DAG-Driven Notebook Priming to Reduce Redundant Queries
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Solution Overview
Problem
Generative AI models face inefficiencies in network usage and computational resources due to iterative queries with limited context, leading to inaccuracies and resource waste when handling dependent data in notebook environments.
Innovation Solution
A supervised machine learning approach utilizing a directed acyclic graph (DAG) structure in a notebook environment to determine cell dependencies and prime a generative AI model with contextual information from notebook, user, and data warehouse graphs, reducing redundant queries and optimizing network and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative queries are used with generative AI models to resolve natural language commands, then the model can refine its responses based on feedback, but network usage and computational resources are wasted due to redundant back-and-forth communications
Solution Approach 1:
The system performs preliminary actions by determining cell dependencies and constructing the execution plan before the generative AI model generates code. The DAG analysis identifies which cells need to be executed and in what order, so when the model responds, the context is already prepared and filtered, eliminating the need for iterative clarification queries about execution context.
Solution Approach 2:
The notebook execution context acts as an intermediary between the user's natural language command and the generative AI model. It translates the command into a structured form with pre-determined cell dependencies and execution plans, which are then passed to the model along with only the relevant context, reducing the need for iterative back-and-forth.
2Productivity
If generative AI models process natural language commands with limited context, then computational efficiency is maintained, but accuracy deteriorates when inputs depend on external data from notebook cells
Solution Approach 1:
The system extracts only the relevant context from the notebook environment based on cell dependencies. Instead of providing the entire notebook context or using limited fixed context, the DAG analysis identifies and extracts only the specific cells that the target cell depends on, filtering out irrelevant information and providing precisely the right amount of context for accurate code generation.
Solution Approach 2:
The system dynamically changes the context parameter based on the specific command and cell dependencies. Rather than using a static context window size or fixed context set, the execution plan adapts the context to include only the necessary precedent cells, optimizing both the amount of context provided and the computational resources required.
3Reliability
If the entire notebook context is provided to the generative AI model, then accuracy improves by including all dependent data, but network usage and computational resources increase significantly
Solution Approach 1:
The system applies local quality by providing different levels of context to different parts of the system. The DAG analysis determines which specific cells are relevant to the current command and provides detailed context only for those cells, while other unrelated cells receive minimal or no context, optimizing the balance between accuracy and resource usage.
Solution Approach 2:
The system uses partial action by providing only the necessary subset of cell contexts rather than the entire notebook. The execution plan identifies the minimal set of precedent cells required to accurately execute the target cell, avoiding the excessive resource consumption that would result from providing all notebook context while ensuring sufficient accuracy.
Data Source
AI summary
An application receives a natural language query from a user into a cell of a notebook environment and responsively determines a set of precedent cells and a profile of the user. The application determines a portion of the data warehouse graph that corresponds to the natural language query. The application primes the large language model with priming context that is based on the portion of the data warehouse graph that corresponds to the natural language query, the precedent cells from which the code cell depends, and the profile of the user, the priming resulting in a primed large language model. The application inputs the natural language query into the primed large language model and receives, as output from the large language model, a response to the natural language query. The application provides the response to the natural language query to the user.