LLMs + Graphs
Business-oriented, responsible, white-box Generative AI - Achieve it through synergy with graphs.
Graph Capabilities Desired in LLMs
Commonly known challenges of Large Language Models (LLMs), such as their black-box nature, hallucinations, and overly general knowledge base, have severely hindered their practical application in organizations.
Deep Reasoning and Relating
LLMs struggle to answer multi-hop questions that require reasoning across temporal or spatial distances and connecting information from different contexts.
Graph Solution
Graphs offer interconnected, traceable, and explicit representations, empowering efficient deep traversal and causality searches.
Analytics and Algorithms
LLMs excel at text generation but struggle with even basic mathematical questions and complex analytical tasks.
Graph Solution
Graphs provide solid analytical and algorithmic frameworks, rendering them reliable problem solvers.
LLMs + Graphs: Powerful Synergy
The integration leverages the textual comprehension abilities of LLMs and the structured reasoning power of graphs to create truly intelligent systems.
Graph Extractor
Utilizes LLM abilities to extract information from raw text and directly visualize them in a graph. Transforms unstructured data into structured knowledge graphs through entity discovery and relation extraction.
ChatGraph
Enables LLMs to understand user inquiries in natural language and translate them into precise graph queries. This conversational data engagement makes complex graph analytics accessible to everyone.
Graph Chatbot
Combines OpenAI, LangChain, Chroma Vector DB and Ultipa for optimal QA System performance. Enables knowledge acquisition through pathfinding and node property analysis for accurate, context-aware responses.
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