Finsight is a RAG (retrieval augmented generation) AI app that helps new investors understand financial markets through experiential learning. With market data streamed from Yahoo Finance, state-of-the-art LLMs (large language models) process that data and deliver high-quality, unbiased market briefings to users through a friendly user interface.
We were motivated by the difficulty of learning more about the stock and financial markets, as they are taking a bigger role in our day-to-day wealth management lives. So, we determined a need for an educational platform to help new investors understand what the financial market looks like daily and provide critical insights on investments. As a result, Finsense came to be. We spent the first few hours planning our idea, including developing our unique “RAG” pipeline from data sources like Yahoo Finance to LLMs to the user interface with LangChain wrappers. We also sketched our idea on a whiteboard while researching similar designs: Yahoo Finance, Robinhood, Motley Fools, etc.
- Top company stock listings dashboard for users to explore and add to their "favorites"
- In-depth RAG-generated stock briefings for day, month, quarter, and year
- User login for saving favorites
- Tutorial and definitions to help users learn about financial terms
- Deploy to a permanent cloud location and custom domain
- Experiment with different LLMs and Prompt Templates to improve response quality
- Streaming tokens for cleaner chat UI
Finsense was developed with the following technologies:
- Frontend: ReactJS
- API: FastAPI, HuggingFace LLM (gemma:2b, published on Feb 21, 2024), Ollama, LangChain
- Database: Firebase Firestore
- Auth: Firebase Auth
To run the project locally, you may clone the repository linked above. You must have Node version 18+ and pip/Python 3.11+ installed on your machine.
Setup
git clone https://github.com/charlestang06/finsightThen, install the dependencies.
cd web-app
npm install
cd ..
cd finance-rag-server
pip install -r requirements.txtRun the server. The server will be listening on port 8000.
cd finance-rag-server
uvicorn main:app --reload
cd ..Open a new command line and run the web-app.
cd web-app
npm startGo to localhost:3000 to view the project.
- Version 1.0 (03/30/2024)
- Initial Release
- See [Commits history]
MIT