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:

• Fetch the content (WebFetch, Read, etc.)

• Extract key information

• Store in Qdrant with metadata

• 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:

• Query Qdrant for relevant documents

• Include top results in context

• Cite sources from metadata

Example: Research Workflow

• Check existing: Query for topic with qdrant-find

• Assess freshness: Check harvested_at in results

• Harvest if needed: Fetch new content

• Store with metadata: Add via qdrant-store

• 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