📝 Introduction

"What if kids could have their very own AI-powered space professor, ready to answer all their astronomy questions, tell fascinating space stories, and even generate mini-podcasts about the cosmos?"

Professor Starstuff, an AI chatbot
that makes astronomy fun and interactive for kids! It combines:

🌟 Natural Language Processing (NLP) to understand kids' questions.
🌟 A vector-based knowledge system for retrieving accurate space facts.
🌟 NASA's Image API to provide real space images and enhance learning.
🌟 Podcast-style responses that turn complex topics into engaging storytelling.

With Professor Starstuff, kids don’t just learn about space - they explore it in a whole new way! 🚀🌠

📊 Dataset

Where’s the Data From?

The knowledge is built from provided YouTube video transcripts.

How is the Data Retrieved?

Using the youtube_transcript_api library:

📌 Fetch video IDs & titles
📌 Extract transcripts from ~8 hours of content

Chunking for Better Searchability:

📌 Text is split using RecursiveCharacterTextSplitter
📌 Chunk size: 500 tokens
📌 Overlap: 100 tokens (for better context retention)

Embedding & Storage:

📌 Chunks are embedded and stored in ChromaDB
📌 Metadata (e.g., video title) for improved retrieval

💻 Technical Summary

Architecture Overview

Professor Starstuff is powered by a LangGraph agent, using:

🛠️ GPT-4 used for decision-making (tool usage).
⚡ GPT-3.5-turbo for faster response time.
🗂️ ChromaDB as a vector store for context-aware retrieval.
📡 NASA Image API for real-time space images.
🔊 OpenAI TTS for converting answers into spoken podcasts.

How It Works Together

⁉️ User Input: The agent first determines if the query is about astronomy or general conversation.
❓ Decision Phase (GPT-4):

☑️ Determines if the query is astronomical or general conversation.
☑️ If general, it takes a shortcut for a quick response.

❓ Retrieval Phase (GPT-3.5 Turbo):

☑️ ChromaDB (retrieves relevant space facts).
☑️ NASA Image API (fetches related images).
☑️ OpenAI TTS (generates a podcast-style response).

‼️ Final Answer: The chatbot returns a text response, related images, and a topic teaser podcast based on the user’s query.

Deployment & Infrastructure

🟢 Django (Backend Framework):
- Handles chatbot logic, API communication, and serves as the main application.
🔵 SQLite (ChromaDB, Embedded in Django):
- Local vector database stored within Django, used for retrieval-augmented generation (RAG) without requiring an external database server.
🔴 Redis (Cloud Memory Storage):
- A fast, cloud-based key-value store, used to maintain conversation memory across user interactions, ensuring context persistence.
🟣 Heroku (PaaS Hosting Platform):
- Deploys and runs the full Django + ChromaDB backend, with Redis connected as a cloud service for optimized performance.