agentscope.formatter._openai_formatter 源代码
# -*- coding: utf-8 -*-
# pylint: disable=too-many-branches, too-many-nested-blocks
"""The OpenAI formatter for agentscope."""
import base64
import json
import os
from typing import Any
from urllib.parse import urlparse
import requests
from ._truncated_formatter_base import TruncatedFormatterBase
from .._logging import logger
from ..message import (
Msg,
URLSource,
TextBlock,
ImageBlock,
AudioBlock,
Base64Source,
ToolUseBlock,
ToolResultBlock,
)
from ..token import TokenCounterBase
def _format_openai_image_block(
image_block: ImageBlock,
) -> dict[str, Any]:
"""Format an image block for OpenAI API.
Args:
image_block (`ImageBlock`):
The image block to format.
Returns:
`dict[str, Any]`:
A dictionary with "type" and "image_url" keys in OpenAI format.
Raises:
`ValueError`:
If the source type is not supported.
"""
source = image_block["source"]
if source["type"] == "url":
url = _to_openai_image_url(source["url"])
elif source["type"] == "base64":
data = source["data"]
media_type = source["media_type"]
url = f"data:{media_type};base64,{data}"
else:
raise ValueError(
f"Unsupported image source type: {source['type']}",
)
return {
"type": "image_url",
"image_url": {
"url": url,
},
}
def _to_openai_image_url(url: str) -> str:
"""Convert an image url to openai format. If the given url is a local
file, it will be converted to base64 format. Otherwise, it will be
returned directly.
Args:
url (`str`):
The local or public url of the image.
"""
# See https://platform.openai.com/docs/guides/vision for details of
# support image extensions.
support_image_extensions = (
".png",
".jpg",
".jpeg",
".gif",
".webp",
)
parsed_url = urlparse(url)
lower_url = url.lower()
# Web url
if not os.path.exists(url) and parsed_url.scheme != "":
if any(lower_url.endswith(_) for _ in support_image_extensions):
return url
# Check if it is a local file
elif os.path.exists(url) and os.path.isfile(url):
if any(lower_url.endswith(_) for _ in support_image_extensions):
with open(url, "rb") as image_file:
base64_image = base64.b64encode(image_file.read()).decode(
"utf-8",
)
extension = parsed_url.path.lower().split(".")[-1]
mime_type = f"image/{extension}"
return f"data:{mime_type};base64,{base64_image}"
raise TypeError(f'"{url}" should end with {support_image_extensions}.')
def _to_openai_audio_data(source: URLSource | Base64Source) -> dict:
"""Covert an audio source to OpenAI format."""
if source["type"] == "url":
extension = source["url"].split(".")[-1].lower()
if extension not in ["wav", "mp3"]:
raise TypeError(
f"Unsupported audio file extension: {extension}, "
"wav and mp3 are supported.",
)
parsed_url = urlparse(source["url"])
if os.path.exists(source["url"]):
with open(source["url"], "rb") as audio_file:
data = base64.b64encode(audio_file.read()).decode("utf-8")
# web url
elif parsed_url.scheme != "":
response = requests.get(source["url"])
response.raise_for_status()
data = base64.b64encode(response.content).decode("utf-8")
else:
raise ValueError(
f"Unsupported audio source: {source['url']}, "
"it should be a local file or a web URL.",
)
return {
"data": data,
"format": extension,
}
if source["type"] == "base64":
data = source["data"]
media_type = source["media_type"]
if media_type not in ["audio/wav", "audio/mp3"]:
raise TypeError(
f"Unsupported audio media type: {media_type}, "
"only audio/wav and audio/mp3 are supported.",
)
return {
"data": data,
"format": media_type.split("/")[-1],
}
raise TypeError(f"Unsupported audio source: {source['type']}.")
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class OpenAIChatFormatter(TruncatedFormatterBase):
"""The OpenAI formatter class for chatbot scenario, where only a user
and an agent are involved. We use the `name` field in OpenAI API to
identify different entities in the conversation.
"""
support_tools_api: bool = True
"""Whether support tools API"""
support_multiagent: bool = True
"""Whether support multi-agent conversation"""
support_vision: bool = True
"""Whether support vision models"""
supported_blocks: list[type] = [
TextBlock,
ImageBlock,
AudioBlock,
ToolUseBlock,
ToolResultBlock,
]
"""Supported message blocks for OpenAI API"""
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def __init__(
self,
promote_tool_result_images: bool = False,
token_counter: TokenCounterBase | None = None,
max_tokens: int | None = None,
) -> None:
"""Initialize the OpenAI chat formatter.
Args:
promote_tool_result_images (`bool`, defaults to `False`):
Whether to promote images from tool results to user messages.
Most LLM APIs don't support images in tool result blocks, but
do support them in user message blocks. When `True`, images are
extracted and appended as a separate user message with
explanatory text indicating their source.
token_counter (`TokenCounterBase | None`, optional):
A token counter instance used to count tokens in the messages.
If not provided, the formatter will format the messages
without considering token limits.
max_tokens (`int | None`, optional):
The maximum number of tokens allowed in the formatted
messages. If not provided, the formatter will not truncate
the messages.
"""
super().__init__(token_counter=token_counter, max_tokens=max_tokens)
self.promote_tool_result_images = promote_tool_result_images
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async def _format(
self,
msgs: list[Msg],
) -> list[dict[str, Any]]:
"""Format message objects into OpenAI API required format.
Args:
msgs (`list[Msg]`):
The list of Msg objects to format.
Returns:
`list[dict[str, Any]]`:
A list of dictionaries, where each dictionary has "name",
"role", and "content" keys.
"""
self.assert_list_of_msgs(msgs)
messages: list[dict] = []
i = 0
while i < len(msgs):
msg = msgs[i]
content_blocks = []
tool_calls = []
for block in msg.get_content_blocks():
typ = block.get("type")
if typ == "text":
content_blocks.append({**block})
elif typ == "tool_use":
tool_calls.append(
{
"id": block.get("id"),
"type": "function",
"function": {
"name": block.get("name"),
"arguments": json.dumps(
block.get("input", {}),
ensure_ascii=False,
),
},
},
)
elif typ == "tool_result":
(
textual_output,
multimodal_data,
) = self.convert_tool_result_to_string(block["output"])
messages.append(
{
"role": "tool",
"tool_call_id": block.get("id"),
"content": ( # type: ignore[arg-type]
textual_output
),
"name": block.get("name"),
},
)
# Then, handle the multimodal data if any
promoted_blocks: list = []
for url, multimodal_block in multimodal_data:
if (
multimodal_block["type"] == "image"
and self.promote_tool_result_images
):
promoted_blocks.extend(
[
TextBlock(
type="text",
text=f"\n- The image from '{url}': ",
),
ImageBlock(
type="image",
source=URLSource(
type="url",
url=url,
),
),
],
)
if promoted_blocks:
# Insert promoted blocks as new user message(s)
promoted_blocks = [
TextBlock(
type="text",
text="<system-info>The following are "
"the image contents from the tool "
f"result of '{block['name']}':",
),
*promoted_blocks,
TextBlock(
type="text",
text="</system-info>",
),
]
msgs.insert(
i + 1,
Msg(
name="user",
content=promoted_blocks,
role="user",
),
)
elif typ == "image":
content_blocks.append(
_format_openai_image_block(
block, # type: ignore[arg-type]
),
)
elif typ == "audio":
input_audio = _to_openai_audio_data(block["source"])
content_blocks.append(
{
"type": "input_audio",
"input_audio": input_audio,
},
)
else:
logger.warning(
"Unsupported block type %s in the message, skipped.",
typ,
)
msg_openai = {
"role": msg.role,
"name": msg.name,
"content": content_blocks or None,
}
if tool_calls:
msg_openai["tool_calls"] = tool_calls
# When both content and tool_calls are None, skipped
if msg_openai["content"] or msg_openai.get("tool_calls"):
messages.append(msg_openai)
# Move to next message
i += 1
return messages
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class OpenAIMultiAgentFormatter(TruncatedFormatterBase):
"""
OpenAI formatter for multi-agent conversations, where more than
a user and an agent are involved.
.. tip:: This formatter is compatible with OpenAI API and
OpenAI-compatible services like vLLM, Azure OpenAI, and others.
"""
support_tools_api: bool = True
"""Whether support tools API"""
support_multiagent: bool = True
"""Whether support multi-agent conversation"""
support_vision: bool = True
"""Whether support vision models"""
supported_blocks: list[type] = [
TextBlock,
ImageBlock,
AudioBlock,
ToolUseBlock,
ToolResultBlock,
]
"""Supported message blocks for OpenAI API"""
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def __init__(
self,
conversation_history_prompt: str = (
"# Conversation History\n"
"The content between <history></history> tags contains "
"your conversation history\n"
),
promote_tool_result_images: bool = False,
token_counter: TokenCounterBase | None = None,
max_tokens: int | None = None,
) -> None:
"""Initialize the OpenAI multi-agent formatter.
Args:
conversation_history_prompt (`str`):
The prompt to use for the conversation history section.
promote_tool_result_images (`bool`, defaults to `False`):
Whether to promote images from tool results to user messages.
Most LLM APIs don't support images in tool result blocks, but
do support them in user message blocks. When `True`, images are
extracted and appended as a separate user message with
explanatory text indicating their source.
token_counter (`TokenCounterBase | None`, optional):
A token counter instance used to count tokens in the messages.
If not provided, the formatter will format the messages
without considering token limits.
max_tokens (`int | None`, optional):
The maximum number of tokens allowed in the formatted
messages. If not provided, the formatter will not truncate
the messages.
"""
super().__init__(token_counter=token_counter, max_tokens=max_tokens)
self.conversation_history_prompt = conversation_history_prompt
self.promote_tool_result_images = promote_tool_result_images
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async def _format_tool_sequence(
self,
msgs: list[Msg],
) -> list[dict[str, Any]]:
"""Given a sequence of tool call/result messages, format them into
the required format for the OpenAI API."""
return await OpenAIChatFormatter(
promote_tool_result_images=self.promote_tool_result_images,
).format(msgs)
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async def _format_agent_message(
self,
msgs: list[Msg],
is_first: bool = True,
) -> list[dict[str, Any]]:
"""Given a sequence of messages without tool calls/results, format
them into the required format for the OpenAI API."""
if is_first:
conversation_history_prompt = self.conversation_history_prompt
else:
conversation_history_prompt = ""
# Format into required OpenAI format
formatted_msgs: list[dict] = []
conversation_blocks: list = []
accumulated_text = []
images = []
audios = []
for msg in msgs:
for block in msg.get_content_blocks():
if block["type"] == "text":
accumulated_text.append(f"{msg.name}: {block['text']}")
elif block["type"] == "image":
images.append(_format_openai_image_block(block))
elif block["type"] == "audio":
input_audio = _to_openai_audio_data(block["source"])
audios.append(
{
"type": "input_audio",
"input_audio": input_audio,
},
)
if accumulated_text:
conversation_blocks.append(
{"text": "\n".join(accumulated_text)},
)
if conversation_blocks:
if conversation_blocks[0].get("text"):
conversation_blocks[0]["text"] = (
conversation_history_prompt
+ "<history>\n"
+ conversation_blocks[0]["text"]
)
else:
conversation_blocks.insert(
0,
{
"text": conversation_history_prompt + "<history>\n",
},
)
if conversation_blocks[-1].get("text"):
conversation_blocks[-1]["text"] += "\n</history>"
else:
conversation_blocks.append({"text": "</history>"})
conversation_blocks_text = "\n".join(
conversation_block.get("text", "")
for conversation_block in conversation_blocks
)
content_list: list[dict[str, Any]] = []
if conversation_blocks_text:
content_list.append(
{
"type": "text",
"text": conversation_blocks_text,
},
)
if images:
content_list.extend(images)
if audios:
content_list.extend(audios)
user_message = {
"role": "user",
"content": content_list,
}
if content_list:
formatted_msgs.append(user_message)
return formatted_msgs