test(openai): add tests for prompt_cache_key parameter and update docs (#32363)

Introduce tests to validate the behavior and inclusion of the
`prompt_cache_key` parameter in request payloads for the `ChatOpenAI`
model.

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -2731,6 +2731,31 @@ class ChatOpenAI(BaseChatOpenAI):  # type: ignore[override]
             Always use ``extra_body`` for custom parameters, **not** ``model_kwargs``.
             Using ``model_kwargs`` for non-OpenAI parameters will cause API errors.
 
+    .. dropdown:: Prompt caching optimization
+
+        For high-volume applications with repetitive prompts, use ``prompt_cache_key``
+        per-invocation to improve cache hit rates and reduce costs:
+
+        .. code-block:: python
+
+            llm = ChatOpenAI(model="gpt-4o-mini")
+
+            response = llm.invoke(
+                messages,
+                prompt_cache_key="example-key-a",  # Routes to same machine for cache hits
+            )
+
+            customer_response = llm.invoke(messages, prompt_cache_key="example-key-b")
+            support_response = llm.invoke(messages, prompt_cache_key="example-key-c")
+
+            # Dynamic cache keys based on context
+            cache_key = f"example-key-{dynamic_suffix}"
+            response = llm.invoke(messages, prompt_cache_key=cache_key)
+
+        Cache keys help ensure requests with the same prompt prefix are routed to
+        machines with existing cache, providing cost reduction and latency improvement on
+        cached tokens.
+
     """  # noqa: E501
 
     max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens")

libs/partners/openai/tests/integration_tests/chat_models/test_base.py

@@ -1110,3 +1110,46 @@ def test_tools_and_structured_output() -> None:
     assert isinstance(aggregated["raw"], AIMessage)
     assert aggregated["raw"].tool_calls
     assert aggregated["parsed"] is None
+
+
+@pytest.mark.scheduled
+def test_prompt_cache_key_invoke() -> None:
+    """Test that prompt_cache_key works with invoke calls."""
+    chat = ChatOpenAI(model="gpt-4o-mini", max_completion_tokens=20)
+    messages = [HumanMessage("Say hello")]
+
+    # Test that invoke works with prompt_cache_key parameter
+    response = chat.invoke(messages, prompt_cache_key="integration-test-v1")
+
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+    assert len(response.content) > 0
+
+    # Test that subsequent call with same cache key also works
+    response2 = chat.invoke(messages, prompt_cache_key="integration-test-v1")
+
+    assert isinstance(response2, AIMessage)
+    assert isinstance(response2.content, str)
+    assert len(response2.content) > 0
+
+
+@pytest.mark.scheduled
+def test_prompt_cache_key_usage_methods_integration() -> None:
+    """Integration test for prompt_cache_key usage methods."""
+    messages = [HumanMessage("Say hi")]
+
+    # Test keyword argument method
+    chat = ChatOpenAI(model="gpt-4o-mini", max_completion_tokens=10)
+    response = chat.invoke(messages, prompt_cache_key="integration-test-v1")
+    assert isinstance(response, AIMessage)
+    assert isinstance(response.content, str)
+
+    # Test model-level via model_kwargs
+    chat_model_level = ChatOpenAI(
+        model="gpt-4o-mini",
+        max_completion_tokens=10,
+        model_kwargs={"prompt_cache_key": "integration-model-level-v1"},
+    )
+    response_model_level = chat_model_level.invoke(messages)
+    assert isinstance(response_model_level, AIMessage)
+    assert isinstance(response_model_level.content, str)

libs/partners/openai/tests/unit_tests/chat_models/test_prompt_cache_key.py

@@ -0,0 +1,84 @@
+"""Unit tests for prompt_cache_key parameter."""
+
+from langchain_core.messages import HumanMessage
+
+from langchain_openai import ChatOpenAI
+
+
+def test_prompt_cache_key_parameter_inclusion() -> None:
+    """Test that prompt_cache_key parameter is properly included in request payload."""
+    chat = ChatOpenAI(model="gpt-4o-mini", max_completion_tokens=10)
+    messages = [HumanMessage("Hello")]
+
+    payload = chat._get_request_payload(messages, prompt_cache_key="test-cache-key")
+    assert "prompt_cache_key" in payload
+    assert payload["prompt_cache_key"] == "test-cache-key"
+
+
+def test_prompt_cache_key_parameter_exclusion() -> None:
+    """Test that prompt_cache_key parameter behavior matches OpenAI API."""
+    chat = ChatOpenAI(model="gpt-4o-mini", max_completion_tokens=10)
+    messages = [HumanMessage("Hello")]
+
+    # Test with explicit None (OpenAI should accept None values (marked Optional))
+    payload = chat._get_request_payload(messages, prompt_cache_key=None)
+    assert "prompt_cache_key" in payload
+    assert payload["prompt_cache_key"] is None
+
+
+def test_prompt_cache_key_per_call() -> None:
+    """Test that prompt_cache_key can be passed per-call with different values."""
+    chat = ChatOpenAI(model="gpt-4o-mini", max_completion_tokens=10)
+    messages = [HumanMessage("Hello")]
+
+    # Test different cache keys per call
+    payload1 = chat._get_request_payload(messages, prompt_cache_key="cache-v1")
+    payload2 = chat._get_request_payload(messages, prompt_cache_key="cache-v2")
+
+    assert payload1["prompt_cache_key"] == "cache-v1"
+    assert payload2["prompt_cache_key"] == "cache-v2"
+
+    # Test dynamic cache key assignment
+    cache_keys = ["customer-v1", "support-v1", "feedback-v1"]
+
+    for cache_key in cache_keys:
+        payload = chat._get_request_payload(messages, prompt_cache_key=cache_key)
+        assert "prompt_cache_key" in payload
+        assert payload["prompt_cache_key"] == cache_key
+
+
+def test_prompt_cache_key_model_kwargs() -> None:
+    """Test prompt_cache_key via model_kwargs and method precedence."""
+    messages = [HumanMessage("Hello world")]
+
+    # Test model-level via model_kwargs
+    chat = ChatOpenAI(
+        model="gpt-4o-mini",
+        max_completion_tokens=10,
+        model_kwargs={"prompt_cache_key": "model-level-cache"},
+    )
+    payload = chat._get_request_payload(messages)
+    assert "prompt_cache_key" in payload
+    assert payload["prompt_cache_key"] == "model-level-cache"
+
+    # Test that per-call cache key overrides model-level
+    payload_override = chat._get_request_payload(
+        messages, prompt_cache_key="per-call-cache"
+    )
+    assert payload_override["prompt_cache_key"] == "per-call-cache"
+
+
+def test_prompt_cache_key_responses_api() -> None:
+    """Test that prompt_cache_key works with Responses API."""
+    chat = ChatOpenAI(
+        model="gpt-4o-mini", use_responses_api=True, max_completion_tokens=10
+    )
+
+    messages = [HumanMessage("Hello")]
+    payload = chat._get_request_payload(
+        messages, prompt_cache_key="responses-api-cache-v1"
+    )
+
+    # prompt_cache_key should be present regardless of API type
+    assert "prompt_cache_key" in payload
+    assert payload["prompt_cache_key"] == "responses-api-cache-v1"