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Zhongwei Li
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skills/pocketflow/scripts/pocketflow_init.py
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243
skills/pocketflow/scripts/pocketflow_init.py
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#!/usr/bin/env python3
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"""
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PocketFlow Project Initializer
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Creates a new PocketFlow project with best-practice structure
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"""
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import os
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import sys
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def create_project(project_name):
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"""Create a new PocketFlow project structure"""
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# Create directories
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dirs = [
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f"{project_name}/nodes",
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f"{project_name}/flows",
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f"{project_name}/utils",
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f"{project_name}/tests",
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f"{project_name}/docs"
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]
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for d in dirs:
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os.makedirs(d, exist_ok=True)
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# Create __init__.py for Python packages
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if d.endswith(('nodes', 'flows', 'utils', 'tests')):
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open(f"{d}/__init__.py", 'w').close()
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# Create main.py
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with open(f"{project_name}/main.py", 'w') as f:
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f.write('''#!/usr/bin/env python3
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"""
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Main entry point for {name}
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"""
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from flows.my_flow import MyFlow
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def main():
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shared = {{
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"input": "Hello, PocketFlow!",
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}}
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flow = MyFlow()
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flow.run(shared)
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print(f"Result: {{shared.get('result')}}")
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if __name__ == "__main__":
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main()
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'''.format(name=project_name))
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# Create example LLM utility
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with open(f"{project_name}/utils/call_llm.py", 'w') as f:
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f.write('''"""
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LLM wrapper - customize for your provider
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"""
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def call_llm(prompt):
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"""Call your LLM provider"""
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# TODO: Implement your LLM call
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# Example for OpenAI:
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# from openai import OpenAI
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# client = OpenAI(api_key="YOUR_API_KEY")
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# response = client.chat.completions.create(
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# model="gpt-4o",
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# messages=[{"role": "user", "content": prompt}]
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# )
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# return response.choices[0].message.content
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raise NotImplementedError("Implement your LLM provider")
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''')
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# Create example node
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with open(f"{project_name}/nodes/my_node.py", 'w') as f:
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f.write('''"""
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Example node implementation
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"""
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from pocketflow import Node
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from utils.call_llm import call_llm
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class ProcessNode(Node):
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"""Example processing node"""
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def prep(self, shared):
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"""Get input from shared store"""
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return shared.get("input", "")
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def exec(self, prep_res):
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"""Process with LLM"""
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prompt = f"Process this: {prep_res}"
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result = call_llm(prompt)
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return result
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def post(self, shared, prep_res, exec_res):
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"""Store result"""
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shared["result"] = exec_res
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return "default"
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''')
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# Create example flow
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with open(f"{project_name}/flows/my_flow.py", 'w') as f:
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f.write('''"""
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Example flow implementation
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"""
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from pocketflow import Flow
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from nodes.my_node import ProcessNode
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class MyFlow(Flow):
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"""Example flow"""
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def __init__(self):
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# Create nodes
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process = ProcessNode()
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# Define flow
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# process >> next_node # Add more nodes as needed
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# Initialize flow
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super().__init__(start=process)
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''')
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# Create requirements.txt
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with open(f"{project_name}/requirements.txt", 'w') as f:
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f.write('''# PocketFlow dependencies
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pocketflow
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# LLM providers (uncomment what you need)
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# openai
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# anthropic
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# google-generativeai
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# Optional utilities
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# beautifulsoup4
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# requests
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# faiss-cpu
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''')
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# Create README
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with open(f"{project_name}/README.md", 'w') as f:
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f.write(f'''# {project_name}
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PocketFlow project for [describe your use case]
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## Setup
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```bash
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# Install dependencies
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pip install -r requirements.txt
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# Configure your LLM provider
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# Edit utils/call_llm.py
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# Run
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python main.py
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```
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## Project Structure
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```
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{project_name}/
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├── main.py # Entry point
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├── nodes/ # Node implementations
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├── flows/ # Flow definitions
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├── utils/ # Utilities (LLM, DB, etc.)
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├── tests/ # Unit tests
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└── docs/ # Documentation
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```
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## Next Steps
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1. Implement your LLM wrapper in `utils/call_llm.py`
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2. Create your nodes in `nodes/`
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3. Define your flow in `flows/`
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4. Run and test!
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''')
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# Create design doc template
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with open(f"{project_name}/docs/design.md", 'w') as f:
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f.write(f'''# {project_name} Design
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## Problem Statement
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What problem are you solving?
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## Solution Overview
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High-level approach using PocketFlow
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## Flow Architecture
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```mermaid
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flowchart LR
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start[Start] --> process[Process]
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process --> end[End]
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```
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## Data Schema
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```python
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shared = {{
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"input": "...",
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"intermediate": "...",
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"result": "..."
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}}
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```
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## Nodes
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### Node 1: ProcessNode
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- **Purpose:** What does it do?
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- **Input:** What does it need from shared?
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- **Output:** What does it produce?
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- **Actions:** What actions can it return?
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## Error Handling
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How will you handle failures?
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## Testing Strategy
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How will you test this?
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''')
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print(f"✅ Created PocketFlow project: {project_name}/")
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print(f"📁 Structure:")
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print(f" ├── main.py")
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print(f" ├── nodes/my_node.py")
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print(f" ├── flows/my_flow.py")
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print(f" ├── utils/call_llm.py")
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print(f" ├── requirements.txt")
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print(f" └── docs/design.md")
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print(f"\n🚀 Next steps:")
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print(f" 1. cd {project_name}")
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print(f" 2. Edit utils/call_llm.py (add your LLM API key)")
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print(f" 3. python main.py")
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print("Usage: python pocketflow_init.py <project_name>")
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sys.exit(1)
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create_project(sys.argv[1])
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76
skills/pocketflow/scripts/test_llm_connection.py
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76
skills/pocketflow/scripts/test_llm_connection.py
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#!/usr/bin/env python3
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"""
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Quick script to test your LLM connection
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"""
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import os
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import sys
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def test_openai():
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"""Test OpenAI connection"""
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try:
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from openai import OpenAI
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "user", "content": "Say 'hello'"}]
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)
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print("✅ OpenAI: Connected")
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print(f" Response: {response.choices[0].message.content}")
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return True
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except Exception as e:
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print(f"❌ OpenAI: Failed - {e}")
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return False
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def test_anthropic():
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"""Test Anthropic connection"""
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try:
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from anthropic import Anthropic
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client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
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response = client.messages.create(
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model="claude-3-5-haiku-20241022",
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max_tokens=100,
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messages=[{"role": "user", "content": "Say 'hello'"}]
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)
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print("✅ Anthropic: Connected")
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print(f" Response: {response.content[0].text}")
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return True
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except Exception as e:
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print(f"❌ Anthropic: Failed - {e}")
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return False
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def test_google():
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"""Test Google Gemini connection"""
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try:
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from google import genai
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client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
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response = client.models.generate_content(
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model='gemini-2.0-flash-exp',
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contents="Say 'hello'"
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)
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print("✅ Google Gemini: Connected")
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print(f" Response: {response.text}")
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return True
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except Exception as e:
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print(f"❌ Google Gemini: Failed - {e}")
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return False
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if __name__ == "__main__":
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print("🔍 Testing LLM connections...\n")
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results = {
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"OpenAI": test_openai(),
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"Anthropic": test_anthropic(),
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"Google": test_google()
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}
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print("\n" + "="*50)
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working = [k for k, v in results.items() if v]
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if working:
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print(f"✅ Working providers: {', '.join(working)}")
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else:
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print("❌ No working providers found")
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print("\nMake sure you've set environment variables:")
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print(" export OPENAI_API_KEY=sk-...")
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print(" export ANTHROPIC_API_KEY=sk-ant-...")
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print(" export GEMINI_API_KEY=...")
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