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This tutorial video guides you through building a multimodal. Learn how to convert unstructured content like text files and pdfs into a vector index that ai agents or other retrievers can query. The unstructured package from unstructured.io extracts clean text from raw source documents like pdfs and word documents
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This page covers how to use the unstructured ecosystem within langchain. To assist us in building our example, we will use the langchain library In this tutorial, we’ll explore how to use docling with langchain’s wrapper to streamline pdf document extraction
For this example, we’re working with a sample pdf from docling and utilizing supabase as our vector store to manage embeddings and search.
Here's an example from langchain itself, this code works and you don't have to worry about dependencies, just run it on colab to extract tables and ingest into the vector store of your choice. Create a pdf/csv chatbot with rag using langchain and streamlit It extracts structured data (tables), unstructured text, and images, then uses vector similarity search combined with multimodal llms to answer questions with full context awareness. I’m currently developing a class that will convert the results to a langchain document objet for use downstream with a rag application for the creation of embeddings.
