Knowledge Graphs and LLMs: Fine-Tuning vs. Retrieval-Augmented Generation
Sep 11 15 mins read
Explore the pros and cons of fine-tuning versus retrieval-augmented generation (RAG) for overcoming large language model (LLM) limitations. Read more →
Explore the pros and cons of fine-tuning versus retrieval-augmented generation (RAG) for overcoming large language model (LLM) limitations. 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 →
A guide to building LLM applications with the Neo4j GenAI Stack on LangChain, from initializing the database to building RAG strategies. Read more →
Learn how to use LangChain and Neo4j vector index to build a simple RAG application that can effectively answer questions. Read more →
Learn when to use graph data models, like parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs. Read more →
A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain. Read more →
Learn how to implement a knowledge graph-based RAG application with LangChain to support your DevOps team. Read more →
In this blog, you will learn how to use the neo4j-advanced-rag template in LangServe Playground to implement advanced RAG strategies. 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photo by author.AbstractExternal knowledge is the key to resolving the problems of LLMs such as hallucination and outdated knowledge, which can make LLMs generate more accurate and reliable… Read more →