初始化：独立 oclaw 仓库首提交

- 在 oclaw/ 下重新初始化 Git 仓库
- 补齐子仓库 .gitignore，避免提交本地运行态数据（_local、node_modules、logs 等）
- 提交当前工程代码与配置

Made-with: Cursor

platform/embeddings/embedding_client.py

@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+import hashlib
+import os
+from dataclasses import dataclass
+from typing import Any
+
+
+@dataclass(frozen=True)
+class EmbeddingResult:
+    model: str
+    vector: list[float]
+
+
+class EmbeddingClient:
+    def embed(self, text: str) -> EmbeddingResult:
+        raise NotImplementedError
+
+
+class OpenAIEmbeddingClient(EmbeddingClient):
+    def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
+        self.model = (model or os.getenv("OPENAI_EMBEDDING_MODEL") or "text-embedding-3-small").strip()
+        self.api_key = (api_key or os.getenv("OPENAI_API_KEY") or "").strip()
+        self.base_url = (base_url or os.getenv("OPENAI_BASE_URL") or "").strip() or None
+        if not self.api_key:
+            raise RuntimeError("OPENAI_API_KEY is required for embeddings")
+        from openai import OpenAI
+
+        kwargs: dict[str, Any] = {"api_key": self.api_key}
+        if self.base_url:
+            kwargs["base_url"] = self.base_url
+        self._client = OpenAI(**kwargs)
+
+    def embed(self, text: str) -> EmbeddingResult:
+        t = (text or "").strip()
+        if not t:
+            return EmbeddingResult(model=self.model, vector=[0.0] * 8)
+        resp = self._client.embeddings.create(model=self.model, input=t)
+        vec = list(resp.data[0].embedding)
+        return EmbeddingResult(model=self.model, vector=[float(x) for x in vec])
+
+
+class HashEmbeddingClient(EmbeddingClient):
+    """Offline fallback: deterministic small vector (NOT semantic)."""
+
+    def __init__(self, *, dim: int = 32, model: str = "hash-embed-32"):
+        self.dim = int(dim)
+        self.model = model
+
+    def embed(self, text: str) -> EmbeddingResult:
+        t = (text or "").encode("utf-8", errors="ignore")
+        h = hashlib.sha256(t).digest()
+        vec = []
+        for i in range(self.dim):
+            b = h[i % len(h)]
+            vec.append((float(b) / 255.0) * 2.0 - 1.0)
+        return EmbeddingResult(model=self.model, vector=vec)
+
+
+def build_default_embedding_client() -> EmbeddingClient:
+    mode = (os.getenv("AIA_RAG_EMBEDDING_MODE") or "").strip().lower()
+    if mode in ("hash", "offline"):
+        return HashEmbeddingClient()
+    try:
+        return OpenAIEmbeddingClient()
+    except Exception:
+        return HashEmbeddingClient()
+
+
+__all__ = [
+    "EmbeddingClient",
+    "EmbeddingResult",
+    "OpenAIEmbeddingClient",
+    "HashEmbeddingClient",
+    "build_default_embedding_client",
+]
+