Self-RAG实战：自我反思提升内容生成质量

从这段伪代码可以很清晰地看到，四大类标记是如何一关一关地介入检索与生成的。它不改变模型本身，而是在模型输出前后加了一道“自我检查”的工序。

Self-RAG的代码实现

下面进入工程环节。在参考Self-RAG原始思路的基础上，我们做了一个“轻量级”的实现——并没有完全照搬论文里的全部设计。比如，我们默认所有情况下都需要检索，因此没有实现检索需求评分器；实用性评分也没有输出1~5的分数，而是简单输出了yes/no。大家如果感兴趣，完全可以自行扩展，写出更复杂的评分器。

检索相关性评分器

第一个评分器的作用是：判断检索回来的文档跟用户问题是否相关。目标是“抓大放小”，不用太严格，关键在于过滤掉那些明显检索错误的垃圾信息。

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI

# Data model
class GradeDocuments(BaseModel):
    """Binary score for relevance check on retrieved documents."""
    binary_score: str = Field(
        description="Documents are relevant to the question, 'yes' or 'no'"
    )

# LLM with function call
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm_grader = llm.with_structured_output(GradeDocuments)

# Prompt
system = """You are a grader assessing relevance of a retrieved document to a user question. \n 
It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n
If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""

grade_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
    ]
)

retrieval_grader = grade_prompt | structured_llm_grader
question = "agent memory"
docs = retriever.get_relevant_documents(question)
doc_txt = docs[1].page_content
print(retrieval_grader.invoke({"question": question, "document": doc_txt}))

生成相关性评分器

第二个评分器主要负责“查幻觉”——看模型生成的内容，是不是真的有被检索到的事实做支撑。如果生成的内容跟检索回来的事实对不上，那就需要标记出来，不能让它蒙混过关。

### Hallucination Grader

# Data model
class GradeHallucinations(BaseModel):
    """Binary score for hallucination present in generation answer."""
    binary_score: str = Field(
        description="Answer is grounded in the facts, 'yes' or 'no'"
    )

# LLM with function call
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm_grader = llm.with_structured_output(GradeHallucinations)

# Prompt
system = """You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \n 
 Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts."""

hallucination_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "Set of facts: \n\n {documents} \n\n LLM generation: {generation}"),
    ]
)

hallucination_grader = hallucination_prompt | structured_llm_grader
hallucination_grader.invoke({"documents": docs, "generation": generation})

回答质量评分器

最后一个评分器负责“终审”——看最终的回答到底有没有解决用户的问题。这个评分器输出的结果，直接决定了这个答案是否可以对外输出。

### Answer Grader

# Data model
class GradeAnswer(BaseModel):
    """Binary score to assess answer addresses question."""
    binary_score: str = Field(
        description="Answer addresses the question, 'yes' or 'no'"
    )

# LLM with function call
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm_grader = llm.with_structured_output(GradeAnswer)

# Prompt
system = """You are a grader assessing whether an answer addresses / resolves a question \n 
 Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question."""

answer_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "User question: \n\n {question} \n\n LLM generation: {generation}"),
    ]
)

answer_grader = answer_prompt | structured_llm_grader
answer_grader.invoke({"question": question, "generation": generation})

篇幅有限，这里只展示了最关键的三个评分器的核心代码。其余的实现逻辑，比如如何串联这些评分器、如何处理排序和重试，这里就不一一贴出了。

总结

通过这篇文章，我们完整走通了一个Self-RAG的轻量级Demo。核心就是四个评分器——检索需求评分器（本文中未实现）、检索相关性评分器、生成相关性评分器和回答质量评分器。它们共同构成了一个“生成-反思-再生成”的闭环，让RAG的输出不再是“黑盒”式的单次结果，而是经过自我审视的可靠内容。

到这一步，一个相对完整的RAG系统其实已经搭建完毕。但放眼整个RAG生态，还有两座大山注定绕不过去：一是将RAG与知识图谱结合，系统性提升推理和总结能力；二是让RAG与Agent融合，真正嵌入到实际业务流程里去。这些，也都是下一阶段值得深耕的方向。

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