💬 Conversational agent

Create a chat.yml file with your configuration before starting the web service.

Below is an example of configuration using the Mixtral GGUF model, without vectorstore, to deploy a generic conversational chatbot

chat.yml

llm:
  model_path: ./models/mixtral-8x7b-instruct-v0.1.Q2_K.gguf # (1)
  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

prompt:
  # Always use input for the human input variable with a generic agent
  variables: [input, history]
  template: |
    Your are an assistant, please help me

    {history}
    User: {input}
    AI Assistant:

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: vincent.emonet@gmail.com
  license_info:
    name: MIT license
    url: https://raw.github.com/vemonet/libre-chat/main/LICENSE.txt
  workers: 4

1. We recommend to predownload the files, but you can provide download URLs that will be used if the files are not present