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Structured Output Demo

A minimal demo application showing how to use PocketFlow to extract structured data from a resume using direct prompting and YAML formatting. Why YAML? Check out the doc.

Features

Extracts structured data using prompt engineering
Validates output structure before processing

Run It

Install the packages you need with this simple command:
```
pip install -r requirements.txt
```
Make sure your OpenAI API key is set:
```
export OPENAI_API_KEY="your-api-key-here"
```
Alternatively, you can edit the utils.py file to include your API key directly.

Let's do a quick check to make sure your API key is working properly:
```
python utils.py
```
Edit data.txt with the resume you want to parse (a sample resume is already included)
Run the application:
```
python main.py
```

How It Works

flowchart LR
    parser[ResumeParserNode]

The Resume Parser application uses a single node that:

Takes resume text from the shared state (loaded from data.txt)
Sends the resume to an LLM with a prompt that requests YAML formatted output
Extracts and validates the structured YAML data
Outputs the structured result

Files

main.py: Implementation of the ResumeParserNode
utils.py: LLM utilities
data.txt: Sample resume text file

Example Output

=== Resume Parser - Structured Output with Indexes & Comments ===


=== STRUCTURED RESUME DATA (Comments & Skill Index List) ===

name: JOHN SMTIH
email: johnsmtih1983@gnail.com
experience:
- {title: SALES MANAGER, company: ABC Corportaion}
- {title: ASST. MANAGER, company: XYZ Industries}
- {title: CUSTOMER SERVICE REPRESENTATIVE, company: Fast Solutions Inc}
skill_indexes: [0, 1, 2, 3, 4]


============================================================

✅ Extracted resume information.

--- Found Target Skills (from Indexes) ---
- Team leadership & management (Index: 0)
- CRM software (Index: 1)
- Project management (Index: 2)
- Public speaking (Index: 3)
- Microsoft Office (Index: 4)
----------------------------------------

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