A Triple Store RAG Retriever
How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models
Matching skills and candidates with Graph RAG
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.
Graph Data Models for RAG Applications
When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.
Why I Chose FalkorDB Knowledge Graph Over Neo4j for Building a RAG Application
A comparison of FalkorDB with other Knowledge Graphs for developing AI applications with.
LLMs, Knowledge Graphs and Property Graphs
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…
Analyzing Annual Reports Using LLMs and Graph Technology
Natural language processing made easy
Constructing knowledge graphs from text using OpenAI functions | by Tomaz Bratanic | Oct, 2023 | Medium
Seamlessy implement information extraction pipeline with LangChain and Neo4j
Graph RAG ：Unleashing the Power of Knowledge Graphs with LLM
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
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…
Working on a LangChain template that adds a custom graph conversational memory to the Neo4j Cypher chain
Integrating TigerGraph and Large Language Models for Generative AI - TigerGraph
See how to integrate a language language model with TigerGraph to empower question answering with your connected data.