TwinMind SDK Patterns
Overview
Production patterns for TwinMind's AI memory and meeting intelligence REST API. TwinMind captures, organizes, and retrieves contextual memories from conversations and meetings.
Prerequisites
- TwinMind API key configured
- Understanding of REST API patterns
- Familiarity with memory/context retrieval concepts
Instructions
Step 1: Client Wrapper with Authentication
import requests
import os
class TwinMindClient:
def __init__(self, api_key: str = None, base_url: str = "https://api.twinmind.com/v1"):
self.api_key = api_key or os.environ["TWINMIND_API_KEY"]
self.base_url = base_url
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
})
def _request(self, method: str, path: str, **kwargs):
response = self.session.request(method, f"{self.base_url}{path}", **kwargs)
response.raise_for_status()
return response.json()
Step 2: Memory Storage and Retrieval
class TwinMindClient:
# ... (continued from Step 1)
def store_memory(self, content: str, context: dict = None, tags: list = None) -> dict:
return self._request("POST", "/memories", json={
"content": content,
"context": context or {},
"tags": tags or [],
"timestamp": datetime.utcnow().isoformat()
})
def search_memories(self, query: str, limit: int = 10, tags: list = None) -> list:
params = {"q": query, "limit": limit}
if tags:
params["tags"] = ",".join(tags)
return self._request("GET", "/memories/search", params=params)
def get_memory(self, memory_id: str) -> dict:
return self._request("GET", f"/memories/{memory_id}")
