How to get response_metadata from a result with StructuredPrompt?

[jeremrav]

This is my code :

prompt_helpfulness: StructuredPrompt = hub.pull(f"evaluator-helpfulness-{ENVIRONMENT}")
evaluator_helpfulness = prompt_helpfulness | llm

result = evaluator_helpfulness.invoke(
    {"assistant_answer": output, "question": content_input, "context": context},
    config={"tags": ["helpful", ENVIRONMENT]},
)
print(result.response_metadata)

How to get response_metadata from a result with StructuredPrompt?

[dosubot[bot]]

Hello, @jeremrav! I'm here to assist you with any bugs, questions, or contributions you might have. Let's get started!

To retrieve response_metadata from a result when using StructuredPrompt, you should access the response_metadata attribute of the AIMessage object returned by the invoke method. Your code already does this correctly:

result = evaluator_helpfulness.invoke(
    {"assistant_answer": output, "question": content_input, "context": context},
    config={"tags": ["helpful", ENVIRONMENT]},
)
print(result.response_metadata)

This will print the response_metadata, which includes details like token_usage, model_name, system_fingerprint, finish_reason, and logprobs, among other metadata ^[1][2].

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[dosubot[bot]]

The error you're encountering suggests that the invoke method is returning a dictionary instead of an object with a response_metadata attribute. This can happen if the schema used in the StructuredPrompt is set to return a dictionary.

To access response_metadata when the result is a dictionary, you need to ensure that the language model's response includes metadata. If the invoke method is configured to return a dictionary, the metadata might not be directly accessible as an attribute. You may need to modify the way the response is structured or check if the metadata is included in the dictionary under a specific key.

If the metadata is not included in the dictionary, you might need to adjust the configuration or the schema used in the StructuredPrompt to ensure that the response includes the necessary metadata. If the language model or the setup allows, you could try to configure it to return an object that includes both the result and the metadata.

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[jeremrav]

@dosu

I'm using Azure OpenAI gpt-4o-mini model and using from langchain import hub to pull the structured prompt.

So how can I do to have response_metadata in the response in this code:

prompt_helpfulness: StructuredPrompt = hub.pull(f"evaluator-helpfulness-{ENVIRONMENT}")
evaluator_helpfulness = prompt_helpfulness | llm

result = evaluator_helpfulness.invoke(
    {"assistant_answer": output, "question": content_input, "context": context},
    config={"tags": ["helpful", ENVIRONMENT]},
)
print(result.response_metadata)

[dosubot[bot]]

Hey @jeremrav! I see you're diving into some interesting stuff with Azure OpenAI and Langchain. However, I'm just a bot designed to help with bugs, technical questions, and engineering knowledge. For anything beyond that, you might want to reach out to the Dosu Team to see if they can provide more insights. Keep up the great work!

To continue talking to Dosu, mention @dosu.

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[jeremrav]

@dosu

I'm using Azure OpenAI gpt-4o-mini model and using from langchain import hub to pull the structured prompt.

So how can I do to have response_metadata in the response in this code:

prompt_helpfulness: StructuredPrompt = hub.pull(f"evaluator-helpfulness-{ENVIRONMENT}")
evaluator_helpfulness = prompt_helpfulness | llm

result = evaluator_helpfulness.invoke(
    {"assistant_answer": output, "question": content_input, "context": context},
    config={"tags": ["helpful", ENVIRONMENT]},
)
print(result.response_metadata)

[dosubot[bot]]

To include response_metadata in the response when using the Azure OpenAI gpt-4o-mini model with LangChain, ensure that the invoke method returns an object that includes metadata. If the result is currently a dictionary, you might need to adjust the configuration or the way the response is structured.

Here's a general approach to ensure response_metadata is included:

1. Check the Model Configuration: Ensure that the language model is configured to return metadata. This might involve setting specific parameters when initializing the model or during the invocation.

2. Modify the Response Handling: If the invoke method is returning a dictionary, you might need to modify the way the response is handled to include metadata. This could involve wrapping the response in an object that includes both the result and the metadata.

3. Ensure Proper Schema: If using a StructuredPrompt, ensure that the schema allows for metadata to be included in the response. This might involve defining a custom schema that includes fields for metadata.

Here's a conceptual example of how you might adjust your code:

from langchain_openai import AzureChatOpenAI

# Initialize the AzureChatOpenAI with the necessary parameters
llm = AzureChatOpenAI(
    azure_deployment="your-deployment",
    api_version="2024-05-01-preview",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
    model="gpt-4o-mini",
    # other params...
)

# Pull the structured prompt
prompt_helpfulness: StructuredPrompt = hub.pull(f"evaluator-helpfulness-{ENVIRONMENT}")
evaluator_helpfulness = prompt_helpfulness | llm

# Invoke the model
result = evaluator_helpfulness.invoke(
    {"assistant_answer": output, "question": content_input, "context": context},
    config={"tags": ["helpful", ENVIRONMENT]},
)

# Access the response metadata if available
if hasattr(result, 'response_metadata'):
    print(result.response_metadata)
else:
    print("Response metadata not available.")

Ensure that the AzureChatOpenAI and StructuredPrompt are set up to handle and return metadata. If the metadata is not included, you may need to adjust the model or prompt configuration to ensure it is part of the response ^[1].

To continue talking to Dosu, mention @dosu.

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