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Qdrant Vector Search

Build and query a vector database for semantic search and RAG with filtering

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
3 files

What it does

This skill teaches an AI agent how to use Qdrant, an open-source vector database, to store text or other content as numerical embeddings and find the most similar items quickly. Once installed, the agent can write code that creates collections, inserts documents with metadata, and runs filtered nearest-neighbour searches, which are the building blocks of retrieval-augmented generation (RAG) and semantic search. It also explains when Qdrant fits better than alternatives such as Chroma, FAISS, Pinecone or Weaviate.

Great for

  • Developers building a production RAG or semantic search feature who need filtering on metadata
  • Teams that want a self-hosted vector store with full control of their data
  • Engineers comparing vector databases who want a quick, working Qdrant setup in Python

Skip it if

  • Simple prototypes or embedded use where a lighter tool like Chroma is enough, since Qdrant needs a running server such as Docker
  • Non-technical users who only want to search their own files, because this skill assumes coding and infrastructure work
  • Projects that need a fully managed service with zero operations, where the skill's self-hosted focus may not match

Try asking

  • “Set up a Qdrant collection for my support documents and write code to insert them with category metadata”
  • “Write a Python function that searches my knowledge base for the top 10 results filtered to category tech”
  • “Add a semantic search endpoint to my app using sentence-transformers embeddings and Qdrant”
  • “Show me how to configure HNSW and quantization to reduce memory use for a 1 million vector collection”
Step-by-step guide: how to use Qdrant Vector SearchInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    qdrant.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip qdrant.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip qdrant.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • State your embedding model and vector size up front so the agent sets the correct VectorParams size
  • Ask the agent to check the Qdrant client version, since some method names in the examples (such as search) have changed in newer releases
  • Review any code that connects to a remote Qdrant host and keep API keys and connection details out of source files

What's inside

  • references/advanced-usage.md
  • references/troubleshooting.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.