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eceb45a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | from agency_swarm.agents import Agent
from agency_swarm.tools import CodeInterpreter
import os
import logging
from .tools.SearchAndScrape import SearchAndScrape
class ValidationAgent(Agent):
def __init__(self):
super().__init__(
name="ValidationAgent",
description="This agent validates market research reports using AI and ensures data completeness.",
instructions="./instructions.md",
files_folder="./files",
schemas_folder="./schemas",
tools=[SearchAndScrape],
tools_folder="./tools",
temperature=0.3,
model="groq/llama-3.3-70b-versatile",
max_prompt_tokens=25000,
)
def validate_data(self, report_data):
"""Validate the report using AI and fill gaps if needed."""
validation_prompt = f"""
Analyze this market research report for quality and completeness:
{report_data}
Please check for:
1. Missing key information
2. Data accuracy and consistency
3. Logical flow and structure
4. Completeness of sections:
- Market Size & Growth
- Competitive Landscape
- Consumer Analysis
- Technology & Innovation
- Future Outlook
Provide a detailed assessment with:
1. Quality score (0-100)
2. List of missing or incomplete sections
3. Specific recommendations for improvement
4. Additional data points needed
Format: JSON with these keys:
{
"quality_score": int,
"missing_sections": list,
"recommendations": list,
"additional_data_needed": list,
"is_complete": boolean
}
"""
try:
model = self._get_model() # Updated method to get model
response = model.generate_content(validation_prompt)
validation_result = response.text
# If validation shows missing data, scrape for it
if '"is_complete": false' in validation_result.lower():
missing_data = self._fill_missing_data(validation_result)
if missing_data:
# Combine original report with new data
updated_report = self._merge_reports(report_data, missing_data)
# Validate again
return self.validate_data(updated_report)
return validation_result
except Exception as e:
logging.error(f"Validation error: {str(e)}")
return {"error": str(e)}
def _fill_missing_data(self, validation_result):
"""Fill missing data based on validation results."""
try:
# Extract missing sections from validation result
import json
result = json.loads(validation_result)
missing_sections = result.get("missing_sections", [])
additional_data = []
for section in missing_sections:
# Create specific search query for missing section
search_query = f"{section} market research data analysis"
tool = SearchAndScrape(query=search_query)
section_data = tool.run()
if section_data:
additional_data.append({
"section": section,
"content": section_data
})
return additional_data if additional_data else None
except Exception as e:
logging.error(f"Error filling missing data: {str(e)}")
return None
def _merge_reports(self, original_report, new_data):
"""Merge original report with newly scraped data."""
merge_prompt = f"""
Merge this original report with new data:
Original Report:
{original_report}
New Data to Add:
{new_data}
Please create a cohesive, well-structured report that incorporates all information without duplication.
Ensure proper flow and transitions between sections.
"""
try:
model = self._get_model() # Updated method to get model
response = model.generate_content(merge_prompt)
return response.text
except Exception as e:
logging.error(f"Error merging reports: {str(e)}")
return original_report
def response_validator(self, message):
return message
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