2019?", + "output": "What is the total interest expense in 2019?" + }, + { + "input": "What was the total amount of interest paid in expenses during the year 2019?", + "output": "What is the total interest expense in 2019?" + }, + { + "input": "What segments of 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"input": "What specific items are included under current liabilities in the consolidated balance sheets?", + "output": "What do the current liabilities reflected in the consolidated balance sheets include?" + }, + { + "input": "What items are typically categorized as Other current liabilities in financial statements?", + "output": "What are the items included under Other current liabilities?" + }, + { + "input": "In which specific year do capital expenditures account for a greater value of accounts payable?", + "output": "Which year has a larger amount of accounts payable associated with capital expenditures?" + }, + { + "input": "Which specific year indicates a higher level of accounts payable specifically linked to capital expenditures?", + "output": "Which year has a larger amount of accounts payable associated with capital expenditures?" + }, + { + "input": "What distinguishes the total grant values of Indraneel Dev and Stacey W. 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Please provide the specific revenue figures for each year.\"", + "output": "What is the average revenue for 2017 and 2018?" + }, + { + "input": "\"What is the revenue change reported specifically for Europe?\"", + "output": "What is the reported change in revenue - europe?" + }, + { + "input": "What is the reported change in revenue for Europe? 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acquisitions during the year ?" + }, + { + "input": "Which year, 2018 or 2019, witnessed a higher number of acquisitions throughout the entire year?", + "output": "Between 2018 and 2019, which year had a greater amount of acquisitions during the year?" + }, + { + "input": "What are the total operating lease commitments?", + "output": "What is the sum of all the commitments related to operating leases?" + }, + { + "input": "What are the total operating lease commitments?", + "output": "What is the sum of all commitments made for operating leases?" + }, + { + "input": "What is the total outstanding amount of financial liabilities that need to be paid within the shortest timeframe?", + "output": "How much financial liabilities are due for payment soonest?" + }, + { + "input": "What is the proportion of financial liabilities in relation to the overall sum of contractual obligations and commitments?", + "output": "What percentage of total contractual obligations and commitments is the 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"input": "What is the difference in the value of managed investment funds from 2018 to 2019?", + "output": "What is the change in managed investment funds between 2018 and 2019?" + }, + { + "input": "What type of long-term borrowings are shown in the table?", + "output": "What are the specific types of long-term borrowings that are presented in the table?" + }, + { + "input": "What type of long-term borrowings are shown in the table?", + "output": "What is the specific category of long-term borrowings that is displayed in the table?" + }, + { + "input": "What is the percentage change in the number of shares acquired from February to March 2019?", + "output": "What is the percentage change between shares purchased in February and March 2019?" + }, + { + "input": "Which financial years' information is shown in the table?", + "output": "Which fiscal years' data is displayed in the given table?" + }, + { + "input": "Which financial years' information is shown in the table?", + "output": "Which specific financial year's information is depicted in the table?" + }, + { + "input": "How much is the 2019 audit fees ?", + "output": "What is the exact cost of the audit fees for the year 2019?" + }, + { + "input": "What were the average fees for audits in 2018 and 2019?", + "output": "What is the average audit fees for 2018 and 2019?" + }, + { + "input": "What were the average audit fees in both 2018 and 2019?", + "output": "What is the average audit fees for 2018 and 2019?" + }, + { + "input": "\"What is the average total fees for the years 2018 and 2019 combined?\"", + "output": "What is the average total fees for 2018 and 2019?" + }, + { + "input": "What is the average sum of fees for the years 2018 and 2019 combined?", + "output": "What is the average total fees for 2018 and 2019?" + }, + { + "input": "What is the discrepancy between the average fees charged for audits and the average total fees incurred in 2018 and 2019?", + "output": "What is the difference between the 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", + "output": "What was the excluded number of potentially anti-dilutive shares for the fiscal years ending June 30, 2017, 2018, and 2019?" + }, + { + "input": "What is the number of potentially anti-dilutive shares that were excluded for fiscal years ended June 30, 2017, 2018 and 2019 respectively? ", + "output": "What is the total count of potentially anti-dilutive shares excluded for the fiscal years ending on June 30, 2017, 2018, and 2019, respectively?" + }, + { + "input": "What is the variance in the weighted-average ordinary shares outstanding for basic and diluted calculations in the fiscal year that ended on June 30, 2019?", + "output": "In fiscal year ended June 30, 2019, what is the difference in the weighted-average ordinary shares outstanding between the basic and the diluted?" + }, + { + "input": "What is the average total non-current operating assets for fiscal years ended June 30, 2018 and 2019?", + "output": "\"What is the average total value of non-current operating assets for the fiscal years that ended on June 30, 2018 and June 30, 2019?\"" + }, + { + "input": "How many geographic regions had non-current operating assets exceeding $10,000 thousand in the fiscal year ending on June 30, 2019?", + "output": "In fiscal year ended June 30, 2019, how many geographic 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"What is the difference in balance of capped call and non-marketable investments as of June 30, 2019?", + "output": "What is the difference in the balance of capped call and non-marketable investments as of June 30, 2019, and how do these two types of investments vary in terms of their marketability?" + }, + { + "input": "What is the difference in balance of capped call and non-marketable investments as of June 30, 2019?", + "output": "What is the balance difference between capped call and non-marketable investments as of June 30, 2019?" + }, + { + "input": "\"What is the chronological order of the share capital amounts from June 30, 2015 to 2019?\"", + "output": "What are the share capital for the years as of June 30, 2015 to 2019 in chronological order?" + }, + { + "input": "List the short-term investments from June 30, 2015 to 2019 in chronological order.", + "output": "What are the short-term investments for the years as of June 30, 2015 to 2019 in chronological order?" + }, + { + "input": "What is the difference in the value of short-term investments between fiscal years 2018 and 2019?", + "output": "What is the variance in the monetary worth of short-term investments from fiscal year 2018 to fiscal year 2019?" + }, + { + "input": "What is the average Cash and cash equivalents for 2015-2019?", + "output": "What is the average amount of Cash and cash equivalents from 2015 to 2019?" + }, + { + "input": "What is the definition of other fees?", + "output": "What does the term \"other fees\" mean? 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"output": "What are the components under other current assets in the table?" + }, + { + "input": "What was the difference in the total value of other current assets between 2019 and 2018?", + "output": "What was the change in total other current assets in 2019 from 2018?" + }, + { + "input": "Which items in the table include the cumulative effect of the 2017 Tax Reform Act in their 2017 amounts?", + "output": "For which items in the table does the amount for 2017 includes the sum related to the impact of the 2017 Tax Reform Act?" + }, + { + "input": "Which items in the table include the sum related to the impact of the 2017 Tax Reform Act in their amount for 2017?", + "output": "For which items in the table does the amount for 2017 includes the sum related to the impact of the 2017 Tax Reform Act?" + }, + { + "input": "What was the year when the Diluted Net Income per Common Share reached its highest value?", + "output": "In which year was the Diluted Net Income per Common Share 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"output": "In which years is income tax benefit calculated?" + }, + { + "input": "What does restricted cash include?", + "output": "What is included in restricted cash and can you provide further details about its components?" + }, + { + "input": "In which year was Restricted cash included in \"Other current assets\" the lowest?", + "output": "In which year did the inclusion of Restricted cash in the category of \"Other current assets\" reach its lowest level?" + }, + { + "input": "What was the difference in the amount of Restricted cash included in the category of \"Deferred charges and other assets\" in the year 2017 compared to 2016?", + "output": "What was the change in Restricted cash included in \"Deferred charges and other assets\" in 2017 from 2016?" + }, + { + "input": "What is the largest recorded year for Net periodic benefit cost?", + "output": "In which year was Net periodic benefit cost the largest?" + }, + { + "input": "What was the difference in the amount of money 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"output": "In which year was service cost larger?" + }, + { + "input": "What was the difference in the amount spent on interest in 2019 compared to 2018?", + "output": "What was the change in interest cost in 2019 from 2018?" + }, + { + "input": "\"What was the gross revenue generated by overseas operations, excluding any income taxes, in the fiscal year 2018?\"", + "output": "What was the Foreign income before income taxes in 2018?" + }, + { + "input": "What was the total income from foreign sources in 2018, before any deductions for income taxes were made?", + "output": "What was the Foreign income before income taxes in 2018?" + }, + { + "input": "What are the components under income before income taxes?", + "output": "What are the individual components that make up the category of income before income taxes?" + }, + { + "input": "In which specific year, prior to the implementation of income taxes, did individuals and/or organizations experience the highest level of income?", + "output": "In which year was income before income taxes the largest?" + }, + { + "input": "What is the largest recorded income before income taxes, and in which specific year was it achieved?", + "output": "In which year was income before income taxes the largest?" + }, + { + "input": "In which specific year did the quantity of an item categorized as \"Other\" exceed the quantity of another item, considering all provided information?", + "output": "In which year was the amount of Other larger?" + }, + { + "input": "In which year did the quantity of a different item exceed the quantity of the rest?", + "output": "In which year was the amount of Other larger?" + }, + { + "input": "What was the year-over-year difference in accrued severance and associated expenses between 2018 and 2019?", + "output": "What was the change in Accrued severance and related costs in 2019 from 2018?" + }, + { + "input": "What was the total payment made by the Company in the year 2019?", + "output": "How much has the Company paid in total in 2019?" + }, + { + "input": "What was the total amount paid by the Company in 2019?", + "output": "How much has the Company paid in total in 2019?" + }, + { + "input": "When did the amount of non-cash impairment charges exceed previous years?", + "output": "In which year was Non-cash impairment charges larger?" + }, + { + "input": "Which year had a greater amount of non-cash impairment charges compared to other years?", + "output": "In which year was Non-cash impairment charges larger?" + }, + { + "input": "During which years is the fair value of restricted shares or restricted stock units (RSUs) vested determined?", + "output": "In which years is the Fair value of restricted shares/RSUs vested calculated?" + }, + { + "input": "What was the change in Number of restricted shares/ RSUs granted in 2019 from 2018?", + "output": "What was the difference in the number of restricted shares/RSUs granted in 2019 compared to 2018?" + }, + { + "input": "What was the change in Number of restricted shares/ RSUs granted in 2019 from 2018?", + "output": "What was the difference in the number of restricted shares/ RSUs granted in 2019 compared to 2018?" + }, + { + "input": "What was the percentage change in Number of restricted shares/ RSUs granted in 2019 from 2018?", + "output": "What was the percentage change in the number of restricted shares/ RSUs granted from 2018 to 2019?" + }, + { + "input": "As of July 1, 2019, what was the total value of stapled securities of NSR listed on the Australian Securities Exchange?", + "output": "How much did NSR had stapled securities quoted on the Australian Securities Exchange as at 1 July 2019?" + }, + { + "input": "As of July 1, 2019, what was the total value of NSR stapled securities quoted on the Australian Securities Exchange?", + "output": "How much did NSR had stapled securities quoted on the Australian Securities Exchange as at 1 July 2019?" + }, + { + "input": "What would happen if the price of a 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"input": "What is the average revenue generated from New Zealand in both 2018 and 2019?", + "output": "What is the average revenue from New Zealand for 2018 and 2019?" + }, + { + "input": "In which specific year did the revenue from New Zealand amount to less than 10,000 thousands?", + "output": "In which year was revenue from New Zealand under 10,000 thousands?" + }, + { + "input": "In what specific year did the revenue generated from New Zealand fall below 10,000 thousands (in the same currency)?", + "output": "In which year was revenue from New Zealand under 10,000 thousands?" + }, + { + "input": "What was the total NLA in Brisbane?", + "output": "What was the total number of Newly Listed Apartments (NLA) in Brisbane?" + }, + { + "input": "What is the difference in the NLA between Sunshine Cost and Brisbane?", + "output": "What are the NLA differences between the Sunshine Coast and Brisbane, and how do they vary from each other in terms of specific aspects?" + }, + { + "input": "In which year is the Net investment hedge negative?", + "output": "Which specific year does the Net investment hedge demonstrate a negative value?" + }, + { + "input": "What is the change in the Taxation impact on revaluation from 2018 to 2019?", + "output": "What is the difference in the taxation impact on revaluation between 2018 and 2019?" + }, + { + "input": "What is the change in the Taxation impact on revaluation from 2018 to 2019?", + "output": "What is the difference in the effect of taxation on revaluation between 2018 and 2019?" + }, + { + "input": "What was the share of profit from associates representing NSRs in 2019 and 2018?", + "output": "What were the percentages of profit from associates, specifically those representing NSRs, for the years 2019 and 2018?" + }, + { + "input": "What is the difference in the percentage of profit attributed to associates between 2018 and 2019?", + "output": "What is the change in the Share of profit from associates from 2018 to 2019?" + }, + { + "input": "What is the difference in the percentage of profit allocated to associates between 2018 and 2019?", + "output": "What is the change in the Share of profit from associates from 2018 to 2019?" + }, + { + "input": "When was the year when the closing balance on June 30th was below 11,000 thousand units?", + "output": "In which year was the Closing balance at 30 June less than 11,000 thousands?" + }, + { + "input": "What is the year-over-year change in face value for New Zealand Dollar (NZD) interest rate swaps from 2018 to 2019?", + "output": "What is the change in Interest rate swaps (NZD) at face value for Current interest rate swaps from 2018 to 2019?" + }, + { + "input": "What was the percentage change in face value for current New Zealand Dollar (NZD) interest rate swaps from 2018 to 2019?", + "output": "What is the change in Interest rate swaps (NZD) at face value for Current interest rate swaps from 2018 to 2019?" + }, + { + "input": "What was the assets held for sale 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"output": "What was the amount of amortization expense recorded by the Company for the fiscal year that ended on March 31, 2017?" + }, + { + "input": "What are the main sources of liquidity in financial markets and institutions?", + "output": "What are the principal sources of liquidity?" + }, + { + "input": "What was the total value of outstanding letters of credit associated with specific operating leases as of March 31, 2018?", + "output": "How much was the outstanding letters of credit related to certain operating leases as of March 31, 2018?" + }, + { + "input": "What were the revenue retention rates for the years 2019, 2018, and 2017 in chronological order?", + "output": "What was the Revenue retention rate in 2019, 2018 and 2017 respectively?" + }, + { + "input": "What is the average number of customers in total from 2017 to 2019?", + "output": "What is the average Total customers between 2017-2019?" + }, + { + "input": "\"In which specific year did the Adjusted EBITDA amount to 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"What time periods are encompassed in the table's content?" + }, + { + "input": "\"What specific items or transactions were encompassed within the category of acquisitions and adjustments?\"", + "output": "What did Acquisitions and adjustments include?" + }, + { + "input": "What were the variations in acquisitions and adjustments made in fiscal year 2018 between the Electronic Manufacturing Services (EMS) and Document Management Systems (DMS)?", + "output": "What is the difference in Acquisitions and adjustments in fiscal 2018 between EMS and DMS?" + }, + { + "input": "What are the distinctions in acquisitions and adjustments, specifically pertaining to fiscal year 2018, when comparing EMS (Emergency Medical Services) and DMS (Document Management Systems)?", + "output": "What is the difference in Acquisitions and adjustments in fiscal 2018 between EMS and DMS?" + }, + { + "input": "What was the balance of EMS as in 2018 as a percentage of the total balance at the end of fiscal 2018?", 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"input": "What was the precise difference in terms of employee severance and benefit costs from 2018 to 2019?", + "output": "What was the change in Employee severance and benefit costs between 2018 and 2019?" + }, + { + "input": "How many years did cash proceeds received exceed $5,000 million?", + "output": "How many years have the cash proceeds received been above $5,000 million?" + }, + { + "input": "How many years did cash proceeds received exceed $5,000 million?", + "output": "For how many years did the cash proceeds received exceed $5,000 million?" + }, + { + "input": "What was the percentage increase or decrease in pre-tax losses on the sale of receivables from 2017 to 2018?", + "output": "What was the percentage change in Pre-tax losses on sale of receivables between 2017 and 2018?" + }, + { + "input": "What was the difference in the amount of money spent on repurchasing shares in 2016 compared to 2017?", + "output": "What was the change in share repurchases between 2016 and 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"output": "In which year was the amount of other financial expenses the largest?" + }, + { + "input": "What was the net difference in the overall financial expenses incurred during the year 2019 as compared to 2018?", + "output": "What was the change in the total financial expenses in 2019 from 2018?" + }, + { + "input": "What was the sum of all financial costs incurred in the year 2019?", + "output": "What is the total amount of financial expenses in 2019?" + }, + { + "input": "What was the specific year when the amount of Current lease liabilities was greater than in any other year?", + "output": "In which year was the amount of Current lease liabilities larger?" + }, + { + "input": "When did the amount of Current lease liabilities exceed the previous year's amount?", + "output": "In which year was the amount of Current lease liabilities larger?" + }, + { + "input": "What is the total financial income for 2019?", + "output": "What was the overall financial income for the year 2019?" 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"What percentage increase or decrease in interest income occurred from 2018 to 2019?", + "output": "What was the percentage change in interest income between 2018 and 2019?" + }, + { + "input": "What is the average amount of net cash provided by operating activities for the years 2017, 2018, and 2019?", + "output": "What was the average net cash provided by operating activities from 2017-2019?" + }, + { + "input": "What was the percentage change in the free flow cash margin from 2017 to 2018?", + "output": "What was the change in free flow cash margin between 2017 and 2018?" + }, + { + "input": "What was the total amount of federal net operating losses incurred by the company in the year 2019?", + "output": "What was the company's federal net operating losses in 2019?" + }, + { + "input": "What was the amount of federal net operating losses incurred by the company during the year 2019 for tax purposes?", + "output": "What was the company's federal net operating losses in 2019?" + }, + { + "input": "What was the company's state net operating losses in 2019?", + "output": "What was the total amount of net operating losses incurred by the company in the state during the year 2019?" + }, + { + "input": "What is the percentage change in Deferred revenue between 2018 and 2019?", + "output": "What is the exact percentage difference in Deferred revenue from 2018 to 2019?" + }, + { + "input": "What was the precise amount spent on sales and marketing in the year 2018?", + "output": "What was the sales and marketing expense in 2018?" + }, + { + "input": "What was the specific fair value amount of cash that is being referred to in the context of a certain situation or event?", + "output": "What was the fair value amount of cash?" + }, + { + "input": "What was the specific fair value figure assigned to the amount of cash? 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total obligations that consists of payments due in 1-3 years?" + }, + { + "input": "\"What are the regulations or guidelines regarding the payment of Foreign income tax applicable for the year 2019?\"", + "output": "What is the provision for Foreign income tax in 2019?" + }, + { + "input": "\"What are the regulations or rules implemented in 2019 regarding the taxation of foreign income?\"", + "output": "What is the provision for Foreign income tax in 2019?" + }, + { + "input": "What is the disparity in the weighted-average grant date fair value when comparing shares that have been granted to those that have been forfeited?", + "output": "What is the difference in weighted-average grant date fair value between granted and forfeited shares?" + }, + { + "input": "What is the disparity in weighted-average grant date fair value when comparing shares that have been granted with those that have been forfeited?", + "output": "What is the difference in weighted-average grant date fair value between granted and forfeited shares?" + }, + { + "input": "What is the Net sales for 2019?", + "output": "What was the total global revenue generated from sales in the year 2019?" + }, + { + "input": "What was the percentage change in diluted net income per share from continuing operations from 2017 to 2018?", + "output": "What was the change in Diluted Net income per share from continuing operations in 2018 from 2017?" + }, + { + "input": "What was the percentage increase or decrease in the Diluted Net income per share from continuing operations between 2018 and 2017?", + "output": "What was the percentage change in Diluted Net income per share from continuing operations in 2018 from 2017?" + }, + { + "input": "What does the Euro Term Loan due 2024 consist of?", + "output": "What is included in the Euro Term Loan due in 2024?" + }, + { + "input": "What does the Euro Term Loan due 2024 consist of?", + "output": "\"What are the components and details of the Euro Term Loan due in 2024?\"" + }, + { + "input": "In which year did the value of the 1.0% State of Connecticut term loan, which has a maturity date in 2023, surpass its previous size?", + "output": "In which year was the 1.0% State of Connecticut term loan due 2023 larger?" + }, + { + "input": "In which year did the size of the 1.0% State of Connecticut term loan due in 2023 increase?", + "output": "In which year was the 1.0% State of Connecticut term loan due 2023 larger?" + }, + { + "input": "What was the change in Capital lease obligations from 2018 to 2019?", + "output": "What was the exact difference in the amount of Capital lease obligations between the years 2018 and 2019?" + }, + { + "input": "In which year was ILS a higher percentage of total net sales?", + "output": "In which specific year did the Incremental Lifetime Sales (ILS) constitute a larger proportion of the overall net sales?" + }, + { + "input": "What were the net sales figures for ILS in both 2018 and 2019, and what was the average amount for each year?", + "output": "What was the average amount of net sales for ILS in 2018 and 2019?" + }, + { + "input": "What were the average net sales for ILS for the years 2018 and 2019?", + "output": "What was the average amount of net sales for ILS in 2018 and 2019?" + }, + { + "input": "What is the estimated duration of a building and any associated enhancements before they are deemed no longer useful?", + "output": "What is the useful life of Buildings and improvements?" + }, + { + "input": "How is property and equipment stated in the table?", + "output": "In what manner is property and equipment presented and described within the table?" + }, + { + "input": "In which year was the amount of Land larger?", + "output": "\"When comparing the years, which year had a larger amount of land?\"" + }, + { + "input": "What was the change in Leasehold improvements from 2018 to 2019?", + "output": "What was the difference in Leasehold improvements value between 2018 and 2019?" + }, + { + "input": "What was the percentage change in Short-term investments in 2019 from 2018?", + "output": "What was the percentage difference in the value of Short-term investments between 2019 and 2018?" + }, + { + "input": "What was the percentage change in Short-term investments in 2019 from 2018?", + "output": "What was the exact percentage difference in the value of Short-term investments between 2018 and 2019?" + }, + { + "input": "What was the net income for the year 2019?", + "output": "What was Net income in 2019?" + }, + { + "input": "\"In which specific year did the company report the highest amount of comprehensive income?\"", + "output": "In which year was Comprehensive income largest?" + }, + { + "input": "What is the largest year for Comprehensive income?", + "output": "In which year was Comprehensive income largest?" + }, + { + "input": "What was the difference in the amount of money earned in 2018 compared to 2017 after all expenses and taxes were accounted for?", + "output": "What was the change in Net income in 2018 from 2017?" + }, + { + "input": "What was the Projected Benefit Obligation (PBO) amount for the year 2019? Please provide the numerical value for the PBO as of December 31st, 2019.", + "output": "What was the Projected benefit obligation in 2019?" + }, + { + "input": "In which years was information on defined benefit plans provided?", + "output": "During which specific years was information regarding defined benefit plans provided?" + }, + { + "input": "In which year was the amount of Research and development largest?", + "output": "In which specific year did Research and development reach its highest amount or expenditure?" + }, + { + "input": "What was the specific amount allocated for income taxes in the year 2019?", + "output": "What was the Provision for income taxes in 2019?" + }, + { + "input": "What specific amount was set aside for income taxes in the fiscal year of 2019?", + "output": "What was the Provision for income taxes in 2019?" + }, + { + "input": "What has been the modification in the amount of state income taxes that individuals are required to pay in 2019 compared to 2018?", + "output": "What was the change in state income taxes that is currently payable in 2019 from 2018?" + }, + { + "input": "How much Microelectronics was present in the year 2019?", + "output": "What is the amount of Microelectronics in 2019?" + }, + { + "input": "What is the total volume or quantity of Microelectronics produced or used worldwide in the year 2019?", + "output": "What is the amount of Microelectronics in 2019?" + }, + { + "input": "In which years is net sales calculated?", + "output": "In which specific years does the calculation of net sales occur?" + }, + { + "input": "In which years is net sales calculated?", + "output": "During which specific years is the calculation of net sales undertaken?" + }, + { + "input": "What was the percentage difference in the quantity of OEM components and instrumentation between 2018 and 2019?", + "output": "What was the percentage change in the amount of OEM components and instrumentation in 2019 from 2018?" + }, + { + "input": "What was the Net cash provided by operating activities in 2019?", + "output": "What was the amount of net cash generated from operating activities during the year 2019?" + }, + { + "input": "What was the Purchases of property and equipment in 2018?", + "output": "What was the total value of purchases made for property and equipment in the year 2018?" + }, + { + "input": "In which specific years are the instances or applications of physical currency emphasized or showcased in the table?", + "output": "In which years are the uses of cash highlighted in the table?" + }, + { + "input": "What years are specified in the table to illustrate the purpose of utilizing cash?", + "output": "In which years are the uses of cash highlighted in the table?" + }, + { + "input": "In which year was the Issuance of shares under employee stock plans larger?", + "output": "When comparing the issuance of shares under employee stock plans, in which specific year was the volume of shares issued found to be greater?" + }, + { + "input": "In which year were the Additions charged to expenses the largest?", + "output": "What is the specific year when the largest Additions were charged to expenses?" + }, + { + "input": "In which year were the Additions charged to expenses the largest?", + "output": "What was the year when the largest amount of Additions were charged to expenses?" + }, + { + "input": "In which year did tax fees have a higher value or amount than in other years?", + "output": "In which year were tax fees larger?" + }, + { + "input": "What was the difference in the amount of all other fees charged in 2019 compared to 2018?", + "output": "What was the change in All other fees in 2019 from 2018?" + }, + { + "input": "What was Long-term taxes payable in 2019?", + "output": "What was the amount of Long-term taxes payable in the year 2019?" + }, + { + "input": "In which year was Other long-term liabilities larger?", + "output": "\"In which specific year did Other long-term liabilities exhibit a greater amount compared to the other years?\"" + }, + { + "input": "In 2019, what was the exact amount of deferred compensation liability?", + "output": "What was the Total deferred compensation liability in 2019?" + }, + { + "input": "For which years was the calculation of Total deferred compensation liability performed?", + "output": "In which years was Total deferred compensation liability calculated?" + }, + { + "input": "How was the calculation for diluted earnings per share derived?", + "output": "How was diluted earnings per share computed?" + }, + { + "input": "In which specific years can we find the earnings per share data that was included in the table?", + "output": "In which years was earnings per share provided in the table?" + }, + { + "input": "What year experienced the highest dilutive impact from employee stock awards?", + "output": "In which year was the Dilutive effect of employee stock awards largest?" + }, + { + "input": "In what specific year did employee stock awards have the most significant dilutive effect?", + "output": "In which year was the Dilutive effect of employee stock awards largest?" + }, + { + "input": "What was the change in Dilutive effect of employee stock awards in 2019 from 2018?", + "output": "What was the difference in the dilutive impact caused by employee stock awards between 2019 and 2018?" + }, + { + "input": "What was the change in Dilutive effect of employee stock awards in 2019 from 2018?", + "output": "What was the difference in how much employee stock awards had a dilutive effect in 2019 compared to 2018?" + }, + { + "input": "What was the percentage change in Dilutive effect of employee stock awards in 2019 from 2018?", + "output": "What was the percentage change in the dilutive effect of employee stock awards from 2018 to 2019?" + }, + { + "input": "In which specific year did the Amortization of intangible assets account for a larger proportion of the overall net sales?", + "output": "In which year was Amortization of intangible assets a higher percentage of total net sales?" + }, + { + "input": "What was the change in the amount of Amortization of intangible assets in 2019 from 2018?", + "output": "What was the difference in the amount of Amortization of intangible assets between the fiscal years 2019 and 2018?" + }, + { + "input": "What was the value of Net deferred tax assets in the financial year of 2019?", + "output": "What was the Net deferred tax assets in 2019?" + }, + { + "input": "What was the difference in the initial balance for 2019 compared to 2018?", + "output": "What was the change in Beginning balance in 2019 from 2018?" + }, + { + "input": "What was the difference in the starting balance in 2019 compared to 2018?", + "output": "What was the change in Beginning balance in 2019 from 2018?" + }, + { + "input": "What was the balance in the accounts as of the start of the year in 2018?", + "output": "What was the Balance as of the beginning of the year in 2018?" + }, + { + "input": "\"What was the exact monetary balance in the accounts as of January 1st, 2018, at the start of the year?\"", + "output": "What was the Balance as of the beginning of the year in 2018?" + }, + { + "input": "What was the percentage change in Balance as of the beginning of the year in 2019 from 2018?", + "output": "What is the percentage change in the Balance as of the start of 2019 compared to the Balance at the start of 2018?" + }, + { + "input": "What was the percentage change in Prepaid and refundable income taxes from 2018 to 2019?", + "output": "What was the percentage difference in the amount of income taxes that were prepaid and refundable between the years 2018 and 2019?" + }, + { + "input": "What was the percentage change in Prepaid and refundable income taxes from 2018 to 2019?", + "output": "What was the percentage difference in the amount of prepaid and refundable income taxes between 2018 and 2019?" + }, + { + "input": "What does the Current portion of Euro Term Loan consist of?", + "output": "What is included in the current portion of the Euro Term Loan?" + }, + { + "input": "What was the net change in the amount of outstanding Capital lease obligations between the years 2018 and 2019?", + "output": "What was the change in Capital lease obligations from 2018 to 2019?" + }, + { + "input": "What was the percentage increase or decrease in Capital lease obligations from 2018 to 2019?", + "output": "What was the percentage change in Capital lease obligations from 2018 to 2019?" + }, + { + "input": "What was the percentage increase or decrease in Capital lease obligations between 2018 and 2019?", + "output": "What was the percentage change in Capital lease obligations from 2018 to 2019?" + }, + { + "input": "What are the respective values of the company's work in process inventories on March 31, 2018 and 2019?", + "output": "What were the specific values of the company's work in process inventories as of March 31 in both 2018 and 2019?" + }, + { + "input": "What are the respective values of the company's work in process inventories on March 31, 2018 and 2019?", + "output": "What is the dollar amount of the work in process inventories for the company on March 31 of both 2018 and 2019?" + }, + { + "input": "What was the value of the company's finished goods on March 31, 2018 and March 31, 2019?", + "output": "What are the respective values of the company's finished goods on March 31, 2018 and 2019?" + }, + { + "input": "What is the change in the company's raw materials on March 31, 2018 and 2019?", + "output": "What is the difference in the amount of raw materials held by the company as of March 31, 2018 and March 31, 2019?" + }, + { + "input": "What was the mean value of the company's raw materials on March 31 in both 2018 and 2019?", + "output": "What is the average value of the company's raw materials on March 31, 2018 and 2019?" + }, + { + "input": "What is the average worth of the company's raw materials specifically on March 31st, both in the years 2018 and 2019?", + "output": "What is the average value of the company's raw materials on March 31, 2018 and 2019?" + }, + { + "input": "What is the mean amount of the company's completed products on March 31, in both the years 2018 and 2019?", + "output": "What is the average value of the company's finished goods on March 31, 2018 and 2019?" + }, + { + "input": "What is the average value of the finished goods held by the company on March 31 in both 2018 and 2019?", + "output": "What is the average value of the company's finished goods on March 31, 2018 and 2019?" + }, + { + "input": "What are the company's respective foreign income before taxes in 2018 and 2019?", + "output": "What were the pre-tax foreign incomes of the company in 2018 and 2019?" + }, + { + "input": "What is the company's average loss before income taxes from the United States in 2018 and 2019?", + "output": "\"What was the average pre-tax loss incurred by the company in the United States for the years 2018 and 2019?\"" + }, + { + "input": "What is the company's average loss before income taxes from the United States in 2018 and 2019?", + "output": "What was the average pre-tax income loss for the company in the United States for both 2018 and 2019?" + }, + { + "input": "What is the company's average foreign income before taxes in 2018 and 2019?", + "output": "What was the average amount of foreign income before taxes for the company in both 2018 and 2019?" + }, + { + "input": "What are the average pre-tax earnings of the company for the fiscal years 2018 and 2019?", + "output": "What is the company's average total income before income taxes in 2018 and 2019?" + }, + { + "input": "What are the specific cost of goods sold for the company in both 2018 and 2019?", + "output": "What are the company's respective cost of goods sold in 2018 and 2019?" + }, + { + "input": "What were the cost of goods sold for the company in 2018 and 2019?", + "output": "What are the company's respective cost of goods sold in 2018 and 2019?" + } +] diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py b/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py new file mode 100644 index 0000000..3891f54 --- /dev/null +++ b/libs/ktem/ktem/reasoning/prompt_optimization/rewrite_question.py @@ -0,0 +1,37 @@ +from ktem.llms.manager import llms + +from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage +from kotaemon.llms import ChatLLM, PromptTemplate + +DEFAULT_REWRITE_PROMPT = ( + "Given the following question, rephrase and expand it " + "to help you do better answering. Maintain all information " + "in the original question. Keep the question as concise as possible. " + "Give answer in {lang}\n" + "Original question: {question}\n" + "Rephrased question: " +) + + +class RewriteQuestionPipeline(BaseComponent): + """Rewrite user question + + Args: + llm: the language model to rewrite question + rewrite_template: the prompt template for llm to paraphrase a text input + lang: the language of the answer. Currently support English and Japanese + """ + + llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) + rewrite_template: str = DEFAULT_REWRITE_PROMPT + + lang: str = "English" + + def run(self, question: str) -> Document: # type: ignore + prompt_template = PromptTemplate(self.rewrite_template) + prompt = prompt_template.populate(question=question, lang=self.lang) + messages = [ + SystemMessage(content="You are a helpful assistant"), + HumanMessage(content=prompt), + ] + return self.llm(messages) diff --git a/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py b/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py new file mode 100644 index 0000000..c5b2b25 --- /dev/null +++ b/libs/ktem/ktem/reasoning/prompt_optimization/suggest_conversation_name.py @@ -0,0 +1,36 @@ +import logging + +from ktem.llms.manager import llms + +from kotaemon.base import AIMessage, BaseComponent, Document, HumanMessage, Node +from kotaemon.llms import ChatLLM, PromptTemplate + +logger = logging.getLogger(__name__) + + +class SuggestConvNamePipeline(BaseComponent): + """Suggest a good conversation name based on the chat history.""" + + llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) + SUGGEST_NAME_PROMPT_TEMPLATE = ( + "You are an expert at suggesting good and memorable conversation name. " + "Based on the chat history above, " + "suggest a good conversation name (max 10 words). " + "Give answer in {lang}. Just output the conversation " + "name without any extra." + ) + prompt_template: str = SUGGEST_NAME_PROMPT_TEMPLATE + lang: str = "English" + + def run(self, chat_history: list[tuple[str, str]]) -> Document: # type: ignore + prompt_template = PromptTemplate(self.prompt_template) + prompt = prompt_template.populate(lang=self.lang) + + messages = [] + for human, ai in chat_history: + messages.append(HumanMessage(content=human)) + messages.append(AIMessage(content=ai)) + + messages.append(HumanMessage(content=prompt)) + + return self.llm(messages) diff --git a/libs/ktem/ktem/reasoning/react.py b/libs/ktem/ktem/reasoning/react.py index 9f92023..afdd931 100644 --- a/libs/ktem/ktem/reasoning/react.py +++ b/libs/ktem/ktem/reasoning/react.py @@ -19,7 +19,10 @@ from kotaemon.agents import ( from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage from kotaemon.llms import ChatLLM, PromptTemplate +from ..utils import SUPPORTED_LANGUAGE_MAP + logger = logging.getLogger(__name__) +DEFAULT_AGENT_STEPS = 4 class DocSearchArgs(BaseModel): @@ -97,7 +100,7 @@ class DocSearchTool(BaseTool): ) print("Retrieved #{}: {}".format(_id, retrieved_content[:100])) - print("Score", retrieved_item.metadata.get("relevance_score", None)) + print("Score", retrieved_item.metadata.get("cohere_reranking_score", None)) # trim context by trim_len if evidence: @@ -190,7 +193,9 @@ class ReactAgentPipeline(BaseReasoning): "Action: {tool}[{input}]\n\nOutput: {output}" ).format( tool=step.tool if status == "thinking" else "", - input=step.tool_input.replace("\n", "") if status == "thinking" else "", + input=step.tool_input.replace("\n", "").replace('"', "") + if status == "thinking" + else "", output=output if status == "thinking" else "Finished", ) return Document( @@ -261,9 +266,17 @@ class ReactAgentPipeline(BaseReasoning): llm_name = settings[f"{prefix}.llm"] llm = llms.get(llm_name, llms.get_default()) + max_context_length_setting = settings.get("reasoning.max_context_length", None) + pipeline = ReactAgentPipeline(retrievers=retrievers) pipeline.agent.llm = llm pipeline.agent.max_iterations = settings[f"{prefix}.max_iterations"] + + if max_context_length_setting: + pipeline.agent.max_context_length = ( + max_context_length_setting // DEFAULT_AGENT_STEPS + ) + tools = [] for tool_name in settings[f"reasoning.options.{_id}.tools"]: tool = TOOL_REGISTRY[tool_name] @@ -273,7 +286,7 @@ class ReactAgentPipeline(BaseReasoning): tool.llm = llm tools.append(tool) pipeline.agent.plugins = tools - pipeline.agent.output_lang = {"en": "English", "ja": "Japanese"}.get( + pipeline.agent.output_lang = SUPPORTED_LANGUAGE_MAP.get( settings["reasoning.lang"], "English" ) pipeline.use_rewrite = states.get("app", {}).get("regen", False) @@ -298,6 +311,7 @@ class ReactAgentPipeline(BaseReasoning): "value": llm, "component": "dropdown", "choices": llm_choices, + "special_type": "llm", "info": ( "The language model to use for generating the answer. If None, " "the application default language model will be used." @@ -325,5 +339,10 @@ class ReactAgentPipeline(BaseReasoning): return { "id": "ReAct", "name": "ReAct Agent", - "description": "Implementing ReAct paradigm", + "description": ( + "Implementing ReAct paradigm: https://arxiv.org/abs/2210.03629. " + "ReAct agent answers the user's request by iteratively formulating " + "plan and executing it. The agent can use multiple tools to gather " + "information and generate the final answer." + ), } diff --git a/libs/ktem/ktem/reasoning/rewoo.py b/libs/ktem/ktem/reasoning/rewoo.py index 87210f7..e4d461f 100644 --- a/libs/ktem/ktem/reasoning/rewoo.py +++ b/libs/ktem/ktem/reasoning/rewoo.py @@ -20,7 +20,10 @@ from kotaemon.agents import ( from kotaemon.base import BaseComponent, Document, HumanMessage, Node, SystemMessage from kotaemon.llms import ChatLLM, PromptTemplate +from ..utils import SUPPORTED_LANGUAGE_MAP + logger = logging.getLogger(__name__) +DEFAULT_AGENT_STEPS = 4 DEFAULT_PLANNER_PROMPT = ( @@ -135,7 +138,7 @@ class DocSearchTool(BaseTool): ) print("Retrieved #{}: {}".format(_id, retrieved_content)) - print("Score", retrieved_item.metadata.get("relevance_score", None)) + print("Score", retrieved_item.metadata.get("cohere_reranking_score", None)) # trim context by trim_len if evidence: @@ -215,7 +218,7 @@ class RewooAgentPipeline(BaseReasoning): use_rewrite: bool = False enable_citation: bool = False - def format_info_panel(self, worker_log): + def format_info_panel_evidence(self, worker_log): header = "" content = [] @@ -223,6 +226,10 @@ class RewooAgentPipeline(BaseReasoning): if line.startswith("#Plan"): # line starts with #Plan should be marked as a new segment header = line + elif line.startswith("#Action"): + # small fix for markdown output + line = "\\" + line + "
" + content.append(line) elif line.startswith("#"): # stop markdown from rendering big headers line = "\\" + line @@ -238,6 +245,17 @@ class RewooAgentPipeline(BaseReasoning): content=Render.collapsible( header=header, content=Render.table("\n".join(content)), + open=False, + ), + ) + + def format_info_panel_planner(self, planner_output): + planner_output = planner_output.replace("\n", "
") + return Document( + channel="info", + content=Render.collapsible( + header="Planner Output", + content=planner_output, open=True, ), ) @@ -285,12 +303,19 @@ class RewooAgentPipeline(BaseReasoning): # line starts with #Plan should be marked as a new segment new_segment = [line] segments.append(new_segment) + elif line.startswith("#Action"): + # small fix for markdown output + line = "\\" + line + "
" + segments[-1].append(line) elif line.startswith("#"): # stop markdown from rendering big headers line = "\\" + line segments[-1].append(line) else: - segments[-1].append(line) + if segments: + segments[-1].append(line) + else: + segments.append([line]) outputs = [] for segment in segments: @@ -337,18 +362,23 @@ class RewooAgentPipeline(BaseReasoning): for item in output_stream: if item.intermediate_steps: for step in item.intermediate_steps: - yield Document( - channel="info", - content=self.format_info_panel(step["worker_log"]), - ) + if "planner_log" in step: + yield Document( + channel="info", + content=self.format_info_panel_planner(step["planner_log"]), + ) + else: + yield Document( + channel="info", + content=self.format_info_panel_evidence(step["worker_log"]), + ) if item.text: + # final answer yield Document(channel="chat", content=item.text) answer = output_stream.value yield Document(channel="info", content=None) - refined_citations = self.prepare_citation(answer) - for _ in refined_citations: - yield _ + yield from self.prepare_citation(answer) return answer @@ -360,6 +390,8 @@ class RewooAgentPipeline(BaseReasoning): prefix = f"reasoning.options.{_id}" pipeline = RewooAgentPipeline(retrievers=retrievers) + max_context_length_setting = settings.get("reasoning.max_context_length", None) + planner_llm_name = settings[f"{prefix}.planner_llm"] planner_llm = llms.get(planner_llm_name, llms.get_default()) solver_llm_name = settings[f"{prefix}.solver_llm"] @@ -367,6 +399,10 @@ class RewooAgentPipeline(BaseReasoning): pipeline.agent.planner_llm = planner_llm pipeline.agent.solver_llm = solver_llm + if max_context_length_setting: + pipeline.agent.max_context_length = ( + max_context_length_setting // DEFAULT_AGENT_STEPS + ) tools = [] for tool_name in settings[f"{prefix}.tools"]: @@ -377,7 +413,7 @@ class RewooAgentPipeline(BaseReasoning): tool.llm = solver_llm tools.append(tool) pipeline.agent.plugins = tools - pipeline.agent.output_lang = {"en": "English", "ja": "Japanese"}.get( + pipeline.agent.output_lang = SUPPORTED_LANGUAGE_MAP.get( settings["reasoning.lang"], "English" ) pipeline.agent.prompt_template["Planner"] = PromptTemplate( @@ -413,6 +449,7 @@ class RewooAgentPipeline(BaseReasoning): "value": llm, "component": "dropdown", "choices": llm_choices, + "special_type": "llm", "info": ( "The language model to use for planning. " "This model will generate a plan based on the " @@ -424,6 +461,7 @@ class RewooAgentPipeline(BaseReasoning): "value": llm, "component": "dropdown", "choices": llm_choices, + "special_type": "llm", "info": ( "The language model to use for solving. " "This model will generate the answer based on the " @@ -457,6 +495,10 @@ class RewooAgentPipeline(BaseReasoning): "id": "ReWOO", "name": "ReWOO Agent", "description": ( - "Implementing ReWOO paradigm " "https://arxiv.org/pdf/2305.18323.pdf" + "Implementing ReWOO paradigm: https://arxiv.org/abs/2305.18323. " + "The ReWOO agent makes a step by step plan in the first stage, " + "then solves each step in the second stage. The agent can use " + "external tools to help in the reasoning process. Once all stages " + "are completed, the agent will summarize the answer." ), } diff --git a/libs/ktem/ktem/reasoning/simple.py b/libs/ktem/ktem/reasoning/simple.py index 5118b8f..8244690 100644 --- a/libs/ktem/ktem/reasoning/simple.py +++ b/libs/ktem/ktem/reasoning/simple.py @@ -1,14 +1,20 @@ -import asyncio import html import logging -import re +import threading from collections import defaultdict +from difflib import SequenceMatcher from functools import partial from typing import Generator +import numpy as np import tiktoken from ktem.llms.manager import llms +from ktem.reasoning.prompt_optimization import ( + DecomposeQuestionPipeline, + RewriteQuestionPipeline, +) from ktem.utils.render import Render +from theflow.settings import settings as flowsettings from kotaemon.base import ( AIMessage, @@ -22,8 +28,8 @@ from kotaemon.base import ( from kotaemon.indices.qa.citation import CitationPipeline from kotaemon.indices.splitters import TokenSplitter from kotaemon.llms import ChatLLM, PromptTemplate -from kotaemon.loaders.utils.gpt4v import generate_gpt4v, stream_gpt4v +from ..utils import SUPPORTED_LANGUAGE_MAP from .base import BaseReasoning logger = logging.getLogger(__name__) @@ -32,6 +38,22 @@ EVIDENCE_MODE_TEXT = 0 EVIDENCE_MODE_TABLE = 1 EVIDENCE_MODE_CHATBOT = 2 EVIDENCE_MODE_FIGURE = 3 +MAX_IMAGES = 10 + + +def find_text(search_span, context): + sentence_list = search_span.split("\n") + matches = [] + # don't search for small text + if len(search_span) > 5: + for sentence in sentence_list: + match = SequenceMatcher( + None, sentence, context, autojunk=False + ).find_longest_match() + if match.size > len(sentence) * 0.35: + matches.append((match.b, match.b + match.size)) + + return matches class PrepareEvidencePipeline(BaseComponent): @@ -44,21 +66,29 @@ class PrepareEvidencePipeline(BaseComponent): chunk of text into smaller ones. The first one will be retained. """ - trim_func: TokenSplitter = TokenSplitter.withx( - chunk_size=3000, - chunk_overlap=0, - separator=" ", - tokenizer=partial( - tiktoken.encoding_for_model("gpt-3.5-turbo").encode, - allowed_special=set(), - disallowed_special="all", - ), - ) + max_context_length: int = 32000 + trim_func: TokenSplitter | None = None def run(self, docs: list[RetrievedDocument]) -> Document: evidence = "" + images = [] table_found = 0 - evidence_mode = EVIDENCE_MODE_TEXT + evidence_modes = [] + + evidence_trim_func = ( + self.trim_func + if self.trim_func + else TokenSplitter( + chunk_size=self.max_context_length, + chunk_overlap=0, + separator=" ", + tokenizer=partial( + tiktoken.encoding_for_model("gpt-3.5-turbo").encode, + allowed_special=set(), + disallowed_special="all", + ), + ) + ) for _id, retrieved_item in enumerate(docs): retrieved_content = "" @@ -67,9 +97,11 @@ class PrepareEvidencePipeline(BaseComponent): if page: source += f" (Page {page})" if retrieved_item.metadata.get("type", "") == "table": - evidence_mode = EVIDENCE_MODE_TABLE + evidence_modes.append(EVIDENCE_MODE_TABLE) if table_found < 5: - retrieved_content = retrieved_item.metadata.get("table_origin", "") + retrieved_content = retrieved_item.metadata.get( + "table_origin", retrieved_item.text + ) if retrieved_content not in evidence: table_found += 1 evidence += ( @@ -78,7 +110,7 @@ class PrepareEvidencePipeline(BaseComponent): + "\n
" ) elif retrieved_item.metadata.get("type", "") == "chatbot": - evidence_mode = EVIDENCE_MODE_CHATBOT + evidence_modes.append(EVIDENCE_MODE_CHATBOT) retrieved_content = retrieved_item.metadata["window"] evidence += ( f"
Chatbot scenario from {filename} (Row {page})\n" @@ -86,15 +118,16 @@ class PrepareEvidencePipeline(BaseComponent): + "\n
" ) elif retrieved_item.metadata.get("type", "") == "image": - evidence_mode = EVIDENCE_MODE_FIGURE + evidence_modes.append(EVIDENCE_MODE_FIGURE) retrieved_content = retrieved_item.metadata.get("image_origin", "") retrieved_caption = html.escape(retrieved_item.get_content()) evidence += ( f"
Figure from {source}\n" - + f"" + "\n
" ) + images.append(retrieved_content) else: if "window" in retrieved_item.metadata: retrieved_content = retrieved_item.metadata["window"] @@ -108,27 +141,27 @@ class PrepareEvidencePipeline(BaseComponent): + " \n
" ) - print("Retrieved #{}: {}".format(_id, retrieved_content)) - print(retrieved_item.metadata) - print("Score", retrieved_item.metadata.get("relevance_score", None)) + # resolve evidence mode + evidence_mode = EVIDENCE_MODE_TEXT + if EVIDENCE_MODE_FIGURE in evidence_modes: + evidence_mode = EVIDENCE_MODE_FIGURE + elif EVIDENCE_MODE_TABLE in evidence_modes: + evidence_mode = EVIDENCE_MODE_TABLE - if evidence_mode != EVIDENCE_MODE_FIGURE: - # trim context by trim_len - print("len (original)", len(evidence)) - if evidence: - texts = self.trim_func([Document(text=evidence)]) - evidence = texts[0].text - print("len (trimmed)", len(evidence)) + # trim context by trim_len + print("len (original)", len(evidence)) + if evidence: + texts = evidence_trim_func([Document(text=evidence)]) + evidence = texts[0].text + print("len (trimmed)", len(evidence)) - print(f"PrepareEvidence with input {docs}\nOutput: {evidence}\n") - - return Document(content=(evidence_mode, evidence)) + return Document(content=(evidence_mode, evidence, images)) DEFAULT_QA_TEXT_PROMPT = ( - "Use the following pieces of context to answer the question at the end. " + "Use the following pieces of context to answer the question at the end in detail with clear explanation. " # noqa: E501 "If you don't know the answer, just say that you don't know, don't try to " - "make up an answer. Keep the answer as concise as possible. Give answer in " + "make up an answer. Give answer in " "{lang}.\n\n" "{context}\n" "Question: {question}\n" @@ -136,15 +169,15 @@ DEFAULT_QA_TEXT_PROMPT = ( ) DEFAULT_QA_TABLE_PROMPT = ( - "List all rows (row number) from the table context that related to the question, " - "then provide detail answer with clear explanation and citations. " + "Use the given context: texts, tables, and figures below to answer the question, " + "then provide answer with clear explanation." "If you don't know the answer, just say that you don't know, " "don't try to make up an answer. Give answer in {lang}.\n\n" "Context:\n" "{context}\n" "Question: {question}\n" "Helpful Answer:" -) +) # noqa DEFAULT_QA_CHATBOT_PROMPT = ( "Pick the most suitable chatbot scenarios to answer the question at the end, " @@ -155,7 +188,7 @@ DEFAULT_QA_CHATBOT_PROMPT = ( "{context}\n" "Question: {question}\n" "Answer:" -) +) # noqa DEFAULT_QA_FIGURE_PROMPT = ( "Use the given context: texts, tables, and figures below to answer the question. " @@ -165,7 +198,7 @@ DEFAULT_QA_FIGURE_PROMPT = ( "{context}\n" "Question: {question}\n" "Answer: " -) +) # noqa DEFAULT_REWRITE_PROMPT = ( "Given the following question, rephrase and expand it " @@ -174,7 +207,9 @@ DEFAULT_REWRITE_PROMPT = ( "Give answer in {lang}\n" "Original question: {question}\n" "Rephrased question: " -) +) # noqa + +CONTEXT_RELEVANT_WARNING_SCORE = 0.7 class AnswerWithContextPipeline(BaseComponent): @@ -193,7 +228,8 @@ class AnswerWithContextPipeline(BaseComponent): """ llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) - vlm_endpoint: str = "" + vlm_endpoint: str = getattr(flowsettings, "KH_VLM_ENDPOINT", "") + use_multimodal: bool = getattr(flowsettings, "KH_REASONINGS_USE_MULTIMODAL", True) citation_pipeline: CitationPipeline = Node( default_callback=lambda _: CitationPipeline(llm=llms.get_default()) ) @@ -210,33 +246,25 @@ class AnswerWithContextPipeline(BaseComponent): def get_prompt(self, question, evidence, evidence_mode: int): """Prepare the prompt and other information for LLM""" - images = [] - if evidence_mode == EVIDENCE_MODE_TEXT: prompt_template = PromptTemplate(self.qa_template) elif evidence_mode == EVIDENCE_MODE_TABLE: prompt_template = PromptTemplate(self.qa_table_template) elif evidence_mode == EVIDENCE_MODE_FIGURE: - prompt_template = PromptTemplate(self.qa_figure_template) + if self.use_multimodal: + prompt_template = PromptTemplate(self.qa_figure_template) + else: + prompt_template = PromptTemplate(self.qa_template) else: prompt_template = PromptTemplate(self.qa_chatbot_template) - if evidence_mode == EVIDENCE_MODE_FIGURE: - # isolate image from evidence - evidence, images = self.extract_evidence_images(evidence) - prompt = prompt_template.populate( - context=evidence, - question=question, - lang=self.lang, - ) - else: - prompt = prompt_template.populate( - context=evidence, - question=question, - lang=self.lang, - ) + prompt = prompt_template.populate( + context=evidence, + question=question, + lang=self.lang, + ) - return prompt, images + return prompt, evidence def run( self, question: str, evidence: str, evidence_mode: int = 0, **kwargs @@ -244,37 +272,22 @@ class AnswerWithContextPipeline(BaseComponent): return self.invoke(question, evidence, evidence_mode, **kwargs) def invoke( - self, question: str, evidence: str, evidence_mode: int = 0, **kwargs + self, + question: str, + evidence: str, + evidence_mode: int = 0, + images: list[str] = [], + **kwargs, ) -> Document: - history = kwargs.get("history", []) - prompt, images = self.get_prompt(question, evidence, evidence_mode) - - output = "" - if evidence_mode == EVIDENCE_MODE_FIGURE: - output = generate_gpt4v(self.vlm_endpoint, images, prompt, max_tokens=768) - else: - messages = [] - if self.system_prompt: - messages.append(SystemMessage(content=self.system_prompt)) - for human, ai in history[-self.n_last_interactions :]: - messages.append(HumanMessage(content=human)) - messages.append(AIMessage(content=ai)) - messages.append(HumanMessage(content=prompt)) - output = self.llm(messages).text - - # retrieve the citation - citation = None - if evidence and self.enable_citation: - citation = self.citation_pipeline.invoke( - context=evidence, question=question - ) - - answer = Document(text=output, metadata={"citation": citation}) - - return answer + raise NotImplementedError async def ainvoke( # type: ignore - self, question: str, evidence: str, evidence_mode: int = 0, **kwargs + self, + question: str, + evidence: str, + evidence_mode: int = 0, + images: list[str] = [], + **kwargs, ) -> Document: """Answer the question based on the evidence @@ -298,127 +311,94 @@ class AnswerWithContextPipeline(BaseComponent): (determined by retrieval pipeline) evidence_mode: the mode of evidence, 0 for text, 1 for table, 2 for chatbot """ - history = kwargs.get("history", []) - prompt, images = self.get_prompt(question, evidence, evidence_mode) - - citation_task = None - if evidence and self.enable_citation: - citation_task = asyncio.create_task( - self.citation_pipeline.ainvoke(context=evidence, question=question) - ) - print("Citation task created") - - output = "" - if evidence_mode == EVIDENCE_MODE_FIGURE: - for text in stream_gpt4v(self.vlm_endpoint, images, prompt, max_tokens=768): - output += text - self.report_output(Document(channel="chat", content=text)) - await asyncio.sleep(0) - else: - messages = [] - if self.system_prompt: - messages.append(SystemMessage(content=self.system_prompt)) - for human, ai in history[-self.n_last_interactions :]: - messages.append(HumanMessage(content=human)) - messages.append(AIMessage(content=ai)) - messages.append(HumanMessage(content=prompt)) - - try: - # try streaming first - print("Trying LLM streaming") - for text in self.llm.stream(messages): - output += text.text - self.report_output(Document(content=text.text, channel="chat")) - await asyncio.sleep(0) - except NotImplementedError: - print("Streaming is not supported, falling back to normal processing") - output = self.llm(messages).text - self.report_output(Document(content=output, channel="chat")) - - # retrieve the citation - print("Waiting for citation task") - if citation_task is not None: - citation = await citation_task - else: - citation = None - - answer = Document(text=output, metadata={"citation": citation}) - - return answer + raise NotImplementedError def stream( # type: ignore - self, question: str, evidence: str, evidence_mode: int = 0, **kwargs + self, + question: str, + evidence: str, + evidence_mode: int = 0, + images: list[str] = [], + **kwargs, ) -> Generator[Document, None, Document]: history = kwargs.get("history", []) - prompt, images = self.get_prompt(question, evidence, evidence_mode) - - output = "" - if evidence_mode == EVIDENCE_MODE_FIGURE: - for text in stream_gpt4v(self.vlm_endpoint, images, prompt, max_tokens=768): - output += text - yield Document(channel="chat", content=text) + print(f"Got {len(images)} images") + # check if evidence exists, use QA prompt + if evidence: + prompt, evidence = self.get_prompt(question, evidence, evidence_mode) else: - messages = [] - if self.system_prompt: - messages.append(SystemMessage(content=self.system_prompt)) - for human, ai in history[-self.n_last_interactions :]: - messages.append(HumanMessage(content=human)) - messages.append(AIMessage(content=ai)) - messages.append(HumanMessage(content=prompt)) - - try: - # try streaming first - print("Trying LLM streaming") - for text in self.llm.stream(messages): - output += text.text - yield Document(channel="chat", content=text.text) - except NotImplementedError: - print("Streaming is not supported, falling back to normal processing") - output = self.llm(messages).text - yield Document(channel="chat", content=output) + prompt = question # retrieve the citation citation = None - if evidence and self.enable_citation: - citation = self.citation_pipeline.invoke( - context=evidence, question=question - ) - answer = Document(text=output, metadata={"citation": citation}) + def citation_call(): + nonlocal citation + citation = self.citation_pipeline(context=evidence, question=question) + + if evidence and self.enable_citation: + # execute function call in thread + citation_thread = threading.Thread(target=citation_call) + citation_thread.start() + else: + citation_thread = None + + output = "" + logprobs = [] + + messages = [] + if self.system_prompt: + messages.append(SystemMessage(content=self.system_prompt)) + for human, ai in history[-self.n_last_interactions :]: + messages.append(HumanMessage(content=human)) + messages.append(AIMessage(content=ai)) + + if self.use_multimodal and evidence_mode == EVIDENCE_MODE_FIGURE: + # create image message: + messages.append( + HumanMessage( + content=[ + {"type": "text", "text": prompt}, + ] + + [ + { + "type": "image_url", + "image_url": {"url": image}, + } + for image in images[:MAX_IMAGES] + ], + ) + ) + else: + # append main prompt + messages.append(HumanMessage(content=prompt)) + + try: + # try streaming first + print("Trying LLM streaming") + for out_msg in self.llm.stream(messages): + output += out_msg.text + logprobs += out_msg.logprobs + yield Document(channel="chat", content=out_msg.text) + except NotImplementedError: + print("Streaming is not supported, falling back to normal processing") + output = self.llm(messages).text + yield Document(channel="chat", content=output) + + if logprobs: + qa_score = np.exp(np.average(logprobs)) + else: + qa_score = None + + if citation_thread: + citation_thread.join() + answer = Document( + text=output, + metadata={"citation": citation, "qa_score": qa_score}, + ) return answer - def extract_evidence_images(self, evidence: str): - """Util function to extract and isolate images from context/evidence""" - image_pattern = r"src='(data:image\/[^;]+;base64[^']+)'" - matches = re.findall(image_pattern, evidence) - context = re.sub(image_pattern, "", evidence) - return context, matches - - -class RewriteQuestionPipeline(BaseComponent): - """Rewrite user question - - Args: - llm: the language model to rewrite question - rewrite_template: the prompt template for llm to paraphrase a text input - lang: the language of the answer. Currently support English and Japanese - """ - - llm: ChatLLM = Node(default_callback=lambda _: llms.get_default()) - rewrite_template: str = DEFAULT_REWRITE_PROMPT - - lang: str = "English" - - def run(self, question: str) -> Document: # type: ignore - prompt_template = PromptTemplate(self.rewrite_template) - prompt = prompt_template.populate(question=question, lang=self.lang) - messages = [ - SystemMessage(content="You are a helpful assistant"), - HumanMessage(content=prompt), - ] - return self.llm(messages) - class AddQueryContextPipeline(BaseComponent): @@ -470,50 +450,72 @@ class FullQAPipeline(BaseReasoning): class Config: allow_extra = True + # configuration parameters + trigger_context: int = 150 + use_rewrite: bool = False + retrievers: list[BaseComponent] evidence_pipeline: PrepareEvidencePipeline = PrepareEvidencePipeline.withx() answering_pipeline: AnswerWithContextPipeline = AnswerWithContextPipeline.withx() - rewrite_pipeline: RewriteQuestionPipeline = RewriteQuestionPipeline.withx() + rewrite_pipeline: RewriteQuestionPipeline | None = None add_query_context: AddQueryContextPipeline = AddQueryContextPipeline.withx() - trigger_context: int = 150 - use_rewrite: bool = False def retrieve( self, message: str, history: list ) -> tuple[list[RetrievedDocument], list[Document]]: """Retrieve the documents based on the message""" - if len(message) < self.trigger_context: - # prefer adding context for short user questions, avoid adding context for - # long questions, as they are likely to contain enough information - # plus, avoid the situation where the original message is already too long - # for the model to handle - query = self.add_query_context(message, history).content - else: - query = message - print(f"Rewritten query: {query}") + # if len(message) < self.trigger_context: + # # prefer adding context for short user questions, avoid adding context for + # # long questions, as they are likely to contain enough information + # # plus, avoid the situation where the original message is already too long + # # for the model to handle + # query = self.add_query_context(message, history).content + # else: + # query = message + # print(f"Rewritten query: {query}") + query = None if not query: - return [], [] + # TODO: previously return [], [] because we think this message as something + # like "Hello", "I need help"... + query = message docs, doc_ids = [], [] - for retriever in self.retrievers: - for doc in retriever(text=query): + plot_docs = [] + + for idx, retriever in enumerate(self.retrievers): + retriever_node = self._prepare_child(retriever, f"retriever_{idx}") + retriever_docs = retriever_node(text=query) + + retriever_docs_text = [] + retriever_docs_plot = [] + + for doc in retriever_docs: + if doc.metadata.get("type", "") == "plot": + retriever_docs_plot.append(doc) + else: + retriever_docs_text.append(doc) + + for doc in retriever_docs_text: if doc.doc_id not in doc_ids: docs.append(doc) doc_ids.append(doc.doc_id) - info = [] - for doc in docs: - info.append( - Document( - channel="info", - content=Render.collapsible( - header=doc.metadata["file_name"], - content=Render.table(doc.text), - open=True, - ), - ) + plot_docs.extend(retriever_docs_plot) + + info = [ + Document( + channel="info", + content=Render.collapsible_with_header(doc, open_collapsible=True), ) + for doc in docs + ] + [ + Document( + channel="plot", + content=doc.metadata.get("data", ""), + ) + for doc in plot_docs + ] return docs, info @@ -521,153 +523,172 @@ class FullQAPipeline(BaseReasoning): """Prepare the citations to show on the UI""" with_citation, without_citation = [], [] spans = defaultdict(list) + has_llm_score = any("llm_trulens_score" in doc.metadata for doc in docs) - if answer.metadata["citation"] is not None: + if answer.metadata["citation"] and answer.metadata["citation"].answer: for fact_with_evidence in answer.metadata["citation"].answer: for quote in fact_with_evidence.substring_quote: + matched_excerpts = [] for doc in docs: - start_idx = doc.text.find(quote) - if start_idx == -1: - continue + matches = find_text(quote, doc.text) - end_idx = start_idx + len(quote) - - current_idx = start_idx - if "|" not in doc.text[start_idx:end_idx]: - spans[doc.doc_id].append( - {"start": start_idx, "end": end_idx} - ) - else: - while doc.text[current_idx:end_idx].find("|") != -1: - match_idx = doc.text[current_idx:end_idx].find("|") + for start, end in matches: + if "|" not in doc.text[start:end]: spans[doc.doc_id].append( { - "start": current_idx, - "end": current_idx + match_idx, + "start": start, + "end": end, } ) - current_idx += match_idx + 2 - if current_idx > end_idx: - break - break + matched_excerpts.append(doc.text[start:end]) + + print("Matched citation:", quote, matched_excerpts), id2docs = {doc.doc_id: doc for doc in docs} not_detected = set(id2docs.keys()) - set(spans.keys()) - for id, ss in spans.items(): + + # render highlight spans + for _id, ss in spans.items(): if not ss: - not_detected.add(id) + not_detected.add(_id) continue + cur_doc = id2docs[_id] + highlight_text = "" + ss = sorted(ss, key=lambda x: x["start"]) - text = id2docs[id].text[: ss[0]["start"]] + text = cur_doc.text[: ss[0]["start"]] for idx, span in enumerate(ss): - text += Render.highlight(id2docs[id].text[span["start"] : span["end"]]) + to_highlight = cur_doc.text[span["start"] : span["end"]] + if len(to_highlight) > len(highlight_text): + highlight_text = to_highlight + text += Render.highlight(to_highlight) if idx < len(ss) - 1: - text += id2docs[id].text[span["end"] : ss[idx + 1]["start"]] - text += id2docs[id].text[ss[-1]["end"] :] + text += cur_doc.text[span["end"] : ss[idx + 1]["start"]] + text += cur_doc.text[ss[-1]["end"] :] + # add to display list with_citation.append( Document( channel="info", - content=Render.collapsible( - header=id2docs[id].metadata["file_name"], - content=Render.table(text), - open=True, + content=Render.collapsible_with_header_score( + cur_doc, + override_text=text, + highlight_text=highlight_text, + open_collapsible=True, ), ) ) - without_citation = [ - Document( - channel="info", - content=Render.collapsible( - header=id2docs[id].metadata["file_name"], - content=Render.table(id2docs[id].text), - open=False, - ), - ) - for id in list(not_detected) - ] + print("Got {} cited docs".format(len(with_citation))) + sorted_not_detected_items_with_scores = [ + (id_, id2docs[id_].metadata.get("llm_trulens_score", 0.0)) + for id_ in not_detected + ] + sorted_not_detected_items_with_scores.sort(key=lambda x: x[1], reverse=True) + + for id_, _ in sorted_not_detected_items_with_scores: + doc = id2docs[id_] + doc_score = doc.metadata.get("llm_trulens_score", 0.0) + is_open = not has_llm_score or ( + doc_score > CONTEXT_RELEVANT_WARNING_SCORE and len(with_citation) == 0 + ) + without_citation.append( + Document( + channel="info", + content=Render.collapsible_with_header_score( + doc, open_collapsible=is_open + ), + ) + ) return with_citation, without_citation + def show_citations(self, answer, docs): + # show the evidence + with_citation, without_citation = self.prepare_citations(answer, docs) + if not with_citation and not without_citation: + yield Document(channel="info", content="
No evidence found.
") + else: + # clear the Info panel + max_llm_rerank_score = max( + doc.metadata.get("llm_trulens_score", 0.0) for doc in docs + ) + has_llm_score = any("llm_trulens_score" in doc.metadata for doc in docs) + # clear previous info + yield Document(channel="info", content=None) + + # yield warning message + if has_llm_score and max_llm_rerank_score < CONTEXT_RELEVANT_WARNING_SCORE: + yield Document( + channel="info", + content=( + "
WARNING! Context relevance score is low. " + "Double check the model answer for correctness.
" + ), + ) + + # show QA score + qa_score = ( + round(answer.metadata["qa_score"], 2) + if answer.metadata.get("qa_score") + else None + ) + if qa_score: + yield Document( + channel="info", + content=f"
Answer confidence: {qa_score}
", + ) + + yield from with_citation + if without_citation: + yield from without_citation + async def ainvoke( # type: ignore self, message: str, conv_id: str, history: list, **kwargs # type: ignore ) -> Document: # type: ignore - if self.use_rewrite: - rewrite = await self.rewrite_pipeline(question=message) - message = rewrite.text - - docs, infos = self.retrieve(message, history) - for _ in infos: - self.report_output(_) - await asyncio.sleep(0.1) - - evidence_mode, evidence = self.evidence_pipeline(docs).content - answer = await self.answering_pipeline( - question=message, - history=history, - evidence=evidence, - evidence_mode=evidence_mode, - conv_id=conv_id, - **kwargs, - ) - - # show the evidence - with_citation, without_citation = self.prepare_citations(answer, docs) - if not with_citation and not without_citation: - self.report_output(Document(channel="info", content="No evidence found.\n")) - else: - self.report_output(Document(channel="info", content=None)) - for _ in with_citation: - self.report_output(_) - if without_citation: - self.report_output( - Document( - channel="info", - content="Retrieved segments without matching evidence:\n", - ) - ) - for _ in without_citation: - self.report_output(_) - - self.report_output(None) - return answer + raise NotImplementedError def stream( # type: ignore self, message: str, conv_id: str, history: list, **kwargs # type: ignore ) -> Generator[Document, None, Document]: - if self.use_rewrite: + if self.use_rewrite and self.rewrite_pipeline: + print("Chosen rewrite pipeline", self.rewrite_pipeline) message = self.rewrite_pipeline(question=message).text + print("Rewrite result", message) + print(f"Retrievers {self.retrievers}") # should populate the context docs, infos = self.retrieve(message, history) - for _ in infos: - yield _ + print(f"Got {len(docs)} retrieved documents") + yield from infos + + evidence_mode, evidence, images = self.evidence_pipeline(docs).content + + def generate_relevant_scores(): + nonlocal docs + docs = self.retrievers[0].generate_relevant_scores(message, docs) + + # generate relevant score using + if evidence and self.retrievers: + scoring_thread = threading.Thread(target=generate_relevant_scores) + scoring_thread.start() + else: + scoring_thread = None - evidence_mode, evidence = self.evidence_pipeline(docs).content answer = yield from self.answering_pipeline.stream( question=message, history=history, evidence=evidence, evidence_mode=evidence_mode, + images=images, conv_id=conv_id, **kwargs, ) # show the evidence - with_citation, without_citation = self.prepare_citations(answer, docs) - if not with_citation and not without_citation: - yield Document(channel="info", content="No evidence found.\n") - else: - yield Document(channel="info", content=None) - for _ in with_citation: - yield _ - if without_citation: - yield Document( - channel="info", - content="Retrieved segments without matching evidence:\n", - ) - for _ in without_citation: - yield _ + if scoring_thread: + scoring_thread.join() + + yield from self.show_citations(answer, docs) return answer @@ -679,12 +700,21 @@ class FullQAPipeline(BaseReasoning): settings: the settings for the pipeline retrievers: the retrievers to use """ - prefix = f"reasoning.options.{cls.get_info()['id']}" - pipeline = cls(retrievers=retrievers) + max_context_length_setting = settings.get("reasoning.max_context_length", 32000) + pipeline = cls( + retrievers=retrievers, + rewrite_pipeline=RewriteQuestionPipeline(), + ) + + prefix = f"reasoning.options.{cls.get_info()['id']}" llm_name = settings.get(f"{prefix}.llm", None) llm = llms.get(llm_name, llms.get_default()) + # prepare evidence pipeline configuration + evidence_pipeline = pipeline.evidence_pipeline + evidence_pipeline.max_context_length = max_context_length_setting + # answering pipeline configuration answer_pipeline = pipeline.answering_pipeline answer_pipeline.llm = llm @@ -693,7 +723,7 @@ class FullQAPipeline(BaseReasoning): answer_pipeline.enable_citation = settings[f"{prefix}.highlight_citation"] answer_pipeline.system_prompt = settings[f"{prefix}.system_prompt"] answer_pipeline.qa_template = settings[f"{prefix}.qa_prompt"] - answer_pipeline.lang = {"en": "English", "ja": "Japanese"}.get( + answer_pipeline.lang = SUPPORTED_LANGUAGE_MAP.get( settings["reasoning.lang"], "English" ) @@ -704,10 +734,11 @@ class FullQAPipeline(BaseReasoning): pipeline.trigger_context = settings[f"{prefix}.trigger_context"] pipeline.use_rewrite = states.get("app", {}).get("regen", False) - pipeline.rewrite_pipeline.llm = llm - pipeline.rewrite_pipeline.lang = {"en": "English", "ja": "Japanese"}.get( - settings["reasoning.lang"], "English" - ) + if pipeline.rewrite_pipeline: + pipeline.rewrite_pipeline.llm = llm + pipeline.rewrite_pipeline.lang = SUPPORTED_LANGUAGE_MAP.get( + settings["reasoning.lang"], "English" + ) return pipeline @classmethod @@ -727,6 +758,7 @@ class FullQAPipeline(BaseReasoning): "value": llm, "component": "dropdown", "choices": choices, + "special_type": "llm", "info": ( "The language model to use for generating the answer. If None, " "the application default language model will be used." @@ -734,7 +766,7 @@ class FullQAPipeline(BaseReasoning): }, "highlight_citation": { "name": "Highlight Citation", - "value": False, + "value": True, "component": "checkbox", }, "system_prompt": { @@ -767,5 +799,163 @@ class FullQAPipeline(BaseReasoning): return { "id": "simple", "name": "Simple QA", - "description": "Simple QA pipeline", + "description": ( + "Simple RAG-based question answering pipeline. This pipeline can " + "perform both keyword search and similarity search to retrieve the " + "context. After that it includes that context to generate the answer." + ), + } + + +class FullDecomposeQAPipeline(FullQAPipeline): + def answer_sub_questions( + self, messages: list, conv_id: str, history: list, **kwargs + ): + output_str = "" + for idx, message in enumerate(messages): + yield Document( + channel="chat", + content=f"
Sub-question {idx + 1}" + f"
{message}
Answer
", + ) + # should populate the context + docs, infos = self.retrieve(message, history) + print(f"Got {len(docs)} retrieved documents") + + yield from infos + + evidence_mode, evidence, images = self.evidence_pipeline(docs).content + answer = yield from self.answering_pipeline.stream( + question=message, + history=history, + evidence=evidence, + evidence_mode=evidence_mode, + images=images, + conv_id=conv_id, + **kwargs, + ) + + output_str += ( + f"Sub-question {idx + 1}-th: '{message}'\nAnswer: '{answer.text}'\n\n" + ) + + return output_str + + def stream( # type: ignore + self, message: str, conv_id: str, history: list, **kwargs # type: ignore + ) -> Generator[Document, None, Document]: + sub_question_answer_output = "" + if self.rewrite_pipeline: + print("Chosen rewrite pipeline", self.rewrite_pipeline) + result = self.rewrite_pipeline(question=message) + print("Rewrite result", result) + if isinstance(result, Document): + message = result.text + elif ( + isinstance(result, list) + and len(result) > 0 + and isinstance(result[0], Document) + ): + yield Document( + channel="chat", + content="

Sub questions and their answers

", + ) + sub_question_answer_output = yield from self.answer_sub_questions( + [r.text for r in result], conv_id, history, **kwargs + ) + + yield Document( + channel="chat", + content=f"

Main question

{message}
Answer
", + ) + + # should populate the context + docs, infos = self.retrieve(message, history) + print(f"Got {len(docs)} retrieved documents") + yield from infos + + evidence_mode, evidence, images = self.evidence_pipeline(docs).content + answer = yield from self.answering_pipeline.stream( + question=message, + history=history, + evidence=evidence + "\n" + sub_question_answer_output, + evidence_mode=evidence_mode, + images=images, + conv_id=conv_id, + **kwargs, + ) + + # show the evidence + with_citation, without_citation = self.prepare_citations(answer, docs) + if not with_citation and not without_citation: + yield Document(channel="info", content="
No evidence found.
") + else: + yield Document(channel="info", content=None) + yield from with_citation + yield from without_citation + + return answer + + @classmethod + def get_user_settings(cls) -> dict: + user_settings = super().get_user_settings() + user_settings["decompose_prompt"] = { + "name": "Decompose Prompt", + "value": DecomposeQuestionPipeline.DECOMPOSE_SYSTEM_PROMPT_TEMPLATE, + } + return user_settings + + @classmethod + def get_pipeline(cls, settings, states, retrievers): + """Get the reasoning pipeline + + Args: + settings: the settings for the pipeline + retrievers: the retrievers to use + """ + prefix = f"reasoning.options.{cls.get_info()['id']}" + pipeline = cls( + retrievers=retrievers, + rewrite_pipeline=DecomposeQuestionPipeline( + prompt_template=settings.get(f"{prefix}.decompose_prompt") + ), + ) + + llm_name = settings.get(f"{prefix}.llm", None) + llm = llms.get(llm_name, llms.get_default()) + + # answering pipeline configuration + answer_pipeline = pipeline.answering_pipeline + answer_pipeline.llm = llm + answer_pipeline.citation_pipeline.llm = llm + answer_pipeline.n_last_interactions = settings[f"{prefix}.n_last_interactions"] + answer_pipeline.enable_citation = settings[f"{prefix}.highlight_citation"] + answer_pipeline.system_prompt = settings[f"{prefix}.system_prompt"] + answer_pipeline.qa_template = settings[f"{prefix}.qa_prompt"] + answer_pipeline.lang = SUPPORTED_LANGUAGE_MAP.get( + settings["reasoning.lang"], "English" + ) + + pipeline.add_query_context.llm = llm + pipeline.add_query_context.n_last_interactions = settings[ + f"{prefix}.n_last_interactions" + ] + + pipeline.trigger_context = settings[f"{prefix}.trigger_context"] + pipeline.use_rewrite = states.get("app", {}).get("regen", False) + if pipeline.rewrite_pipeline: + pipeline.rewrite_pipeline.llm = llm + return pipeline + + @classmethod + def get_info(cls) -> dict: + return { + "id": "complex", + "name": "Complex QA", + "description": ( + "Use multi-step reasoning to decompose a complex question into " + "multiple sub-questions. This pipeline can " + "perform both keyword search and similarity search to retrieve the " + "context. After that it includes that context to generate the answer." + ), } diff --git a/libs/ktem/ktem/settings.py b/libs/ktem/ktem/settings.py index dec1d4c..89f5c65 100644 --- a/libs/ktem/ktem/settings.py +++ b/libs/ktem/ktem/settings.py @@ -19,6 +19,7 @@ class SettingItem(BaseModel): choices: list = Field(default_factory=list) metadata: dict = Field(default_factory=dict) component: str = "text" + special_type: str = "" class BaseSettingGroup(BaseModel): @@ -55,6 +56,9 @@ class BaseSettingGroup(BaseModel): option = self.options[option_id] return option.get_setting_item(sub_path) + def __bool__(self): + return bool(self.settings) or bool(self.options) + class SettingReasoningGroup(BaseSettingGroup): def _get_options(self) -> dict: diff --git a/libs/ktem/ktem/utils/__init__.py b/libs/ktem/ktem/utils/__init__.py index e69de29..009c60a 100644 --- a/libs/ktem/ktem/utils/__init__.py +++ b/libs/ktem/ktem/utils/__init__.py @@ -0,0 +1,3 @@ +from .lang import SUPPORTED_LANGUAGE_MAP + +__all__ = ["SUPPORTED_LANGUAGE_MAP"] diff --git a/libs/ktem/ktem/utils/conversation.py b/libs/ktem/ktem/utils/conversation.py new file mode 100644 index 0000000..2550aa7 --- /dev/null +++ b/libs/ktem/ktem/utils/conversation.py @@ -0,0 +1,20 @@ +def sync_retrieval_n_message( + messages: list[list[str]], + retrievals: list[str], +) -> list[str]: + """Ensure len of messages history and retrieval history are equal + Empty string/Truncate will be used in case any difference exist + """ + n_message = len(messages) # include previous history + n_retrieval = min(n_message, len(retrievals)) + + diff = n_message - n_retrieval + retrievals = retrievals[:n_retrieval] + ["" for _ in range(diff)] + + assert len(retrievals) == n_message + + return retrievals + + +if __name__ == "__main__": + print(sync_retrieval_n_message([[""], [""], [""]], [])) diff --git a/libs/ktem/ktem/utils/lang.py b/libs/ktem/ktem/utils/lang.py new file mode 100644 index 0000000..af6601d --- /dev/null +++ b/libs/ktem/ktem/utils/lang.py @@ -0,0 +1 @@ +SUPPORTED_LANGUAGE_MAP = {"en": "English", "ja": "Japanese", "vi": "Vietnamese"} diff --git a/libs/ktem/ktem/utils/render.py b/libs/ktem/ktem/utils/render.py index 5890d33..b1695aa 100644 --- a/libs/ktem/ktem/utils/render.py +++ b/libs/ktem/ktem/utils/render.py @@ -1,4 +1,36 @@ +import os.path + import markdown +from fast_langdetect import detect + +from kotaemon.base import RetrievedDocument + + +def is_close(val1, val2, tolerance=1e-9): + return abs(val1 - val2) <= tolerance + + +def replace_mardown_header(text: str) -> str: + textlines = text.splitlines() + newlines = [] + for line in textlines: + if line.startswith("#"): + line = "" + line.replace("#", "") + "" + if line.startswith("=="): + line = "" + newlines.append(line) + + return "\n".join(newlines) + + +def get_header(doc: RetrievedDocument) -> str: + """Get the header for the document""" + header = "" + if "page_label" in doc.metadata: + header += f" [Page {doc.metadata['page_label']}]" + + header += f" {doc.metadata.get('file_name', '')}" + return header.strip() class Render: @@ -13,9 +45,152 @@ class Render: @staticmethod def table(text: str) -> str: """Render table from markdown format into HTML""" + text = replace_mardown_header(text) return markdown.markdown(text, extensions=["markdown.extensions.tables"]) + @staticmethod + def preview( + html_content: str, + doc: RetrievedDocument, + highlight_text: str | None = None, + ) -> str: + text = doc.content + pdf_path = doc.metadata.get("file_path", "") + + if not os.path.isfile(pdf_path): + print(f"pdf-path: {pdf_path} does not exist") + return html_content + + is_pdf = doc.metadata.get("file_type", "") == "application/pdf" + page_idx = int(doc.metadata.get("page_label", 1)) + + if not is_pdf: + print("Document is not pdf") + return html_content + + if page_idx < 0: + print("Fail to extract page number") + return html_content + + if not highlight_text: + try: + lang = detect(text.replace("\n", " "))["lang"] + print("lang", lang) + if lang not in ["ja", "cn"]: + highlight_words = [ + t[:-1] if t.endswith("-") else t for t in text.split("\n") + ] + highlight_text = highlight_words[0] + phrase = "true" + else: + highlight_text = text.replace("\n", "") + phrase = "false" + + print("highlight_text", highlight_text, phrase) + except Exception as e: + print(e) + highlight_text = text + else: + phrase = "true" + + return f""" + {html_content} + + [Preview] + + """ # noqa + @staticmethod def highlight(text: str) -> str: """Highlight text""" return f"{text}" + + @staticmethod + def image(url: str, text: str = "") -> str: + """Render an image""" + img = f'
' + if text: + caption = f"

{text}

" + return f"
{img}{caption}

" + return img + + @staticmethod + def collapsible_with_header( + doc: RetrievedDocument, + open_collapsible: bool = False, + ) -> str: + header = f"{get_header(doc)}" + if doc.metadata.get("type", "") == "image": + doc_content = Render.image(url=doc.metadata["image_origin"], text=doc.text) + else: + doc_content = Render.table(doc.text) + + return Render.collapsible( + header=Render.preview(header, doc), + content=doc_content, + open=open_collapsible, + ) + + @staticmethod + def collapsible_with_header_score( + doc: RetrievedDocument, + override_text: str | None = None, + highlight_text: str | None = None, + open_collapsible: bool = False, + ) -> str: + """Format the retrieval score and the document""" + # score from doc_store (Elasticsearch) + if is_close(doc.score, -1.0): + vectorstore_score = "" + text_search_str = " (full-text search)
" + else: + vectorstore_score = str(round(doc.score, 2)) + text_search_str = "
" + + llm_reranking_score = ( + round(doc.metadata["llm_trulens_score"], 2) + if doc.metadata.get("llm_trulens_score") is not None + else 0.0 + ) + cohere_reranking_score = ( + round(doc.metadata["cohere_reranking_score"], 2) + if doc.metadata.get("cohere_reranking_score") is not None + else 0.0 + ) + item_type_prefix = doc.metadata.get("type", "") + item_type_prefix = item_type_prefix.capitalize() + if item_type_prefix: + item_type_prefix += " from " + + rendered_score = Render.collapsible( + header=f" Relevance score: {llm_reranking_score}", + content="  Vectorstore score:" + f" {vectorstore_score}" + f"{text_search_str}" + "  LLM relevant score:" + f" {llm_reranking_score}
" + "  Reranking score:" + f" {cohere_reranking_score}
", + ) + + text = doc.text if not override_text else override_text + if doc.metadata.get("type", "") == "image": + rendered_doc_content = Render.image( + url=doc.metadata["image_origin"], + text=text, + ) + else: + rendered_doc_content = Render.table(text) + + rendered_header = Render.preview( + f"{item_type_prefix}{get_header(doc)}" + f" [score: {llm_reranking_score}]", + doc, + highlight_text=highlight_text, + ) + + return Render.collapsible( + header=rendered_header, + content=rendered_score + rendered_doc_content, + open=open_collapsible, + ) diff --git a/libs/ktem/pyproject.toml b/libs/ktem/pyproject.toml index eb6ac12..a995495 100644 --- a/libs/ktem/pyproject.toml +++ b/libs/ktem/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "SQLAlchemy>=2.0.29,<3", "sqlmodel>=0.0.16,<0.1", "tiktoken>=0.6.0,<1", - "gradio>=4.26.0,<5", + "gradio>=4.31.0,<5", "markdown>=3.6,<4", ] authors = [ diff --git a/scripts/migrate/__init__.py b/scripts/migrate/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/migrate/migrate_chroma_db.py b/scripts/migrate/migrate_chroma_db.py new file mode 100644 index 0000000..96fa920 --- /dev/null +++ b/scripts/migrate/migrate_chroma_db.py @@ -0,0 +1,191 @@ +import uuid + +import chromadb +from ktem.index.models import Index +from sqlalchemy import ( + JSON, + Column, + DateTime, + Integer, + String, + UniqueConstraint, + create_engine, + select, +) +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.ext.mutable import MutableDict +from sqlalchemy.orm import Session +from sqlalchemy.sql import func + + +def _init_resource(private: bool = True, id: int = 1): + """Init schemas. Hard-code""" + Base = declarative_base() + + if private: + Source = type( + "Source", + (Base,), + { + "__tablename__": f"index__{id}__source", + "__table_args__": ( + UniqueConstraint("name", "user", name="_name_user_uc"), + ), + "id": Column( + String, + primary_key=True, + default=lambda: str(uuid.uuid4()), + unique=True, + ), + "name": Column(String), + "path": Column(String), + "size": Column(Integer, default=0), + "date_created": Column( + DateTime(timezone=True), server_default=func.now() + ), + "user": Column(Integer, default=1), + "note": Column( + MutableDict.as_mutable(JSON), # type: ignore + default={}, + ), + }, + ) + else: + Source = type( + "Source", + (Base,), + { + "__tablename__": f"index__{id}__source", + "id": Column( + String, + primary_key=True, + default=lambda: str(uuid.uuid4()), + unique=True, + ), + "name": Column(String, unique=True), + "path": Column(String), + "size": Column(Integer, default=0), + "date_created": Column( + DateTime(timezone=True), server_default=func.now() + ), + "user": Column(Integer, default=1), + "note": Column( + MutableDict.as_mutable(JSON), # type: ignore + default={}, + ), + }, + ) + Index = type( + "IndexTable", + (Base,), + { + "__tablename__": f"index__{id}__index", + "id": Column(Integer, primary_key=True, autoincrement=True), + "source_id": Column(String), + "target_id": Column(String), + "relation_type": Column(Integer), + "user": Column(Integer, default=1), + }, + ) + + return {"Source": Source, "Index": Index} + + +def get_chromadb_collection( + db_dir: str = "../ktem_app_data/user_data/vectorstore", + collection_name: str = "index_1", +): + """Extract collection from chromadb""" + client = chromadb.PersistentClient(path=db_dir) + collection = client.get_or_create_collection(collection_name) + + return collection + + +def update_metadata(metadata, file_id): + """Update file_id""" + metadata["file_id"] = file_id + return metadata + + +def migrate_chroma_db( + chroma_db_dir: str, sqlite_path: str, is_private: bool = True, int_index: int = 1 +): + chroma_collection_name = f"index_{int_index}" + + """Update chromadb with metadata.file_id""" + engine = create_engine(sqlite_path) + resource = _init_resource(private=is_private, id=int_index) + print("Load sqlalchemy engine successfully!") + + chroma_db_collection = get_chromadb_collection( + db_dir=chroma_db_dir, collection_name=chroma_collection_name + ) + print( + f"Load chromadb collection: {chroma_collection_name}, " + f"path: {chroma_db_dir} successfully!" + ) + + # Load docs id of user + with Session(engine) as session: + stmt = select(resource["Source"]) + results = session.execute(stmt) + doc_ids = [r[0].id for r in results.all()] + print(f"Retrieve n-docs: {len(doc_ids)}") + print(doc_ids) + + for doc_id in doc_ids: + print("-") + # Find corresponding vector ids + with Session(engine) as session: + stmt = select(resource["Index"]).where( + resource["Index"].relation_type == "vector", + resource["Index"].source_id.in_([doc_id]), + ) + results = session.execute(stmt) + vs_ids = [r[0].target_id for r in results.all()] + + print(f"Got {len(vs_ids)} vs_ids for doc {doc_id}") + + # Update file_id + if len(vs_ids) > 0: + batch = chroma_db_collection.get(ids=vs_ids, include=["metadatas"]) + batch.update( + ids=batch["ids"], + metadatas=[ + update_metadata(metadata, doc_id) for metadata in batch["metadatas"] + ], + ) + + # Assert file_id. Skip + print(f"doc-{doc_id} got updated") + + +def main(chroma_db_dir: str, sqlite_path: str): + engine = create_engine(sqlite_path) + + with Session(engine) as session: + stmt = select(Index) + + results = session.execute(stmt) + file_indices = [r[0] for r in results.all()] + + for file_index in file_indices: + _id = file_index.id + _is_private = file_index.config["private"] + + print(f"Migrating for Index id: {_id}, is_private: {_is_private}") + + migrate_chroma_db( + chroma_db_dir=chroma_db_dir, + sqlite_path=sqlite_path, + is_private=_is_private, + int_index=_id, + ) + + +if __name__ == "__main__": + chrome_db_dir: str = "./vectorstore/kan_db" + sqlite_path: str = "sqlite:///../ktem_app_data/user_data/sql.db" + + main(chrome_db_dir, sqlite_path)

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