91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
# rag/pipeline.py
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import asyncio
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import os
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from ..config import LLAMACPP_RERANKER_URL, LLAMACPP_API_KEY
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from typing import List
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from langchain_core.documents import Document
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from langchain_core.language_models import BaseLanguageModel
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from .reranker import LLaMaCPPReranker
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from .query_transform import MultiQueryGenerator
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from .fusion import reciprocal_rank_fusion
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class RAGPipeline:
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"""
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固定流程的 RAG 检索流水线：
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多路改写 → 并行检索 → RRF融合 → 重排序 → 返回父文档
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"""
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def __init__(
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self,
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retriever, # 基础检索器（应返回父文档，例如 ParentDocumentRetriever 实例）
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llm: BaseLanguageModel,
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num_queries: int = 3,
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rerank_top_n: int = 5,
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):
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"""
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Args:
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retriever: 基础检索器对象，需实现 ainvoke(query) 异步方法
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llm: 用于生成多路查询的语言模型
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num_queries: 生成的查询变体数量
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rerank_top_n: 最终返回的文档数量
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rerank_model: 重排序模型名称
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"""
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self.retriever = retriever
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self.llm = llm
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self.num_queries = num_queries
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self.rerank_top_n = rerank_top_n
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# 初始化组件
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self.query_generator = MultiQueryGenerator(llm=llm, num_queries=num_queries)
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self.reranker = LLaMaCPPReranker(
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base_url=LLAMACPP_RERANKER_URL,
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api_key=LLAMACPP_API_KEY,
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top_n=rerank_top_n,
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)
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async def aretrieve(self, query: str) -> List[Document]:
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"""
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异步执行完整检索流程
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"""
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# Step 1: 生成多路查询
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queries = await self.query_generator.agenerate(query)
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# 包含原始查询，确保至少有一条
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if query not in queries:
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queries.insert(0, query)
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else:
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# 如果原始查询已在列表中，将其移至首位
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queries.remove(query)
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queries.insert(0, query)
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# Step 2: 并行检索（每个查询获取文档列表）
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tasks = [self.retriever.ainvoke(q) for q in queries]
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doc_lists = await asyncio.gather(*tasks)
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# Step 3: RRF 融合
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fused_docs = reciprocal_rank_fusion(doc_lists)
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# Step 4: 重排序
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try:
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final_docs = self.reranker.compress_documents(fused_docs, query)
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except Exception:
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# 若重排序器不可用，直接返回融合后的前 N 条
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final_docs = fused_docs[:self.rerank_top_n]
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return final_docs
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def retrieve(self, query: str) -> List[Document]:
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"""同步检索入口（内部调用异步方法）"""
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return asyncio.run(self.aretrieve(query))
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def format_context(self, documents: List[Document]) -> str:
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"""将文档列表格式化为上下文字符串"""
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if not documents:
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return ""
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parts = []
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for i, doc in enumerate(documents, 1):
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source = doc.metadata.get("source", "未知来源")
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parts.append(f"【资料 {i}】来源：{source}\n{doc.page_content}\n---\n")
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return "\n".join(parts) |