Enterprise Document RAG Knowledge Assistant
Semantic search and grounded question-answering system over private company documentation
Team members waste hours searching across hundreds of internal PDFs, handbooks, and operational specifications, frequently relying on guesswork or outdated information.
Built a Retrieval-Augmented Generation (RAG) assistant that indexes company documents into a vector-enabled PostgreSQL database, retrieves context using hybrid vector-keyword search, and produces cited answers.
Staff receive fast, factual answers that cite the exact internal source document, with safety guardrails that refuse to speculate when knowledge base context is insufficient.
Key Architecture Components
- 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 keyword matching with cosine similarity embeddings
- 4Context-grounded prompt engineering with explicit citation requirements and confidence scoring
Engineering Decisions & Business Rationale
pgvector over standalone vector databases
Keeping relational access control, document versions, and vector embeddings in a single PostgreSQL instance minimized operational complexity and backup overhead.
Semantic chunking over fixed token cuts
Chunking along logical headers and sections preserved semantic boundaries, significantly improving retrieval precision.
Challenges & Trade-offs
- •Higher upfront document processing time during ingestion in exchange for markedly higher answer accuracy during live 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 explicit fallback ('I do not have enough context in the verified documents to answer') to prevent speculation.
Interested in a similar build for your operations?
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