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Building A RAG Pipeline for Semi-structured Data with Langchain - Analytics Vidhya

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Retrieval Augmented Generation has been here for a while. Many tools and applications are being built around this concept, like vector stores, retrieval frameworks, and LLMs, making it convenient to work with custom documents, especially Semi-structured Data with Langchain. Working with long, dense texts has never been so easy and fun. The conventional RAG works well with unstructured text-heavy files like DOC, PDFs, etc. However, this approach does not sit well with semi-structured data, such as embedded tables in PDFs. While working with semi-structured data, there are usually two concerns. So, in this article, we will build a Retrieval generation pipeline for semi-structured data with Langchain to address these two concerns with semistructured data. This article was published as a part of the Data Science Blogathon. There are usually three types of data. Structured, Semi-structured, and Unstructured. RAG stands for Retrieval Augmented Generation. It is the simplest way to feed the L

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