About This Project
Ready Tensor is a capstone project for the Agentic AI Essentials Certification Program. It demonstrates a production-ready Retrieval-Augmented Generation (RAG) system that combines sophisticated document retrieval with large language models to answer questions about custom knowledge bases.
This implementation showcases best practices in building intelligent Q&A systems using modern AI frameworks and vector databases.
Key Features
📚 Document Ingestion
Efficiently load and process documents into a searchable vector store
🔍 Semantic Search
Retrieve relevant context using embeddings and similarity search
🤖 LLM Integration
Generate contextual answers using OpenAI GPT or compatible models
⚡ Optimized Pipeline
Streamlined architecture for fast inference and minimal latency
💾 Vector Databases
Support for FAISS, Chroma, and other vector store backends
🔧 Extensible Design
Modular components that can be customized for various use cases
Getting Started
Installation
cd ready-tensor
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
Configuration
echo "OPENAI_API_KEY=your_api_key_here" > .env
Running the Assistant
Project Structure
├── src/
│ ├── rag_assistant.py
│ ├── document_loader.py
│ └── retriever.py
├── data/
│ └── documents/
├── notebooks/
│ └── demo.ipynb
├── requirements.txt
└── README.md
🎓 Certification Program
This project is part of the Agentic AI Essentials certification program offered by Ready Tensor. Upon successful completion, you'll earn a certificate demonstrating proficiency in:
- Building RAG systems with LangChain
- Vector database integration and management
- Prompt engineering and optimization
- LLM integration and chaining
- Production-ready AI system design
📖 Documentation
Comprehensive guides and examples are available in the project repository: