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https://github.com/RYDE-WORK/Langchain-Chatchat.git
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253168a187
* 修复Azure 不设置Max token的bug * 重写agent 1. 修改Agent实现方式，支持多参数，仅剩 ChatGLM3-6b和 OpenAI GPT4 支持，剩余模型将在暂时缺席Agent功能 2. 删除agent_chat 集成到llm_chat中 3. 重写大部分工具，适应新Agent * 更新架构 * 删除web_chat,自动融合 * 移除所有聊天，都变成Agent控制 * 更新配置文件 * 更新配置模板和提示词 * 更改参数选择bug
124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
import sys
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from fastchat.conversation import Conversation
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from typing import List, Literal, Dict
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from fastchat import conversation as conv
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from server.model_workers.base import *
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from server.model_workers.base import ApiEmbeddingsParams
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from configs import logger, log_verbose
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class QwenWorker(ApiModelWorker):
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DEFAULT_EMBED_MODEL = "text-embedding-v1"
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def __init__(
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self,
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*,
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version: Literal["qwen-turbo", "qwen-plus"] = "qwen-turbo",
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model_names: List[str] = ["qwen-api"],
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controller_addr: str = None,
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worker_addr: str = None,
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**kwargs,
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):
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kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
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kwargs.setdefault("context_len", 16384)
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super().__init__(**kwargs)
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self.version = version
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def do_chat(self, params: ApiChatParams) -> Dict:
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import dashscope
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params.load_config(self.model_names[0])
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if log_verbose:
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logger.info(f'{self.__class__.__name__}:params: {params}')
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gen = dashscope.Generation()
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responses = gen.call(
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model=params.version,
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temperature=params.temperature,
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api_key=params.api_key,
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messages=params.messages,
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result_format='message', # set the result is message format.
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stream=True,
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)
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for resp in responses:
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if resp["status_code"] == 200:
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if choices := resp["output"]["choices"]:
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yield {
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"error_code": 0,
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"text": choices[0]["message"]["content"],
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}
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else:
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data = {
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"error_code": resp["status_code"],
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"text": resp["message"],
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"error": {
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"message": resp["message"],
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"type": "invalid_request_error",
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"param": None,
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"code": None,
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}
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}
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self.logger.error(f"请求千问 API 时发生错误：{data}")
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yield data
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def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
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import dashscope
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params.load_config(self.model_names[0])
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if log_verbose:
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logger.info(f'{self.__class__.__name__}:params: {params}')
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result = []
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i = 0
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while i < len(params.texts):
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texts = params.texts[i:i+25]
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resp = dashscope.TextEmbedding.call(
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model=params.embed_model or self.DEFAULT_EMBED_MODEL,
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input=texts, # 最大25行
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api_key=params.api_key,
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)
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if resp["status_code"] != 200:
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data = {
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"code": resp["status_code"],
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"msg": resp.message,
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"error": {
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"message": resp["message"],
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"type": "invalid_request_error",
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"param": None,
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"code": None,
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}
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}
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self.logger.error(f"请求千问 API 时发生错误：{data}")
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return data
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else:
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embeddings = [x["embedding"] for x in resp["output"]["embeddings"]]
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result += embeddings
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i += 25
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return {"code": 200, "data": result}
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def get_embeddings(self, params):
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print("embedding")
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print(params)
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def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
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return conv.Conversation(
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name=self.model_names[0],
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system_message="你是一个聪明、对人类有帮助的人工智能，你可以对人类提出的问题给出有用、详细、礼貌的回答。",
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messages=[],
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roles=["user", "assistant", "system"],
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sep="\n### ",
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stop_str="###",
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)
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if __name__ == "__main__":
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import uvicorn
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from server.utils import MakeFastAPIOffline
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from fastchat.serve.model_worker import app
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worker = QwenWorker(
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controller_addr="http://127.0.0.1:20001",
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worker_addr="http://127.0.0.1:20007",
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)
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sys.modules["fastchat.serve.model_worker"].worker = worker
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MakeFastAPIOffline(app)
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uvicorn.run(app, port=20007)
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