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Unified relational and vector data architecture.

pgvector Database Expert

Integrate vector search directly into your PostgreSQL database using pgvector. Build scalable semantic search without external tools.

pgvectorPostgreSQLVector SearchEmbeddings

Core Capabilities

pgvector Extension Setup
Cosine Similarity Search
HNSW Indexing
Prisma Integration

Related Case Studies

OmniBid Intelligence Engine

OmniBid Intelligence Engine

Finding good freelance jobs on platforms like Upwork is exhausting. Many jobs are scams, have insulting budgets, or use legacy technologies. Applying to them wastes hours of time and real money (Connects).

Standard AI models are prone to hallucination—if a client posts a job without a budget, standard LLMs will confidently guess or invent details instead of flagging missing data.

Frequently Asked Questions

Why use pgvector instead of Pinecone?

pgvector allows you to keep your standard app data (users, posts) and your AI vectors in the exact same database. This allows for powerful queries that combine keyword filtering with semantic search.

Is pgvector fast enough for production?

Yes, with the recent addition of HNSW (Hierarchical Navigable Small World) indexes, pgvector can query millions of embeddings in milliseconds.