📸 OCR

Extracting structure and meaning from visual data and documents.

• AI Receipt and Expense Tracker — Extracts structured data from receipt photos and tracks spending in a local SQLite ledger. Powered by Gemma 4 E2B vision via llama-cpp-python. Fully offline after first run.
• Image-to-Structured-Data Extractor — Converts images into validated, structured JSON using Mistral Large 3 and Instructor.
• LaTeX Formula OCR — Extracts math formulas from images and PDFs into LaTeX using a local vision-language model.
• Medical Prescription Digitizer — Digitizes handwritten or printed prescriptions into structured output using Mistral Large 3, with real-time drug name validation against RxNorm.

🎧 Audio

Projects for audio understanding and analysis.

• Music Explorer — Chat with any audio file or YouTube video using Gemini 3 Flash. Ask for transcriptions, emotion analysis, instrument identification, and timestamp-aware breakdowns.
• Multilingual Audio Translator — Upload or record audio in any language, get it transcribed with faster-whisper, translated via Gemini, and played back as synthesized speech using Kokoro TTS.

🎬 Multimodal

Projects combining vision, video, and language models.

• GLM-OCR Pro — Structured document extraction using GLM-OCR via Ollama, transforming images and PDFs into formatted Markdown locally.
• Video Understanding Agent — Summarizes YouTube videos into chapters, key takeaways, and action items using Gemini Flash.
• Multimodal Weather App — Upload a map image and get live weather. Mistral Small 4 identifies the city via vision, then fetches real-time conditions through native tool calling.
• Multimodal RAG — RAG system that ingests text, URLs, PDFs, images, audio, and video into a shared ChromaDB index. Gemini Embedding 2 handles retrieval and Gemini 3 Flash generates grounded answers, passing actual file URIs for media sources.
• Image Question Answering — Upload a PDF, select a page, and ask visual questions answered by Gemma 4 with thinking mode. PyMuPDF renders each page to a full-resolution image for grounded reasoning over charts, tables, and figures.
• Medical Document Parser - Extracts a structured clinical profile from medical PDFs and images using Gemma 4 vision.

📚 RAG Applications

Retrieval-Augmented Generation systems for knowledge-enhanced AI applications.

• Agentic RAG with O3-Mini & DuckDuckGo — RAG system using O3-Mini with DuckDuckGo for real-time web search.
• Agentic RAG with Qwen & FireCrawl — RAG system using Qwen and FireCrawl for web scraping and retrieval.
• Vision RAG — Multimodal RAG system for processing and querying visual content.
• Clinical RAG with ADE — High-precision clinical RAG using LandingAI ADE for visual-first document parsing and Mistral Large for grounded reasoning.
• YouTube Transcript RAG — Chat with any YouTube video using Whisper transcription, ChromaDB retrieval, and Mistral Small 4, with timestamp-linked answers.
• GraphRAG Knowledge System — Builds a local knowledge graph from uploaded documents using Mistral Small 4 and NetworkX, supporting both entity-level and thematic queries.
• Hybrid RAG System — Indexes documents into a knowledge graph and a vector store in parallel. Mistral Small 4 answers questions with fused context from both retrieval paths.
• HyDE RAG — RAG pipeline using Hypothetical Document Embeddings. Gemini 3 Flash generates hypothetical answers, Gemini Embedding 2 embeds and averages them, and the result retrieves more precise chunks from ChromaDB.
• Rock Music RAG — Custom rock music knowledge base built from Wikipedia. Add any band, ask questions across all of them, and get sourced answers powered by BM25 retrieval and Gemma 4.
• RAG Agent with Database Routing — Routes queries across three specialized Qdrant databases (products, support, financial) using an Agno router agent. Falls back to a LangGraph ReAct web search agent when no relevant documents are found.
• Reasoning RAG - Ask questions against any web source and get cited answers with a live, step-by-step reasoning trace via Gradio.

🤝 Contributing

We welcome contributions! Whether you're adding new projects, improving existing ones, or fixing bugs, your help makes this repository better for everyone.

How to Contribute

1. Read the guidelines: Check CONTRIBUTING.md for detailed instructions
2. Create an issue: Propose your project or improvement
3. Follow the structure: Use the appropriate category folder
4. Submit a PR: One project per pull request

Project Structure Requirements

• Each project must be in its own folder within the appropriate category
• Must include a comprehensive README.md (use our template)
• Must include requirements.txt or pyproject.toml
• Must include .env.example for required API keys
• Follow snake_case naming convention

📜 License

This repository is licensed under the MIT License. See the LICENSE file for details.

🙏 Acknowledgments

Thank you to all contributors who have helped build this collection of AI engineering projects!