Why AI Agents Need Verifiable Evidence: Building an MCP-Native Retrieval Engine with PostgreSQL

说明与用途

Knowledge Fabric 通过在 PostgreSQL 中结合 BM25 与向量搜索，为代理工作流提供可验证的证据，提供每段检索文本的元数据和哈希。

问题

Standard Retrieval-Augmented Generation (RAG) systems lack provenance, exact-term recall, and efficient context sizing, making them unsuitable for agentic workflows that require verifiable evidence and policy compliance.

结果

Agents can now retrieve context with exact-term recall, provenance, and efficient prompt sizing, reducing policy failures and improving execution reliability.

工作流

• 1. Index documents into PostgreSQL with pgvector and tsvector for hybrid search.
• 2. Use Knowledge Fabric’s retrieve_evidence MCP tool to perform RRF‑based hybrid search and obtain cited evidence packages.
• 3. Agent calls get_document to fetch full content if needed.
• 4. Agent uses explain_retrieval to understand scoring breakdown.
• 5. Agent plans actions via Intent Fabric and executes writes through Enterprise Adapters, ensuring provenance and policy compliance.

证据

Knowledge Fabric is built directly on PostgreSQL + pgvector: Hybrid Search with Reciprocal Rank Fusion (RRF) fuses PostgreSQL BM25-style lexical search with semantic vector embeddings

来源

retrieve_evidence: Performs hybrid search and returns bounded, cited evidence packages

来源