MCOS: Planning Evidence-Backed Content Search with pgvector

Content intelligence platforms often hallucinate because they treat generative LLMs as search engines. In reality, language models must be paired with strict vector grounding and human approval gates before publishing facts or analyses.

I designed MCOS: a content intelligence operating system using FastAPI, PostgreSQL, and pgvector HNSW indexing for high-precision semantic evidence retrieval.

1. Vector Storage Schema with pgvector

-- postgresql/schema.sql
CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE research_chunks (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    document_title TEXT NOT NULL,
    chunk_content TEXT NOT NULL,
    embedding vector(1536) NOT NULL,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Fast Approximate Nearest Neighbor HNSW Index
CREATE INDEX ON research_chunks USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);