Adds agentic procurement example (#932)

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## 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.

examples/python/agentic_reasoning_procurement_example.py

@@ -0,0 +1,204 @@
+import os
+import logging
+import cognee
+import asyncio
+import json
+
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+from dotenv import load_dotenv
+from cognee.api.v1.search import SearchType
+from cognee.modules.engine.models import NodeSet
+from cognee.shared.logging_utils import setup_logging
+
+
+load_dotenv()
+
+os.environ["LLM_API_KEY"] = ""
+# Notes: Nodesets cognee feature only works with kuzu and Neo4j graph databases
+os.environ["GRAPH_DATABASE_PROVIDER"] = "kuzu"
+
+
+class ProcurementMemorySystem:
+    """Procurement system with persistent memory using Cognee"""
+
+    async def setup_memory_data(self):
+        """Load and store procurement data in memory"""
+
+        # Procurement system dummy data
+        vendor_conversation_text_techsupply = """
+        Assistant: Hello! This is Sarah from TechSupply Solutions.
+        Thanks for reaching out for your IT procurement needs.
+
+        User: We're looking to procure 50 high-performance enterprise laptops.
+        Specs: Intel i7, 16GB RAM, 512GB SSD, dedicated graphics card.
+        Budget: $80,000. What models do you have?
+
+        Assistant: TechSupply Solutions can offer Dell Precision 5570 ($1,450) and Lenovo ThinkPad P1 ($1,550).
+        Both come with a 3-year warranty. Delivery: 2–3 weeks (Dell), 3–4 weeks (Lenovo).
+
+        User: Do you provide bulk discounts? We're planning another 200 units next quarter.
+
+        Assistant: Yes! Orders over $50,000 get 8% off.
+        So for your current order:
+        - Dell = $1,334 each ($66,700 total)
+        - Lenovo = $1,426 each ($71,300 total)
+
+        And for 200 units next quarter, we can offer 12% off with flexible delivery.
+        """
+
+        vendor_conversation_text_office_solutions = """
+        Assistant: Hi, this is Martin from vendor Office Solutions. How can we assist you?
+
+        User: We need 50 laptops for our engineers.
+        Specs: i7 CPU, 16GB RAM, 512GB SSD, dedicated GPU.
+        We can spend up to $80,000. Can you meet this?
+
+        Assistant: Office Solutions can offer HP ZBook Power G9 for $1,600 each.
+        Comes with 2-year warranty, delivery time is 4–5 weeks.
+
+        User: That's a bit long — any options to speed it up?
+
+        Assistant: We can expedite for $75 per unit, bringing delivery to 3–4 weeks.
+        Also, for orders over $60,000 we give 6% off.
+
+        So:
+        - Base price = $1,600 → $1,504 with discount
+        - Expedited price = $1,579
+
+        User: Understood. Any room for better warranty terms?
+
+        Assistant: We’re working on adding a 3-year warranty option next quarter for enterprise clients.
+        """
+
+        previous_purchases_text = """
+        Previous Purchase Records:
+        1. Vendor: TechSupply Solutions
+           Item: Desktop computers - 25 units
+           Amount: $35,000
+           Date: 2024-01-15
+           Performance: Excellent delivery, good quality, delivered 2 days early
+           Rating: 5/5
+           Notes: Responsive support team, competitive pricing
+
+        2. Vendor: Office Solutions
+           Item: Office furniture
+           Amount: $12,000
+           Date: 2024-02-20
+           Performance: Delayed delivery by 1 week, average quality
+           Rating: 2/5
+           Notes: Poor communication, but acceptable product quality
+        """
+
+        procurement_preferences_text = """
+        Procurement Policies and Preferences:
+        1. Preferred vendors must have 3+ year warranty coverage
+        2. Maximum delivery time: 30 days for non-critical items
+        3. Bulk discount requirements: minimum 5% for orders over $50,000
+        4. Prioritize vendors with sustainable/green practices
+        5. Vendor rating system: require minimum 4/5 rating for new contracts
+        """
+
+        # Initializing and pruning databases
+        await cognee.prune.prune_data()
+        await cognee.prune.prune_system(metadata=True)
+
+        # Store data in different memory categories
+        await cognee.add(
+            data=[vendor_conversation_text_techsupply, vendor_conversation_text_office_solutions],
+            node_set=["vendor_conversations"],
+        )
+
+        await cognee.add(data=previous_purchases_text, node_set=["purchase_history"])
+
+        await cognee.add(data=procurement_preferences_text, node_set=["procurement_policies"])
+
+        # Process all data through Cognee's knowledge graph
+        await cognee.cognify()
+
+    async def search_memory(self, query, search_categories=None):
+        """Search across different memory layers"""
+        results = {}
+        for category in search_categories:
+            category_results = await cognee.search(
+                query_type=SearchType.GRAPH_COMPLETION,
+                query_text=query,
+                node_type=NodeSet,
+                node_name=[category],
+                top_k=30,
+            )
+            results[category] = category_results
+
+        return results
+
+
+async def run_procurement_example():
+    """Main function demonstrating procurement memory system"""
+    print("Building AI Procurement System with Memory: Cognee Integration...\n")
+
+    # Initialize the procurement memory system
+    procurement_system = ProcurementMemorySystem()
+
+    # Setup memory with procurement data
+    print("Setting up procurement memory data...")
+    await procurement_system.setup_memory_data()
+    print("Memory successfully populated and processed.\n")
+
+    research_questions = {
+        "vendor_conversations": [
+            "What are the laptops that are discussed, together with their vendors?",
+            "What pricing was offered by each vendor before and after discounts?",
+            "What were the delivery time estimates for each product?",
+        ],
+        "purchase_history": [
+            "Which vendors have we worked with in the past?",
+            "What were the satisfaction ratings for each vendor?",
+            "Were there any complaints or red flags associated with specific vendors?",
+        ],
+        "procurement_policies": [
+            "What are our company’s bulk discount requirements?",
+            "What is the maximum acceptable delivery time for non-critical items?",
+            "What is the minimum vendor rating for new contracts?",
+        ],
+    }
+
+    research_notes = {}
+    print("Running contextual research questions...\n")
+    for category, questions in research_questions.items():
+        print(f"Category: {category}")
+        research_notes[category] = []
+        for q in questions:
+            print(f"Question: \n{q}")
+            results = await procurement_system.search_memory(q, search_categories=[category])
+            top_answer = results[category][0]
+            print(f"Answer: \n{top_answer.strip()}\n")
+            research_notes[category].append({"question": q, "answer": top_answer})
+
+    print("Contextual research complete.\n")
+
+    print("Compiling structured research information for decision-making...\n")
+    research_information = "\n\n".join(
+        f"Q: {note['question']}\nA: {note['answer'].strip()}"
+        for section in research_notes.values()
+        for note in section
+    )
+
+    print("Compiled Research Summary:\n")
+    print(research_information)
+    print("\nPassing research to LLM for final procurement recommendation...\n")
+
+    final_decision = await get_llm_client().acreate_structured_output(
+        text_input=research_information,
+        system_prompt="""You are a procurement decision assistant. Use the provided QA pairs that were collected through a research phase. Recommend the best vendor,
+         based on pricing, delivery, warranty, policy fit, and past performance. Be concise and justify your choice with evidence.
+         """,
+        response_model=str,
+    )
+
+    print("Final Decision:")
+    print(final_decision.strip())
+
+
+# Run the example
+if __name__ == "__main__":
+    setup_logging(logging.ERROR)
+    asyncio.run(run_procurement_example())