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🤖 AutoRAG: Autonomous RAG with GPT-4o and Vector Database

This Streamlit application implements an Autonomous Retrieval-Augmented Generation (RAG) system using OpenAI's GPT-4o model and PgVector database. It allows users to upload PDF documents, add them to a knowledge base, and query the AI assistant with context from both the knowledge base and web searches. Features

Freatures

  • Chat interface for interacting with the AI assistant
  • PDF document upload and processing
  • Knowledge base integration using PostgreSQL and Pgvector
  • Web search capability using DuckDuckGo
  • Persistent storage of assistant data and conversations

How to get Started?

  1. Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd awesome-llm-apps/rag_tutorials/autonomous_rag
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Ensure PgVector Database is running: The app expects PgVector to be running on localhost:5532. Adjust the configuration in the code if your setup is different.
docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  phidata/pgvector:16
  1. Run the Streamlit App
streamlit run autorag.py