Mengikat beberapa tool

Anda baru saja membuat beberapa tool! Kini Anda dapat menambahkannya ke chatbot dengan terlebih dahulu mengikatnya ke LLM. Tool wikipedia_tool, palindrome_checker, dan historical_events semuanya telah ditambahkan ke lingkungan Anda bersama dengan llm. Anda juga akan membuat sebuah node tool untuk ditambahkan ke chatbot yang berisi daftar seluruh tool yang tersedia.

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

Petunjuk latihan

Dari langgraph.prebuilt, impor modul yang diperlukan untuk mendefinisikan node tool.
Lengkapi daftar tool dengan menambahkan nama tool yang sesuai yang ada di lingkungan Anda.
Teruskan daftar tools ke kelas ToolNode() dan tetapkan ke tool_node.
Ikat tools ke llm menggunakan .bind_tools().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import modules required for defining tool nodes
from ____.____ import ____

# List of tools
tools = [____, ____, ____]

# Pass the tools to the ToolNode()
____ = ____(____)

# Bind tools to the LLM
model_with_tools = ____.____(____)

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: Building graphs for chatbots Exercise 2: Graph and agent states Exercise 3: Adding nodes and edges Exercise 4: Generating chatbot responses Exercise 5: Queries and responses Exercise 6: Generate a graph diagram Exercise 7: Adding external tools to a chatbot Exercise 8: Build a Wikipedia tool Exercise 9: Add a tool to a graph Exercise 10: Adding memory and conversation Exercise 11: Visualize the graph and return tool outputs Exercise 12: Adding graph memory Exercise 13: Using graph memory for conversation

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: Mendefinisikan banyak tool Exercise 2: Bangun alat yang memanggil LLM Exercise 3: Bangun alat dengan kode Python Exercise 4: Mengikat beberapa tool

Latihan Saat Ini

Exercise 5: Mendefinisikan node dan edge untuk pemanggilan fungsi yang fleksibel Exercise 6: Definisikan fungsi untuk menghentikan chatbot Exercise 7: Buat fungsi untuk mengembalikan respons LLM Exercise 8: Buat alur kerja graph untuk beberapa tool Exercise 9: Mengatur keluaran chatbot dengan memori Exercise 10: Konfigurasikan keluaran untuk beberapa alat Exercise 11: Aktifkan percakapan multi-giliran dengan memori Exercise 12: Selamat!