Inspiration
The evolved ability of LLM, and also nowadays too much information to process as a human
What it does
It helps you to efficiently navigate through vast documents or stay updated on topics that matter to us without drowning in tuns of data.
How we built it
Retrieval Augmented Generation(RAG), Crawling, Data Aggregating, React as frontend, Flask as backend, Save Index for the vector data in Mongo DB
Challenges we ran into
The speed for the information aggregation and the processing time for LLM
Accomplishments that we're proud of
It really helps a lot of people to save time in terms of information gain
What we learned
In the future, no UI is the best UI, less info is more info
What's next for Info Beta
Tailor the model to satisfy the special need for each industry, like medical, finance, climate, etc.
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