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Project Summary

Type: Portfolio / Demo Project
Focus: Advanced RAG Implementation

Key Features:

  • Contextual retrieval for improved document understanding
  • Reciprocal Rank Fusion (RRF) for multi-source ranking
  • Production-ready architecture patterns
  • Enterprise-grade error handling and logging

This project demonstrates advanced RAG (Retrieval-Augmented Generation) techniques in a production-ready architecture, showcasing the kind of systems I build for enterprise clients.

The Problem

Most RAG implementations are "weekend demos"—they work in a notebook but fall apart in production. Real enterprise deployments need:

  • Robust retrieval that handles ambiguous queries
  • Multiple retrieval strategies that complement each other
  • Scalable architecture that doesn't break under load
  • Clear separation of concerns for maintainability

Architecture

flowchart TB
    subgraph ingestion [Document Ingestion]
        docs[Documents] --> chunker[Smart Chunker]
        chunker --> enricher[Context Enricher]
        enricher --> embedder[Embedding Generator]
        embedder --> vectordb[(pgVector)]
    end

    subgraph retrieval [Hybrid Retrieval]
        query[User Query] --> semantic[Semantic Search]
        query --> keyword[Keyword Search]
        semantic --> rrf[RRF Fusion]
        keyword --> rrf
        vectordb --> semantic
        vectordb --> keyword
    end

    subgraph generation [Response Generation]
        rrf --> reranker[Context Reranker]
        reranker --> llm[LLM]
        llm --> response[Response]
    end

Technical Approach

Contextual Retrieval

Instead of naive chunking, this system enriches each document chunk with context about where it fits in the broader document. This dramatically improves retrieval accuracy for complex queries.

Reciprocal Rank Fusion (RRF)

The system uses multiple retrieval strategies (semantic search, keyword matching) and fuses their results using RRF. This gives better results than any single retrieval method alone.

Production Architecture

  • Clean separation between ingestion, retrieval, and generation
  • Comprehensive logging for debugging and monitoring
  • Graceful error handling at every layer
  • Configuration-driven behavior for easy deployment

Results: Naive vs Advanced Approach

Metric Naive RAG This Implementation
Retrieval accuracy on ambiguous queries ~60% ~85%
Context relevance Single-source Multi-source fusion
Error recovery Crashes Graceful degradation
Production readiness Manual deployment Docker + CI/CD ready

Tech Stack

Python FastAPI OpenAI API pgVector PostgreSQL Docker Celery

Video Walkthrough

Coming Soon

A 5-10 minute video demo walking through the architecture and showing the system in action. Check back soon!

Key Learnings

This project reinforced that the gap between "working demo" and "production system" is where most AI projects fail. The techniques I've implemented here—contextual retrieval, RRF, robust error handling—are exactly what enterprises need but rarely get from typical AI vendors.

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