🦜️✂️ Text Splitters: Smart Text Division with Langchain
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In the fascinating world of natural language processing, tools for transforming documents and splitting texts have become essential, thanks in large part to the emergence of “Retrieval Augmented Generation” or RAG.
RAG has revolutionized the way we train language models by allowing them to access external data after initial training. This is why effective information division has become indispensable.
In this article, we will delve into the Document Transformers and Text Splitters of #langchain, along with their applications and customization options.
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Text Splitters
Text Splitters are tools that divide text into smaller fragments with semantic meaning, often corresponding to sentences. But here’s where the intelligence lies: it’s not just about splitting; it’s about combining these fragments strategically. They work as follows:
- Divide the text into small fragments with semantic meaning, such as sentences.
- Then, combine these small fragments into a larger fragment until a certain size is reached, usually measured by some function.
- Once that size is reached, that fragment becomes its own unit of text. Then, the creation of a new text fragment begins, with some overlap to maintain context between the fragments.
This means that Text Splitters are highly customizable in two fundamental aspects:
- How the text is divided: You can define division rules based on characters, words, or tokens.
- How the fragment size is measured: You can adjust the fragment size according to your specific needs.
Types of Text Splitters in #langchain
RecursiveCharacterTextSplitter: Divides the text into fragments based on characters, starting with the first character. If the fragments turn out to be too large, it moves on to the next character. It offers flexibility by allowing you to define the division characters and fragment size.
CharacterTextSplitter: Similar to the RecursiveCharacterTextSplitter, but with the ability to define a custom separator for more specific division. By default, it tries to split on characters like “\n\n”, “\n”, “ “, and “”.
RecursiveTextSplitter: Unlike the previous ones, the RecursiveTextSplitter divides text into fragments based on words or tokens instead of characters. This provides a more semantic view and is ideal for content analysis rather than structure.
TokenTextSplitter: Uses the OpenAI language model to split text into fragments based on tokens, allowing for precise and contextualized segmentation, ideal for advanced natural language processing applications.
I hope this article and examples are helpful in understanding this new challenge of working with AI. I leave you with the demo and the code to experiment with and use in your projects.
If you need assistance, please don’t hesitate to reach out.
