Building a Self-Correcting RAG System with LangChain

Standard Retrieval-Augmented Generation (RAG) pipelines often suffer from hallucinations, irrelevant context retrieval, and low answer relevance. When the initial retrieval fails to fetch the correct data, the LLM is forced to guess or produce inaccurate outputs.

This article explores how to construct a robust, production-ready **Self-Correcting RAG** (often referred to as Corrective RAG or CRAG) pipeline using **LangChain**, featuring automated document grading, query rewriting, and web search fallback.

1. Architecture of Self-Correcting RAG

Unlike traditional RAG which follows a linear path (Retrieve -> Generate), a self-correcting architecture introduces inspection and decision loops using state graphs:

- **Retrieve:** Fetches documents from the vector database based on the user query. - **Retrieval Grader:** Evaluates whether retrieved documents are relevant to the query to filter out noise. - **Transform Query:** If documents are irrelevant, the query is rewritten for better retrieval, or a fallback web search is triggered. - **Hallucination Grader:** Checks if the generated answer is grounded in the retrieved facts. - **Answer Grader:** Assesses whether the generated response actually answers the user's question.

2. Implementation with LangGraph and LangChain

Setting up the state graph and evaluation nodes using LangChain and LangGraph.

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

# Data model for document relevance grading
class GradeDocuments(BaseModel):
    binary_score: str = Field(description="Documents are relevant to the question, 'yes' or 'no'")

llm = ChatOpenAI(model="gpt-4o", temperature=0)
structured_llm_grader = llm.with_structured_output(GradeDocuments)

system = """You are a grader assessing relevance of a retrieved document to a user question. 
If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. Give a binary score 'yes' or 'no'."""

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

retrieval_grader = grading_prompt | structured_llm_grader

Conclusion

By incorporating self-correcting mechanisms, developers can dramatically reduce hallucination rates and build resilient LLM applications capable of handling edge cases, messy data, and complex multi-step reasoning.