update structrued output demo
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zachary62
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@ -9,17 +9,27 @@ A minimal demo application showing how to use PocketFlow to extract structured d
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## Run It
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1. Make sure your OpenAI API key is set:
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1. Install the packages you need with this simple command:
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```bash
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pip install -r requirements.txt
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```
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2. 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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Alternatively, you can edit the [`utils.py`](./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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Let's do a quick check to make sure your API key is working properly:
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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 utils.py
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```
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3. Edit [data.txt](./data.txt) with the resume you want to parse (a sample resume is already included)
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4. Run the application:
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```bash
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python main.py
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```
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@ -45,24 +55,29 @@ The Resume Parser application uses a single node that:
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## Example Output
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```
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=== STRUCTURED RESUME DATA ===
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=== Resume Parser - Structured Output with Indexes & Comments ===
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name: John Smith
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=== STRUCTURED RESUME DATA (Comments & Skill Index List) ===
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name: JOHN SMTIH
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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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- {title: SALES MANAGER, company: ABC Corportaion}
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- {title: ASST. MANAGER, company: XYZ Industries}
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- {title: CUSTOMER SERVICE REPRESENTATIVE, company: Fast Solutions Inc}
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skill_indexes: [0, 1, 2, 3, 4]
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============================
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============================================================
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✅ Extracted resume information.
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--- Found Target Skills (from Indexes) ---
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- Team leadership & management (Index: 0)
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- CRM software (Index: 1)
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- Project management (Index: 2)
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- Public speaking (Index: 3)
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- Microsoft Office (Index: 4)
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----------------------------------------
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```
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@ -1,74 +1,148 @@
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from pocketflow import Node, Flow
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from utils import call_llm
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import yaml
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import os # Needed for the utils import below
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from pocketflow import Node, Flow
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from utils import call_llm # Assumes utils.py with call_llm exists
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class ResumeParserNode(Node):
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def prep(self, shared):
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"""Return resume text from shared state"""
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return shared["resume_text"]
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def exec(self, resume_text):
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"""Extract structured data from resume using prompt engineering"""
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"""Return resume text and target skills from shared state."""
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return {
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"resume_text": shared["resume_text"],
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"target_skills": shared.get("target_skills", [])
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}
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def exec(self, prep_res):
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"""Extract structured data from resume using prompt engineering.
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Requests YAML output with comments and skill indexes as a list.
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"""
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resume_text = prep_res["resume_text"]
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target_skills = prep_res["target_skills"]
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# Format skills with indexes for the prompt
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skill_list_for_prompt = "\n".join([f"{i}: {skill}" for i, skill in enumerate(target_skills)])
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# Simplified Prompt focusing on key instructions and format
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prompt = f"""
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Please extract the following information from this resume and format it as YAML:
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- name
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- email
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- experience (list of positions with title and company)
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- skills (list of skills)
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Analyze the resume below. Output ONLY the requested information in YAML format.
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**Resume:**
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```
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{resume_text}
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```
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Now, output:
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**Target Skills (use these indexes):**
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```
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{skill_list_for_prompt}
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```
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**YAML Output Requirements:**
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- Extract `name` (string).
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- Extract `email` (string).
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- Extract `experience` (list of objects with `title` and `company`).
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- Extract `skill_indexes` (list of integers found from the Target Skills list).
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- **Add a YAML comment (`#`) explaining the source BEFORE `name`, `email`, `experience`, each item in `experience`, and `skill_indexes`.**
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**Example Format:**
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```yaml
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name: John Doe
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email: john@example.com
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# Found name at top
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name: Jane Doe
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# Found email in contact info
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email: jane@example.com
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# Experience section analysis
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experience:
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- title: Software Engineer
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company: Tech Company
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- title: Developer
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company: Another Company
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skills:
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- Python
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- JavaScript
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- HTML/CSS
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```"""
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# First job listed
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- title: Manager
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company: Corp A
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# Second job listed
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- title: Assistant
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company: Corp B
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# Skills identified from the target list based on resume content
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skill_indexes:
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# Found 0 at top
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- 0
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# Found 2 in experience
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- 2
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```
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Generate the YAML output now:
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"""
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response = call_llm(prompt)
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# Extract YAML content from markdown code blocks
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# --- Minimal YAML Extraction ---
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# Assumes LLM correctly uses ```yaml blocks
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yaml_str = response.split("```yaml")[1].split("```")[0].strip()
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structured_result = yaml.safe_load(yaml_str)
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# Validate structure
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assert "name" in structured_result
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assert "experience" in structured_result
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assert isinstance(structured_result["experience"], list)
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assert "skills" in structured_result
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assert isinstance(structured_result["skills"], list)
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return structured_result
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def post(self, shared, prep_res, exec_res):
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"""Store and display structured resume data in YAML"""
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shared["structured_data"] = exec_res
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# Print structured data in YAML format
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print("\n=== STRUCTURED RESUME DATA ===\n")
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print(yaml.dump(exec_res, sort_keys=False))
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print("\n============================\n")
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print("✅ Extracted basic resume information")
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# Create and run the flow
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if __name__ == "__main__":
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print("=== Simple Resume Parser - YAML Output ===\n")
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# Read resume text from file
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shared = {}
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with open('data.txt', 'r') as file:
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resume_text = file.read()
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shared["resume_text"] = resume_text
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# --- End Minimal Extraction ---
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flow = Flow(start=ResumeParserNode())
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flow.run(shared)
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# --- Basic Validation ---
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assert structured_result is not None, "Validation Failed: Parsed YAML is None"
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assert "name" in structured_result, "Validation Failed: Missing 'name'"
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assert "email" in structured_result, "Validation Failed: Missing 'email'"
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assert "experience" in structured_result, "Validation Failed: Missing 'experience'"
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assert isinstance(structured_result.get("experience"), list), "Validation Failed: 'experience' is not a list"
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assert "skill_indexes" in structured_result, "Validation Failed: Missing 'skill_indexes'"
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skill_indexes_val = structured_result.get("skill_indexes")
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assert skill_indexes_val is None or isinstance(skill_indexes_val, list), "Validation Failed: 'skill_indexes' is not a list or None"
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if isinstance(skill_indexes_val, list):
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for index in skill_indexes_val:
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assert isinstance(index, int), f"Validation Failed: Skill index '{index}' is not an integer"
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# --- End Basic Validation ---
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return structured_result
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def post(self, shared, prep_res, exec_res):
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"""Store structured data and print it."""
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shared["structured_data"] = exec_res
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print("\n=== STRUCTURED RESUME DATA (Comments & Skill Index List) ===\n")
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# Dump YAML ensuring block style for readability
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print(yaml.dump(exec_res, sort_keys=False, allow_unicode=True, default_flow_style=None))
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print("\n============================================================\n")
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print("✅ Extracted resume information.")
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# === Main Execution Logic ===
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if __name__ == "__main__":
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print("=== Resume Parser - Structured Output with Indexes & Comments ===\n")
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# --- Configuration ---
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target_skills_to_find = [
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"Team leadership & management", # 0
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"CRM software", # 1
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"Project management", # 2
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"Public speaking", # 3
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"Microsoft Office", # 4
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"Python", # 5
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"Data Analysis" # 6
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]
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resume_file = 'data.txt' # Assumes data.txt contains the resume
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# --- Prepare Shared State ---
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shared = {}
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try:
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with open(resume_file, 'r') as file:
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shared["resume_text"] = file.read()
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except FileNotFoundError:
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print(f"Error: Resume file '{resume_file}' not found.")
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exit(1) # Exit if resume file is missing
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shared["target_skills"] = target_skills_to_find
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# --- Define and Run Flow ---
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parser_node = ResumeParserNode()
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flow = Flow(start=parser_node)
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flow.run(shared) # Execute the parsing node
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# --- Display Found Skills ---
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if "structured_data" in shared and "skill_indexes" in shared["structured_data"]:
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print("\n--- Found Target Skills (from Indexes) ---")
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found_indexes = shared["structured_data"]["skill_indexes"]
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if found_indexes: # Check if the list is not empty or None
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for index in found_indexes:
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if 0 <= index < len(target_skills_to_find):
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print(f"- {target_skills_to_find[index]} (Index: {index})")
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else:
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print(f"- Warning: Found invalid skill index {index}")
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else:
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print("No target skills identified from the list.")
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print("----------------------------------------\n")
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