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"""Quick test of diverse generation with high temperature."""
import json
import random
import os
from dotenv import load_dotenv
load_dotenv()
import cohere
client = cohere.ClientV2(api_key=os.getenv("COHERE_API_KEY"))
INDUSTRIES = ["fintech startup", "healthcare SaaS", "e-commerce fashion"]
STARTERS = ["So about", "Following up on", "I've been thinking about"]
# Test 3 different categories with HIGH temperature
test_cases = [
("company.tools_config", "fintech startup", "growth hacker"),
("user.communication_style", "healthcare SaaS", "CMO"),
("none", "e-commerce fashion", "marketing manager")
]
for category, industry, role in test_cases:
starter = random.choice(STARTERS)
turns = random.randint(3, 6)
if category == "none":
prompt = f"""Create a UNMEMORABLE conversation between a {role} at a {industry} and AI.
Purely transactional - status check, scheduling, confirmation. NO specific details.
{turns} turns. Start with "{starter}..."
Return JSON: {{"conversation": [...], "labels": {{"categories": ["none"]}}}}"""
else:
prompt = f"""Create a marketing conversation for a {role} at a {industry}.
Must demonstrate: {category}
{turns} turns. Start with "{starter}..."
Be SPECIFIC with realistic details unique to {industry}.
Return JSON: {{"conversation": [...], "labels": {{"categories": ["{category}"]}}}}"""
response = client.chat(
messages=[{"role": "user", "content": prompt}],
temperature=0.95,
model="command-r-plus-08-2024",
response_format={"type": "json_object"}
)
content = response.message.content[0].text
data = json.loads(content)
print(f"\n{'='*60}")
print(f"Category: {category} | Industry: {industry}")
print(f"Output categories: {data.get('labels', {}).get('categories', [])}")
conv = data.get("conversation", [])
if conv:
first = conv[0]
if isinstance(first, dict):
print(f"First turn: {first.get('content', '')[:120]}...")
else:
print(f"First turn: {str(first)[:120]}...")
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