truchain = TruChain(
chain,
app_id='Chain1_ChatApplication',
tru=tru
)
truchain("This will be automatically logged.")
Feedback functions can also be logged automatically by providing them in a list to the feedbacks arg.
truchain = TruChain(
chain,
app_id='Chain1_ChatApplication',
feedbacks=[f_lang_match], # feedback functions
tru=tru
)
truchain("This will be automatically logged.")
tc = TruChain(chain, app_id='Chain1_ChatApplication')
Set up logging and instrumentation¶
Making the first call to your wrapped LLM Application will now also produce a log or "record" of the chain execution.
prompt_input = 'que hora es?'
gpt3_response, record = tc.call_with_record(prompt_input)
We can log the records but first we need to log the chain itself.
tru.add_app(app=truchain)
Then we can log the record:
tru.add_record(record)
Log App Feedback¶
Capturing app feedback such as user feedback of the responses can be added with one call.
thumb_result = True
tru.add_feedback(name="👍 (1) or 👎 (0)",
record_id=record.record_id,
result=thumb_result)
Evaluate Quality¶
Following the request to your app, you can then evaluate LLM quality using feedback functions. This is completed in a sequential call to minimize latency for your application, and evaluations will also be logged to your local machine.
To get feedback on the quality of your LLM, you can use any of the provided feedback functions or add your own.
To assess your LLM quality, you can provide the feedback functions to tru.run_feedback() in a list provided to feedback_functions.
feedback_results = tru.run_feedback_functions(
record=record,
feedback_functions=[f_lang_match]
)
display(feedback_results)
After capturing feedback, you can then log it to your local database.
tru.add_feedbacks(feedback_results)
Out-of-band Feedback evaluation¶
In the above example, the feedback function evaluation is done in the same process as the chain evaluation. The alternative approach is the use the provided persistent evaluator started via tru.start_deferred_feedback_evaluator. Then specify the feedback_mode for TruChain as deferred to let the evaluator handle the feedback functions.
For demonstration purposes, we start the evaluator here but it can be started in another process.
truchain: TruChain = TruChain(
chain,
app_id='Chain1_ChatApplication',
feedbacks=[f_lang_match],
tru=tru,
feedback_mode="deferred"
)
tru.start_evaluator()
truchain("This will be logged by deferred evaluator.")
tru.stop_evaluator()