Graf dan state agen

Anda diminta membuat chatbot dasar yang dapat menjawab pertanyaan dalam aplikasi pendidikan tingkat sekolah menengah. Pihak sekolah ingin Anda menggunakan versi ChatGPT dari OpenAI sebagai LLM-nya. Anda memutuskan bahwa tugas ini dapat dikelola secara efisien dengan menggunakan LangGraph untuk membangun agen chatbot berbasis node. Pertama, Anda akan mendefinisikan State() agen untuk menyimpan data agen, dan menyiapkan objek StateGraph() untuk mengelola alur kerja agen.

Modul yang diperlukan sudah diimpor untuk latihan ini dan latihan-latihan berikutnya:

from langchain_openai import ChatOpenAI
from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

Petunjuk latihan

Siapkan llm menggunakan ChatOpenAI() dan model "gpt-4o-mini".
Definisikan kelas State menggunakan TypedDict untuk mengelola data chatbot.
Tentukan messages sebagai list bertipe Annotated menggunakan add_messages.
Inisialisasi instance StateGraph dengan State untuk menyusun alur kerja chatbot.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define the llm
llm = ____(model="____", api_key="OPENAI_API_KEY")

# Define the State
class State(____):
    
    # Define messages with metadata
    messages: ____[____, ____]

# Initialize StateGraph
graph_builder = ____(____)

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

Latihan Saat Ini

Exercise 3: Menambahkan node dan edge Exercise 4: Menghasilkan respons chatbot Exercise 5: Kueri dan respons Exercise 6: Hasilkan diagram graph 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!