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

# Pinecone Vector Database

> Use Pinecone as a vector database for your Knowledge Base.

## Setup

Follow the instructions in the [Pinecone Setup Guide](https://docs.pinecone.io/guides/get-started/quickstart) to get started quickly with Pinecone.

```shell theme={null}
uv pip install pinecone
```

<Info>
  We do not yet support Pinecone v6.x.x. We are actively working to achieve
  compatibility. In the meantime, we recommend using **Pinecone v5.4.2** for the
  best experience.
</Info>

## Example

```python agent_with_knowledge.py theme={null}
import os
import typer
from typing import Optional
from rich.prompt import Prompt

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pineconedb import PineconeDb

api_key = os.getenv("PINECONE_API_KEY")
index_name = "thai-recipe-hybrid-search"

vector_db = PineconeDb(
    name=index_name,
    dimension=1536,
    metric="cosine",
    spec={"serverless": {"cloud": "aws", "region": "us-east-1"}},
    api_key=api_key,
    use_hybrid_search=True,
    hybrid_alpha=0.5,
)

knowledge_base = Knowledge(
    vector_db=vector_db,
)

def pinecone_agent(user: str = "user"):
    agent = Agent(
        knowledge=knowledge_base,
        debug_mode=True,
    )

    while True:
        message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
        if message in ("exit", "bye"):
            break
        agent.print_response(message)

if __name__ == "__main__":
    # Comment out after first run
    knowledge_base.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )

    typer.run(pinecone_agent)
```

<Card title="Async Support ⚡">
  <div className="mt-2">
    <p>
      Pinecone also supports asynchronous operations, enabling concurrency and leading to better performance.
    </p>

    ```python async_pinecone.py theme={null}
    import asyncio
    from os import getenv

    from agno.agent import Agent
    from agno.knowledge.knowledge import Knowledge
    from agno.vectordb.pineconedb import PineconeDb

    api_key = getenv("PINECONE_API_KEY")
    index_name = "thai-recipe-index"

    vector_db = PineconeDb(
        name=index_name,
        dimension=1536,
        metric="cosine",
        spec={"serverless": {"cloud": "aws", "region": "us-east-1"}},
        api_key=api_key,
    )

    knowledge_base = Knowledge(
        vector_db=vector_db,
    )

    agent = Agent(
        knowledge=knowledge_base,
        # Enable the agent to search the knowledge base
        search_knowledge=True,
        # Enable the agent to read the chat history
        read_chat_history=True,
    )

    if __name__ == "__main__":
        # Load knowledge base asynchronously
        asyncio.run(knowledge_base.ainsert(
                url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
            )
        )

        # Create and use the agent asynchronously
        asyncio.run(agent.aprint_response("How to make Tom Kha Gai", markdown=True))
    ```

    <Tip className="mt-4">
      Use <code>aload()</code> and <code>aprint\_response()</code> methods with <code>asyncio.run()</code> for non-blocking operations in high-throughput applications.
    </Tip>
  </div>
</Card>

## PineconeDb Params

<Snippet file="vectordb_pineconedb_params.mdx" />