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Selecting Components

LLM applications come in all shapes and sizes and with a variety of different control flows. As a result it’s a challenge to consistently evaluate parts of an LLM application trace.

Therefore, we’ve adapted the use of lenses to refer to parts of an LLM stack trace and use those when defining evaluations. For example, the following lens refers to the input to the retrieve step of the app called query.



Such lenses can then be used to define evaluations as so:


# Context relevance between question and each context chunk.
f_context_relevance = (
    Feedback(provider.context_relevance_with_cot_reasons, name = "Context Relevance")

In most cases, the Select object produces only a single item but can also address multiple items.

For example: Select.RecordCalls.retrieve.args.query refers to only one item.

However, Select.RecordCalls.retrieve.rets refers to multiple items. In this case, the documents returned by the retrieve method. These items can be evaluated separately, as shown above, or can be collected into an array for evaluation with .collect(). This is most commonly used for groundedness evaluations.


f_groundedness = (
    Feedback(provider.groundedness_measure_with_cot_reasons, name = "Groundedness")

Selectors can also access multiple calls to the same component. In agentic applications, this is an increasingly common practice. For example, an agent could complete multiple calls to a retrieve method to complete the task required.

For example, the following method returns only the returned context documents from the first invocation of retrieve.

context = Select.RecordCalls.retrieve.rets.rets[:]
# Same as context = context_method[0].rets[:]

Alternatively, adding [:] after the method name retrieve returns context documents from all invocations of retrieve.

context_all_calls = Select.RecordCalls.retrieve[:].rets.rets[:]

See also other Select shortcuts.

Understanding the structure of your app

Because LLM apps have a wide variation in their structure, the feedback selector construction can also vary widely. To construct the feedback selector, you must first understand the structure of your application.

In python, you can access the JSON structure with with_record methods and then calling layout_calls_as_app.

For example:

response = my_llm_app(query)

from trulens_eval import TruChain
tru_recorder = TruChain(

response, tru_record = tru_recorder.with_record(my_llm_app, query)
json_like = tru_record.layout_calls_as_app()

If a selector looks like the below

It can be accessed via the JSON-like via


The application structure can also be viewed in the TruLens user inerface. You can view this structure on the Evaluations page by scrolling down to the Timeline.

The top level record also contains these helper accessors

  • RecordInput = Record.main_input -- points to the main input part of a Record. This is the first argument to the root method of an app (for LangChain Chains this is the __call__ method).

  • RecordOutput = Record.main_output -- points to the main output part of a Record. This is the output of the root method of an app (i.e. __call__ for LangChain Chains).

  • RecordCalls = -- points to the root of the app-structured mirror of calls in a record. See App-organized Calls Section above.

Multiple Inputs Per Argument

As in the f_qs_relevance example, a selector for a single argument may point to more than one aspect of a record/app. These are specified using the slice or lists in key/index poisitions. In that case, the feedback function is evaluated multiple times, its outputs collected, and finally aggregated into a main feedback result.

The collection of values for each argument of feedback implementation is collected and every combination of argument-to-value mapping is evaluated with a feedback definition. This may produce a large number of evaluations if more than one argument names multiple values. In the dashboard, all individual invocations of a feedback implementation are shown alongside the final aggregate result.

App/Record Organization (What can be selected)

The top level JSON attributes are defined by the class structures.

For a Record:

class Record(SerialModel):
    record_id: RecordID
    app_id: AppID

    cost: Optional[Cost] = None
    perf: Optional[Perf] = None

    ts: datetime = pydantic.Field(default_factory=lambda:

    tags: str = ""

    main_input: Optional[JSON] = None
    main_output: Optional[JSON] = None  # if no error
    main_error: Optional[JSON] = None  # if error

    # The collection of calls recorded. Note that these can be converted into a
    # json structure with the same paths as the app that generated this record
    # via `layout_calls_as_app`.
    calls: Sequence[RecordAppCall] = []

For an App:

class AppDefinition(WithClassInfo, SerialModel, ABC):

    app_id: AppID

    feedback_definitions: Sequence[FeedbackDefinition] = []

    feedback_mode: FeedbackMode = FeedbackMode.WITH_APP_THREAD

    root_class: Class

    root_callable: ClassVar[FunctionOrMethod]

    app: JSON

For your app, you can inspect the JSON-like structure by using the dict method:

tru = ... # your app, extending App

Calls made by App Components

When evaluating a feedback function, Records are augmented with app/component calls. For example, if the instrumented app contains a component combine_docs_chain then app.combine_docs_chain will contain calls to methods of this component. app.combine_docs_chain._call will contain a RecordAppCall (see with information about the inputs/outputs/metadata regarding the _call call to that component. Selecting this information is the reason behind the Select.RecordCalls alias.

You can inspect the components making up your app via the App method print_instrumented.