Building RAG Applications With the Neo4j GenAI Stack: A Comprehensive Guide
Apr 25 9 mins read
A guide to building LLM applications with the Neo4j GenAI Stack on LangChain, from initializing the database to building RAG strategies. Read more →
A guide to building LLM applications with the Neo4j GenAI Stack on LangChain, from initializing the database to building RAG strategies. Read more →
In this blog post, we will explore extracting information from unstructured data to construct a knowledge graph. Read more →
Learn how Neo4j can help you make sense of your unstructured data. Enroll in this new free course on GraphAcademy.There’s a new course on GraphAcademy: Introduction to Vector Indexes and Unstructured Data.This course teaches you to understand unstructured data using… Read more →
Learn how to write graph retrieval queries that supplement or ground the LLM’s answer for your RAG application, using Python and Langchain. Read more →
Learn how to use PDF documents to build a graph and LLM-powered retrieval augmented generation application. Read more →
Exploring the Shortcomings of Text Embedding Retrieval for LLM GenerationLoch Awe in Scotland, photo by author.AbstractExternal knowledge is the key to resolving the problems of LLMs such as hallucination and outdated knowledge, which can make LLMs generate more accurate and reliable… Read more →
Learn how to build a support agent that relies on information from Stack Overflow using the GenAI Stack – Neo4j, LangChain & Ollama in Docker. Read more →
As the final blog post of the Project NaLLM blog series, we reflect on the positive aspects and challenges encountered during this project. Read more →
Learn how Adam built an educational chatbot for GraphAcademy with Neo4j using Large Language Models and vector search. Read more →