113 lines
3.9 KiB
Python
113 lines
3.9 KiB
Python
from fastchat.conversation import Conversation
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import os
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from .base import *
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from fastchat import conversation as conv
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import sys
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from typing import List, Dict, Iterator, Literal
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from loguru import logger
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# from configs import logger, log_verbose
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log_verbose = os.environ.get("log_verbose", False)
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class ChatGLMWorker(ApiModelWorker):
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DEFAULT_EMBED_MODEL = "text_embedding"
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def __init__(
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self,
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*,
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model_names: List[str] = ["zhipu-api"],
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controller_addr: str = None,
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worker_addr: str = None,
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version: Literal["chatglm_turbo"] = "chatglm_turbo",
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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", 32768)
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super().__init__(**kwargs)
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self.version = version
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def do_chat(self, params: ApiChatParams) -> Iterator[Dict]:
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# TODO: 维护request_id
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import zhipuai
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params.load_config(self.model_names[0])
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zhipuai.api_key = params.api_key
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if log_verbose:
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logger.info(f'{self.__class__.__name__}:params: {params}')
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response = zhipuai.model_api.sse_invoke(
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model=params.version,
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prompt=params.messages,
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temperature=params.temperature,
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top_p=params.top_p,
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incremental=False,
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)
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for e in response.events():
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if e.event == "add":
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yield {"error_code": 0, "text": e.data}
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elif e.event in ["error", "interrupted"]:
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data = {
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"error_code": 500,
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"text": str(e),
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"error": {
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"message": str(e),
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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"请求智谱 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 zhipuai
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params.load_config(self.model_names[0])
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zhipuai.api_key = params.api_key
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embeddings = []
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try:
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for t in params.texts:
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response = zhipuai.model_api.invoke(model=params.embed_model or self.DEFAULT_EMBED_MODEL, prompt=t)
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if response["code"] == 200:
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embeddings.append(response["data"]["embedding"])
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else:
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self.logger.error(f"请求智谱 API 时发生错误：{response}")
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return response # dict with code & msg
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except Exception as e:
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self.logger.error(f"请求智谱 API 时发生错误：{data}")
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data = {"code": 500, "msg": f"对文本向量化时出错：{e}"}
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return data
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return {"code": 200, "data": embeddings}
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def get_embeddings(self, params):
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# TODO: 支持embeddings
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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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# 这里的是chatglm api的模板，其它API的conv_template需要定制
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return conv.Conversation(
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name=self.model_names[0],
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system_message="你是一个聪明的助手，请根据用户的提示来完成任务",
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messages=[],
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roles=["Human", "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 = ChatGLMWorker(
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controller_addr="http://127.0.0.1:20001",
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worker_addr="http://127.0.0.1:21001",
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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=21001)
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