Python SDK

The Pinecone Python SDK is distributed on PyPI using the package name pinecone. By default, the pinecone package has a minimal set of dependencies and interacts with Pinecone via HTTP requests. However, you can install the following extras to unlock additional functionality:

• pinecone[grpc] adds dependencies on grpcio and related libraries needed to run data operations such as upserts and queries over gRPC for a modest performance improvement.

• pinecone[asyncio] adds a dependency on aiohttp and enables usage of async methods for use with asyncio. For more details, see Asyncio support.

​

Requirements

The Pinecone Python SDK requires Python 3.9 or later. It has been tested with CPython versions from 3.9 to 3.13.

​

SDK versions

SDK versions are pinned to specific API versions. When a new API version is released, a new version of the SDK is also released.

The mappings between API versions and Python SDK versions are as follows:

When a new stable API version is released, you should upgrade your SDK to the latest version to ensure compatibility with the latest API changes.

​

Install

To install the latest version of the Python SDK, run the following command:

# Install the latest version
pip install pinecone

# Install the latest version with gRPC extras
pip install "pinecone[grpc]"

# Install the latest version with asyncio extras
pip install "pinecone[asyncio]"

To install a specific version of the Python SDK, run the following command:

# Install a specific version
pip install pinecone==<version>

# Install a specific version with gRPC extras
pip install "pinecone[grpc]"==<version>

# Install a specific version with asyncio extras
pip install "pinecone[asyncio]"==<version>

To check your SDK version, run the following command:

pip show pinecone

​

Install the Pinecone Assistant Python plugin

As of Python SDK v7.0.0, the pinecone-plugin-assistant package is included by default. It is only necessary to install the package if you are using a version of the Python SDK prior to v7.0.0.

pip install --upgrade pinecone pinecone-plugin-assistant

​

Upgrade

If you already have the Python SDK, upgrade to the latest version as follows:

# Upgrade to the latest version
pip install pinecone --upgrade

# Upgrade to the latest version with gRPC extras
pip install "pinecone[grpc]" --upgrade

# Upgrade to the latest version with asyncio extras
pip install "pinecone[asyncio]" --upgrade

​

Initialize

Once installed, you can import the library and then use an API key to initialize a client instance:

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

When creating an index, import the ServerlessSpec or PodSpec class as well:

from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec

pc = Pinecone(api_key="YOUR_API_KEY")

pc.create_index(
  name="docs-example",
  dimension=1536,
  metric="cosine",
  spec=ServerlessSpec(
    cloud="aws",
    region="us-east-1"
  )
)

​

Proxy configuration

If your network setup requires you to interact with Pinecone through a proxy, you will need to pass additional configuration using optional keyword parameters:

• proxy_url: The location of your proxy. This could be an HTTP or HTTPS URL depending on your proxy setup.
• proxy_headers: Accepts a python dictionary which can be used to pass any custom headers required by your proxy. If your proxy is protected by authentication, use this parameter to pass basic authentication headers with a digest of your username and password. The make_headers utility from urllib3 can be used to help construct the dictionary. Note: Not supported with Asyncio.
• ssl_ca_certs: By default, the client will perform SSL certificate verification using the CA bundle maintained by Mozilla in the certifi package. If your proxy is using self-signed certicates, use this parameter to specify the path to the certificate (PEM format).
• ssl_verify: SSL verification is enabled by default, but it is disabled when set to False. It is not recommened to go into production with SSL verification disabled.

from pinecone import Pinecone
import urllib3 import make_headers

pc = Pinecone(
    api_key="YOUR_API_KEY",
    proxy_url='https://your-proxy.com',
    proxy_headers=make_headers(proxy_basic_auth='username:password'),
    ssl_ca_certs='path/to/cert-bundle.pem'
)

​

Async requests

Pinecone Python SDK versions 6.0.0 and later provide async methods for use with asyncio. Asyncio support makes it possible to use Pinecone with modern async web frameworks such as FastAPI, Quart, and Sanic, and should significantly increase the efficiency of running requests in parallel.

Use the PineconeAsyncio class to create and manage indexes and the IndexAsyncio class to read and write index data. To ensure that sessions are properly closed, use the async with syntax when creating PineconeAsyncio and IndexAsyncio objects.

# pip install "pinecone[asyncio]"
import asyncio
from pinecone import PineconeAsyncio, ServerlessSpec

async def main():
    async with PineconeAsyncio(api_key="YOUR_API_KEY") as pc:
        if not await pc.has_index(index_name):
            desc = await pc.create_index(
                name="docs-example",
                dimension=1536,
                metric="cosine",
                spec=ServerlessSpec(
                    cloud="aws",
                    region="us-east-1"
                ),
                deletion_protection="disabled",
                tags={
                    "environment": "development"
                }
            )

asyncio.run(main())

​

Query across namespaces

Each query is limited to a single namespace. However, the Pinecone Python SDK provides a query_namespaces utility method to run a query in parallel across multiple namespaces in an index and then merge the result sets into a single ranked result set with the top_k most relevant results.

The query_namespaces method accepts most of the same arguments as query with the addition of a required namespaces parameter.

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index(
    name="docs-example",
    pool_threads=50,             # <-- make sure to set these
    connection_pool_maxsize=50,  # <-- make sure to set these
)

query_vec = [ 0.1, ...] # an embedding vector with same dimension as the index
combined_results = index.query_namespaces(
    vector=query_vec,
    namespaces=['ns1', 'ns2', 'ns3', 'ns4'],
    metric="cosine",
    top_k=10,
    include_values=False,
    include_metadata=True,
    filter={"genre": { "$eq": "comedy" }},
    show_progress=False,
)

for scored_vec in combined_results.matches:
    print(scored_vec)
print(combined_results.usage)

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index(
    name="docs-example",
    pool_threads=50,             # <-- make sure to set these
    connection_pool_maxsize=50,  # <-- make sure to set these
)

query_vec = [ 0.1, ...] # an embedding vector with same dimension as the index
combined_results = index.query_namespaces(
    vector=query_vec,
    namespaces=['ns1', 'ns2', 'ns3', 'ns4'],
    metric="cosine",
    top_k=10,
    include_values=False,
    include_metadata=True,
    filter={"genre": { "$eq": "comedy" }},
    show_progress=False,
)

for scored_vec in combined_results.matches:
    print(scored_vec)
print(combined_results.usage)

from pinecone.grpc import PineconeGRPC

pc = PineconeGRPC(api_key="API_KEHY")
index = pc.Index(
    name="docs-example",
    pool_threads=50, # <-- make sure to set this
)

query_vec = [ 0.1, ...] # an embedding vector with same dimension as the index
combined_results = index.query_namespaces(
    vector=query_vec,
    namespaces=['ns1', 'ns2', 'ns3', 'ns4'],
    metric="cosine",
    top_k=10,
    include_values=False,
    include_metadata=True,
    filter={"genre": { "$eq": "comedy" }},
    show_progress=False,
)

for scored_vec in combined_results.matches:
    print(scored_vec)
print(combined_results.usage)

​

Upsert from a dataframe

To quickly ingest data when using the Python SDK, use the upsert_from_dataframe method. The method includes retry logic andbatch_size, and is performant especially with Parquet file data sets.

The following example upserts the uora_all-MiniLM-L6-bm25 dataset as a dataframe.

from pinecone import Pinecone, ServerlessSpec
from pinecone_datasets import list_datasets, load_dataset

pc = Pinecone(api_key="API_KEY")

dataset = load_dataset("quora_all-MiniLM-L6-bm25")

pc.create_index(
  name="docs-example",
  dimension=384,
  metric="cosine",
  spec=ServerlessSpec(
    cloud="aws",
    region="us-east-1"
  )
)

# To get the unique host for an index, 
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")

index.upsert_from_dataframe(dataset.drop(columns=["blob"]))