Pinecone Vector Database

Setup

Follow the instructions in the Pinecone Setup Guide to get started quickly with Pinecone.

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).

export PINECONE_API_KEY=your_pinecone_api_key_here
export OPENAI_API_KEY=your_openai_api_key_here

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

agent_with_knowledge.py

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().

async_pinecone.py

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))

Use ainsert() and aprint_response() methods with asyncio.run() for non-blocking operations in high-throughput applications.

PineconeDb Params

Parameter	Type	Default	Description
`name`	`Optional[str]`	`None`	Pinecone index name. Index creation still requires a usable name.
`dimension`	`int`	-	The dimension of the embeddings
`spec`	`Union[Dict, ServerlessSpec, PodSpec]`	-	The index spec
`embedder`	`Optional[Embedder]`	`None`	Embedder instance for creating embeddings (defaults to OpenAIEmbedder if not provided)
`metric`	`Optional[str]`	`"cosine"`	The metric used for similarity search
`additional_headers`	`Optional[Dict[str, str]]`	`None`	Additional headers to pass to the Pinecone client
`pool_threads`	`Optional[int]`	`1`	The number of threads to use for the Pinecone client
`namespace`	`Optional[str]`	`None`	The namespace for the Pinecone index
`timeout`	`Optional[int]`	`None`	The timeout for Pinecone operations
`index_api`	`Optional[Any]`	`None`	The Index API object
`api_key`	`Optional[str]`	`None`	The Pinecone API key
`host`	`Optional[str]`	`None`	The Pinecone host
`config`	`Optional[Config]`	`None`	The Pinecone config
`use_hybrid_search`	`bool`	`False`	Whether to use hybrid search
`hybrid_alpha`	`float`	`0.5`	The alpha value for hybrid search
`reranker`	`Optional[Reranker]`	`None`	Rerank retrieved documents.
`description`	`Optional[str]`	`None`	Description of the vector database.
`id`	`Optional[str]`	`None`	Optional vector database ID.
`**kwargs`	`Any`	—	Additional client arguments.

Next Steps

Task	Guide
Insert, search, and delete content	Pinecone usage
Call async Agno methods	Async Pinecone usage