EnergyInsight: Chat with Renewable Energy Reports Using RAG

🌱 EnergyInsight is an AI-powered tool that allows you to interact with renewable energy documents like “Renewable Energy: Emerging Technologies and Innovations to Reduce Climate Change” by Robyn Lui (UNICEF). Using the power of Retrieval-Augmented Generation (RAG), you can ask questions and get accurate, context-based answers directly from the report.

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What Is EnergyInsight?

EnergyInsight leverages RAG to turn static documents into dynamic, interactive Q&A assistants. By combining retrieval (searching relevant text) with generation (producing human-like responses), it ensures:

• Accurate Answers: Every response is based on real data from the document.
• Time Efficiency: No need to manually sift through hundreds of pages.
• Ease of Use: Ideal for students, researchers, and professionals in renewable energy.

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Key Features

• 📖 Document Search: Seamlessly query insights from renewable energy reports.
• ⚡ RAG Workflow: Combines document retrieval with AI-powered response generation.
• 🔍 Fast & Accurate Retrieval: Uses embeddings and vector search for lightning-fast results.
• 🌿 Sustainability Focus: Specifically designed to assist in understanding renewable energy innovations.

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How It Works

1. Load the PDF: Extract text from the renewable energy report.
2. Split the Text: Break the document into manageable chunks.
3. Embed the Text: Convert chunks into vector representations for search.
4. Retrieve Relevant Chunks: Use FAISS to find the best matches for your query.
5. Generate an Answer: Craft an insightful response with OpenAI’s GPT model.

Diagram of the RAG Workflow

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Installation

Prerequisites

• Python 3.7+
• API Key for OpenAI (to use GPT models)

Setup Steps

1. Clone the Repository:

   git clone https://github.com/your-repo-link.git
   cd your-repo-link

2. Install Dependencies:

   pip install -r requirements.txt

3. Add Your OpenAI API Key:

   • Create a .env file in the project root and add:

     OPENAI_API_KEY=your_api_key_here
     

4. Run the Tool:

   python main.py

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Usage

Once you’ve set up the tool, you can ask questions like:

• “What renewable energy technologies does UNICEF highlight?”
• “How can renewable energy combat climate change?”

The tool retrieves relevant insights from the report and generates a precise answer.

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Demo Video

📺 Watch the detailed walkthrough on YouTube:
👉 How to Build EnergyInsight with RAG

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Folder Structure

📂 EnergyInsight
├── 📄 main.py          # Main application script
├── 📄 requirements.txt # Dependencies
├── 📂 data             # Contains the PDF document
├── 📂 embeddings       # Stores chunk embeddings
├── 📂 utils            # Utility scripts for preprocessing and retrieval
└── 📄 README.md        # This file

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Sample Code

Here’s a glimpse of how retrieval works:

query = "What are the emerging renewable energy technologies UNICEF highlights?"
retrieved_chunks = retrieve(query)
prompt = create_prompt(query, retrieved_chunks)
answer = generate_answer(prompt)
print("\nAnswer:\n", answer)

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Contributing

Contributions are welcome! If you have suggestions for improving the tool or adding features, feel free to open a pull request or issue.

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Start interacting with renewable energy insights now!

👉 GitHub Code Repository
📺 YouTube Walkthrough

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Let me know if you'd like further customizations! 😊