This project is a Retrieval-Augmented Generation (RAG) assistant developed as part of Module 1: Foundations of Agentic AI in the Agentic AI Developer Certification (AAIDC) by Ready Tensor.
The assistant answers user questions by retrieving relevant information from a custom document set stored in a vector database and generating grounded responses using Google Gemini.
User Query
↓
Retriever (Chroma Vector Database)
↓
Relevant Document Chunks
↓
Prompt + Context
↓
Gemini LLM
↓
Final Answer
AAIDC-Module1-RAG-Gemini/
├── main.py
├── ingest.py
├── data/
│ └── docs.txt
├── requirements.txt
├── .env.example
└── README.md
git clone https://github.com/your-username/AAIDC-Module1-RAG-Gemini.git cd AAIDC-Module1-RAG-Gemini
pip install -r requirements.txt
Add a Codespaces Secret:
Restart the Codespace after adding the secret.
Create a .env file:
GEMINI_API_KEY=your_api_key_here
python ingest.py
python main.py
Type exit to stop the assistant.
You: What is RAG?
Bot: RAG stands for Retrieval-Augmented Generation. It combines document retrieval with language models to generate grounded responses.
You: What is LangChain?
Bot: LangChain is a framework for building applications powered by language models, including tools for retrieval, memory, and agents.
This project fulfills the requirements for AAIDC Module 1: Foundations of Agentic AI by demonstrating:
This project is intended for educational purposes as part of the Ready Tensor Agentic AI Developer Certification program.