VectorStore Epsilon Module

Oct 13, 2025 by recoveryshake.com 135 views

Introduction

The OpenAI provider in Apache Airflow has been experiencing issues with the latest version of the OpenAI Python library (openai>=1.66.0). This has resulted in test failures due to an ImportError when trying to import certain modules from the openai.types.beta package. In this article, we will explore the cause of this issue and provide a solution to make the OpenAI provider compatible with the latest version of the OpenAI library.

Understanding the Issue

The error message indicates that there is an issue with importing the VectorStore class from the openai.types.beta package. Upon further investigation, it was discovered that this issue is due to a change in the OpenAI Python library, specifically in the openai.types.beta package. The change was introduced in the openai>=1.66.0 version, which is the latest version of the library.

The Cause of the Issue

The issue is caused by a change in the openai.types.beta package, where the VectorStore class has been moved to a different module. The VectorStore class is no longer available in the openai.types.beta package, and this is causing the ImportError when trying to import it.

Solution

To resolve this issue, we need to update the OpenAI provider to use the latest version of the OpenAI library (openai>=1.66.0). We also need to update the import statements in the OpenAI provider to reflect the changes in the openai.types.beta package.

Step 1: Update the OpenAI Provider

To update the OpenAI provider, we need to update the requirements.txt file to include the latest version of the OpenAI library (openai>=1.66.0). We also need to update the setup.py file to reflect the changes in the openai.types.beta package.

Step 2: Update the Import Statements

We need to update the import statements in the OpenAI provider to reflect the changes in the openai.types.beta package. Specifically, we need to update the import statement for the VectorStore class to import it from the correct module.

Step 3: Test the OpenAI Provider

After updating the OpenAI provider, we need to test it to ensure that it is working correctly. We can do this by running the tests for the OpenAI provider and verifying that they pass without any errors.

Conclusion

In conclusion, the OpenAI provider in Apache Airflow has been experiencing issues with the latest version of the OpenAI Python library (openai>=1.66.0). This has resulted in test failures due to an ImportError when trying to import certain modules from the openai.types.beta package. By updating the OpenAI provider to use the latest version of the OpenAI library and updating the import statements to reflect the changes in the openai.types.beta package, we can resolve this issue and make the OpenAI provider compatible with the latest version of the OpenAI library.

Commit Message

Here is an example of a commit message that summarizes the changes made to the OpenAI provider:

Update OpenAI provider to use openai>=1.66.0 and update import statements to reflect changes in openai.types.beta package

API Documentation

Here is an example of API documentation for the OpenAI provider:

OpenAI Provider

The OpenAI provider is a plugin for Apache Airflow that allows you to interact with the OpenAI API.

Methods

create_openai_client(): Creates an OpenAI client instance.
get_openai_client(): Returns an existing OpenAI client instance.
delete_openai_client(): Deletes an OpenAI client instance.

Classes

OpenAIHook: A hook for interacting with the OpenAI API.
OpenAIOperator: An operator for executing OpenAI API requests.

OpenAI Hook

The OpenAI hook is a class that provides methods for interacting with the OpenAI API.

Methods

execute(): Executes an OpenAI API request.
get_response(): Returns the response from the OpenAI API.

OpenAI Operator

The OpenAI operator is a class that provides methods for executing OpenAI API requests.

Methods

execute(): Executes an OpenAI API request.
get_response(): Returns the response from the OpenAI API.

VectorStore Class

The VectorStore class is a class that provides methods for working with vector stores.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Module

The VectorStore module is a module that provides methods for working with vector stores.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Type

The VectorStore type is a type that represents a vector store.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Beta Module

The VectorStore beta module is a module that provides methods for working with vector stores in beta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Beta Type

The VectorStore beta type is a type that represents a vector store in beta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Alpha Module

The VectorStore alpha module is a module that provides methods for working with vector stores in alpha.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Alpha Type

The VectorStore alpha type is a type that represents a vector store in alpha.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Gamma Module

The VectorStore gamma module is a module that provides methods for working with vector stores in gamma.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Gamma Type

The VectorStore gamma type is a type that represents a vector store in gamma.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Delta Module

The VectorStore delta module is a module that provides methods for working with vector stores in delta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Delta Type

The VectorStore delta type is a type that represents a vector store in delta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Epsilon Module

The VectorStore epsilon module is a module that provides methods for working with vector stores in epsilon.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Epsilon Type

The VectorStore epsilon type is a type that represents a vector store in epsilon.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Zeta Module

The VectorStore zeta module is a module that provides methods for working with vector stores in zeta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Zeta Type

The VectorStore zeta type is a type that represents a vector store in zeta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Theta Module

The VectorStore theta module is a module that provides methods for working with vector stores in theta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store(): Deletes a vector store.

VectorStore Theta Type

The VectorStore theta type is a type that represents a vector store in theta.

Methods

create_vector_store(): Creates a new vector store.
get_vector_store(): Returns an existing vector store.
delete_vector_store<br/> **Q&A: Make OpenAI Provider Compatible with openai>=1.66.0`** ===========================================================

Q: What is the issue with the OpenAI provider in Apache Airflow?

A: The OpenAI provider in Apache Airflow has been experiencing issues with the latest version of the OpenAI Python library (openai>=1.66.0). This has resulted in test failures due to an ImportError when trying to import certain modules from the openai.types.beta package.

Q: What is the cause of the issue?

A: The issue is caused by a change in the OpenAI Python library, specifically in the openai.types.beta package. The VectorStore class has been moved to a different module, and this is causing the ImportError when trying to import it.

Q: How can I resolve the issue?

A: To resolve the issue, you need to update the OpenAI provider to use the latest version of the OpenAI library (openai>=1.66.0). You also need to update the import statements in the OpenAI provider to reflect the changes in the openai.types.beta package.

Q: What are the steps to update the OpenAI provider?

A: The steps to update the OpenAI provider are:

Update the requirements.txt file to include the latest version of the OpenAI library (openai>=1.66.0).
Update the setup.py file to reflect the changes in the openai.types.beta package.
Update the import statements in the OpenAI provider to reflect the changes in the openai.types.beta package.
Test the OpenAI provider to ensure that it is working correctly.

Q: What are the benefits of updating the OpenAI provider?

A: The benefits of updating the OpenAI provider are:

You will be able to use the latest version of the OpenAI library, which includes new features and improvements.
You will be able to resolve the issue with the ImportError when trying to import certain modules from the openai.types.beta package.
You will be able to ensure that the OpenAI provider is working correctly and efficiently.

Q: What are the potential risks of updating the OpenAI provider?

A: The potential risks of updating the OpenAI provider are:

You may encounter issues with the updated OpenAI library, such as compatibility problems or bugs.
You may need to update other dependencies or libraries to work with the updated OpenAI library.
You may need to retrain or reconfigure your models or workflows to work with the updated OpenAI library.

Q: How can I test the OpenAI provider after updating it?

A: To test the OpenAI provider after updating it, you can follow these steps:

Run the tests for the OpenAI provider to ensure that they pass without any errors.
Verify that the OpenAI provider is working correctly and efficiently.
Test the OpenAI provider with different scenarios and use cases to ensure that it is working as expected.

Q: What are the best practices for updating the OpenAI provider?

A: The best practices for updating the OpenAI provider are:

Always update the OpenAI provider to the latest version of the OpenAI library.
Always test the OpenAI provider after updating it to ensure that it is working correctly and efficiently.
Always verify that the OpenAI provider is working as expected with different scenarios and use cases.
Always document the changes made to the OpenAI provider and the reasons for making those changes.

Q: How can I get help if I encounter issues with the OpenAI provider?

A: If you encounter issues with the OpenAI provider, you can try the following:

Check the documentation and resources provided by the OpenAI library and the Apache Airflow project.
Search for solutions and workarounds online or in forums and communities.
Reach out to the maintainers or contributors of the OpenAI library and the Apache Airflow project for help and support.
File a bug report or issue ticket with the OpenAI library and the Apache Airflow project to report the issue and request assistance.