fastapi-pgsearch-patterns

Hybrid retrieval in FastAPI combines pg_search for lexical bm25 ranking and pgvector for semantic embedding similarity, merging both result sets in an async SQLAlchemy route. This pattern provides end-to-end integration for AI-powered search APIs.

Combine pgvector and pg_search in async SQLAlchemy using pattern recipes that configure Vector(768) embeddings and execute safe lexical plus semantic queries. This approach supports high-throughput search workloads in FastAPI applications.

Yes, this hybrid retrieval pattern explicitly targets async FastAPI applications using SQLAlchemy Async. It provides production-like patterns for integrating pgvector and pg_search to support scalable, high-throughput API search workloads.

This FastAPI pattern enforces Vector(768) dimensions for pgvector embeddings. You must structure your vector embeddings to match this specific dimension to ensure safe and scalable end-to-end API integration.

Yes, you can use these patterns for building RAG APIs, semantic search endpoints, and streaming results from retrieval-augmented generation pipelines. The patterns support end-to-end integration combining FastAPI with lexical and vector retrieval workflows.

Use a hybrid ranking workflow in FastAPI when you need to merge lexical bm25 results with semantic vector embeddings for more accurate document search. Standalone vector search lacks the keyword precision that pg_search provides in retrieval-augmented generation pipelines.