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LangChain is a framework designed to simplify the creation of applications using large language models. To get started with LangChain, follow the instructions.

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Document loader

Cube’s integration with LangChain comes as the document loader that is intended to be used to populate a vector database with embeddings derived from the data model. Later, this vector database can be queried to find best-matching entities of the semantic layer. This is useful to match free-form input, e.g., queries in a natural language, with the views and their members in the data model.
We’re also providing an chat-based demo application (see source code on GitHub) with example OpenAI prompts for constructing queries to Cube’s SQL API. If you wish to create an AI-powered conversational interface for the semantic layer, these prompts can be a good starting point.

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Configuring the connection to Cube

The document loader connects to Cube using the REST (JSON) API, and will need a JWT to authenticate. If you’re using Cube Cloud, you can retrieve these details from a deployment’s Overview page.

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Querying Cube

Please refer to the blog post for details on querying Cube and building a complete AI-based application. Also, please feel free to review a chat-based demo application source code on GitHub.