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GitHub - SynaLinks/HybridAGI: The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
GitHub - SynaLinks/HybridAGI: The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI
·github.com·
GitHub - SynaLinks/HybridAGI: The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
(PDF) BIFROST: A Future Graph Database Runtime
(PDF) BIFROST: A Future Graph Database Runtime
PDF | BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It... | Find, read and cite all the research you need on ResearchGate
·researchgate.net·
(PDF) BIFROST: A Future Graph Database Runtime
GitHub - a-s-g93/neo4j-runway: End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages.
GitHub - a-s-g93/neo4j-runway: End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages.
End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages. - a-s-g93/neo4j-runway
·github.com·
GitHub - a-s-g93/neo4j-runway: End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages.
Apple: After 6 years of using GraphQL in production we aren't reaching for it as much as we once did.
Apple: After 6 years of using GraphQL in production we aren't reaching for it as much as we once did.
After 6 years of using GraphQL in production we aren't reaching for it as much as we once did. First up, security. GraphQL's self-documenting query API… | 161 comments on LinkedIn
After 6 years of using GraphQL in production we aren't reaching for it as much as we once did.
·linkedin.com·
Apple: After 6 years of using GraphQL in production we aren't reaching for it as much as we once did.
Matching skills and candidates with Graph RAG
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.
·semanticpartners.com·
Matching skills and candidates with Graph RAG
GQL and Cypher
GQL and Cypher
A short introduction what a GQL query looks like relative to Cypher
·milowski.com·
GQL and Cypher
GQL in code | LinkedIn
GQL in code | LinkedIn
Lots of gratifying announcements about the GQL standard: Neo4j, TigerGraph, JTC 1, AWS/Neo4j, Memgraph, Stefan the editor, The Register ..
·linkedin.com·
GQL in code | LinkedIn
Graph-based metadata filtering for improving vector search in RAG applications
Graph-based metadata filtering for improving vector search in RAG applications
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
·blog.langchain.dev·
Graph-based metadata filtering for improving vector search in RAG applications