Enterprise RAGKnowledge System
Solve hallucination at scale. Design a production RAG system using Hybrid Search, advanced chunking, and re-ranking to deliver accurate context to LLMs.
fig 1 — hybrid search retrieval pipeline
RETRIEVAL
<150ms
Query Latency
CHUNKING
Recursive
Overlap Strategy
PRECISION
Top-K
Cross-Encoder Rerank
DOCUMENTS
10K+
Production Scale
What You'll Build
A complete, production-ready RAG system with semantic search, streaming responses, and observability.
Document Ingestion
Multi-format parsing (PDF, DOCX, MD) with configurable recursive and semantic chunking strategies
Hybrid Search
BM25 keyword + vector similarity search with cross-encoder reranking for 40% precision boost
Streaming RAG Chat
Real-time GPT-4 responses with source citations, page numbers, and retrieval score breakdowns
Production Stack
Retrieval explainability, monitoring dashboard, security hardening, and Docker deployment
Curriculum
4 parts, each with a clear checkpoint. Build incrementally, test as you go.
Technical Standards
Production patterns you'll implement across the retrieval pipeline.
Sub-second retrieval with Redis caching, batch embeddings, and optimized vector indices
Hybrid BM25 + vector search with cross-encoder reranking and configurable Top-K
Production-scale ingestion with monitoring, explainability, and security hardening
Environment Setup
Launch the RAG stack and run your first hybrid search query.
# Clone the project & launch RAG stack$ git clone https://github.com/aide-hub/enterprise-rag.git$ cd enterprise-rag# Start FastAPI + Chroma + Redis + PostgreSQL$ docker-compose -f docker-compose.rag.yml up -d# Run a hybrid search query$ curl -X POST http://localhost:8000/api/search \$ -H "Content-Type: application/json" \$ -d '{"query": "quarterly revenue trends", "top_k": 5}'
Tech Stack
Prerequisites
- Python fundamentals (classes, async/await, packages)
- REST API design (HTTP methods, JSON)
- Basic ML concepts (embeddings, vectors, similarity)
- Docker basics (containers, compose)
Related Learning Path
Deepen your understanding of RAG architectures, vector databases, retrieval strategies, and production deployment patterns.
RAG Learning PathNew to RAG? Read the complete guide covering chunking, embeddings, hybrid search, and reranking.
What is RAG? — Full GuideReady to build your RAG system?
Start with Part 1: Document Ingestion Pipeline