How to Analyze Tables In Large Financial Reports Using GPT-4 (w/Jerry Liu, LlamaIndex)

Video Description

In this video, you'll learn how to ask complex questions on tables embedded within large text using GPT. In this presentation we use Openai's latest `gpt-4-1106-preview` model. The challenge for performing simple RAG (retrieval augmented generation) is that text splitting may break up tables and similarity search is likely to fail. To overcome this problem, we store raw tables and text along with table summaries better suited for retrieval. The tool used to orchestrate and enable this solution is LlamaIndex: a data framework for LLM applications to ingest, structure, and access private or domain-specific data. During this presentation, Jerry Liu, Co-Founder of LlamaIndex, covers the use case of two "semi structured" data sources: 1) A large wikipedia page containing tables of billionaires 2) 200+ pages of Tesla's 10-k financial report and discusses the strategy with Mayo Oshin. ----------------------- LlamaIndex links:- LlamaIndex: https://www.llamaindex.ai/ slides: https://docs.google.com/presentation/d/1ug9jHtMFsGjNV7zp85hPUNjiiCGKz53wQb9mZh0B_ZI/edit?usp=sharing Colab notebook: https://colab.research.google.com/drive/1DldMhszgSI4KKI2UziNHHM4w8Cb5OxEL#scrollTo=Ht4oSN2PvzUJ Chat with data links:- Twitter: https://twitter.com/mayowaoshin Website: https://www.mayooshin.com/ Send a tip to support the channel: https://ko-fi.com/mayochatdata Timestamps: 01:30 Wikipedia article demo and architecture 05:19 Codebase walkthrough 19:45 Analysing Tesla 10-k filing 30:15 Question and answer session #openai #gpt #finance #investing