Your own local RAG Agent GPT

Interface for multiple LLM use-cases

Note: This project is a work in progress.

Features

• Local or API model execution
• RAG capabilities
• Agentic pipelines
• Llama index and Langchain usage

Modules

• Basic GPT
• Basic RAG
• RAG Agent
• CV Generator (under development)

How to use

Run project

• clone and start the project under a virtual environment python -m venv .venv
• activate virtual env source .venv/bin/activate (for windows: .\.venv\Scripts\activate)
• install requirements pip install -r requirements.txt
• install Ollama https://ollama.com/
• rename .env.example to .env and set it up with your keys and secrets when applicable
• make necessary changes under settings.py
• run the app: python app.py
• open http://127.0.0.1:8123

Documents usage

• add documents under the data/module_name/source_data folder for the corresponding module

Database usage (under development)

• add SQlite3 file under dbs folder
• add a database url under .env file