Project - Building a RAG Chatbot from Your Website Data using OpenAI, Langchain and Vector Database

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Step 3: Converting Data to Documents

Now that we have cleaned the website text, it's time to prepare it for efficient use by our RAG chatbot. This involves converting the text into smaller document chunks.

Why Break Down the Text?

Imagine searching a library for a specific fact, but the entire library is just one massive, unwieldy book. Not exactly efficient, right? Text splitters in RAG chatbots tackle this challenge in a similar way.

Large chunks of text can be a burden for both information retrieval and processing in a RAG chatbot. Text splitters act like librarians, helping us:

  1. Find relevant information faster: Just like you wouldn't scan the entire library, text splitters avoid wading through oceans of text. It's faster to search over small documents than the large documents.
  2. Improve search accuracy: Smaller text chunks allow for more precise retrieval of documents. Text splitters can leverage keyword matching and semantic similarity (understanding the meaning behind words) to identify the most relevant sections for the user's question.
  3. Enhance model performance: Large Language Models (LLMs) have limitations. We can only process a specific amount of text at a time as they have a token limit. By breaking text into smaller chunks, we ensure that on including documents in the LLM prompt, we do not surpass the token limit. enter image description here

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