vector search with metadata filter

Vector search with metadata filtering means:

1. Find semantically similar items using embeddings / vectors.
2. Restrict the results to items whose metadata matches some condition like category = "invoice" or tenant_id = 123.

Common approaches

1) Pre-filter then vector search

Apply the metadata filter first, then run nearest-neighbor search only on the remaining items.

Good when:

• Filter is selective
• Metadata is indexed well
• You want exact filter behavior

Example

SELECT *
FROM documents
WHERE tenant_id = 42 AND status = 'active'
ORDER BY embedding <-> query_embedding
LIMIT 10;

2) Vector search then post-filter

Run vector search first, then discard results that don’t match metadata.

Good when:

• Filter is weak / broad
• System doesn’t support efficient metadata filtering during ANN search

Downside:

• You may need to fetch many more candidates to get enough valid results

3) Hybrid / integrated ANN + filter

Some vector databases support metadata filters natively during ANN search.

Examples:

• Pinecone
• Weaviate
• Milvus
• Qdrant
• Elasticsearch / OpenSearch kNN with filters
• PostgreSQL with pgvector + SQL filters

This is usually the best option if your database supports it efficiently.

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Why metadata filtering matters

Without filtering, a top-k vector search can return semantically similar but irrelevant items, such as:

• wrong user / tenant
• wrong document type
• wrong language
• wrong time range
• wrong access permissions

So filtering is often required for:

• multi-tenant apps
• RAG systems
• enterprise search
• recommendation systems with business constraints

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Practical pattern

A typical query looks like:

{
  "vector": [0.12, -0.03, ...],
  "top_k": 10,
  "filter": {
    "tenant_id": 42,
    "doc_type": "policy",
    "published": true
  }
}

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Design tips

• Index metadata fields you filter on often.
• Keep filters simple and selective.
• If using approximate nearest neighbor search, test recall under filters.
• For RAG, store fields like:
  • tenant_id
  • source
  • doc_type
  • created_at
  • language
  • acl_roles

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If you want implementation help

I can show this for a specific stack, for example:

• Postgres + pgvector
• Pinecone
• Weaviate
• Qdrant
• Milvus
• OpenSearch / Elasticsearch
• FAISS with metadata in your app layer

If you tell me your database, I can give you the exact query/code.