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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Read More:

Agents Component of LangChain
LangChain with RAG - Process
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