init

functionality/long_term_memory_mem0/memory_example.py

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+# -*- coding: utf-8 -*-
+"""Memory example demonstrating long-term memory functionality with mem0.
+
+This module provides examples of how to use the Mem0LongTermMemory class
+for recording and retrieving persistent memories.
+"""
+
+import asyncio
+import os
+
+from agentscope.agent import ReActAgent
+from agentscope.embedding import DashScopeTextEmbedding
+from agentscope.formatter import DashScopeChatFormatter
+from agentscope.memory import InMemoryMemory, Mem0LongTermMemory
+from agentscope.message import Msg
+from agentscope.model import DashScopeChatModel
+from agentscope.tool import Toolkit
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+async def main() -> None:
+    """Run the memory examples."""
+    # Initialize long term memory
+    long_term_memory = Mem0LongTermMemory(
+        agent_name="Friday",
+        user_name="user_123",
+        model=DashScopeChatModel(
+            model_name="qwen-max-latest",
+            api_key=os.environ.get("DASHSCOPE_API_KEY"),
+            stream=False,
+        ),
+        embedding_model=DashScopeTextEmbedding(
+            model_name="text-embedding-v2",
+            api_key=os.environ.get("DASHSCOPE_API_KEY"),
+        ),
+        on_disk=False,
+    )
+
+    print("=== Long Term Memory Examples with mem0 ===\n")
+
+    # Example 1: Basic conversation recording
+    print("1. Basic Conversation Recording")
+    print("-" * 40)
+    results = await long_term_memory.record(
+        msgs=[
+            Msg(
+                role="user",
+                content="Please help me book a hotel, preferably homestay",
+                name="user",
+            ),
+        ],
+    )
+    print(f"Recorded conversation: {results}\n")
+
+    # Example 2: Retrieving memories
+    print("2. Retrieving Memories")
+    print("-" * 40)
+    print("Searching for weather-related memories...")
+    weather_memories = await long_term_memory.retrieve(
+        msg=[
+            Msg(
+                role="user",
+                content="What's the weather like today?",
+                name="user",
+            ),
+        ],
+    )
+    print(f"Retrieved weather memories: {weather_memories}\n")
+
+    print("Searching for user preference memories...")
+    preference_memories = await long_term_memory.retrieve(
+        msg=[
+            Msg(
+                role="user",
+                content=(
+                    "I prefer temperatures in Celsius and wind speed in km/h"
+                ),
+                name="user",
+            ),
+        ],
+    )
+    print(f"Retrieved preference memories: {preference_memories}\n")
+
+    # Example 3: ReActAgent with long term memory
+    print("3. ReActAgent with long term memory")
+    print("-" * 40)
+
+    toolkit = Toolkit()
+    agent = ReActAgent(
+        name="Friday",
+        sys_prompt="You are a helpful assistant named Friday.",
+        model=DashScopeChatModel(
+            model_name="qwen-max-latest",
+            api_key=os.environ.get("DASHSCOPE_API_KEY"),
+            stream=False,
+        ),
+        formatter=DashScopeChatFormatter(),
+        toolkit=toolkit,
+        memory=InMemoryMemory(),
+        long_term_memory=long_term_memory,
+        long_term_memory_mode="both",
+    )
+
+    await agent.memory.clear()
+    msg = Msg(
+        role="user",
+        content="When I travel to Hangzhou, I prefer to stay in a homestay",
+        name="user",
+    )
+    msg = await agent(msg)
+    print(f"ReActAgent response: {msg.get_text_content()}\n")
+
+    msg = Msg(role="user", content="what preference do I have?", name="user")
+    msg = await agent(msg)
+    print(f"ReActAgent response: {msg.get_text_content()}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())