> ## Documentation Index
> Fetch the complete documentation index at: https://docs.llmcontrols.ai/llms.txt
> Use this file to discover all available pages before exploring further.

#  Resume Extraction 

<img src="https://mintcdn.com/devrel/mIs5bV2YHX7MUNHO/images/ResumeExtraction.jpg?fit=max&auto=format&n=mIs5bV2YHX7MUNHO&q=85&s=3ca2e6e4efb7c7dee71168c55ce4be37" alt="Resume Extraction Jp" width="2400" height="1640" data-path="images/ResumeExtraction.jpg" />

This advanced template automates the process of extracting, analyzing, and organizing resumes using Gmail, Google Drive, and an LLM-powered extraction workflow. It’s designed for recruitment automation, helping you efficiently process job applications and structure candidate data into Google Sheets.

## **Prerequisites**

Before you begin, make sure you have:

1. A Google Cloud Project with access to Gmail and Google Drive APIs.
2. A Gmail Credentials JSON file for authentication.
3. Access to a Google Sheet to store structured results.
4. An OpenAI API key (optional if using a model component that requires it)**.**

## **Create the Resume Extraction Flow**

1. From the LLM Controls dashboard, click **New Flow.**
2. Select **Resume Extraction.**

   The flow will appear with pre-connected components for Gmail, Google Drive, and LLM-based resume parsing.

## **Component Overview & Configuration**

### **1. Gmail Manager (Trigger & Setup)**

**Component**: Gmail Manager

**Purpose:** Monitors a labeled Gmail inbox, downloads resume attachments, and applies post-processing labels.

* **User Configuration:**
  1. **Gmail Credentials JSON:** Upload your credentials file from Google Cloud.
  2. **Target Label:** Gmail label to fetch (e.g., Job-Applications).
  3. **Maximum Emails to Fetch:** Number of emails to process per batch.
  4. **Drive Folder Name:** Folder in Google Drive where resumes will be saved.
  5. **Processed Label:** Label applied after successful processing (e.g., Processed).

### **2. File Handling and Processing**

**Component:** File Extract and Process

**Purpose:** Extracts resume PDFs from Gmail attachments and prepares them for batch LLM processing.

### **3. HR Analyst Description**

**Componen**t: User Input

**Purpose**: Defines how the LLM should interpret and analyze resumes.

**Action:**\
Paste your custom analysis prompt here, ideally, one optimized using the Prompt Optimizer Flow.

**This prompt guides the model in extracting structured details such as:**

1. Candidate Name
2. Contact Information
3. Skills
4. Experience Summary
5. Fit Evaluation

### **4. Contextual Inputs for LLM Understanding**

#### **a. Job Description**

**Component**: Job Description

**Purpose**: Provide the target job role or requirements to guide candidate-fit analysis.

#### **b. Prompt Configuration**

**Component**: Prompt

**Purpose**: Combines the job description, analyst instruction, and resume text into a formatted LLM input.

### **5. Batch Resume Processing**

**Component**: Batch Run

**Purpose**: Manages multiple resume analyses at once using the defined prompt template.

**Input**: Combined text from the resume file, job description, and HR prompt.

### **6.  LLM Execution**

**Component**: Language Model (LLM)

**Purpose**: Runs the structured extraction using the configured prompt.

**Tip**: You can select models like gpt-4o or your custom LLM Executor for faster batch inference.

### **7. Resume Classification and Scoring**

**Component**: Resume Classifier

**Purpose**: Assigns a classification (e.g., Junior, Mid-level, Senior) and generates a match score based on the job description.

**Custom Setting:** Adjust the Resume Classification Score threshold to control fit categorization.

### **8.  Resume Results Formatter**

**Component**: Results Formatter

**Purpose**: Cleans up and standardizes the LLM’s output.

**Output Fields:**

1. Candidate Name
2. Email Address
3. Role Classification
4. Executive Summary
5. Fit Score

### **9.  Google Sheets Writer**

**Component:** Google Sheets

**Purpose**: Writes structured data to a predefined Google Sheet.

**Configuration Required:**

1. Spreadsheet ID – Your Google Sheet identifier.
2. Sheet Name –The target tab name for data insertion.
3. Field Mapping – Connect output fields to sheet columns.

### **10.  Final Output: Sheet Link Display**

**Component**: URL Output

**Purpose**: Displays a clickable Google Sheet link for quick access to processed data.

## **Run the Resume Extraction Flow**

1. Click **Playground.**
2. Ensure your Gmail credentials and Google Drive folder are correctly configured.
3. Press Run to start fetching and processing resumes.
4. **Once complete:**
   * Resume PDFs appear in your Google Drive.
   * Candidate data is added as new rows in your Google Sheet.

## **Modify or Extend the Flow**

You can extend this template by:

1. Replacing the LLM component with a Prompt Optimizer Flow for automated prompt evaluation.
2. Adding a Qdrant or Vector Store for long-term candidate profile storage.
3. Connecting additional Sheets or CRMs for HR integration.

## **Output Summary**

After each run, you’ll get:

1. Resume PDFs saved in Drive
2. Extracted and structured candidate insights
3. A consolidated Google Sheet with name, skills, and fit score.

   <Note>
     When importing into your workspace, replace the default Gmail credentials provider with your own Gmail Credentials JSON.
   </Note>