A Coding Information to Unlock mem0 Reminiscence for Anthropic Claude Bot: Enabling Context-Wealthy Conversations

On this tutorial, we stroll you thru establishing a totally useful bot in Google Colab that leverages Anthropic’s Claude mannequin alongside mem0 for seamless reminiscence recall. Combining LangGraph’s intuitive state-machine orchestration with mem0’s highly effective vector-based reminiscence retailer will empower our assistant to recollect previous conversations, retrieve related particulars on demand, and keep pure continuity throughout classes. Whether or not you’re constructing help bots, digital assistants, or interactive demos, this information will equip you with a strong basis for memory-driven AI experiences.

!pip set up -qU langgraph mem0ai langchain langchain-anthropic anthropic

First, we set up and improve LangGraph, the Mem0 AI consumer, LangChain with its Anthropic connector, and the core Anthropic SDK, making certain we’ve got all the most recent libraries required for constructing a memory-driven Claude chatbot in Google Colab. Working it upfront will keep away from dependency points and streamline the setup course of.

import os
from typing import Annotated, TypedDict, Record


from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_anthropic import ChatAnthropic
from mem0 import MemoryClient

We convey collectively the core constructing blocks for our Colab chatbot: it masses the operating-system interface for API keys, Python’s typed dictionaries and annotation utilities for outlining conversational state, LangGraph’s graph and message decorators to orchestrate chat move, LangChain’s message lessons for establishing prompts, the ChatAnthropic wrapper to name Claude, and Mem0’s consumer for persistent reminiscence storage.

os.environ("ANTHROPIC_API_KEY") = "Use Your Personal API Key"
MEM0_API_KEY = "Use Your Personal API Key"

We securely inject our Anthropic and Mem0 credentials into the surroundings and an area variable, making certain that the ChatAnthropic consumer and Mem0 reminiscence retailer can authenticate correctly with out hard-coding delicate keys all through our pocket book. Centralizing our API keys right here, we keep a clear separation between code and secrets and techniques whereas enabling seamless entry to the Claude mannequin and chronic reminiscence layer.

llm = ChatAnthropic(
    mannequin="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)

We initialize our conversational AI core: first, it creates a ChatAnthropic occasion configured to speak with Claude 3.5 Sonnet at zero temperature for deterministic replies and as much as 1024 tokens per response, utilizing our saved Anthropic key for authentication. Then it spins up a Mem0 MemoryClient with our Mem0 API key, giving our bot a persistent vector-based reminiscence retailer to save lots of and retrieve previous interactions seamlessly.

class State(TypedDict):
    messages: Annotated(Record(HumanMessage | AIMessage), add_messages)
    mem0_user_id: str


graph = StateGraph(State)


def chatbot(state: State):
    messages = state("messages")
    user_id = state("mem0_user_id")


    reminiscences = mem0.search(messages(-1).content material, user_id=user_id)


    context = "n".be part of(f"- {m('reminiscence')}" for m in reminiscences)
    system_message = SystemMessage(content material=(
        "You're a useful buyer help assistant. "
        "Use the context under to personalize your solutions:n" + context
    ))


    full_msgs = (system_message) + messages
    ai_resp: AIMessage = llm.invoke(full_msgs)


    mem0.add(
        f"Person: {messages(-1).content material}nAssistant: {ai_resp.content material}",
        user_id=user_id
    )


    return {"messages": (ai_resp)}

We outline the conversational state schema and wire it right into a LangGraph state machine: the State TypedDict tracks the message historical past and a Mem0 consumer ID, and graph = StateGraph(State) units up the move controller. Throughout the chatbot, the latest consumer message is used to question Mem0 for related reminiscences, a context-enhanced system immediate is constructed, Claude generates a reply, and that new change is saved again into Mem0 earlier than returning the assistant’s response.

graph.add_node("chatbot", chatbot)
graph.add_edge(START, "chatbot")
graph.add_edge("chatbot", "chatbot")
compiled_graph = graph.compile()

We plug our chatbot perform into LangGraph’s execution move by registering it as a node named “chatbot,” then connecting the built-in START marker to that node. Therefore, the dialog begins there, and eventually creates a self-loop edge so every new consumer message re-enters the identical logic. Calling graph.compile() then transforms this node-and-edge setup into an optimized, runnable graph object that may handle every flip of our chat session mechanically.

def run_conversation(user_input: str, mem0_user_id: str):
    config = {"configurable": {"thread_id": mem0_user_id}}
    state = {"messages": (HumanMessage(content material=user_input)), "mem0_user_id": mem0_user_id}
    for occasion in compiled_graph.stream(state, config):
        for node_output in occasion.values():
            if node_output.get("messages"):
                print("Assistant:", node_output("messages")(-1).content material)
                return


if __name__ == "__main__":
    print("Welcome! (kind 'exit' to give up)")
    mem0_user_id = "customer_123"  
    whereas True:
        user_in = enter("You: ")
        if user_in.decrease() in ("exit", "give up", "bye"):
            print("Assistant: Goodbye!")
            break
        run_conversation(user_in, mem0_user_id)

We tie the whole lot collectively by defining run_conversation, which packages our consumer enter into the LangGraph state, streams it via the compiled graph to invoke the chatbot node, and prints out Claude’s reply. The __main__ guard then launches a easy REPL loop, prompting us to kind messages, routing them via our memory-enabled graph, and gracefully exiting after we enter “exit”.

In conclusion, we’ve assembled a conversational AI pipeline that mixes Anthropic’s cutting-edge Claude mannequin with mem0’s persistent reminiscence capabilities, all orchestrated through LangGraph in Google Colab. This structure permits our bot to recall user-specific particulars, adapt responses over time, and ship customized help. From right here, take into account experimenting with richer memory-retrieval methods, fine-tuning Claude’s prompts, or integrating extra instruments into your graph.

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