快速入门

import requests
import time

BASE_URL = "https://app.memorylake.cn/openapi/memorylake/api/v3"
API_KEY = "YOUR_API_KEY"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}

# 1. Create a workspace
workspace = requests.post(
    f"{BASE_URL}/workspaces",
    headers=headers,
    json={
        "custom_id": "ws-quickstart-001",
        "name": "My First Workspace",
        "description": "Testing MemoryLake memory extraction",
    },
).json()
workspace_id = workspace["data"]["id"]
print(f"Workspace created: {workspace_id}")

# 2. Create an actor
actor = requests.post(
    f"{BASE_URL}/actors",
    headers=headers,
    json={
        "custom_id": "jane-001",
        "display_name": "Jane",
        "actor_type": "HUMAN",
    },
).json()
actor_id = actor["data"]["id"]
print(f"Actor created: {actor_id}")

# 3. Create a project
project = requests.post(
    f"{BASE_URL}/workspaces/{workspace_id}/projects",
    headers=headers,
    json={
        "custom_id": "proj-support-001",
        "name": "Customer Support Bot",
        "description": "Knowledge base for our support assistant",
    },
).json()
project_id = project["data"]["id"]
print(f"Project created: {project_id}")

# 4. Create a conversation
conversation = requests.post(
    f"{BASE_URL}/workspaces/{workspace_id}/memories/conversations",
    headers=headers,
    json={
        "custom_id": "conv-onboarding-001",
        "kind": "DIRECT",
        "rw_project_ids": [project_id],
        "name": "Onboarding chat with Jane",
    },
).json()
conversation_id = conversation["data"]["id"]
print(f"Conversation created: {conversation_id}")

# 5. Append messages (one at a time)
requests.post(
    f"{BASE_URL}/conversations/{conversation_id}/messages",
    headers=headers,
    json={
        "custom_id": "msg-001",
        "actor_id": actor_id,
        "content": [
            {
                "block_type": "TEXT",
                "text": "Hi, I am Jane. I work at Acme Corp as a product manager. I prefer concise answers.",
            }
        ],
    },
)

requests.post(
    f"{BASE_URL}/conversations/{conversation_id}/messages",
    headers=headers,
    json={
        "custom_id": "msg-002",
        "actor_id": actor_id,
        "content": [
            {
                "block_type": "TEXT",
                "text": "Welcome Jane! I have noted your preferences. How can I help you today?",
            }
        ],
    },
)
print("Messages submitted")

# 6. Wait for fact extraction
print("Waiting for fact extraction...")
time.sleep(5)

# 7. Search memories
results = requests.post(
    f"{BASE_URL}/workspaces/{workspace_id}/memories/search",
    headers=headers,
    json={"query": "What are Jane preferences?"},
).json()

print("\nExtracted memories:")
for fact in results.get("data", {}).get("facts", []):
    print(f"  - {fact['fact']} (score: {fact['score']})")