feat(regulatory): 增加本地法规RAG索引检索

review_agent/regulatory_review/services/rag_embedding.py

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+from __future__ import annotations
+
+import hashlib
+import random
+from typing import Callable, Iterable
+
+import httpx
+from django.conf import settings
+
+
+EmbeddingFunction = Callable[[list[str]], list[list[float]]]
+
+
+class EmbeddingConfigurationError(RuntimeError):
+    pass
+
+
+class SiliconFlowEmbeddingProvider:
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        base_url: str,
+        model: str,
+        dimensions: int,
+        timeout: float = 60.0,
+    ):
+        if not api_key:
+            raise EmbeddingConfigurationError("SILICONFLOW_API_KEY 未配置。")
+        self.api_key = api_key
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.dimensions = dimensions
+        self.timeout = timeout
+
+    def embed(self, texts: Iterable[str]) -> list[list[float]]:
+        inputs = list(texts)
+        response = httpx.post(
+            f"{self.base_url}/embeddings",
+            headers={"Authorization": f"Bearer {self.api_key}"},
+            json={
+                "model": self.model,
+                "input": inputs,
+                "dimensions": self.dimensions,
+            },
+            timeout=self.timeout,
+        )
+        response.raise_for_status()
+        payload = response.json()
+        return [item["embedding"] for item in payload.get("data", [])]
+
+    def __call__(self, texts: list[str]) -> list[list[float]]:
+        return self.embed(texts)
+
+
+class DeterministicEmbeddingProvider:
+    """Small local embedding substitute for tests and explicit dry runs."""
+
+    def __init__(self, dimensions: int = 16):
+        self.dimensions = dimensions
+
+    def __call__(self, texts: list[str]) -> list[list[float]]:
+        vectors = []
+        for text in texts:
+            seed = int(hashlib.sha256(text.encode("utf-8")).hexdigest()[:16], 16)
+            rng = random.Random(seed)
+            vectors.append([rng.uniform(-1, 1) for _ in range(self.dimensions)])
+        return vectors
+
+
+def get_embedding_provider(provider_name: str | None = None) -> EmbeddingFunction:
+    provider = provider_name or settings.REGULATORY_RAG_PROVIDER
+    if provider == "siliconflow":
+        return SiliconFlowEmbeddingProvider(
+            api_key=settings.SILICONFLOW_API_KEY,
+            base_url=settings.SILICONFLOW_BASE_URL,
+            model=settings.SILICONFLOW_EMBEDDING_MODEL,
+            dimensions=settings.SILICONFLOW_EMBEDDING_DIMENSIONS,
+        )
+    if provider in {"deterministic", "local"}:
+        return DeterministicEmbeddingProvider()
+    raise EmbeddingConfigurationError(f"不支持的 embedding provider：{provider}")