jipso 0.1.29

JIPSO framework for prompt orchestration and comparison

📰 Paper

🛠️ INSTALL jipso-py

pip install jipso

🚀 QUICK START jipso-py

Example 1

from jipso.pvp import pvp
import os

os.environ['OPENAI_API_KEY'] = 'sk-proj-...'

prompt1 = 'Write leave request email'
prompt2 = 'Write formal leave request email with clear reason and timeline'
o_eval = pvp(prompt1, prompt2)
print(o_eval)

# ✅ **Function executed:** pvp("Write leave request email", "Write formal leave request email with clear reason and timeline"

# **Test Input Generated:** Employee needs 3 days off next week for medical appointment

# **Results:**
# - **P1 Output:** Generic leave request mentioning time off needed
# - **P2 Output:** Structured email with specific dates, medical reason, coverage arrangements, and professional formatting

# 📊 **Score:** P1 = 3.2/10 (P2 baseline = 5.0)
# 📝 **Reasoning:** P1 produces vague, incomplete emails missing key details like specific dates, reasons, and professional structure. P2's explicit requirements for "clear reason and timeline" generate comprehensive, actionable requests that managers can easily approve. P2 consistently outperforms P1 in completeness, professionalism, and practical utility.

Example 2 (2235)

from jipso.Prompt import Prompt

os.environ['ANTHROPIC_API_KEY'] = 'sk-ant-...'

p1 = Prompt('Collect sales figures this week')
print(p1.add('Customer trend analysis'))
# Collect sales figures this week and perform customer trend analysis

p2 = Prompt('Customer trend analysis')
p = p1 | p2
print(p)
# Collect sales figures this week and perform customer trend analysis

print(p > p2)
# True

p3 = p.enhance()
print(p3)
# Collect detailed sales figures for this week including revenue, units sold, and transaction counts by product category and customer segment, then perform comprehensive customer trend analysis identifying purchasing patterns, seasonal variations, and emerging opportunities with actionable insights and recommendations

print(set(p3))
# {
#   'Collect detailed sales figures for this week',
#   'Include revenue data', 
#   'Include units sold data',
#   'Include transaction counts',
#   'Categorize by product category',
#   'Categorize by customer segment', 
#   'Perform comprehensive customer trend analysis',
#   'Identify purchasing patterns',
#   'Identify seasonal variations', 
#   'Identify emerging opportunities',
#   'Provide actionable insights',
#   'Provide recommendations'
# }

print(dict(p3))
# {
#   "name": "comprehensive_sales_analysis",
#   "description": "Collect detailed sales data and perform customer trend analysis",
#   "data_collection": {
#     "timeframe": "this week",
#     "metrics": ["revenue", "units_sold", "transaction_counts"],
#     "segmentation": ["product_category", "customer_segment"]
#   },
#   "analysis": {
#     "type": "comprehensive_customer_trend_analysis",
#     "focus_areas": ["purchasing_patterns", "seasonal_variations", "emerging_opportunities"]
#   },
#   "output": {
#     "format": ["actionable_insights", "recommendations"],
#     "detail_level": "comprehensive"
#   }
# }

Example 4

from jipso.Compute import Compute

os.environ['ALIBABACLOUD_API_KEY'] = 'sk-...'

compute = Compute(
  j = 'qwen-turbo',
  i = 'Hi, I would like to ask about the Dell XPS 13 laptop. What is the current price and are there any promotions? Thank you!',
  p = 'Please categorize this email into one of the following categories: Product Advice, Complaints, Technical Support, Orders, Other',
)
compute.s = 'Based on the main content and purpose of the email. Choose only 1 category that best fits.'
compute.exe()
print(compute.o)

🕌 ARCHITECT jipso-stack

Pod	Docker Image	Engine	Role
Client Pod	-	jipso-py	Request jipso.Compute.exe()
Worker Pod	jipsofoundation/jipso	celery	Run jipso.Compute, wrap all AI model
Cache Pod	-	Redis GPU? (please build it or we will jipso-cache)	Cache VRAM
Proxy Pod	nginx	Nginx	Rate limiting
Broker Pod	bitnami/kafka	Kafka	Message Queue Broker
Database Pod	postgres	PostgreSQL	Database for jipso.Compute
Storage Pod	minio/minio	Minio, S3, CDN	Media content
Metric Pod	influxdb	InfluxDB	Metric: cost, SLA. Metric database and monitoring. Worker Pod proactive push
Auth Pod	keycloak/keycloak	Keycloak	Authentication, API key management

• Self-Build: Deploy on top Kubernetes
• Multi-Vendor
  • AI Providers (OpenAI, Anthropic) and Individual: Worker Pod, Cache Pod, Proxy Pod
  • Cloud Providers (AWS, Alibaba Cloud): Broker Pod, Database Pod, Storage Pod, Metric Pod, Auth Pod
  • SME Partners: Client Pod with UI/UX

💰 SPONSORSHIP

This project has received no external funding, sponsorship, or investment. All development is fully volunteer-based at this stage.