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
186
tests/populate_test_data.py
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186
tests/populate_test_data.py
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#!/usr/bin/env python3
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"""
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Populate LangMem with test data for Supabase web UI viewing
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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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from datetime import datetime
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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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# Test memories to store
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test_memories = [
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{
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"content": "Claude Code is an AI-powered CLI tool that helps with software development tasks. It can read files, search codebases, and generate code.",
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"user_id": "demo_user",
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"session_id": "demo_session_1",
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"metadata": {
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"category": "tools",
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"subcategory": "ai_development",
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"importance": "high",
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"tags": ["claude", "ai", "cli", "development"]
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}
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},
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{
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"content": "FastAPI is a modern, fast web framework for building APIs with Python. It provides automatic API documentation and type hints.",
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"user_id": "demo_user",
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"session_id": "demo_session_1",
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"metadata": {
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"category": "frameworks",
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"subcategory": "python_web",
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"importance": "medium",
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"tags": ["fastapi", "python", "web", "api"]
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}
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},
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{
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"content": "Docker containers provide lightweight virtualization for applications. They package software with all dependencies for consistent deployment.",
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"user_id": "demo_user",
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"session_id": "demo_session_2",
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"metadata": {
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"category": "devops",
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"subcategory": "containerization",
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"importance": "high",
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"tags": ["docker", "containers", "devops", "deployment"]
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}
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},
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{
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"content": "PostgreSQL with pgvector extension enables vector similarity search for embeddings. This is useful for semantic search and AI applications.",
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"user_id": "demo_user",
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"session_id": "demo_session_2",
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"metadata": {
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"category": "databases",
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"subcategory": "vector_search",
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"importance": "high",
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"tags": ["postgresql", "pgvector", "embeddings", "search"]
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}
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},
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{
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"content": "N8N is an open-source workflow automation tool that connects different services and APIs. It provides a visual interface for building workflows.",
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"user_id": "demo_user",
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"session_id": "demo_session_3",
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"metadata": {
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"category": "automation",
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"subcategory": "workflow_tools",
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"importance": "medium",
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"tags": ["n8n", "automation", "workflow", "integration"]
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}
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},
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{
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"content": "Ollama runs large language models locally on your machine. It supports models like Llama, Mistral, and provides embedding capabilities.",
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"user_id": "demo_user",
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"session_id": "demo_session_3",
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"metadata": {
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"category": "ai",
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"subcategory": "local_models",
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"importance": "high",
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"tags": ["ollama", "llm", "local", "embeddings"]
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}
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},
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{
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"content": "Supabase is an open-source Firebase alternative that provides database, authentication, and real-time subscriptions with PostgreSQL.",
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"user_id": "demo_user",
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"session_id": "demo_session_4",
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"metadata": {
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"category": "backend",
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"subcategory": "baas",
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"importance": "medium",
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"tags": ["supabase", "database", "authentication", "backend"]
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}
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},
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{
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"content": "Neo4j is a graph database that stores data as nodes and relationships. It's excellent for modeling complex relationships and network data.",
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"user_id": "demo_user",
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"session_id": "demo_session_4",
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"metadata": {
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"category": "databases",
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"subcategory": "graph_database",
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"importance": "medium",
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"tags": ["neo4j", "graph", "relationships", "cypher"]
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}
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}
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]
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async def store_test_memories():
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"""Store test memories in LangMem API"""
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print("🧪 Populating LangMem with test data...")
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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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stored_memories = []
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for i, memory in enumerate(test_memories, 1):
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try:
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print(f"\n{i}. Storing: {memory['content'][:50]}...")
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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,
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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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stored_memories.append(data)
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print(f" ✅ Stored with ID: {data['id']}")
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else:
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print(f" ❌ 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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print(f"\n🎉 Successfully stored {len(stored_memories)} memories!")
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print("\n📊 Summary:")
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print(f" - Total memories: {len(stored_memories)}")
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print(f" - User: demo_user")
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print(f" - Sessions: {len(set(m['session_id'] for m in test_memories))}")
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print(f" - Categories: {len(set(m['metadata']['category'] for m in test_memories))}")
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# Test search functionality
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print("\n🔍 Testing search functionality...")
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search_tests = [
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"Python web development",
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"AI and machine learning",
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"Database and storage",
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"Docker containers"
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]
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for query in search_tests:
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try:
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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": query,
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"user_id": "demo_user",
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"limit": 3,
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"threshold": 0.5
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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 response.status_code == 200:
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data = response.json()
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print(f" Query: '{query}' -> {data['total_count']} results")
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for memory in data['memories']:
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print(f" - {memory['content'][:40]}... ({memory['similarity']:.3f})")
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else:
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print(f" Query: '{query}' -> Failed ({response.status_code})")
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except Exception as e:
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print(f" Query: '{query}' -> Error: {e}")
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print("\n✅ Test data population complete!")
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print(" You can now view the memories in Supabase web UI:")
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print(" - Table: langmem_documents")
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print(" - URL: http://localhost:8000")
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if __name__ == "__main__":
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asyncio.run(store_test_memories())
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