deepdata: core product code (backend, frontend, runtime, deepflow module)

2026-08-30 19:23:38 +08:00
commit bd3db30be9
1405 changed files with 271727 additions and 0 deletions
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+# app/core/llm/__init__.py
+"""
+LangChain 模型工厂：统一创建 Chat、Embedding 及健康检查，便于各模块复用。
+"""
+from .factory import LLMFactory
+
+__all__ = ["LLMFactory"]
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+# app/module/shared/llm/factory.py
+"""
+LangChain 模型工厂：基于 OpenAI 兼容接口封装 Chat / Embedding 的创建、健康检查与同步调用。
+- DashScope compatible‑mode：重写embedding逻辑，规避tiktoken token数组输入问题
+- 其他OpenAI兼容服务商：完全使用原生 OpenAIEmbeddings / ChatOpenAI
+"""
+from typing import Literal, List, Optional
+
+import httpx
+from langchain_core.language_models import BaseChatModel
+from langchain_core.embeddings import Embeddings
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from pydantic import SecretStr
+
+
+class DashScopeOpenAIEmbeddings(OpenAIEmbeddings, Embeddings):
+    """
+    仅用于阿里云DashScope compatible‑mode
+    1. 绕过原生tiktoken逻辑，直接传入原始字符串，不传入token数字数组
+    2. 适配新版openai SDK返回Pydantic Embedding对象，使用属性 .embedding
+    """
+
+    def _get_len_safe_embeddings(self, texts: List[str], *, engine: str, **kwargs) -> List[List[float]]:
+        batch_size = self.chunk_size
+        embeddings: List[List[float]] = []
+        for i in range(0, len(texts), batch_size):
+            batch = texts[i:i + batch_size]
+            resp = self.client.create(
+                input=batch,
+                model=self.model,
+                encoding_format="float",
+            )
+            embeddings.extend([data.embedding for data in resp.data])
+        return embeddings
+
+
+class LLMFactory:
+    @staticmethod
+    def _is_dashscope_compatible(base_url: Optional[str]) -> bool:
+        if not base_url:
+            return False
+        return "dashscope.aliyuncs.com/compatible-mode" in base_url
+
+    @staticmethod
+    def create_chat(
+        model_name: str,
+        base_url: str,
+        api_key: str | None = None,
+    ) -> BaseChatModel:
+        """创建对话模型，兼容 OpenAI 及任意 base_url 的 OpenAI 兼容服务。"""
+        # DashScope容器环境关闭SSL校验；其余服务商使用默认httpx客户端（开启证书校验）
+        if LLMFactory._is_dashscope_compatible(base_url):
+            http_client = httpx.Client(verify=False)
+        else:
+            http_client = None
+
+        return ChatOpenAI(
+            model=model_name,
+            base_url=base_url or None,
+            api_key=SecretStr(api_key or ""),
+            http_client=http_client,
+        )
+
+    @staticmethod
+    def create_embedding(
+        model_name: str,
+        base_url: str,
+        api_key: str | None = None,
+    ) -> Embeddings:
+        is_dashscope = LLMFactory._is_dashscope_compatible(base_url)
+
+        # DashScope才关闭SSL校验，其他服务商走默认SSL校验
+        if is_dashscope:
+            http_client = httpx.Client(verify=False)
+        else:
+            http_client = None
+
+        kwargs = {
+            "model": model_name,
+            "base_url": base_url or None,
+            "api_key": SecretStr(api_key or ""),
+            "http_client": http_client,
+        }
+
+        # 仅DashScope注入encoding_format="float"，其他服务商不强制注入，避免参数不兼容
+        if is_dashscope:
+            import inspect
+            sig = inspect.signature(OpenAIEmbeddings.__init__)
+            if "encoding_format" in sig.parameters:
+                kwargs["encoding_format"] = "float"
+            return DashScopeOpenAIEmbeddings(**kwargs)
+
+        # 其余所有OpenAI兼容模型，走原生实现
+        return OpenAIEmbeddings(**kwargs)
+
+    @staticmethod
+    def check_health(
+        model_name: str,
+        base_url: str,
+        api_key: str | None,
+        model_type: Literal["CHAT", "EMBEDDING"] | str,
+    ) -> None:
+        """对配置做一次最小化调用进行健康检查，失败则抛出。"""
+        if model_type == "CHAT":
+            model = LLMFactory.create_chat(model_name, base_url, api_key)
+            model.invoke("hello")
+        else:
+            model = LLMFactory.create_embedding(model_name, base_url, api_key)
+            model.embed_query("text")
+
+    @staticmethod
+    def get_embedding_dimension(
+        model_name: str,
+        base_url: str,
+        api_key: str | None = None,
+    ) -> int:
+        """创建 Embedding 模型并返回向量维度。"""
+        emb = LLMFactory.create_embedding(model_name, base_url, api_key)
+        return len(emb.embed_query("text"))
+
+    @staticmethod
+    def invoke_sync(chat_model: BaseChatModel, prompt: str) -> str:
+        """同步调用对话模型并返回 content，供 run_in_executor 等场景使用。"""
+        return chat_model.invoke(prompt).content