Membuat agen ReAct

Sekarang setelah Anda mempelajari komponen dasar LangChain, Anda akan langsung membuat agen ReAct yang dapat menghitung berapa banyak huruf 'r' dalam kata apa pun menggunakan tool count_r_in_word.

Berikut telah dimuat untuk Anda: tool, ChatOpenAI, create_react_agent, math, dan model.

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

Petunjuk latihan

Siapkan agen app menggunakan create_react_agent() dengan meneruskan model dan count_r_in_word ke daftar tools.
Definisikan variabel query yang menerima pertanyaan pengguna sebagai string.
Panggil app dengan .invoke() dan teruskan sebuah dictionary dengan kunci "messages", menandai query sebagai "human".
Akses pesan terakhir dalam response dan cetak atribut .content-nya untuk mendapatkan jawaban agen.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create the agent
____ = ____(model=____, tools=[____])

# Create a query
____ = "How many r's are in the word 'Terrarium'?"

# Invoke the agent and store the response
response = app.____({"messages": [("human", ____)]})

# Print the agent's response
print(____['messages'][____].____)

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: Agen di LangChain Exercise 2: Membuat agen ReAct

Latihan Saat Ini

Exercise 3: Komponen agen LangChain Exercise 4: Membangun alat kustom Exercise 5: Buat tool untuk perhitungan matematika Exercise 6: Mengintegrasikan alat kustom dan kueri Exercise 7: Percakapan dengan agen ReAct Exercise 8: Menyiapkan percakapan Exercise 9: Ajukan pertanyaan tentang riwayat percakapan

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: 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!