Building a Production-Grade RAG System with LangChain

Large Language Models (LLMs) are powerful, but their knowledge is frozen in time. Retrieval-Augmented Generation (RAG) allows us to ground these models in our own private data. This case study explores how we built a technical support bot for a complex SaaS product using LangChain.

The Challenge: Contextual Accuracy

Our support team was overwhelmed by questions about obscure technical configurations.

• The Data: 5,000+ pages of documentation across PDFs, Markdown, and Notion.
• The Problem: Generic LLMs often hallucinated settings or provided outdated advice.

The Solution: The LangChain RAG Pipeline

We implemented a robust pipeline using LangChain’s modular architecture.

1. Ingestion & Chunking

Using PyPDFLoader and RecursiveCharacterTextSplitter, we processed our documents into overlapping 1000-character chunks to preserve context.

2. Vector Storage

We chose ChromaDB with OpenAI Embeddings for our vector store, allowing for high-speed semantic search.

3. The Retrieval Chain

We used the create_retrieval_chain to combine the retriever with a ChatPromptTemplate that strictly instructs the model to only use the provided context.

Implementation Example (TypeScript)

import { ChatOpenAI } from "@langchain/openai";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { ChatPromptTemplate } from "@langchain/core/prompts";

const model = new ChatOpenAI({ modelName: "gpt-4o" });

const systemPrompt = `
  You are an expert support assistant. 
  Use the following pieces of retrieved context to answer the user's question.
  If you don't know the answer, say that you don't know.
  
  Context: {context}
`;

const prompt = ChatPromptTemplate.fromMessages([
  ["system", systemPrompt],
  ["human", "{input}"],
]);

const combineDocsChain = await createStuffDocumentsChain({
  llm: model,
  prompt,
});

const retrievalChain = await createRetrievalChain({
  combineDocsChain,
  retriever: vectorStore.asRetriever(),
});

const response = await retrievalChain.invoke({
  input: "How do I configure the API timeout?",
});

Results & Impact

Conclusion

LangChain provided the “glue” that made this complex pipeline easy to manage. By separating the retrieval logic from the generation logic, we built a system that is both accurate and scalable.