Multi-Agent Resume Parser

Project Overview

This project implements a multi-agent workflow for processing resumes using Large Language Models (LLMs). It automates the extraction of key information from multi-page resumes, validates the extracted data, and allows for human intervention and feedback at various stages of the process.

Features

• Resume Reading: Processes multi-page resumes in various formats (PDF, DOCX)
• Entity Extraction: Extracts key information such as personal details, education, work experience, and skills
• Entity Validation: Validates extracted information for accuracy and completeness
• Human Feedback Loop: Allows for user intervention and feedback at each stage
• JSON Output: Compiles validated entities into a predefined JSON format
• Monitoring: Utilizes LangGraph and LangSmith for monitoring LLM calls

Installation

1. Clone the repository:

   git clone [repository-url]
   cd [repository-name]
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install required packages:

   pip install -r requirements.txt
   

Usage

• Set the src as the root file

To process a resume, run the following command:

python main.py --file_path /path/to/your/resume.pdf

Human Feedback Loop

The system allows for human intervention at each stage of the process. When prompted:

1. Enter the state you want to modify, or type 'next' to proceed to the next stage.
2. If modifying a state, enter the new value when prompted.
3. Type 'quit' at any time to exit the process.

Monitoring

This project uses LangGraph and LangSmith for monitoring LLM calls. To view the monitoring data:

1. [Instructions for accessing LangGraph monitoring]
2. [Instructions for accessing LangSmith monitoring]

Project Structure

• main.py: Entry point of the application
• agents/:
  • state.py: Defines the ResumeState class
  • resume_reader.py: Contains the resume reading logic
  • entity_extractor.py: Implements entity extraction
  • entity_validation.py: Handles validation of extracted entities
  • store_entities.py: Manages storage of validated entities
  • entity_corrector.py: Implements correction logic for extracted entities

Testing

Testing of all the agents have been implemented using the pytest library

• tests :
  • test_resume_reader.py : Define unit tests for resume reader agent
  • test_entity_extractor.py : Define unit test cases for entity extraction agent
  • test_entity_Validation.py : Define unit test cases for the entity validation agent

Assumptions and Design Decisions

• [List any important assumptions or design decisions made during development]

Future Improvements

• Implement comprehensive unit tests for each component
• Implement a graphical user interface for easier interaction
• Enhance error handling and edge case management
• Improve scalability to handle a larger volume of resumes

Contributing

License