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RAG高级优化:检索后处理模块成竹在胸 原创
通过上文的方法RAG高级优化:一文看尽query的转换之路,我们召回了一些相关片段,本文我们将介绍在将召回片段送入大模型之前的一些优化手段,它们能帮助大模型更好的理解上下文知识,给出最佳的回答:
- Long-text Reorder
- Contextual compression
- Refine
- Emotion Prompt
Long-text Reorder
根据论文 Lost in the Middle: How Language Models Use Long Contexts,的实验表明,大模型更容易记忆开头和结尾的文档,而对中间部分的文档记忆能力不强,因此可以根据召回的文档和query的相关性进行重排序。
核心的代码可以参考langchain的实现:
def _litm_reordering(documents: List[Document]) -> List[Document]:
"""Lost in the middle reorder: the less relevant documents will be at the
middle of the list and more relevant elements at beginning / end.
See: https://arxiv.org/abs//2307.03172"""
documents.reverse()
reordered_result = []
for i, value in enumerate(documents):
if i % 2 == 1:
reordered_result.append(value)
else:
reordered_result.insert(0, value)
return reordered_result
Contextual compression
本质上利用LLM去判断检索之后的文档和用户query的相关性,只返回相关度最高的k个。
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.get_relevant_documents(
"What did the president say about Ketanji Jackson Brown"
)
print(compressed_docs)
Refine
对最后大模型生成的回答进行进一步的改写,保证回答的准确性。主要涉及提示词工程,参考的提示词如下:
The original query is as follows: {query_str}
We have provided an existing answer: {existing_answer}
We have the opportunity to refine the existing answer (only if needed) with some more context below.
------------
{context_msg}
------------
Given the new context, refine the original answer to better answer the query. If the context isn't useful, return the original answer.
Refined Answer:
Emotion Prompt
同样是提示词工程的一部分,思路来源于微软的论文:
Large Language Models Understand and Can Be Enhanced by Emotional Stimuli
在论文中,微软研究员提出,在提示词中增加一些情绪情感相关的提示,有助于大模型输出高质量的回答。
参考提示词如下:
emotion_stimuli_dict = {
"ep01": "Write your answer and give me a confidence score between 0-1 for your answer. ",
"ep02": "This is very important to my career. ",
"ep03": "You'd better be sure.",
# add more from the paper here!!
}
# NOTE: ep06 is the combination of ep01, ep02, ep03
emotion_stimuli_dict["ep06"] = (
emotion_stimuli_dict["ep01"]
+ emotion_stimuli_dict["ep02"]
+ emotion_stimuli_dict["ep03"]
)
from llama_index.prompts import PromptTemplate
qa_tmpl_str = """\
Context information is below.
---------------------
{context_str}
---------------------
Given the context information and not prior knowledge, \
answer the query.
{emotion_str}
Query: {query_str}
Answer: \
"""
qa_tmpl = PromptTemplate(qa_tmpl_str)
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