Een few-shot prompttemplate bouwen

Nu je voorbeelden in een gestructureerd formaat staan, is het tijd om de few-shot prompttemplate te maken! Je maakt een template die de vraag-antwoordparen omzet naar het volgende formaat:

Question: Example question
Example Answer

Alle benodigde LangChain-klassen voor deze oefening zijn alvast voor je geladen.

Deze oefening maakt deel uit van de cursus

LLM-toepassingen ontwikkelen met LangChain

Cursus bekijken

Oefeninstructies

Maak de prompt af voor het formatteren van antwoorden zodat deze de sleutels question en answer bevat.
Maak de few-shot prompt met FewShotPromptTemplate met examples en example_prompt.
Vul de lijst met invoervariabelen aan op basis van de gegeven suffix.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Complete the prompt for formatting answers
example_prompt = PromptTemplate.from_template("Question: {____}\n{____}")

# Create the few-shot prompt
prompt_template = ____(
    examples=____,
    example_prompt=____,
    suffix="Question: {input}",
    input_variables=["____"],
)

prompt = prompt_template.invoke({"input": "What is Jack's favorite technology on DataCamp?"})
print(prompt.text)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

LLM-toepassingen ontwikkelen met LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: Het LangChain-ecosysteem Exercise 2: OpenAI-modellen in LangChain!Exercise 3: Hugging Face-modellen in LangChain!Exercise 4: Prompt-templates Exercise 5: Prompttemplates en chaining Exercise 6: Chatprompt-sjablonen Exercise 7: Few-shot prompting Exercise 8: De few-shot-voorbeelden maken Exercise 9: Een few-shot prompttemplate bouwen

Huidige oefening

Exercise 10: Few-shot prompting implementeren

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!