draft

docs/evaluation/how_to_guides/summary.mdx

@@ -8,7 +8,9 @@ import {
 
 Some metrics can only be defined on the entire experiment level as opposed to the individual runs of the experiment.
 For example, you may want to compute the overall pass rate or f1 score of your evaluation target across all examples in the dataset.
-These are called `summary_evaluators`. Instead of taking in a single `Run` and `Example`, these evaluators take a list of each.
+These are called `summary_evaluators`.
+
+## Basic example
 
 Below, we'll implement a very simple summary evaluator that computes overall pass rate:
 
@@ -47,7 +49,7 @@ You can then pass this evaluator to the `evaluate` method as follows:
       
       ls_client = Client()
       dataset = ls_client.clone_public_dataset(
-        "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d
+        "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d"
       )
       
       def bad_classifier(inputs: dict) -> dict:
@@ -73,7 +75,7 @@ You can then pass this evaluator to the `evaluate` method as follows:
       const client = new Client();
       const datasetName = "Toxic queries";
       const dataset = await client.clonePublicDataset(
-        "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d,
+        "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d",
         { datasetName: datasetName }
       );
 
@@ -100,3 +102,25 @@ You can then pass this evaluator to the `evaluate` method as follows:
 In the LangSmith UI, you'll the summary evaluator's score displayed with the corresponding key.
 
 ![](./static/summary_eval.png)
+
+## Summary evaluator args
+
+Summary evaluator functions must have specific argument names. They can take any subset of the following arguments:
+
+- `inputs: list[dict]`: A list of the inputs corresponding to a single example in a dataset.
+- `outputs: list[dict]`: A list of the dict outputs produced by each experiment on the given inputs.
+- `reference_outputs/referenceOutputs: list[dict]`: A list of the reference outputs associated with the example, if available.
+- `runs: list[Run]`: A list of the full [Run](/reference/data_formats/run_data_format) objects generated by the two experiments on the given example. Use this if you need access to intermediate steps or metadata about each run.
+- `examples: list[Example]`: All of the dataset [Example](/reference/data_formats/example_data_format) objects, including the example inputs, outputs (if available), and metdata (if available).
+
+## Summary evaluator output
+
+Summary evaluators are expected to return one of the following types:
+
+Python and JS/TS
+
+- `dict`: dicts of the form `{"score": ..., "name": ...}` allow you to pass a numeric or boolean score and metric name.
+
+Currently Python only
+
+- `int | float | bool`: this is interepreted as an continuous metric that can be averaged, sorted, etc. The function name is used as the name of the metric.