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LLM + Knowledge Graph
ArangoDB is unique in how it bridges the gap between two very different yet highly complementary data domains: Knowledge Graphs and Large Language Models (LLMs).
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LLMs: Unveiling
Language Potential
Large Language Models, for example OpenAI's ChatGPT, have become powerful language transformers. These models, with the help of advanced neural networks, possess the uncanny ability to understand, generate, and engage in contextually-aware conversations. They excel at natural language understanding and generation capabilities.
Why it matters: Many use cases require (or may require in the future) the power of LLMs combined with other data assets and analytics.
Knowledge Graphs for Information Interconnections
Knowledge Graphs contain carefully structured data and are designed to capture intricate relationships among discrete and seemingly unrelated information. These graph-based structures organize data hierarchically while interconnecting data points and relationships. Knowledge Graphs are great at contextual insights, allowing users to explore and
comprehend data associations.
Why it matters: Many use cases benefit from extremely rapid and simple traversal of interconnected data to yield actionable and predictive insights.
Better together - LLMs + Knowledge Graphs
Take, for example, the requirement for personalized recommendations. Knowledge Graphs power personalized recommendations based on user connections. Now imagine adding the unique power of LLMs to enhance this by considering not just connections but also textural preferences expressed by users. Recommendations will be far more accurate.
Why it matters: There is tremendous added power of LLMs when layered on top of core Knowledge Graph capabilities.
ArangoDB - Unique for
LLM Integration
ArangoDB’s model flexibility allows it to stand out when integrating LLM capabilities and output. Consider the situation where an LLM is analyzing and generating unstructured output to produce insights. Because ArangoDB has native Document support, the unstructured LLM content can be stored efficiently in collections. This dramatically simplifies and speeds up retrieval, manipulation, and search of textual data when combining it with Knowledge Graph content.
Why it matters: Intermediate data integration of LLM + Knowledge graph is often unacceptable.
ArangoDB’s Unified Query Support
Combining LLMs and Knowledge Graphs often requires complex queries that span both structured and unstructured data. ArangoDB's AQL (ArangoDB Query Language) supports combining graph traversal with document and full text queries, allowing for sophisticated combined searches.
Why it matters: What if you could use a single query language instead of multiple to gain insights from combined LLM and Knowledge Graph date?
Get started with
Graph today
(no credit card required), and experience the shortest time to value for a hosted graph DB.
Download The Graph DB Buyer’s Guide
Questions to ask Legacy Graph DB Vendors
Read the
Case Studies
Learn why companies across industries are switching to ArangoDB for Graph.