How does smaller chunk size help ? Why is it required t...

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The community member asked how smaller chunk size helps and why it is required to chunk the data into smaller sizes. A response from another community member explained that chunking data into smaller sizes can help make the embeddings more precise. When documents are ingested into an index in LlamaIndex, they are split into chunks with a certain amount of overlap. The default chunk size is 1024 and the default chunk overlap is 20. Changing these parameters can affect the embeddings that are calculated. A smaller chunk size means the embeddings are more precise, while a larger chunk size means the embeddings may be more general but can miss fine-grained details. The community member provided an example of how to set the chunk size to 512 and the chunk overlap to 50, and also double the similarity_top_k parameter to 4 to better represent the amount of data to retrieve for each query.

ppraveenks30#120294

@kapa.ai How does smaller chunk size help ? Why is it required to chunk the data into smaller size ?

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kkapa.ai

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kkapa.ai

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Chunking data into smaller sizes can help in making the embeddings more precise. When documents are ingested into an index in LlamaIndex, they are split into chunks with a certain amount of overlap. The default chunk size is 1024, and the default chunk overlap is 20. If you change these parameters, it will change the embeddings that are calculated. A smaller chunk size means the embeddings are more precise, while a larger chunk size means that the embeddings may be more general, but can miss fine-grained details.

For example, if you halve the default chunk size, you may also want to double the similarity_top_k from the default of 2 to 4 to better represent the amount of data to retrieve for each query. Here is an example of how to do this:

Plain Text

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core import Settings

documents = SimpleDirectoryReader("./data").load_data()

Settings.chunk_size = 512
Settings.chunk_overlap = 50

index = VectorStoreIndex.from_documents(
    documents,
)

query_engine = index.as_query_engine(similarity_top_k=4)

In this example, the chunk size is set to 512 and the chunk overlap is set to 50. The similarity_top_k parameter is set to 4, which is double the default value of 2.

For more information, you can refer to the LlamaIndex documentation on chunk sizes ().
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How does smaller chunk size help ? Why is it required to chunk the data into smaller size