https://github.com/adityaadaki21/fastapi-rag
FASTapi-RAG is a FastAPI-based Retrieval-Augmented Generation system that lets users query PDF documents via an AI-powered chatbot. It integrates Ollama for language generation and ChromaDB for document indexing, offering features like document upload, natural language querying, and an interactive web interface.
https://github.com/adityaadaki21/fastapi-rag
chromadb fastapi llm llms ollama pdf-processing rag retrieval-augmented-generation
Last synced: about 1 month ago
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FASTapi-RAG is a FastAPI-based Retrieval-Augmented Generation system that lets users query PDF documents via an AI-powered chatbot. It integrates Ollama for language generation and ChromaDB for document indexing, offering features like document upload, natural language querying, and an interactive web interface.
- Host: GitHub
- URL: https://github.com/adityaadaki21/fastapi-rag
- Owner: AdityaAdaki21
- Created: 2025-03-10T18:25:32.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-03-19T11:39:59.000Z (11 months ago)
- Last Synced: 2025-03-19T12:33:39.462Z (11 months ago)
- Topics: chromadb, fastapi, llm, llms, ollama, pdf-processing, rag, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 32.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# F1-AI: Formula 1 RAG Application
F1-AI is a Retrieval-Augmented Generation (RAG) application specifically designed for Formula 1 information. It features an intelligent web scraper that automatically discovers and extracts Formula 1-related content from the web, stores it in a vector database, and enables natural language querying of the stored information.
## Features
- Web scraping of Formula 1 content with automatic content extraction
- Vector database storage using Pinecone for efficient similarity search
- OpenRouter integration for advanced LLM capabilities
- RAG-powered question answering with contextual understanding and source citations
- Command-line interface for automation and scripting
- User-friendly Streamlit web interface with chat history
- Asynchronous data ingestion and processing for improved performance
## Architecture
F1-AI is built on a modern tech stack:
- **LangChain**: Orchestrates the RAG pipeline and manages interactions between components
- **Pinecone**: Vector database for storing and retrieving embeddings
- **OpenRouter**: Primary LLM provider with Mistral-7B-Instruct model
- **Ollama**: Alternative local LLM provider for embeddings
- **Playwright**: Handles web scraping with JavaScript support
- **BeautifulSoup4**: Processes HTML content and extracts relevant information
- **Streamlit**: Provides an interactive web interface with chat functionality
## Prerequisites
- Python 3.8 or higher
- OpenRouter API key (set as OPENROUTER_API_KEY environment variable)
- Pinecone API key (set as PINECONE_API_KEY environment variable)
- 8GB RAM minimum (16GB recommended)
- Internet connection for web scraping
- Ollama installed (optional, for local embeddings)
- Download from [Ollama's website](https://ollama.ai)
- Pull the required model: `ollama pull all-minilm-l6-v2`
## Installation
1. Clone the repository:
```bash
git clone
cd FASTapi-RAG
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Install Playwright browsers:
```bash
playwright install
```
4. Set up environment variables:
Create a .env file with:
```
OPENROUTER_API_KEY=your_api_key_here # Required for LLM functionality
PINECONE_API_KEY=your_api_key_here # Required for vector storage
```
## Usage
### Command Line Interface
1. Scrape and ingest F1 content:
```bash
python f1_scraper.py --start-urls https://www.formula1.com/ --max-pages 100 --depth 2 --ingest
```
Options:
- `--start-urls`: Space-separated list of URLs to start crawling from
- `--max-pages`: Maximum number of pages to crawl (default: 100)
- `--depth`: Maximum crawl depth (default: 2)
- `--ingest`: Flag to ingest discovered content into RAG system
- `--max-chunks`: Maximum chunks per URL for ingestion (default: 50)
- `--llm-provider`: Choose LLM provider (openrouter, ollama)
2. Ask questions about Formula 1:
```bash
python f1_ai.py ask "Who won the 2023 F1 World Championship?"
```
### Streamlit Interface
Run the Streamlit app:
```bash
streamlit run streamlit_app.py
```
This will open a web interface where you can:
- Ask questions about Formula 1
- View responses in a chat-like interface
- See source citations for answers
- Track conversation history
- Get real-time updates on response generation
## Project Structure
- `f1_scraper.py`: Intelligent web crawler implementation
- Automatically discovers F1-related content
- Handles content relevance detection
- Manages crawling depth and limits
- `f1_ai.py`: Core RAG application implementation
- Handles data ingestion and chunking
- Manages vector database operations
- Implements question-answering logic
- `llm_manager.py`: LLM provider management
- Integrates with OpenRouter for advanced LLM capabilities
- Handles embeddings generation
- Manages API interactions
- `streamlit_app.py`: Streamlit web interface
- Provides chat-based UI
- Manages conversation history
- Handles async operations
## Contributing
Contributions are welcome! Please follow these steps:
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Submit a Pull Request