68 lines
1.7 KiB
Markdown
68 lines
1.7 KiB
Markdown
# Structured Output Demo
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A minimal demo application showing how to use PocketFlow to extract structured data from a resume using direct prompting and YAML formatting.
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## Features
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- Extracts structured data using prompt engineering
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- Validates output structure before processing
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## Run It
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1. Make sure your OpenAI API key is set:
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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```
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Alternatively, you can edit the `utils.py` file to include your API key directly.
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2. Edit data.txt with the resume you want to parse (a sample resume is already included)
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3. Install requirements and run the application:
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```bash
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pip install -r requirements.txt
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python main.py
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```
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## How It Works
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```mermaid
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flowchart LR
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parser[ResumeParserNode]
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```
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The Resume Parser application uses a single node that:
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1. Takes resume text from the shared state (loaded from data.txt)
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2. Sends the resume to an LLM with a prompt that requests YAML formatted output
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3. Extracts and validates the structured YAML data
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4. Outputs the structured result
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## Files
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- [`main.py`](./main.py): Implementation of the ResumeParserNode
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- [`utils.py`](./utils.py): LLM utilities
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- [`data.txt`](./data.txt): Sample resume text file
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## Example Output
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```
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=== STRUCTURED RESUME DATA ===
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name: John Smith
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email: johnsmtih1983@gnail.com
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experience:
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- title: Sales Manager
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company: ABC Corporation
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- title: Assistant Manager
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company: XYZ Industries
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- title: Customer Service Representative
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company: Fast Solutions Inc
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skills:
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- Microsoft Office: Excel, Word, PowerPoint (Advanced)
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- Customer relationship management (CRM) software
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- Team leadership & management
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- Project management
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- Public speaking
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- Time management
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============================
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``` |