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Retrieval-Augmented Generation (RAG) Pipeline

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In the age of large language models (LLMs), Retrieval-Augmented Generation (RAG) is emerging as a powerful pattern that blends the strengths of retrieval-based systems and generative AI. It solves a key limitation of LLMs — limited context and outdated knowledge — by dynamically bringing in relevant information during inference.

What is a RAG Pipeline?

RAG combines two components
  1. Retriever: Finds relevant documents or chunks from a knowledge base (e.g., PDFs, web pages, databases).
  2. Generator (LLM): Uses both the retrieved content and the user query to generate a contextual, grounded answer.

Why Use RAG?

  • Reduces hallucinations by grounding outputs in factual data.
  • Keeps answers fresh without retraining the model.
  • Supports domain-specific use cases (legal, healthcare, finance, etc.).

RAG Pipeline: Step-by-Step

Here’s a simplified breakdown:

1. Data Ingestion
  • Upload various sources like PDFs, HTML, CSVs, or even website URLs.
  • Convert these sources into plain text using tools like Apache Tika, PDF parsers, or OCR for scanned images.
  • Perform chunking (breaking down long texts into smaller, semantically meaningful units).
  • Optionally clean, filter, or tag the content before storage.
2. Embed and Index
  • Each chunk is converted into a vector (numerical form) using embedding models like Gemini Embedding, BERT, or OpenAI Embeddings.
  • Store these vectors in a vector database such as Vertex AI Vector Search, Pinecone, or FAISS.
  • Metadata like document title, page number, and tags are stored for filtering later.
3. Query Execution (User Prompt Flow)
  • The user enters a natural language query (e.g., “Summarize this contract”).
  • Steps triggered:
  1. The query is embedded using the same embedding model.
  2. A top-k similarity search is run against the vector database.
  3. Retrieved chunks are ranked (possibly filtered using metadata).
  4. These chunks, along with the original query, are passed as context to the LLM.
  5. The LLM generates a grounded, high-quality answer.
4. Generate Answer
  • The LLM outputs the response, often with inline citations or references to source documents.
  • Optionally, the system may include links or highlights from the source.

Use Cases

  • Enterprise chatbots
  • Document Q&A (legal contracts, manuals)
  • Contextual search engines
  • Internal knowledge assistants

Further reading

If you want to explore the implementation of the RAG pipeline then you can refer the below blogs from Langchain: