File size: 6,834 Bytes
34b531b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | from __future__ import annotations
import logging
import math
import time
from dataclasses import replace
from functools import lru_cache
import requests
from app.config import (
RERANK_API_RETRIES,
RERANK_API_RETRY_BACKOFF,
RERANK_API_TIMEOUT,
RERANK_API_URL,
RERANK_BATCH_SIZE,
RERANK_ENABLED,
)
from app.runtime_auth import get_hf_api_key
from app.schemas import RetrievedChunk
logger = logging.getLogger(__name__)
RETRYABLE_STATUS_CODES = {408, 429, 500, 502, 503, 504}
class BGEReranker:
def __init__(self) -> None:
self.enabled = RERANK_ENABLED
def rerank(self, query: str, chunks: list[RetrievedChunk], top_k: int) -> list[RetrievedChunk]:
if not chunks:
return []
if not self.enabled:
return chunks[:top_k]
if not get_hf_api_key():
return self._fallback(chunks, top_k, "missing_hf_api_key")
try:
scores: list[float] = []
for start in range(0, len(chunks), RERANK_BATCH_SIZE):
batch = chunks[start : start + RERANK_BATCH_SIZE]
scores.extend(self._api_scores(query, [chunk.text for chunk in batch]))
if len(scores) != len(chunks):
raise RuntimeError(
f"Reranker returned {len(scores)} scores for {len(chunks)} candidates"
)
ranked = sorted(
zip(chunks, scores),
key=lambda item: item[1],
reverse=True,
)
return [
replace(
chunk,
score=round(sigmoid(raw_score), 6),
metadata={
**chunk.metadata,
"hybrid_score": chunk.score,
"rerank_score": raw_score,
"rerank_status": "success",
},
)
for chunk, raw_score in ranked[:top_k]
]
except (requests.RequestException, RuntimeError, TypeError, ValueError) as exc:
logger.warning(
"Reranker API unavailable; using hybrid ranking fallback: %s",
exc,
)
return self._fallback(chunks, top_k, type(exc).__name__)
def _fallback(
self,
chunks: list[RetrievedChunk],
top_k: int,
reason: str,
) -> list[RetrievedChunk]:
return [
replace(
chunk,
metadata={
**chunk.metadata,
"hybrid_score": chunk.score,
"rerank_status": "fallback",
"rerank_fallback_reason": reason,
},
)
for chunk in chunks[:top_k]
]
def _api_scores(self, query: str, documents: list[str]) -> list[float]:
api_key = get_hf_api_key()
if not api_key:
raise RuntimeError("Enter a Hugging Face token to use reranking")
headers = {"Authorization": f"Bearer {api_key}"}
payload = {
"inputs": [{"text": query, "text_pair": document} for document in documents],
"options": {"wait_for_model": True},
}
response: requests.Response | None = None
attempts = max(1, RERANK_API_RETRIES + 1)
for attempt in range(1, attempts + 1):
try:
response = requests.post(
RERANK_API_URL,
headers=headers,
json=payload,
timeout=RERANK_API_TIMEOUT,
)
if response.status_code not in RETRYABLE_STATUS_CODES:
break
if attempt == attempts:
response.raise_for_status()
logger.warning(
"Reranker API returned HTTP %s; retrying (%s/%s)",
response.status_code,
attempt,
attempts - 1,
)
except (requests.Timeout, requests.ConnectionError) as exc:
if attempt == attempts:
raise
logger.warning(
"Reranker API request failed; retrying (%s/%s): %s",
attempt,
attempts - 1,
exc,
)
delay = RERANK_API_RETRY_BACKOFF * (2 ** (attempt - 1))
if delay > 0:
time.sleep(delay)
if response is None:
raise RuntimeError("Reranker API did not return a response")
if response.status_code == 400 and len(documents) > 1:
return [self._api_scores(query, [document])[0] for document in documents]
response.raise_for_status()
response_payload = response.json()
if isinstance(response_payload, dict) and response_payload.get("error"):
raise RuntimeError(str(response_payload["error"]))
return self._coerce_scores(response_payload, expected_count=len(documents))
def _coerce_scores(self, payload, expected_count: int) -> list[float]:
if isinstance(payload, dict) and "scores" in payload:
scores = payload["scores"]
else:
scores = payload
if isinstance(scores, list) and len(scores) == 1 and isinstance(scores[0], list):
scores = scores[0]
if not isinstance(scores, list) or len(scores) != expected_count:
raise RuntimeError(
f"Unexpected rerank API response shape: expected {expected_count}, "
f"received {type(scores).__name__}"
)
return [self._score_from_item(item) for item in scores]
def _score_from_item(self, item) -> float:
if isinstance(item, int | float):
return float(item)
if isinstance(item, dict):
if "score" in item:
return float(item["score"])
if "logit" in item:
return float(item["logit"])
if isinstance(item, list) and item:
candidate = max(
item,
key=lambda value: (
float(value.get("score", 0.0)) if isinstance(value, dict) else 0.0
),
)
return self._score_from_item(candidate)
raise RuntimeError("Unexpected rerank score item from API")
def sigmoid(value: float) -> float:
if value >= 0:
z = math.exp(-value)
return 1 / (1 + z)
z = math.exp(value)
return z / (1 + z)
@lru_cache(maxsize=1)
def get_reranker() -> BGEReranker:
return BGEReranker()
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