LLM Prompt Orchestration With Tool Selection for Document Extraction
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Solution Overview
Problem
Existing systems struggle to efficiently and accurately extract information from electronic documents using machine learning models, particularly in complex tasks involving multiple steps and tools.
Innovation Solution
A system utilizing machine learning models that generate prompts based on user queries, select appropriate tools, and perform tasks to enhance information extraction, including document analysis and tool utilization, enabling more efficient and accurate document processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models directly extract information from documents without preparation steps, then the process is simpler and faster, but the accuracy and completeness of information extraction deteriorates
Solution Approach 1:
The system performs preliminary actions by generating task plans and selecting appropriate tools before the machine learning model extracts information from documents. This preparation phase includes analyzing the query, identifying required tasks, and preparing extraction configurations, which improves extraction accuracy while managing complexity through structured preprocessing
2Measurement precision
If machine learning models perform multiple preparation steps and tool selections, then information extraction accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The information extraction process is segmented into distinct phases: query analysis, task planning, tool selection, and information extraction. Each phase handles specific sub-tasks independently, allowing for optimized processing at each stage and enabling parallel execution where possible, thus improving accuracy without linearly increasing total time
3Adaptability or versatility
If machine learning models use multiple tools for task completion, then the versatility and capability to handle complex tasks improves, but the system complexity and difficulty of operation increase
Solution Approach 1:
The machine learning model autonomously performs tool selection and task planning based on the query and document context. The system self-determines which tools are needed and in what sequence, eliminating the need for manual configuration or user intervention in tool selection, thus maintaining versatility while preserving ease of operation
Data Source
AI summary
Systems and methods to use one or more machine learning models to perform tasks as prompted using one or more tools are disclosed. Exemplary implementations may receive user input indicating a query; generate prompt information defining a prompt based on the query; provide the prompt as input to one or more machine learning models; obtain one or more replies for individual tasks; and present the one or more replies. The prompt may be configured to prompt the one or more machine learning models to identify the individual tasks based on the prompt; determine a step to be completed; select a tool for performing the step; perform the step using the tool to generate a first tool result; generate an intermediary prompt based on the first tool result; determine whether the intermediary prompt is sufficient; identify tools for generating the one or more replies; and generate the one or more replies.