# To use the Python SDK, install the package:
# pip install evermemos

from evermemos import EverMemOS

memory = EverMemOS(api_key="<api_key>").v0.memories

response = memory.search(extra_query={"user_id": "<user_id>", "query": "<query>"})
print(response.result.total_count)

{
  "message": "Memory search successful",
  "result": {
    "has_more": false,
    "memories": [
      {
        "group_id": "group_456",
        "memory_type": "episodic_memory",
        "summary": "User mentioned controlling their diet recently, eating only two meals a day, with dinner mainly being salad",
        "timestamp": "2024-01-15T10:30:00+00:00",
        "user_id": "user_123"
      }
    ],
    "metadata": {
      "episodic_count": 1,
      "latency_ms": 156,
      "profile_count": 2
    },
    "original_data": [],
    "pending_messages": [],
    "profiles": [
      {
        "category": "Dietary Preferences",
        "description": "Prefers light flavors, favoring vegetables and seafood",
        "item_type": "explicit_info",
        "score": 0.89
      },
      {
        "description": "Prioritizes dietary health, preferring low oil and low salt",
        "item_type": "implicit_trait",
        "score": 0.75,
        "trait_name": "Health Conscious"
      }
    ],
    "query_metadata": {
      "memory_type": "retrieve",
      "source": "hybrid_search",
      "user_id": "user_123"
    },
    "scores": [
      0.82
    ],
    "total_count": 3
  },
  "status": "ok"
}