[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-metadata-filtering::en":3,"gloss-cluster-metadata-filtering::en":20,"gloss-next-metadata-filtering::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"metadata-filtering","data-infra","Metadata Filtering","Metadata filtering is the practice of attaching structured attributes (metadata) — like a date, a category, a user ID, a status flag, a price — to each vector stored in a vector database, and then constraining a similarity search to only consider vectors whose metadata matches specified filter conditions, alongside the semantic similarity ranking itself. It answers a query pattern that pure vector similarity search can't handle alone: \"find the passages most similar to this question, but only from documents published after 2025 and tagged `product: enterprise`.\" Why it matters for AI\u002FSaaS builders: nearly every real-world RAG or semantic-search feature needs this. A support-desk AI shouldn't surface an outdated article that's been superseded; a multi-tenant app must never return another customer's data no matter how semantically similar it is; a marketplace search needs to combine \"find products like this\" with \"only show ones in stock and under $50.\" Vector similarity alone is oblivious to all of these business-logic constraints — it only knows about meaning, not about access control, recency, or inventory state — so metadata filtering is the mechanism that reconnects semantic search to the real, structured constraints every production application actually has. How it works: at insert\u002Fupsert time, each vector is stored alongside a metadata object (commonly JSON: `{\"category\": \"billing\", \"published\": true, \"date\": \"2026-03-14\", \"tenant_id\": \"t_88\"}`). At query time, the application passes both the query vector and a filter expression (syntax varies by vector database — Pinecone uses a Mongo-style operator syntax like `{\"tenant_id\": {\"$eq\": \"t_88\"}, \"date\": {\"$gte\": \"2026-01-01\"}}`; pgvector, being plain Postgres, uses ordinary SQL `WHERE` clauses alongside the `ORDER BY embedding \u003C=> $1`). Some vector databases apply the filter before the ANN search (pre-filtering, which can be slower if the filter is very selective and the index has to search harder to find enough matches within a narrow subset) while others apply it after (post-filtering, which can return fewer than `top_k` results if too many top matches get filtered out) — the trade-off is implementation-specific and worth checking in a given database's docs when precision matters. Worked example: a job-board SaaS's AI-powered candidate search embeds resumes and lets recruiters search in natural language (\"senior backend engineer with Kubernetes experience\"), but a recruiter should only ever see candidates who opted into being searchable and who are located in their hiring region. The query combines vector similarity with a filter: `{\"opted_in\": true, \"region\": {\"$in\": [\"EU\", \"UK\"]}, \"years_experience\": {\"$gte\": 5}}` — ensuring the semantically-best matches returned are also the only ones the recruiter is legally and contractually allowed to see.","Metadata filtering narrows a vector search to only vectors matching specified attributes, combining semantic similarity with exact structured filters.",null,[11,14,17],{"slug":12,"name":13},"hybrid-search","Hybrid Search",{"slug":15,"name":16},"namespace","Namespace",{"slug":18,"name":19},"vector-store","Vector Store",[21,25,28,31,34,38,41,44,47,50,54,57],{"slug":22,"category":5,"name":23,"updated_at":24},"acid","ACID","2026-08-24T02:46:37+00:00",{"slug":26,"category":5,"name":27,"updated_at":24},"ann-search","ANN Search",{"slug":29,"category":5,"name":30,"updated_at":24},"backpressure","Backpressure",{"slug":32,"category":5,"name":33,"updated_at":24},"batch-processing","Batch Processing",{"slug":35,"category":5,"name":36,"updated_at":37},"bm25","BM25","2026-08-24T02:46:38+00:00",{"slug":39,"category":5,"name":40,"updated_at":24},"cache","Cache",{"slug":42,"category":5,"name":43,"updated_at":24},"cap-theorem","CAP Theorem",{"slug":45,"category":5,"name":46,"updated_at":24},"change-data-capture","Change Data Capture (CDC)",{"slug":48,"category":5,"name":49,"updated_at":24},"chroma","Chroma",{"slug":51,"category":5,"name":52,"updated_at":53},"chunk-overlap","Chunk Overlap","2026-08-24T03:30:02+00:00",{"slug":55,"category":5,"name":56,"updated_at":24},"columnar-storage","Columnar Storage",{"slug":58,"category":5,"name":59,"updated_at":24},"connection-pooling","Connection Pooling"]