Intelligent Answers Powered By PuppyGraph’s Agentic GraphRAG

Our GraphRAG transforms how your LLMs understand and utilize the interconnectedness of data. Not just retrieving it—PuppyGraph equips your LLMs to get smarter, context-rich answers instantly.

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Databricks provides the Data Intelligence Platform allowing for ingestion, transformation, and analysis of data. Databricks also provides an interface for Large Language Models (LLMs) either through SQL or leveraging the LangChain API. PuppyGraph allows the data stored in Databricks to be queried as a graph with its graph query engine and offers advanced graph analytics capabilities.

Ajmal Aziz

Solution Architect at Databricks

Why GraphRAG?

Problems with Traditional RAG

Traditional RAG systems struggle in linking varied data points.

Disconnected chunks cause missed relationships, yielding disjointed responses.

More data can obscure, not clarify, crucial details.

Available data may still lead to unreliable or incorrect outcomes.

GraphRAG Benefits

GraphRAG enriches RAG with a Knowledge Graph, enhancing data structure understanding.

It recognizes and utilizes connections among data points, focusing on node and edge relationships.

GraphRAG synthesizes meta-information across a network, moving beyond isolated data.

Graph RAG enhances LLMs' understanding and navigation in complex data environments.

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Why PuppyGraph’s Agentic Graph RAG Is a Game-Changer

Discover hidden patterns and connections at scale.

Zero ETL, Maximum Efficiency

Directly connect to various data sources and simplify knowledge graph creation with PuppyGraph—deploy and query in just 10 minutes.

Agentic & Goal-Oriented Intelligence

PuppyGraph’s Agentic Graph RAG adopts a goal-oriented approach. It plans, executes multiple graph queries, and intelligently reasons based on results, doing a re-plan or summarization.

Flexible Query Languages

PuppyGraph supports both Gremlin or Cypher, the Agentic Graph RAG can choose the most suitable query language based on the request (or both!). It enhances accuracy and allows for optimized queries.

Petabyte Scale, Ultra-Low Latency

Manage petabyte-scale data effortlessly, with ultra-low latency queries. Making it capable of pinpointing even the most hidden insights in vast datasets, like finding a needle in a haystack.

Use Case Highlight

See PuppyGraph Agentic GraphRAG Improves Meta Queries by 5x

Business use case: In mining maintenance, the key performance indicator for the Reliability teams is the Mean Time To Recovery (MTTR). A 15 minute improvement to MTTR can lead to millions of dollars in recovered revenue for some of the largest mine operators in the world.

Results with VectorDatabase RAG

Limited Context: Struggled with fragmentary data insights, missing crucial connections between data elements.

Inadequate Response Time: Slow response times due to inefficient data retrieval across multiple pieces hurt user experience and operational efficiency.

Increased Operational Costs: Maintaining multiple data copies and ensuring synchronization increases infrastructure and management costs.

After PuppyGraph

Comprehensive Data Integration: GraphRAG provided a unified view of all relevant data, enabling a deeper understanding of asset conditions and maintenance requirements.

Enhanced Diagnostic Speed: Significantly reduced the time required to diagnose failures, with complex queries about equipment status and history resolved in seconds.

Cost Efficiency: Improved operational efficiency and reduced maintenance costs by accelerating decision-making and reducing equipment downtime.

Figure 1: Each point on the plot represents a question; GraphRAG achieves the top score of 5 for the majority of questions, whereas the vector-based RAG generally scores fairly low.

Figure 2: GraphRAG consistently delivers higher accuracy across both Direct and Meta queries*, while traditional vector-based RAG underperforms particularly in Meta questions.

*Direct Query: Require only a single table to answer a question. These questions are specific and only enquire about a single chunk or row of information.
Meta Query: Multiple tables need to be referenced to answer this query.

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Seamless LLM Integration for Deeper Insights

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