MCP Client Implementation using Python, LangGraph and Gemini
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Updated
Mar 21, 2025 - Python
MCP Client Implementation using Python, LangGraph and Gemini
An AI-powered code review tool leveraging Google's Gemini API to provide instant code analysis, bug detection, and best practice suggestions with syntax highlighting.
MediQuery is an AI-driven web application that helps users analyze and gain insights from medical reports. Leveraging state-of-the-art AI technologies such as Google Gemini, Pinecone, and a modern web stack with Next.js and Shadcn/ui, it allows users to upload PDFs or images of their medical reports and get precise, personalized insights.
In this Project, we will create a simple LangChain RAG Colab Notebook that uses the Google Gemini Flash model to answer user questions from the document provided. This example below is provided to help you get started assumes you have access to the Gemini API, Pinecone and a basic Python environment.
In this Project, we will create a simple LangChain Colab Notebook that uses the Google Gemini Flash model to answer user calculation questions. This example below is provided to help you get started assumes you have access to the Gemini API and a basic Python environment.
Your AI-driven dietary detection companion that scans ingredients through text, URLs, or photos to instantly identify potential allergens and dietary restrictions, ensuring safer food choices.
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Conversational chatbot that understands and analyzes images using AI-powered recognition.
TechSuggest is an AI-powered Messenger bot for quick, personalized gadget recommendations.
Snap Story is a mobile application that creates imaginative short stories from images.
AI-Powered Interactive Learning Assistant
A Telegram bot powered by Google's Gemini AI. Responds to text and voice messages with intelligent, AI-generated replies. Built with Python.
Projeto desenvolvido na Imersão Dev Back-end em parceria da Alura e Google Gemini.
MedBuddy is a GENAI-based platform for symptom analysis, doctor matching, and appointment scheduling. It uses FastAPI, React, and MongoDB, integrated with Google Gemini API for intelligent healthcare support.
This repository demonstrates the usage of Google Gemini API for describing visuals and personalizing text responses.
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From a table file containing clinic's name and websites, a python module can output relevant results regarding existing psychologist names, types, pricing, ... using LLM.
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