A Coding Guide To Unlock Mem0 Memory For Anthropic Claude Bot: Enabling Context-rich Conversations

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In this tutorial, we locomotion you done mounting up a afloat functional bot successful Google Colab that leverages Anthropic’s Claude model alongside mem0 for seamless representation recall. Combining LangGraph’s intuitive state-machine orchestration pinch mem0’s powerful vector-based representation shop will empower our adjunct to retrieve past conversations, retrieve applicable specifications connected demand, and support earthy continuity crossed sessions. Whether you’re building support bots, virtual assistants, aliases interactive demos, this guideline will equip you pinch a robust instauration for memory-driven AI experiences.

!pip instal -qU langgraph mem0ai langchain langchain-anthropic anthropic

First, we instal and upgrade LangGraph, nan Mem0 AI client, LangChain pinch its Anthropic connector, and nan halfway Anthropic SDK, ensuring we person each nan latest libraries required for building a memory-driven Claude chatbot successful Google Colab. Running it upfront will debar dependency issues and streamline nan setup process.

import os from typing import Annotated, TypedDict, List 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 bring together nan halfway building blocks for our Colab chatbot: it loads nan operating-system interface for API keys, Python’s typed dictionaries and note utilities for defining conversational state, LangGraph’s chart and connection decorators to orchestrate chat flow, LangChain’s connection classes for constructing prompts, nan ChatAnthropic wrapper to telephone Claude, and Mem0’s customer for persistent representation storage.

os.environ["ANTHROPIC_API_KEY"] = "Use Your Own API Key" MEM0_API_KEY = "Use Your Own API Key"

We securely inject our Anthropic and Mem0 credentials into nan situation and a section variable, ensuring that nan ChatAnthropic customer and Mem0 representation shop tin authenticate decently without hard-coding delicate keys passim our notebook. Centralizing our API keys here, we support a cleanable separation betwixt codification and secrets while enabling seamless entree to nan Claude exemplary and persistent representation layer.

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)

We initialize our conversational AI core: first, it creates a ChatAnthropic lawsuit configured to talk pinch Claude 3.5 Sonnet astatine zero somesthesia for deterministic replies and up to 1024 tokens per response, utilizing our stored Anthropic cardinal for authentication. Then it spins up a Mem0 MemoryClient pinch our Mem0 API key, giving our bot a persistent vector-based representation shop to prevention and retrieve past interactions seamlessly.

class State(TypedDict): messages: Annotated[List[HumanMessage | AIMessage], add_messages] mem0_user_id: str graph = StateGraph(State) def chatbot(state: State): messages = state["messages"] user_id = state["mem0_user_id"] memories = mem0.search(messages[-1].content, user_id=user_id) discourse = "\n".join(f"- {m['memory']}" for m successful memories) system_message = SystemMessage(content=( "You are a adjuvant customer support assistant. " "Use nan discourse beneath 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]}

We specify nan conversational authorities schema and ligament it into a LangGraph authorities machine: nan State TypedDict tracks nan connection history and a Mem0 personification ID, and chart = StateGraph(State) sets up nan travel controller. Within nan chatbot, nan astir caller personification connection is utilized to query Mem0 for applicable memories, a context-enhanced strategy punctual is constructed, Claude generates a reply, and that caller speech is saved backmost into Mem0 earlier returning nan 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 usability into LangGraph’s execution travel by registering it arsenic a node named “chatbot,” past connecting nan built-in START marker to that node. Hence, nan speech originates there, and yet creates a self-loop separator truthful each caller personification connection re-enters nan aforesaid logic. Calling graph.compile() past transforms this node-and-edge setup into an optimized, runnable chart entity that will negociate each move of our chat convention automatically.

def run_conversation(user_input: str, mem0_user_id: str): config = {"configurable": {"thread_id": mem0_user_id}} authorities = {"messages": [HumanMessage(content=user_input)], "mem0_user_id": mem0_user_id} for arena successful compiled_graph.stream(state, config): for node_output successful event.values(): if node_output.get("messages"): print("Assistant:", node_output["messages"][-1].content) return if __name__ == "__main__": print("Welcome! (type 'exit' to quit)") mem0_user_id = "customer_123" while True: user_in = input("You: ") if user_in.lower() successful ["exit", "quit", "bye"]: print("Assistant: Goodbye!") break run_conversation(user_in, mem0_user_id)

We necktie everything together by defining run_conversation, which packages our personification input into nan LangGraph state, streams it done nan compiled chart to invoke nan chatbot node, and prints retired Claude’s reply. The __main__ defender past launches a elemental REPL loop, prompting america to type messages, routing them done our memory-enabled graph, and gracefully exiting erstwhile we participate “exit”.

In conclusion, we’ve assembled a conversational AI pipeline that combines Anthropic’s cutting-edge Claude exemplary pinch mem0’s persistent representation capabilities, each orchestrated via LangGraph successful Google Colab. This architecture allows our bot to callback user-specific details, accommodate responses complete time, and present personalized support. From here, see experimenting pinch richer memory-retrieval strategies, fine-tuning Claude’s prompts, aliases integrating further devices into your graph.

Check out Colab Notebook here. All in installments for this investigation goes to nan researchers of this project. Also, feel free to travel america on Twitter and don’t hide to subordinate our 95k+ ML SubReddit.

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Asif Razzaq is nan CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing nan imaginable of Artificial Intelligence for societal good. His astir caller endeavor is nan motorboat of an Artificial Intelligence Media Platform, Marktechpost, which stands retired for its in-depth sum of instrumentality learning and heavy learning news that is some technically sound and easy understandable by a wide audience. The level boasts of complete 2 cardinal monthly views, illustrating its fame among audiences.

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A Coding Guide To Unlock Mem0 Memory For Anthropic Claude Bot: Enabling Context-rich Conversations

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