# Running with your app

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

To do so, you first need to define the metric by wrapping a metric implementation with `Metric` and specifying selectors that define what components of your app to evaluate. Optionally, you can also specify an aggregation method.

## Example

```
from trulens.core import Metric, Selector
import numpy as np

f_context_relevance = Metric(
    implementation=openai.context_relevance,
    selectors={
        "question": Selector.select_record_input(),
        "context": Selector.select_context(collect_list=False),
    },
    agg=np.mean,
)

# Implementation signature:
# def context_relevance(self, question: str, context: str) -> float:
```

Once you've defined the metrics 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`.

## Example

```
from trulens.apps.langchain import TruChain

# f_lang_match, f_qa_relevance, f_context_relevance are metrics
tru_recorder = TruChain(
    chain,
    app_name='ChatApplication',
    app_version="Chain1",
    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, metrics will be run in the same process as the app. This is known as the feedback mode: `WITH_APP_THREAD`.

## Example

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

In addition to `WITH_APP_THREAD`, there are a number of other manners of running metrics. These are accessed by the feedback mode and included when you construct the recorder.

## Example

```
from trulens.core import FeedbackMode

tru_recorder = TruChain(
    chain,
    app_name='ChatApplication',
    app_version="Chain1",
    feedbacks=[f_lang_match, f_qa_relevance, f_context_relevance],
    feedback_mode=FeedbackMode.DEFERRED,
)
```

Here are the different feedback modes you can use:

- `WITH_APP_THREAD`: This is the default mode. Metrics 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 metrics are specified.
- `WITH_APP`: Metrics will run immediately and before the app returns a record.
- `DEFERRED`: Metrics will be evaluated later via the process started by `tru.start_evaluator`.