Clean and organize project structure
Major reorganization: - Created scripts/ directory for all utility scripts - Created config/ directory for configuration files - Moved all test files to tests/ directory - Updated all script paths to work with new structure - Updated README.md with new project structure diagram New structure: ├── src/ # Source code (API + MCP) ├── scripts/ # Utility scripts (start-*.sh, docs_server.py, etc.) ├── tests/ # All test files and debug utilities ├── config/ # Configuration files (JSON, Caddy config) ├── docs/ # Documentation website └── logs/ # Log files All scripts updated to use relative paths from project root. Documentation updated with new folder structure. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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Docker Config Backup
165
tests/test_langmem_with_neo4j.py
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165
tests/test_langmem_with_neo4j.py
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#!/usr/bin/env python3
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"""
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Test LangMem API integration with Neo4j graph relationships
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"""
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import asyncio
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import httpx
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import json
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# Configuration
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API_BASE_URL = "http://localhost:8765"
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API_KEY = "langmem_api_key_2025"
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async def test_langmem_with_neo4j():
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"""Test LangMem API with Neo4j graph relationships"""
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print("🧪 Testing LangMem API + Neo4j Integration")
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print("=" * 50)
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headers = {"Authorization": f"Bearer {API_KEY}"}
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async with httpx.AsyncClient() as client:
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# Store a memory with graph relationships
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print("\n1. Storing memory with graph relationships...")
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memory_with_relationships = {
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"content": "LangMem is a long-term memory system for LLM projects that combines vector search with graph relationships",
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"user_id": "graph_test_user",
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"session_id": "graph_test_session",
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"metadata": {
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"category": "ai_systems",
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"subcategory": "memory_systems",
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"importance": "high",
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"tags": ["langmem", "llm", "vector", "graph", "memory"]
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},
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"relationships": [
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{
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"entity_name": "Vector Search",
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"entity_type": "Technology",
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"relationship": "USES",
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"confidence": 0.95,
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"properties": {
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"implementation": "pgvector",
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"embedding_model": "nomic-embed-text"
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}
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},
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{
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"entity_name": "Graph Database",
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"entity_type": "Technology",
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"relationship": "USES",
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"confidence": 0.90,
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"properties": {
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"implementation": "neo4j",
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"query_language": "cypher"
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}
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},
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{
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"entity_name": "LLM Projects",
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"entity_type": "Domain",
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"relationship": "SERVES",
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"confidence": 0.98,
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"properties": {
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"purpose": "long_term_memory",
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"integration": "mcp_server"
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}
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}
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]
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}
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try:
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response = await client.post(
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f"{API_BASE_URL}/v1/memories/store",
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json=memory_with_relationships,
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headers=headers,
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timeout=30.0
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)
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if response.status_code == 200:
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data = response.json()
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memory_id = data["id"]
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print(f" ✅ Memory stored with ID: {memory_id}")
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print(f" ✅ Graph relationships will be processed in background")
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# Wait a moment for background task
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await asyncio.sleep(2)
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# Search for the memory with graph relationships
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print("\n2. Searching memory with graph relationships...")
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search_response = await client.post(
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f"{API_BASE_URL}/v1/memories/search",
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json={
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"query": "memory system for AI projects",
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"user_id": "graph_test_user",
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"limit": 5,
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"threshold": 0.5,
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"include_graph": True
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},
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headers=headers,
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timeout=30.0
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)
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if search_response.status_code == 200:
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search_data = search_response.json()
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print(f" ✅ Found {search_data['total_count']} memories")
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for memory in search_data['memories']:
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print(f" - Content: {memory['content'][:60]}...")
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print(f" Similarity: {memory['similarity']:.3f}")
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if 'relationships' in memory:
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print(f" Relationships: {len(memory['relationships'])}")
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for rel in memory['relationships']:
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print(f" → {rel['relationship']} {rel['entity_name']} ({rel['confidence']})")
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else:
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print(" Relationships: Not included")
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else:
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print(f" ❌ Search failed: {search_response.status_code}")
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else:
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print(f" ❌ Memory storage failed: {response.status_code}")
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print(f" Response: {response.text}")
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except Exception as e:
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print(f" ❌ Error: {e}")
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# Test retrieval for conversation
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print("\n3. Testing memory retrieval with graph context...")
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try:
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retrieval_response = await client.post(
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f"{API_BASE_URL}/v1/memories/retrieve",
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json={
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"messages": [
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{"role": "user", "content": "Tell me about memory systems"},
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{"role": "assistant", "content": "I can help with memory systems. What specific aspect?"},
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{"role": "user", "content": "How do vector databases work with graph relationships?"}
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],
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"user_id": "graph_test_user",
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"session_id": "graph_test_session"
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},
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headers=headers,
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timeout=30.0
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)
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if retrieval_response.status_code == 200:
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retrieval_data = retrieval_response.json()
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print(f" ✅ Retrieved {retrieval_data['total_count']} relevant memories")
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for memory in retrieval_data['memories']:
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print(f" - {memory['content'][:50]}... (similarity: {memory['similarity']:.3f})")
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if 'relationships' in memory:
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print(f" Graph relationships: {len(memory['relationships'])}")
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else:
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print(f" ❌ Retrieval failed: {retrieval_response.status_code}")
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except Exception as e:
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print(f" ❌ Retrieval error: {e}")
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print("\n" + "=" * 50)
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print("🎉 LangMem + Neo4j Integration Test Complete!")
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print("✅ Vector search and graph relationships working together")
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print("🌐 Check Neo4j Browser: http://localhost:7474")
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print(" Look for Memory, Entity nodes and their relationships")
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if __name__ == "__main__":
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asyncio.run(test_langmem_with_neo4j())
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