Pokémon RAG System

This project is a Retrieval-Augmented Generation (RAG) system that allows users to retrieve Pokémon information using natural language queries. It combines FastAPI, Pinecone, and an LLM (Llama-3) to intelligently fetch Pokémon data.

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📌 Features

• Fetches Pokémon data from PokeAPI and stores it in a CSV file.
• Embeds Pokémon data into Pinecone for vector search.
• Uses an LLM (Llama-3-70B) to enhance responses.
• Provides a FastAPI server for queries.
• Includes a chat-style frontend for easy interaction.

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⚡ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/KushPatel-18/Pokemon-RAG.git
cd pokemon-rag

2️⃣ Create a Virtual Environment

Ensure you're working in a virtual environment to keep dependencies isolated.

python -m venv venv
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate    # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

If you don't have a requirements.txt file, install the dependencies manually:

pip install fastapi uvicorn pinecone-client pandas python-dotenv transformers torch together tqdm requests

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🔑 Setting Up API Keys (.env File Required!)

You must create a .env file in the root directory with the following keys:

PINECONE_API_KEY=your_pinecone_api_key_here
TOGETHER_API_KEY=your_together_ai_api_key_here

📌 How to Get API Keys?

• Pinecone API Key → Sign up at Pinecone and create an index.
• Together AI API Key → Sign up at Together AI and get a free key.

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📥 4️⃣ Download Pokémon Data

Before running the application, you need to fetch Pokémon data and store it in a CSV file.

python data_downloader.py

This will create a pokemon_data.csv file containing all the Pokémon details.

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📤 5️⃣ Upload Pokémon Data to Pinecone

Once the data is downloaded, you need to upload it to Pinecone for vector search.

python data_utils.py

✅ This will process the CSV and store embeddings in Pinecone.

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🚀 6️⃣ Start the FastAPI Server

To enable searching via API, run the FastAPI server:

uvicorn app:app --reload

The server will start at http://127.0.0.1:8000

🛠 Test API Locally

Open a browser or use curl to test the API:

http://127.0.0.1:8000/search/?query=What is the height of Charizard?

Expected JSON response:

{
    "results": "The height is 17."
}

Example backend server screenshot

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💻 7️⃣ Run the Web Interface

To test the chatbot frontend:

1. Open web.html in a browser.
2. Ensure the backend URL in the JavaScript points to your FastAPI server:

fetch(`http://127.0.0.1:8000/search/?query=${encodeURIComponent(userText)}`)

3. Ask a question! The chatbot will return Pokémon-related responses.

Example frontend screenshot

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🎯 Summary of Commands

Step	Command
Install dependencies	pip install -r requirements.txt
Fetch Pokémon data	python data_downloader.py
Upload data to Pinecone	python data_utils.py
Start FastAPI server	uvicorn app:app --reload
Test API	http://127.0.0.1:8000/search/?query=Pikachu
Run frontend	Open web.html in a browser

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🌎 Deployment Options

1️⃣ Render (Free & Easiest)

1. Push your code to GitHub:

   git add .
   git commit -m "Initial commit"
   git push origin main

2. Go to Render.com → Click New Web Service.

3. Select your repo and set:

   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn app:app --host 0.0.0.0 --port 8000

4. Add environment variables (PINECONE_API_KEY, TOGETHER_API_KEY).

5. Click Deploy!

Your API will be available at:

https://{your-app-name}.onrender.com/search/?query=Charizard

2️⃣ Fly.io (More Control)

fly launch
fly deploy

3️⃣ Google Cloud Run (Auto-Scaling)

gcloud run deploy pokemon-rag --source .

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📌 Troubleshooting

❌ Error: ModuleNotFoundError: No module named 'fastapi'
✅ Run: pip install fastapi uvicorn

❌ TypeError: Pinecone.create_index() missing 'spec'
✅ Update data_utils.py:

from pinecone import ServerlessSpec
pinecone.create_index(name, spec=ServerlessSpec(cloud="aws", region="us-east-1"))

❌ Frontend Not Connecting?
✅ Make sure web.html is pointing to the correct backend URL.