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build python version rag-flow (#21)
* clean rust version project * clean rust version project * build python version rag-flow
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89
rag/llm/cv_model.py
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89
rag/llm/cv_model.py
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#
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# Copyright 2019 The FATE 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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from abc import ABC
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from openai import OpenAI
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import os
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import base64
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from io import BytesIO
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class Base(ABC):
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def __init__(self, key, model_name):
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pass
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def describe(self, image, max_tokens=300):
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raise NotImplementedError("Please implement encode method!")
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def image2base64(self, image):
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if isinstance(image, BytesIO):
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return base64.b64encode(image.getvalue()).decode("utf-8")
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buffered = BytesIO()
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try:
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image.save(buffered, format="JPEG")
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except Exception as e:
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image.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode("utf-8")
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def prompt(self, b64):
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return [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "请用中文详细描述一下图中的内容,比如时间,地点,人物,事情,人物心情等。",
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{b64}"
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},
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},
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],
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}
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]
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class GptV4(Base):
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def __init__(self, key, model_name="gpt-4-vision-preview"):
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self.client = OpenAI(key)
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self.model_name = model_name
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def describe(self, image, max_tokens=300):
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b64 = self.image2base64(image)
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res = self.client.chat.completions.create(
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model=self.model_name,
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messages=self.prompt(b64),
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max_tokens=max_tokens,
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)
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return res.choices[0].message.content.strip()
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class QWenCV(Base):
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def __init__(self, key, model_name="qwen-vl-chat-v1"):
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import dashscope
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dashscope.api_key = key
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self.model_name = model_name
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def describe(self, image, max_tokens=300):
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from http import HTTPStatus
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from dashscope import MultiModalConversation
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response = MultiModalConversation.call(model=self.model_name,
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messages=self.prompt(self.image2base64(image)))
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if response.status_code == HTTPStatus.OK:
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return response.output.choices[0]['message']['content']
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return response.message
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