How are documents fragmented to facilitate indexing?
In the world of natural language processing, structuring and breaking documents into smaller fragments is key to creating efficient and manageable indexes. Learning how to use TextSplitters, specifically the Recursive Character TextSplitter, is a critical step. Defining the right size for these text fragments can significantly improve the way documents are parsed and indexed. In the following, we will explore this approach and highlight how to convert multiple documents into smaller fragments for analysis.
What are TextSplitters and how to use them?
TextSplitters are tools that transform documents into more manageable fragments. The goal is to divide a large document into chapters and subchapters. To get started, the Recursive Character TextSplitter from the Langsteam library is used.
from langsteam import TextSplitter
text_splitter = TextSplitter()
The first step is to define the text size. Instead of measuring it by characters or words, a measurement in tokens is used.
What are tokens and how are they used in TextSplitter?
A token is a basic unit of data in natural language processing. To determine the size of fragments, functions that count the length in tokens are used. It is possible to use advanced methods such as OpenAI tokenizers or Jogging Face, although a simple length function is usually sufficient.
Importance of overlap in fragments
Overlap is essential to ensure that continuity is not lost between consecutive fragments. A text may contain important parts right at the beginning and end of a fragment, so introducing a 200-character overlap ensures that relevant information is not cut off abruptly.
Key features of the Recursive Character TextSplitter
This splitter has the feature of taking into account the ends of sentences and paragraphs, making sure not to cut sentences in half. This improves readability and comprehension of the text.
Why is chunk size and overlap important?
Chunk size and overlap are essential parameters when chunking documents:
- Chunk Size: To be effective, a Chunk Size must be consistent with the Embedding model employed.
- Models such as Cohere handle between 500 to 600 tokens.
- Sentence Transformers from Jogging Face accept around 250 tokens.
- OpenAI - offers a margin of up to 8000 tokens.
- Chunk Overlap: It is recommended that the overlap be between 10% to 20% of the total chunk. This allows the chunks to maintain their connection without being overly repetitive.
Practical exploration
Once the documents are split, a much more extensive and detailed list of chunks is obtained. Originally, 18 documents were transformed into 142 more manageable snippets, ready to be indexed more accurately.
documents = text_splitter.splitDocuments(data)
With this methodology, indexing is facilitated and access to information is optimized by breaking down large documents into more precise and concise fragments.
Tips for advanced practices
- When adjusting Chunk Size and Overlap values, experimenting with different settings can reveal which one is best suited to a specific task.
- Keeping up to date with advances in Embedding models can allow you to handle larger chunks as technology capabilities increase.
- Using libraries such as Langteam and Jogging Face for advanced tokenization can provide more accurate size measurement in tokens.
Learning to work with TextSplitters not only improves how information is indexed, but also transforms the way natural language is processed for data and knowledge retrieval. We encourage you to continue exploring and experimenting with these tools to maximize their potential.
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