feat(agent-core): 增加智能编排与模型工具基础

agent_core/llm_provider.py

@@ -0,0 +1,132 @@
+from dataclasses import dataclass
+import json
+from urllib.error import URLError
+from urllib.request import Request, urlopen
+
+
+class LLMConfigurationError(ValueError):
+    pass
+
+
+class EmbeddingConfigurationError(ValueError):
+    pass
+
+
+@dataclass
+class LLMResponse:
+    content: str = ""
+    model_name: str = ""
+    success: bool = True
+    error: Exception | None = None
+
+
+class MockLLMProvider:
+    def __init__(self, model_name: str = "mock-model"):
+        self.model_name = model_name or "mock-model"
+
+    def generate(self, messages: list[dict], response_format: dict | None = None) -> LLMResponse:
+        user_content = ""
+        for message in reversed(messages):
+            if message.get("role") == "user":
+                user_content = message.get("content", "")
+                break
+        return LLMResponse(
+            content=f"模拟模型回答：{user_content}",
+            model_name=self.model_name,
+            success=True,
+        )
+
+
+class OpenAICompatibleProvider:
+    def __init__(self, api_key: str, base_url: str, model_name: str):
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model_name = model_name
+
+    def generate(self, messages: list[dict], response_format: dict | None = None) -> LLMResponse:
+        if not self.api_key:
+            return LLMResponse(
+                model_name=self.model_name,
+                success=False,
+                error=LLMConfigurationError("LLM_API_KEY 未配置，无法调用 OpenAI 兼容模型接口"),
+            )
+        payload = {
+            "model": self.model_name,
+            "messages": messages,
+        }
+        if response_format:
+            payload["response_format"] = response_format
+        try:
+            data = _post_json(
+                base_url=self.base_url,
+                endpoint="chat/completions",
+                api_key=self.api_key,
+                payload=payload,
+            )
+            choice = data.get("choices", [{}])[0]
+            content = choice.get("message", {}).get("content", "")
+            return LLMResponse(
+                content=content,
+                model_name=data.get("model", self.model_name),
+                success=True,
+            )
+        except Exception as exc:
+            return LLMResponse(model_name=self.model_name, success=False, error=exc)
+
+
+class OpenAICompatibleEmbeddingProvider:
+    def __init__(self, api_key: str, base_url: str, model_name: str):
+        self.api_key = api_key
+        self.base_url = base_url
+        self.model_name = model_name
+
+    def embed_texts(self, texts: list[str]) -> list[list[float]]:
+        if not self.api_key:
+            raise EmbeddingConfigurationError("EMBEDDING_API_KEY 未配置，无法调用 OpenAI 兼容 Embedding 接口")
+        data = _post_json(
+            base_url=self.base_url,
+            endpoint="embeddings",
+            api_key=self.api_key,
+            payload={"model": self.model_name, "input": texts},
+        )
+        return [item.get("embedding", []) for item in data.get("data", [])]
+
+
+def _post_json(base_url: str, endpoint: str, api_key: str, payload: dict) -> dict:
+    url = f"{base_url.rstrip('/')}/{endpoint}"
+    request = Request(
+        url,
+        data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+        method="POST",
+    )
+    try:
+        with urlopen(request, timeout=60) as response:
+            return json.loads(response.read().decode("utf-8"))
+    except URLError as exc:
+        raise RuntimeError(f"OpenAI 兼容接口调用失败：{exc}") from exc
+
+
+def create_llm_provider(config: dict | None = None):
+    config = config or {}
+    provider_name = config.get("LLM_PROVIDER", "mock")
+    model_name = config.get("LLM_MODEL", "mock-model")
+    if provider_name == "mock":
+        return MockLLMProvider(model_name=model_name)
+    return OpenAICompatibleProvider(
+        api_key=config.get("LLM_API_KEY", ""),
+        base_url=config.get("LLM_BASE_URL", "https://api.openai.com/v1"),
+        model_name=model_name,
+    )
+
+
+def create_embedding_provider(config: dict | None = None):
+    config = config or {}
+    return OpenAICompatibleEmbeddingProvider(
+        api_key=config.get("EMBEDDING_API_KEY", config.get("LLM_API_KEY", "")),
+        base_url=config.get("EMBEDDING_BASE_URL", config.get("LLM_BASE_URL", "https://api.openai.com/v1")),
+        model_name=config.get("EMBEDDING_MODEL", "text-embedding-3-small"),
+    )