Integrate Criteria Evaluation with langflow

[sampratha28]

I want to make use of criteria evaluation in langflow to verify if the model's response is grounded to the vector store or not. I see that there is already code available in the langchain repository. How to enable it?

I am using a Vector Store agent and also, note that i am creating a custom LLM component

[dosubot[bot]]

🤖

Hello @sampratha28! 👋

Nice to meet you! I'm Dosu, a bot here on GitHub. I can assist you with bug fixes, answer your questions, and guide you on how to become a contributor. While you're waiting for a human maintainer to respond, feel free to ask me anything. I'm here to help!

Based on the information available in the langflow repository, it appears that the current version (v0.6.4) does not support integration with Criteria Evaluation out of the box. There are no references or dependencies on Criteria Evaluation in the repository. However, langflow's architecture allows for the import of custom components, which means you can manually integrate Criteria Evaluation into langflow.

To do this, you would need to create a custom component for Criteria Evaluation. Here's a general guide on how you can do it:

1. Create a new Python file for your Criteria Evaluation class. This class should contain all the necessary methods and attributes for evaluating criteria.

2. In the importing/utils.py file, add a new function to import your Criteria Evaluation class. This function should be similar to the other import functions in the file, like import_custom_component, import_agent, etc.

def import_criteria_evaluation(criteria_evaluation: str) -> Any:
    """Import criteria evaluation from criteria evaluation name"""
    return import_class(f"your_module_path.{criteria_evaluation}")

3. Add a new entry to the func_dict in the import_by_type function for your Criteria Evaluation class.

func_dict = {
    ...
    "criteria_evaluations": import_criteria_evaluation,
    ...
}

4. Now, you can use your Criteria Evaluation class in the configuration file by specifying its type as "criteria_evaluations" and its name as the name of your class.

Please note that this is a general guide and the actual implementation may vary depending on the specifics of your Criteria Evaluation class and how you want to integrate it into langflow.

For creating a custom component, you can refer to the Component class in the component.py file. This class defines the structure and requirements of a custom component in langflow. You can also refer to the CustomComponent.py file for a basic template on how to create a custom component.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Source Code

Sources

• src/frontend/package.json
• src/backend/langflow/interface/importing/utils.py
• src/backend/langflow/interface/custom/custom_component/component.py
• src/backend/langflow/components/custom_components/CustomComponent.py

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[ogabrielluiz]

Hi @sampratha28

How do you see that being implemented to make it useful? Could you share more details?

[sampratha28]

hey @ogabrielluiz this feature can be used to check how grounded the model's response is with respect to the given knowledge base. This can help in improving model response quality. Please correct me if i am wrong, also feel free to give your suggestions for the same

[dosubot[bot]]

Hi, @sampratha28,

I'm helping the langflow team manage their backlog and am marking this issue as stale. From what I understand, you were seeking guidance on integrating criteria evaluation with langflow to verify a model's response grounding to the vector store. Dosubot provided a detailed response, explaining that the current version of langflow does not support this integration out of the box, but it can be achieved by creating a custom component for criteria evaluation. Ogabrielluiz also engaged in the discussion, asking for more details on how the feature would be implemented to be useful. You responded, explaining that the feature could be used to assess the model's response grounding and improve response quality.

Could you please confirm if this issue is still relevant to the latest version of the langflow repository? If it is, please let the langflow team know by commenting on the issue. Otherwise, feel free to close the issue yourself, or the issue will be automatically closed in 7 days. Thank you!