Adds agentic procurement example (#932)
<!-- .github/pull_request_template.md --> ## Description Adds agentic procurement example ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin.
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examples/python/agentic_reasoning_procurement_example.py
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examples/python/agentic_reasoning_procurement_example.py
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import os
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import logging
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import cognee
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import asyncio
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import json
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from cognee.infrastructure.llm.get_llm_client import get_llm_client
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from dotenv import load_dotenv
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from cognee.api.v1.search import SearchType
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from cognee.modules.engine.models import NodeSet
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from cognee.shared.logging_utils import setup_logging
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load_dotenv()
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os.environ["LLM_API_KEY"] = ""
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# Notes: Nodesets cognee feature only works with kuzu and Neo4j graph databases
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os.environ["GRAPH_DATABASE_PROVIDER"] = "kuzu"
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class ProcurementMemorySystem:
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"""Procurement system with persistent memory using Cognee"""
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async def setup_memory_data(self):
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"""Load and store procurement data in memory"""
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# Procurement system dummy data
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vendor_conversation_text_techsupply = """
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Assistant: Hello! This is Sarah from TechSupply Solutions.
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Thanks for reaching out for your IT procurement needs.
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User: We're looking to procure 50 high-performance enterprise laptops.
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Specs: Intel i7, 16GB RAM, 512GB SSD, dedicated graphics card.
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Budget: $80,000. What models do you have?
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Assistant: TechSupply Solutions can offer Dell Precision 5570 ($1,450) and Lenovo ThinkPad P1 ($1,550).
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Both come with a 3-year warranty. Delivery: 2–3 weeks (Dell), 3–4 weeks (Lenovo).
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User: Do you provide bulk discounts? We're planning another 200 units next quarter.
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Assistant: Yes! Orders over $50,000 get 8% off.
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So for your current order:
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- Dell = $1,334 each ($66,700 total)
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- Lenovo = $1,426 each ($71,300 total)
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And for 200 units next quarter, we can offer 12% off with flexible delivery.
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"""
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vendor_conversation_text_office_solutions = """
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Assistant: Hi, this is Martin from vendor Office Solutions. How can we assist you?
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User: We need 50 laptops for our engineers.
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Specs: i7 CPU, 16GB RAM, 512GB SSD, dedicated GPU.
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We can spend up to $80,000. Can you meet this?
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Assistant: Office Solutions can offer HP ZBook Power G9 for $1,600 each.
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Comes with 2-year warranty, delivery time is 4–5 weeks.
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User: That's a bit long — any options to speed it up?
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Assistant: We can expedite for $75 per unit, bringing delivery to 3–4 weeks.
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Also, for orders over $60,000 we give 6% off.
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So:
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- Base price = $1,600 → $1,504 with discount
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- Expedited price = $1,579
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User: Understood. Any room for better warranty terms?
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Assistant: We’re working on adding a 3-year warranty option next quarter for enterprise clients.
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"""
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previous_purchases_text = """
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Previous Purchase Records:
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1. Vendor: TechSupply Solutions
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Item: Desktop computers - 25 units
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Amount: $35,000
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Date: 2024-01-15
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Performance: Excellent delivery, good quality, delivered 2 days early
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Rating: 5/5
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Notes: Responsive support team, competitive pricing
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2. Vendor: Office Solutions
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Item: Office furniture
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Amount: $12,000
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Date: 2024-02-20
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Performance: Delayed delivery by 1 week, average quality
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Rating: 2/5
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Notes: Poor communication, but acceptable product quality
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"""
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procurement_preferences_text = """
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Procurement Policies and Preferences:
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1. Preferred vendors must have 3+ year warranty coverage
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2. Maximum delivery time: 30 days for non-critical items
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3. Bulk discount requirements: minimum 5% for orders over $50,000
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4. Prioritize vendors with sustainable/green practices
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5. Vendor rating system: require minimum 4/5 rating for new contracts
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"""
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# Initializing and pruning databases
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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# Store data in different memory categories
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await cognee.add(
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data=[vendor_conversation_text_techsupply, vendor_conversation_text_office_solutions],
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node_set=["vendor_conversations"],
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)
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await cognee.add(data=previous_purchases_text, node_set=["purchase_history"])
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await cognee.add(data=procurement_preferences_text, node_set=["procurement_policies"])
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# Process all data through Cognee's knowledge graph
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await cognee.cognify()
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async def search_memory(self, query, search_categories=None):
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"""Search across different memory layers"""
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results = {}
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for category in search_categories:
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category_results = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION,
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query_text=query,
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node_type=NodeSet,
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node_name=[category],
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top_k=30,
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)
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results[category] = category_results
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return results
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async def run_procurement_example():
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"""Main function demonstrating procurement memory system"""
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print("Building AI Procurement System with Memory: Cognee Integration...\n")
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# Initialize the procurement memory system
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procurement_system = ProcurementMemorySystem()
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# Setup memory with procurement data
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print("Setting up procurement memory data...")
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await procurement_system.setup_memory_data()
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print("Memory successfully populated and processed.\n")
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research_questions = {
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"vendor_conversations": [
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"What are the laptops that are discussed, together with their vendors?",
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"What pricing was offered by each vendor before and after discounts?",
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"What were the delivery time estimates for each product?",
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],
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"purchase_history": [
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"Which vendors have we worked with in the past?",
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"What were the satisfaction ratings for each vendor?",
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"Were there any complaints or red flags associated with specific vendors?",
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],
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"procurement_policies": [
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"What are our company’s bulk discount requirements?",
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"What is the maximum acceptable delivery time for non-critical items?",
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"What is the minimum vendor rating for new contracts?",
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],
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}
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research_notes = {}
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print("Running contextual research questions...\n")
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for category, questions in research_questions.items():
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print(f"Category: {category}")
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research_notes[category] = []
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for q in questions:
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print(f"Question: \n{q}")
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results = await procurement_system.search_memory(q, search_categories=[category])
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top_answer = results[category][0]
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print(f"Answer: \n{top_answer.strip()}\n")
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research_notes[category].append({"question": q, "answer": top_answer})
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print("Contextual research complete.\n")
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print("Compiling structured research information for decision-making...\n")
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research_information = "\n\n".join(
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f"Q: {note['question']}\nA: {note['answer'].strip()}"
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for section in research_notes.values()
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for note in section
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)
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print("Compiled Research Summary:\n")
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print(research_information)
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print("\nPassing research to LLM for final procurement recommendation...\n")
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final_decision = await get_llm_client().acreate_structured_output(
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text_input=research_information,
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system_prompt="""You are a procurement decision assistant. Use the provided QA pairs that were collected through a research phase. Recommend the best vendor,
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based on pricing, delivery, warranty, policy fit, and past performance. Be concise and justify your choice with evidence.
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""",
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response_model=str,
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)
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print("Final Decision:")
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print(final_decision.strip())
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# Run the example
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if __name__ == "__main__":
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setup_logging(logging.ERROR)
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asyncio.run(run_procurement_example())
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