COLAB_GUIDE.md

🎓 Study Ocean RAG — Google Colab Guide

Your laptop stays cool. Colab's T4 GPU does all the heavy lifting.

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📋 One-Time Setup (First Time Only)

1. Upload ZIP to Google Drive

1. Go to drive.google.com
2. Create a folder: MyDrive/StudyOcean/
3. Upload StudyOceanPDFs.zip into that folder
4. ⏳ Wait for upload to complete (6 GB — may take 15–30 min on slow wifi)

2. Get a Gemini API Key (Free)

1. Go to aistudio.google.com/apikey
2. Click Create API Key
3. Copy the key — you'll paste it into the notebook

3. Open the Notebook in Colab

1. Go to colab.research.google.com
2. Click File → Upload notebook
3. Upload StudyOcean_RAG_Colab.ipynb from this folder

4. Set T4 GPU Runtime

This is critical — without it, ingestion will be very slow!

1. In Colab: Runtime → Change runtime type
2. Set Hardware accelerator = T4 GPU
3. Click Save

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🚀 Running the Pipeline

First Time (Fresh Ingestion)

Run cells in order: Steps 1 → 2 → 3 → 4 → 5 → 6 → 7 → 8 → 9 → 10

Step	What it does	Time
1	Verify T4 GPU + mount Drive	30s
2	Install packages	2–3 min
3	Configure settings (paste API key here!)	instant
4	Extract PDFs from ZIP	5–10 min
5	Ingest PDFs & build vector store	30–60 min
6	Save vector store to Drive	2–5 min
7	Test retrieval	instant
8	Build RAG chain	instant
9	Test full pipeline	5s
10	Launch Gradio chat UI	instant

Future Sessions (Vector Store Already Built)

Run cells: Steps 1 → 2 → 3 → 11 → 8 → 10

Step	What it does	Time
11	Load saved vector store from Drive	1–2 min
8	Build RAG chain	instant
10	Launch Gradio chat UI	instant

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💻 Running Locally (After Ingestion)

Once the vector store is built, you can run the chat locally:

Option A — Download via Colab (Step 12)

• Run Step 12 in the notebook
• It downloads vector_store_export.zip to your computer
• Extract it to: Study_Ocean Rag/data/vector_store/

Option B — Download from Google Drive

• Go to Drive → StudyOcean/vector_store/
• Download the folder
• Place it at: Study_Ocean Rag/data/vector_store/

Then run locally:

# Make sure .env has your GOOGLE_API_KEY
python main.py chat    # Web UI at http://localhost:7860
# OR
python main.py ask     # Terminal Q&A

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⚡ Performance on T4 GPU

Task	CPU (your laptop)	T4 GPU (Colab)
Embedding batch size	64	256
~10,000 chunks	~45 min	~8 min
~50,000 chunks	~4 hours	~35 min
~100,000 chunks	~8 hours	~70 min

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❓ Troubleshooting

"ZIP not found" error → Update ZIP_DRIVE_PATH in Step 3 to match your actual Drive path

"CUDA not available"
→ You forgot to set T4 GPU runtime! Runtime → Change runtime type → T4 GPU

Colab session disconnects during ingestion
→ The vector store saves in batches. Re-run from Step 5 — FAISS will rebuild from scratch. Save to Drive often.

"Invalid API key"
→ Make sure you pasted the full Gemini API key in Step 3

Gradio link doesn't work
→ Gradio public links expire after 72 hours. Re-run Step 10 to get a new link.