At Semantic Partners, we wanted to build our informed opinion over the strengths and weaknesses of graph RAG for RDF triple stores. We considered a simple use case: matching a job opening with Curriculum Vitae. We show how we used Ontotext GraphDB to build a simple graph RAG retriever using open, offline LLM models – the graph acting like a domain expert for improving search accuracy.

Another once specialized database technology from the previous big data era might find its way into AI products thanks to retrieval augmented generation.

When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.

Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Neo4j is a graph database and analytics company which helps organizations find hidden relationships and patterns

A comparison of FalkorDB with other Knowledge Graphs for developing AI applications with.

In response to my last blog, LLMs and Knowledge — the study continues, I got a lot of questions LinkedIn, which I decided to answer in…

🚀 Exciting News Alert! 🚀 We're over the moon to announce the launch of TigerGraph CoPilot's public alpha release! 🌟 🔗 Get ready to explore the future of…

The NLQ integration for Ontotext GraphDB in LangChain: a framework designed to simplify the creation of applications using LLMs

Go beyond typical RAG strategies

An architecture review of Neo4j as both vector and graph store

AWS announced a new capability today called Neptune Analytics that uses vector search to understand the relationships in a graph database.

Neo4j vector search provides a simple approach for quickly finding contextually related information and uncovering hidden relationships.

We're excited to announce the partnership between Neo4j and AWS to speed up enterprise AI development and resolve key LLM challenges.

Natural language processing made easy

Seamlessy implement information extraction pipeline with LangChain and Neo4j

In the era of information overload, sifting through vast amounts of data to provide accurate search results in an engaging and…

Working on a LangChain template that adds a custom graph conversational memory to the Neo4j Cypher chain, which uses LLMs to generate Cypher statements. This…

Learn how to build a support agent that relies on information from Stack Overflow using the GenAI Stack – Neo4j, LangChain & Ollama in Docker.

We're excited to launch the new GenAI stack for building GenAI-powered applications with Neo4j, Docker, LangChain and Ollama.

See how to integrate a language language model with TigerGraph to empower question answering with your connected data.

“📈 More Graph DBs in @LangChainAI Graphs can store structured information in a way embeddings can't capture, and we're excited to support even more of them in LangChain: HugeGraph and SPARQL Not only can you query data, but you can also update graph data (!!!) 🧵”