[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/model_workers/openai.py

@@ -0,0 +1,124 @@
+import json
+import sys
+from typing import List, Dict
+
+from fastchat.conversation import Conversation
+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 OpenAIWorker(ApiModelWorker):
+    """
+    支持 OpenAI 格式 API 的 Worker，用于 embedding 和 chat
+    """
+    DEFAULT_EMBED_MODEL = "text-embedding-ada-002"
+
+    def __init__(
+        self,
+        *,
+        model_names: List[str] = ["openai-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", 8192)
+        super().__init__(**kwargs)
+
+    def do_chat(self, params: ApiChatParams) -> Dict:
+        from openai import OpenAI
+        params.load_config(self.model_names[0])
+
+        client = OpenAI(
+            api_key=params.api_key,
+            base_url=params.api_base_url,
+        )
+
+        if log_verbose:
+            logger.info(f'{self.__class__.__name__}:params: {params}')
+
+        try:
+            response = client.chat.completions.create(
+                model=params.version or self.model_names[0],
+                messages=params.messages,
+                temperature=params.temperature,
+                max_tokens=params.max_tokens,
+                top_p=params.top_p,
+                stream=True,
+            )
+            for chunk in response:
+                if chunk.choices:
+                    delta = chunk.choices[0].delta
+                    if delta.content:
+                        yield {
+                            "error_code": 0,
+                            "text": delta.content,
+                        }
+        except Exception as e:
+            logger.error(f"OpenAI API 请求错误: {e}")
+            yield {
+                "error_code": 500,
+                "text": f"请求错误: {str(e)}",
+            }
+
+    def do_embeddings(self, params: ApiEmbeddingsParams) -> Dict:
+        from openai import OpenAI
+        # embed_texts 里用 worker_class() 默认 model_names 为 ["openai-api"]，会错加载成 openai-api 的 base_url；
+        # 在线嵌入应使用 ONLINE_LLM_MODEL 的键（如 bge-m3-api），由调用方写入 params.worker_name。
+        params.load_config(params.worker_name or self.model_names[0])
+
+        client = OpenAI(
+            api_key=params.api_key,
+            base_url=params.api_base_url,
+        )
+
+        if log_verbose:
+            logger.info(f'{self.__class__.__name__}:params: {params}')
+
+        try:
+            # OpenAI embedding API 每次最多处理 2048 个文本，这里分批处理
+            result = []
+            batch_size = 100
+            for i in range(0, len(params.texts), batch_size):
+                batch_texts = params.texts[i:i+batch_size]
+                response = client.embeddings.create(
+                    model=params.embed_model or self.DEFAULT_EMBED_MODEL,
+                    input=batch_texts,
+                    encoding_format="float",
+                )
+                embeddings = [item.embedding for item in response.data]
+                result.extend(embeddings)
+
+            return {"code": 200, "data": result}
+        except Exception as e:
+            logger.error(f"OpenAI Embedding API 请求错误: {e}")
+            return {
+                "code": 500,
+                "msg": f"Embedding 请求错误: {str(e)}",
+            }
+
+    def make_conv_template(self, conv_template: str = None, model_path: str = None) -> Conversation:
+        return conv.Conversation(
+            name=self.model_names[0],
+            system_message="You are a helpful assistant.",
+            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 = OpenAIWorker(
+        controller_addr="http://127.0.0.1:20001",
+        worker_addr="http://127.0.0.1:20008",
+    )
+    sys.modules["fastchat.serve.model_worker"].worker = worker
+    MakeFastAPIOffline(app)
+    uvicorn.run(app, port=20008)