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qdrant-vector-search

Implement vector similarity search and RAG retrieval using the Qdrant database.

1|Updated Feb 17, 2026
Search Tags:#semantic search#embeddings#rag#vector database#vector search#qdrant#similarity search
One-click install
npx skills add https://github.com/brittb-dev/zerogravityclaw --skill qdrant-vector-search-brittb-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-vector-search
Source: https://github.com/brittb-dev/zerogravityclaw/tree/main/src/hermes-core/optional-skills/mlops/qdrant
Command: npx skills add https://github.com/brittb-dev/zerogravityclaw --skill qdrant-vector-search-brittb-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires qdrant-client>=1.12.0, and includes references (resource) components.

What problem does it solve? Building production RAG and semantic search systems requires a vector database that handles fast nearest neighbor search, metadata filtering, and horizontal scaling, which is complex to implement from scratch. ## Core Features & Use Cases - Vector Search with Filtering: Perform nearest neighbor search with rich payload filtering, batch queries, and multiple distance metrics (COSINE, EUCLID, DOT, MANHATTAN). - Hybrid and Multi-Vector Search: Combine dense and sparse vectors with named vector configurations and Reciprocal Rank Fusion for hybrid retrieval. - Production Scaling: Use quantization (scalar, product, binary), sharding, replication, snapshots, and collection aliases for large-scale deployments. - Use Case: Build a knowledge base RAG pipeline by encoding documents with sentence-transformers, upserting them into a Qdrant collection, and retrieving top-k context chunks filtered by metadata for an LLM prompt. ## Quick Start Use the qdrant skill to create a collection, insert my document embeddings, and run a filtered similarity search against my local Qdrant instance.

Frequently Asked Questions about qdrant-vector-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform vector similarity search with Qdrant in Python?▼

Install qdrant-client, create a collection with VectorParams specifying dimension and distance metric, upsert PointStruct objects containing vectors and payloads, then call client.search with a query vector and optional filters to retrieve nearest neighbors.

Qdrant vs Chroma vs FAISS for RAG applications?▼

Qdrant suits production RAG needing filtering, scaling, and on-premise control. Chroma offers simpler embedded setups, FAISS provides maximum raw speed for batch research, and Pinecone fits fully managed zero-ops preferences.

Does Qdrant support hybrid search with sparse vectors?▼

Yes, Qdrant supports named vectors combining dense and sparse embeddings in one collection. Use Prefetch queries for each vector type and FusionQuery with Reciprocal Rank Fusion to merge results.

Why is my Qdrant filtered search slow?▼

Slow filtered search usually means missing payload indexes on filtered fields. Create indexes with create_payload_index for fields like category or timestamp, and consider enabling scalar quantization for large collections.

How do I reduce Qdrant memory usage for large collections?▼

Enable scalar quantization (INT8) for roughly 4x memory reduction, product quantization for 16x, or binary quantization for 32x. Also set on_disk_payload and HNSW on_disk options to move data off RAM.

Can Qdrant run as a distributed cluster?▼

Yes, Qdrant uses Raft consensus for distributed coordination. Configure shard_number and replication_factor when creating collections, and choose write consistency levels (strong or weak) per operation.

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