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Qdrant Integration

How Simili Bot uses Qdrant for semantic search and vector storage.

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Overview

Qdrant is the vector database backend for Simili Bot. It stores:
  • Issue embeddings (768-dimensional vectors)
  • Issue metadata (title, repo, number, labels, etc.)
  • Enables fast semantic similarity search

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Key Features

Collection Management
  • Automatic collection creation
  • Configurable collection names
  • Payload storage for metadata
Search Capabilities
  • Cosine similarity scoring
  • Configurable thresholds
  • Fast retrieval (100ms-1s typical)
Scalability
  • Handles thousands to millions of vectors
  • Efficient indexing and retrieval
  • Cloud or self-hosted options

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Configuration

qdrant:
  url: "https://cluster.qdrant.io:6333"
  api_key: "${QDRANT_API_KEY}"
  collection: "issues"
  tls: true
  timeout: 30
  max_retries: 3

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Storage

Per-issue vector storage: 
Vector: 768 dimensions (float32)
Metadata: ~1-2KB (title, body, labels, etc.)
Total: ~3-4KB per issue
For 10,000 issues: ~30-40MB of storage

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Performance

Typical Operations:
  • Point insertion: 100-500ms
  • Similarity search: 500ms-1s
  • Batch operations: Parallelizable
Scaling:
  • Linear with vector count
  • Optimized for cosine distance
  • Supports fuzzy search

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Deployment

Qdrant Cloud (Recommended)
  • Managed service
  • Automatic backups
  • 1GB free tier
  • Pay-as-you-go pricing
Self-Hosted
  • Docker container
  • Docker Compose
  • Kubernetes deployment
  • Full control

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Integration Points

  1. Indexing - Add issues to Qdrant
  2. Search - Find similar issues
  3. Updates - Re-index modified issues
  4. Cleanup - Archive old issues

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Next Steps

Qdrant Configuration

Configure Qdrant connection

Semantic Search

How semantic search works