Build a RAG Pipeline from Scratch in Python: A Step-by-Step Guide
This article provides a step-by-step guide on how to build a Retrieval-Augmented Generation (RAG) pipeline in Python, which can turn any folder of documents into an AI assistant that provides accurate, grounded responses backed by the source data.
Why it matters
RAG is important for businesses and applications where accuracy and grounded responses are critical, such as legal, medical, or financial contexts.
Key Points
- 1Large language models can hallucinate and provide inaccurate information due to their pattern-completion nature
- 2RAG fixes this by retrieving relevant documents from a knowledge base and using them to augment the model's responses
- 3The three key components of a RAG system are document processing, vector embeddings, and the retrieval-generation loop
Details
The article explains that large language models like ChatGPT can confidently provide fabricated information when asked about topics not covered in their training data. This is because they are pattern-completion machines that try to generate plausible-sounding responses, even if they are factually incorrect. To address this, the article introduces Retrieval-Augmented Generation (RAG), which integrates a document retrieval system with the language model. The key components of a RAG pipeline are: 1) Document processing - splitting documents into manageable chunks, 2) Vector embeddings - converting text into numerical vectors to enable semantic search, and 3) The retrieval-generation loop - embedding the input query, finding the most relevant document chunks, and then using that context to generate an accurate response. By grounding the language model's outputs in the source data, RAG can provide responses that are backed by real information, rather than hallucinations.
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