Generative AI and Retrieval-Augmented Generation

This page highlights my work with retrieval-augmented generation, local language models, vector databases, and document-based question answering. My current focus is controlled, practical exposure to generative AI systems that connect technical implementation with useful real-world workflows.

RAG Project Demo

This project implements a retrieval-augmented generation application that allows users to query a document collection using natural language. Instead of relying only on a language model's general training, the system retrieves relevant document context and uses that retrieved context to support the response.

The demo was built around a practical document-querying use case, such as asking questions about policy and procedure documents. This kind of workflow is useful when the goal is not just to generate text, but to connect a language model to a specific body of documents.

The main value of this project is that it demonstrates my ability to understand and implement the structure of a RAG workflow: document ingestion, embedding, vector storage, retrieval, prompt construction, local model inference, and user-facing interaction.

Technical Stack

Core Components

LangChain: orchestration of the RAG workflow
Pinecone: vector database for document embeddings
HuggingFace Transformers: embedding model support
Streamlit: front-end interface for user interaction
Ollama: local language model serving

Workflow Concepts

Document loading and preprocessing
Embedding generation
Vector search and retrieval
Context-aware question answering
Local LLM experimentation
Modular project organization

Project Structure

The project was organized into separate files for the application interface, document ingestion, query handling, logging, exception handling, dependencies, and environment configuration.

rag_project/
├── .env                    # Environment variables, such as Pinecone keys
├── app.py                  # Main Streamlit app
├── logger.py               # Custom logging logic
├── exceptions.py           # Custom exception handling
├── requirements.txt        # Python dependencies
├── README.md               # Project documentation
├── ingestion.py            # Document loading and embedding workflow
└── query.py                # Retrieval and question-answering logic

What This Project Demonstrates

Understanding of retrieval-augmented generation as a practical AI architecture
Ability to connect language models to specific document collections
Use of vector databases for semantic retrieval
Experience with local model serving through Ollama
Modular Python project organization
Practical experimentation with LangChain and Streamlit
Interest in responsible, document-grounded generative AI workflows

This project supports my broader direction in applied AI, data science, and quantitative decision-support systems. It is part of my controlled development in generative AI rather than a claim of senior-level GenAI engineering expertise.