 Falcon Hackathon

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Falcon Masters

Faiqkhan

MinalAli

hamna_dev

advanced medical assistant application that utilizes Retrieval-Augmented Generation (RAG) with the Falcon Large Language Model (LLM) to provide accurate and context-aware medical information.

Features
Audio Interaction Endpoint
Speech-to-Text (S2T) conversion
LLM processing using Falcon
Text-to-Speech (T2S) conversion for audible responses

Text-based Interaction Endpoint
Direct text input
LLM processing using Falcon
Text output

Retrieval-Augmented Generation (RAG)
Enhances responses with relevant medical knowledge
Improves accuracy and context-awareness of the AI

uses a Retrieval-Augmented Generation (RAG) architecture:

User input (text or transcribed audio) is processed.
Relevant medical information is retrieved from the Qdrant vector database.
The Falcon LLM generates a response based on the user query and retrieved information.
The response is returned as text or converted to speech.

advanced medical assistant application that utilizes Retrieval-Augmented Generation (RAG) with the Falcon Large Language Model (LLM) to provide accurate and context-aware medical information

"Falcon Masters team on Falcon Hackathon Hackathon"

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Submission