Using Parameters In Chains | Variables And Parameters

Introduction

LangChain is a powerful tool for building complex data processing pipelines. One of the essential features of LangChain is the ability to use parameters in chains, which allows for dynamic and flexible data processing workflows. In this tutorial, we'll cover everything you need to know about using parameters in chains, from basic concepts to advanced examples.

What are Parameters?

Parameters are variables that can be passed into functions or chains to influence their behavior dynamically. In the context of LangChain, parameters allow you to customize the execution of a chain without hardcoding values.

Basic Example

Let's start with a simple example to illustrate how parameters can be used in a LangChain chain.

Suppose we have a chain that processes a list of numbers and we want to add a parameter to specify a multiplier for each number in the list.

def multiply_chain(numbers, multiplier):
    return [n * multiplier for n in numbers]

numbers = [1, 2, 3, 4, 5]
multiplier = 2
result = multiply_chain(numbers, multiplier)
print(result)

Output: [2, 4, 6, 8, 10]

Advanced Example with Multiple Parameters

Now, let's look at a more advanced example where we use multiple parameters to control the behavior of the chain. We'll create a chain that filters and multiplies numbers based on specified criteria.

We want to filter out numbers less than a certain threshold and then multiply the remaining numbers by a specified multiplier.

def filter_and_multiply_chain(numbers, threshold, multiplier):
    filtered_numbers = [n for n in numbers if n >= threshold]
    return [n * multiplier for n in filtered_numbers]

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
threshold = 5
multiplier = 3
result = filter_and_multiply_chain(numbers, threshold, multiplier)
print(result)

Output: [15, 18, 21, 24, 27, 30]

Using Parameters in Real-World Scenarios

In real-world applications, parameters in LangChain can be used to handle various data processing tasks such as data cleaning, transformation, and aggregation. Parameters can be passed from external sources such as configuration files, user inputs, or APIs.

Here's an example of a chain that takes user input to process data:

def user_input_chain(data, user_params):
    filtered_data = [d for d in data if d.get('value', 0) >= user_params['min_value']]
    return [d['value'] * user_params['multiplier'] for d in filtered_data]

data = [{'value': 1}, {'value': 5}, {'value': 8}, {'value': 10}]
user_params = {'min_value': 5, 'multiplier': 2}
result = user_input_chain(data, user_params)
print(result)

Output: [10, 16, 20]

Conclusion

Using parameters in chains is a powerful feature of LangChain that allows for flexible and dynamic data processing workflows. By understanding how to use parameters effectively, you can build more robust and adaptable data processing pipelines. We hope this tutorial has provided you with a comprehensive understanding of using parameters in LangChain chains.