qdrant-client and qdrant-multi-node-cluster
The official Python client library complements the community-managed clustering solution, as developers would use the client to interact with a Qdrant instance deployed via the multi-node cluster setup.
About qdrant-client
qdrant/qdrant-client
Python client for Qdrant vector search engine
Provides type-safe bindings for all Qdrant API methods with dual REST and gRPC transports, plus a local in-memory or disk-persisted mode for development without a server. Built-in embedding inference via FastEmbed (CPU/GPU) or Qdrant Cloud models enables end-to-end vector workflows in a single client, simplifying document upload and semantic search operations. Supports both synchronous and asynchronous request patterns with helper methods like `upload_collection` that handle chunking and batch operations automatically.
About qdrant-multi-node-cluster
Mohitkr95/qdrant-multi-node-cluster
Scalable Qdrant vector database cluster with Docker Compose, monitoring, and comprehensive documentation for high-performance similarity search applications.
Implements dynamic sharding and replication across 3+ nodes with customizable shard counts and distance metrics (COSINE, etc.), coordinated via bootstrap peer discovery. Includes a Python client library and demo application for cluster interaction, alongside production-ready Prometheus/Grafana monitoring with real-time performance dashboards and detailed configuration guides for vector parameters, node scaling, and performance tuning.
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