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ragex

An AI app to help users in crafting their resumes by engaging in a simple conversation

Leveraging LlamaIndex, ChromaDB, the app uses a Retrieval Augmented Generation system designed to process data from files of varying formats. This system can ingest data from files, store them in a ChromaDB vector database, and be used to query them with precision thanks to LlamaIndex’s retrieval tools.

The backend is a FastAPI server handling file upload and chat connection requests made from a Streamlit client app.

Conversation examples



System Architecture:

Programming Language - Python
Model Provider - Groq, Google Vertex
LLM Orchestrators and Frameworks - LlamaIndex, FastAPI, Streamlit
Operational and Vector Database - ChromaDB, SQLite
Monitoring and Observability - Python Logger
Deployment - Streamlit, GCP

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An AI app to help users in crafting their resumes by engaging in a simple conversation

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