Implementing Memory-Driven AI with Claude and Mem0
In this guide, we will explore how to set up a functional chatbot using Google Colab that utilizes Anthropic’s Claude model and Mem0 for memory recall. This combination allows for a more interactive and personalized experience in applications such as customer support bots and virtual assistants.
1. Setting Up Your Environment
To start, we need to install essential libraries that will help us build our chatbot. This includes LangGraph, Mem0 AI client, LangChain with Anthropic connector, and the Anthropic SDK. By ensuring we have the latest versions, we can avoid potential issues later on.
!pip install -qU langgraph mem0ai langchain langchain-anthropic anthropic
2. Configuring API Keys
Next, we will securely set our API keys for Anthropic and Mem0. This step is crucial for authenticating our chatbot without exposing sensitive information in the code.
os.environ["ANTHROPIC_API_KEY"] = "Use Your Own API Key"
MEM0_API_KEY = "Use Your Own API Key"
3. Initializing the Chatbot
We will create instances of the ChatAnthropic model and Mem0 MemoryClient. The ChatAnthropic instance will handle conversations, while the Mem0 client will store and retrieve past interactions.
llm = ChatAnthropic(
model="claude-3-5-haiku-latest",
temperature=0.0,
max_tokens=1024,
anthropic_api_key=os.environ["ANTHROPIC_API_KEY"]
)
mem0 = MemoryClient(api_key=MEM0_API_KEY)
4. Defining the Conversational State
We will set up a state machine to manage the conversation flow. This includes tracking user messages and utilizing the Mem0 user ID for memory retrieval.
class State(TypedDict):
messages: Annotated[List[HumanMessage | AIMessage], add_messages]
mem0_user_id: str
5. Building the Chatbot Logic
The chatbot function will query Mem0 for relevant memories based on the latest user message, construct a personalized response, and save the interaction back into memory.
def chatbot(state: State):
messages = state["messages"]
user_id = state["mem0_user_id"]
memories = mem0.search(messages[-1].content, user_id=user_id)
context = "n".join(f"- {m['memory']}" for m in memories)
system_message = SystemMessage(content=(
"You are a helpful customer support assistant. "
"Use the context below to personalize your answers:n" + context
))
full_msgs = [system_message] + messages
ai_resp: AIMessage = llm.invoke(full_msgs)
mem0.add(
f"User: {messages[-1].content}nAssistant: {ai_resp.content}",
user_id=user_id
)
return {"messages": [ai_resp]}
6. Compiling the Conversation Flow
We will register our chatbot function within the LangGraph framework, allowing it to manage the conversation automatically.
graph.add_node("chatbot", chatbot)
graph.add_edge(START, "chatbot")
graph.add_edge("chatbot", "chatbot")
compiled_graph = graph.compile()
7. Running the Chatbot
Finally, we will implement a simple loop to interact with the chatbot, allowing users to input messages and receive responses in real-time.
def run_conversation(user_input: str, mem0_user_id: str):
config = {"configurable": {"thread_id": mem0_user_id}}
state = {"messages": [HumanMessage(content=user_input)], "mem0_user_id": mem0_user_id}
for event in compiled_graph.stream(state, config):
for node_output in event.values():
if node_output.get("messages"):
print("Assistant:", node_output["messages"][-1].content)
return
Conclusion
In summary, we have successfully built a memory-driven chatbot using Anthropic’s Claude model and Mem0’s memory capabilities. This setup allows for personalized and context-aware interactions, enhancing user experience. Businesses can leverage this technology to improve customer support and engagement.
Next Steps
- Experiment with different memory retrieval strategies.
- Fine-tune the prompts for Claude to enhance responses.
- Integrate additional tools to expand functionality.
For further assistance in implementing AI solutions in your business, feel free to reach out to us at hello@itinai.ru.
