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

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.

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LlamaIndex links:-

LlamaIndex: https://www.llamaindex.ai/
slides: https://docs.google.com/presentation/...
Colab notebook: https://colab.research.google.com/dri...

Chat with data links:-

Twitter:   / 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