Hasilkan diagram graph

Setelah Anda menunjukkan kepada pihak administrasi sekolah bahwa chatbot berfungsi, Anda menyarankan untuk membuat diagram yang menjelaskan bagaimana chatbot menjawab pertanyaan. Anda sepakat untuk membuat diagram LangGraph yang menunjukkan bagaimana chatbot dikonfigurasi.

Image dan display dari IPython.display telah dimuat untuk Anda. Kami telah membuat fungsi display_fallback() untuk menampilkan gambar dalam cache jika API Mermaid (layanan yang digunakan LangGraph untuk membuat plot) mengembalikan error.

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

Petunjuk latihan

Di dalam blok try, panggil metode .get_graph() pada graph, diikuti dengan .draw_mermaid_png() untuk menampilkan diagram.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Membangun grafik untuk chatbot Exercise 2: Graf dan state agen Exercise 3: Menambahkan node dan edge Exercise 4: Menghasilkan respons chatbot Exercise 5: Kueri dan respons Exercise 6: Hasilkan diagram graph

Latihan Saat Ini

Exercise 7: Menambahkan alat eksternal ke chatbot Exercise 8: Membangun tool Wikipedia Exercise 9: Tambahkan sebuah tool ke dalam graph Exercise 10: Menambahkan memori dan percakapan Exercise 11: Visualisasikan graph dan kembalikan output tool Exercise 12: Menambahkan memori graf Exercise 13: Menggunakan memori graph untuk percakapan

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!