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Knowledge Graphs for

Integrate Disparate Data Sources. Ensure GenAI is Grounded in Real-World Context.

Why knowledge graphs for GenAI

Memgraph enables you to integrate disparate data sources into a unified knowledge graph that captures domain specific information. Knowledge graphs ground GenAI applications with real-world context, enhancing their relevance and accuracy.
Fewer Hallucinations
Enable GenAI models to better understand and leverage context as guardrails against errors.
Better Accuracy
Explicitly represent facts and logic ensuring accurate information retrieval and validation.
Greater Productivity
Easily incorporate rich, contextual data into LLMs and GenAI content for increased efficiency.
More Transparency
Track data origin and transformations to ensure transparency and trustworthiness.

Knowledge graphs provide context for each step of GenAI

Feature Store

As the number of features used in ML and GenAI explodes, a feature store based on a knowledge graph that is contextually rich provides a much-needed single repository that eliminates the headaches of unmanaged features (duplicates, outdated, etc.) and multiple pipelines.

Using Memgraph, you gain a naturally context rich, scalable centralized feature store that makes it easy to track the origin and transformations of features. Feature lineage provides transparency, enhances feature quality, and improves explainability in the GenAI process.

Feature Discovery

Combining graph and vector search enables you to return both semantically and topologically similar entities within a single query — especially powerful when you have queries that require mixed starting parameters.

With Memgraph, you can easily find what you need in the haystack of features. This approach streamlines feature discovery in knowledge graphs, enhancing productivity by swiftly retrieving relevant entities based on similarities, minimizing time and computational resources needed.

Feature Engineering

Relationships are highly predictive but traditional machine learning processes throw away this valuable data type when transforming data for ML. Graph algorithms and embeddings capture and transform the shape of data to increase accuracy in LLMs and GenAI.

Memgraph includes state-of-the-art graph algorithms as well as key GNN and ML capabilities to streamline processes. Unlike alternatives, Memgraph’s dynamic, streaming algorithms work in-memory and continually compute and update features.

LLM Enrichment

Large Language Models (LLMs) often err when it comes to company and domain-specific details due to their limited capacity to process structured data and inaccessible proprietary knowledge.

Retrieval augmented generation (RAG) techniques use graphs and vectors to codify proprietary data with its context and bring together structured and unstructured data for LLMs. Incorporating your enterprise knowledge graph with Memgraph reduces hallucinations, increases accuracy, and helps tailor responses to your specific industry and needs.

GenAI Applications

Just as humans need to make decisions in context, so must GenAI for it to be production-worthy. Knowledge graphs add that much-needed contextual awareness necessary for grounding GenAI.

Memgraph is a high performance graph foundation for more explainable, accurate, and situationally aware applications such as interactive chatbots and generative content. With Memgraph, you can trust that your GenAI application is returning the best and most appropriate results — taking everything into account.

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