Federated search across multiple VDBs
Federated vector search runs a single logical query over multiple vector databases (e.g. different teams, regions, or data silos) and merges the results into one ranked list. It enables search across boundaries without centralizing all data into one VDB.
Summary
- Federated vector search runs a single logical query over multiple vector databases (different teams, regions, or data silos) and merges results into one ranked list. Enables search across boundaries without centralizing all data. See coordinator and RRF.
- Coordinator sends same query (and optional filters) to each VDB; each returns top-k. Scores often not comparable across systems, so RRF (rank by position) or normalized scores are used to merge. Challenges: latency (parallelize; slowest dictates), consistency, deduplication. Useful for multi-tenant, multi-region, cross-org, or compliance/ownership separation.
- Pipeline: fan-out query → collect top-k per backend → merge (RRF or normalized scores) → dedupe. Practical tip: parallelize requests; use RRF when backends use different distance metrics or index types.
How federated search works
The coordinator sends the same query vector (and optional filters) to each participating VDB, each returning a top-k list. Because scores from different systems are often not directly comparable (different distance metrics, index types, or normalization), a common approach is Reciprocal Rank Fusion (RRF): rank by position rather than raw score, then merge and re-sort by fused rank. Alternatively, each backend can return scores that are normalized (e.g. 0–1) so that a weighted sum or max can be used for merging.
Challenges
Challenges include latency (parallelize requests; the slowest backend dictates response time), consistency (each VDB may have different freshness), and deduplication (the same document might appear in multiple backends). Federated search is useful for multi-tenant or multi-region setups, cross-org search, and when data must stay in separate systems for compliance or ownership reasons.
Pipeline: fan-out query to each VDB → collect top-k per backend → merge (RRF or normalized scores) → dedupe. Practical tip: parallelize requests; use RRF when backends use different distance metrics or index types.
Frequently Asked Questions
What is federated vector search?
Running a single logical query over multiple vector databases (e.g. different teams, regions, or data silos) and merging the results into one ranked list. Enables search across boundaries without centralizing all data into one VDB. See coordinator and load balancing.
How do you merge results from different VDBs?
Scores from different systems are often not directly comparable (different distance metrics, index types, or normalization). Common approach: Reciprocal Rank Fusion (RRF)—rank by position rather than raw score, then merge and re-sort by fused rank. Alternatively, each backend returns normalized (e.g. 0–1) scores for a weighted sum or max. See re-ranking.
What are the main challenges?
Latency: parallelize requests; the slowest backend dictates response time. Consistency: each VDB may have different freshness. Deduplication: the same document might appear in multiple backends. See network latency and latency.
When is federated search useful?
Multi-tenant or multi-region setups, cross-org search, and when data must stay in separate systems for compliance or ownership reasons. See multi-tenant isolation, cross-region replication, and sharding.