Sequential chains with LCEL

With your prompt templates created, it's time to tie everything together, including the LLM, using chains and LCEL. An llm has already been defined for you that uses OpenAI's gpt-4o-mini model

For the final step of calling the chain, feel free to insert any activity you wish! If you're struggling for ideas, try inputting "play the harmonica".

This exercise is part of the course

Developing LLM Applications with LangChain

Exercise instructions

Create a sequential chain using LCEL that passes learning_prompt into the llm, and feeds the output into time_prompt for resending to the llm.
Call the chain with the activity of your choice!

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

learning_prompt = PromptTemplate(
    input_variables=["activity"],
    template="I want to learn how to {activity}. Can you suggest how I can learn this step-by-step?"
)

time_prompt = PromptTemplate(
    input_variables=["learning_plan"],
    template="I only have one week. Can you create a concise plan to help me hit this goal: {learning_plan}."
)

# Complete the sequential chain with LCEL
seq_chain = ({"____": ____ | ____ | StrOutputParser()}
    | ____
    | ____
    | StrOutputParser())

# Call the chain
print(seq_chain.____({"____": "____"}))

Edit and Run Code

This exercise is part of the course

Developing LLM Applications with LangChain

IntermediateSkill Level

4.8+

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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

Current Exercise

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!