116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
|
from contextlib import contextmanager
|
|||
|
|
|
|||
|
|
import httpx
|
|||
|
|
from fastchat.conversation import Conversation
|
|||
|
|
from httpx_sse import EventSource
|
|||
|
|
|
|||
|
|
from server.model_workers.base import *
|
|||
|
|
from fastchat import conversation as conv
|
|||
|
|
import sys
|
|||
|
|
from typing import List, Dict, Iterator, Literal, Any
|
|||
|
|
import jwt
|
|||
|
|
import time
|
|||
|
|
|
|||
|
|
|
|||
|
|
@contextmanager
|
|||
|
|
def connect_sse(client: httpx.Client, method: str, url: str, **kwargs: Any):
|
|||
|
|
with client.stream(method, url, **kwargs) as response:
|
|||
|
|
yield EventSource(response)
|
|||
|
|
|
|||
|
|
|
|||
|
|
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] = ["deepseek-chat"],
|
|||
|
|
controller_addr: str = None,
|
|||
|
|
worker_addr: str = None,
|
|||
|
|
version: Literal["glm-4"] = "glm-4",
|
|||
|
|
**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"
|
|||
|
|
with httpx.Client(headers=headers) as client:
|
|||
|
|
response = client.post(url, json=data)
|
|||
|
|
response.raise_for_status()
|
|||
|
|
chunk = response.json()
|
|||
|
|
print(chunk)
|
|||
|
|
yield {"error_code": 0, "text": chunk["choices"][0]["message"]["content"]}
|
|||
|
|
|
|||
|
|
# with connect_sse(client, "POST", url, json=data) as event_source:
|
|||
|
|
# for sse in event_source.iter_sse():
|
|||
|
|
# chunk = json.loads(sse.data)
|
|||
|
|
# if len(chunk["choices"]) != 0:
|
|||
|
|
# text += chunk["choices"][0]["delta"]["content"]
|
|||
|
|
# yield {"error_code": 0, "text": text}
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_embeddings(self, params):
|
|||
|
|
print("embedding")
|
|||
|
|
print(params)
|
|||
|
|
|
|||
|
|
def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
|
|||
|
|
# 这里的是chatglm api的模板，其它API的conv_template需要定制
|
|||
|
|
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)
|