RAG

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

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.

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...

This blog post presents a new modular workflow for converting PDFs and similar documents to structured data and shows you how to build end-to-end document understanding and information extraction pipelines for industry use cases.

Here’s a cheat sheet of 7 of the most popular RAG architectures. Which variants did we miss? — Weaviate • vector database (@weaviate_io)

A question-based extraction approach

Discover how QueryGPT revolutionizes SQL query generation at Uber! Learn about the cutting-edge AI that turns natural language prompts into efficient SQL queries, boosting productivity at Uber. Dive into our journey of innovation and transformation.

Entity resolution, the process of determining which digital descriptions correspond to the same real-world entities, is an important graph use case. It is also a crucial precursor to many graph data science projects. In this session, you will learn steps that the Neo4j professional services team has used in many entity resolution projects. The steps include designing a graph data model that highlights shared identifiers, standardizing the format of node properties, identifying outlier nodes that should be excluded from the matching process, using graph data science algorithms to identify duplicate entities, using string similarity to identify misspellings, and capturing the results of entity resolution in your graph. Get certified with GraphAcademy: https://dev.neo4j.com/learngraph Neo4j AuraDB https://dev.neo4j.com/auradb Knowledge Graph Builder https://dev.neo4j.com/KGBuilder Neo4j GenAI https://dev.neo4j.com/graphrag

The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI

Learn how to overcome them using knowledge graphs and structured tools

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.

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

❇️ 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.

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).

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

(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)