Aggiungere la memoria al grafo

Ottimo! Il tuo chatbot può usare Wikipedia per rispondere alle domande in base alla richiesta dell’utente. Ora puoi fare un passo in più permettendo agli utenti di porre domande di follow-up! Per riuscirci, devi prima abilitare la memoria all’interno del tuo chatbot. La memoria darà al tuo chatbot accesso all’intero contesto della conversazione, così potrà gestire anche i follow-up.

Questo esercizio fa parte del corso

Progettare sistemi agentici con LangChain

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Istruzioni dell'esercizio

Inizia importando MemorySaver dal modulo checkpoint.memory in LangGraph.
Crea un’istanza di MemorySaver() chiamata memory.
Ricompila il grafo del chatbot impostando l’argomento checkpointer su memory nel metodo .compile() applicato a graph_builder.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import the modules for saving memory
from langgraph.____.____ import ____

# Modify the graph with memory checkpointing
memory = ____()

# Compile the graph passing in memory
graph = ____.___(checkpointer=____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Progettare sistemi agentici con LangChain

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

Build intelligent agentic systems! Discover the key components of LangChain agents, including how prompts, LLMs, and tools work together for reasoning and action. You'll set up an agent with OpenAI's API, define custom tools, and tackle real-world tasks like math calculations. Plus, explore how LangChain organizes data using graphs, nodes, and edges.

Exercise 1: Agents in LangChain Exercise 2: Creating a ReAct agent Exercise 3: Components of LangChain agents Exercise 4: Building custom tools Exercise 5: Create a tool for math calculations Exercise 6: Integrating custom tools and queries Exercise 7: Conversation with a ReAct agent Exercise 8: Conversation setup Exercise 9: Ask questions about conversation history

Build dynamic, tool-augmented chatbots with LangChain and LangGraph! You’ll explore how to create a chatbot that adapts based on user input by defining states and integrating external APIs for real-time information retrieval. You'll connect these components into a responsive graph structure, enabling smooth transitions between conversation and tool-assisted responses. By the end, you’ll have a visually represented chatbot framework with enhanced reasoning and multi-step workflows.

Exercise 1: Creare grafi per chatbot Exercise 2: Stati del grafo e dell'agente Exercise 3: Aggiungere nodi e archi Exercise 4: Generare le risposte del chatbot Exercise 5: Query e risposte Exercise 6: Genera un diagramma del grafo Exercise 7: Aggiungere strumenti esterni a un chatbot Exercise 8: Crea uno strumento Wikipedia Exercise 9: Aggiungi uno strumento a un grafo Exercise 10: Aggiungere memoria e conversazione Exercise 11: Visualizza il grafo e restituisci gli output degli strumenti Exercise 12: Aggiungere la memoria al grafo

Esercizio in corso

Exercise 13: Usare la memoria del grafo per la conversazione

Expand your chatbot with dynamic tools and memory! Define and integrate multiple tools into flexible workflows, build functions for dynamic tool calling, and configure your chatbot for multiple-tool handling. Organize memory and outputs to enable interleaved, multi-turn conversations. By the end, you'll have created a sophisticated chatbot capable of complex interactions.

Exercise 1: Defining multiple tools Exercise 2: Build a tool that invokes the LLM Exercise 3: Build a tool with Python code Exercise 4: Binding multiple tools Exercise 5: Defining nodes and edges for flexible function calling Exercise 6: Define a function that stops the chatbot Exercise 7: Create a function to return an LLM response Exercise 8: Create the graph workflow for multiple tools Exercise 9: Organize chatbot outputs with memory Exercise 10: Configure outputs for multiple tools Exercise 11: Enable multi-turn conversation with memory Exercise 12: Congratulations!