Creating the few-shot example set

PromptTemplate and ChatPromptTemplate are great for integrating variables, but struggle with integrating datasets containing many examples. This is where FewShotPromptTemplate comes in! In this exercise, you'll create a dataset, in the form of a list of dictionaries, to contain the follow question-answer pairs.

Question: How many DataCamp courses has Jack completed?
Answer: 36
Question: How much XP does Jack have on DataCamp?
Answer: 284,320XP
Question: What technology does Jack learn about most on DataCamp?
Answer: Python

In the next exercise, you'll convert this information into a few-shot prompt template.

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Developing LLM Applications with LangChain

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Egzersiz talimatları

Create a list of dictionaries for the questions and answers provided, using the keys "question" and "answer".

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# Create the examples list of dicts
examples = [
  {
    "question": "____",
    "answer": "____"
  },
  {
    "question": "____",
    "answer": "____"
  },
  {
    "question": "____",
    "answer": "____"
  }
]

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Developing LLM Applications with LangChain

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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 Exercise 6: Chat prompt templates Exercise 7: Few-shot prompting Exercise 8: Creating the few-shot example set

Geçerli Egzersiz

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!