How TigerGraph CoPilot implements graph-enhanced AI

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By comparing the shortcomings of other commercial LLMs, this article details the main functions, advantages, and two key use cases of TigerGraph CoPilot.

In recent years, data, as a production factor, has been able to provide us with transformative business insights across different industries. However, how to make good use of the available big data often brings us considerable challenges. On the one hand, there is an overload of data, and on the other hand, there are a large number of underutilized data islands. Many professional data scientists and analysts are in urgent need of professional knowledge to enable their organizations to process the continuously growing data in a timely manner without sacrificing performance and operational efficiency, and to extract from complex data what is hidden beneath the surface. insights below.

Recent breakthroughs in natural language processing with artificial intelligence (AI) have changed the way data is accessed centrally. By taking full advantage of AI CoPilot's ability to process and analyze large-scale data in real time, more users can easily query and parse complex data sets, which in turn helps organizations make informed decisions quickly. At the same time, artificial intelligence CoPilot can also manage and control the high cost of large data sets by automating complex data processes and empowering technical personnel to conduct in-depth data analysis, thereby optimizing the overall allocation of resources.

Generating AI and natural language models (LLMs are not without doubts. Since most LLMs are built on common public knowledge, they cannot know the specific data of a specific organization, let alone those confidential data At the same time, in addition to the inability of LLM to understand the changing information world, another more serious problem is "illusion". In other words, the conclusions generated by the generative model in its statistical process may be just wishful thinking and not true at all. Real.

It can be considered that we urgently need an AI that is more contextual and less error-prone, and can provide high-quality data that can directly improve the accuracy of business decisions in the process of predictive analysis and machine learning.

Introduction to TigerGraph CoPilot

TigerGraph CoPilot is an AI-assisted tool that combines the power of a graph database and generative AI to improve productivity in analyzing, developing, and managing a variety of business tasks. TigerGraph TigerGraph CoPilot allows business analysts, data scientists, and developers to use natural language to perform real-time queries on large-scale data to obtain data insights that can be presented and analyzed from natural language, graph visualization, and other perspectives. In response to the AI ​​shortcomings mentioned above, TigerGraph CoPilot can fully tap the potential of data for organizations by improving accuracy and reducing illusions, and promote informed decision-making in areas such as customer service, marketing, product sales, data science, development and engineering. Really embody the value of generative AI applications.

The main functions and advantages of TigerGraph CoPilot

Graphic enhanced natural language query

• Generative for graph enhancement. AI
• Reliable and responsible (as opposed to hallucinatory) AI
• High performance and scalability
• Graphically enhanced natural language query

TigerGraph CoPilot allows non-technical users to query and analyze data using the language of everyday communication, allowing them to focus more on uncovering insights rather than having to learn a new technology or computer language. CoPilot adopts a language for every user question. The novel three-stage interaction method (below) interacts with the TigerGraph database and the user-selected LLM simultaneously to obtain accurate and relevant answers. In the first stage, TigerGraph CoPilot will answer the question. Compare it to specific data available in the database. Specifically, it uses LLM to compare the schema of the problem to the graph and replace the entities in the problem with graph elements. For example, if there is a vertex of type BareMetalNode, And the user asks "How many servers are there?", then the question will be converted into "How many BareMetalNode vertices are there?"

In the second stage, TigerGraph CoPilot uses LLM to combine the converted question with a set of carefully planned The database query is compared with the function to select the best match. Using pre-approved queries generally has two benefits:

• First, because each query undergoes validation for its meaning and behavior, they effectively reduce the likelihood of hallucinations.
• Second, the system has the potential to predict the execution resources that will be invoked to answer the question.

In the third phase, TigerGraph CoPilot executes those identified queries and returns relevant results in natural language, along with the reasoning behind them. At the same time, CoPilot's graph-enhanced natural language query provides a good barrier, which can not only reduce the risk of model illusion, but also clarify the meaning of each query and provide an understanding of the results.

• Generative AI for graph enhancement

  TigerGraph CoPilot can create chatbots with graph-enhanced AI based on users’ own documents and data, without the need for an existing graph database. In this mode of operation, TigerGraph CoPilot builds a knowledge graph from resource materials and applies a variant of its unique Retrieval Augmented Generation (RAG) to improve the contextual relevance and accuracy of answers to natural language questions.

  First, when loading a user document, TigerGraph CoPilot extracts entities and relationships from document blocks and constructs a knowledge graph from the document. Knowledge graphs often connect data points through relationships to organize information in a structured format. At the same time, CoPilot will also identify concepts, build ontology, and add semantics and reasoning to the knowledge graph. Of course, users can also provide their own concept ontology.

  Then, by using this comprehensive knowledge graph, CoPilot will perform hybrid retrieval, that is, combining traditional vector search and graph traversal to collect more relevant information and richer context to answer the user's questions All kinds of problems.

  By organizing data into knowledge graphs, chatbots can quickly and efficiently access accurate, fact-based information, reducing the reliance on learning patterns based on the training process to generate responses. After all, such models may sometimes be incorrect or even outdated.

  

  Reliable and Responsible AI

  As mentioned earlier, TigerGraph CoPilot alleviates hallucinations by allowing LLM to manage queries to access the graph database. At the same time, it ensures responsible AI by applying the same role-based access control and security measures that are already part of the TigerGraph database. Additionally, TigerGraph CoPilot maintains openness and transparency by opening up its major components and allowing users to choose its LLM services.

  High performance and scalability

  By leveraging the TigerGraph database, TigerGraph CoPilot provides high performance for graph analysis. As a graph-based RAG solution, it also provides a large-scale scalable knowledge base for knowledge graph-driven question answering.

  TigerGraph CoPilot Key Use Cases

  • From Natural Language to Data Insights
  • Informative Q&A

  From Natural Language to Data Insights

  Whether you are a business analyst, data expert, or investigator, TigerGraph CoPilot allows you to quickly obtain information and insights from your data. For example, CoPilot can generate relevant reports for fraud investigators by answering questions such as "Show me a list of recent false positive fraud cases." At the same time, CoPilot can also facilitate more accurate investigations, such as: "Who has made transactions of more than $1,000 with 123 accounts in the past month?"

  TigerGraph CoPilot can even follow dependencies Traverse the graph to answer questions such as "What if?" For example, you can easily find out from the supply chain diagram "Which suppliers can fill the shortage of part 123?" or from the digital infrastructure diagram "Which services will be affected by the upgrade of server 321?"

  content-rich Q&A

  TigerGraph CoPilot can achieve accurate retrieval of contextual information based on the RAG method of knowledge graphs when building a Q&A chatbot for users' data and documents, so as to give better answers and more information. Smart decisions. It can be said that the rich Q&A provided by CoPilot directly improves the productivity of typical Q&A applications (such as call centers, customer service, knowledge search and other scenarios) and reduces construction costs.

  In addition, by merging the document knowledge graph and existing business graphs (such as product graphs) into a smart graph, TigerGraph CoPilot can solve problems that other RAG solutions cannot cope with. For example, by combining a customer's purchase history with product charts, CoPilot can make more accurate, personalized recommendations as customers type a search query or request recommendations. Typical scenarios include: By combining a patient's medical history with a health chart, a doctor or health professional can obtain more practical information about the patient to provide better diagnosis or treatment.

  Summary

  To sum up, compared with other commercial LLM applications, TigerGraph CoPilot solves the challenges of complex data management and related analysis. Through its powerful capabilities in natural language processing and advanced algorithms, organizations can gain tremendous business insights in overcoming data overload and responding to suboptimal data access. At the same time, by utilizing graph-based RAG, it can also ensure the accuracy and relevance of LLM output.

  Whereas CoPilot allows a wider range of users to effectively utilize data, make informed decisions, and optimize resource allocation across organizations. We believe this is a significant step forward in democratizing access to data and empowering organizations to leverage the full potential of their data assets.

  Translator Introduction

  Julian Chen (Julian Chen), 51CTO community editor, has more than ten years of experience in IT project implementation, is good at managing and controlling internal and external resources and risks, and focuses on spreading network and Information security knowledge and experience.

  Original title: How TigerGraph CoPilot enables graph-augmented AI, author: Hamid Azzawe

  Link: https://www.infoworld.com/article/3715344/how-tigergraph-copilot-enables-graph -augmented-ai.html.

  To learn more about AIGC, please visit:

  51CTO AI.x Community

  https://www.51cto.com/ aigc/

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