Chain Dependencies | Chain Management

Introduction

In LangChain, chain dependencies are a crucial component for managing complex workflows. They allow you to define sequences of operations where the output of one operation is the input to another. This tutorial will guide you through the concept of chain dependencies, from basic principles to advanced usage, with practical examples.

Understanding Chain Dependencies

Chain dependencies enable you to create a series of dependent tasks. Each task in the chain can depend on the output of previous tasks. This is essential for workflows where each step builds on the results of the previous steps.

For instance, consider a workflow where you need to fetch data, process it, and then store the results. Each of these steps can be managed as a separate task with dependencies:

Fetch data from an API
Process the fetched data
Store the processed data

Setting Up LangChain

Before diving into chain dependencies, ensure you have LangChain installed. You can install it using pip:

pip install langchain

Creating Basic Chains

Let’s start by creating a basic chain in LangChain. Below is a simple example where we define two dependent tasks:


from langchain.chains import SimpleChain, Chain

# Define the first task
def fetch_data():
    return {"data": "Sample data"}

# Define the second task which depends on the first
def process_data(input_data):
    data = input_data["data"]
    return {"processed_data": data.upper()}

# Create the chain
fetch_chain = SimpleChain(fetch_data)
process_chain = SimpleChain(process_data, dependencies=[fetch_chain])

# Execute the chain
result = process_chain.run()
print(result)

{'processed_data': 'SAMPLE DATA'}

Complex Chain Dependencies

In more complex scenarios, you might have multiple chains interdependent on each other. Here's an example demonstrating a more intricate setup:


from langchain.chains import SimpleChain, Chain

# Task 1: Fetch data
def fetch_data():
    return {"data": "Sample data"}

# Task 2: Process data
def process_data(input_data):
    data = input_data["data"]
    return {"processed_data": data.upper()}

# Task 3: Store data
def store_data(processed_data):
    # Simulate storing data
    return f"Data stored: {processed_data['processed_data']}"

# Create the chains
fetch_chain = SimpleChain(fetch_data)
process_chain = SimpleChain(process_data, dependencies=[fetch_chain])
store_chain = SimpleChain(store_data, dependencies=[process_chain])

# Execute the chain
result = store_chain.run()
print(result)

Data stored: SAMPLE DATA

Handling Errors in Chains

Error handling is an essential aspect of managing chain dependencies. LangChain provides mechanisms to handle errors gracefully within chains. Here's an example:


from langchain.chains import SimpleChain, Chain, ChainError

# Task 1: Fetch data with a potential error
def fetch_data():
    raise ChainError("Failed to fetch data")

# Task 2: Process data
def process_data(input_data):
    data = input_data["data"]
    return {"processed_data": data.upper()}

# Create the chains
fetch_chain = SimpleChain(fetch_data)
process_chain = SimpleChain(process_data, dependencies=[fetch_chain])

try:
    # Execute the chain
    result = process_chain.run()
    print(result)
except ChainError as e:
    print(f"Error: {e}")

Error: Failed to fetch data

Conclusion

Chain dependencies in LangChain enable you to build complex workflows by linking multiple tasks. By understanding and utilizing these dependencies, you can create robust and efficient workflows that handle data processing, error management, and more. Experiment with different chain setups to find what works best for your specific use case.

Chain Dependencies in LangChain