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qdrant

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

Updated Sep 10, 2026
Search Tags:#semantic search#embeddings#rag#vector database#vector search#qdrant#similarity search
One-click install
npx skills add https://github.com/loteiron/ZeusAgent --skill qdrant-loteiron
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant
Source: https://github.com/loteiron/ZeusAgent/tree/main/optional-skills/mlops/qdrant
Command: npx skills add https://github.com/loteiron/ZeusAgent --skill qdrant-loteiron

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Building production RAG and semantic search systems requires a vector database that handles low-latency similarity search, metadata filtering, and scaling, which is complex to implement from scratch. ## Core Features & Use Cases - Vector Search with Filtering: Store dense and sparse vectors with payloads, then run nearest-neighbor queries combined with keyword, range, geo, and full-text filters. - RAG Integration: Connect with sentence-transformers, LangChain, and LlamaIndex to retrieve context for retrieval-augmented generation pipelines. - Production Scaling: Use quantization, payload indexing, sharding, replication, and snapshots for large collections and distributed deployments. - Use Case: Index a knowledge base of documents as embeddings, then retrieve the top-k most relevant passages filtered by category to ground an LLM's answers. ## Quick Start Use the qdrant skill to create a collection, upsert document embeddings, and run a filtered similarity search against a local Qdrant instance.

Frequently Asked Questions about qdrant

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 defining dimension and distance metric, upsert PointStruct items with vectors and payloads, then call client.query_points with your query vector and limit to retrieve nearest neighbors.

Qdrant vs Chroma vs FAISS for RAG applications?▼

Qdrant suits production RAG needing filtering, sharding, and on-premise control. Chroma is simpler for embedded use cases, FAISS offers maximum raw speed for batch research, and Pinecone fits teams preferring fully managed services.

Does Qdrant support hybrid search with dense and sparse vectors?▼

Yes, Qdrant supports named vectors allowing dense and sparse vectors per point in one collection. You can combine both using prefetch queries with Reciprocal Rank Fusion to merge semantic and keyword search results.

Why is my Qdrant filtered search slow?▼

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

How do I fix vector dimension mismatch errors in Qdrant?▼

Dimension mismatch occurs when your embedding size differs from the collection's configured vector size. Check the expected dimension with client.get_collection, then recreate the collection with VectorParams matching your embedding model's output size.

Can Qdrant reduce memory usage for large vector collections?▼

Yes, Qdrant offers scalar quantization for roughly 4x memory reduction, product quantization for 16x, and binary quantization for 32x. You can also enable on_disk_payload and on-disk HNSW to move data off RAM.

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