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45 lines
1.4 KiB
Markdown
45 lines
1.4 KiB
Markdown
# 🤖 AutoRAG: Autonomous RAG with GPT-4o and Vector Database
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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.
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Features
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### Freatures
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- Chat interface for interacting with the AI assistant
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- PDF document upload and processing
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- Knowledge base integration using PostgreSQL and Pgvector
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- Web search capability using DuckDuckGo
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- Persistent storage of assistant data and conversations
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd awesome-llm-apps/rag_tutorials/autonomous_rag
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Ensure PgVector Database is running:
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The app expects PgVector to be running on [localhost:5532](http://localhost:5532/). Adjust the configuration in the code if your setup is different.
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```bash
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docker run -d \
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-e POSTGRES_DB=ai \
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-e POSTGRES_USER=ai \
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-e POSTGRES_PASSWORD=ai \
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-e PGDATA=/var/lib/postgresql/data/pgdata \
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-v pgvolume:/var/lib/postgresql/data \
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-p 5532:5432 \
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--name pgvector \
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phidata/pgvector:16
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```
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4. Run the Streamlit App
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```bash
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streamlit run autorag.py
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```
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