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praisonai-triage-agent[bot]andDhivya-Bharathy
92c2b6dd64
AgentTeam.__init__ accepts neither verbose= nor **kwargs, so passing verbose= raised TypeError at construction. Verbosity is controlled via the consolidated output= param (silent/minimal/verbose) and iteration limits via execution=. Migrated all internal CLI, template generators, and examples off the removed verbose=/max_iter=/session= kwargs, and added an AST lint guard that scans examples + internal CLI for invalid AgentTeam kwargs to prevent regression. Co-authored-by: Dhivya-Bharathy <[email protected]>
370 lines
14 KiB
Python
370 lines
14 KiB
Python
# praisonai: skip=true
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"""
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CSV Processing with PraisonAI Agents
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This example demonstrates multiple ways to process CSV files with agents:
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1. Simple CSV Loop Processing (easiest)
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2. Manual CSV Processing with read_csv tools
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3. URL Processing from CSV files
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4. Custom CSV Processing with validation
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Author: Generated for GitHub Issue #23
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"""
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import os
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from praisonaiagents import Agent, Task, AgentTeam
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from praisonaiagents.tools import read_csv, write_csv
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def create_sample_urls_csv():
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"""Create a sample CSV file with URLs for testing"""
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urls_data = [
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{"id": 1, "url": "https://example.com", "description": "Example website"},
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{"id": 2, "url": "https://github.com", "description": "GitHub platform"},
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{"id": 3, "url": "https://stackoverflow.com", "description": "Stack Overflow Q&A"},
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{"id": 4, "url": "https://python.org", "description": "Python official site"},
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{"id": 5, "url": "https://docs.python.org", "description": "Python documentation"}
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]
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# Ensure the CSV file exists
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write_csv("sample_urls.csv", urls_data)
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print("✅ Created sample_urls.csv with 5 URLs")
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return "sample_urls.csv"
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def create_sample_tasks_csv():
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"""Create a sample CSV file with tasks for loop processing"""
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tasks_data = [
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{"task": "Analyze the homepage of example.com and summarize its content"},
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{"task": "Check if github.com is accessible and describe its main features"},
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{"task": "Research what Stack Overflow is used for"},
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{"task": "Summarize the key features of Python programming language"},
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{"task": "Describe what you can find in Python documentation"}
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]
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write_csv("sample_tasks.csv", tasks_data)
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print("✅ Created sample_tasks.csv with 5 tasks")
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return "sample_tasks.csv"
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# ==============================================================================
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# METHOD 1: SIMPLE CSV LOOP PROCESSING (RECOMMENDED FOR BEGINNERS)
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# ==============================================================================
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def method_1_simple_loop():
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"""
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The easiest way to process CSV files with PraisonAI agents.
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This method automatically loops through each row in your CSV file.
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Each row becomes input for the agent to process.
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"""
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print("\n" + "="*60)
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print("METHOD 1: Simple CSV Loop Processing")
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print("="*60)
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# Create sample CSV file
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csv_file = create_sample_tasks_csv()
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# Create an agent that will process each row
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loop_agent = Agent(
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name="CSVLoopProcessor",
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role="CSV Task Processor",
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goal="Process each task from the CSV file efficiently",
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backstory="Expert at handling repetitive tasks from CSV data",
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instructions="Process each task thoroughly and provide detailed output",
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llm="gpt-4o-mini"
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)
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# Create a loop task that will automatically process each CSV row
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loop_task = Task(
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description="Process each task from the CSV file",
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expected_output="Completed task with detailed results",
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agent=loop_agent,
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task_type="loop", # This enables CSV loop processing
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input_file=csv_file # The CSV file to process
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)
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# Initialize the agents system
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agents = AgentTeam(
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agents=[loop_agent],
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tasks=[loop_task],
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process="workflow",
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execution="balanced" # max_iter=10 preset to prevent infinite loops
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)
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print(f"🚀 Starting loop processing of {csv_file}")
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print("Each row will be processed as a separate task...")
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# Start processing
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agents.start()
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# ==============================================================================
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# METHOD 2: MANUAL CSV PROCESSING WITH TOOLS
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# ==============================================================================
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def method_2_manual_csv():
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"""
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Manual CSV processing using built-in CSV tools.
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This gives you more control over how the CSV is processed,
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including validation, filtering, and custom logic.
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"""
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print("\n" + "="*60)
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print("METHOD 2: Manual CSV Processing with Tools")
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print("="*60)
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# Create sample CSV file
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csv_file = create_sample_urls_csv()
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# Create an agent with CSV tools
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csv_agent = Agent(
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name="CSVAnalyzer",
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role="CSV Data Processor",
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goal="Read, validate, and process CSV data manually",
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backstory="Specialist in data validation and processing",
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tools=[read_csv, write_csv],
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instructions="""
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You are an expert at processing CSV files. When given a CSV file:
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1. Read the CSV file using read_csv tool
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2. Validate the data structure
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3. Process each row according to the requirements
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4. Create a summary of the processing results
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5. Save results to a new CSV file if needed
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""",
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llm="gpt-4o-mini"
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)
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# Create a task for manual CSV processing
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csv_task = Task(
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description=f"""
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Process the CSV file '{csv_file}' manually:
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1. Read the CSV file using the read_csv tool
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2. Validate that it contains 'url' and 'description' columns
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3. For each URL, describe what type of website it is
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4. Create a summary report of all URLs processed
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5. Save the results to 'processed_urls.csv'
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""",
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expected_output="A detailed report of CSV processing with validation results",
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agent=csv_agent
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)
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# Initialize and run
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agents = AgentTeam(
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agents=[csv_agent],
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tasks=[csv_task],
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process="sequential"
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)
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print(f"🚀 Starting manual processing of {csv_file}")
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agents.start()
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# ==============================================================================
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# METHOD 3: URL PROCESSING FROM CSV (SPECIFIC TO ISSUE REQUEST)
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# ==============================================================================
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def method_3_url_processing():
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"""
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Specific example for processing URLs from CSV files.
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This addresses the original question about processing a CSV list of URLs
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where agents work through the list sequentially.
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"""
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print("\n" + "="*60)
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print("METHOD 3: URL Processing from CSV Files")
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print("="*60)
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# Create sample CSV file with URLs
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csv_file = create_sample_urls_csv()
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# Create a URL processing agent
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url_agent = Agent(
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name="URLProcessor",
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role="Website Analyzer",
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goal="Analyze websites from CSV URL list",
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backstory="Expert web analyst who can evaluate websites",
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tools=[read_csv, write_csv],
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instructions="""
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You are a website analysis expert. When given a CSV with URLs:
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1. Read the CSV file to get the list of URLs
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2. For each URL, analyze what kind of website it is
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3. Determine the purpose and main features of each site
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4. Create a detailed analysis report
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5. Save results with analysis data
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""",
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llm="gpt-4o-mini"
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)
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# Create the URL processing task
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url_task = Task(
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description=f"""
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Process URLs from '{csv_file}':
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1. Read the CSV file containing URLs and descriptions
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2. For each URL in the list, provide an analysis of:
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- What type of website it is
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- Its main purpose and features
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- Target audience
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- Key characteristics
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3. Work through the URLs sequentially (one by one)
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4. Create a comprehensive report with all analyses
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5. Save the results to 'url_analysis_results.csv'
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Make sure to process each URL individually and thoroughly.
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""",
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expected_output="Detailed analysis of each URL with comprehensive results saved to CSV",
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agent=url_agent
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)
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# Initialize and run
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agents = AgentTeam(
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agents=[url_agent],
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tasks=[url_task],
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process="sequential"
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)
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print(f"🚀 Starting URL processing from {csv_file}")
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print("URLs will be processed sequentially...")
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agents.start()
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# ==============================================================================
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# METHOD 4: ADVANCED CSV PROCESSING WITH VALIDATION
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# ==============================================================================
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def method_4_advanced_processing():
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"""
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Advanced CSV processing with data validation and error handling.
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This method shows how to handle complex CSV processing scenarios
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with validation, error handling, and structured outputs.
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"""
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print("\n" + "="*60)
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print("METHOD 4: Advanced CSV Processing with Validation")
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print("="*60)
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# Create a more complex CSV file
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complex_data = [
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{"id": 1, "url": "https://example.com", "priority": "high", "category": "test"},
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{"id": 2, "url": "https://github.com", "priority": "medium", "category": "development"},
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{"id": 3, "url": "invalid-url", "priority": "low", "category": "test"}, # Invalid URL for testing
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{"id": 4, "url": "https://stackoverflow.com", "priority": "high", "category": "development"},
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{"id": 5, "url": "", "priority": "medium", "category": "empty"} # Empty URL for testing
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]
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csv_file = "complex_urls.csv"
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write_csv(csv_file, complex_data)
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print(f"✅ Created {csv_file} with complex data including invalid entries")
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# Create a validation agent
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validator_agent = Agent(
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name="DataValidator",
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role="Data Quality Specialist",
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goal="Validate and clean CSV data before processing",
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backstory="Expert in data validation and quality assurance",
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tools=[read_csv, write_csv],
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instructions="""
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You are a data validation expert. Your job is to:
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1. Read CSV files and validate data quality
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2. Identify invalid or problematic entries
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3. Clean and standardize data where possible
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4. Create separate files for valid and invalid data
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5. Provide detailed validation reports
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""",
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llm="gpt-4o-mini"
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)
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# Create a processing agent
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processor_agent = Agent(
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name="ValidatedProcessor",
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role="Validated Data Processor",
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goal="Process only validated and clean data",
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backstory="Specialist in processing pre-validated data efficiently",
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tools=[read_csv, write_csv],
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instructions="""
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You process validated data efficiently:
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1. Read validated CSV data
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2. Process each valid entry according to requirements
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3. Generate comprehensive results
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4. Create detailed output reports
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""",
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llm="gpt-4o-mini"
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)
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# Create validation task
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validation_task = Task(
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description=f"""
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Validate the data in '{csv_file}':
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1. Read the CSV file
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2. Check each row for:
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- Valid URL format (must start with http:// or https://)
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- Non-empty required fields
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- Valid priority values (high, medium, low)
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- Valid category values
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3. Separate valid and invalid entries
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4. Save valid entries to 'valid_urls.csv'
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5. Save invalid entries to 'invalid_urls.csv' with error descriptions
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6. Create a validation summary report
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""",
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expected_output="Validation complete with separate files for valid/invalid data and summary report",
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agent=validator_agent
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)
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# Create processing task (depends on validation)
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processing_task = Task(
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description="""
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Process the validated data from 'valid_urls.csv':
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1. Read the validated CSV file
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2. For each valid URL, provide analysis based on priority and category
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3. High priority items get detailed analysis
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4. Medium priority items get standard analysis
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5. Low priority items get basic analysis
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6. Save results to 'final_results.csv'
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""",
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expected_output="Processed results for all valid entries saved to final CSV",
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agent=processor_agent
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)
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# Initialize and run with sequential processing
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agents = AgentTeam(
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agents=[validator_agent, processor_agent],
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tasks=[validation_task, processing_task],
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process="sequential" # Validation must complete before processing
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)
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print(f"🚀 Starting advanced processing with validation")
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agents.start()
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# ==============================================================================
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# MAIN EXECUTION
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# ==============================================================================
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if __name__ == "__main__":
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print("CSV Processing with PraisonAI Agents - Multiple Methods Demo")
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print("=" * 70)
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print("\nThis demo shows 4 different ways to process CSV files with agents:")
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print("1. Simple CSV Loop Processing (easiest)")
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print("2. Manual CSV Processing with tools")
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print("3. URL Processing from CSV (addresses the GitHub issue)")
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print("4. Advanced processing with validation")
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# You can run all methods or comment out ones you don't need
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try:
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# Method 1: Simple loop processing
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method_1_simple_loop()
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# Method 2: Manual CSV processing
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method_2_manual_csv()
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# Method 3: URL processing (specific to the GitHub issue)
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method_3_url_processing()
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# Method 4: Advanced processing with validation
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method_4_advanced_processing()
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print("\n" + "="*70)
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print("✅ All CSV processing methods completed!")
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print("Check the generated CSV files for results.")
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print("="*70)
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except Exception as e:
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print(f"❌ Error during processing: {e}")
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print("Make sure you have:")
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print("- OpenAI API key set (export OPENAI_API_KEY=your_key)")
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print("- praisonaiagents package installed (pip install praisonaiagents)")
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print("- pandas package installed (pip install pandas)") |