Vector Graph
Highly Relevant Query Results with Vector Graph
Vector Graph generates and stores graph data automatically upon data ingestion. Programmatically create nodes and edges that define the graph relationships of your data. Vector Graph is the only solution available to build graph data with no additional work for developers.
Control for RAG issues
Iterate and Improve Graph Data Easily
Using graph data and adding edges gives AI developers control over query results, improving relevancy and increasing the reliability of their AI applications. Vector Graph makes it easy to alter the relationship models and improve query relevancy. This simple, drop-in enhancement has consistently improved performance of AI queries compared to plain vector queries. But creating graph relationships can be resource-intensive. Developing and testing effective models consumes development cycles.
Just Two Lines of Code!
Two lines of code added to your ingestion routine instructs Vector Graph to automatically build a vector store with graph data connections at any scale, without needing a graph DB.
Better Data, Better Relevance
Enhancing vector queries by including graph data significantly improves query relevance. Benchmarks show query accuracy improving 2.8x with the addition of knowledge to complex vector queries (source). Vector Graph generates this relationship data automatically.
Understanding Graph Data
Good AI applications need relevant, accurate query results.
Knowledge Graphs Enhance Vector Searches
Knowledge graphs provide a way of dramatically improving query results because they give you the ability to add and remove links between related contexts LLMs need.
Making Your Data AI-Ready
Effective AI requires the right data in the right place. Learn about data types, what it takes to get data AI-ready, and how to handle massive amounts of relevant data, and why this matters no matter where you are on your AI journey.
Resources
Scaling Knowledge Graphs by Eliminating Edges
Knowledge graphs enable the linking of related content in a way that complements vector similarity. This article explains how to reduce complexity and speed up traversals by eliminating edges in the graph.
Langflow and Astra DB Are Integrated with NVIDIA NeMo Retriever NIM!
The DataStax AI PaaS is now integrated with NVIDIA’s NeMo Retriever for state-of-the-art embedding retrievals.
Demystifying LLM-based Systems
Dig into design patterns, architectural examples, and tools to help navigate the complex world of large language models and get generative AI applications to production.
