ChromaDB: Metadata Filtering for Precise Semantic Search
Plus semantic search in PostgreSQL with pgvector
Grab your coffee. Here are this week’s highlights.
📅 Today’s Picks
ChromaDB: Metadata Filtering for Precise Semantic Search
Problem
Search for “latest ML research” and semantic search might return highly relevant papers from 2019.
That’s because similarity doesn’t understand constraints. You need metadata filtering to enforce “year >= 2024” at the database level.
Solution
ChromaDB’s where clause lets you combine “find similar” with “but only from 2024.” The database filters first, then ranks by similarity.
Key operators:
$eq, $ne for exact matching
$gt, $gte, $lt, $lte for range queries
$in, $nin for set membership
$and, $or for combining conditions
📖 View the full article | 🧪 Run code | ⭐ View GitHub
⭐ Worth Revisiting
Semantic Search in PostgreSQL with pgvector
Problem
Traditional PostgreSQL keyword queries return limited results because they require exact string matches. This approach misses semantically related data that shares meaning but uses different terminology.
Solution
pgvector enables vector search within PostgreSQL. This allows semantic matching of contextually similar content.
Key benefits:
Native PostgreSQL integration with existing databases
Fast exact and approximate nearest neighbor search
Six distance metrics including L2, cosine, inner product, and Hamming
Seamless Python integration via SQLAlchemy or psycopg2
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