[全量] 初始化项目代码、配置、文档及Agent协同harness

2026-04-02 11:36:05 +08:00
parent 0553309cdf
commit 87e571d9ec
1133 changed files with 221948 additions and 0 deletions
--- a/langchain-chat/server/model_workers/qwen.py
+++ b/langchain-chat/server/model_workers/qwen.py
@@ -0,0 +1,128 @@
+import json
+import sys
+
+from fastchat.conversation import Conversation
+from configs import TEMPERATURE
+from http import HTTPStatus
+from typing import List, Literal, Dict
+
+from fastchat import conversation as conv
+from server.model_workers.base import *
+from server.model_workers.base import ApiEmbeddingsParams
+from configs import logger, log_verbose
+
+
+class QwenWorker(ApiModelWorker):
+    DEFAULT_EMBED_MODEL = "text-embedding-v1"
+
+    def __init__(
+        self,
+        *,
+        version: Literal["qwen-turbo", "qwen-plus"] = "qwen-turbo",
+        model_names: List[str] = ["qwen-api"],
+        controller_addr: str = None,
+        worker_addr: str = None,
+        **kwargs,
+    ):
+        kwargs.update(model_names=model_names, controller_addr=controller_addr, worker_addr=worker_addr)
+        kwargs.setdefault("context_len", 16384)
+        super().__init__(**kwargs)
+        self.version = version
+
+    def do_chat(self, params: ApiChatParams) -> Dict:
+        import dashscope
+        params.load_config(self.model_names[0])
+        if log_verbose:
+            logger.info(f'{self.__class__.__name__}:params: {params}')
+
+        gen = dashscope.Generation()
+        responses = gen.call(
+            model=params.version,
+            temperature=params.temperature,
+            api_key=params.api_key,
+            messages=params.messages,
+            result_format='message',  # set the result is message format.
+            stream=True,
+        )
+
+        for resp in responses:
+            if resp["status_code"] == 200:
+                if choices := resp["output"]["choices"]:
+                    yield {
+                        "error_code": 0,
+                        "text": choices[0]["message"]["content"],
+                    }
+            else:
+                data = {
+                    "error_code": resp["status_code"],
+                    "text": resp["message"],
+                    "error": {
+                        "message": resp["message"],
+                        "type": "invalid_request_error",
+                        "param": None,
+                        "code": None,
+                    }
+                }
+                self.logger.error(f"请求千问 API 时发生错误：{data}")
+                yield data
+
+    def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
+        import dashscope
+        params.load_config(self.model_names[0])
+        if log_verbose:
+            logger.info(f'{self.__class__.__name__}:params: {params}')
+        result = []
+        i = 0
+        while i < len(params.texts):
+            texts = params.texts[i:i+25]
+            resp = dashscope.TextEmbedding.call(
+                model=params.embed_model or self.DEFAULT_EMBED_MODEL,
+                input=texts, # 最大25行
+                api_key=params.api_key,
+            )
+            if resp["status_code"] != 200:
+                data = {
+                            "code": resp["status_code"],
+                            "msg": resp.message,
+                            "error": {
+                                "message": resp["message"],
+                                "type": "invalid_request_error",
+                                "param": None,
+                                "code": None,
+                            }
+                        }
+                self.logger.error(f"请求千问 API 时发生错误：{data}")
+                return data
+            else:
+                embeddings = [x["embedding"] for x in resp["output"]["embeddings"]]
+                result += embeddings
+            i += 25
+        return {"code": 200, "data": result}
+
+    def get_embeddings(self, params):
+        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="你是一个聪明、对人类有帮助的人工智能，你可以对人类提出的问题给出有用、详细、礼貌的回答。",
+            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 = QwenWorker(
+        controller_addr="http://127.0.0.1:20001",
+        worker_addr="http://127.0.0.1:20007",
+    )
+    sys.modules["fastchat.serve.model_worker"].worker = worker
+    MakeFastAPIOffline(app)
+    uvicorn.run(app, port=20007)