三大策略深度优化RAG，打造智能问答新高度

一、优化被检索的知识内容

1、结构化处理：从无序到有序的转变

案例：将非结构化文本转化为表格

import pandas as pd

# 模拟医疗报告数据
data = {
'patient_id': [101, 102, 103],
'diagnosis': ['Diabetes', 'Hypertension', 'Coronary Artery Disease'],
'treatment': ['Insulin', 'Lisinopril', 'Aspirin'],
'doctor_notes': ['Patient responds well to treatment', 'Blood pressure needs monitoring', 'Recommend lifestyle changes']
}

# 转化为DataFrame
df = pd.DataFrame(data)
print(df)

结果：

 patient_id diagnosistreatmentdoctor_notes
0 101DiabetesInsulin Patient responds well to treatment
1 102 HypertensionLisinoprilBlood pressure needs monitoring
2 103Coronary Artery Disease Aspirin Recommend lifestyle changes

2、标准化处理：确保数据一致性

案例：标准化处理时间格式

from datetime import datetime

# 原始事件记录
event_data = ['12-08-2021', '08/12/2021', '2021.08.12']

# 标准化处理
standardized_dates = [datetime.strptime(date, '%d-%m-%Y').strftime('%Y-%m-%d') for date in event_data]
print(standardized_dates)

结果：

['2021-08-12', '2021-08-12', '2021-08-12']

3、聚焦处理：通过业务场景缩小检索范围

案例：聚焦处理在医疗领域的应用

# 假设我们有一系列文档，其中部分与糖尿病有关
documents = [
"This research discusses the effects of insulin on diabetes treatment.",
"This paper explores hypertension treatment methods.",
"An analysis on the causes of coronary artery disease."
]

# 聚焦处理，筛选出与糖尿病相关的文档
focused_docs = [doc for doc in documents if "diabetes" in doc.lower()]
print(focused_docs)

结果：

['This research discusses the effects of insulin on diabetes treatment.']

二、专注于生成（G）的能力提升

1、模型微调（Finetune）：增强领域适应性

案例：对BERT进行微调以应对医疗问答

from transformers import BertForQuestionAnswering, BertTokenizer, Trainer, TrainingArguments

# 加载预训练的BERT模型和tokenizer
model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# 假设我们有医疗领域的问答数据集
train_dataset = ...  # 数据加载代码省略

# 微调模型
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
sa ve_steps=10_000,
sa ve_total_limit=2,
)

trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)

trainer.train()

2、多轮对话：在上下文中提升答案质量

案例：基于对话的RAG问答系统

from transformers import Conversation, ConversationalPipeline

# 假设我们使用一个训练好的生成模型
pipeline = ConversationalPipeline(model=model)

# 创建对话
conversation = Conversation("What are the symptoms of diabetes?")
conversation.add_user_input("How is it diagnosed?")

# 模型生成答案
response = pipeline(conversation)
print(response)

3、Prompt Engineering：优化输入提示

案例：Prompt设计在生成问答中的应用

prompt = "Based on the research papers on diabetes treatment, explain the role of insulin and cite the relevant sources."
response = model.generate(prompt)
print(response)

三、专注于检索（R）的能力提升

1、选择合适的Embedding和Rank模型：精准语义匹配

案例：使用Sentence-BERT进行语义检索

from sentence_transformers import SentenceTransformer, util

# 加载Sentence-BERT模型
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# 知识库文档
documents = ["Insulin is important for diabetes treatment.", 
 "Hypertension is treated with Lisinopril.", 
 "Aspirin is used for coronary artery disease."]

# 用户问题
query = "What is used to treat diabetes?"

# 将文档和查询向量化
doc_embeddings = model.encode(documents)
query_embedding = model.encode(query)

# 计算相似度
similarities = util.cos_sim(query_embedding, doc_embeddings)
most_similar_doc = documents[similarities.argmax()]
print(most_similar_doc)

结果：

'Insulin is important for diabetes treatment.'

2、引入Rerank与精排机制：提高检索精度

案例：简单的Rerank机制

# 初步检索结果
documents = [
{"text": "Older study on insulin", "date": "2010", "citations": 50},
{"text": "Recent study on insulin", "date": "2022", "citations": 10},
]

# 基于业务规则重新排序
reranked_docs = sorted(documents, key=lambda x: x['date'], reverse=True)
print(reranked_docs)

结果：

[{'text': 'Recent study on insulin', 'date': '2022', 'citations': 10}, {'text': 'Older study on insulin', 'date': '2010', 'citations': 50}]

3、问题改写（Rewrite）：提升问题的表达清晰度

案例：问题改写提高检索精度

from transformers import pipeline

# 使用一个简单的问答改写模型
question_rewriter = pipeline("text2text-generation", model="t5-small")

# 用户问题
original_question = "How can I manage diabetes?"

# 改写问题
rewritten_question = question_rewriter(original_question)
print(rewritten_question)

4、自行判断问题可回答性：提升模型的准确性

案例：基于置信度的可回答性判断

# 假设我们通过生成模型计算置信度
def can_answer(query, docs):
    # 模拟模型返回的置信度
    confidence = model.predict_confidence(query, docs)
    return confidence > 0.5

# 判断是否能回答
if can_answer("What is diabetes?", documents):
    print("Generating answer...")
else:
    print("Unable to answer the question.")

四、总结

你是一名 AI 行业编辑，请围绕下面这条热点输出一份资讯解读：
热点：三大策略深度优化RAG，打造智能问答新高度要求：
1. 先用一句话解释这条热点在讲什么
2. 再总结它为什么重要
3. 说明会影响哪些 AI 产品或内容方向
4. 最后给出 3 个适合资讯站使用的标题