fix 文档管理模块 & 法规对话模块

backend/app/infrastructure/vectorstore/cross_encoder_reranker.py

@@ -0,0 +1,84 @@
+"""Implement cross-encoder reranking via an OpenAI-compatible reranker API."""
+
+from __future__ import annotations
+
+import time
+
+import requests
+from loguru import logger
+
+from app.config.settings import settings
+from app.domain.retrieval import Reranker, RetrievedChunk
+
+
+class OpenAICompatibleReranker(Reranker):
+    """Call a TEI / Cohere-style reranker endpoint to re-score retrieved chunks."""
+
+    def __init__(
+        self,
+        base_url: str | None = None,
+        model: str | None = None,
+        api_key: str | None = None,
+        timeout: int = 30,
+    ) -> None:
+        self._base_url = (base_url or settings.reranker_base_url).rstrip("/")
+        self._model = model or settings.reranker_model
+        self._api_key = api_key or settings.reranker_api_key
+        self._timeout = timeout
+
+    def rerank(self, query: str, chunks: list[RetrievedChunk], top_k: int) -> list[RetrievedChunk]:
+        """Return up to top_k chunks re-sorted by cross-encoder score."""
+        if not chunks:
+            return []
+
+        texts = [chunk.content for chunk in chunks]
+        start = time.time()
+        try:
+            scores = self._call_reranker(query, texts)
+        except Exception as exc:
+            logger.warning("Reranker call failed ({}), falling back to original order: {}", type(exc).__name__, exc)
+            return chunks[:top_k]
+
+        elapsed_ms = int((time.time() - start) * 1000)
+        logger.debug("Reranker scored {} chunks in {}ms", len(chunks), elapsed_ms)
+
+        ranked = sorted(
+            [(score, chunk) for score, chunk in zip(scores, chunks)],
+            key=lambda x: x[0],
+            reverse=True,
+        )
+        result = []
+        for score, chunk in ranked[:top_k]:
+            chunk.score = float(score)
+            result.append(chunk)
+        return result
+
+    def _call_reranker(self, query: str, texts: list[str]) -> list[float]:
+        """Call the reranker API and return a score per text."""
+        headers = {"Content-Type": "application/json"}
+        if self._api_key:
+            headers["Authorization"] = f"Bearer {self._api_key}"
+
+        # Try TEI format first: POST /rerank
+        payload = {"query": query, "texts": texts, "raw_scores": False, "return_text": False}
+        url = f"{self._base_url}/rerank"
+        resp = requests.post(url, json=payload, headers=headers, timeout=self._timeout)
+
+        if resp.status_code == 404:
+            # Fall back to Cohere / OpenAI-style: POST /v1/rerank
+            payload_v1 = {"model": self._model, "query": query, "documents": texts}
+            url = f"{self._base_url}/v1/rerank"
+            resp = requests.post(url, json=payload_v1, headers=headers, timeout=self._timeout)
+
+        resp.raise_for_status()
+        data = resp.json()
+
+        # TEI response: list of {"index": N, "score": F}
+        if isinstance(data, list):
+            ordered = sorted(data, key=lambda x: x["index"])
+            return [float(item["score"]) for item in ordered]
+
+        # Cohere/OpenAI response: {"results": [{"index": N, "relevance_score": F}]}
+        results = data.get("results", [])
+        ordered = sorted(results, key=lambda x: x["index"])
+        return [float(item.get("relevance_score", item.get("score", 0))) for item in ordered]