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 →
Knowledge Graph vs. Vector RAG: Benchmarking, Optimization Levers, and a Financial Analysis Example
Jun 05 20 mins read
Learn how graph and vector search systems can work together to improve retrieval-augmented generation (RAG) systems. Read more →
Going Meta: Wrapping Up GraphRAG, Vectors, and Knowledge Graphs
May 14 3 mins read
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 →
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Graph-based Metadata Filtering to Improve Vector Search in RAG Applications
Apr 28 11 mins read
Optimizing vector retrieval with advanced graph-based metadata filtering techniques using LangChain and Neo4j. Read more →
graphgraph-databaseknowledge-graphlangchainllmmetadatametadata filteringNeo4jopenairagretrieval augmented generationunstructured-datavector searchvector similarity search
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 →
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LangChain Library Adds Full Support for Neo4j Vector Index
Apr 16 5 mins read
Learn how to use LangChain and Neo4j vector index to build a simple RAG application that can effectively answer questions. Read more →
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Graph Data Models for RAG Applications
Apr 09 6 mins read
Learn when to use graph data models, like parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs. Read more →
data-modelinggraph data modelknowledge graphsllmNeo4jparent childragretrieval augmented generationretrieval-augmented
Enhancing the Accuracy of RAG Applications With Knowledge Graphs
Mar 30 8 mins read
A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain. Read more →
Using a Knowledge Graph to Implement a RAG Application
Mar 12 9 mins read
Learn how to implement a knowledge graph-based RAG application with LangChain to support your DevOps team. Read more →
chatgptdependency graphknowledge graphslangchainllmNeo4jragretrieval augmented generationunstructured-datavector similarity searchvector-database
Implementing Advanced Retrieval RAG Strategies With Neo4j
Mar 12 7 mins read
In this blog, you will learn how to use the neo4j-advanced-rag template in LangServe Playground to implement advanced RAG strategies. Read more →
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Implementing RAG: How to Write a Graph Retrieval Query in LangChain
Mar 05 6 mins read
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 →
Building A Graph & LLM-Powered RAG Application from PDF Documents
Jan 19 5 mins read
Learn how to use PDF documents to build a graph and LLM-powered retrieval augmented generation application. Read more →
Why Vector Search Didn’t Work for Your RAG Solution?
Jan 05 10 mins read
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 →
