Mengintegrasikan alat kustom dengan agent

Sekarang setelah Anda memiliki alat-alat yang diperlukan, saatnya menyiapkan alur kerja berbasis agent! Anda akan kembali menggunakan agent ReAct yang, ingat, menalar langkah-langkah yang perlu dilakukan dan memilih alat berdasarkan konteks serta deskripsi alat. Sebuah llm telah didefinisikan untuk Anda yang menggunakan model gpt-4o-mini dari OpenAI

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

Petunjuk latihan

Buat agent ReAct menggunakan create_react_agent() dengan llm dan sebuah daftar yang memuat alat retrieve_customer_info Anda.
Panggil agent dengan agent.invoke() pada masukan yang disediakan.
Cetak konten dari pesan terakhir.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

@tool
def retrieve_customer_info(name: str) -> str:
    """Retrieve customer information based on their name."""
    customer_info = customers[customers['name'] == name]
    return customer_info.to_string()

# Create a ReAct agent
agent = create_react_agent(____, [____])

# Invoke the agent on the input
messages = agent.____({"messages": [("human", "Create a summary of our customer: Peak Performance Co.")]})
print(messages['messages'][-1].____)

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

Latihan Saat Ini

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!