This notebook demonstrates the process of retrieving Cube's data model metadata in a format suitable for passing to LLMs as embeddings, thereby enhancing contextual information.
Cube is the Semantic Layer for building data apps. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application.
Cube’s data model provides structure and definitions that are used as a context for LLM to understand data and generate correct queries. LLM doesn’t need to navigate complex joins and metrics calculations because Cube abstracts those and provides a simple interface that operates on the business-level terminology, instead of SQL table and column names. This simplification helps LLM to be less error-prone and avoid hallucinations.
import jwt
from langchain_community.document_loaders import CubeSemanticLoader
api_url = "https://api-example.gcp-us-central1.cubecloudapp.dev/cubejs-api/v1/meta"
cubejs_api_secret = "api-secret-here"
security_context = {}
api_token = jwt.encode(security_context, cubejs_api_secret, algorithm="HS256")
loader = CubeSemanticLoader(api_url, api_token)
documents = loader.load()
page_content = "Users View City, None"
metadata = {
"table_name": "users_view",
"column_name": "users_view.city",
"column_data_type": "string",
"column_title": "Users View City",
"column_description": "None",
"column_member_type": "dimension",
"column_values": [
"Austin",
"Chicago",
"Los Angeles",
"Mountain View",
"New York",
"Palo Alto",
"San Francisco",
"Seattle",
],
"cube_data_obj_type": "view",
}
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