The Power of Bridging Knowledge Graphs with Language Models<\/h3>\n\n\n\n
In our journey to make the web more intelligent and accessible, we’ve recognized the tremendous potential of combining the structured world of knowledge graphs with the fluid, context-aware capabilities of language models (LMs)<\/strong>. This fusion that we call neuro-symbolic AI<\/a> is a game-changer. With their ability to store and organize vast amounts of information, knowledge graphs provide a solid foundation of relevant facts and relationships<\/strong>. On the other hand, LLMs<\/strong>, with their capacity to understand and generate human-like text, bring a level of personalization that static web pages simply can’t match<\/strong>. We’re enhancing and fundamentally transforming the user experience by bridging these two powerful technologies. <\/p>\n\n\n\n We’re moving from a world where information is passively consumed to a digital ecosystem where users engage in rich, vibrant, and personalized conversations with the help of semantic concepts and structured data. <\/strong><\/p>\n\n\n\n The emerging architecture for LLM applications is a fascinating blend of new and existing technologies. I live the AI hype and the evolution of its stack with similar enthusiasm that I had back in the mid-nineties when Mosaic was the interface to the Internet. <\/p>\n\n\n\n Large Language Models (LLMs) are a powerful new tool for building software<\/strong>, but their unique characteristics require a combination of old ETL techniques and innovative approaches<\/strong> (embeddings<\/a>, prompting, intent matching) to leverage their capabilities fully. The reference architecture for the Large Language Model<\/a> app stack includes standard systems, tools, and design patterns used by AI startups and tech companies.<\/p>\n\n\n\n The architecture, as depicted by a16z<\/a>, is primarily focused on in-context learning; this is the ability of LLMs to be controlled through clever prompting techniques and access to private \u201ccontextual\u201d data. <\/p>\n\n\n\n The architecture can be divided into three core areas:<\/p>\n\n\n\n LlamaIndex, LangChain, Semantic Kernel and other emerging frameworks orchestrate the process and represent an abstraction layer on top of the LLM. <\/p>\n\n\n\n The WordLift connector loads the data we need from the knowledge graph using GraphQL, a query language introduced by Facebook that allows for precise, efficient data fetching. With only a few lines of code, we can specify exactly what data we need by extracting from our KG the sub-graphs we want. We do this by using specific properties of the schema.org vocabulary (or any custom ontology we use).<\/p>\n\n\n\n GraphQL enables the connector (WordLift Reader<\/a><\/strong>) to stay updated with the latest changes on the website without the need for crawling<\/strong>, ensuring that all conversations are always based on the most current data. This seamless integration via GraphQL between our KGs and Llama Index (or LangChain) enables the connector to transform static, structured data into dynamic, interactive conversations.<\/p>\n\n\n\n We are incorporating the concept of modularization into the new AI generative stack for SEO<\/a>. We can see blocks like the “TalkMe” widget introduced in my previous blog post to “modularize” conversations; the connector has been designed to use different sub-graphs and provide specific responses. This feature allows us to tailor conversations to the specific needs of users.<\/p>\n\n\n\n In the example below, a Shopping assistant is built by accessing the following:<\/p>\n\n\n\n Let\u2019s review the dialogue intents that we can address using only product data and using product data and Q&A content. <\/p>\n\n\n\n The power to leverage your Knowledge Graph built using schema.org for LLMs<\/em>. On the left the AI uses only schema:Product; on the right the AI uses schema:Product and schema:FAQPage. <\/p>\n\n\n\n The notebook below provides a reference implementation that uses our demo e-commerce website. To see how things work, open the link and follow the steps. <\/p>\n\n\n\n Ready to jump to the code?<\/strong><\/em> \ud83e\ude84 wor.ai\/wl-reader-demo<\/a> (here is a Colab Notebook to see how things work).<\/p>\n\n\n\n \ud83d\udcd6 Here is a link to the documentation<\/a>.<\/p>\n<\/blockquote>\n\n\n\n We\u2019ll build an AI-powered shopping assistant using LlamaIndex and the Knowledge Graph behind our e-commerce demo website (https:\/\/product-finder.wordlift.io\/). You must add your OpenAI API key and WordLift Key to run this tutorial. We\u2019ll begin by setting up the environment and installing the required libraries like langchain, openai, llama-index, and API keys.<\/p>\n\n\n\n The code’s core part is focused on using the WordLiftReader class to build a vector index of the shop\u2019s products.<\/p>\n\n\n\n We\u2019ll also see how to create multiple indices based on different schemas<\/a> classes like schema:Product<\/strong> and schema:FAQPage<\/strong>. This allows the AI agent to handle different types of user queries effectively. For example, the agent can suggest products based on user preferences or provide information about the shop’s return policy.<\/p>\n\n\n\n <\/p>\n\n\n\n To see our connector in action, watch the video with Andrea Volpini, Teodora Petkova and Jason Barnard. Here, you can learn how to create an enriching and interactive experience<\/strong> using the data in the knowledge graph, bridging the gap between users and content.<\/p>\n\n\n\nModern Architecture for Generative AI Apps<\/h3>\n\n\n\n
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Modularization: Tailoring Conversations to User Needs<\/h2>\n\n\n\n
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