Few-shot prompt uygulama

Bileşenlerini bir zincirde birleştirme zamanı! Önceki egzersizde oluşturduğun few-shot prompt hâlâ kullanımına hazır; ayrıca examples ve example_prompt da elinde.

Bu egzersizi tamamlamak için gerekli tüm LangChain sınıfları senin için önceden yüklendi.

Bu egzersiz

LangChain ile LLM Uygulamaları Geliştirme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

ChatOpenAI sınıfını kullanarak bir OpenAI sohbet LLM’i başlat.
Prompt şablonu ile LLM’den | operatörüyle bir zincir oluştur ve ardından .invoke() yöntemiyle çalıştır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

prompt_template = FewShotPromptTemplate(
    examples=examples,
    example_prompt=example_prompt,
    suffix="Question: {input}",
    input_variables=["input"],
)

# Create an OpenAI chat LLM
llm = ____(model="gpt-4o-mini", api_key='')

# Create and invoke the chain
llm_chain = ____
print(____({"input": "What is Jack's favorite technology on DataCamp?"}))

Kodu Düzenle ve Çalıştır

Bu egzersiz

LangChain ile LLM Uygulamaları Geliştirme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: LangChain ekosistemi Exercise 2: LangChain'de OpenAI modelleri!Exercise 3: LangChain'de Hugging Face modelleri!Exercise 4: İstem şablonları Exercise 5: Prompt şablonları ve zincirleme Exercise 6: Sohbet istemi şablonları Exercise 7: Birkaç atışlık yönlendirme Exercise 8: Birkaç çekimlik örnek setinin oluşturulması Exercise 9: Az örnekli (few-shot) istem şablonu oluşturma Exercise 10: Few-shot prompt uygulama

Geçerli Egzersiz

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: Sequential chains Exercise 2: Building prompts for sequential chains Exercise 3: Sequential chains with LCEL Exercise 4: Introduction to LangChain agents Exercise 5: What's an agent?Exercise 6: ReAct agents Exercise 7: Custom tools for agents Exercise 8: Defining a function for tool use Exercise 9: Creating custom tools Exercise 10: Integrating custom tools with agents

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!