[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/model_workers/zhipu.py

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+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)