AI Knowledge Assistant (RAG Engine)
An advanced, production-ready RAG system built to solve the contextual limitations of base LLMs by dynamically indexing complex research papers.
Business Context
Enterprise knowledge bases are massive and unstructured. Precise retrieval saves thousands of hours of manual document review.
Technical Context
Required a sophisticated hybrid search (Dense + Sparse) coupled with a neural cross-encoder reranker to handle massive document ingestion.
Architecture Flow
Component Breakdown
- Ingestion Pipeline: Document loaders and text splitters for PDFs and arXiv API.
- Retrieval Engine: FAISS (dense) and BM25 (sparse) running in parallel.
- Re-Ranking Phase: HuggingFace ms-marco-TinyBERT Cross-Encoder.
- Generation Engine: FastAPI backend streaming Server-Sent Events from Groq Llama 3.
Data Flow
- Raw documents are ingested, chunked, and embedded.
- User query hits both FAISS and BM25 indices.
- Candidate chunks are scored and resorted by the Cross-Encoder.
- Top-K chunks are injected into the LLM prompt and streamed to the UI.
Infrastructure
Engineering Deep Dive
Challenges
- Keyword Loss: Pure vector search (FAISS) consistently failed to retrieve documents when the user queried for exact acronyms or specific part numbers.
- Context Window limits: Stuffing too many retrieved chunks into the LLM caused performance degradation and API limits.
- PDF Parsing: Extracting clean text from multi-column academic PDFs with math equations.
Technical Decisions
Implementing Hybrid Search (BM25 + FAISS)
FAISS excels at semantic meaning, while BM25 guarantees that exact technical keywords and acronyms are never missed.
Adding a Cross-Encoder Re-Ranker
Retrieving 20 chunks from hybrid search and re-ranking them neutrally proved far more accurate than just taking the top 5 from vector search directly.
Results & Metrics
Lessons Learned
- Vector databases alone are insufficient for production RAG. Hybrid search is mandatory.
- Cross-encoders add ~200ms latency but double retrieval accuracy; it's a necessary tradeoff.
Recruiter Takeaways
AI Engineer
Understands advanced RAG concepts (Hybrid Search, Re-Ranking, Chunking Strategies) beyond basic tutorials.
ML Engineer
Familiar with deploying and running HuggingFace Cross-Encoders locally.
Startup Founder
Built a highly performant, production-ready enterprise knowledge tool.
Software Engineer
Solid architectural boundaries between ingestion, retrieval, and generation.