Semantic Model Pushdown

Large Language Models (LLMs) promise to revolutionize data interaction, but they need proper grounding to provide trustworthy results. Semantic model pushdown provides this grounding by mapping natural language queries to well-defined data models.

LLMs are trained on vast amounts of text and code, granting them impressive language processing abilities. But they lack a deep understanding of the underlying structure and semantics of your data warehouse. When directly querying raw data, LLMs can struggle with:

• Ambiguous Queries: Natural language can be inherently ambiguous. An LLM might misinterpret a user’s intent, leading to incorrect queries.
• Data Complexity: Understanding complex relationships between tables, aggregations, and filters requires a strong grasp of the data model. LLMs can easily make mistakes.
• Lack of Context: LLMs operate on the surface level of the data. They don’t inherently understand the business meaning behind the data, leading to potentially misleading results.
• Hallucinations: LLMs might fabricate data or invent relationships that don’t exist in the database, leading to untrustworthy answers.

• Raw Data Access: Bypassing a semantic layer and letting LLMs directly query raw data is akin to giving them a map without a legend. They can navigate the terrain (the data), but they don’t understand the meaning of the landmarks (the data elements). This leads to misinterpretations, inaccuracies, and hallucinations.

• BI Tool Silos: Some organizations rely on BI tools like PowerBI to manage their semantic models. While these tools provide valuable business logic, locking the semantic model within the BI tool creates a new problem. LLMs cannot directly access this embedded logic, limiting their ability to generate accurate queries. Microsoft’s approach of integrating Copilot into Power BI is a step in the right direction, allowing natural language interaction within the BI tool itself. However, this approach still keeps the semantic model siloed and may not be ideal for broader data exploration outside the BI tool’s confines.

Semantic model pushdown addresses these challenges by acting as an intermediary between the LLM and the data warehouse. Here’s how it works:

• User Input: The user provides a natural language query
• LLM Interpretation: The LLM interprets the user’s intent
• Semantic Model Mapping: The semantic model maps the LLM’s interpretation to specific entities
• SQL Generation: Based on the mapping, a precise and unambiguous SQL query is generated
• Data Retrieval: The SQL query is executed against the data warehouse
• Answer Presentation: The results are presented to the user in a natural language format

• Trustworthy Answers: By grounding the LLM in a well-defined semantic model
• Deterministic Results: Given the same query, consistent results
• Improved Accuracy: Better disambiguation of natural language queries
• Simplified Querying: Natural language access to data
• Enhanced Data Governance: Enforced access control policies

Several approaches are emerging for implementing semantic model pushdown:

• Custom Development: Building your own semantic model and integration with an LLM gives you maximum flexibility but requires significant development effort.
• Specialized Platforms: Platforms specifically designed for semantic layer management are gaining traction. These platforms offer tools for defining and managing semantic models and integrating with LLMs.

• Cube.dev: Cube.dev offers a headless BI layer that acts as a powerful semantic layer. It allows you to define a data model using a declarative syntax, including measures, dimensions, and relationships. Cube.dev then handles the translation of user queries into efficient SQL queries against your data warehouse. Its focus on API-first access makes it well-suited for integration with LLMs, providing a robust foundation for building natural language interfaces. Critically, it provides a well-defined and governed structure for the LLM to interact with, minimizing the risk of misinterpretations and hallucinations. Cube.dev’s approach allows for consistent and reliable results, ensuring that the LLM queries are aligned with the defined business logic.
• AtScale: AtScale provides a semantic layer platform that connects to various data sources and allows you to define a business-friendly view of your data. It offers a query engine that can translate user queries into optimized queries for the underlying data sources. AtScale’s focus on performance and scalability makes it suitable for handling large datasets and complex analytical workloads. By abstracting the physical data layer, AtScale provides a consistent and reliable interface for LLMs, ensuring that queries are executed accurately and efficiently. It helps bridge the gap between complex data structures and the natural language understanding of LLMs. AtScale’s emphasis on performance ensures that even complex queries are translated and executed quickly, providing a seamless user experience.
• MetricFlow: MetricFlow takes a slightly different approach, focusing on defining and managing metrics. It provides a declarative way to define metrics based on your data model, including calculations, aggregations, and transformations. MetricFlow can then be used to generate SQL queries that compute these metrics. For LLM integration, MetricFlow provides a structured way for the LLM to understand and query available metrics, ensuring that the queries are semantically correct and consistent with business definitions. This is particularly useful for analytical applications where specific metrics are the focus of user queries. MetricFlow enables LLMs to understand the precise definitions of key metrics, ensuring that the results are accurate and aligned with business expectations.

• AI-powered Semantic Modeling: Using AI to automatically discover relationships
• Integration with Data Catalogs: Leveraging existing metadata
• Explainable AI (XAI): Providing transparency in query generation
• Personalized Semantic Models: Tailoring to user roles and preferences

While cloud-based LLMs offer convenience, having a choice of AI models is crucial. Different LLMs have different strengths and weaknesses. Being able to select the best model for a specific task is essential for optimizing performance and accuracy. Furthermore, when highly sensitive data is involved, self-hosting LLMs becomes a critical requirement for maintaining data privacy and control. Self-hosting allows organizations to keep their data within their own secure environment, mitigating the risks associated with sending sensitive information to external providers.

Semantic model pushdown is a critical component for building trustworthy and deterministic natural language interfaces to data warehouses. By grounding LLMs in a well-defined semantic model, we can unlock the true potential of these powerful models, making data access more accessible, accurate, and reliable. Moving beyond raw data access and siloed BI tools, embracing semantic model pushdown with specialized platforms like Cube.dev, AtScale, and MetricFlow, is key to achieving trustworthy data insights. As the technology continues to evolve, we can expect even more sophisticated solutions to emerge, further bridging the gap between human language and the world of data. The ability to choose and even self-host LLMs adds another layer of control and security, especially when dealing with sensitive information, ensuring that organizations can leverage the power of LLMs responsibly and securely.