Genera un diagrama del grafo

Ahora que has demostrado a la administración del centro que el chatbot funciona, propones generar un diagrama que explique cómo responde a las preguntas. Has acordado crear un diagrama de LangGraph que muestre cómo se ha configurado el chatbot.

Image y display de IPython.display ya están cargados. Hemos creado una función display_fallback() para mostrar una imagen en caché en caso de que la API de Mermaid (el servicio que usa LangGraph para crear la gráfica) devuelva un error.

Este ejercicio forma parte del curso

Diseño de sistemas agénticos con LangChain

Instrucciones del ejercicio

Dentro de un bloque try, llama al método .get_graph() sobre graph, seguido de .draw_mermaid_png() para mostrar el diagrama.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Try generating and displaying the diagram of the graph
try:
    display(Image(graph.____().____()))

# Return an exception if the generation fails
except Exception:
    print("Plot generation failed... falling back to cached asset.")
    display_fallback()

Editar y ejecutar código

Este ejercicio forma parte del curso

Diseño de sistemas agénticos con LangChain

IntermedioNivel de habilidad

4.8+

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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: Construir grafos para chatbots Exercise 2: Estados del grafo y del agente Exercise 3: Añadir nodos y aristas Exercise 4: Generar respuestas del chatbot Exercise 5: Consultas y respuestas Exercise 6: Genera un diagrama del grafo

Ejercicio actual

Exercise 7: Añadir herramientas externas a un chatbot Exercise 8: Crea una herramienta de Wikipedia Exercise 9: Añade una herramienta a un grafo Exercise 10: Añadir memoria y conversación Exercise 11: Visualiza el grafo y devuelve las salidas de las herramientas Exercise 12: Añadir memoria al grafo Exercise 13: Usar la memoria del grafo para la conversación

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!