Prompt templates and chaining

In this exercise, you'll begin using two of the core components in LangChain: prompt templates and chains!

The classes necessary for completing this exercise, including ChatOpenAI, have been pre-loaded for you.

Bu egzersiz

Developing LLM Applications with LangChain

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Kursu Görüntüle

Egzersiz talimatları

Convert the template text provided into a standard (non-chat) prompt template.
Create a chain to pass the prompt template into the LLM.
Invoke the chain on the question variable provided.

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Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create a prompt template from the template string
template = "You are an artificial intelligence assistant, answer the question. {question}"
prompt = PromptTemplate.____(
    template=____
)

llm = ChatOpenAI(model="gpt-4o-mini", api_key='')	

# Create a chain to integrate the prompt template and LLM
llm_chain = ____ | ____

# Invoke the chain on the question
question = "How does LangChain make LLM application development easier?"
print(llm_chain.invoke({"question": ____}))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Developing LLM Applications with LangChain

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: 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

Geçerli Egzersiz

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: 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!