LangChain
LangChain is a framework designed to simplify the creation of applications using large language models.
To get started with LangChain, follow the instructions .
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.
Configuring the connection to Cube
The document loader connects to Cube using the REST API, and will need a JWT to authenticate.
If you’re using Cube Cloud, you can retrieve these details from a deployment’s page.
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.
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