AI & LLMs
#pgvector & LangChain
8 min read
Building Domain-Specific Retrieval-Augmented Generation (RAG) with Vector Databases
How to implement production-ready RAG architectures using PostgreSQL pgvector, semantic re-ranking, and strict hallucination guardrails.
A
AI Systems Engineering
Principal AI Engineer
Published:
Sep 10, 2026
Executive Summary & Key Takeaways
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Hierarchical document chunking with metadata headers increases semantic retrieval precision by 37%.
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Bi-encoder vector search combined with cross-encoder re-ranking filters out irrelevant context chunks.
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Strict JSON output validation guarantees deterministic responses for downstream business logic.
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Client data is strictly isolated with zero training retention policies.
### 1. Chunking Strategies and Context Preservation
Naive fixed-size character chunking frequently breaks conceptual cohesion. Implementing semantic boundary chunking with parent-child relationship indexing allows the vector engine to retrieve precise excerpts while passing the broader surrounding context window to the synthesis model.
### 2. Hybrid Search: Dense Vectors + BM25 Lexical Matching
Dense embedding vectors excel at semantic similarity, but struggle with precise alphanumeric part numbers, SKUs, and error codes. Combining dense embeddings with PostgreSQL full-text search (BM25) via Reciprocal Rank Fusion (RRF) delivers the most resilient production search engine.
### 3. Guardrails & Output Validation
Every enterprise AI agent must operate within strict boundaries. Automated schema verification validates that LLM outputs conform to required typed payloads before executing financial transactions or updating customer records.
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