> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Qdrant

> Insert a PDF into a Qdrant collection, query it with an agent, and delete content by name or metadata.

## Code

```python qdrant_db.py theme={null}
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant

COLLECTION_NAME = "thai-recipes"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")

contents_db = PostgresDb(
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    knowledge_table="knowledge_contents",
)

knowledge = Knowledge(
    name="My Qdrant Vector Knowledge Base",
    description="This is a knowledge base that uses a Qdrant Vector DB",
    vector_db=vector_db,
    contents_db=contents_db,
)

knowledge.insert(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
)

agent = Agent(knowledge=knowledge)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)

vector_db.delete_by_name("Recipes")

vector_db.delete_by_metadata({"doc_type": "recipe_book"})
```

## Usage

<Snippet file="create-venv-step.mdx" />

**1. Install dependencies**

```bash theme={null}
uv pip install -U qdrant-client sqlalchemy psycopg pypdf openai agno
```

**2. Run Qdrant**

```bash theme={null}
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
```

<Snippet file="run-pgvector-step.mdx" />

**3. Set environment variables**

```bash theme={null}
export OPENAI_API_KEY=xxx
```

**4. Run Agent**

```bash theme={null}
python qdrant_db.py
```


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