mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-26 00:33:35 +08:00
42aa900566
- 修复：
- Qwen Agent 的 OutputParser 不再抛出异常，遇到非 COT 文本直接返回
- CallbackHandler 正确处理工具调用信息
- 重写 tool 定义方式：
- 添加 regist_tool 简化 tool 定义:
- 可以指定一个用户友好的名称
- 自动将函数的 __doc__ 作为 tool.description
- 支持用 Field 定义参数，不再需要额外定义 ModelSchema
- 添加 BaseToolOutput 封装 tool 返回结果，以便同时获取原始值、给LLM的字符串值
- 支持工具热加载（有待测试）
- 增加 openai 兼容的统一 chat 接口，通过 tools/tool_choice/extra_body 不同参数组合支持：
- Agent 对话
- 指定工具调用（如知识库RAG）
- LLM 对话
- 根据后端功能更新 webui
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
import base64
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import json
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import os
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from PIL import Image
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from typing import List
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import uuid
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from chatchat.server.pydantic_v1 import Field
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from chatchat.server.utils import get_tool_config
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from .tools_registry import regist_tool, BaseToolOutput
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import openai
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from chatchat.configs.basic_config import MEDIA_PATH
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from chatchat.server.utils import MsgType
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def get_image_model_config() -> dict:
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from chatchat.configs.model_config import LLM_MODEL_CONFIG, ONLINE_LLM_MODEL
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model = LLM_MODEL_CONFIG.get("image_model")
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if model:
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name = list(model.keys())[0]
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if config := ONLINE_LLM_MODEL.get(name):
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config = {**list(model.values())[0], **config}
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config.setdefault("model_name", name)
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return config
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@regist_tool(title="文生图", return_direct=True)
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def text2images(
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prompt: str,
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n: int = Field(1, description="需生成图片的数量"),
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width: int = Field(512, description="生成图片的宽度"),
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height: int = Field(512, description="生成图片的高度"),
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) -> List[str]:
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'''根据用户的描述生成图片'''
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model_config = get_image_model_config()
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assert model_config is not None, "请正确配置文生图模型"
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client = openai.Client(
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base_url=model_config["api_base_url"],
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api_key=model_config["api_key"],
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timeout=600,
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)
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resp = client.images.generate(prompt=prompt,
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n=n,
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size=f"{width}*{height}",
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response_format="b64_json",
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model=model_config["model_name"],
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)
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images = []
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for x in resp.data:
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uid = uuid.uuid4().hex
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filename = f"image/{uid}.png"
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with open(os.path.join(MEDIA_PATH, filename), "wb") as fp:
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fp.write(base64.b64decode(x.b64_json))
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images.append(filename)
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return BaseToolOutput({"message_type": MsgType.IMAGE, "images": images}, format="json")
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if __name__ == "__main__":
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from io import BytesIO
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from matplotlib import pyplot as plt
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from pathlib import Path
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import sys
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sys.path.append(str(Path(__file__).parent.parent.parent.parent))
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prompt = "draw a house with trees and river"
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prompt = "画一个带树、草、河流的山中小屋"
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params = text2images.args_schema.parse_obj({"prompt": prompt}).dict()
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print(params)
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image = text2images.invoke(params)[0]
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buffer = BytesIO(base64.b64decode(image))
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image = Image.open(buffer)
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plt.imshow(image)
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plt.show()
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