Claude/claude md mhyyoe7gu6p8y6fq 01 t3 co rftbgo rx nt zqa gmp8v

examples/mcp/advanced_workflow_example.py

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+#!/usr/bin/env python3
+"""
+Advanced MCP Workflow Example
+
+This example demonstrates more complex workflows using the Memori MCP server,
+including:
+- Multi-session management
+- Category-based filtering
+- Importance-based search
+- Memory lifecycle management
+"""
+
+import sys
+import os
+from datetime import datetime
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+# Set environment variables
+os.environ["MEMORI_DATABASE_URL"] = "sqlite:///advanced_mcp.db"
+os.environ["OPENAI_API_KEY"] = "your-api-key-here"
+
+from mcp.memori_mcp_server import (
+    record_conversation,
+    search_memories,
+    get_recent_memories,
+    get_memory_statistics,
+    clear_session_memories,
+)
+
+
+def simulate_project_conversation():
+    """Simulate a conversation about a software project"""
+
+    print("\n" + "=" * 80)
+    print("SCENARIO 1: Software Project Discussion")
+    print("=" * 80)
+
+    user_id = "developer_123"
+    session_id = "project_planning"
+
+    # Day 1: Initial project discussion
+    print("\nDay 1: Initial Planning")
+    conversations = [
+        {
+            "user": "I'm starting a new e-commerce platform project",
+            "ai": "Exciting! Let's plan this carefully. What's your tech stack preference?",
+        },
+        {
+            "user": "I want to use Next.js for frontend and Python FastAPI for backend",
+            "ai": "Great choices! Next.js provides excellent SEO and FastAPI is very performant.",
+        },
+        {
+            "user": "I'll need user authentication, product catalog, and payment processing",
+            "ai": "Those are the core features. For auth, I'd suggest JWT tokens. For payments, Stripe is a solid choice.",
+        },
+    ]
+
+    for conv in conversations:
+        result = record_conversation(
+            user_input=conv["user"],
+            ai_response=conv["ai"],
+            user_id=user_id,
+            session_id=session_id,
+        )
+        print(f"  Recorded: {conv['user'][:50]}...")
+
+    # Check what was recorded
+    stats = get_memory_statistics(user_id=user_id)
+    print(f"\n  Stats after Day 1: {stats['statistics']['total_memories']} memories")
+
+
+def simulate_preference_learning():
+    """Simulate learning user preferences over time"""
+
+    print("\n" + "=" * 80)
+    print("SCENARIO 2: Learning User Preferences")
+    print("=" * 80)
+
+    user_id = "power_user_456"
+
+    preferences = [
+        {
+            "user": "I always prefer TypeScript over JavaScript",
+            "ai": "Noted! I'll suggest TypeScript solutions from now on.",
+        },
+        {
+            "user": "I like to write comprehensive tests for everything",
+            "ai": "Great practice! I'll make sure to include test examples in my suggestions.",
+        },
+        {
+            "user": "I prefer functional programming patterns when possible",
+            "ai": "Understood! I'll focus on functional approaches and avoid mutations.",
+        },
+        {
+            "user": "I use VS Code with Vim keybindings",
+            "ai": "Nice setup! I'll keep that in mind when suggesting editor configurations.",
+        },
+    ]
+
+    for pref in preferences:
+        result = record_conversation(
+            user_input=pref["user"],
+            ai_response=pref["ai"],
+            user_id=user_id,
+        )
+        print(f"  Learned: {pref['user']}")
+
+    # Search for preferences
+    print("\n  Searching for programming preferences...")
+    results = search_memories(
+        query="programming preferences",
+        user_id=user_id,
+        category="preference",
+        limit=10,
+    )
+
+    print(f"  Found {results['total_results']} preferences:")
+    for mem in results.get('results', []):
+        print(f"    - {mem.get('summary', 'N/A')}")
+
+
+def simulate_multi_session_workflow():
+    """Simulate working across multiple sessions"""
+
+    print("\n" + "=" * 80)
+    print("SCENARIO 3: Multi-Session Workflow")
+    print("=" * 80)
+
+    user_id = "researcher_789"
+
+    # Session 1: Research on AI
+    print("\n  Session 1: AI Research")
+    session1_id = "ai_research"
+    for conv in [
+        ("Tell me about transformer architectures", "Transformers revolutionized NLP..."),
+        ("How do attention mechanisms work?", "Attention allows models to focus on relevant parts..."),
+    ]:
+        record_conversation(conv[0], conv[1], user_id, session_id=session1_id)
+        print(f"    Recorded: {conv[0][:40]}...")
+
+    # Session 2: Research on databases
+    print("\n  Session 2: Database Research")
+    session2_id = "database_research"
+    for conv in [
+        ("What are the benefits of PostgreSQL?", "PostgreSQL offers ACID compliance, advanced features..."),
+        ("Explain database indexing", "Indexes speed up queries by creating lookup structures..."),
+    ]:
+        record_conversation(conv[0], conv[1], user_id, session_id=session2_id)
+        print(f"    Recorded: {conv[0][:40]}...")
+
+    # Session 3: Temporary brainstorming
+    print("\n  Session 3: Temporary Brainstorming")
+    session3_id = "temp_brainstorm"
+    for conv in [
+        ("Random idea: AI-powered code reviewer", "Interesting! That could help catch bugs..."),
+        ("Another idea: Automated documentation generator", "That would save a lot of time..."),
+    ]:
+        record_conversation(conv[0], conv[1], user_id, session_id=session3_id)
+        print(f"    Recorded: {conv[0][:40]}...")
+
+    # Now clear the temporary session
+    print("\n  Clearing temporary brainstorming session...")
+    clear_result = clear_session_memories(
+        session_id=session3_id,
+        user_id=user_id,
+    )
+    print(f"    {clear_result.get('message', 'Done')}")
+
+    # Check remaining memories
+    stats = get_memory_statistics(user_id=user_id)
+    print(f"\n  Final stats: {stats['statistics']['total_memories']} memories (temp session cleared)")
+
+
+def demonstrate_search_filtering():
+    """Demonstrate advanced search with filtering"""
+
+    print("\n" + "=" * 80)
+    print("SCENARIO 4: Advanced Search Filtering")
+    print("=" * 80)
+
+    user_id = "data_scientist_101"
+
+    # Record various types of information
+    print("\n  Recording diverse information...")
+
+    facts = [
+        "I work with pandas and numpy daily",
+        "My current dataset has 10 million rows",
+        "I'm using scikit-learn for machine learning",
+    ]
+
+    for fact in facts:
+        record_conversation(fact, "Got it, I'll remember that.", user_id)
+
+    # Search with different filters
+    print("\n  Searching for 'data' with category filter...")
+    results = search_memories(
+        query="data science tools",
+        user_id=user_id,
+        category="fact",
+        limit=5,
+    )
+    print(f"    Found {results['total_results']} fact-type memories")
+
+    print("\n  Searching for high-importance memories...")
+    results = search_memories(
+        query="machine learning",
+        user_id=user_id,
+        min_importance=0.7,
+        limit=5,
+    )
+    print(f"    Found {results['total_results']} high-importance memories")
+
+
+def main():
+    """Run all advanced workflow examples"""
+
+    print("=" * 80)
+    print("Memori MCP Server - Advanced Workflow Examples")
+    print("=" * 80)
+
+    # Run each scenario
+    simulate_project_conversation()
+    simulate_preference_learning()
+    simulate_multi_session_workflow()
+    demonstrate_search_filtering()
+
+    print("\n" + "=" * 80)
+    print("All scenarios completed! Check advanced_mcp.db for stored data.")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()

examples/mcp/basic_usage_example.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""
+Basic MCP Usage Example
+
+This example demonstrates how to test the Memori MCP server tools directly
+without needing a full MCP client like Claude Desktop.
+
+This is useful for:
+- Testing the MCP server functionality
+- Understanding how the tools work
+- Debugging issues
+- Development and iteration
+"""
+
+import sys
+import os
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+# Set environment variables
+os.environ["MEMORI_DATABASE_URL"] = "sqlite:///test_mcp.db"
+os.environ["OPENAI_API_KEY"] = "your-api-key-here"
+
+# Import the MCP server tools
+from mcp.memori_mcp_server import (
+    record_conversation,
+    search_memories,
+    get_recent_memories,
+    get_memory_statistics,
+    get_conversation_history,
+)
+
+
+def main():
+    """Run basic MCP tool examples"""
+
+    print("=" * 80)
+    print("Memori MCP Server - Basic Usage Example")
+    print("=" * 80)
+    print()
+
+    user_id = "demo_user"
+
+    # Example 1: Record a conversation
+    print("1. Recording a conversation...")
+    result = record_conversation(
+        user_input="I'm building a web application with FastAPI and PostgreSQL",
+        ai_response="That's great! FastAPI is excellent for building high-performance APIs. Would you like help with the database setup?",
+        user_id=user_id,
+    )
+    print(f"   Result: {result}")
+    print()
+
+    # Example 2: Record another conversation
+    print("2. Recording another conversation...")
+    result = record_conversation(
+        user_input="Yes, I need help setting up SQLAlchemy models",
+        ai_response="I can help you with that. Let's start by defining your database models using SQLAlchemy ORM.",
+        user_id=user_id,
+    )
+    print(f"   Result: {result}")
+    print()
+
+    # Example 3: Record a preference
+    print("3. Recording a user preference...")
+    result = record_conversation(
+        user_input="I prefer using async/await patterns in Python",
+        ai_response="Noted! I'll keep that in mind and suggest async patterns when appropriate.",
+        user_id=user_id,
+    )
+    print(f"   Result: {result}")
+    print()
+
+    # Example 4: Get memory statistics
+    print("4. Getting memory statistics...")
+    stats = get_memory_statistics(user_id=user_id)
+    print(f"   Statistics: {stats}")
+    print()
+
+    # Example 5: Search for memories
+    print("5. Searching for memories about 'Python'...")
+    search_results = search_memories(
+        query="Python programming",
+        user_id=user_id,
+        limit=5,
+    )
+    print(f"   Found {search_results.get('total_results', 0)} results")
+    for i, memory in enumerate(search_results.get('results', []), 1):
+        print(f"   {i}. {memory.get('summary', 'N/A')} (Category: {memory.get('category', 'N/A')})")
+    print()
+
+    # Example 6: Get recent memories
+    print("6. Getting recent memories...")
+    recent = get_recent_memories(
+        user_id=user_id,
+        limit=5,
+    )
+    print(f"   Found {recent.get('total_results', 0)} recent memories")
+    for i, memory in enumerate(recent.get('memories', []), 1):
+        print(f"   {i}. {memory.get('summary', 'N/A')}")
+    print()
+
+    # Example 7: Get conversation history
+    print("7. Getting conversation history...")
+    history = get_conversation_history(
+        user_id=user_id,
+        limit=10,
+    )
+    print(f"   Found {history.get('total_conversations', 0)} conversations")
+    for i, conv in enumerate(history.get('conversations', []), 1):
+        print(f"   {i}. User: {conv.get('user_input', 'N/A')[:50]}...")
+        print(f"      AI:   {conv.get('ai_output', 'N/A')[:50]}...")
+    print()
+
+    print("=" * 80)
+    print("Example completed! Check test_mcp.db for stored data.")
+    print("=" * 80)
+
+
+if __name__ == "__main__":
+    main()