> ## 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.

# 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==5.4.2 pinecone-text typer rich pypdf openai agno
```

You need `pinecone-text` for hybrid search (`use_hybrid_search=True`).

```shell theme={null}
export PINECONE_API_KEY=your_pinecone_api_key_here
export OPENAI_API_KEY=your_openai_api_key_here
```

> [!NOTE]
>
> 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.

## Example

```python agent_with_knowledge.py theme={null}
import os
import typer
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)
```

> **Async Support ⚡**
>
> Pinecone also supports asynchronous operations with `ainsert()` and `aprint_response()`.
>
> ```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]
> >
> > Use `ainsert()` and `aprint_response()` methods with `asyncio.run()` for non-blocking operations in high-throughput applications.

## PineconeDb Params

<Snippet file="vectordb_pineconedb_params.mdx" />

## Next Steps

| Task | Guide |
| - | - |
| Insert, search, and delete content | [Pinecone usage](https://agno-v2.mintlify.site/knowledge/vector-stores/pinecone/usage/pinecone-db.md) |
| Call async Agno methods | [Async Pinecone usage](https://agno-v2.mintlify.site/knowledge/vector-stores/pinecone/usage/async-pinecone-db.md) |


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