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fmt
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Bagatur
parent
b31faf6572
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@ -44,7 +44,7 @@ from langchain_core.messages.utils import (
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_message_from_dict,
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convert_to_messages,
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filter_messages,
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format_messages_as,
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format_messages,
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get_buffer_string,
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merge_message_runs,
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message_chunk_to_message,
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@ -84,5 +84,5 @@ __all__ = [
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"filter_messages",
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"merge_message_runs",
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"trim_messages",
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"format_messages_as",
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"format_messages",
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]
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@ -934,30 +934,52 @@ def _runnable_generator(func: Callable) -> Callable:
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@_runnable_generator
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def format_messages_as(
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def format_messages(
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messages: Union[MessageLikeRepresentation, Sequence[MessageLikeRepresentation]],
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*,
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format: Literal["openai", "anthropic"],
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format: Literal["langchain-openai", "langchain-anthropic"],
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text_format: Literal["string", "block"],
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) -> Union[BaseMessage, List[BaseMessage]]:
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"""Convert message contents into a standard format.
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.. versionadded:: 0.2.36
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Can be used imperatively (pass in messages, get out messages) or can be used
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declaratively (call without messages, use resulting Runnable in a chain).
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.. versionadded:: 0.2.37
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Args:
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messages: Message-like object or iterable of objects whose contents are already
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messages: Message-like object or iterable of objects whose contents are
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in OpenAI, Anthropic, Bedrock Converse, or VertexAI formats.
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format: Format to convert message contents to.
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text_format: How to format text contents. If ``text='string'`` then any string
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contents are left as strings. If a message has content blocks that are all
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of type 'text', these are joined with a newline to make a single string. If
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a message has content blocks and at least one isn't of type 'text', then
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all blocks are left as dicts. If ``text='block'`` then all contents are
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turned into a list of dicts.
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format: Output message format:
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- "langchain-openai":
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BaseMessages with OpenAI-style contents.
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- "langchain-anthropic":
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BaseMessages with Anthropic-style contents.
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text_format: How to format string or text block contents:
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- "string":
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If a message has a string content, this is left as a string. If
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a message has content blocks that are all of type 'text', these are
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joined with a newline to make a single string. If a message has
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content blocks and at least one isn't of type 'text', then
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all blocks are left as dicts.
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- "block":
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If a message has a string content, this is turned into a list
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with a single content block of type 'text'. If a message has content
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blocks these are left as is.
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Returns:
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A single BaseMessage is a single message-like object was passed in, else list
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of BaseMessages.
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The return type depends on the input type:
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- BaseMessage:
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If a single message-like object is passed in, a BaseMessage is
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returned.
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- List[BaseMessage]:
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If a sequence of message-like objects are passed in, a list
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of BaseMessages are returned.
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- Runnable:
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If no messages are passed in, a Runnable is generated that formats
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messages (per the above) when invoked.
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.. dropdown:: Basic usage
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:open:
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@ -965,7 +987,7 @@ def format_messages_as(
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.. code-block:: python
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from langchain_core.messages import (
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format_messages_as,
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format_messages,
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AIMessage,
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HumanMessage,
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SystemMessage,
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@ -979,7 +1001,7 @@ def format_messages_as(
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ToolMessage("foobar", tool_call_id="1", name="bar"),
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{"role": "assistant", "content": "thats nice"},
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]
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oai_strings = format_messages_as(messages, format="openai", text="string")
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oai_strings = format_messages(messages, format="langchain-openai", text="string")
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# -> [
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# SystemMessage(content='foo'),
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# HumanMessage(content=[{'type': 'text', 'text': 'whats in this'}, {'type': 'image_url', 'image_url': {'url': "data:image/png;base64,'/9j/4AAQSk'"}}]),
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@ -988,7 +1010,7 @@ def format_messages_as(
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# AIMessage(content='thats nice')
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# ]
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anthropic_blocks = format_messages_as(messages, format="anthropic", text="block")
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anthropic_blocks = format_messages(messages, format="langchain-anthropic", text="block")
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# -> [
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# SystemMessage(content=[{'type': 'text', 'text': 'foo'}]),
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# HumanMessage(content=[{'type': 'text', 'text': 'whats in this'}, {'type': 'image', 'source': {'type': 'base64', 'media_type': 'image/png', 'data': "'/9j/4AAQSk'"}}]),
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@ -997,15 +1019,15 @@ def format_messages_as(
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# AIMessage(content=[{'type': 'text', 'text': 'thats nice'}])
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# ]
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.. dropdown:: Chain usage
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.. dropdown:: Chaining
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:open:
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.. code-block:: python
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from langchain_core.messages import format_messages_as
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from langchain_core.messages import format_messages
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from langchain.chat_models import init_chat_model
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formatter = format_messages_as(format="openai", text="string")
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formatter = format_messages(format="langchain-openai", text="string")
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llm = init_chat_model() | formatter
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llm.invoke(
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@ -1020,7 +1042,16 @@ def format_messages_as(
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)
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# -> AIMessage(["My name is...], ...)
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.. dropdown:: Streaming
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:open:
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.. code-block:: python
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from langchain_core.messages import format_messages
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from langchain.chat_models import init_chat_model
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formatter = format_messages(format="langchain-openai", text="string")
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def multiply(a: int, b: int) -> int:
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'''Return product of a and b.'''
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return a * b
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@ -1031,40 +1062,41 @@ def format_messages_as(
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"what's 5 times 2", config={"model": "claude-3-5-sonnet-20240620"}
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):
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print(chunk)
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# -> AIMessageChunk(content='', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75', usage_metadata={'input_tokens': 370, 'output_tokens': 0, 'total_tokens': 370}),
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# AIMessageChunk(content='Certainly', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content='! To', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' calculate', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' 5 times ', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content='2, we can use', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' the "', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content='multiply" function that', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content="'s", id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' available to', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' us.', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' Let', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content="'s use", id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' this tool', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' to', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' get', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content=' the result.', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75'),
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# AIMessageChunk(content='', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75', tool_calls=[{'name': 'multiply', 'args': {}, 'id': 'toolu_01PW8o6BkATCecjsJX8QgG6z', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'multiply', 'args': '', 'id': 'toolu_01PW8o6BkATCecjsJX8QgG6z', 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75', tool_calls=[{'name': '', 'args': {}, 'id': None, 'type': 'tool_call'}], tool_call_chunks=[{'name': None, 'args': '', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75', tool_calls=[{'name': '', 'args': {'a': 5}, 'id': None, 'type': 'tool_call'}], tool_call_chunks=[{'name': None, 'args': '{"a": 5', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75', invalid_tool_calls=[{'name': None, 'args': ', "b": 2}', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], tool_call_chunks=[{'name': None, 'args': ', "b": 2}', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', response_metadata={'stop_reason': 'tool_use', 'stop_sequence': None}, id='run-64757cb2-b85f-4d51-8f34-5a6c1d40ad75', usage_metadata={'input_tokens': 0, 'output_tokens': 104, 'total_tokens': 104})
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# -> AIMessageChunk(content='', id='run-6...', usage_metadata={'input_tokens': 370, 'output_tokens': 0, 'total_tokens': 370}),
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# AIMessageChunk(content='Certainly', id='run-6...'),
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# AIMessageChunk(content='! To', id='run-6...'),
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# AIMessageChunk(content=' calculate', id='run-6...'),
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# AIMessageChunk(content=' 5 times ', id='run-6...'),
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# AIMessageChunk(content='2, we can use', id='run-6...'),
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# AIMessageChunk(content=' the "', id='run-6...'),
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# AIMessageChunk(content='multiply" function that', id='run-6...'),
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# AIMessageChunk(content="'s", id='run-6...'),
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# AIMessageChunk(content=' available to', id='run-6...'),
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# AIMessageChunk(content=' us.', id='run-6...'),
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# AIMessageChunk(content=' Let', id='run-6...'),
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# AIMessageChunk(content="'s use", id='run-6...'),
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# AIMessageChunk(content=' this tool', id='run-6...'),
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# AIMessageChunk(content=' to', id='run-6...'),
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# AIMessageChunk(content=' get', id='run-6...'),
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# AIMessageChunk(content=' the result.', id='run-6...'),
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# AIMessageChunk(content='', id='run-6...', tool_calls=[{'name': 'multiply', 'args': {}, 'id': 'toolu_0...', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'multiply', 'args': '', 'id': 'toolu_0...', 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', id='run-6...', tool_calls=[{'name': '', 'args': {}, 'id': None, 'type': 'tool_call'}], tool_call_chunks=[{'name': None, 'args': '', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', id='run-6...', tool_calls=[{'name': '', 'args': {'a': 5}, 'id': None, 'type': 'tool_call'}], tool_call_chunks=[{'name': None, 'args': '{"a": 5', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', id='run-6...', invalid_tool_calls=[{'name': None, 'args': ', "b": 2}', 'id': None, 'error': None, 'type': 'invalid_tool_call'}], tool_call_chunks=[{'name': None, 'args': ', "b": 2}', 'id': None, 'index': 1, 'type': 'tool_call_chunk'}]),
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# AIMessageChunk(content='', response_metadata={'stop_reason': 'tool_use', 'stop_sequence': None}, id='run-6...', usage_metadata={'input_tokens': 0, 'output_tokens': 104, 'total_tokens': 104})
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""" # noqa: E501
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if is_single := isinstance(messages, (BaseMessage, dict)):
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messages = [messages]
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messages = convert_to_messages(messages, copy=True)
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if format.lower() == "openai":
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formatted = _format_messages_as_openai(messages, text_format=text_format)
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elif format.lower() == "anthropic":
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formatted = _format_messages_as_anthropic(messages, text_format=text_format)
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if format.lower().replace("_", "-") == "langchain-openai":
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formatted = _format_messages_openai(messages, text_format=text_format)
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elif format.lower().replace("_", "-") == "langchain-anthropic":
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formatted = _format_messages_anthropic(messages, text_format=text_format)
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else:
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raise ValueError(
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f"Unrecognized {format=}. Expected one of ('openai', 'anthropic')."
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f"Unrecognized {format=}. Expected one of ('langchain-openai', "
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f"'langchain-anthropic')."
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)
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if is_single:
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return formatted[0]
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@ -1072,7 +1104,7 @@ def format_messages_as(
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return formatted
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def _format_messages_as_openai(
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def _format_messages_openai(
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messages: Sequence[BaseMessage], *, text_format: Literal["string", "block"]
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) -> List[BaseMessage]:
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"""Mutates messages so their contents match OpenAI messages API."""
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@ -1226,7 +1258,7 @@ def _format_messages_as_openai(
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)
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# Recurse to make sure tool message contents are OpenAI format.
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tool_messages.extend(
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_format_messages_as_openai(
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_format_messages_openai(
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[tool_message], text_format=text_format
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)
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)
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@ -1307,7 +1339,7 @@ def _format_messages_as_openai(
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_OPTIONAL_ANTHROPIC_KEYS = ("cache_control", "is_error", "index")
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def _format_messages_as_anthropic(
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def _format_messages_anthropic(
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messages: Sequence[BaseMessage], *, text_format: Literal["string", "block"]
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) -> List[BaseMessage]:
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"""Mutates messages so their contents match Anthropic messages API."""
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@ -32,7 +32,7 @@ EXPECTED_ALL = [
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"filter_messages",
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"merge_message_runs",
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"trim_messages",
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"format_messages_as",
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"format_messages",
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]
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@ -17,7 +17,7 @@ from langchain_core.messages.utils import (
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_bytes_to_b64_str,
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convert_to_messages,
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filter_messages,
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format_messages_as,
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format_messages,
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merge_message_runs,
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trim_messages,
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)
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@ -566,20 +566,20 @@ def create_base64_image(format: str = "jpeg") -> str:
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return f"data:image/{format};base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCAABAAEDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iiigD//2Q==" # noqa: E501
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def test_format_messages_as_single_message() -> None:
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def test_format_messages_single_message() -> None:
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message = HumanMessage(content="Hello")
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result = format_messages_as(message, format="openai", text_format="string")
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result = format_messages(message, format="langchain-openai", text_format="string")
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assert isinstance(result, BaseMessage)
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assert result.content == "Hello"
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def test_format_messages_as_multiple_messages() -> None:
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def test_format_messages_multiple_messages() -> None:
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messages = [
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SystemMessage(content="System message"),
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HumanMessage(content="Human message"),
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AIMessage(content="AI message"),
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]
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result = format_messages_as(messages, format="openai", text_format="string")
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result = format_messages(messages, format="langchain-openai", text_format="string")
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assert isinstance(result, list)
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assert len(result) == 3
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assert all(isinstance(msg, BaseMessage) for msg in result)
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@ -590,7 +590,7 @@ def test_format_messages_as_multiple_messages() -> None:
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]
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def test_format_messages_as_openai_string() -> None:
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def test_format_messages_openai_string() -> None:
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messages = [
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HumanMessage(
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content=[
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@ -602,23 +602,23 @@ def test_format_messages_as_openai_string() -> None:
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content=[{"type": "text", "text": "Hi"}, {"type": "text", "text": "there"}]
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),
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]
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result = format_messages_as(messages, format="openai", text_format="string")
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result = format_messages(messages, format="langchain-openai", text_format="string")
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assert [msg.content for msg in result] == ["Hello\nWorld", "Hi\nthere"]
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def test_format_messages_as_openai_block() -> None:
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def test_format_messages_openai_block() -> None:
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messages = [
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HumanMessage(content="Hello"),
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AIMessage(content="Hi there"),
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]
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result = format_messages_as(messages, format="openai", text_format="block")
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result = format_messages(messages, format="langchain-openai", text_format="block")
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assert [msg.content for msg in result] == [
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[{"type": "text", "text": "Hello"}],
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[{"type": "text", "text": "Hi there"}],
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||||
]
|
||||
|
||||
|
||||
def test_format_messages_as_anthropic_string() -> None:
|
||||
def test_format_messages_anthropic_string() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
@ -630,30 +630,34 @@ def test_format_messages_as_anthropic_string() -> None:
|
||||
content=[{"type": "text", "text": "Hi"}, {"type": "text", "text": "there"}]
|
||||
),
|
||||
]
|
||||
result = format_messages_as(messages, format="anthropic", text_format="string")
|
||||
result = format_messages(
|
||||
messages, format="langchain-anthropic", text_format="string"
|
||||
)
|
||||
assert [msg.content for msg in result] == ["Hello\nWorld", "Hi\nthere"]
|
||||
|
||||
|
||||
def test_format_messages_as_anthropic_block() -> None:
|
||||
def test_format_messages_anthropic_block() -> None:
|
||||
messages = [
|
||||
HumanMessage(content="Hello"),
|
||||
AIMessage(content="Hi there"),
|
||||
]
|
||||
result = format_messages_as(messages, format="anthropic", text_format="block")
|
||||
result = format_messages(
|
||||
messages, format="langchain-anthropic", text_format="block"
|
||||
)
|
||||
assert [msg.content for msg in result] == [
|
||||
[{"type": "text", "text": "Hello"}],
|
||||
[{"type": "text", "text": "Hi there"}],
|
||||
]
|
||||
|
||||
|
||||
def test_format_messages_as_invalid_format() -> None:
|
||||
def test_format_messages_invalid_format() -> None:
|
||||
with pytest.raises(ValueError, match="Unrecognized format="):
|
||||
format_messages_as(
|
||||
format_messages(
|
||||
[HumanMessage(content="Hello")], format="invalid", text_format="string"
|
||||
)
|
||||
|
||||
|
||||
def test_format_messages_as_openai_image() -> None:
|
||||
def test_format_messages_openai_image() -> None:
|
||||
base64_image = create_base64_image()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
@ -663,12 +667,12 @@ def test_format_messages_as_openai_image() -> None:
|
||||
]
|
||||
)
|
||||
]
|
||||
result = format_messages_as(messages, format="openai", text_format="block")
|
||||
result = format_messages(messages, format="langchain-openai", text_format="block")
|
||||
assert result[0].content[1]["type"] == "image_url"
|
||||
assert result[0].content[1]["image_url"]["url"] == base64_image
|
||||
|
||||
|
||||
def test_format_messages_as_anthropic_image() -> None:
|
||||
def test_format_messages_anthropic_image() -> None:
|
||||
base64_image = create_base64_image()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
@ -678,21 +682,25 @@ def test_format_messages_as_anthropic_image() -> None:
|
||||
]
|
||||
)
|
||||
]
|
||||
result = format_messages_as(messages, format="anthropic", text_format="block")
|
||||
result = format_messages(
|
||||
messages, format="langchain-anthropic", text_format="block"
|
||||
)
|
||||
assert result[0].content[1]["type"] == "image"
|
||||
assert result[0].content[1]["source"]["type"] == "base64"
|
||||
assert result[0].content[1]["source"]["media_type"] == "image/jpeg"
|
||||
|
||||
|
||||
def test_format_messages_as_tool_message() -> None:
|
||||
def test_format_messages_tool_message() -> None:
|
||||
tool_message = ToolMessage(content="Tool result", tool_call_id="123")
|
||||
result = format_messages_as([tool_message], format="openai", text_format="block")
|
||||
result = format_messages(
|
||||
[tool_message], format="langchain-openai", text_format="block"
|
||||
)
|
||||
assert isinstance(result[0], ToolMessage)
|
||||
assert result[0].content == [{"type": "text", "text": "Tool result"}]
|
||||
assert result[0].tool_call_id == "123"
|
||||
|
||||
|
||||
def test_format_messages_as_tool_use() -> None:
|
||||
def test_format_messages_tool_use() -> None:
|
||||
messages = [
|
||||
AIMessage(
|
||||
content=[
|
||||
@ -700,21 +708,21 @@ def test_format_messages_as_tool_use() -> None:
|
||||
]
|
||||
)
|
||||
]
|
||||
result = format_messages_as(messages, format="openai", text_format="block")
|
||||
result = format_messages(messages, format="langchain-openai", text_format="block")
|
||||
assert result[0].tool_calls[0]["id"] == "123"
|
||||
assert result[0].tool_calls[0]["name"] == "calculator"
|
||||
assert result[0].tool_calls[0]["args"] == "2+2"
|
||||
|
||||
|
||||
def test_format_messages_as_json() -> None:
|
||||
def test_format_messages_json() -> None:
|
||||
json_data = {"key": "value"}
|
||||
messages = [HumanMessage(content=[{"type": "json", "json": json_data}])]
|
||||
result = format_messages_as(messages, format="openai", text_format="block")
|
||||
result = format_messages(messages, format="langchain-openai", text_format="block")
|
||||
assert result[0].content[0]["type"] == "text"
|
||||
assert json.loads(result[0].content[0]["text"]) == json_data
|
||||
|
||||
|
||||
def test_format_messages_as_guard_content() -> None:
|
||||
def test_format_messages_guard_content() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
@ -725,12 +733,12 @@ def test_format_messages_as_guard_content() -> None:
|
||||
]
|
||||
)
|
||||
]
|
||||
result = format_messages_as(messages, format="openai", text_format="block")
|
||||
result = format_messages(messages, format="langchain-openai", text_format="block")
|
||||
assert result[0].content[0]["type"] == "text"
|
||||
assert result[0].content[0]["text"] == "Protected content"
|
||||
|
||||
|
||||
def test_format_messages_as_vertexai_image() -> None:
|
||||
def test_format_messages_vertexai_image() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
@ -738,7 +746,7 @@ def test_format_messages_as_vertexai_image() -> None:
|
||||
]
|
||||
)
|
||||
]
|
||||
result = format_messages_as(messages, format="openai", text_format="block")
|
||||
result = format_messages(messages, format="langchain-openai", text_format="block")
|
||||
assert result[0].content[0]["type"] == "image_url"
|
||||
assert (
|
||||
result[0].content[0]["image_url"]["url"]
|
||||
@ -746,27 +754,27 @@ def test_format_messages_as_vertexai_image() -> None:
|
||||
)
|
||||
|
||||
|
||||
def test_format_messages_as_invalid_block() -> None:
|
||||
def test_format_messages_invalid_block() -> None:
|
||||
messages = [HumanMessage(content=[{"type": "invalid", "foo": "bar"}])]
|
||||
with pytest.raises(ValueError, match="Unrecognized content block"):
|
||||
format_messages_as(messages, format="openai", text_format="block")
|
||||
format_messages(messages, format="langchain-openai", text_format="block")
|
||||
with pytest.raises(ValueError, match="Unrecognized content block"):
|
||||
format_messages_as(messages, format="anthropic", text_format="block")
|
||||
format_messages(messages, format="langchain-anthropic", text_format="block")
|
||||
|
||||
|
||||
def test_format_messages_as_empty_message() -> None:
|
||||
result = format_messages_as(
|
||||
HumanMessage(content=""), format="openai", text_format="string"
|
||||
def test_format_messages_empty_message() -> None:
|
||||
result = format_messages(
|
||||
HumanMessage(content=""), format="langchain-openai", text_format="string"
|
||||
)
|
||||
assert result.content == ""
|
||||
|
||||
|
||||
def test_format_messages_as_empty_list() -> None:
|
||||
result = format_messages_as([], format="openai", text_format="string")
|
||||
def test_format_messages_empty_list() -> None:
|
||||
result = format_messages([], format="langchain-openai", text_format="string")
|
||||
assert result == []
|
||||
|
||||
|
||||
def test_format_messages_as_mixed_content_types() -> None:
|
||||
def test_format_messages_mixed_content_types() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
@ -776,21 +784,23 @@ def test_format_messages_as_mixed_content_types() -> None:
|
||||
]
|
||||
)
|
||||
]
|
||||
result = format_messages_as(messages, format="openai", text_format="block")
|
||||
result = format_messages(messages, format="langchain-openai", text_format="block")
|
||||
assert len(result[0].content) == 3
|
||||
assert isinstance(result[0].content[0], dict)
|
||||
assert isinstance(result[0].content[1], dict)
|
||||
assert isinstance(result[0].content[2], dict)
|
||||
|
||||
|
||||
def test_format_messages_as_anthropic_tool_calls() -> None:
|
||||
def test_format_messages_anthropic_tool_calls() -> None:
|
||||
message = AIMessage(
|
||||
"blah",
|
||||
tool_calls=[
|
||||
{"type": "tool_call", "name": "foo", "id": "1", "args": {"bar": "baz"}}
|
||||
],
|
||||
)
|
||||
result = format_messages_as(message, format="anthropic", text_format="string")
|
||||
result = format_messages(
|
||||
message, format="langchain-anthropic", text_format="string"
|
||||
)
|
||||
assert result.content == [
|
||||
{"type": "text", "text": "blah"},
|
||||
{"type": "tool_use", "id": "1", "name": "foo", "input": {"bar": "baz"}},
|
||||
@ -798,8 +808,8 @@ def test_format_messages_as_anthropic_tool_calls() -> None:
|
||||
assert result.tool_calls == message.tool_calls
|
||||
|
||||
|
||||
def test_format_messages_as_declarative() -> None:
|
||||
formatter = format_messages_as(format="openai", text_format="block")
|
||||
def test_format_messages_declarative() -> None:
|
||||
formatter = format_messages(format="langchain-openai", text_format="block")
|
||||
base64_image = create_base64_image()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
@ -996,7 +1006,7 @@ def _stream_anthropic(input_: Any) -> Iterator:
|
||||
|
||||
@pytest.mark.parametrize("stream", [_stream_oai, _stream_anthropic])
|
||||
def test_format_messages_openai_string_stream(stream: Callable) -> None:
|
||||
formatter = format_messages_as(format="openai", text_format="string")
|
||||
formatter = format_messages(format="langchain-openai", text_format="string")
|
||||
|
||||
chain = RunnableLambda(stream) | formatter
|
||||
tool_call_idx = 1 if stream == _stream_anthropic else 0
|
||||
@ -1090,7 +1100,7 @@ def test_format_messages_openai_string_stream(stream: Callable) -> None:
|
||||
|
||||
@pytest.mark.parametrize("stream", [_stream_oai, _stream_anthropic])
|
||||
def test_format_messages_openai_block_stream(stream: Callable) -> None:
|
||||
formatter = format_messages_as(format="openai", text_format="block")
|
||||
formatter = format_messages(format="langchain-openai", text_format="block")
|
||||
|
||||
chain = RunnableLambda(stream) | formatter
|
||||
tool_call_idx = 1 if stream == _stream_anthropic else 0
|
||||
@ -1183,7 +1193,7 @@ def test_format_messages_openai_block_stream(stream: Callable) -> None:
|
||||
|
||||
@pytest.mark.parametrize("stream", [_stream_oai, _stream_anthropic])
|
||||
def test_format_messages_anthropic_block_stream(stream: Callable) -> None:
|
||||
formatter = format_messages_as(format="anthropic", text_format="block")
|
||||
formatter = format_messages(format="langchain-anthropic", text_format="block")
|
||||
|
||||
chain = RunnableLambda(stream) | formatter
|
||||
expected = [
|
||||
@ -1285,7 +1295,7 @@ def test_format_messages_anthropic_block_stream(stream: Callable) -> None:
|
||||
|
||||
@pytest.mark.parametrize("stream", [_stream_oai, _stream_anthropic])
|
||||
def test_format_messages_anthropic_string_stream(stream: Callable) -> None:
|
||||
formatter = format_messages_as(format="anthropic", text_format="string")
|
||||
formatter = format_messages(format="langchain-anthropic", text_format="string")
|
||||
|
||||
chain = RunnableLambda(stream) | formatter
|
||||
expected = [
|
||||
|
||||
Loading…
Reference in New Issue
Block a user