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UNLOCK THE FULL POTENTIAL OF LLMS

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, such as uncovering connections between historical figures who lived in different times and places.

Graphs are renowned for their interconnected, traceable, and explicit representations, empowering efficient deep traversal and causality searches.

genghis-khan-isaac-newton

Analytics and Algorithms

LLMs excel in generating coherent text responses to various inputs but face challenges with even basic mathematical questions.

Graphs, rooted in the principles of graph theory, provide solid analytical and algorithmic frameworks, rendering them reliable problem solvers.

shortest-path

HYBRID SOLUTIONS

LLMs + Graphs

LLMs and graphs exhibit inherent complementarity. By harnessing the textual comprehension abilities of LLMs and the structured reasoning power of graphs, their integration opens up compelling opportunities to enhance the capabilities, intelligence, and interpretability of AI systems.

TRANSFORM UNSTRUCTURED DATA INTO GRAPH

Graph Extractor

Leverage the entity discovery and relation extraction capabilities of LLM to extract information from raw text and directly visualize them in a graph.

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graph-extractor
chatgraph

ENGAGE WITH DATA CONVERSATIONALLY

ChatGraph

LLMs can effectively understand user inquiries in natural language and translate them into precise graph queries or algorithms in UQL.

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FROM RELEVANT TO PRECISE

Graph Chatbot

The strategic integration of OpenAI, LangChain, Chroma Vector DB and Ultipa for optimal QA System performance, utilizing graphs for knowledge acquisition through pathfinding queries and node properties.

Read More

graph-chatbot

Looking into the Future

While LLMs have showcased remarkable capabilities in understanding and generating natural language, the concerns and criticisms around them have also cast shadows on their applicability in serious business contexts. The synergy between LLMs and graphs holds the promise of mitigating the limitations associated with LLMs. By harnessing the explicit and structured representation of relationships provided by graphs, this harmonious integration anticipates a forthcoming era in data processing and problem-solving. In this envisioned future, the inherent strengths of both technologies will collaborate to overcome existing challenges and obstacles.

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