颠覆传统RAG！Corrective-RAG引入自我反思与自我评估，让文档检索更智能更精准！

from langchain.schema import Document

def retrieve(state):    """    Retrieve documents
    Args:        state (dict): The current graph state
    Returns:        state (dict): New key added to state, documents, that contains retrieved documents    """    print("---RETRIEVE---")    question = state["question"]
    # Retrieval    documents = retriever.get_relevant_documents(question)    return {"documents": documents, "question": question}

def generate(state):    """    Generate answer
    Args:        state (dict): The current graph state
    Returns:        state (dict): New key added to state, generation, that contains LLM generation    """    print("---GENERATE---")    question = state["question"]    documents = state["documents"]
    # RAG generation    generation = rag_chain.invoke({"context": documents, "question": question})    return {"documents": documents, "question": question, "generation": generation}

def grade_documents(state):    """    Determines whether the retrieved documents are relevant to the question.
    Args:        state (dict): The current graph state
    Returns:        state (dict): Updates documents key with only filtered relevant documents    """
    print("---CHECK DOCUMENT RELEVANCE TO QUESTION---")    question = state["question"]    documents = state["documents"]
    # Score each doc    filtered_docs = []    web_search = "No"    for d in documents:        score = retrieval_grader.invoke(            {"question": question, "document": d.page_content}        )        grade = score.binary_score        if grade == "yes":            print("---GRADE: DOCUMENT RELEVANT---")            filtered_docs.append(d)        else:            print("---GRADE: DOCUMENT NOT RELEVANT---")            web_search = "Yes"            continue    return {"documents": filtered_docs, "question": question, "web_search": web_search}

def transform_query(state):    """    Transform the query to produce a better question.
    Args:        state (dict): The current graph state
    Returns:        state (dict): Updates question key with a re-phrased question    """
    print("---TRANSFORM QUERY---")    question = state["question"]    documents = state["documents"]
    # Re-write question    better_question = question_rewriter.invoke({"question": question})    return {"documents": documents, "question": better_question}

def web_search(state):    """    Web search based on the re-phrased question.
    Args:        state (dict): The current graph state
    Returns:        state (dict): Updates documents key with appended web results    """
    print("---WEB SEARCH---")    question = state["question"]    documents = state["documents"]
    # Web search    docs = web_search_tool.invoke({"query": question})    web_results = "\n".join([d["content"] for d in docs])    web_results = Document(page_content=web_results)    documents.append(web_results)
    return {"documents": documents, "question": question}

### Edges

def decide_to_generate(state):    """    Determines whether to generate an answer, or re-generate a question.
    Args:        state (dict): The current graph state
    Returns:        str: Binary decision for next node to call    """
    print("---ASSESS GRADED DOCUMENTS---")    state["question"]    web_search = state["web_search"]    state["documents"]
    if web_search == "Yes":        # All documents have been filtered check_relevance        # We will re-generate a new query        print(            "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---"        )        return "transform_query"    else:        # We have relevant documents, so generate answer        print("---DECISION: GENERATE---")        return "generate"