Membangun tool Wikipedia

Pihak administrasi sekolah sangat puas dengan pekerjaan Anda! Mereka ingin membuat chatbot menjadi lebih andal dengan menggabungkan sumber daya eksternal. Anda menyarankan untuk membekali chatbot dengan akses ke Wikipedia. Pihak administrasi setuju untuk memperpanjang penugasan Anda, sehingga Anda memutuskan untuk membangun sebuah tool untuk chatbot menggunakan Wikipedia API.

Modul berikut telah diimpor agar Anda dapat memulai: WikipediaQueryRun, WikipediaAPIWrapper

Latihan ini adalah bagian dari kursus

Merancang Sistem Agentic dengan LangChain

Petunjuk latihan

Buat api_wrapper yang menginisialisasi WikipediaAPIWrapper() dengan top_k_results=1 untuk mengambil hanya hasil teratas dari Wikipedia.
Buat tool WikipediaQueryRun() bernama wikipedia_tool, teruskan api_wrapper sebagai masukannya, lalu simpan ke dalam sebuah daftar bernama tools.
Ikat daftar tools ke llm dengan meneruskannya ke metode .bind_tools().
Untuk mengarahkan chatbot agar merespons menggunakan tool, teruskan konten "messages" dalam state ke metode .invoke() dan terapkan pada llm_with_tools.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Initialize Wikipedia API wrapper to fetch top one result
api_wrapper = ____(top_k_results=1)

# Create a Wikipedia query tool using the API wrapper
wikipedia_tool = ____(api_wrapper=____)
tools = [____]

# Bind the Wikipedia tool to the language model
llm_with_tools = ____.____(____)

# Modify chatbot function to respond with Wikipedia
def chatbot(state: State):
    return {"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: 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 Exercise 7: Menambahkan alat eksternal ke chatbot Exercise 8: Membangun tool Wikipedia

Latihan Saat Ini

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!