🔍 AI-Powered Search Engine

Welcome to the AI-Powered Search Engine, an intelligent and dynamic search solution combining advanced hybrid Search [Dense + sparse & Rerankers] Visual Search, and LLM-powered Recommendations. Designed to revolutionize product discovery in e-commerce and knowledge bases, this system helps users effortlessly find the most relevant products or services.

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Here's how our AI-powered search engine looks in action:

🚀 Features

1. Semantic Search

• Sentence Transformers: Efficient semantic embeddings for contextual matching.
• FAISS Vector Store: Fast similarity search powered by cosine similarity.

3. Visual Search

• Image Similarity: Quickly finds visually similar items using image embeddings and efficient nearest-neighbor retrieval.

4. Hybrid Search

• Combines Dense (Semantic) and Sparse (Keyword) retrieval methods for comprehensive results.

5. Cross-Encoder Re-ranking

• Improves search quality by re-ranking initial search results based on query relevance.

6. LLM-Enhanced Recommendations

• Uses Large Language Models (LLMs) like LLaMA for generating personalized and context-aware recommendations and summaries.

7. Retrieval-Augmented Generation (RAG)

• Integrates retrieval methods with generative models to provide contextually accurate, informative, and coherent responses based on retrieved knowledge.
• Utilizes LangChain and Pinecone for efficient context retrieval and dynamic query handling.

8. User-Friendly Interface

• Built with Streamlit, ensuring a smooth, interactive, and intuitive user experience.

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🛠️ Tech Stack

• Frontend: Streamlit
• Backend: FastAPI
• Semantic Models: Sentence-BERT, CrossEncoder
• Vector DB: FAISS
• Search Engine: Elasticsearch
• LLM Integration: LangChain, LLaMA, LangGraph
• Model Tracking: MLflow
• Monitoring & Logging: Kibana

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🎯 How It Works

1. Text/Visual Query: User inputs a query or uploads an image.
2. Search & Retrieve: AI-powered retrieval engines fetch relevant results.
3. Re-ranking: Cross-Encoder re-ranks based on detailed relevance.
4. Contextual Generation (RAG): Context retrieved using dense and sparse methods is fed into LLM to generate comprehensive answers and recommendations.

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🧪 Model Management (MLOps)

• MLflow Integration: Tracks experiments, logs models, and manages deployments.
• Automated Model Promotion: Streamlined workflows for staging and production.
• Continuous Integration & Delivery (CI/CD): Automates testing, deployment, and monitoring, ensuring reliable and efficient updates.
• Versioning & Governance: Clear version control and model governance strategies for reproducibility and compliance.
• Monitoring & Alerting: Real-time tracking of model performance and usage with automated alerts for anomalies or performance degradation.[yet to update]

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🚀 High Level Architecture

Here's how our AI-powered search engine looks in action:

🚦 Getting Started

Installation

Clone this repository:

git clone https://github.com/yourusername/ai-search-engine.git
cd ai-search-engine
pip install -r requirements.txt

Configuration

Create your config.yaml file based on config-example.yaml provided.

Run Application

Start Streamlit app:

streamlit run streamlit/app.py

Start API:

uvicorn api.hybrid:app --reload

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📚 Documentation & Support

• Check out Docs for detailed architecture, deployment instructions, and API guides.
• For support, raise an issue or submit a pull request.

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🤝 Contributing

We welcome community contributions:

• Fork this repo
• Create your feature branch (git checkout -b feature/my-feature)
• Commit your changes (git commit -m 'Add some feature')
• Push to the branch (git push origin feature/my-feature)
• Open a Pull Request

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📜 License

Distributed under the MIT License. See LICENSE for more information.

Jsearch_ai project