> ## Documentation Index
> Fetch the complete documentation index at: https://docs.parea.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Feedback

> Record feedback from users on the quality LLM results.

If you collect feedback from your users, such as thumbs up or down, you can record that feedback on your trace logs.

## Record feedback

To record feedback you need the trace\_id of the trace log you want to record feedback on.
You can get the trace\_id using the `get_current_trace_id()` helper function, or from the completion method's response.

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    from parea.schemas import FeedbackRequest
    from parea import Parea, get_current_trace_id, trace

    p = Parea(api_key="PAREA_API_KEY")

    @trace
    def argument_chain(messages: list[dict]) -> tuple[str, str]:
        # get_current_trace_id will return the trace_id of the most recent trace
        trace_id = get_current_trace_id()
        return call_llm(messages), trace_id

    result, trace_id = argument_chain([{"role": "user", "content": "Hello"}])
    p.record_feedback(
        FeedbackRequest(
            trace_id=trace_id,
            # insert your user score here, score must be float
            score=USER_SCORE,
            # Optionally, you can also provide a ground truth or
            # target answer. This could also be from your user.
            target="ground_truth",
        )
    )
    ```

    Alternatively, if you used `p.completion` for your LLM request, you can access the `inference_id` field on the response to get the trace\_id.

    ```python theme={null}
    from parea.schemas import LLMInputs, Message, Role, Completion, CompletionResponse, FeedbackRequest

    result: CompletionResponse = p.completion(
        Completion(
            llm_configuration=LLMInputs(
                model="gpt-3.5-turbo",
                messages=[Message(role=Role.user, content="Hello")],
            )
        )
    )
    p.record_feedback(
        FeedbackRequest(
            trace_id=result.inference_id,
            score=USER_SCORE,
        )
    )
    ```
  </Tab>

  <Tab title="Typescript">
    ```typescript theme={null}
    import {getCurrentTraceId, trace} from "parea-ai";
    const p = new Parea("PAREA_API_KEY");

    const argumentChain = trace('argumentChain', async (messages) => {
        // getCurrentTraceId will return the trace_id of current traced function
        const traceId = getCurrentTraceId()
        const result = await call_llm(messages)
        return [result, traceId]
      },
    );
    const [result, traceId] = await argumentChain([{role: "user", content: "Hello"}]);
    await p.recordFeedback({
        trace_id: traceId,
        // insert your user score here, score must be float
        score: USER_SCORE,
    });
    ```

    Alternatively, if you used `p.completion` for your LLM request, you can access the `inference_id` field on the response to get the trace\_id.

    ```typescript theme={null}
    const result = await p.completion({
        llm_configuration: {
            model: 'gpt-3.5-turbo',
            messages: [{role: 'user', content: "Hello"}],
        }
    })
    await p.recordFeedback({
        trace_id: result.inference_id,
        score: USER_SCORE,
    });
    ```
  </Tab>
</Tabs>