大模型系列——解读RAG#

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graph LR
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    A[用户查询] --> B[查询向量化]
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    B --> C[向量检索]
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    C --> D[检索相关文档]
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    D --> E[上下文整合]
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    E --> F[LLM 生成回答]

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# 智能客服 RAG 系统
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class CustomerServiceRAG:
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    def __init__(self):
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        self.product_knowledge = self.load_product_docs()
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        self.faq_knowledge = self.load_faq_docs()
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        self.chat_history = []
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    def answer_customer_query(self, query):
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        # 1. 检索相关产品信息
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        product_info = self.retrieve_from_kb(
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            query,
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            self.product_knowledge
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        )
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        # 2. 检索相关 FAQ
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        faq_info = self.retrieve_from_kb(
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            query,
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            self.faq_knowledge
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        )
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        # 3. 获取对话上下文
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        context = self.get_conversation_context()
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        # 4. 整合信息生成回答
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        response = self.generate_response(
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            query,
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            product_info,
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            faq_info,
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            context
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        )
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        # 5. 更新对话历史
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        self.chat_history.append((query, response))
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        return response

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class EducationRAG:
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    def __init__(self, course_materials):
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        self.knowledge_base = self.build_course_kb(course_materials)
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        self.student_profiles = {}
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    def provide_personalized_help(self, student_id, question):
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        # 获取学生信息和学习进度
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        student_info = self.student_profiles[student_id]
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        # 检索相关知识点
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        relevant_concepts = self.retrieve_concepts(
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            question,
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            student_info.current_topics
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        )
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        # 检索相关例题
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        similar_problems = self.retrieve_problems(
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            question,
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            student_info.learning_history
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        )
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        # 生成个性化解答
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        answer = self.generate_explanation(
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            question,
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            relevant_concepts,
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            similar_problems,
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            student_info.learning_level
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        )
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        return answer

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from langchain.retrievers import BM25Retriever
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from langchain.retrievers import EnsembleRetriever
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# BM25 关键词检索器
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bm25_retriever = BM25Retriever.from_documents(chunks)
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bm25_retriever.k = 5
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# 向量检索器
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vector_retriever = vector_store.as_retriever(
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    search_kwargs={"k": 5}
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)
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# 集成检索器（混合检索）
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ensemble_retriever = EnsembleRetriever(
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    retrievers=[bm25_retriever, vector_retriever],
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    weights=[0.3, 0.7]  # BM25 权重 0.3，向量检索权重 0.7
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)

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from PIL import Image
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import base64
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def encode_image(image_path):
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    with open(image_path, "rb") as image_file:
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        return base64.b64encode(image_file.read()).decode('utf-8')
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# 多模态 RAG 查询
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query_with_image = {
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    "text": "描述这张图片中的内容",
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    "image": encode_image("example.jpg")
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}
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# 使用支持多模态的模型
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response = multimodal_rag_system.query(query_with_image)

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class RealTimeRAG:
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    def __init__(self):
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        self.knowledge_base = KnowledgeBase()
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        self.update_scheduler = UpdateScheduler()
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    def start_real_time_updates(self, data_sources):
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        # 设置数据源监控
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        for source in data_sources:
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            self.update_scheduler.watch(source)
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    def on_data_change(self, source, new_data):
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        # 实时更新知识库
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        self.knowledge_base.update(
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            source=source,
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            data=new_data
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        )
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        # 重建索引
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        self.knowledge_base.rebuild_index()

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class PersonalizedRAG:
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    def __init__(self):
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        self.user_profiles = {}
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        self.personalization_models = {}
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    def get_personalized_answer(self, user_id, query):
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        # 获取用户画像
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        profile = self.user_profiles[user_id]
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        # 应用个性化检索策略
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        personalization = self.personalization_models[user_id]
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        # 检索相关内容
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        relevant_docs = self.retrieve_with_personalization(
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            query,
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            profile,
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            personalization
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        )
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        # 生成个性化回答
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        answer = self.generate_personalized_response(
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            query,
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            relevant_docs,
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            profile.preferences
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        )
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        return answer