How to use SQLAlchemy ORM to access Pinecone Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Pinecone-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Pinecone data to query Pinecone data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Pinecone data in Python. When you issue complex SQL queries from Pinecone, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Pinecone and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Pinecone Data

Connecting to Pinecone data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Authentication

To authenticate to Pinecone, and connect to your own data or to allow other users to connect to their data, you can use API Key authentication.

Using API Key Authentication

To authenticate using an API Key, you need to obtain your API Key from your Pinecone console at https://app.pinecone.io/.

You can then connect by setting the AuthScheme to APIKey and providing your API key:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your API key from Pinecone.

Example connection strings:

Standard API Key Configuration:

Profile=C:\profiles\Pinecone.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key;APIVersion=2025-10';

Follow the procedure below to install SQLAlchemy and start accessing Pinecone through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Pinecone Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Pinecone data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("api:///?Profile=C:\profiles\Pinecone.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key&APIVersion=2025-10'")

Declare a Mapping Class for Pinecone Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Indexes table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Indexes(base):
	__tablename__ = "Indexes"
	 = Column(String,primary_key=True)
	 = Column(String)
	...

Query Pinecone Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("api:///?Profile=C:\profiles\Pinecone.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key&APIVersion=2025-10'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Indexes).filter_by(Name="my-index"):
	print(": ", instance.)
	print(": ", instance.)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Indexes_table = Indexes.metadata.tables["Indexes"]
for instance in session.execute(Indexes_table.select().where(Indexes_table.c.Name == "my-index")):
	print(": ", instance.)
	print(": ", instance.)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Pinecone data. Reach out to our Support Team if you have any questions.