Summary Note Generation Agent

This agent system is designed to transform intake forms filled out by patients, containing question-and-answer pairs (typically with short answers), into concise, human-readable summary notes. The agent uses a state-based workflow defined with LangGraph to accomplish this.

Key Features

• Input Parsing: The system reads a JSON-formatted input that contains questions and their respective answers.
• Categorization: The questions and answers are categorized based on their content, allowing for organized and relevant summaries.
• Summary Generation: Generates human-readable notes based on the categorized content.
• Summary Correction: In case of incorrect categorizations or summaries, the system iterates through a correction and regeneration loop until acceptable results are produced.
• Grading and Decision Making: Summaries are graded for accuracy, and decisions are made on whether corrections are needed or if the final summary can be accepted.

Workflow Overview

1. Split QA: The system first splits the question-answer (QA) pairs from the JSON input.
   • If the splits are correct, the workflow proceeds to generate summaries.
   • If splits are incorrect, the process loops back to correct them.
2. Generate Summaries: Based on the categorized QA pairs, the system generates initial summaries.
3. Grade Summaries: The generated summaries are evaluated for accuracy.
   • If the summaries are acceptable, the process ends.
   • If not, the workflow moves on to correct the summaries.
4. Correct Summaries: The system corrects any detected issues in the summaries.
5. Grade Regenerated Summaries: The corrected summaries are graded again.
   • If acceptable, the process ends.
   • Otherwise, the correction loop is repeated.

Input and Output

The input to this system is a set of questions and answers filled out by the patient. The output is a human-readable summary note, such as: "Patient reports vomiting without any visible blood in the vomit."