A few weeks ago, the LlamaIndex team released a demonstration for the analysis of 10-K (annual reports) and 10-Q (quarterly report) forms called secinsights.ai. 👇
In simple terms, it's about: Using ChatGPT seamlessly to answer your questions on 10K and 10-Q forms without teething problems.
A bit more formally: It's a production-ready RAG framework with LLMs on SEC forms with minimal hallucinations.
The entire framework is open sourced and, for me, currently represents the most compelling free-to-use end-to-end system architecture for Retrieval Augmented Generation.
If you were to take away just one thing from this example, it should be this:
There is no universal RAG application. A framework that promises to provide an information system for all types of data, a solution to all user questions, will only lead to disappointments. Everything, from the database to the indexing, the LLM, right up to the user UI, must be subordinate to the use case.
Once this insight has matured and the use case has been specified, as the example shows, it's possible to develop things that weren't feasible three years ago...
Here's the link to the app:
https://lnkd.in/esFuyqSm
#largelanguagemodels #chatgpt #gpt4 #llms #generativeai #rag #llamaindex
