From ea129d72be7a50ebf3cc82fbcfaf1a4cafb12c77 Mon Sep 17 00:00:00 2001
From: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
Date: Thu, 29 Aug 2024 20:46:57 -0700
Subject: [PATCH] @unit

---
 .../how_to_guides/evaluation/unit_testing.mdx | 26 +++++++++----------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/versioned_docs/version-2.0/how_to_guides/evaluation/unit_testing.mdx b/versioned_docs/version-2.0/how_to_guides/evaluation/unit_testing.mdx
index 851b05ef..0445763f 100644
--- a/versioned_docs/version-2.0/how_to_guides/evaluation/unit_testing.mdx
+++ b/versioned_docs/version-2.0/how_to_guides/evaluation/unit_testing.mdx
@@ -9,12 +9,12 @@ We recommend using LangSmith to track any unit tests, end-to-end integration tes
 These should run on every commit in your CI pipeline to catch regressions early.
 
 :::note
-`@test` currently requires `langsmith` python version `>=0.1.74` (named `@unit` for versions `>=0.1.42`). If you are interested in unit testing functionality in TypeScript or other languages, please let us know at [support@langchain.dev](mailto:support@langchain.dev).  
+`@unit` currently requires `langsmith` python version `>=0.1.74` (named `@unit` for versions `>=0.1.42`). If you are interested in unit testing functionality in TypeScript or other languages, please let us know at [support@langchain.dev](mailto:support@langchain.dev).  
 :::
 
-## Write a @test
+## Write a @unit
 
-To write a LangSmith functional test, decorate your test function with `@test`.
+To write a LangSmith functional test, decorate your test function with `@unit`.
 If you want to track the full nested trace of the system or component being tested, you can mark those functions with `@traceable`. For example:
 
 ```python
@@ -35,7 +35,7 @@ Then define your test:
 from langsmith import test
 from my_app.main import generate_sql
 
-@test
+@unit
 def test_sql_generation_select_all():
     user_query = "Get all users from the customers table"
     sql = generate_sql(user_query)
@@ -61,7 +61,7 @@ The test suite syncs to a corresponding dataset named after your package or gith
 
 ## Going further
 
-`@test` is designed to stay out of your way and works well with familiar `pytest` features. For example:
+`@unit` is designed to stay out of your way and works well with familiar `pytest` features. For example:
 
 #### Defining inputs as fixtures
 
@@ -80,7 +80,7 @@ def expected_sql():
 
 # output_keys indicate which test arguments to save as 'outputs' in the dataset (Optional)
 # Otherwise, all arguments are saved as 'inputs'
-@test(output_keys=["expected_sql"])
+@unit(output_keys=["expected_sql"])
 def test_sql_generation_with_fixture(user_query, expected_sql):
     sql = generate_sql(user_query)
     assert sql == expected_sql
@@ -91,7 +91,7 @@ def test_sql_generation_with_fixture(user_query, expected_sql):
 Parametrizing tests lets you run the same assertions across multiple sets of inputs. Use `pytest`'s `parametrize` decorator to achieve this. For example:
 
 ```python
-@test
+@unit
 @pytest.mark.parametrize(
     "user_query, expected_sql",
     [
@@ -113,7 +113,7 @@ LangSmith provides an `expect` utility to help define expectations about your LL
 ```python
 from langsmith import expect
 
-@test
+@unit
 def test_sql_generation_select_all():
     user_query = "Get all users from the customers table"
     sql = generate_sql(user_query)
@@ -125,7 +125,7 @@ This will log the binary "expectation" score to the experiment results, addition
 `expect` also provides "fuzzy match" methods. For example:
 
 ```python
-@test
+@unit
 @pytest.mark.parametrize(
     "query, expectation",
     [
@@ -188,7 +188,7 @@ LANGCHAIN_TEST_CACHE=tests/cassettes ptw tests/my_llm_tests
 
 ## Explanations
 
-The `@test` test decorator converts any test into a parametrized LangSmith example. By default, all tests within a given file will be grouped as a single "test suite" with a corresponding dataset. You can configure which test suite a test belongs to by passing the `test_suite_name` parameter to `@test`.
+The `@unit` test decorator converts any test into a parametrized LangSmith example. By default, all tests within a given file will be grouped as a single "test suite" with a corresponding dataset. You can configure which test suite a test belongs to by passing the `test_suite_name` parameter to `@unit`.
 
 The following metrics are available off-the-shelf:
 
@@ -207,7 +207,7 @@ from langsmith.run_helpers import get_current_run_tree
 
 client = Client()
 
-@test
+@unit
 def test_foo():
     run_tree = get_current_run_tree()
     client.create_feedback(run_id=run_tree.id, key="my_custom_feedback", score=1)
@@ -306,9 +306,9 @@ Assert the expectation value against a custom function.
 
 ### `test` API
 
-The `@test` decorator is used to mark a function as a test case for LangSmith. It ensures that the necessary example data is created and associated with the test function. The decorated function will be executed as a test case, and the results will be recorded and reported by LangSmith.
+The `@unit` decorator is used to mark a function as a test case for LangSmith. It ensures that the necessary example data is created and associated with the test function. The decorated function will be executed as a test case, and the results will be recorded and reported by LangSmith.
 
-#### `@test(id=None, output_keys=None, client=None, test_suite_name=None)`
+#### `@unit(id=None, output_keys=None, client=None, test_suite_name=None)`
 
 Create a test case in LangSmith.