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

# Qdrant CLI

> CLI commands for Qdrant vector store

# Qdrant CLI

## Docker Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
docker run -d --name qdrant \
  -p 6333:6333 \
  -p 6334:6334 \
  qdrant/qdrant
```

## Quick Start

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Test connection
praisonai persistence doctor \
  --knowledge-backend qdrant \
  --knowledge-url "http://localhost:6333"

# Run with knowledge store
praisonai persistence run \
  --knowledge-backend qdrant \
  --knowledge-url "$QDRANT_URL" \
  "Search my documents"
```

## Commands

### Doctor

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai persistence doctor \
  --knowledge-backend qdrant \
  --knowledge-url "http://localhost:6333"
```

### Run with Knowledge

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai persistence run \
  --knowledge-backend qdrant \
  --knowledge-url "$QDRANT_URL" \
  --session-id my-session \
  "What do my documents say about AI?"
```

## Python Test

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
python3 -c "
from praisonai.persistence import create_knowledge_store
store = create_knowledge_store('qdrant', url='http://localhost:6333')
print('Qdrant OK:', store.list_collections())
"
```