## Inspiration

Large Language Models (LLMs) are limited by a token cap, making it difficult for them to process large contexts, such as entire codebases. We wanted to overcome this limitation and provide a solution that enables LLMs to handle extensive projects more efficiently.

## What it does

LLM Pro Max intelligently breaks a codebase into manageable chunks and feeds only the relevant information to the LLM, ensuring token efficiency and improved response accuracy. It also provides an interactive dependency graph that visualizes the relationships between different parts of the codebase, making it easier to understand complex dependencies.

## How we built it

Our landing page and chatbot interface were developed using React. We used Python and Pyvis to create an interactive visualization graph, while FastAPI powered the backend for dependency graph content. We've added third-party authentication using the GitHub Social Identity Provider on Auth0. We set up our project's backend using Convex and also added a Convex database to store the chats. We implemented Chroma for vector embeddings of GitHub codebases, leveraging advanced Retrieval-Augmented Generation (RAG) techniques, including query expansion and re-ranking. This enhanced the Cohere-powered chatbot’s ability to respond with high accuracy by focusing on relevant sections of the codebase.

## Challenges we ran into

We faced a learning curve with vector embedding codebases and applying new RAG techniques. Integrating all the components—especially since different team members worked on separate parts—posed a challenge when connecting everything at the end.

## Accomplishments that we're proud of

We successfully created a fully functional repo agent capable of retrieving and presenting highly relevant and accurate information from GitHub repositories. This feat was made possible through RAG techniques, surpassing the limits of current chatbots restricted by character context.

## What we learned

We deepened our understanding of vector embedding, enhanced our skills with RAG techniques, and gained valuable experience in team collaboration and merging diverse components into a cohesive product.

## What's next for LLM Pro Max

We aim to improve the user interface and refine the chatbot’s interactions, making the experience even smoother and more visually appealing. (Please Fund Us)
## Inspiration: We're trying to get involved in the AI chat-bot craze and pull together cool pieces of technology -> including Google Cloud for our backend, Microsoft Cognitive Services and Facebook Messenger API

## What it does: Have a look - message Black Box on Facebook and find out!

## How we built it: SO MUCH PYTHON

## Challenges we ran into: State machines (i.e. mapping out the whole user flow and making it as seamless as possible) and NLP training

## Accomplishments that we're proud of: Working NLP, Many API integrations including Eventful and Zapato

## What we learned

## What's next for BlackBox: Integration with google calendar - and movement towards a more general interactive calendar application. Its an assistant that will actively engage with you to try and get your tasks/events/other parts of your life managed. This has a lot of potential - but for the sake of the hackathon, we thought we'd try do it on a topic that's more fun (and of course, I'm sure quite a few us can benefit from it's advice :) )
## Inspiration

We we wanted to make learning and understanding each other more equitable and fair. Thus we decided to solve a problem that exists everywhere.

## What it does

## How we built it

React/Vite + Tailwind + Convex Frontend
Convex Backend
Convex Database
Convex API
Symphonic Labs Video to Text
Chroma RAG
Cohere Multilingual LLM

## Challenges we ran into

The main Challenge that we all ran into was fitting our experiences and intuition from unopinionated technologies onto convex, which was very opinionated. We struggled in learning and fitting our existing knowledge (JS, NodeJS), onto the opinionated format, which slowed us down significantly. We alos struggled to host the service on AWS and Azure.## Accomplishments that we're proud of

## Accomplishments

We were able to get a end-end user case working, from uploading the video, to getting the transcript with Symphonic and using the Cohere LLM chatbot.

## What we learned

We learned a ton about effective system design, full stack info

## What's next for Linguist

We hope to build for more users in the future, and add more features