GenAI

Learn to build an agentic RAG system with LangChain, MyScaleDB, VoyageAI, and Tavily for dynamic Q&A that adapts to real-time data and knowledge base searches.

✒️Kiroku is a multi-agent system that helps you organize and write documents Really complex agent (see the diagram below!) that HEAVILY involves a "human in…

Learn how to find the data types of properties in Neo4j Graph Database using the Cypher query language and APOC.

📃 A better UX for chat, writing content, and coding with LLMs. - langchain-ai/open-canvas

❇️ Quickly build knowledge graphs from unstructured text documents Easily implement knowledge graph retrievers combining graph traversals and vector… — Neo4j (@neo4j)

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

Manage multiple AI agents and handle complex conversations

Explore advanced GraphRAG retrieval patterns and how graph structures enhance RAG systems. Learn actionable strategies to implement and optimize GraphRAG.

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/ - microsoft/generative-ai-for-beginners

This post will explain how you can implement complex agentic ReAct flows using LangGraph and LangGraph Studio. Basic Python knowledge is…

We’re publishing 2 full-length tutorial videos showing you how to implement various agentic RAG techniques - adding LLM layers to reason over inputs and post… | 12 comments on LinkedIn

This weekend, check out one of the most comprehensive tutorials we’ve seen on building an e2e research agent with nested workflows for 1) research, and 2) generating a Powerpoint deck with human-in-the-loop.… — LlamaIndex 🦙 (@llama_index)

(And why should anyone working with RAG over PDFs care?) ColPali makes information retrieval from complex document types - like PDFs - easier. Information retrieval from PDFs is hard because they contain various components: Text, images, tables,… — Leonie (@helloiamleonie)

Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for… — Victoria Slocum (@victorialslocum)

TL;DR: 🗂️ Parses numerous… — Philipp Schmid (@_philschmid)

Build language agents as graphs

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Create a Neo4j GraphRAG workflow using LangChain and LangGraph, combining graph queries, vector search, and dynamic prompting for advanced RAG.

Explore the future of RAG in report generation, enhancing decision-making and resource allocation for businesses.

Explore low-hanging fruit strategies to enhance your RAG search systems and improve user experience with practical techniques.

Building an agent with LangChain's LangGraph and Graphiti