feat: implement pgvector semantic and hybrid rrf search with bgd indexing#10
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Prakhar-Sethi012 wants to merge 1 commit into
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feat: implement pgvector semantic and hybrid rrf search with bgd indexing#10Prakhar-Sethi012 wants to merge 1 commit into
Prakhar-Sethi012 wants to merge 1 commit into
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Title: feat: Implement Hybrid Search Engine & Background Indexer
Description
This PR introduces the core AI search domain for the WhereTF backend. It sets up the pgvector database infrastructure, implements a background asynchronous indexing service to prevent API blocking, and exposes a high-performance Reciprocal Rank Fusion (RRF) search route that combines semantic vector search with full-text keyword matching.
Type of Change
Architecture & Technical Highlights
pgvectorextension. Created SQLAlchemy models (filesandfile_content) and generated Alembic migrations to handle vector storage.app/services/indexer.pyusing FastAPIBackgroundTasks. It opens an isolated SQLAlchemy session, chunks incoming text, generates 384-dimensional embeddings, stores them in PostgreSQL, and cleans up the server's/tmp/directory.processing/search.pymodule to execute complex raw SQL via SQLAlchemy'sdb.execute().POST /search/endpoint supportingvector,keyword, andhybridmodes.How This Was Tested
backendsafely waits fordbhealth check)./test-indexer/route to ingest a dummy syllabus file.POST /search/(Hybrid Mode) via Swagger UI, receiving a200 OKwith correctly fused vector/keyword scoring and text chunks.Next Steps & Blockers
POST /files/upload route to be completed so we can route real user files from the web client directly into this background indexing pipeline.