Running with your app

The primary method for evaluating LLM apps is by running feedback functions with your app.

To do so, you first need to define the wrap the specified feedback implementation with Feedback and select what components of your app to evaluate. Optionally, you can also select an aggregation method.

f_context_relevance = Feedback(openai.qs_relevance)

# Implementation signature:
# def qs_relevance(self, question: str, statement: str) -> float:

Once you've defined the feedback functions to run with your application, you can then pass them as a list to the instrumentation class of your choice, along with the app itself. These make up the recorder.

from trulens_eval import TruChain
# f_lang_match, f_qa_relevance, f_context_relevance are feedback functions
tru_recorder = TruChain(
    feedbacks=[f_lang_match, f_qa_relevance, f_context_relevance])

Now that you've included the evaluations as a component of your recorder, they are able to be run with your application. By default, feedback functions will be run in the same process as the app. This is known as the feedback mode: with_app_thread.

with tru_recorder as recording:
    chain(""What is langchain?")

In addition to with_app_thread, there are a number of other manners of running feedback functions. These are accessed by the feedback mode and included when you construct the recorder, like so:

from trulens_eval import FeedbackMode

tru_recorder = TruChain(
    feedbacks=[f_lang_match, f_qa_relevance, f_context_relevance],

Here are the different feedback modes you can use:

  • WITH_APP_THREAD: This is the default mode. Feedback functions will run in the same process as the app, but only after the app has produced a record.
  • NONE: In this mode, no evaluation will occur, even if feedback functions are specified.
  • WITH_APP: Feedback functions will run immediately and before the app returns a record.
  • DEFERRED: Feedback functions will be evaluated later via the process started by tru.start_evaluator.