Adding Retrieval-Augmented Generation (RAG) to Your GraphQL API

The text discusses how to add Retrieval Augmented Generation (RAG) capabilities to a GraphQL API using Neo4j. It highlights the Weaviate vector database's native support for RAG and explores how to replicate this functionality in a Neo4j GraphQL project using Langchain.js, an open-source library that enables developers to integrate large language models into their applications. The article provides step-by-step instructions on setting up a basic Neo4j GraphQL project, installing LangChain.js, creating a chain to invoke an LLM, and defining a custom resolver to generate reviews based on user input. It also discusses how to create a generic generate resolver that can be applied to multiple types, such as Movie and Actor, and provides a mapping function to automatically assign resolvers to types that inherit the CanGenerate interface. The article concludes by highlighting the benefits of adding RAG capabilities to GraphQL APIs for dynamic content creation and personalization at scale.

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