mirror of
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164 lines
4.8 KiB
Python
164 lines
4.8 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import uuid
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from copy import deepcopy
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from typing import List, Literal, Union
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from urllib.parse import urlparse
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import requests
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from openai.types.chat import (
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ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam,
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)
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from openai.types.chat import (
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ChatCompletionMessageParam as OpenAIChatCompletionMessageParam,
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)
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from typing_extensions import Required, TypeAlias, TypedDict
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from fastdeploy.input.multimodal.image import ImageMediaIO
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from fastdeploy.input.multimodal.video import VideoMediaIO
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class VideoURL(TypedDict, total=False):
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"""Video URL object"""
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url: Required[str]
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"""Either a URL of the video or the base64 encoded video data"""
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class CustomChatCompletionContentPartVideoParam(TypedDict, total=False):
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"""Custom Video URL object"""
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video_url: Required[VideoURL]
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type: Required[Literal["video_url"]]
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"""The type of the content type."""
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CustomChatCompletionContentPartParam: TypeAlias = Union[
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OpenAIChatCompletionContentPartParam,
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CustomChatCompletionContentPartVideoParam,
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]
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class CustomChatCompletionMessageParam(TypedDict, total=False):
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"""Custom User chat message parameter."""
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content: Required[Union[str, List[CustomChatCompletionContentPartParam]]]
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"""The contents of the user message"""
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role: Required[str]
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"""The role of the messages author, in this case `user`."""
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name: str
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"""An optional name for the participant
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Provides the model information to differentiate between participants of the same role.
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"""
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ChatCompletionMessageParam = Union[OpenAIChatCompletionMessageParam, CustomChatCompletionMessageParam]
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class MultiModalPartParser:
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"""Multi Modal Part parser"""
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def __init__(self):
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self.image_io = ImageMediaIO()
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self.video_io = VideoMediaIO()
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def parse_image(self, image_url):
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""" "Parse Image"""
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return self.load_from_url(image_url, self.image_io)
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def parse_video(self, video_url):
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"""Parse Video"""
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return self.load_from_url(video_url, self.video_io)
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def load_from_url(self, url, media_io):
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"""Load media from URL"""
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parsed = urlparse(url)
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if parsed.scheme.startswith("http"):
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media_bytes = requests.get(url).content
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return media_io.load_bytes(media_bytes)
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if parsed.scheme.startswith("data"):
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data_spec, data = parsed.path.split(",", 1)
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media_type, data_type = data_spec.split(";", 1)
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return media_io.load_base64(media_type, data)
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if parsed.scheme.startswith("file"):
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localpath = parsed.path
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return media_io.load_file(localpath)
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def parse_content_part(mm_parser, part):
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"""only support openai compatible format for now"""
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part_type = part.get("type", None)
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if part_type == "text":
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return part
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if part_type == "image_url":
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content = part.get("image_url", {}).get("url", None)
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image = mm_parser.parse_image(content)
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parsed = deepcopy(part)
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del parsed["image_url"]["url"]
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parsed["image"] = image
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parsed["type"] = "image"
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return parsed
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if part_type == "video_url":
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content = part.get("video_url", {}).get("url", None)
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video = mm_parser.parse_video(content)
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parsed = deepcopy(part)
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del parsed["video_url"]["url"]
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parsed["video"] = video
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parsed["type"] = "video"
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return parsed
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raise ValueError(f"Unknown content part type: {part_type}")
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# TODO async
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# def parse_chat_messages(messages: List[ChatCompletionMessageParam]):
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def parse_chat_messages(messages):
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"""Parse chat messages to [dict]"""
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mm_parser = MultiModalPartParser()
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conversation = []
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for message in messages:
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role = message["role"]
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content = message["content"]
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parsed_content = []
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if content is None:
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parsed_content = []
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elif isinstance(content, str):
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parsed_content = [{"type": "text", "text": content}]
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else:
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parsed_content = [parse_content_part(mm_parser, part) for part in content]
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conversation.append({"role": role, "content": parsed_content})
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return conversation
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def random_tool_call_id() -> str:
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return f"chatcmpl-tool-{str(uuid.uuid4().hex)}"
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