RAG-Match3-AMD is an intelligent document question-answering system designed to help users find reliable information inside PDF, DOCX, spreadsheet, and text documents. The project uses Chroma Cloud as its vector database and retrieval engine. Documents are parsed, divided into manageable chunks, and transformed into dense embeddings with Chroma Cloud Qwen and sparse embeddings with Chroma Cloud SPLADE. Hybrid search and Reciprocal Rank Fusion combine semantic and keyword results to retrieve the most relevant evidence.
OpenAI GPT-4.1 Mini then analyzes the retrieved context and generates a concise answer supported by citations to the original document. The system can also process images and scanned content through a vision-capable model. Document metadata, chunk indexes, and source identifiers are preserved to improve traceability and prevent duplicate results.
The project provides a practical RAG workflow that separates document retrieval from answer generation. Chroma Cloud handles embedding and search, while OpenAI handles language understanding and response generation. This architecture makes the application suitable for learning materials, technical documentation, reports, and other knowledge-intensive document collections
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