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Bonzepiss
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WebRover is an autonomous AI agent designed to interpret user input and execute actions by interacting with web elements to accomplish tasks or answer questions. It leverages advanced language mode…
Deep Graph Infomax (https://arxiv.org/abs/1809.10341)
🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
Make websites accessible for AI agents
Connect to your customer data using any LLM and gain actionable insights. IdentityRAG creates a single comprehensive customer 360 view (golden record) by unifying, consolidating, disambiguating and…
Simplifies the retrieval, extraction, and training of structured data from various unstructured sources.
We propose the Text-to-CQL task and provide the dataset.
File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.
LLaVA-CoT, a visual language model capable of spontaneous, systematic reasoning
Open-source platform for extracting structured data from documents using AI.
A next-generation crawling and spidering framework.
Open source DocuSign alternative. Create, fill, and sign digital documents ✍️
RepoGPT: AI-powered GitHub assistant to chat, manage, and explore your repos effortlessly.
A system for agentic LLM-powered data processing and ETL
PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis
This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive A…
Loki: Open-source solution designed to automate the process of verifying factuality
AI-Driven Research Assistant: An advanced multi-agent system for automating complex research processes. Leveraging LangChain, OpenAI GPT, and LangGraph, this tool streamlines hypothesis generation,…
🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
An open-source RAG-based tool for chatting with your documents.
Graph Attention Networks (https://arxiv.org/abs/1710.10903)