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 →
Learn how graph and vector search systems can work together to improve retrieval-augmented generation (RAG) systems. Read more →
In the 27 episodes of our Going Meta livestream series, Jesús Barrasa and I explored the many aspects of semantics, ontologies, and knowledge graphs. Read more →
Optimizing vector retrieval with advanced graph-based metadata filtering techniques using LangChain and Neo4j. Read more →
Learn how to use LangChain and Neo4j vector index to build a simple RAG application that can effectively answer questions. Read more →
Combining Neo4j knowledge graphs, vector search, and Cypher LangChain templates using LangChain agents for enhanced information retrieval. Read more →
Discover a simple but effective way to improve vector similarity search for RAG using OpenAI Embedding, AWS Bedrock, or Google VertexAI. Read more →
We will build a catalog of songs and lyrics with Neo4j, and use its built-in GenAI to find songs from a synopsis of what they are about. Read more →
Learn how to scrape YouTube video transcripts into a knowledge graph for Retrieval Augmented Generation (RAG) applications. 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photo by author.AbstractExternal knowledge is the key to resolving the problems of LLMs such as hallucination and outdated knowledge, which can make LLMs generate more accurate and reliable… Read more →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 Adam built an educational chatbot for GraphAcademy with Neo4j using Large Language Models and vector search. Read more →
