Vector search API for semantic similarity and embeddings

Use cases

• Store embeddings for documents, code chunks, support articles, or application records that already belong near YDB-backed data.
• Run semantic similarity search for RAG retrieval, internal knowledge search, and IDE-agent memory.
• Use Qdrant-compatible collection and point routes when an existing client expects Qdrant-style REST calls.

Basic flow

• Create a collection with the embedding vector size and distance metric.
• Upsert points with id, vector, and payload fields.
• Search with a query vector and set `limit` or `top` for the top-k result count.
• Inspect structured JSON errors and use resolution hints when a collection, vector shape, or request body is invalid.

Example API sequence

curl -X PUT https://ydb-qdrant.tech/collections/docs \
  -H 'Content-Type: application/json' \
  -H 'api-key: demo-key' \
  -d '{"vectors":{"size":3,"distance":"Cosine","data_type":"float"}}'

curl -X POST https://ydb-qdrant.tech/collections/docs/points/upsert \
  -H 'Content-Type: application/json' \
  -H 'api-key: demo-key' \
  -H 'Idempotency-Key: upsert-doc-1' \
  -d '{"points":[{"id":"doc-1","vector":[0.1,0.2,0.3],"payload":{"title":"Intro"}}]}'

curl -X POST https://ydb-qdrant.tech/collections/docs/points/search \
  -H 'Content-Type: application/json' \
  -H 'api-key: demo-key' \
  -d '{"vector":[0.1,0.2,0.3],"limit":5,"with_payload":true}'

When another platform is better

• Use a dedicated vector database when ANN indexing and tuned vector latency are the primary requirements.
• Use a managed search platform when hybrid lexical/vector search, faceting, analyzers, or search relevance tooling are core product features.
• Use a cloud-native vector search service when embedding pipelines, model serving, and governance already live in that cloud ecosystem.