refactor: root-package imports (svc/runtime/interfaces) and fix PYTHONPATH

- Rename platform/ to svc/ to avoid shadowing stdlib platform.
- Replace from oclaw.* with from svc/runtime/interfaces; update -m CLI paths.
- tests/conftest: prepend repo root to sys.path (no parent-folder package name).
- CI: paths and offline_eval script under repo root.
- Ops scripts: PYTHONPATH must be repo root for python -m runtime.* (fixes gateway/WhatsApp sidecar startup).
- Fix default oclaw.json path in tabular/file attachment limits; stabilize attachment test config.

Co-authored-by: Cursor <cursoragent@cursor.com>

platform/embeddings/embedding_client.py

@@ -1,77 +0,0 @@
-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",
-]
-