01 Mar LangChain with RAG – Workflow

We saw various use cases of LangChain and its components. Let us see how LangChain can be used with RAG. The following is a quick workflow displaying how LangChain can be used to build a RAG system:

1. Document Loaders → Load PDFs into Document objects.
2. Text Splitters → Split documents into chunks.
3. Embedding Models → Generate embeddings for the chunks.
4. Indexes → Store embeddings in a vector store (FAISS).
5. Chains → Combine the retriever and LLM into a QA pipeline.
6. Prompts → Pass the user’s question to the QA chain.
7. Memory → Use the vector store to retrieve relevant chunks.

We will see the complete code with snippets and explanations in the next lessons.

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