- Client
Data engineering research
- Technologies
Python 3.13+, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs)
- Links
Overview
A CLI tool enabling natural language querying of document collections using Retrieval-Augmented Generation (RAG).
Technologies
- Python 3.13+
- Retrieval-Augmented Generation (RAG)
- Large Language Models (LLMs)
- Vector Embeddings
- llama-index Framework
- DeepSeek API
- Ollama Integration
- Natural Language Processing
- Command Line Interface
Key Features
- Natural language document querying
- Multi-model LLM backend support
- Semantic search with embeddings
- Environment-based configuration
- Interactive CLI interface
- Local document loading
- Vector indexing
- Modular architecture
- Lightweight dependencies
- Cross-platform support
Challenges & Solutions
Model Integration and Abstraction
Unified multiple LLM backends under a single interface.
Efficient Document Processing
Implemented scalable vector indexing for semantic retrieval.
CLI Design and User Experience
Designed a simple yet flexible CLI workflow.
My Role
Designer and developer responsible for RAG architecture, model integration, indexing, and CLI implementation.
Project Links
Results
Delivered a versatile RAG tool for developers and researchers.