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from fastchat.conversation import Conversation
from server.model_workers.base import *
from fastchat import conversation as conv
import sys
from typing import List, Dict, Iterator, Literal
from configs import logger, log_verbose
import requests
import jwt
import time
import json
def generate_token(apikey: str, exp_seconds: int):
try:
id, secret = apikey.split(".")
except Exception as e:
raise Exception("invalid apikey", e)
payload = {
"api_key": id,
"exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
"timestamp": int(round(time.time() * 1000)),
}
return jwt.encode(
payload,
secret,
algorithm="HS256",
headers={"alg": "HS256", "sign_type": "SIGN"},
)
class ChatGLMWorker(ApiModelWorker):
def __init__(
self,
*,
model_names: List[str] = ["zhipu-api"],
controller_addr: str = None,
worker_addr: str = None,
version: Literal["chatglm_turbo"] = "chatglm_turbo",
**kwargs,
):
kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
kwargs.setdefault("context_len", 4096)
super().__init__(**kwargs)
self.version = version
def do_chat(self, params: ApiChatParams) -> Iterator[Dict]:
params.load_config(self.model_names[0])
token = generate_token(params.api_key, 60)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {token}"
}
data = {
"model": params.version,
"messages": params.messages,
"max_tokens": params.max_tokens,
"temperature": params.temperature,
"stream": False
}
url = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
response = requests.post(url, headers=headers, json=data)
# for chunk in response.iter_lines():
# if chunk:
# chunk_str = chunk.decode('utf-8')
# json_start_pos = chunk_str.find('{"id"')
# if json_start_pos != -1:
# json_str = chunk_str[json_start_pos:]
# json_data = json.loads(json_str)
# for choice in json_data.get('choices', []):
# delta = choice.get('delta', {})
# content = delta.get('content', '')
# yield {"error_code": 0, "text": content}
ans = response.json()
content = ans["choices"][0]["message"]["content"]
yield {"error_code": 0, "text": content}
def get_embeddings(self, params):
# 临时解决方案，不支持embedding
print("embedding")
print(params)
def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
return conv.Conversation(
name=self.model_names[0],
system_message="你是智谱AI小助手，请根据用户的提示来完成任务",
messages=[],
roles=["user", "assistant", "system"],
sep="\n###",
stop_str="###",
)
if __name__ == "__main__":
import uvicorn
from server.utils import MakeFastAPIOffline
from fastchat.serve.model_worker import app
worker = ChatGLMWorker(
controller_addr="http://127.0.0.1:20001",
worker_addr="http://127.0.0.1:21001",
)
sys.modules["fastchat.serve.model_worker"].worker = worker
MakeFastAPIOffline(app)
uvicorn.run(app, port=21001)
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