Biarkan Agen Menghitung: Wawasan Pengeluaran

Anda selama ini mencatat pengeluaran bulanan secara manual di spreadsheet, tetapi menganalisis pola belanja memakan waktu lama.

Teman Anda menyarankan untuk mencoba smolagents guna mengotomatisasi analisis. Data pengeluaran mingguan Anda untuk bulan lalu tersedia dalam kamus expense_data berikut:

expense_data = {
    "groceries": [120, 95, 110, 140],
    "utilities": [85, 92, 78, 88],
    "entertainment": [45, 0, 75, 30],
    "transportation": [60, 55, 70, 65]
}

Mari jalankan sebuah agen yang dapat membantu Anda memahami kebiasaan belanja dan membuat rencana anggaran.

Catatan: agent dan kamus expense_data telah diinisialisasi untuk Anda.

Latihan ini merupakan bagian dari kursus

Agen AI dengan Hugging Face smolagents

Instruksi latihan

Jalankan kode yang disediakan untuk melihat smolagents menganalisis data pengeluaran Anda.
Amati hasilnya dan bagaimana agen menulis kode Python untuk menemukan pola pengeluaran.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Task for the agent
task = f"""Analyze my monthly expense data by category. Calculate total spending per category, find my highest expense area, and suggest a realistic budget for next month. Use simple text format in your final answer. Here is my weekly expense data for the past four weeks:

{expense_data}
"""

# Execute the financial analysis
result = agent.run(task)

print("Personal finance analysis:\n")
print(result)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Agen AI dengan Hugging Face smolagents

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus Gratis

Discover what makes code agents special and how they use Python to reason and act. Build your first agent with smolagents, add built-in and community tools for web access, and create custom tools to connect agents with data.

Exercise 1: Pengantar Hugging Face smolagents Exercise 2: Biarkan Agen Menghitung: Wawasan Pengeluaran

Latihan Saat Ini

Exercise 3: Tunggu, Anda Tidak Menulis Fungsi Apa Pun?Exercise 4: Membuat Agen dengan Tools Exercise 5: Menulis Kode Melalui Prompt: Bunga Majemuk Exercise 6: Biarkan Agent Menelusuri Web Exercise 7: Membuat Agen dengan Alat Kustom Exercise 8: Cukup Tambahkan @tool: Menulis Tool Kustom Exercise 9: Permisi, Tagihannya! Memberi Akses Data kepada Agen Exercise 10: Satu Prompt, Banyak Tool

Transform your traditional RAG pipeline into an agentic system that retrieves information iteratively and reasons across multiple steps. Build stateful tools to support advanced retrieval, guide agents with planning intervals to improve outcomes, and use callbacks to track and customize agent behavior at runtime.

Exercise 1: RAG Refresher Exercise 2: RAG in Order: From Manuals to Answers Exercise 3: So, What’s Actually Happening in RAG?Exercise 4: The Step That Adds Meaning Exercise 5: Introduction to Agentic RAG Exercise 6: Stateful by Design: Build a RAG Search Tool Exercise 7: Connect the Tool, Unlock Agentic RAG Exercise 8: Working With Multi-Step Agents Exercise 9: Track Agent Steps, Monitor the Action Exercise 10: Game Time: Run the Full Agent

Tackle complex workflows by orchestrating teams of specialized agents under a coordinating manager. Add memory to retain context across interactions, debug agent behavior using execution traces and reasoning steps, and implement robust validation strategies to ensure high-quality, reliable responses.

Exercise 1: Introduction to Multi-Agent Systems Exercise 2: Divide and Conquer: Creating Specialist Agents Exercise 3: Meet the Team: Agent Coordination Exercise 4: Managing Agent Memory Exercise 5: Don't Forget: Keeping Memory Between Calls Exercise 6: Tracing the Agent’s Code Execution Exercise 7: Agent Final Answer Validation Exercise 8: Say More, Agent: Enforcing Answers Length Exercise 9: Smarter Agents: Validate and Retry Exercise 10: Congratulations!