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qdrant-vector-search

Perform vector similarity search with metadata filtering via the qdrant-client API.

1|Updated May 16, 2026
Search Tags:#semantic search#embeddings#rag#vector search#qdrant#hnsw#hybrid filtering
One-click install
npx skills add https://github.com/devMoez/titan --skill qdrant-vector-search-devmoez
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-vector-search
Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/qdrant
Command: npx skills add https://github.com/devMoez/titan --skill qdrant-vector-search-devmoez

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Qdrant vector similarity search solves the problem of quickly finding the most relevant items for a query by comparing embeddings, while also supporting metadata filters for practical RAG and search pipelines.

Core Features & Use Cases

  • High-performance vector search (Rust): Low-latency nearest-neighbor retrieval suitable for production semantic search and RAG.
  • Hybrid retrieval with filtering: Combine vector similarity with payload-based constraints (e.g., category, timestamps, tenant_id) to narrow results.
  • Scalable and feature-rich storage: Support distributed operation, sharding/replication, multi-vector points, quantization for memory efficiency, and REST/gRPC access.
  • Use case: Build a knowledge-base RAG pipeline where you embed documents, upsert them into Qdrant, and retrieve top-k relevant passages for a user question with optional category or time-range filtering.

Quick Start

Use the qdrant-vector-search skill to run a local Qdrant instance, create a collection with an embedding vector size that matches your model (and choose a distance metric like COSINE), upsert points with both vectors and payload metadata, then perform a filtered search to retrieve the most relevant contexts for your RAG query.

Frequently Asked Questions about qdrant-vector-search

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

FAQPage Schema
How do I implement filtered vector search for a RAG pipeline?▼

Filtered vector search for RAG combines nearest-neighbor queries with payload metadata constraints. You upsert points with embeddings and payloads, then retrieve top-k relevant passages using category or time-range filters via the qdrant-client API.

How does hybrid retrieval with metadata filtering work?▼

Hybrid retrieval narrows results by applying payload-based constraints like tenant_id or timestamps alongside vector similarity. This ensures the nearest-neighbor search only matches items that satisfy specific metadata conditions within the collection.

Can I use semantic search with sharding and replication for scalable deployments?▼

Semantic search supports distributed operation through sharding and replication. This enables scalable deployments while maintaining low-latency nearest-neighbor retrieval, and includes quantization features to optimize memory efficiency for large vector collections.

What do I need to set up a Qdrant collection for embedding-based question answering?▼

Setting up a collection requires a local Qdrant instance and an embedding vector size matching your model. You must choose a distance metric like COSINE, upsert points with vectors and payload metadata, then execute search operations for retrieval.

Does Qdrant support multi-vector representations and quantization?▼

Qdrant supports multi-vector points and quantization to optimize memory efficiency. These features allow you to store complex embeddings and reduce memory overhead while performing fast similarity search across large datasets.

What is the best way to retrieve top-k relevant contexts for semantic retrieval?▼

The best way to retrieve top-k contexts is using vector similarity search with optional metadata filtering. This approach finds the most relevant items by comparing embeddings while applying payload constraints to narrow results for production RAG pipelines.

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