Repositório criado para o Workshop de Agentes de IA

A template repository for multi-agents architecture using LangGraph tool from LangChain

Harness the Power of Intelligent Search to Uncover Relevant Information from your Documents Instantly using generative AI

Django-based text utility service, offering multi-language translation, sentiment analysis, and text summarization, all seamlessly deployed on AWS Lambda with Docker for robust and scalable performance.

Users can upload documents and seamlessly ask questions about their content, leveraging Retrieval-Augmented Generation (RAG) for precise and context-aware answers.

This repository offers a hands-on guide to mastering Generative AI with Langchain and Huggingface. It covers key concepts, practical implementation, and deployment strategies to help AI enthusiasts, developers, and professionals build and optimize AI models efficiently

Using LangGraph to create agents, Pytests for testing and deployment on Streamlit

This project showcases how Retrieval-Augmented Generation (RAG) can be applied to create an efficient and user-friendly system for querying large text documents. With this approach, users can easily extract and interact with relevant information from PDFs in real time.

🛍️ Automate your sales process with an AI-driven multi-agent system that improves efficiency from product inquiries to payment links.

Explore Genkit agents for YouTube video searches and content generation. Find workflows to streamline your video discovery and blogging process. 🚀📂

LLM complaint insights: Structured summaries, risk tags, and entity extraction on CFPB consumer complaints — built with OpenAI + LangChain, MLflow, Streamlit.

Building ReAct AI agents with sandboxed Python code execution using AWS Bedrock and LangGraph

Trackify offers helpful insights, including thorough descriptions of the CV's strengths, recommendations for improving skills, the discovery of missing keywords, and an overall percentage match, by examining the content of the job description and the resume.

A summarisation assistant powered by voice + RAG + Gen AI

This project allows you to search for academic papers on arXiv, download and process them, and generate responses to specific questions using embeddings and language models. The application leverages several tools including Gradio for the interface, ChromaDB for embedding storage, and LangChain for text processing.

Um agente de consultas musicais que utiliza a API do Spotify e Inteligência Artificial.

This repository demonstrates how to use LangChain’s with_structured_output to generate consistent, machine-readable responses. It includes examples with TypedDict, Pydantic, and JSON Schema, and how they help enforce reliable data formats in LLM applications.

A chat-based agent utilizing the Mistral 7B Large Language Model (LLM), Langchain, Ollama, and Streamlit to answer questions about files through Retrieval-Augmented Generation (RAG).