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core[patch]: add convert_to_openai_messages util (#27263)
Co-authored-by: Erick Friis <erick@langchain.dev>
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@ -43,6 +43,7 @@ from langchain_core.messages.utils import (
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MessageLikeRepresentation,
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_message_from_dict,
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convert_to_messages,
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convert_to_openai_messages,
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filter_messages,
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get_buffer_string,
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merge_message_runs,
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@ -83,4 +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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"convert_to_openai_messages",
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]
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@ -9,6 +9,7 @@ Some examples of what you can do with these functions include:
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from __future__ import annotations
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import base64
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import inspect
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import json
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from collections.abc import Iterable, Sequence
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@ -34,7 +35,7 @@ from langchain_core.messages.function import FunctionMessage, FunctionMessageChu
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from langchain_core.messages.human import HumanMessage, HumanMessageChunk
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from langchain_core.messages.modifier import RemoveMessage
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from langchain_core.messages.system import SystemMessage, SystemMessageChunk
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from langchain_core.messages.tool import ToolMessage, ToolMessageChunk
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from langchain_core.messages.tool import ToolCall, ToolMessage, ToolMessageChunk
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if TYPE_CHECKING:
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from langchain_text_splitters import TextSplitter
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@ -171,7 +172,7 @@ def messages_from_dict(messages: Sequence[dict]) -> list[BaseMessage]:
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messages: Sequence of messages (as dicts) to convert.
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Returns:
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List of messages (BaseMessages).
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list of messages (BaseMessages).
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"""
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return [_message_from_dict(m) for m in messages]
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@ -217,9 +218,9 @@ def _create_message_from_message_type(
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content: (str) the content string.
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name: (str) the name of the message. Default is None.
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tool_call_id: (str) the tool call id. Default is None.
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tool_calls: (List[Dict[str, Any]]) the tool calls. Default is None.
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tool_calls: (list[dict[str, Any]]) the tool calls. Default is None.
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id: (str) the id of the message. Default is None.
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**additional_kwargs: (Dict[str, Any]) additional keyword arguments.
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**additional_kwargs: (dict[str, Any]) additional keyword arguments.
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Returns:
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a message of the appropriate type.
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@ -337,7 +338,7 @@ def convert_to_messages(
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messages: Sequence of messages to convert.
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Returns:
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List of messages (BaseMessages).
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list of messages (BaseMessages).
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"""
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# Import here to avoid circular imports
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from langchain_core.prompt_values import PromptValue
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@ -359,7 +360,8 @@ def _runnable_support(func: Callable) -> Callable:
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) -> list[BaseMessage]: ...
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def wrapped(
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messages: Optional[Sequence[MessageLikeRepresentation]] = None, **kwargs: Any
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messages: Union[Sequence[MessageLikeRepresentation], None] = None,
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**kwargs: Any,
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) -> Union[
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list[BaseMessage],
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Runnable[Sequence[MessageLikeRepresentation], list[BaseMessage]],
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@ -479,7 +481,7 @@ def merge_message_runs(
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Default is "\n".
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Returns:
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List of BaseMessages with consecutive runs of message types merged into single
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list of BaseMessages with consecutive runs of message types merged into single
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messages. By default, if two messages being merged both have string contents,
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the merged content is a concatenation of the two strings with a new-line separator.
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The separator inserted between message chunks can be controlled by specifying
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@ -655,7 +657,7 @@ def trim_messages(
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newlines.
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Returns:
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List of trimmed BaseMessages.
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list of trimmed BaseMessages.
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Raises:
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ValueError: if two incompatible arguments are specified or an unrecognized
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@ -668,7 +670,7 @@ def trim_messages(
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.. code-block:: python
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from typing import List
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from typing import list
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from langchain_core.messages import (
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AIMessage,
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@ -768,7 +770,7 @@ def trim_messages(
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AIMessage("This is a 4 token text. The full message is 10 tokens.", id="fourth"),
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]
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def dummy_token_counter(messages: List[BaseMessage]) -> int:
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def dummy_token_counter(messages: list[BaseMessage]) -> int:
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# treat each message like it adds 3 default tokens at the beginning
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# of the message and at the end of the message. 3 + 4 + 3 = 10 tokens
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# per message.
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@ -868,6 +870,328 @@ def trim_messages(
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raise ValueError(msg)
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def convert_to_openai_messages(
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messages: Union[MessageLikeRepresentation, Sequence[MessageLikeRepresentation]],
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*,
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text_format: Literal["string", "block"] = "string",
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) -> Union[dict, list[dict]]:
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"""Convert LangChain messages into OpenAI message dicts.
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.. versionadded:: 0.3.11
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Args:
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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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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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The return type depends on the input type:
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- dict:
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If a single message-like object is passed in, a single OpenAI message
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dict is returned.
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- list[dict]:
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If a sequence of message-like objects are passed in, a list of OpenAI
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message dicts is returned.
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Example:
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.. code-block:: python
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from langchain_core.messages import (
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convert_to_openai_messages,
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AIMessage,
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SystemMessage,
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ToolMessage,
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)
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messages = [
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SystemMessage([{"type": "text", "text": "foo"}]),
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{"role": "user", "content": [{"type": "text", "text": "whats in this"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,'/9j/4AAQSk'"}}]},
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AIMessage("", tool_calls=[{"name": "analyze", "args": {"baz": "buz"}, "id": "1", "type": "tool_call"}]),
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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_messages = convert_to_openai_messages(messages)
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# -> [
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# {'role': 'system', 'content': 'foo'},
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# {'role': 'user', 'content': [{'type': 'text', 'text': 'whats in this'}, {'type': 'image_url', 'image_url': {'url': "data:image/png;base64,'/9j/4AAQSk'"}}]},
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# {'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': '1','function': {'name': 'analyze', 'arguments': '{"baz": "buz"}'}}], 'content': ''},
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# {'role': 'tool', 'name': 'bar', 'content': 'foobar'},
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# {'role': 'assistant', 'content': 'thats nice'}
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# ]
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""" # noqa: E501
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if text_format not in ("string", "block"):
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err = f"Unrecognized {text_format=}, expected one of 'string' or 'block'."
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raise ValueError(err)
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oai_messages: list = []
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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)
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for i, message in enumerate(messages):
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oai_msg: dict = {"role": _get_message_openai_role(message)}
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tool_messages: list = []
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content: Union[str, list[dict]]
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if message.name:
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oai_msg["name"] = message.name
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if isinstance(message, AIMessage) and message.tool_calls:
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oai_msg["tool_calls"] = _convert_to_openai_tool_calls(message.tool_calls)
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if message.additional_kwargs.get("refusal"):
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oai_msg["refusal"] = message.additional_kwargs["refusal"]
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if isinstance(message, ToolMessage):
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oai_msg["tool_call_id"] = message.tool_call_id
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if not message.content:
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content = "" if text_format == "string" else []
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elif isinstance(message.content, str):
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if text_format == "string":
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content = message.content
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else:
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content = [{"type": "text", "text": message.content}]
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else:
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if text_format == "string" and all(
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isinstance(block, str) or block.get("type") == "text"
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for block in message.content
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):
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content = "\n".join(
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block if isinstance(block, str) else block["text"]
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for block in message.content
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)
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else:
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content = []
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for j, block in enumerate(message.content):
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# OpenAI format
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if isinstance(block, str):
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content.append({"type": "text", "text": block})
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elif block.get("type") == "text":
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if missing := [k for k in ("text",) if k not in block]:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': 'text' "
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f"but is missing expected key(s) "
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f"{missing}. Full content block:\n\n{block}"
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)
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raise ValueError(err)
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content.append({"type": block["type"], "text": block["text"]})
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elif block.get("type") == "image_url":
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if missing := [k for k in ("image_url",) if k not in block]:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': 'image_url' "
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f"but is missing expected key(s) "
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f"{missing}. Full content block:\n\n{block}"
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)
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raise ValueError(err)
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content.append(
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{"type": "image_url", "image_url": block["image_url"]}
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)
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# Anthropic and Bedrock converse format
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elif (block.get("type") == "image") or "image" in block:
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# Anthropic
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if source := block.get("source"):
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if missing := [
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k
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for k in ("media_type", "type", "data")
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if k not in source
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]:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': 'image' "
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f"but 'source' is missing expected key(s) "
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f"{missing}. Full content block:\n\n{block}"
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)
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raise ValueError(err)
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content.append(
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{
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"type": "image_url",
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"image_url": {
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"url": (
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f"data:{source['media_type']};"
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f"{source['type']},{source['data']}"
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)
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},
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}
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)
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# Bedrock converse
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elif image := block.get("image"):
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if missing := [
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k for k in ("source", "format") if k not in image
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]:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has key 'image', "
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f"but 'image' is missing expected key(s) "
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f"{missing}. Full content block:\n\n{block}"
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)
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raise ValueError(err)
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b64_image = _bytes_to_b64_str(image["source"]["bytes"])
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content.append(
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{
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"type": "image_url",
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"image_url": {
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"url": (
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f"data:image/{image['format']};"
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f"base64,{b64_image}"
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)
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},
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}
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)
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else:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': 'image' "
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f"but does not have a 'source' or 'image' key. Full "
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f"content block:\n\n{block}"
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)
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raise ValueError(err)
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elif block.get("type") == "tool_use":
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if missing := [
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k for k in ("id", "name", "input") if k not in block
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]:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': "
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f"'tool_use', but is missing expected key(s) "
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f"{missing}. Full content block:\n\n{block}"
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)
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raise ValueError(err)
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if not any(
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tool_call["id"] == block["id"]
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for tool_call in cast(AIMessage, message).tool_calls
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):
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oai_msg["tool_calls"] = oai_msg.get("tool_calls", [])
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oai_msg["tool_calls"].append(
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{
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"type": "function",
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"id": block["id"],
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"function": {
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"name": block["name"],
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"arguments": json.dumps(block["input"]),
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},
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}
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)
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elif block.get("type") == "tool_result":
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if missing := [
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k for k in ("content", "tool_use_id") if k not in block
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]:
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msg = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': "
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f"'tool_result', but is missing expected key(s) "
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f"{missing}. Full content block:\n\n{block}"
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)
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raise ValueError(msg)
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tool_message = ToolMessage(
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block["content"],
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tool_call_id=block["tool_use_id"],
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status="error" if block.get("is_error") else "success",
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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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convert_to_openai_messages(
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[tool_message], text_format=text_format
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)
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)
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elif (block.get("type") == "json") or "json" in block:
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if "json" not in block:
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msg = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': 'json' "
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f"but does not have a 'json' key. Full "
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f"content block:\n\n{block}"
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)
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raise ValueError(msg)
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content.append(
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{"type": "text", "text": json.dumps(block["json"])}
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)
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elif (
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block.get("type") == "guard_content"
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) or "guard_content" in block:
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if (
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"guard_content" not in block
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or "text" not in block["guard_content"]
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):
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msg = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': "
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f"'guard_content' but does not have a "
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f"messages[{i}].content[{j}]['guard_content']['text'] "
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f"key. Full content block:\n\n{block}"
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)
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raise ValueError(msg)
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text = block["guard_content"]["text"]
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if isinstance(text, dict):
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text = text["text"]
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content.append({"type": "text", "text": text})
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# VertexAI format
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elif block.get("type") == "media":
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if missing := [
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k for k in ("mime_type", "data") if k not in block
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]:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] has 'type': "
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f"'media' but does not have key(s) {missing}. Full "
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f"content block:\n\n{block}"
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)
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raise ValueError(err)
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if "image" not in block["mime_type"]:
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err = (
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f"OpenAI messages can only support text and image data."
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f" Received content block with media of type:"
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f" {block['mime_type']}"
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)
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raise ValueError(err)
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b64_image = _bytes_to_b64_str(block["data"])
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content.append(
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{
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"type": "image_url",
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"image_url": {
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"url": (
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f"data:{block['mime_type']};base64,{b64_image}"
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)
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},
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}
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)
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else:
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err = (
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f"Unrecognized content block at "
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f"messages[{i}].content[{j}] does not match OpenAI, "
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f"Anthropic, Bedrock Converse, or VertexAI format. Full "
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f"content block:\n\n{block}"
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)
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raise ValueError(err)
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if text_format == "string" and not any(
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block["type"] != "text" for block in content
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):
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content = "\n".join(block["text"] for block in content)
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oai_msg["content"] = content
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if message.content and not oai_msg["content"] and tool_messages:
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oai_messages.extend(tool_messages)
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else:
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oai_messages.extend([oai_msg, *tool_messages])
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if is_single:
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return oai_messages[0]
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else:
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return oai_messages
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def _first_max_tokens(
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messages: Sequence[BaseMessage],
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*,
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@ -1036,3 +1360,39 @@ def _is_message_type(
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types_types = tuple(t for t in types if isinstance(t, type))
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return message.type in types_str or isinstance(message, types_types)
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def _bytes_to_b64_str(bytes_: bytes) -> str:
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return base64.b64encode(bytes_).decode("utf-8")
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def _get_message_openai_role(message: BaseMessage) -> str:
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if isinstance(message, AIMessage):
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return "assistant"
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elif isinstance(message, HumanMessage):
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return "user"
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elif isinstance(message, ToolMessage):
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return "tool"
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elif isinstance(message, SystemMessage):
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return "system"
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elif isinstance(message, FunctionMessage):
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return "function"
|
||||
elif isinstance(message, ChatMessage):
|
||||
return message.role
|
||||
else:
|
||||
msg = f"Unknown BaseMessage type {message.__class__}."
|
||||
raise ValueError(msg)
|
||||
|
||||
|
||||
def _convert_to_openai_tool_calls(tool_calls: list[ToolCall]) -> list[dict]:
|
||||
return [
|
||||
{
|
||||
"type": "function",
|
||||
"id": tool_call["id"],
|
||||
"function": {
|
||||
"name": tool_call["name"],
|
||||
"arguments": json.dumps(tool_call["args"]),
|
||||
},
|
||||
}
|
||||
for tool_call in tool_calls
|
||||
]
|
||||
|
||||
@ -32,6 +32,7 @@ EXPECTED_ALL = [
|
||||
"filter_messages",
|
||||
"merge_message_runs",
|
||||
"trim_messages",
|
||||
"convert_to_openai_messages",
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -1,3 +1,4 @@
|
||||
import base64
|
||||
import json
|
||||
|
||||
import pytest
|
||||
@ -13,6 +14,7 @@ from langchain_core.messages import (
|
||||
)
|
||||
from langchain_core.messages.utils import (
|
||||
convert_to_messages,
|
||||
convert_to_openai_messages,
|
||||
filter_messages,
|
||||
merge_message_runs,
|
||||
trim_messages,
|
||||
@ -565,6 +567,307 @@ def test_convert_to_messages() -> None:
|
||||
assert expected == actual
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="AI message does not support refusal key yet.")
|
||||
def test_convert_to_messages_openai_refusal() -> None:
|
||||
convert_to_messages([{"role": "assistant", "refusal": "9.1"}])
|
||||
actual = convert_to_messages(
|
||||
[{"role": "assistant", "content": "", "refusal": "9.1"}]
|
||||
)
|
||||
expected = [AIMessage("", additional_kwargs={"refusal": "9.1"})]
|
||||
assert actual == expected
|
||||
|
||||
# Raises error if content is missing.
|
||||
with pytest.raises(ValueError):
|
||||
convert_to_messages([{"role": "assistant", "refusal": "9.1"}])
|
||||
|
||||
|
||||
def create_image_data() -> str:
|
||||
return "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" # noqa: E501
|
||||
|
||||
|
||||
def create_base64_image(format: str = "jpeg") -> str:
|
||||
data = create_image_data()
|
||||
return f"data:image/{format};base64,{data}" # noqa: E501
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_single_message() -> None:
|
||||
message = HumanMessage(content="Hello")
|
||||
result = convert_to_openai_messages(message)
|
||||
assert result == {"role": "user", "content": "Hello"}
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_multiple_messages() -> None:
|
||||
messages = [
|
||||
SystemMessage(content="System message"),
|
||||
HumanMessage(content="Human message"),
|
||||
AIMessage(content="AI message"),
|
||||
]
|
||||
result = convert_to_openai_messages(messages)
|
||||
expected = [
|
||||
{"role": "system", "content": "System message"},
|
||||
{"role": "user", "content": "Human message"},
|
||||
{"role": "assistant", "content": "AI message"},
|
||||
]
|
||||
assert result == expected
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_openai_string() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": "Hello"},
|
||||
{"type": "text", "text": "World"},
|
||||
]
|
||||
),
|
||||
AIMessage(
|
||||
content=[{"type": "text", "text": "Hi"}, {"type": "text", "text": "there"}]
|
||||
),
|
||||
]
|
||||
result = convert_to_openai_messages(messages)
|
||||
expected = [
|
||||
{"role": "user", "content": "Hello\nWorld"},
|
||||
{"role": "assistant", "content": "Hi\nthere"},
|
||||
]
|
||||
assert result == expected
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_openai_block() -> None:
|
||||
messages = [HumanMessage(content="Hello"), AIMessage(content="Hi there")]
|
||||
result = convert_to_openai_messages(messages, text_format="block")
|
||||
expected = [
|
||||
{"role": "user", "content": [{"type": "text", "text": "Hello"}]},
|
||||
{"role": "assistant", "content": [{"type": "text", "text": "Hi there"}]},
|
||||
]
|
||||
assert result == expected
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_invalid_format() -> None:
|
||||
with pytest.raises(ValueError, match="Unrecognized text_format="):
|
||||
convert_to_openai_messages(
|
||||
[HumanMessage(content="Hello")],
|
||||
text_format="invalid", # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_openai_image() -> None:
|
||||
base64_image = create_base64_image()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": "Here's an image:"},
|
||||
{"type": "image_url", "image_url": {"url": base64_image}},
|
||||
]
|
||||
)
|
||||
]
|
||||
result = convert_to_openai_messages(messages, text_format="block")
|
||||
expected = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "Here's an image:"},
|
||||
{"type": "image_url", "image_url": {"url": base64_image}},
|
||||
],
|
||||
}
|
||||
]
|
||||
assert result == expected
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_anthropic() -> None:
|
||||
image_data = create_image_data()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
{
|
||||
"type": "text",
|
||||
"text": "Here's an image:",
|
||||
"cache_control": {"type": "ephemeral"},
|
||||
},
|
||||
{
|
||||
"type": "image",
|
||||
"source": {
|
||||
"type": "base64",
|
||||
"media_type": "image/jpeg",
|
||||
"data": image_data,
|
||||
},
|
||||
},
|
||||
]
|
||||
),
|
||||
AIMessage(
|
||||
content=[
|
||||
{"type": "tool_use", "name": "foo", "input": {"bar": "baz"}, "id": "1"}
|
||||
]
|
||||
),
|
||||
HumanMessage(
|
||||
content=[
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": "1",
|
||||
"is_error": False,
|
||||
"content": [
|
||||
{
|
||||
"type": "image",
|
||||
"source": {
|
||||
"type": "base64",
|
||||
"media_type": "image/jpeg",
|
||||
"data": image_data,
|
||||
},
|
||||
},
|
||||
],
|
||||
}
|
||||
]
|
||||
),
|
||||
]
|
||||
result = convert_to_openai_messages(messages)
|
||||
expected = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "Here's an image:"},
|
||||
{"type": "image_url", "image_url": {"url": create_base64_image()}},
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "",
|
||||
"tool_calls": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "foo",
|
||||
"arguments": json.dumps({"bar": "baz"}),
|
||||
},
|
||||
"id": "1",
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": [
|
||||
{"type": "image_url", "image_url": {"url": create_base64_image()}}
|
||||
],
|
||||
"tool_call_id": "1",
|
||||
},
|
||||
]
|
||||
assert result == expected
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_bedrock_converse_image() -> None:
|
||||
image_data = create_image_data()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": "Here's an image:"},
|
||||
{
|
||||
"image": {
|
||||
"format": "jpeg",
|
||||
"source": {"bytes": base64.b64decode(image_data)},
|
||||
}
|
||||
},
|
||||
]
|
||||
)
|
||||
]
|
||||
result = convert_to_openai_messages(messages)
|
||||
assert result[0]["content"][1]["type"] == "image_url"
|
||||
assert result[0]["content"][1]["image_url"]["url"] == create_base64_image()
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_vertexai_image() -> None:
|
||||
image_data = create_image_data()
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": "Here's an image:"},
|
||||
{
|
||||
"type": "media",
|
||||
"mime_type": "image/jpeg",
|
||||
"data": base64.b64decode(image_data),
|
||||
},
|
||||
]
|
||||
)
|
||||
]
|
||||
result = convert_to_openai_messages(messages)
|
||||
assert result[0]["content"][1]["type"] == "image_url"
|
||||
assert result[0]["content"][1]["image_url"]["url"] == create_base64_image()
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_tool_message() -> None:
|
||||
tool_message = ToolMessage(content="Tool result", tool_call_id="123")
|
||||
result = convert_to_openai_messages([tool_message], text_format="block")
|
||||
assert len(result) == 1
|
||||
assert result[0]["content"] == [{"type": "text", "text": "Tool result"}]
|
||||
assert result[0]["tool_call_id"] == "123"
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_tool_use() -> None:
|
||||
messages = [
|
||||
AIMessage(
|
||||
content=[
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": "123",
|
||||
"name": "calculator",
|
||||
"input": {"a": "b"},
|
||||
}
|
||||
]
|
||||
)
|
||||
]
|
||||
result = convert_to_openai_messages(messages, text_format="block")
|
||||
assert result[0]["tool_calls"][0]["type"] == "function"
|
||||
assert result[0]["tool_calls"][0]["id"] == "123"
|
||||
assert result[0]["tool_calls"][0]["function"]["name"] == "calculator"
|
||||
assert result[0]["tool_calls"][0]["function"]["arguments"] == json.dumps({"a": "b"})
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_json() -> None:
|
||||
json_data = {"key": "value"}
|
||||
messages = [HumanMessage(content=[{"type": "json", "json": json_data}])]
|
||||
result = convert_to_openai_messages(messages, text_format="block")
|
||||
assert result[0]["content"][0]["type"] == "text"
|
||||
assert json.loads(result[0]["content"][0]["text"]) == json_data
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_guard_content() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
{
|
||||
"type": "guard_content",
|
||||
"guard_content": {"text": "Protected content"},
|
||||
}
|
||||
]
|
||||
)
|
||||
]
|
||||
result = convert_to_openai_messages(messages, text_format="block")
|
||||
assert result[0]["content"][0]["type"] == "text"
|
||||
assert result[0]["content"][0]["text"] == "Protected content"
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_invalid_block() -> None:
|
||||
messages = [HumanMessage(content=[{"type": "invalid", "foo": "bar"}])]
|
||||
with pytest.raises(ValueError, match="Unrecognized content block"):
|
||||
convert_to_openai_messages(messages, text_format="block")
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_empty_message() -> None:
|
||||
result = convert_to_openai_messages(HumanMessage(content=""))
|
||||
assert result == {"role": "user", "content": ""}
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_empty_list() -> None:
|
||||
result = convert_to_openai_messages([])
|
||||
assert result == []
|
||||
|
||||
|
||||
def test_convert_to_openai_messages_mixed_content_types() -> None:
|
||||
messages = [
|
||||
HumanMessage(
|
||||
content=[
|
||||
"Text message",
|
||||
{"type": "text", "text": "Structured text"},
|
||||
{"type": "image_url", "image_url": {"url": create_base64_image()}},
|
||||
]
|
||||
)
|
||||
]
|
||||
result = convert_to_openai_messages(messages, 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)
|
||||
|
||||
@ -30,7 +30,7 @@ from langchain_core.language_models.chat_models import (
|
||||
generate_from_stream,
|
||||
)
|
||||
from langchain_core.messages import AnyMessage, BaseMessage
|
||||
from langchain_core.runnables import Runnable, RunnableConfig
|
||||
from langchain_core.runnables import Runnable, RunnableConfig, ensure_config
|
||||
from langchain_core.runnables.schema import StreamEvent
|
||||
from langchain_core.tools import BaseTool
|
||||
from langchain_core.tracers import RunLog, RunLogPatch
|
||||
@ -530,7 +530,7 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
|
||||
return model
|
||||
|
||||
def _model_params(self, config: Optional[RunnableConfig]) -> dict:
|
||||
config = config or {}
|
||||
config = ensure_config(config)
|
||||
model_params = {
|
||||
_remove_prefix(k, self._config_prefix): v
|
||||
for k, v in config.get("configurable", {}).items()
|
||||
|
||||
@ -1218,12 +1218,12 @@ def _make_message_chunk_from_anthropic_event(
|
||||
content_block = event.delta.model_dump()
|
||||
content_block["index"] = event.index
|
||||
content_block["type"] = "tool_use"
|
||||
tool_call_chunk = {
|
||||
"index": event.index,
|
||||
"id": None,
|
||||
"name": None,
|
||||
"args": event.delta.partial_json,
|
||||
}
|
||||
tool_call_chunk = create_tool_call_chunk(
|
||||
index=event.index,
|
||||
id=None,
|
||||
name=None,
|
||||
args=event.delta.partial_json,
|
||||
)
|
||||
message_chunk = AIMessageChunk(
|
||||
content=[content_block],
|
||||
tool_call_chunks=[tool_call_chunk], # type: ignore
|
||||
|
||||
Loading…
Reference in New Issue
Block a user