Vector RAG (Retrieval-Augmented Generation) Project

Overview

Implement a robust data processing and query resolution system using vector embeddings and advanced language models.

Project Components

1. Environment Setup

• Python 3.8+
• Virtual environment
• API integrations (Groq, OpenAI)

2. Data Processing Workflow

1. Chunk large JSON datasets
2. Convert chunks to vector embeddings
3. Store embeddings in vector database
4. Query and retrieve relevant information

3. Key Technologies

• Langchain
• FAISS
• Sentence Transformers
• OpenAI/Groq APIs

Installation

Virtual Environment

python -m venv venv
source venv/bin/activate  # Unix/macOS
venv\Scripts\activate     # Windows

Dependencies

pip install -r requirements.txt

Configuration

Environment Variables

Create .env file:

GROQ_API_KEY=your_groq_api_key
OPENAI_API_KEY=your_openai_api_key

Implementation Details

Data Chunking

• Split large JSON files into manageable chunks
• Ensure semantic coherence in chunks
• Convert chunks to embeddings

Vector Database

• Use FAISS for efficient similarity search
• Store document embeddings
• Support fast retrieval

Query Handling

• Semantic search in vector database
• Fallback to customer support if no relevant match

Security Considerations

• Use environment variables
• Never hardcode API keys
• Implement proper access controls

u like me to elaborate on any specific section?