From 943be92fb2eb4b8eb46f30db542cef812263c972 Mon Sep 17 00:00:00 2001
From: isaac hershenson <ihershenson@hmc.edu>
Date: Tue, 17 Dec 2024 13:29:01 -0800
Subject: [PATCH] tests

---
 python/tests/integration_tests/test_client.py | 108 ++++++++++++++++++
 1 file changed, 108 insertions(+)

diff --git a/python/tests/integration_tests/test_client.py b/python/tests/integration_tests/test_client.py
index 3bcd9d04c..bb5f80714 100644
--- a/python/tests/integration_tests/test_client.py
+++ b/python/tests/integration_tests/test_client.py
@@ -1253,6 +1253,114 @@ def test_list_examples_attachments_keys(langchain_client: Client) -> None:
 
     langchain_client.delete_dataset(dataset_id=dataset.id)
 
+@pytest.mark.skip(reason="Need to land https://github.com/langchain-ai/langchainplus/pull/7415 first")
+def test_mime_type_is_propogated(langchain_client: Client) -> None:
+    """Test that the mime type is propogated correctly."""
+    dataset_name = "__test_mime_type_is_propogated" + uuid4().hex[:4]
+    dataset = langchain_client.create_dataset(dataset_name=dataset_name)
+
+    langchain_client.upload_examples_multipart(
+        dataset_id=dataset.id,
+        uploads=[
+            ExampleUploadWithAttachments(
+                inputs={"text": "hello world"},
+                outputs={"response": "hi there"},
+                attachments={
+                    "test_file": ("text/plain", b"test content"),
+                },
+            )
+        ],
+    )
+
+    example = next(langchain_client.list_examples(dataset_id=dataset.id))
+    assert example.attachments["test_file"]["mime_type"] == "text/plain"
+
+    example = langchain_client.read_example(example_id=example.id)
+    assert example.attachments["test_file"]["mime_type"] == "text/plain"
+
+    langchain_client.delete_dataset(dataset_id=dataset.id)
+
+@pytest.mark.skip(reason="Need to land https://github.com/langchain-ai/langchainplus/pull/7415 first")
+def test_evaluate_mime_type_is_propogated(langchain_client: Client) -> None:
+    """Test that the mime type is propogated correctly when evaluating."""
+    dataset_name = "__test_evaluate_mime_type_is_propogated" + uuid4().hex[:4]
+    dataset = langchain_client.create_dataset(dataset_name=dataset_name)
+
+    langchain_client.upload_examples_multipart(
+        dataset_id=dataset.id,
+        uploads=[
+            ExampleUploadWithAttachments(
+                inputs={"text": "hello world"},
+                outputs={"response": "hi there"},
+                attachments={
+                    "test_file": ("text/plain", b"test content"),
+                },
+            )
+        ],
+    )
+
+    def target(inputs: Dict[str, Any], attachments: Dict[str, Any]) -> Dict[str, Any]:
+        # Verify we receive the attachment data
+        assert attachments["test_file"]["mime_type"] == "text/plain"
+        return {"answer": "hi there"}
+
+    def evaluator(outputs: dict, reference_outputs: dict, attachments: dict) -> Dict[str, Any]:
+        # Verify we receive the attachment data
+        assert attachments["test_file"]["mime_type"] == "text/plain"
+        return {
+            "score": float(
+                reference_outputs.get("answer") == outputs.get("answer")  # type: ignore
+            )
+        }
+
+    langchain_client.evaluate(
+        target,
+        data=dataset_name,
+        evaluators=[evaluator]
+    )
+
+    langchain_client.delete_dataset(dataset_name=dataset_name)
+
+@pytest.mark.skip(reason="Need to land https://github.com/langchain-ai/langchainplus/pull/7415 first")
+async def test_evaluate_mime_type_is_propogated(langchain_client: Client) -> None:
+    """Test that the mime type is propogated correctly when evaluating."""
+    dataset_name = "__test_evaluate_mime_type_is_propogated" + uuid4().hex[:4]
+    dataset = langchain_client.create_dataset(dataset_name=dataset_name)
+
+    langchain_client.upload_examples_multipart(
+        dataset_id=dataset.id,
+        uploads=[
+            ExampleUploadWithAttachments(
+                inputs={"text": "hello world"},
+                outputs={"response": "hi there"},
+                attachments={
+                    "test_file": ("text/plain", b"test content"),
+                },
+            )
+        ],
+    )
+
+    async def target(inputs: Dict[str, Any], attachments: Dict[str, Any]) -> Dict[str, Any]:
+        # Verify we receive the attachment data
+        assert attachments["test_file"]["mime_type"] == "text/plain"
+        return {"answer": "hi there"}
+
+    async def evaluator(outputs: dict, reference_outputs: dict, attachments: dict) -> Dict[str, Any]:
+        # Verify we receive the attachment data
+        assert attachments["test_file"]["mime_type"] == "text/plain"
+        return {
+            "score": float(
+                reference_outputs.get("answer") == outputs.get("answer")  # type: ignore
+            )
+        }
+
+    await langchain_client.aevaluate(
+        target,
+        data=dataset_name,
+        evaluators=[evaluator]
+    )
+
+    langchain_client.delete_dataset(dataset_name=dataset_name)
 
 def test_evaluate_with_attachments_multiple_evaluators(
     langchain_client: Client,