Split Documents

As a starting point on generic text, we recommend the use of LangChain’s RecursiveCharacterTextSplitter.

Splitting documents into smaller segments called chunks is an essential step when embedding your data into a vector store. RAG pipelines retrieve relevant chunks to serve as context for the LLM to pull from when generating responses, which makes it important that the retrieved chunks provide the right amount of contextual information to answer the question, and no more than that.

The RecursiveCharacterTextSplitter tries to split on a list of characters, in order, until the chunks are small enough. The default list is ["\n\n", "\n", " ", ""]. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.

The LangChain TextSplitters have several parameters that can be tuned:

chunk_size: Determining the best chunk size for your setup can be difficult. Smaller chunk sizes may result in lower retrieval and generation cost by providing fewer tokens to the context window, but the embedding may miss out on broader contextual information. Larger chunk sizes include that contextual information but may produce diluted responses due to unnecessary information being included in the context.
chunk_overlap: Chunk overlap is used to specify the number of overlapping tokens between consecutive chunks. This is useful when splitting text to maintain context continuity between chunks. By including some overlapping tokens, you can ensure that a small portion of context is shared between adjacent chunks, which can help with preserving the meaning and coherence when processing the text with language models.

The chunk_size represents the total size of the chunk. (The total size is not chunk_size + chunk_overlap.)

For example, if chunk_size is 1000 and chunk_overlap is 100, then the first 100 tokens will be copied from the end of the previous chunk, and the next 900 tokens will be new.

We recommend starting with a chunk_size of 1024 tokens and a chunk_overlap of 128. Chunk sizes over 1500 tokens may not work with Astra DB at this time.

Additionally you should make sure to use a length function that splits on tokens and not individual characters. The token splitter should match the embedding model you are using. LangChain includes shortcuts for OpenAI and HuggingFace embedding models:

OpenAI:

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    model_name="text-embedding-ada-002",
    chunk_size = 1024,
    chunk_overlap = 128,
)

HuggingFace:

from langchain.text_splitter import RecursiveCharacterTextSplitter
from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
    tokenizer=tokenizer,
    chunk_size = 1024,
    chunk_overlap = 128,
)

Split Documents with RecursiveCharacterTextSplitter

In this example we will split Edgar Allan Poe’s "The Cask of Amontillado" using the RecursiveCharacterTextSplitter.

We assume that the text will be embedded using OpenAI’s text-embedding-ada-002 model.
For illustrative purposes, the chunk_size and chunk_overlap are set to smaller values than recommended.

Download a copy of the text from our repository.

curl https://raw.githubusercontent.com/CassioML/cassio-website/main/docs/frameworks/langchain/texts/amontillado.txt --output amontillado.txt

Use the RecursiveCharacterTextSplitter to split the text into smaller chunks. Output the count of chunks and the contents of the first two chunks. Observe the chunk overlap.

Python
Result

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document

with open("amontillado.txt") as textfile:
    amontillado = textfile.read()

doc = Document(page_content=amontillado)

splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
    model_name="text-embedding-ada-002",
    chunk_size = 100,
    chunk_overlap = 20,
)

chunks = splitter.split_documents([doc])

print(f"Chunk count: {len(chunks)}\n")
print(f"Chunk 1 contents:\n\n{chunks[0].page_content}\n")
print(f"Chunk 2 contents:\n\n{chunks[1].page_content}\n")

Chunk count: 48

Chunk 1 contents:

The thousand injuries of Fortunato I had borne as I best could, but
when he ventured upon insult, I vowed revenge.  You, who so well know
the nature of my soul, will not suppose, however, that I gave utterance
to a threat.  _At length_ I would be avenged; this was a point definitely
settled--but the very definitiveness with which it was resolved,

Chunk 2 contents:

settled--but the very definitiveness with which it was resolved,
precluded the idea of risk.  I must not only punish, but punish with
impunity.  A wrong is unredressed when retribution overtakes its
redresser.  It is equally unredressed when the avenger fails to make
himself felt as such to him who has done the wrong.

Split Documents

Split Documents with RecursiveCharacterTextSplitter

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