Explore a comprehensive workshop on leveraging GPT-4 Vision and LangChain to analyze multimodal data in financial reports. Learn how to implement a novel Retrieval-Augmented Generation (RAG) strategy for processing documents containing diverse data types, including images, text, and tables. Discover techniques for creating and embedding summaries, utilizing vectorstores, and synthesizing answers with multimodal language models. Gain insights into evaluating results with LangSmith, addressing challenges in multimodal RAG, and applying these concepts to real-world financial analysis scenarios. Access accompanying resources, including slides and a Colab notebook, to enhance your understanding and practical application of the presented techniques.
Using LangChain with Multimodal AI to Analyze Images in Financial Reports
Overview
Syllabus
Demo of financial report
Multimodal architecture
Codebase walkthrough
Evaluating results using LangSmith
Showcasing results
Multimodal RAG problems and solutions
Taught by
Chat with data
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