Mendefinisikan fungsi untuk penggunaan tool

Anda bekerja untuk perusahaan SaaS (software as a service) dengan target besar untuk meluncurkan tool yang membantu karyawan di semua level organisasi membuat keputusan berbasis data. Anda membuat PoC untuk sebuah aplikasi yang memungkinkan customer success manager berinteraksi dengan data perusahaan menggunakan bahasa alami untuk mengambil data pelanggan penting.

Anda telah disediakan sebuah pandas DataFrame bernama customers yang berisi sampel kecil data pelanggan. Langkah pertama Anda dalam proyek ini adalah mendefinisikan fungsi Python untuk mengekstrak informasi dari tabel ini berdasarkan nama pelanggan. pandas sudah diimpor sebagai pd.

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

Petunjuk latihan

Definisikan fungsi retrieve_customer_info() yang menerima argumen string, name.
Saring DataFrame customers untuk mengembalikan baris dengan "name" yang sama dengan argumen masukan, name.
Panggil fungsi tersebut pada nama pelanggan, "Peak Performance Co.".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define a function to retrieve customer info by-name
def retrieve_customer_info(____: ____) -> str:
    """Retrieve customer information based on their name."""
    # Filter customers for the customer's name
    customer_info = customers[customers['name'] == ____]
    return customer_info.to_string()
  
# Call the function on Peak Performance Co.
print(retrieve_customer_info("____"))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

Welcome to the LangChain framework for building applications on LLMs! You'll learn about the main components of LangChain, including models, chains, agents, prompts, and parsers. You'll create chatbots using both open-source models from Hugging Face and proprietary models from OpenAI, create prompt templates, and integrate different chatbot memory strategies to manage context and resources during conversations.

Exercise 1: The LangChain ecosystem Exercise 2: OpenAI models in LangChain!Exercise 3: Hugging Face models in LangChain!Exercise 4: Prompt templates Exercise 5: Prompt templates and chaining Exercise 6: Chat prompt templates Exercise 7: Few-shot prompting Exercise 8: Creating the few-shot example set Exercise 9: Building the few-shot prompt template Exercise 10: Implementing few-shot prompting

Time to level up your LangChain chains! You'll learn to use the LangChain Expression Language (LCEL) for defining chains with greater flexibility. You'll create sequential chains, where inputs are passed between components to create more advanced applications. You'll also begin to integrate agents, which use LLMs for decision-making.

Exercise 1: Rantai berurutan Exercise 2: Menyusun prompt untuk sequential chains Exercise 3: Sequential chain dengan LCEL Exercise 4: Pengantar agent LangChain Exercise 5: Apa itu agent?Exercise 6: Agen ReAct Exercise 7: Alat kustom untuk agen Exercise 8: Mendefinisikan fungsi untuk penggunaan tool

Latihan Saat Ini

Exercise 9: Membuat tool kustom Exercise 10: Mengintegrasikan alat kustom dengan agent

One limitation of LLMs is that they have a knowledge cut-off due to being trained on data up to a certain point. In this chapter, you'll learn to create applications that use Retrieval Augmented Generation (RAG) to integrate external data with LLMs. The RAG workflow contains a few different processes, including splitting data, creating and storing the embeddings using a vector database, and retrieving the most relevant information for use in the application. You'll learn to master the entire workflow!

Exercise 1: Integrating document loaders Exercise 2: PDF document loaders Exercise 3: CSV document loaders Exercise 4: HTML document loaders Exercise 5: Splitting external data for retrieval Exercise 6: Splitting by character Exercise 7: Recursively splitting by character Exercise 8: Splitting HTML Exercise 9: RAG storage and retrieval using vector databases Exercise 10: Preparing the documents and vector database Exercise 11: Building a retrieval prompt template Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!