Since the only usage of prompt is within LLMs, it is reasonable to keep it within the LLM module. This way, it would be easier to discover module, and make the code base less complicated.
Changes:
* Move prompt components into llms
* Bump version 0.3.1
* Make pip install dependencies in eager mode
---------
Co-authored-by: ian <ian@cinnamon.is>
This change speeds up OCR extraction by allowing bypassing OCR for texts that are irrelevant (not in table).
---------
Co-authored-by: Nguyen Trung Duc (john) <trungduc1992@gmail.com>
This change remove `BaseComponent`'s:
- run_raw
- run_batch_raw
- run_document
- run_batch_document
- is_document
- is_batch
Each component is expected to support multiple types of inputs and a single type of output. Since we want the component to work out-of-the-box with both standardized and customized use cases, supporting multiple types of inputs are expected. At the same time, to reduce the complexity of understanding how to use a component, we restrict a component to only have a single output type.
To accommodate these changes, we also refactor some components to remove their run_raw, run_batch_raw... methods, and to decide the common output interface for those components.
Tests are updated accordingly.
Commit changes:
* Add kwargs to vector store's query
* Simplify the BaseComponent
* Update tests
* Remove support for Python 3.8 and 3.9
* Bump version 0.3.0
* Fix github PR caching still use old environment after bumping version
---------
Co-authored-by: ian <ian@cinnamon.is>
By allowing specifying the UI outputs in the code, any time user runs `kh export ...`, that outputs in the code will be included in the UI YAML file. Otherwise, any time the user runs `kh export ...`, the output section in the UI YAML file will be reset to the default output.
* add base Tool
* minor update test_tool
* update test dependency
* update test dependency
* Fix namespace conflict
* update test
* add base Agent Interface, add ReWoo Agent
* minor update
* update test
* fix typo
* remove unneeded print
* update rewoo agent
* add LLMTool
* update BaseAgent type
* add ReAct agent
* add ReAct agent
* minor update
* minor update
* minor update
* minor update
* update base reader with BaseComponent
* add splitter
* update agent and tool
* update vectorstores
* update load/save for indexing and retrieving pipeline
* update test_agent for more use-cases
* add missing dependency for test
* update test case for in memory vectorstore
* add TextSplitter to BaseComponent
* update type hint basetool
---------
Co-authored-by: trducng <trungduc1992@gmail.com>
* add test case for Chroma save/load
* minor name change
* add delete_collection support for chroma
* move save load to chroma
---------
Co-authored-by: Nguyen Trung Duc (john) <john@cinnamon.is>
This CL implements:
- The logic to export log to Excel.
- Route the export logic in the UI.
- Demonstrate this functionality in `./examples/promptui` project.
From pipeline > config > UI. Provide example project for promptui
- Pipeline to config: `kotaemon.contribs.promptui.config.export_pipeline_to_config`. The config follows schema specified in this document: https://cinnamon-ai.atlassian.net/wiki/spaces/ATM/pages/2748711193/Technical+Detail. Note: this implementation exclude the logs, which will be handled in AUR-408.
- Config to UI: `kotaemon.contribs.promptui.build_from_yaml`
- Example project is located at `examples/promptui/`
* [AUR-362] Add In-memory vector store
* [AUR-362] fix delete fun input format
* [AUR-362] revise persist and from persist path to save and load
* [AUR-362] revise simple.py to in_memory.py
Document store handles storing and indexing Documents. It supports the following interfaces:
- add: add 1 or more documents into document store
- get: get a list of documents
- get_all: get all documents in a document store
- delete: delete 1 or more document
- save: persist a document store into disk
- load: load a document store from disk
Design the base interface of vector store, and apply it to the Chroma Vector Store (wrapped around llama_index's implementation). Provide the pipelines to populate and retrieve from vector store.
This change provides the base interface of an embedding, and wrap the Langchain's OpenAI embedding. Usage as follow:
```python
from kotaemon.embeddings import AzureOpenAIEmbeddings
model = AzureOpenAIEmbeddings(
model="text-embedding-ada-002",
deployment="embedding-deployment",
openai_api_base="https://test.openai.azure.com/",
openai_api_key="some-key",
)
output = model("Hello world")
```
- Use cases related to LLM call: https://cinnamon-ai.atlassian.net/browse/AUR-388?focusedCommentId=34873
- Sample usages: `test_llms_chat_models.py` and `test_llms_completion_models.py`:
```python
from kotaemon.llms.chats.openai import AzureChatOpenAI
model = AzureChatOpenAI(
openai_api_base="https://test.openai.azure.com/",
openai_api_key="some-key",
openai_api_version="2023-03-15-preview",
deployment_name="gpt35turbo",
temperature=0,
request_timeout=60,
)
output = model("hello world")
```
For the LLM-call component, I decide to wrap around Langchain's LLM models and Langchain's Chat models. And set the interface as follow:
- Completion LLM component:
```python
class CompletionLLM:
def run_raw(self, text: str) -> LLMInterface:
# Run text completion: str in -> LLMInterface out
def run_batch_raw(self, text: list[str]) -> list[LLMInterface]:
# Run text completion in batch: list[str] in -> list[LLMInterface] out
# run_document and run_batch_document just reuse run_raw and run_batch_raw, due to unclear use case
```
- Chat LLM component:
```python
class ChatLLM:
def run_raw(self, text: str) -> LLMInterface:
# Run chat completion (no chat history): str in -> LLMInterface out
def run_batch_raw(self, text: list[str]) -> list[LLMInterface]:
# Run chat completion in batch mode (no chat history): list[str] in -> list[LLMInterface] out
def run_document(self, text: list[BaseMessage]) -> LLMInterface:
# Run chat completion (with chat history): list[langchain's BaseMessage] in -> LLMInterface out
def run_batch_document(self, text: list[list[BaseMessage]]) -> list[LLMInterface]:
# Run chat completion in batch mode (with chat history): list[list[langchain's BaseMessage]] in -> list[LLMInterface] out
```
- The LLMInterface is as follow:
```python
@dataclass
class LLMInterface:
text: list[str]
completion_tokens: int = -1
total_tokens: int = -1
prompt_tokens: int = -1
logits: list[list[float]] = field(default_factory=list)
```
