langhchain

Repositório criado para o Workshop de Agentes de IA

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

⚠️ ARCHIVED — migrated to github.com/devsper-com (runtime, registry, platform)

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.

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

A modular Agentic AI support system powered by LangChain, featuring multi-tool orchestration, RAG, SQL querying, web search, conversational memory, and configurable workflows.

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

LangGraph agent (RAG + SQL tool routing) for e-commerce support with PII masking, prompt-injection detection, JSON-Schema-validated output, and SQLite checkpointing. Offline-first, FastAPI + MCP server included.

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.

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

A repository-aware AI assistant that uses RAG to understand and answer questions about GitHub codebases.

A multilingual, multimodal PDF RAG assistant that lets you ask questions about documents and get AI-powered answers with source citations.

Authorization library for RAG (Retrieval-Augmented Generation) pipelines using SpiceDB. Designed for LangChain and LangGraph integrations with support for any vector store

AI-powered CV-to-job match analyzer — upload a CV and job description, get a deterministic 0–100 ATS-style match score with evidence-based feedback.

Financial news research tool using LangChain and Gemini to parse article URLs, build FAISS vector embeddings, and answer queries with citations.

매직아이(SIRDS) 이미지를 해석하는 AI 모델을 구축하고, 학습 데이터량에 따른 지능 지수를 단계별 난이도로 구성하여 인간과 대결하는 게이미피케이션 AI 서비스

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

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.