Survival RAG

> Overview

A Retrieval-Augmented Generation (RAG) system specialized in survival and emergency preparedness knowledge. This project demonstrates advanced RAG implementation techniques, combining vector databases with LLMs to provide accurate, context-aware information retrieval.

The system showcases how domain-specific knowledge can be effectively organized and retrieved using modern AI techniques, making it an excellent example of practical RAG architecture for specialized applications.

> RAG Architecture

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Document       │────▶│  Embedding       │────▶│  Vector         │
│  Ingestion      │     │  Generation      │     │  Database       │
└─────────────────┘     └──────────────────┘     └─────────────────┘
                                                           │
                                                           ▼
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  User Query     │────▶│  Semantic        │────▶│  Retrieved      │
│                 │     │  Search          │     │  Context        │
└─────────────────┘     └──────────────────┘     └─────────────────┘
                                                           │
                                                           ▼
                                                  ┌─────────────────┐
                                                  │  LLM Response   │
                                                  │  Generation     │
                                                  └─────────────────┘
                

> Key Features

> Technical Implementation

The RAG system implements a sophisticated pipeline that combines vector similarity search with LLM generation to provide accurate, context-aware responses. Core technical components include:

• • Embedding Generation: Sentence Transformers (all-MiniLM-L6-v2) for converting text to high-quality vector representations
• • Vector Storage: ChromaDB for efficient similarity search with metadata filtering capabilities
• • Document Processing: Custom chunking strategies that preserve context across splits
• • Retrieval Logic: Hybrid approach combining semantic search with keyword matching for improved recall
• • Prompt Engineering: Dynamic prompt templates that inject retrieved context while maintaining coherence
• • LLM Integration: Async interface to OpenAI/Anthropic APIs with streaming support

The architecture emphasizes modularity, allowing independent optimization of each component. The system includes comprehensive logging and metrics collection for monitoring retrieval quality and response accuracy, enabling continuous improvement through data-driven insights.

> Technology Stack

> AI Engineering Insights

This RAG implementation demonstrates several advanced concepts:

• • Hybrid Search: Combining semantic and keyword search for optimal retrieval
• • Chunking Strategies: Intelligent document segmentation for better context preservation
• • Reranking: Using cross-encoders to improve retrieval quality
• • Evaluation Metrics: Implementing RAGAS for systematic performance assessment