初始化提交

backend/app/ai_provider/embedding/openai_format.py

@@ -0,0 +1,169 @@
+"""OpenAI 兼容格式的 Embedding 适配器 — ``POST /v1/embeddings``。
+
+覆盖 OpenAI、DeepSeek、以及任何兼容 OpenAI 嵌入端点的提供商。
+
+请求::
+
+    POST {base_url}/v1/embeddings
+    Authorization: Bearer {api_key}
+    Content-Type: application/json
+
+    {
+        "model": "text-embedding-3-small",
+        "input": ["要嵌入的文本"],
+        "encoding_format": "float"
+    }
+
+响应::
+
+    {
+        "object": "list",
+        "data": [
+            {"object": "embedding", "index": 0, "embedding": [0.xxx, ...]}
+        ],
+        "model": "text-embedding-3-small",
+        "usage": {"prompt_tokens": 5, "total_tokens": 5}
+    }
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+import httpx
+
+from app.ai_provider.embedding.base import (
+    EmbeddingAdapterBase,
+    EmbeddingProtocol,
+    EmbeddingRequest,
+    EmbeddingResponse,
+    EmbeddingResult,
+    EmbeddingServiceError,
+)
+from app.core.logging import logger
+
+
+class OpenAIFormatEmbeddingAdapter(EmbeddingAdapterBase, EmbeddingProtocol):
+    """OpenAI 兼容嵌入适配器，覆盖 95%+ 的嵌入服务提供商。"""
+
+    _adapter_type = "embedding"
+    _default_timeout = 60
+
+    def __init__(
+        self,
+        *,
+        base_url: str,
+        api_key: str,
+        model: str = "text-embedding-3-small",
+        request_timeout_seconds: int = 60,
+    ) -> None:
+        self._base_url = base_url.rstrip("/")
+        self._api_key = api_key
+        self._model = model
+        self._timeout = request_timeout_seconds
+        self._client: httpx.AsyncClient | None = None
+
+    # ── 客户端 ───────────────────────────────────────────────────────────
+
+    @property
+    def _http(self) -> httpx.AsyncClient:
+        if self._client is None or self._client.is_closed:
+            self._client = httpx.AsyncClient(timeout=httpx.Timeout(self._timeout))
+        return self._client
+
+    async def close(self) -> None:
+        if self._client is not None and not self._client.is_closed:
+            await self._client.aclose()
+
+    # ── EmbeddingProtocol ────────────────────────────────────────────────
+
+    async def embed(self, request: EmbeddingRequest) -> EmbeddingResponse:
+        """通过 ``POST /v1/embeddings`` 将文本转换为向量。"""
+        url = f"{self._base_url}/v1/embeddings"
+
+        payload: dict[str, Any] = {
+            "model": request.model or self._model,
+            "input": request.input,
+            "encoding_format": request.encoding_format,
+        }
+        if request.dimensions is not None:
+            payload["dimensions"] = request.dimensions
+        if request.user is not None:
+            payload["user"] = request.user
+
+        headers = {
+            "Content-Type": "application/json",
+        }
+        if self._api_key:
+            headers["Authorization"] = f"Bearer {self._api_key}"
+
+        logger.info(
+            "embedding.request",
+            model=payload["model"],
+            input_count=len(request.input) if isinstance(request.input, list) else 1,
+            dimensions=request.dimensions,
+            url=url,
+        )
+
+        try:
+            response = await self._http.post(url, json=payload, headers=headers)
+        except httpx.TimeoutException as exc:
+            raise EmbeddingServiceError(
+                status_code=504,
+                code="embedding_timeout",
+                message="Embedding request timed out.",
+            ) from exc
+        except httpx.RequestError as exc:
+            raise EmbeddingServiceError(
+                status_code=503,
+                code="embedding_connection_error",
+                message=f"Embedding connection error: {exc}",
+            ) from exc
+
+        if response.status_code != 200:
+            body = response.text[:500]
+            raise EmbeddingServiceError(
+                status_code=response.status_code,
+                code="embedding_api_error",
+                message=f"Embedding API returned {response.status_code}: {body}",
+            )
+
+        result = self._parse_response(response.json())
+
+        logger.info(
+            "embedding.success",
+            model=payload["model"],
+            total_vectors=len(result.data),
+            usage=result.usage,
+        )
+        return result
+
+    # ── 辅助方法 ──────────────────────────────────────────────────────────
+
+    @staticmethod
+    def _parse_response(body: dict) -> EmbeddingResponse:
+        """从 OpenAI 格式的嵌入响应中提取结果。"""
+        try:
+            raw_data = body.get("data", [])
+            results = []
+            for item in raw_data:
+                results.append(
+                    EmbeddingResult(
+                        embedding=item.get("embedding", []),
+                        index=item.get("index", 0),
+                    )
+                )
+            # 按 index 排序确保顺序
+            results.sort(key=lambda r: r.index)
+
+            return EmbeddingResponse(
+                data=results,
+                model=body.get("model", ""),
+                usage=body.get("usage"),
+            )
+        except Exception as exc:
+            raise EmbeddingServiceError(
+                status_code=500,
+                code="embedding_response_parse_error",
+                message=f"Failed to parse embedding response: {exc}",
+            ) from exc