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https://github.com/hansjone/oclaw.git
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oliver
420abac9f1
- 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>
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
from __future__ import annotations
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import hashlib
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import os
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from dataclasses import dataclass
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from typing import Any
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@dataclass(frozen=True)
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class EmbeddingResult:
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model: str
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vector: list[float]
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class EmbeddingClient:
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def embed(self, text: str) -> EmbeddingResult:
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raise NotImplementedError
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class OpenAIEmbeddingClient(EmbeddingClient):
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def __init__(self, *, model: str | None = None, api_key: str | None = None, base_url: str | None = None):
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self.model = (model or os.getenv("OPENAI_EMBEDDING_MODEL") or "text-embedding-3-small").strip()
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self.api_key = (api_key or os.getenv("OPENAI_API_KEY") or "").strip()
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self.base_url = (base_url or os.getenv("OPENAI_BASE_URL") or "").strip() or None
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if not self.api_key:
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raise RuntimeError("OPENAI_API_KEY is required for embeddings")
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from openai import OpenAI
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kwargs: dict[str, Any] = {"api_key": self.api_key}
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if self.base_url:
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kwargs["base_url"] = self.base_url
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self._client = OpenAI(**kwargs)
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def embed(self, text: str) -> EmbeddingResult:
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t = (text or "").strip()
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if not t:
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return EmbeddingResult(model=self.model, vector=[0.0] * 8)
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resp = self._client.embeddings.create(model=self.model, input=t)
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vec = list(resp.data[0].embedding)
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return EmbeddingResult(model=self.model, vector=[float(x) for x in vec])
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class HashEmbeddingClient(EmbeddingClient):
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"""Offline fallback: deterministic small vector (NOT semantic)."""
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def __init__(self, *, dim: int = 32, model: str = "hash-embed-32"):
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self.dim = int(dim)
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self.model = model
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def embed(self, text: str) -> EmbeddingResult:
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t = (text or "").encode("utf-8", errors="ignore")
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h = hashlib.sha256(t).digest()
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vec = []
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for i in range(self.dim):
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b = h[i % len(h)]
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vec.append((float(b) / 255.0) * 2.0 - 1.0)
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return EmbeddingResult(model=self.model, vector=vec)
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def build_default_embedding_client() -> EmbeddingClient:
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mode = (os.getenv("AIA_RAG_EMBEDDING_MODE") or "").strip().lower()
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if mode in ("hash", "offline"):
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return HashEmbeddingClient()
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try:
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return OpenAIEmbeddingClient()
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except Exception:
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return HashEmbeddingClient()
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__all__ = [
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"EmbeddingClient",
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"EmbeddingResult",
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"OpenAIEmbeddingClient",
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"HashEmbeddingClient",
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"build_default_embedding_client",
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]
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