Enterprise Document RAG Knowledge Assistant
Semantic search and grounded question-answering system over private technical documentation
Interactive Architecture & Pipeline Inspector
Hybrid RAG Architecture & Vector Retrieval
Enterprise documentation search uniting PostgreSQL pgvector HNSW indexing and grounded citations
Document Ingestion & Chunking
Role: Recursive Syntax ParserSplits engineering documentation into 512-token chunks with 10% overlap preserving code blocks and header hierarchy.
Engineers and support teams struggle to locate precise answers within hundreds of pages of API specifications, architecture decision records, and operational runbooks.
Built a retrieval-augmented generation (RAG) assistant that indexes internal Markdown and PDF documentation into pgvector, applies hybrid keyword-vector retrieval, and provides cited, verifiable answers.
Implemented document chunking strategies with contextual metadata, built the hybrid retrieval pipeline in pgvector, and engineered prompt templates that mandate document citations.
Architectural Breakdown
- 1Document ingestion pipeline with semantic chunking and sliding window token overlaps
- 2Vector database utilizing PostgreSQL with pgvector extension for unified relational & vector queries
- 3Hybrid search combining BM25 keyword matching with cosine similarity embeddings
- 4Context-grounded prompt engineering with explicit citation requirements and confidence scoring
Key Engineering Decisions & Rationale
pgvector over standalone vector DBs
Keeping relational data (user permissions, document versions) and embeddings in the same PostgreSQL database reduced infrastructure complexity and backup overhead.
Semantic chunking over fixed token cuts
Chunking along Markdown headers and paragraphs preserved context boundaries, cutting query hallucination rates dramatically.
Challenges & Trade-offs
- •Higher ingestion time during semantic chunking in exchange for substantially more accurate retrieval precision during user queries.
What I Learned
- ✓Chunk size and overlap tuning has a far greater impact on response accuracy than switching LLM model versions.
- ✓Always include an 'I do not have enough context to answer' prompt instruction to stop speculative completions.
Interested in similar architecture for your team?
Let's evaluate your current requirements and engineering constraints.