e244b8bf30
- embed_dense_multi now sends one batch request (N texts → 1 API call) instead of N parallel requests, avoiding rate-limit errors when question has variants/hyde - Extra dense embeddings (variants/hyde) wrapped in try/except so primary query always succeeds - Reranker now retries up to 5 times with exponential backoff on 429, matching Lotus reference Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
from typing import Any
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import httpx
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from config import RERANK_LIMIT, RERANKER_MODEL, RERANKER_URL, get_upstream_kwargs, logger
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from retrieval import extract_page_content
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async def get_rerank_scores(
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client: httpx.AsyncClient,
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query: str,
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targets: list[str],
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) -> list[float]:
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if not targets:
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return []
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import asyncio as _asyncio
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for attempt in range(5):
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try:
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response = await client.post(
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str(RERANKER_URL),
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**get_upstream_kwargs(),
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json={
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"model": RERANKER_MODEL,
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"encoding_format": "float",
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"text_1": query,
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"text_2": targets,
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},
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)
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if response.status_code == 429:
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wait = 2 ** attempt
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logger.warning("Rerank 429, retry %d/5 in %ds", attempt + 1, wait)
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await _asyncio.sleep(wait)
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continue
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response.raise_for_status()
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data = response.json().get("data") or []
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return [float(sample["score"]) for sample in data]
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except Exception as exc:
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if attempt < 4:
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await _asyncio.sleep(2 ** attempt)
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continue
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raise exc
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return []
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async def rerank_points(
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client: httpx.AsyncClient,
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query: str,
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points: list[Any],
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) -> tuple[list[Any], list[Any]]:
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"""Return (reranked_head, retrieval_tail) so we don't lose candidates."""
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if not points:
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return [], []
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rerank_candidates = points[:RERANK_LIMIT]
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tail = points[RERANK_LIMIT:]
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targets = [extract_page_content(p) for p in rerank_candidates]
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try:
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scores = await get_rerank_scores(client, query, targets)
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except Exception as exc:
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logger.warning("Rerank failed, using retrieval order: %s", exc)
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return rerank_candidates, tail
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if len(scores) != len(rerank_candidates):
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logger.warning("Rerank score count mismatch, using retrieval order")
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return rerank_candidates, tail
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paired = sorted(zip(scores, rerank_candidates), key=lambda x: x[0], reverse=True)
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reranked = [p for _, p in paired]
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return reranked, tail
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