skills.rest
kaminocorp
kaminocorpOFFICIAL
Explore all skills by this author →
Website𝕏@kaminocorpoGitHub

qdrant-vector-search

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

1|Updated Mar 12, 2026
Search Tags:#semantic search#embeddings#rag#vector database#vector search#qdrant#similarity search
One-click install
npx skills add https://github.com/kaminocorp/hermes-alpha-hunter --skill qdrant-vector-search-kaminocorp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-vector-search
Source: https://github.com/kaminocorp/hermes-alpha-hunter/tree/main/skills/mlops/vector-databases/qdrant
Command: npx skills add https://github.com/kaminocorp/hermes-alpha-hunter --skill qdrant-vector-search-kaminocorp

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, geo queries, and full-text conditions using the qdrant-client Python library. - Hybrid and Multi-Vector Search: Combine dense and sparse vectors with Reciprocal Rank Fusion, and store multiple named vectors per point. - Production Scaling: Configure HNSW parameters, scalar/product/binary quantization, sharding, replication, and snapshots for large-scale deployments. - Use Case: Build a RAG pipeline where documents are embedded with sentence-transformers, upserted into a Qdrant collection, and retrieved with category filters to ground LLM answers. ## 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 with vectors and payloads, then call client.search with a query vector and limit. Filters can be added via the query_filter parameter.

Qdrant vs Pinecone vs FAISS for vector search?▼

Qdrant offers self-hosted deployment with rich payload filtering and Rust-based performance. FAISS suits raw-speed batch research, Pinecone is fully managed with zero ops, and Chroma fits simpler embedded use cases.

Does Qdrant support hybrid search with sparse vectors?▼

Yes, Qdrant supports collections with both dense and sparse vector configurations. You can prefetch results from each vector type and fuse them using Reciprocal Rank Fusion via the query_points API.

Why is my Qdrant search slow when using filters?▼

Filtered search is slow when payload fields lack indexes. Create a payload index with client.create_payload_index for each filtered field, and consider enabling scalar quantization or tuning HNSW ef parameters.

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

Enable scalar, product, or binary quantization for 4x to 32x memory reduction, set on_disk_payload to true, and configure HNSW on-disk storage. Rescoring during search recovers accuracy lost to quantization.

Related Skills

Explore More Software Engineering Skills→
qcmuuqcmuu

sentence-transformers

Generate semantic embeddings from text using sentence-transformers models.

Community
Intermediate
JCETools-PetraJCETools-Petra
16

ai-llm-engineering

Design RAG pipelines with chunking, embedding, retrieval, and evaluation metrics.

Community
Advanced
sourcegraphsourcegraph
32

ir-analysis

Compute precision, recall, MRR, nDCG, and MAP for file retrieval comparisons.

Official
Advanced
jillescajillesca
3

langchain-rag

Build end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.

Community
Advanced