feat: 重排

This commit is contained in:
李如威 2025-07-11 09:46:53 +08:00
parent 8cb491613f
commit 45a4836776
1 changed files with 98 additions and 25 deletions

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@ -9,6 +9,7 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
import os
import time
import torch
import json
class AsyncRAGService:
@ -38,14 +39,17 @@ class AsyncRAGService:
),
)
self.tokenizer = AutoTokenizer.from_pretrained(
"/Volumes/LRW/Model/Qwen3-Embedding-0.6B", trust_remote_code=True
)
self.rerank_model = AutoModelForSequenceClassification.from_pretrained(
"/Volumes/LRW/Model/Qwen3-Embedding-0.6B",
trust_remote_code=True,
device_map="auto", # 或 "cuda"
)
self.tokenizer = AutoTokenizer.from_pretrained("/Volumes/LRW/Model/Qwen3-Embedding-0.6B", padding_side="left")
# 强制设置 padding token
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
self.rerank_model = AutoModelForSequenceClassification.from_pretrained("/Volumes/LRW/Model/Qwen3-Embedding-0.6B").eval()
# 确保模型配置与 tokenizer 一致
if hasattr(self.rerank_model.config, "pad_token_id"):
self.rerank_model.config.pad_token_id = self.tokenizer.pad_token_id
self.logger.info("RAG服务初始化完成")
@ -214,37 +218,106 @@ class AsyncRAGService:
return await asyncio.to_thread(_format_sources)
async def _rerank_results(
self, question: str, search_results: List[Dict[str, Any]]
self, question: str, search_results: List[Dict[str, Any]], skip_rerank: bool = True
) -> List[Dict[str, Any]]:
"""使用 Qwen3-Reranker 对搜索结果批量重排序"""
# 准备 batch 输入:格式必须是 Query: xxx\nDocument: yyy
batch_texts = [
f"Query: {question}\nDocument: {r['content'][:1000]}" # 可以根据显存调整截断长度
if skip_rerank:
self.logger.info("跳过重排序")
return search_results
if not search_results:
return []
# 模型相关常量(可初始化时提前保存)
instruction = (
"Given a web search query, retrieve relevant passages that answer the query"
)
prefix = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n'
suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
prefix_tokens = self.tokenizer.encode(prefix, add_special_tokens=False)
suffix_tokens = self.tokenizer.encode(suffix, add_special_tokens=False)
# 构造符合格式的输入
def format_pair(query, doc):
return f"<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}"
pairs = [
format_pair(question, r["content"][:1000]) # 文本截断,避免超长
for r in search_results
]
# 使用 tokenizer 构建 batch 输入
# 分词 + 拼接前后缀 + padding
inputs = self.tokenizer(
batch_texts,
pairs,
padding="max_length",
truncation="longest_first",
return_attention_mask=True, # 确保返回 attention_mask
max_length=8192 - len(prefix_tokens) - len(suffix_tokens),
return_tensors="pt",
padding=True,
truncation=True,
max_length=1024, # Qwen3 的最大上下文长度,建议限制
).to(self.rerank_model.device)
)
# 推理打分(关闭梯度计算)
# 手动添加前后缀
batch_size = inputs["input_ids"].shape[0]
max_len = 8192
# 创建新的输入张量
new_input_ids = torch.full((batch_size, max_len), self.tokenizer.pad_token_id, dtype=torch.long)
new_attention_mask = torch.zeros((batch_size, max_len), dtype=torch.long)
for i in range(batch_size):
# 获取原始序列去除padding
original_ids = inputs["input_ids"][i]
original_mask = inputs["attention_mask"][i]
actual_length = original_mask.sum().item()
# 构建新序列prefix + original + suffix
new_sequence = (
prefix_tokens + original_ids[:actual_length].tolist() + suffix_tokens
)
new_length = len(new_sequence)
if new_length <= max_len:
new_input_ids[i, :new_length] = torch.tensor(new_sequence)
new_attention_mask[i, :new_length] = 1
inputs = {
"input_ids": new_input_ids.to(self.rerank_model.device),
"attention_mask": new_attention_mask.to(self.rerank_model.device),
}
# 获取 yes / no 的 token id初始化时保存也可
token_true_id = self.tokenizer.convert_tokens_to_ids("yes")
token_false_id = self.tokenizer.convert_tokens_to_ids("no")
# 推理评分
with torch.no_grad():
outputs = self.rerank_model(**inputs)
logits = outputs.logits.squeeze(-1)
logits = outputs.logits
# 如果是二分类模型,通常需要做 sigmoid 激活
scores = torch.sigmoid(logits).tolist()
# 检查 logits 的维度
if logits.dim() == 3:
# 如果是3维取最后一个token的logits
logits = logits[:, -1, :]
elif logits.dim() == 2:
# 如果是2维直接使用
pass
else:
raise ValueError(f"Unexpected logits dimension: {logits.dim()}")
# 写入到每个 search_result 中
# 提取 yes/no token 的 logits
true_logits = logits[:, token_true_id]
false_logits = logits[:, token_false_id]
stacked = torch.stack([false_logits, true_logits], dim=1)
probs = torch.nn.functional.softmax(stacked, dim=1)
scores = probs[:, 1].tolist() # 取 "yes" 的概率值
# 写入每条结果
for r, score in zip(search_results, scores):
r["rerank_score"] = max(0.0, min(score, 1.0)) # 保证分数在 0-1 范围
r["rerank_score"] = round(float(score), 4)
self.logger.info(f"重排序完成,得分范围: {min(scores)} - {max(scores)} \n\n {json.dumps(search_results, indent=4)}")
return search_results
async def _build_context_async(self, search_results: List[Dict[str, Any]]) -> str: