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name: qdrant-patterns description: Store and retrieve documents using Qdrant for RAG workflows. Use for persistent memory, research storage, and semantic search.

Qdrant Patterns

Use the qdrant MCP server tools for persistent vector storage and semantic retrieval.

Available Tools

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:

CopyTool: 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:

CopyTool: 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:

CopyTool: 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

CopyTool: 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