Building a retrieval prompt template

Now your documents have been ingested into vector database and are ready for retrieval, you'll need to design a chat prompt template to combine the retrieved document chunks with the user input question.

The general structure of the prompt has already been provided; your goal is to insert the correct input variable placeholders into the message string and convert the string into a chat prompt template.

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Developing LLM Applications with LangChain

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Istruzioni dell'esercizio

Complete the message string to add a placeholder for dynamic insertion of the retrieved documents called context and user input question question.
Create a chat prompt template from message using the .from_messages() method with the "human" role.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Add placeholders to the message string
message = """
Answer the following question using the context provided:

Context:
____

Question:
____

Answer:
"""

# Create a chat prompt template from the message string
prompt_template = ChatPromptTemplate.from_messages([("____", ____)])

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Developing LLM Applications with LangChain

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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: 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

Esercizio in corso

Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!