Advanced Module Usage | Modules | Langchain Tutorial

Introduction

LangChain is a powerful library designed to handle natural language processing tasks. It allows you to work with language models in a modular way. This tutorial will guide you through advanced usage of LangChain modules, covering topics such as custom modules, chaining, and integration with other tools.

Creating Custom Modules

LangChain allows you to create custom modules to extend its functionality. Let's start by creating a simple custom module.

Example: Simple Custom Module

First, let's create a custom module that reverses text:

class ReverseTextModule:
    def __init__(self):
        pass

    def process(self, text):
        return text[::-1]

Next, integrate this module with LangChain:

from langchain import LangChain

lc = LangChain()
reverse_module = ReverseTextModule()
lc.add_module('reverse', reverse_module)
result = lc.process('reverse', 'Hello, LangChain!')
print(result)

Output: !niahCgnaL ,olleH

Chaining Modules

One of the powerful features of LangChain is the ability to chain multiple modules together. This allows you to create complex processing pipelines.

Example: Chaining Modules

Let's chain the reverse text module with a module that converts text to uppercase:

class UppercaseModule:
    def __init__(self):
        pass

    def process(self, text):
        return text.upper()

Now, chain both modules:

lc.add_module('uppercase', UppercaseModule())
result = lc.chain_process(['reverse', 'uppercase'], 'Hello, LangChain!')
print(result)

Output: !NIAHCGNAL ,OLLEH

Integrating with Other Tools

LangChain can be integrated with other NLP tools and libraries to enhance its capabilities. For example, you can integrate it with the SpaCy library for advanced text processing.

Example: Integrating with SpaCy

First, install SpaCy:

pip install spacy

Then, create a module that uses SpaCy for named entity recognition (NER):

import spacy

class SpaCyNERModule:
    def __init__(self):
        self.nlp = spacy.load('en_core_web_sm')

    def process(self, text):
        doc = self.nlp(text)
        return [(ent.text, ent.label_) for ent in doc.ents]

Integrate this NER module with LangChain:

lc.add_module('spacy_ner', SpaCyNERModule())
result = lc.process('spacy_ner', 'Google was founded by Larry Page and Sergey Brin.')
print(result)

Output: [('Google', 'ORG'), ('Larry Page', 'PERSON'), ('Sergey Brin', 'PERSON')]

Error Handling

When working with multiple modules, it's important to handle errors gracefully. LangChain allows you to catch and handle errors within your modules.

Example: Error Handling

Let's modify the reverse text module to handle errors:

class ReverseTextModule:
    def __init__(self):
        pass

    def process(self, text):
        if not isinstance(text, str):
            raise ValueError('Input must be a string')
        return text[::-1]

Now, handle the error when processing:

try:
    result = lc.process('reverse', 12345)
except ValueError as e:
    print(f'Error: {e}')

Output: Error: Input must be a string

Conclusion

In this tutorial, we explored advanced usage of LangChain modules, including creating custom modules, chaining modules, integrating with other tools, and handling errors. By leveraging these capabilities, you can build powerful and flexible NLP pipelines tailored to your specific needs.

Advanced Module Usage in LangChain