30c7bda389
- Removed multiple failed document entries from `documents.json`. - Added a new document entry with updated metadata and changed the index name to `regulations_dense_1024_v2`. - Updated architecture documentation to reflect changes in the Milvus collection name. - Adjusted requirements by removing the sqlalchemy dependency. - Modified test cases to align with new document structure and naming conventions. - Introduced a new test file for Milvus vector index runtime recovery and error handling. - Updated assertions in various test files to ensure compatibility with the new schema.
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
"""Implement cross-encoder reranking via an OpenAI-compatible reranker API."""
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from __future__ import annotations
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import time
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import requests
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from loguru import logger
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from app.config.settings import settings
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from app.domain.retrieval import Reranker, RetrievedChunk
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class OpenAICompatibleReranker(Reranker):
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"""Call a TEI / Cohere-style reranker endpoint to re-score retrieved chunks."""
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def __init__(
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self,
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base_url: str | None = None,
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model: str | None = None,
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api_key: str | None = None,
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timeout: int = 30,
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) -> None:
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self._base_url = (base_url or settings.reranker_base_url).rstrip("/")
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self._model = model or settings.reranker_model
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self._api_key = api_key or settings.reranker_api_key
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self._timeout = timeout
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def rerank(self, query: str, chunks: list[RetrievedChunk], top_k: int) -> list[RetrievedChunk]:
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"""Return up to top_k chunks re-sorted by cross-encoder score."""
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if not chunks:
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return []
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texts = [chunk.text for chunk in chunks]
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start = time.time()
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try:
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scores = self._call_reranker(query, texts)
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except Exception as exc:
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logger.warning("Reranker call failed ({}), falling back to original order: {}", type(exc).__name__, exc)
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return chunks[:top_k]
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elapsed_ms = int((time.time() - start) * 1000)
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logger.debug("Reranker scored {} chunks in {}ms", len(chunks), elapsed_ms)
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ranked = sorted(
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[(score, chunk) for score, chunk in zip(scores, chunks)],
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key=lambda x: x[0],
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reverse=True,
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)
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result = []
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for score, chunk in ranked[:top_k]:
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chunk.score = float(score)
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result.append(chunk)
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return result
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def _call_reranker(self, query: str, texts: list[str]) -> list[float]:
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"""Call the reranker API and return a score per text."""
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headers = {"Content-Type": "application/json"}
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if self._api_key:
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headers["Authorization"] = f"Bearer {self._api_key}"
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# Try TEI format first: POST /rerank
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payload = {"query": query, "texts": texts, "raw_scores": False, "return_text": False}
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url = f"{self._base_url}/rerank"
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resp = requests.post(url, json=payload, headers=headers, timeout=self._timeout)
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if resp.status_code == 404:
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# Fall back to Cohere / OpenAI-style: POST /v1/rerank
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payload_v1 = {"model": self._model, "query": query, "documents": texts}
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url = f"{self._base_url}/v1/rerank"
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resp = requests.post(url, json=payload_v1, headers=headers, timeout=self._timeout)
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resp.raise_for_status()
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data = resp.json()
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# TEI response: list of {"index": N, "score": F}
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if isinstance(data, list):
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ordered = sorted(data, key=lambda x: x["index"])
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return [float(item["score"]) for item in ordered]
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# Cohere/OpenAI response: {"results": [{"index": N, "relevance_score": F}]}
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results = data.get("results", [])
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ordered = sorted(results, key=lambda x: x["index"])
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return [float(item.get("relevance_score", item.get("score", 0))) for item in ordered]
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