21c0eae78f
Implemented two new MCP tools and enhanced workflow instructions to enable effective Personal Knowledge Management across all use cases (architecture decisions, projects, coaching, research, etc.). ## New Tools 1. **get_entity_connections** - Direct graph traversal showing ALL relationships - Returns complete connection data for an entity - Guarantees completeness vs semantic search - Essential for pattern detection and exploration - Leverages EntityEdge.get_by_node_uuid() 2. **get_entity_timeline** - Chronological episode history - Shows ALL episodes mentioning an entity - Enables temporal tracking and evolution analysis - Critical for understanding how concepts evolved - Leverages EpisodicNode.get_by_entity_node_uuid() ## Enhanced Workflow Instructions Updated GRAPHITI_MCP_INSTRUCTIONS with: - Clear "SEARCH FIRST, THEN ADD" workflow with decision flowcharts - Tool selection guide (when to use each tool) - Distinction between graph traversal vs semantic search - Multiple concrete examples across different domains - Key principles for effective PKM usage ## Updated add_memory Docstring Added prominent warning to search before adding: - Step-by-step workflow guidance - Emphasizes creating connections vs isolated nodes - References new exploration tools ## Benefits - Prevents disconnected/duplicate entities - Enables reliable pattern recognition with complete data - Cost-effective (single graph query vs multiple semantic searches) - Temporal tracking for evolution analysis - Works equally well for technical and personal knowledge ## Implementation Details - 0 changes to graphiti_core (uses existing features only) - All new code in mcp_server/src/graphiti_mcp_server.py - Backward compatible (adds tools, doesn't modify existing) - Follows existing MCP tool patterns and conventions - Passes all lint and syntax checks Related: DOCS/IMPLEMENTATION-Graph-Exploration-Tools.md
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""Check what's in the source database."""
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import os
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from neo4j import GraphDatabase
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NEO4J_URI = 'bolt://192.168.1.25:7687'
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NEO4J_USER = 'neo4j'
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NEO4J_PASSWORD = '!"MiTa1205'
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SOURCE_DATABASE = 'neo4j'
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SOURCE_GROUP_ID = 'lvarming73'
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driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))
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print('=' * 70)
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print('Checking Source Database')
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print('=' * 70)
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with driver.session(database=SOURCE_DATABASE) as session:
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# Check total nodes
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result = session.run(
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"""
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MATCH (n {group_id: $group_id})
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RETURN count(n) as total
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""",
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group_id=SOURCE_GROUP_ID,
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)
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total = result.single()['total']
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print(f"\n✓ Total nodes with group_id '{SOURCE_GROUP_ID}': {total}")
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# Check date range
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result = session.run(
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"""
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MATCH (n:Episodic {group_id: $group_id})
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WHERE n.created_at IS NOT NULL
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RETURN
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min(n.created_at) as earliest,
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max(n.created_at) as latest,
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count(n) as total
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""",
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group_id=SOURCE_GROUP_ID,
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)
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dates = result.single()
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if dates and dates['total'] > 0:
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print(f'\n✓ Episodic date range:')
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print(f' Earliest: {dates["earliest"]}')
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print(f' Latest: {dates["latest"]}')
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print(f' Total episodes: {dates["total"]}')
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else:
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print('\n⚠️ No episodic nodes with dates found')
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# Sample episodic nodes by date
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result = session.run(
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"""
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MATCH (n:Episodic {group_id: $group_id})
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RETURN n.name as name, n.created_at as created_at
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ORDER BY n.created_at
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LIMIT 10
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""",
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group_id=SOURCE_GROUP_ID,
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)
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print(f'\n✓ Oldest episodic nodes:')
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for record in result:
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print(f' - {record["name"]}: {record["created_at"]}')
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# Check for other group_ids in neo4j database
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result = session.run("""
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MATCH (n)
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WHERE n.group_id IS NOT NULL
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RETURN DISTINCT n.group_id as group_id, count(n) as count
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ORDER BY count DESC
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""")
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print(f"\n✓ All group_ids in '{SOURCE_DATABASE}' database:")
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for record in result:
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print(f' {record["group_id"]}: {record["count"]} nodes')
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driver.close()
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print('\n' + '=' * 70)
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