| name | qdrant-memory |
| description | Use this skill for semantic search, long-term memory storage, and RAG (Retrieval Augmented Generation). Enables vector-based knowledge retrieval and persistent memory across sessions. |
Qdrant Vector Memory
This skill enables semantic search and long-term memory using Qdrant vector database.
Problem Solved
Traditional keyword search:
- Exact match only
- Misses semantically similar content
- No context understanding
With Qdrant Vector Search:
- Semantic similarity matching
- Finds conceptually related information
- Understands context and meaning
- Persistent memory across sessions
When to Use
- Storing knowledge for later retrieval
- Semantic code search across codebase
- Building RAG (Retrieval Augmented Generation) systems
- Long-term memory for AI agents
- Finding similar documents/code
- Knowledge base management
Available Tools
1. qdrant-store
Stores text with vector embeddings for later retrieval.
Input: { "text": "React hooks are functions...", "metadata": { "topic": "react" } }
Output: Stored with vector embedding
2. qdrant-find
Finds semantically similar content.
Input: { "query": "how to manage state in React" }
Output: Related documents ranked by similarity
3. qdrant-delete
Removes stored memories by ID or filter.
Input: { "filter": { "topic": "outdated" } }
Output: Deleted matching entries
4. qdrant-list-collections
Lists all available collections.
Output: Collection names and stats
Example Usage
Store Knowledge
User: このReactパターンを覚えておいて
AI: [Calls qdrant-store]
[Embeds content with sentence-transformers]
[Stores in taisun_memory collection]
→ 永続的に保存され、後で検索可能
Semantic Search
User: 以前話した状態管理のパターンは？
AI: [Calls qdrant-find with semantic query]
[Returns top-k similar documents]
[Provides context from stored memories]
RAG for Code Generation
User: 前回実装したAPIパターンを参考に新しいエンドポイント作って
AI: [Searches for similar API implementations]
[Retrieves relevant code patterns]
[Generates new code based on retrieved context]
Architecture
┌─────────────────────────────────────────────────────┐
│ TAISUN Agent │
└─────────────────────┬───────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Qdrant MCP Server │
│ ┌───────────────┐ ┌───────────────────────────┐ │
│ │ Embedding │ │ Vector Store │ │
│ │ (MiniLM-L6) │──│ (taisun_memory collection)│ │
│ └───────────────┘ └───────────────────────────┘ │
└─────────────────────┬───────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Qdrant Server (localhost:6333) │
│ - Persistent storage │
│ - Fast ANN search │
│ - Metadata filtering │
└─────────────────────────────────────────────────────┘
Required Environment Variables
| Variable | Description | Default |
|---|
QDRANT_URL | Qdrant server URL | http://localhost:6333 |
QDRANT_COLLECTION_NAME | Collection name | taisun_memory |
QDRANT_API_KEY | API key (cloud only) | - |
Setup
Option 1: Local Docker (Recommended)
docker run -p 6333:6333 -v $(pwd)/qdrant_data:/qdrant/storage qdrant/qdrant
curl http://localhost:6333/health
Option 2: Qdrant Cloud
- Sign up at https://cloud.qdrant.io
- Create a cluster
- Get API key and URL
- Set in .env:
QDRANT_URL=https://xxx-xxx.aws.cloud.qdrant.io:6333
QDRANT_API_KEY=your-api-key
Integration with TAISUN
Qdrant MCP integrates with existing memory systems:
| Layer | System | Purpose |
|---|
| 短期記憶 | taisun-proxy memory | セッション内コンテキスト |
| 長期記憶 | Qdrant | 永続的な知識・パターン |
| エピソード | claude-mem | 観察・決定の履歴 |
Best Practices
-
Store with meaningful metadata
{ "topic": "react", "type": "pattern", "date": "2026-01-19" }
-
Use specific queries
❌ "前の話"
✅ "ReactのuseStateパターンについて"
-
Regular cleanup
Outdated knowledge should be deleted to maintain relevance
-
Combine with other tools
- Context7 for docs + Qdrant for project-specific knowledge
- GPT Researcher for external + Qdrant for internal knowledge
Sources
