Survival RAG - Retrieval-Augmented Generation System

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

Key Features

Vector Search

Semantic similarity search using state-of-the-art embedding models

Context Augmentation

Intelligent context injection for improved response accuracy

Knowledge Curation

Structured ingestion pipeline for domain-specific content

Response Synthesis

Advanced prompt engineering for coherent, factual outputs

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

Python 3.11+ LangChain ChromaDB Sentence Transformers OpenAI API FAISS

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