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qdrant-vector-search

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

14|5|Updated Apr 9, 2026
Search Tags:#semantic search#embeddings#rag#vector database#vector search#qdrant#similarity search
One-click install
npx skills add https://github.com/MLT-OSS/hermes-agent-go --skill qdrant-vector-search-mlt-oss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-vector-search
Source: https://github.com/MLT-OSS/hermes-agent-go/tree/main/optional-skills/mlops/qdrant
Command: npx skills add https://github.com/MLT-OSS/hermes-agent-go --skill qdrant-vector-search-mlt-oss

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 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 combined with payload filters, batch queries, and HNSW tuning via the qdrant-client Python library. - Hybrid and Multi-Vector Search: Combine dense and sparse vectors with Reciprocal Rank Fusion, plus quantization (scalar, product, binary) for memory optimization. - Production Deployment: Configure sharding, replication, snapshots, collection aliases, and multitenancy for distributed clusters. - Use Case: Build a RAG pipeline by embedding documents with sentence-transformers, upserting them into a Qdrant collection, and retrieving top-k context chunks to augment LLM prompts. ## Quick Start Use the qdrant skill to create a collection, upsert embedded documents, 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 limit. Filters can restrict results by payload fields.

Qdrant vs Chroma vs FAISS for RAG applications?▼

Qdrant suits production RAG needing filtering, sharding, and replication. 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. Use query_points with Prefetch for each vector type and FusionQuery with RRF to combine results into a single ranked list.

Why is my Qdrant filtered search slow?▼

Slow filtered search usually means missing payload indexes. Create an index with client.create_payload_index for each filtered field, enable scalar quantization for large collections, or tune HNSW parameters like ef_construct and m.

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

The error occurs when query or point vectors differ from the collection's configured size. Check the expected dimension via client.get_collection, then recreate the collection with the correct size matching your embedding model output.

Can Qdrant reduce memory usage for large vector collections?▼

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

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