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https://github.com/Cinnamon/kotaemon
An open-source RAG-based tool for chatting with your document locally.
https://github.com/Cinnamon/kotaemon
chatbot llms open-source rag
Last synced: 2 months ago
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An open-source RAG-based tool for chatting with your document locally.
- Host: GitHub
- URL: https://github.com/Cinnamon/kotaemon
- Owner: Cinnamon
- License: apache-2.0
- Created: 2024-03-25T08:16:42.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-04-20T11:21:36.000Z (7 months ago)
- Last Synced: 2024-04-20T19:52:17.280Z (7 months ago)
- Topics: chatbot, llms, open-source, rag
- Language: Python
- Homepage: https://cinnamon.github.io/kotaemon/
- Size: 14.4 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# kotaemon
An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and
developers in mind.
[Live Demo](https://huggingface.co/spaces/cin-model/kotaemon-demo) |
[Source Code](https://github.com/Cinnamon/kotaemon)
[User Guide](https://cinnamon.github.io/kotaemon/) |
[Developer Guide](https://cinnamon.github.io/kotaemon/development/) |
[Feedback](https://github.com/Cinnamon/kotaemon/issues)
[](https://www.python.org/downloads/release/python-31013/)
[](https://github.com/psf/black)
[](https://codeium.com)
## 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 clean & minimalistic UI for RAG-based QA.
- Supports LLM API providers (OpenAI, AzureOpenAI, Cohere, etc) and local LLMs
(via `ollama` and `llama-cpp-python`).
- Easy installation scripts.
- For developers:
- 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
+----------------------------------------------------------------------------+
| End users: Those who use apps built with `kotaemon`. |
| (You use an app like the one in the demo above) |
| +----------------------------------------------------------------+ |
| | Developers: Those who built with `kotaemon`. | |
| | (You have `import kotaemon` somewhere in your project) | |
| | +----------------------------------------------------+ | |
| | | Contributors: Those who make `kotaemon` better. | | |
| | | (You make PR to this repo) | | |
| | +----------------------------------------------------+ | |
| +----------------------------------------------------------------+ |
+----------------------------------------------------------------------------+
```
This repository is under active development. Feedback, issues, and PRs are highly
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.
## Installation
### For end users
This document is intended for developers. If you just want to install and use the app as
it is, please follow the non-technical [User Guide](https://cinnamon.github.io/kotaemon/) (WIP).
### For developers
#### With Docker (recommended)
- Use this command to launch the server
```
docker run \
-e GRADIO_SERVER_NAME=0.0.0.0 \
-e GRADIO_SERVER_PORT=7860 \
-p 7860:7860 -it --rm \
taprosoft/kotaemon:v1.0
```
Navigate to `http://localhost:7860/` to access the web UI.
#### Without Docker
- Clone and install required packages on a fresh python environment.
```shell
# optional (setup env)
conda create -n kotaemon python=3.10
conda activate kotaemon
# clone this repo
git clone https://github.com/Cinnamon/kotaemon
cd kotaemon
pip install -e "libs/kotaemon[all]"
pip install -e "libs/ktem"
```
- View and edit your environment variables (API keys, end-points) in `.env`.
- (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`
- 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.
## Setup local models (for local / private RAG)
See [Local model setup](docs/local_model.md).
## 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`
- `.env`
### `flowsettings.py`
This file contains the configuration of your application. You can use the example
[here](flowsettings.py) as the
starting point.
Notable settings
```
# setup your preferred document store (with full-text search capabilities)
KH_DOCSTORE=(Elasticsearch | LanceDB | SimpleFileDocumentStore)
# setup your preferred vectorstore (for vector-based search)
KH_VECTORSTORE=(ChromaDB | LanceDB | InMemory)
# 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`
This file provides another way to configure your models and credentials.
Configure model via the .env file
Alternatively, you can configure the models via the `.env` file with the information needed to connect to the LLMs. This file is located in
the folder of the application. If you don't see it, you can create one.
Currently, the following providers are supported:
#### OpenAI
In the `.env` file, set the `OPENAI_API_KEY` variable with your OpenAI API key in order
to enable access to OpenAI's models. There are other variables that can be modified,
please feel free to edit them to fit your case. Otherwise, the default parameter should
work for most people.
```shell
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
```
#### Azure OpenAI
For OpenAI models via Azure platform, you need to provide your Azure endpoint and API
key. Your might also need to provide your developments' name for the chat model and the
embedding model depending on how you set up Azure development.
```shell
AZURE_OPENAI_ENDPOINT=
AZURE_OPENAI_API_KEY=
OPENAI_API_VERSION=2024-02-15-preview
AZURE_OPENAI_CHAT_DEPLOYMENT=gpt-35-turbo
AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT=text-embedding-ada-002
```
#### Local models
##### Using ollama OpenAI compatible server
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.
##### 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:
- GGUF
You should choose a model whose size is less than your device's memory and should leave
about 2 GB. For example, if you have 16 GB of RAM in total, of which 12 GB is available,
then you should choose a model that takes up at most 10 GB of RAM. Bigger models tend to
give better generation but also take more processing time.
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
Add a new LlamaCpp model with the provided model name on the web uI.
## Adding your own RAG pipeline
#### Custom reasoning pipeline
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.
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.
#### Custom indexing pipeline
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.
## Star History
