🔍 Semantic Search & Recommendation Engine

This repository contains a comprehensive Semantic Search and Recommendation Engine built entirely within a single Jupyter Notebook. It integrates advanced techniques including embeddings, vector stores, and Retrieval-Augmented Generation (RAG) to dynamically retrieve relevant products and services.

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🚀 Demo

🚀 Key Features

• Semantic Search: Powered by fine-tuned Sentence-BERT embeddings for precise context-aware matching.
• Vector Database (Pinecone): Efficient storage and fast retrieval of embeddings for similarity search.
• Retrieval-Augmented Generation (RAG): Utilizes LangChain and ChatGroq (LLaMA3) to provide coherent and contextually accurate recommendations.
• Dynamic Query Handling: Includes metadata-based filtering (e.g., product or service, gender, category).
• Natural Language Processing (spaCy): Contextual extraction and filtering based on query content.

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📂 Project Structure

• Single Jupyter Notebook: End-to-end implementation covering data ingestion, preprocessing, embedding creation, semantic search, and RAG-powered recommendations.

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🛠️ Technologies Used

• Python
• Jupyter Notebook
• LangChain
• ChatGroq (LLaMA3)
• HuggingFace Embeddings (Sentence-BERT)
• Pinecone Vector Store
• spaCy

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🚦 How to Run

1. Setup Environment:

   pip install -r requirements.txt

2. Configure API Keys:

   • Create a .env file with your GROQ_API_KEY and PINECONE_KEY.

3. Run Jupyter Notebook:

   jupyter notebook

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📖 Example Queries

• "Show me men's blue jeans"
• "I need painting services"
• "Find women's jeans"
• "Recommend a product"
• "I need automotive services"

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🎯 Use Case

Ideal for e-commerce platforms, knowledge bases, and recommendation systems where accurate semantic retrieval and personalized recommendations are critical.

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🤝 Contributing

Feel free to fork, modify, and enhance this project. Contributions are always welcome!

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📜 License

Distributed under the MIT License.