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README.md
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README.md
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# kotaemon
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Build and use local RAG-based Question Answering (QA) applications.
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https://github.com/Cinnamon/kotaemon/assets/25688648/815ecf68-3a02-4914-a0dd-3f8ec7e75cd9
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[Source Code](https://github.com/Cinnamon/kotaemon) |
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[Demo](https://huggingface.co/spaces/lone17/kotaemon-app)
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[Live Demo](https://huggingface.co/spaces/lone17/kotaemon-app)
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[User Guide](https://cinnamon.github.io/kotaemon/) |
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[Developer Guide](https://cinnamon.github.io/kotaemon/development/) |
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@ -13,9 +15,7 @@
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[](https://github.com/psf/black)
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[](https://codeium.com)
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Build and use local RAG-based Question Answering (QA) applications.
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This repository would like to appeal to both end users who want to do QA on their
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This project would like to appeal to both end users who want to do QA on their
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documents and developers who want to build their own QA pipeline.
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- For end users:
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@ -32,47 +32,150 @@ This repository is under active development. Feedback, issues, and PRs are highl
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appreciated. Your input is valuable as it helps us persuade our business guys to support
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open source.
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## Setting up
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## Installation
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- Clone the repo
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### For end users
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```shell
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git clone git@github.com:Cinnamon/kotaemon.git
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cd kotaemon
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```
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This document is intended for developers. If you just want to install and use the app as
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it, please follow the [User Guide](https://cinnamon.github.io/kotaemon/).
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- Install the environment
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### For developers
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- Create a conda environment (python >= 3.10 is recommended)
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```shell
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# Create a environment
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python -m venv kotaemon-env
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```shell
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conda create -n kotaemon python=3.10
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conda activate kotaemon
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# Activate the environment
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source kotaemon-env/bin/activate
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# install dependencies
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cd libs/kotaemon
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pip install -e ".[all]"
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```
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# Install the package
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pip install git+https://github.com/Cinnamon/kotaemon.git
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```
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- Or run the installer (one of the `scripts/run_*` scripts depends on your OS), then
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you will have all the dependencies installed as a conda environment at
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`install_dir/env`.
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## Creating your application
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```shell
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conda activate install_dir/env
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```
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In order to create your own application, you need to prepare these files:
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- Pre-commit
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- `flowsettings.py`
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- `app.py`
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- `.env` (Optional)
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```shell
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pre-commit install
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```
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### `flowsettings.py`
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- Test
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This file contains the configuration of your application. You can use the example
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[here](https://github.com/Cinnamon/kotaemon/blob/main/libs/ktem/flowsettings.py) as the
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starting point.
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```shell
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pytest tests
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```
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### `app.py`
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This file is where you create your Gradio app object. This can be as simple as:
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```python
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from ktem.main import App
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app = App()
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demo = app.make()
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demo.launch()
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```
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### `.env` (Optional)
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This file provides another way to configure your models and credentials.
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<details markdown>
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<summary>Configure model via the .env file</summary>
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Alternatively, you can configure the models via the `.env` file with the information needed to connect to the LLMs. This file is located in
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the folder of the application. If you don't see it, you can create one.
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Currently, the following providers are supported:
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#### OpenAI
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In the `.env` file, set the `OPENAI_API_KEY` variable with your OpenAI API key in order
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to enable access to OpenAI's models. There are other variables that can be modified,
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please feel free to edit them to fit your case. Otherwise, the default parameter should
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work for most people.
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```shell
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OPENAI_API_BASE=https://api.openai.com/v1
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OPENAI_API_KEY=<your OpenAI API key here>
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OPENAI_CHAT_MODEL=gpt-3.5-turbo
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OPENAI_EMBEDDINGS_MODEL=text-embedding-ada-002
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```
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#### Azure OpenAI
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For OpenAI models via Azure platform, you need to provide your Azure endpoint and API
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key. Your might also need to provide your developments' name for the chat model and the
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embedding model depending on how you set up Azure development.
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```shell
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AZURE_OPENAI_ENDPOINT=
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AZURE_OPENAI_API_KEY=
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OPENAI_API_VERSION=2024-02-15-preview
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AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-35-turbo
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AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=text-embedding-ada-002
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```
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#### Local models
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- Pros:
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- Privacy. Your documents will be stored and process locally.
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- Choices. There are a wide range of LLMs in terms of size, domain, language to choose
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from.
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- Cost. It's free.
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- Cons:
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- Quality. Local models are much smaller and thus have lower generative quality than
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paid APIs.
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- Speed. Local models are deployed using your machine so the processing speed is
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limited by your hardware.
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##### Find and download a LLM
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You can search and download a LLM to be ran locally from the [Hugging Face
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Hub](https://huggingface.co/models). Currently, these model formats are supported:
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- GGUF
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You should choose a model whose size is less than your device's memory and should leave
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about 2 GB. For example, if you have 16 GB of RAM in total, of which 12 GB is available,
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then you should choose a model that takes up at most 10 GB of RAM. Bigger models tend to
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give better generation but also take more processing time.
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Here are some recommendations and their size in memory:
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- [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):
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around 2 GB
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##### Enable local models
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To add a local model to the model pool, set the `LOCAL_MODEL` variable in the `.env`
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file to the path of the model file.
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```shell
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LOCAL_MODEL=<full path to your model file>
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```
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Here is how to get the full path of your model file:
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- On Windows 11: right click the file and select `Copy as Path`.
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</details>
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## Start your application
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Simply run the following command:
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```shell
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python app.py
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```
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The app will be automatically launched in your browser.
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## Customize your application
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Please refer to the [Developer Guide](https://cinnamon.github.io/kotaemon/development/)
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for more details.
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# Contributing
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## Setting up
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- Clone the repo
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```shell
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git clone git@github.com:Cinnamon/kotaemon.git
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cd kotaemon
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```
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- Install the environment
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- Create a conda environment (python >= 3.10 is recommended)
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```shell
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conda create -n kotaemon python=3.10
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conda activate kotaemon
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# install dependencies
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cd libs/kotaemon
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pip install -e ".[all]"
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```
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- Or run the installer (one of the `scripts/run_*` scripts depends on your OS), then
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you will have all the dependencies installed as a conda environment at
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`install_dir/env`.
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```shell
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conda activate install_dir/env
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```
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- Pre-commit
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```shell
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pre-commit install
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```
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- Test
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```shell
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pytest tests
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```
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## Package overview
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`kotaemon` library focuses on the AI building blocks to implement a RAG-based QA application. It consists of base interfaces, core components and a list of utilities:
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@ -1,6 +1,6 @@
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# Getting Started with Kotaemon
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This page is intended for **end users** who want to use the `kotaemon` tool for Question
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Answering on local documents. If you are a **developer** who wants contribute to the project, please visit the [development](development/index.md) page.
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@ -25,9 +25,9 @@ Download and upzip the latest version of `kotaemon` by clicking this
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## Launch
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To launch the app after initial setup or any changes, simply run the `run_*` script again.
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To launch the app after initial setup or any change, simply run the `run_*` script again.
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A browser window will be opened and greet you with this screen:
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A browser window will be opened and greets you with this screen:
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@ -12,9 +12,10 @@ nav:
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# - Functional description: pages/app/functional-description.md
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- Development:
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- development/index.md
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- Contributing: development/contributing.md
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# - Data & Data Structure Components: development/data-components.md
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# - Features: pages/app/features.md
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- Customize flow logic: pages/app/customize-flows.md
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- Creating a Component: development/create-a-component.md
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- Components:
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- Index:
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- File index: pages/app/index/file.md
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- pages/app/settings/user-settings.md
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- Extension:
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- User management: pages/app/ext/user-management.md
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- Customize flow logic: pages/app/customize-flows.md
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- Creating a Component: development/create-a-component.md
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- Contributing: development/contributing.md
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# generated using gen-files + literate-nav
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- API Reference: reference/
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- Issue Tracker: "https://github.com/Cinnamon/kotaemon/issues"
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type: timeago
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fallback_to_build_date: true
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- section-index
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- mkdocs-video
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theme:
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features:
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