Defining a function for tool use

You're working for a SaaS (software as a service) company with big goals for rolling out tools to help employees at all levels of the organization to make data-informed decisions. You're creating a PoC for an application that allows customer success managers to interface with company data using natural language to retrieve important customer data.

You've been provided with a pandas DataFrame called customers that contains a small sample of customer data. Your first step in this project is to define a Python function to extract information from this table given a customer's name. pandas has already been imported as pd.

This exercise is part of the course

Developing LLM Applications with LangChain

Exercise instructions

Define a retrieve_customer_info() function that takes a string argument, name.
Filter the customers DataFrame to return rows with "name" equal to the input argument, name.
Call the function on the customer name, "Peak Performance Co.".

Hands-on interactive exercise

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

# Define a function to retrieve customer info by-name
def retrieve_customer_info(____: ____) -> str:
    """Retrieve customer information based on their name."""
    # Filter customers for the customer's name
    customer_info = customers[customers['name'] == ____]
    return customer_info.to_string()
  
# Call the function on Peak Performance Co.
print(retrieve_customer_info("____"))

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 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

Current Exercise

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!