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https://github.com/vemonet/libre-chat

🦙 Free and Open Source Large Language Model (LLM) chatbot web UI and API. Self-hosted, offline capable and easy to setup. Powered by LangChain.
https://github.com/vemonet/libre-chat

chatbot chatgpt langchain large-language-models llama2 llm llm-inference mixtral no-code open-source openapi question-answering self-hosted

Last synced: 2 days ago
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🦙 Free and Open Source Large Language Model (LLM) chatbot web UI and API. Self-hosted, offline capable and easy to setup. Powered by LangChain.

Host: GitHub
URL: https://github.com/vemonet/libre-chat
Owner: vemonet
License: mit
Created: 2023-07-26T15:13:50.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-07-28T09:33:40.000Z (4 months ago)
Last Synced: 2024-11-08T20:53:42.132Z (6 days ago)
Topics: chatbot, chatgpt, langchain, large-language-models, llama2, llm, llm-inference, mixtral, no-code, open-source, openapi, question-answering, self-hosted
Language: Python
Homepage: https://vemonet.github.io/libre-chat
Size: 4.32 MB
Stars: 130
Watchers: 5
Forks: 18
Open Issues: 8
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Citation: CITATION.cff

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README

        


#  Libre Chat

[![Test package](https://github.com/vemonet/libre-chat/actions/workflows/test.yml/badge.svg)](https://github.com/vemonet/libre-chat/actions/workflows/test.yml) [![Coverage](https://coverage-badge.samuelcolvin.workers.dev/vemonet/libre-chat.svg)](https://coverage-badge.samuelcolvin.workers.dev/redirect/vemonet/libre-chat)

[![PyPI - Version](https://img.shields.io/pypi/v/libre-chat.svg?logo=pypi&label=PyPI&logoColor=silver)](https://pypi.org/project/libre-chat/)

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/libre-chat.svg?logo=python&label=Python&logoColor=silver)](https://pypi.org/project/libre-chat/)

[![License](https://img.shields.io/pypi/l/libre-chat)](https://github.com/vemonet/libre-chat/blob/main/LICENSE.txt) [![Pull requests welcome](https://img.shields.io/badge/pull%20requests-welcome-brightgreen)](https://github.com/vemonet/libre-chat/fork)

Easily configure and deploy a **fully self-hosted chatbot web service** based on open source Large Language Models (LLMs), such as [Mixtral](https://mistral.ai/news/mixtral-of-experts) or [Llama 2](https://ai.meta.com/llama/), without the need for knowledge in machine learning.



- 🌐 Free and Open Source chatbot web service with UI and API.

- 🏡 Fully self-hosted, not tied to any service, and offline capable. Forget about API keys! Models and embeddings can be pre-downloaded, and the training and inference processes can run off-line if necessary.

- 🔌 Web API described using OpenAPI specs: GET/POST operations, websocket for streaming response

- 🪶 Chat web UI working well on desktop and mobile, with streaming response, and markdown rendering. Alternative gradio-based UI also available.

- 🚀 Easy to setup, no need to program, just configure the service with a [YAML](https://yaml.org/) file, and start it with 1 command

- 📦 Available as a `pip` package 🐍, or `docker` image 🐳

- 🐌 No need for GPU, this will work even on your laptop CPU! That said, just running on CPUs can be quite slow (up to 1min to answer a documents-base question on recent laptops).

- 🦜 Powered by [`LangChain`](https://python.langchain.com) and [`llama.cpp`](https://github.com/ggerganov/llama.cpp) to perform inference locally.

- 🤖 Various types of agents can be deployed:

  - **💬 Generic conversation**: do not need any additional training, just configure settings such as the template prompt

  - **📚 Documents-based question answering** (experimental): automatically build similarity vectors from documents uploaded through the API UI, the chatbot will use them to answer your question, and return which documents were used to generate the answer (PDF, CSV, HTML, JSON, markdown, and more supported).

- 🔍 Readable logs to understand what is going on.

## 📖 Documentation

For more details on how to use Libre Chat check the documentation at **[vemonet.github.io/libre-chat](http://vemonet.github.io/libre-chat)**

![UI screenshot](https://raw.github.com/vemonet/libre-chat/main/docs/docs/assets/screenshot.png)

![UI screenshot](https://raw.github.com/vemonet/libre-chat/main/docs/docs/assets/screenshot-light.png)

## 🏗️ Work in progress

> [!WARNING]

> This project is a work in progress, use it with caution.

Those checkpoints are features we plan to work on in the future, feel free to let us know in the issues if you have any comment or request.

- [x] Stream response to the websocket to show words as they are generated

- [ ] Add button to let the user stop the chatbot generation

- [ ] Add authentication mechanisms? (OAuth/OpenID Connect) https://github.com/vemonet/libre-chat/issues/5

- [ ] Add conversational history? https://milvus.io/blog/conversational-memory-in-langchain.md

- [ ] Add an admin dashboard web UI to enable users to upload/inspect/delete documents for QA, see/edit the config of the chatbot.

- [ ] Kubernetes deployment (Helm chart?)

## 🐳 Deploy with docker

If you just want to quickly deploy it using the pre-trained model `Mixtral-8x7B-Instruct`, you can use docker:

```bash

docker run -it -p 8000:8000 ghcr.io/vemonet/libre-chat:main

```

You can configure the deployment using environment variables. For this using a `docker compose` and a `.env` file is easier, first create the `docker-compose.yml` file:

```yaml

version: "3"

services:

  libre-chat:

    image: ghcr.io/vemonet/libre-chat:main

    volumes:

      # ⚠️ Share folders from the current directory to the /data dir in the container

      - ./chat.yml:/data/chat.yml

      - ./models:/data/models

      - ./documents:/data/documents

      - ./embeddings:/data/embeddings

      - ./vectorstore:/data/vectorstore

    ports:

      - 8000:8000

```

And create a `chat.yml` file with your configuration in the same folder as the `docker-compose.yml`:

```yaml

llm:

  model_path: ./models/mixtral-8x7b-instruct-v0.1.Q2_K.gguf

  model_download: https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q2_K.gguf

  temperature: 0.01    # Config how creative, but also potentially wrong, the model can be. 0 is safe, 1 is adventurous

  max_new_tokens: 1024 # Max number of words the LLM can generate

  # Always use input for the human input variable with a generic agent

  prompt_variables: [input, history]

  prompt_template: |

    Your are an assistant, please help me

    {history}

    User: {input}

    AI Assistant:

vector:

  vector_path: null # Path to the vectorstore to do QA retrieval, e.g. ./vectorstore/db_faiss

  # Set to null to deploy a generic conversational agent

  vector_download: null

  embeddings_path: ./embeddings/all-MiniLM-L6-v2 # Path to embeddings used to generate the vectors, or use directly from HuggingFace: sentence-transformers/all-MiniLM-L6-v2

  embeddings_download: https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/all-MiniLM-L6-v2.zip

  documents_path: ./documents # Path to documents to vectorize

  chunk_size: 500             # Maximum size of chunks, in terms of number of characters

  chunk_overlap: 50           # Overlap in characters between chunks

  chain_type: stuff           # Or: map_reduce, reduce, map_rerank. More details: https://docs.langchain.com/docs/components/chains/index_related_chains

  search_type: similarity     # Or: similarity_score_threshold, mmr. More details: https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore

  return_sources_count: 2     # Number of sources to return when generating an answer

  score_threshold: null       # If using the similarity_score_threshold search type. Between 0 and 1

info:

  title: "Libre Chat"

  version: "0.1.0"

  description: |

    Open source and free chatbot powered by [LangChain](https://python.langchain.com) and [llama.cpp](https://github.com/ggerganov/llama.cpp)

  examples:

  - What is the capital of the Netherlands?

  - Which drugs are approved by the FDA to mitigate Alzheimer symptoms?

  - How can I create a logger with timestamp using python logging?

  favicon: https://raw.github.com/vemonet/libre-chat/main/docs/docs/assets/logo.png

  repository_url: https://github.com/vemonet/libre-chat

  public_url: https://chat.semanticscience.org

  contact:

    name: Vincent Emonet

    email: [email protected]

  license_info:

    name: MIT license

    url: https://raw.github.com/vemonet/libre-chat/main/LICENSE.txt

```

Finally start your chat service with:

```bash

docker compose up

```

## 📦️ Usage with pip

This package requires Python >=3.8, simply install it with `pipx` or `pip`:

```bash

pip install libre-chat

```

### ⌨️ Use as a command-line interface

You can easily start a new chat web service including UI and API using your terminal:

```bash

libre-chat start

```

Provide a specific config file:

```bash

libre-chat start config/chat-vectorstore-qa.yml

```

For re-build of the vectorstore:

```bash

libre-chat build --vector vectorstore/db_faiss --documents documents

```

Get a full rundown of the available options with:

```bash

libre-chat --help

```

### 🐍 Use with python

Or you can use this package in python scripts:

```python

import logging

import uvicorn

from libre_chat import ChatConf, ChatEndpoint, Llm

logging.basicConfig(level=logging.getLevelName("INFO"))

conf = ChatConf(

  model_path="./models/mixtral-8x7b-instruct-v0.1.Q2_K.gguf",

  vector_path=None

)

llm = Llm(conf=conf)

print(llm.query("What is the capital of the Netherlands?"))

# Create and deploy a FastAPI app based on your LLM

app = ChatEndpoint(llm=llm, conf=conf)

uvicorn.run(app)

```

## 🤝 Credits

Inspired by:

- https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference

- https://github.com/lm-sys/FastChat

- https://github.com/liltom-eth/llama2-webui

Llama icons created by Freepik - Flaticon