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formatting/nits/docs
This commit is contained in:
Mason Daugherty
parent
d5b26bc358
commit
cd8d6ae7cd
@ -629,15 +629,16 @@ def tool_example_to_messages(
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The list of messages per example by default corresponds to:
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1) HumanMessage: contains the content from which content should be extracted.
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2) AIMessage: contains the extracted information from the model
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3) ToolMessage: contains confirmation to the model that the model requested a tool
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correctly.
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1. ``HumanMessage``: contains the content from which content should be extracted.
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2. ``AIMessage``: contains the extracted information from the model
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3. ``ToolMessage``: contains confirmation to the model that the model requested a
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tool correctly.
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If `ai_response` is specified, there will be a final AIMessage with that response.
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If ``ai_response`` is specified, there will be a final ``AIMessage`` with that
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response.
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The ToolMessage is required because some chat models are hyper-optimized for agents
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rather than for an extraction use case.
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The ``ToolMessage`` is required because some chat models are hyper-optimized for
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agents rather than for an extraction use case.
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Arguments:
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input: string, the user input
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@ -646,7 +647,7 @@ def tool_example_to_messages(
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tool_outputs: Optional[list[str]], a list of tool call outputs.
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Does not need to be provided. If not provided, a placeholder value
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will be inserted. Defaults to None.
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ai_response: Optional[str], if provided, content for a final AIMessage.
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ai_response: Optional[str], if provided, content for a final ``AIMessage``.
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Returns:
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A list of messages
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@ -728,6 +729,7 @@ def _parse_google_docstring(
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"""Parse the function and argument descriptions from the docstring of a function.
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Assumes the function docstring follows Google Python style guide.
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"""
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if docstring:
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docstring_blocks = docstring.split("\n\n")
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@ -190,7 +190,7 @@ def _format_ls_structured_output(ls_structured_output_format: Optional[dict]) ->
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class BaseChatModel(RunnableSerializable[LanguageModelInput, AIMessageV1], ABC):
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"""Base class for chat models.
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"""Base class for v1 chat models.
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Key imperative methods:
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Methods that actually call the underlying model.
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@ -61,7 +61,7 @@ class ResponseMetadata(TypedDict, total=False):
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@dataclass
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class AIMessage:
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"""A message generated by an AI assistant.
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"""A v1 message generated by an AI assistant.
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Represents a response from an AI model, including text content, tool calls,
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and metadata about the generation process.
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@ -133,7 +133,7 @@ class AIMessage:
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invalid_tool_calls: Optional[list[types.InvalidToolCall]] = None,
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parsed: Optional[Union[dict[str, Any], BaseModel]] = None,
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):
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"""Initialize an AI message.
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"""Initialize a v1 AI message.
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Args:
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content: Message content as string or list of content blocks.
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@ -263,7 +263,7 @@ class AIMessageChunk(AIMessage):
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parsed: Optional[Union[dict[str, Any], BaseModel]] = None,
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chunk_position: Optional[Literal["last"]] = None,
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):
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"""Initialize an AI message.
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"""Initialize a v1 AI message.
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Args:
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content: Message content as string or list of content blocks.
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@ -541,7 +541,7 @@ class HumanMessage:
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id: Optional[str] = None,
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name: Optional[str] = None,
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):
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"""Initialize a human message.
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"""Initialize a v1 human message.
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Args:
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content: Message content as string or list of content blocks.
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@ -623,7 +623,7 @@ class SystemMessage:
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custom_role: Optional[str] = None,
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name: Optional[str] = None,
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):
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"""Initialize a human message.
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"""Initialize a v1 system message.
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Args:
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content: Message content as string or list of content blocks.
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@ -711,7 +711,7 @@ class ToolMessage(ToolOutputMixin):
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artifact: Optional[Any] = None,
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status: Literal["success", "error"] = "success",
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):
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"""Initialize a human message.
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"""Initialize a v1 tool message.
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Args:
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content: Message content as string or list of content blocks.
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@ -374,11 +374,6 @@ class TestFakeChatModelV1Integration(ChatModelV1IntegrationTests):
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"""Enable non-standard blocks support."""
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return True
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@property
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def requires_api_key(self) -> bool:
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"""This fake model doesn't require an API key."""
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return False
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# Example of a more realistic integration test configuration
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# that would require API keys and external services
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@ -108,6 +108,7 @@ def magic_function_no_args() -> int:
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def _validate_tool_call_message(message: BaseMessage) -> None:
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assert isinstance(message, AIMessage)
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assert len(message.tool_calls) == 1
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tool_call = message.tool_calls[0]
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assert tool_call["name"] == "magic_function"
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assert tool_call["args"] == {"input": 3}
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@ -118,6 +119,7 @@ def _validate_tool_call_message(message: BaseMessage) -> None:
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def _validate_tool_call_message_no_args(message: BaseMessage) -> None:
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assert isinstance(message, AIMessage)
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assert len(message.tool_calls) == 1
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tool_call = message.tool_calls[0]
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assert tool_call["name"] == "magic_function_no_args"
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assert tool_call["args"] == {}
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@ -750,10 +752,10 @@ class ChatModelIntegrationTests(ChatModelTests):
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First, debug
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`.
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because `stream` has a default implementation that calls `invoke` and yields
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the result as a single chunk.
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because ``stream`` has a default implementation that calls ``invoke`` and
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yields the result as a single chunk.
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If that test passes but not this one, you should make sure your _stream
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If that test passes but not this one, you should make sure your ``_stream``
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method does not raise any exceptions, and that it yields valid
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:class:`~langchain_core.outputs.chat_generation.ChatGenerationChunk`
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objects like so:
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@ -769,6 +771,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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for chunk in model.stream("Hello"):
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assert chunk is not None
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assert isinstance(chunk, AIMessageChunk)
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assert isinstance(chunk.content, (str, list))
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num_chunks += 1
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assert num_chunks > 0
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@ -785,11 +788,11 @@ class ChatModelIntegrationTests(ChatModelTests):
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_stream`.
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and
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_ainvoke`.
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because `astream` has a default implementation that calls `_stream` in an
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async context if it is implemented, or `ainvoke` and yields the result as a
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single chunk if not.
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because ``astream`` has a default implementation that calls ``_stream`` in
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an async context if it is implemented, or ``ainvoke`` and yields the result
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as a single chunk if not.
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If those tests pass but not this one, you should make sure your _astream
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If those tests pass but not this one, you should make sure your ``_astream``
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method does not raise any exceptions, and that it yields valid
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:class:`~langchain_core.outputs.chat_generation.ChatGenerationChunk`
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objects like so:
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@ -819,12 +822,13 @@ class ChatModelIntegrationTests(ChatModelTests):
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First, debug
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`
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because `batch` has a default implementation that calls `invoke` for each
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message in the batch.
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because ``batch`` has a default implementation that calls ``invoke`` for
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each message in the batch.
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If that test passes but not this one, you should make sure your `batch`
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If that test passes but not this one, you should make sure your ``batch``
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method does not raise any exceptions, and that it returns a list of valid
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:class:`~langchain_core.messages.AIMessage` objects.
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"""
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batch_results = model.batch(["Hello", "Hey"])
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assert batch_results is not None
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@ -848,10 +852,10 @@ class ChatModelIntegrationTests(ChatModelTests):
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_batch`
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and
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_ainvoke`
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because `abatch` has a default implementation that calls `ainvoke` for each
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message in the batch.
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because ``abatch`` has a default implementation that calls ``ainvoke`` for
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each message in the batch.
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If those tests pass but not this one, you should make sure your `abatch`
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If those tests pass but not this one, you should make sure your ``abatch``
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method does not raise any exceptions, and that it returns a list of valid
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:class:`~langchain_core.messages.AIMessage` objects.
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@ -877,7 +881,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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First, debug
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`
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because this test also uses `model.invoke()`.
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because this test also uses ``model.invoke()``.
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If that test passes but not this one, you should verify that:
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1. Your model correctly processes the message history
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@ -890,6 +894,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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AIMessage("hello"),
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HumanMessage("how are you"),
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]
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result = model.invoke(messages)
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assert result is not None
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assert isinstance(result, AIMessage)
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@ -907,18 +912,17 @@ class ChatModelIntegrationTests(ChatModelTests):
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First, debug
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_invoke`
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because this test also uses `model.invoke()`.
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because this test also uses ``model.invoke()``.
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Second, debug
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:meth:`~langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests.test_conversation`
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because this test is the "basic case" without double messages.
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If that test passes those but not this one, you should verify that:
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1. Your model API can handle double messages, or the integration should
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merge messages before sending them to the API.
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1. Your model API can handle double messages, or the integration should merge messages before sending them to the API.
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2. The response is a valid :class:`~langchain_core.messages.AIMessage`
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"""
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""" # noqa: E501
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messages = [
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SystemMessage("hello"),
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SystemMessage("hello"),
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@ -928,6 +932,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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AIMessage("hello"),
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HumanMessage("how are you"),
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]
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result = model.invoke(messages)
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assert result is not None
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assert isinstance(result, AIMessage)
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@ -1023,9 +1028,11 @@ class ChatModelIntegrationTests(ChatModelTests):
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"""
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if not self.returns_usage_metadata:
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pytest.skip("Not implemented.")
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result = model.invoke("Hello")
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assert result is not None
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assert isinstance(result, AIMessage)
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assert result.usage_metadata is not None
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assert isinstance(result.usage_metadata["input_tokens"], int)
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assert isinstance(result.usage_metadata["output_tokens"], int)
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@ -1201,6 +1208,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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"""
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if not self.returns_usage_metadata:
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pytest.skip("Not implemented.")
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full: Optional[AIMessageChunk] = None
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for chunk in model.stream("Write me 2 haikus. Only include the haikus."):
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assert isinstance(chunk, AIMessageChunk)
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@ -1339,6 +1347,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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"""
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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tool_choice_value = None if not self.has_tool_choice else "any"
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# Emit warning if tool_choice_value property is overridden
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if inspect.getattr_static(
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@ -1413,6 +1422,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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"""
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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tool_choice_value = None if not self.has_tool_choice else "any"
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model_with_tools = model.bind_tools(
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[magic_function], tool_choice=tool_choice_value
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@ -1522,10 +1532,10 @@ class ChatModelIntegrationTests(ChatModelTests):
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If this test fails, check that:
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1. The model can correctly handle message histories that include AIMessage objects with ``""`` content.
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2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.
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3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for ``tool_call_id``.
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assert tool_call.get("type") == "tool_call"
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1. The model can correctly handle message histories that include ``AIMessage`` objects with ``""`` content.
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2. The ``tool_calls`` attribute on ``AIMessage`` objects is correctly handled and passed to the model in an appropriate format.
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3. The model can correctly handle ``ToolMessage`` objects with string content and arbitrary string values for ``tool_call_id``.
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You can ``xfail`` the test if tool calling is implemented but this format
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is not supported.
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@ -1538,6 +1548,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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""" # noqa: E501
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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model_with_tools = model.bind_tools([my_adder_tool])
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function_name = "my_adder_tool"
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function_args = {"a": "1", "b": "2"}
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@ -1623,6 +1634,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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""" # noqa: E501
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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model_with_tools = model.bind_tools([my_adder_tool])
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function_name = "my_adder_tool"
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function_args = {"a": 1, "b": 2}
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@ -1695,7 +1707,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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pytest.skip("Test requires tool choice.")
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@tool
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def get_weather(location: str) -> str: # pylint: disable=unused-argument
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def get_weather(location: str) -> str:
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"""Get weather at a location."""
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return "It's sunny."
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@ -1753,6 +1765,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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""" # noqa: E501
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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tool_choice_value = None if not self.has_tool_choice else "any"
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model_with_tools = model.bind_tools(
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[magic_function_no_args], tool_choice=tool_choice_value
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@ -1770,7 +1783,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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def test_tool_message_error_status(
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self, model: BaseChatModel, my_adder_tool: BaseTool
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) -> None:
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"""Test that ToolMessage with ``status="error"`` can be handled.
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"""Test that ``ToolMessage`` with ``status="error"`` can be handled.
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These messages may take the form:
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@ -1809,6 +1822,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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"""
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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model_with_tools = model.bind_tools([my_adder_tool])
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messages = [
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HumanMessage("What is 1 + 2"),
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@ -1863,8 +1877,9 @@ class ChatModelIntegrationTests(ChatModelTests):
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.. dropdown:: Troubleshooting
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This test uses a utility function in ``langchain_core`` to generate a
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sequence of messages representing "few-shot" examples: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.tool_example_to_messages.html
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This test uses `a utility function <https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.tool_example_to_messages.html>`__
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in ``langchain_core`` to generate a sequence of messages representing
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"few-shot" examples.
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If this test fails, check that the model can correctly handle this
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sequence of messages.
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@ -1881,6 +1896,7 @@ class ChatModelIntegrationTests(ChatModelTests):
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"""
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if not self.has_tool_calling:
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pytest.skip("Test requires tool calling.")
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model_with_tools = model.bind_tools([my_adder_tool], tool_choice="any")
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function_result = json.dumps({"result": 3})
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@ -1924,10 +1940,12 @@ class ChatModelIntegrationTests(ChatModelTests):
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If this test fails, ensure that the model's ``bind_tools`` method
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properly handles both JSON Schema and Pydantic V2 models.
|
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``langchain_core`` implements a utility function that will accommodate
|
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most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
|
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See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
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``langchain_core`` implements `a utility function <https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html>`__
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that will accommodate most formats.
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See `example implementation <https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output>`__
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of ``with_structured_output``.
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"""
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if not self.has_structured_output:
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@ -2003,10 +2021,12 @@ class ChatModelIntegrationTests(ChatModelTests):
|
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|
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If this test fails, ensure that the model's ``bind_tools`` method
|
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properly handles both JSON Schema and Pydantic V2 models.
|
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``langchain_core`` implements a utility function that will accommodate
|
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most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
|
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|
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See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
|
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``langchain_core`` implements `a utility function <https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html>`__
|
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that will accommodate most formats.
|
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See `example implementation <https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output>`__
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of ``with_structured_output``.
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"""
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if not self.has_structured_output:
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@ -2055,10 +2075,9 @@ class ChatModelIntegrationTests(ChatModelTests):
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@pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Test requires pydantic 2.")
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def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None:
|
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"""Test to verify we can generate structured output using
|
||||
``pydantic.v1.BaseModel``.
|
||||
"""Test to verify we can generate structured output using ``pydantic.v1.BaseModel``.
|
||||
|
||||
``pydantic.v1.BaseModel`` is available in the pydantic 2 package.
|
||||
``pydantic.v1.BaseModel`` is available in the Pydantic 2 package.
|
||||
|
||||
This test is optional and should be skipped if the model does not support
|
||||
structured output (see Configuration below).
|
||||
@ -2082,12 +2101,14 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
|
||||
If this test fails, ensure that the model's ``bind_tools`` method
|
||||
properly handles both JSON Schema and Pydantic V1 models.
|
||||
``langchain_core`` implements a utility function that will accommodate
|
||||
most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
|
||||
|
||||
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
|
||||
``langchain_core`` implements `a utility function <https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html>`__
|
||||
that will accommodate most formats.
|
||||
|
||||
"""
|
||||
See `example implementation <https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output>`__
|
||||
of ``with_structured_output``.
|
||||
|
||||
""" # noqa: E501
|
||||
if not self.has_structured_output:
|
||||
pytest.skip("Test requires structured output.")
|
||||
|
||||
@ -2144,10 +2165,12 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
|
||||
If this test fails, ensure that the model's ``bind_tools`` method
|
||||
properly handles Pydantic V2 models with optional parameters.
|
||||
``langchain_core`` implements a utility function that will accommodate
|
||||
most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
|
||||
|
||||
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
|
||||
``langchain_core`` implements `a utility function <https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html>`__
|
||||
that will accommodate most formats.
|
||||
|
||||
See `example implementation <https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output>`__
|
||||
of ``with_structured_output``.
|
||||
|
||||
"""
|
||||
if not self.has_structured_output:
|
||||
@ -2228,7 +2251,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
# Type ignoring since the interface only officially supports pydantic 1
|
||||
# or pydantic.v1.BaseModel but not pydantic.BaseModel from pydantic 2.
|
||||
# We'll need to do a pass updating the type signatures.
|
||||
chat = model.with_structured_output(Joke, method="json_mode") # type: ignore[arg-type]
|
||||
chat = model.with_structured_output(Joke, method="json_mode")
|
||||
msg = (
|
||||
"Tell me a joke about cats. Return the result as a JSON with 'setup' and "
|
||||
"'punchline' keys. Return nothing other than JSON."
|
||||
@ -2291,6 +2314,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
"""
|
||||
if not self.supports_pdf_inputs:
|
||||
pytest.skip("Model does not support PDF inputs.")
|
||||
|
||||
url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
|
||||
pdf_data = base64.b64encode(httpx.get(url).content).decode("utf-8")
|
||||
|
||||
@ -2367,6 +2391,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
"""
|
||||
if not self.supports_audio_inputs:
|
||||
pytest.skip("Model does not support audio inputs.")
|
||||
|
||||
url = "https://upload.wikimedia.org/wikipedia/commons/3/3d/Alcal%C3%A1_de_Henares_%28RPS_13-04-2024%29_canto_de_ruise%C3%B1or_%28Luscinia_megarhynchos%29_en_el_Soto_del_Henares.wav"
|
||||
audio_data = base64.b64encode(httpx.get(url).content).decode("utf-8")
|
||||
|
||||
@ -2468,6 +2493,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
"""
|
||||
if not self.supports_image_inputs:
|
||||
pytest.skip("Model does not support image message.")
|
||||
|
||||
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
|
||||
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
|
||||
|
||||
@ -2575,6 +2601,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
"""
|
||||
if not self.supports_image_tool_message:
|
||||
pytest.skip("Model does not support image tool message.")
|
||||
|
||||
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
|
||||
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
|
||||
|
||||
@ -2845,7 +2872,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
chat model.
|
||||
|
||||
Check also that all required information (e.g., tool calling identifiers)
|
||||
from AIMessage objects is propagated correctly to model payloads.
|
||||
from ``AIMessage`` objects is propagated correctly to model payloads.
|
||||
|
||||
This test may fail if the chat model does not consistently generate tool
|
||||
calls in response to an appropriate query. In these cases you can ``xfail``
|
||||
@ -2862,7 +2889,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
pytest.skip("Test requires tool calling.")
|
||||
|
||||
@tool
|
||||
def get_weather(location: str) -> str: # pylint: disable=unused-argument
|
||||
def get_weather(location: str) -> str:
|
||||
"""Call to surf the web."""
|
||||
return "It's sunny."
|
||||
|
||||
@ -2956,7 +2983,7 @@ class ChatModelIntegrationTests(ChatModelTests):
|
||||
|
||||
Args:
|
||||
model: The chat model to test
|
||||
tool_choice: Tool choice parameter to pass to bind_tools (provider-specific)
|
||||
tool_choice: Tool choice parameter to pass to ``bind_tools()`` (provider-specific)
|
||||
force_tool_call: Whether to force a tool call (use ``tool_choice=True`` if None)
|
||||
|
||||
Tests that Unicode characters in tool call arguments are preserved correctly,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user