GenAI

mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding - X-PLUG/mPLUG-DocOwl

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. - mlabonne/llm-course

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EyeLevel.ai's GroundX APIs are the fastest way to build enterprise-grade RAG on prem or cloud. Trusted by Air France, Dartmouth, UltraCommerce and hundreds more.

Quickly understand inscrutable LLM frameworks by intercepting API calls.

DocWrangler is an IDE that provides instant feedback, visual exploration tools, and AI assistance for building and iterating on LLM-powered data processing pipelines

Chatbox AI is an AI client application and smart assistant. Compatible with many cutting-edge AI models and APIs. Available on Windows, MacOS, Android, iOS, Web, and Linux.

Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Foundation models enable many new application interfaces, but one that has especially grown in popularity is the conversational interface, such as with chatbots and assistants. The conversational interface makes it easier for users to give feedback but harder for developers to extract signals. This post will discuss what conversational AI feedback looks like and how to design a system to collect the right feedback without hurting user experience.

PDF - Paperback - Hardcover - — Joshua Starmer (@joshuastarmer)

Upgrade your RAG applications with the power of knowledge graphs./b Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Knowledge Graph-Enhanced RAG/i shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. Inside Knowledge Graph-Enhanced RAG/i you’ll learn: The benefits of using Knowledge Graphs in a RAG system/li How to implement a GraphRAG system from scratch/li The process of building a fully working production RAG system/li Constructing knowledge graphs using LLMs/li Evaluating performance of a RAG pipeline/li /ul Knowledge Graph-Enhanced RAG/i is a practical guide to empowering LLMs with RAG. You’ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.

Everything you need to know to build your own RAG application - bRAGAI/bRAG-langchain

In this blog post, we’ll be focusing on how to write the retrieval query that supplements or grounds the LLM’s answer.

A post for developers with advice and workflows for building effective AI agents

— Jo Kristian Bergum (@jobergum)

What do apps look like when you design AI first? Starting a thread to collect some examples I've found. — Lee Robinson (@leeerob)

Explore the capabilities of AI Agents and their real-world applications. CrewAI showcases the power and versatility of AI technologies across various sectors.

See how AppFolio's AI-powered copilot Realm-X has saved property managers over 10 hours per week. Learn how they improved Realm-X's performance 2x using LangSmith and built an agent architecture with LangGraph.

Neo4j’s LLM Graph Builder is an app for automatically constructing knowledge graphs from unstructured data sources. It can be run locally…

Graphs are ubiquitous data structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. Graph neural networks (GNNs) have become...

Evaluating the quality of outputs from Large Language Models (LLMs) is an intricate task due to the open-ended nature of many LLM tasks…

I made a tutorial showing you how to build a contract review agentic workflow - given a vendor agreement, parse it into a set of key clauses, match it with relevant clauses from a set of guidelines (GDPR),… — Jerry Liu (@jerryjliu0)

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