{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "qdrant-qdrant--run-a-local-vector-database-for-rag",
 "repo": "qdrant/qdrant",
 "repo_url": "https://github.com/qdrant/qdrant",
 "repo_slug": "qdrant-qdrant",
 "title": "Run a local vector database for your RAG app",
 "page_title": "How to run a local vector database for your RAG app with Qdrant",
 "summary": "Start Qdrant in Docker, connect from Python and store your embeddings with payloads.",
 "who": "A developer prototyping a RAG or semantic search app who wants a real vector store on a laptop.",
 "problem": "Keeping embeddings in memory or in files does not scale, and filtering by metadata gets messy fast.",
 "outcome": "A Qdrant server on localhost with a collection your app can search and filter.",
 "steps": [
  "Run `docker run -p 6333:6333 qdrant/qdrant` to start the server",
  "Install the Python client and connect with `QdrantClient(url=\"http://localhost:6333\")`",
  "Store your embeddings as points with a JSON payload such as source, date or tags",
  "Use the Web UI to explore collections and test queries",
  "Read the installation and security guides before moving anything to production"
 ],
 "agent_prompt": "Start qdrant/qdrant locally with Docker, create a collection for my <documents>, embed them with <model>, and upsert them with payloads for source and date. Then run a search for <query> filtered by <condition> and show me the results.",
 "agents": [
  "claude-code",
  "codex",
  "cursor"
 ],
 "difficulty": "Beginner",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "Qdrant stores and searches vectors; you still need an embedding model to create them.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "rust",
 "requirements": [
  "Docker for the local server, or a free tier cluster on Qdrant Cloud",
  "A client library, for example the Python client",
  "Embeddings from a model of your choice"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "insecure-default",
   "notice": "The Docker command starts an instance without authentication, open to all network interfaces, so keep it on a private machine or secure it first."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/qdrant-qdrant/run-a-local-vector-database-for-rag/",
 "md": "https://dedupedai.com/use-cases/qdrant-qdrant/run-a-local-vector-database-for-rag/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/qdrant-qdrant--run-a-local-vector-database-for-rag.json",
 "api_version": "v1"
}