Skip to content

Latest commit

 

History

History
101 lines (62 loc) · 4.64 KB

README.md

File metadata and controls

101 lines (62 loc) · 4.64 KB

RAGENT

English | 中文文档 | 日本語

Probably one of the lightest native RAG + Agent apps out there，experience the power of Agent-powered models and Agent-driven knowledge bases in one click, without complex configuration.

image

Features

Chat and Agent interactions:

  • 💭 Simple, easy-to-use chat box interface.
  • 🌏️ Language options (Simplified Chinese, English)
  • 🔧 Inference support for multiple (local) model sources (Azure OpenAI, Groq, ollama, llamafile)
  • Native Function Call (OpenAI, Azure OpenAI, OpenAI Like, Ollama)
  • 🤖 Multiple Agent modes on-premises
  • 🖥️ Local storage of dialog data and management
    • Multiple export formats(Markdown, HTML)
    • Multiple themes(HTML)

Knowledgebase:

  • Native implementation of Retrieval Augmentation Generation (RAG), lightweight and efficient
  • Optional embedding models (Hugging Face/OpenAI)
  • Easy-to-use knowledge base management
  • Multiple search methods available: Hybrid search, reranking, and specified file retrieval

If you like this project, please star it, it's the biggest encouragement for me!

More details

General

Export

Support export format, theme selection and export range control:

Export settings and preview

Currently supported themes:

Default Glassmorphism
default theme Glassmorphism theme

RAG Chat

image

You can set up the model (sidebar) and view detailed references:

image

Configure RAG：

image

Function Call

Function calls are supported on Chat now, and AgentChat will be supported in the future.

The Function Calls on this page are native and work for all OpenAI Compatible models, but require the model itself to support Function calls.

Function Call can significantly enhance the capabilities of LLM, allowing it to complete tasks that it was previously unable to complete (such as mathematical calculations), as shown below:

image

Or summarize the content of a webpage:

image

You can also customize the function you want to call, please refer to toolkits.py for writing rules.

Quick start

Git

  1. Use git clone https://github.com/Wannabeasmartguy/RAGENT.git to pull the code; Then open your runtime environment in command prompt (CMD) and use pip install -r requirements.txt to install the runtime dependencies.

  2. Configure the model dependencies: Modify the .env_sample file to .env and fill in the following:

    • LANGUAGE: Support English and 简体中文, if not set, default is English;
    • OPENAI_API_KEY : If you are using an OpenAI model, fill in the api key here;
    • AZURE_OAI_KEY : If you are using an Azure OpenAI model, fill in the api key here;
    • AZURE_OAI_ENDPOINT : If you are using an OpenAI model, fill in the end_point here;
    • API_VERSION: If you are using an Azure OpenAI model, fill in the api version here;
    • API_TYPE: if you are using an Azure OpenAI model, fill in the api type here;
    • GROQ_API_KEY : if you are using Groq as the model source, fill in the api key here;
    • COZE_ACCESS_TOKEN: if you need to use the created Coze Bot, fill in the access token here;

If you are using Llamafile, please set the endpoint within the application after starting the Llamafile model.

  1. launch the application:

Run: Run streamlit run RAGENT.py on the command line can start it.

If you want to use the AgentChat page, please use python startup.py to start the application rather than streamlit run RAGENT.py.

Contribution

For any issues encountered during use or new ideas, please submit issues and PRs!