qdrant-patterns
Store and retrieve documents using Qdrant for RAG workflows. Use for persistent memory, research storage, and semantic search.
What this skill does
# Qdrant Patterns
Use the `qdrant` MCP server tools for persistent vector storage and semantic retrieval.
## Available Tools
| Tool | Purpose |
|------|---------|
| `qdrant-store` | Store information with automatic embedding |
| `qdrant-find` | Semantic search for stored information |
## Collection Configuration
The collection name is configured via environment variable:
- `COLLECTION_NAME` - Set to `${WORKSPACE_PROFILE:-default}_memories`
This provides workspace isolation - each profile gets its own collection.
## Storing Documents
Store information with the `qdrant-store` tool:
```
Tool: qdrant-store
Information: "GitHub REST API uses OAuth tokens for authentication. Personal access tokens (PATs) provide scoped access to repositories, issues, and other resources. Fine-grained PATs offer more granular permissions than classic tokens."
Metadata:
source: "https://docs.github.com/rest/authentication"
type: "documentation"
harvested_at: "2025-01-04"
tags: "github,api,authentication"
```
### Metadata Best Practices
Always include:
- `source` - Original URL or file path
- `type` - Content type (documentation, code, article, etc.)
- `harvested_at` - ISO date of collection
- `tags` - Comma-separated searchable keywords
Optional but useful:
- `project` - Related project name
- `language` - Programming language if code
- `version` - API or library version
- `summary` - Brief content summary
## Querying Documents
### Semantic Search
Find related content by meaning:
```
Tool: qdrant-find
Query: "how to authenticate with OAuth"
```
The tool returns the most semantically similar stored information.
### Search Tips
- Use natural language queries
- Be specific about what you're looking for
- The embedding model (fastembed) handles semantic matching
## RAG Workflow
### 1. Check Existing Knowledge
Before researching, query for existing content:
```
Tool: qdrant-find
Query: "GitHub Actions workflow syntax"
```
If results are relevant and recent (check metadata), use them. Otherwise, harvest fresh content.
### 2. Harvest and Store
When gathering new information:
1. Fetch the content (WebFetch, Read, etc.)
2. Extract key information
3. Store in Qdrant with metadata
4. Reference the stored content
```
Tool: qdrant-store
Information: "<extracted content here>"
Metadata:
source: "<url or path>"
type: "documentation"
harvested_at: "<today's date>"
tags: "<relevant,keywords>"
```
### 3. Retrieve for Context
When answering questions or implementing features:
1. Query Qdrant for relevant documents
2. Include top results in context
3. Cite sources from metadata
## Example: Research Workflow
1. **Check existing**: Query for topic with `qdrant-find`
2. **Assess freshness**: Check `harvested_at` in results
3. **Harvest if needed**: Fetch new content
4. **Store with metadata**: Add via `qdrant-store`
5. **Use for response**: Include relevant chunks
## Tips
- Keep stored information focused (one topic per entry)
- Use consistent metadata schemas
- Include enough context in each entry to be useful standalone
- Use descriptive tags for easier filtering
- Check existing knowledge before harvesting new content
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