From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
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Query options - Neo4j Tutorial
From the course: Knowledge Graph Data Engineering for Generative AI Use Cases
Query options
- [Instructor] As we've stated previously, when it comes to AI, semantic context is what supports a precise and accurate AI application. But AI can also be used to help query your graph, especially when the graph is being used by other applications that are AI-enabled, like search, recommendations and analytics. Let's show this. Most LLMs know the graph query languages, so you can ask for a SPARQL or a Cypher query for all customers from Texas with reviews of four stars or higher, and the AI can then construct the query that you can use. This can be a canned prompt that connects to a call to the graph API, or you can set it up so that the AI will call the API with that query when a salesperson needs the information for whichever AI chat experience or analytics experience that they are using. This can be a canned prompt connected to a call that calls the API for your graph. Or you can have the AI call the API with the query when sales or the product managers or whoever needs to use…
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