mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-21 14:23:33 +08:00
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
from contextlib import contextmanager
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import httpx
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from fastchat.conversation import Conversation
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from httpx_sse import EventSource
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from server.model_workers.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, Any
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import jwt
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import time
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@contextmanager
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def connect_sse(client: httpx.Client, method: str, url: str, **kwargs: Any):
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with client.stream(method, url, **kwargs) as response:
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yield EventSource(response)
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def generate_token(apikey: str, exp_seconds: int):
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try:
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id, secret = apikey.split(".")
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except Exception as e:
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raise Exception("invalid apikey", e)
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payload = {
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"api_key": id,
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"exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
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"timestamp": int(round(time.time() * 1000)),
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}
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return jwt.encode(
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payload,
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secret,
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algorithm="HS256",
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headers={"alg": "HS256", "sign_type": "SIGN"},
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)
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class ChatGLMWorker(ApiModelWorker):
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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["glm-4"] = "glm-4",
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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", 4096)
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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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params.load_config(self.model_names[0])
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token = generate_token(params.api_key, 60)
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {token}"
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}
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data = {
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"model": params.version,
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"messages": params.messages,
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"max_tokens": params.max_tokens,
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"temperature": params.temperature,
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"stream": False
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}
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url = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
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with httpx.Client(headers=headers) as client:
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response = client.post(url, json=data)
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response.raise_for_status()
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chunk = response.json()
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print(chunk)
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yield {"error_code": 0, "text": chunk["choices"][0]["message"]["content"]}
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# with connect_sse(client, "POST", url, json=data) as event_source:
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# for sse in event_source.iter_sse():
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# chunk = json.loads(sse.data)
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# if len(chunk["choices"]) != 0:
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# text += chunk["choices"][0]["delta"]["content"]
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# yield {"error_code": 0, "text": text}
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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="你是智谱AI小助手，请根据用户的提示来完成任务",
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messages=[],
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roles=["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 = 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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