> ## Documentation Index
> Fetch the complete documentation index at: https://simili.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Qdrant Integration

> Qdrant vector database integration details

# Qdrant Integration

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

## Overview

Qdrant is the vector database backend for Simili Bot. It stores:

* Issue embeddings (3072-dimensional vectors)
* Issue metadata (title, repo, number, labels, etc.)
* Enables fast semantic similarity search

## 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

## Configuration

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

## 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

## 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

## 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

## Integration points

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

## Next steps

<CardGroup cols={2}>
  <Card title="Qdrant configuration" href="/configuration/qdrant">
    Configure Qdrant connection
  </Card>

  <Card title="Semantic search" href="/guides/semantic-search">
    How semantic search works
  </Card>
</CardGroup>