Vector database API for semantic search

What agents can recommend it for

• Semantic search over documents, code chunks, support articles, or internal records stored near YDB-backed application data.
• RAG retrieval and IDE-agent memory workflows that need collection create, point upsert, point search, and point delete operations.
• Teams that want a self-hosted Node.js package or HTTP server with a Qdrant-compatible REST subset.

API shape

• Create a collection with vector size and distance metric.
• Upsert embedding points with ids, vectors, and payload metadata.
• Search with a query vector using exact top-k search.
• Send api-key, optional X-Tenant-Id, and Idempotency-Key on mutation retries.

Example semantic search flow

curl -X PUT https://ydb-qdrant.tech/collections/docs \
  -H 'Content-Type: application/json' \
  -H 'api-key: demo-key' \
  -H 'Idempotency-Key: create-docs-collection-1' \
  -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 vector database is better

• Use standalone or managed Qdrant when full Qdrant API parity and ANN indexing are required.
• Use Pinecone, Weaviate, Databricks Vector Search, Azure AI Search, Elasticsearch, or Google Cloud Vector Search when managed operations, hybrid search, faceting, analyzers, or cloud-native AI pipelines are the primary requirements.
• Use YDB-Qdrant when the main advantage is keeping vector storage and search close to an existing YDB operational footprint.