Baseball Wiki

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Inspiration

The inspiration for Baseball Wiki came from the desire to create an interactive, AI-powered platform where users could quickly get detailed information about baseball players and their performance.

What it does

The inspiration for Baseball Wiki came from the desire to create an interactive, AI-powered platform where users could quickly get detailed information about baseball players and their performance.

How we built it

We built the platform using Flask as the backend, we used google bigQuery , google translate integrated with LangChain and Google Cloud's Vertex AI for natural language processing. The frontend consists of HTML and Bootstrap to display player cards and an interactive chatbot modal where users can ask about players.

Challenges we ran into

One of the main challenges was managing API quota limits for Google Vertex AI, which restricted how frequently we could make requests. We also had to handle multi-language support seamlessly, translating between Spanish, Japanese, and English

Accomplishments that we're proud of

We successfully integrated the chatbot with Google Gemini and FAISS to answer player-specific questions, implemented multi-language support, and built a user-friendly interface that allows easy access to player data

What we learned

We learned how to integrate Google Cloud AI services, handle dynamic data fetching, and manage multi-language interactions. Additionally, we gained experience in integrating Flask, LangChain, and Google Vertex AI to build a scalable chatbot.

What's next for Baseball Wiki

Next, we plan to expand Baseball Wiki by adding features like real-time tool tips

Built With

• app-engine
• bigquery
• gemini
• google-storage
• langchain
• vertexai