what the parameter chunk_size in langchain.embeddings.openai.OpenAIEmbeddings() refers to?

[GennariAl]

The documentation says:(https://api.python.langchain.com/en/latest/embeddings/langchain.embeddings.openai.OpenAIEmbeddings.html)

param chunk_size: int = 1000 -> Maximum number of texts to embed in each batch

what does 'batch' mean in this case?

It could be that the embedding is performed in batch whose dimension are defined by chunk_size ?

[dosubot[bot]]

🤖

You're correct in your understanding of the 'chunk_size' parameter in the 'langchain.embeddings.openai.OpenAIEmbeddings()' function. The 'batch' in this context refers to the number of tokens to be embedded at once. The 'chunk_size' parameter is used to define the maximum number of tokens to embed in each batch.

This is done to manage memory usage and improve the efficiency of the embedding process, especially when dealing with a large number of tokens. The text is first tokenized and then the tokens are batched for embedding. Here's a snippet of the code that does this:

_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
    response = await async_embed_with_retry(
        self,
        input=tokens[i : i + _chunk_size],
        **self._invocation_params,
    )
    batched_embeddings.extend(r["embedding"] for r in response["data"])

The 'chunk_size' parameter can be adjusted according to the specific requirements of your application and the capabilities of your system. A larger 'chunk_size' may allow the function to process tokens more quickly, but it may also require more system resources. Conversely, a smaller 'chunk_size' may use fewer system resources, but it may also take longer to process all tokens.

I hope this clarifies your question. If you have any more questions, feel free to ask.

Sources

• libs/langchain/langchain/embeddings/openai.py

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[marcusRB]

I think you are wrong. AzureOpenAIEmbeddings chunk_size default value is 2048, and for OpenAIEmbedding is 1000. Both are the maximum number of texts to embed in each batch, not tokens.

Tokens for chunk_size is used when call the specific method to embed one document in vector, right?

chunk_size: int = 2048
    """Maximum number of texts to embed in each batch"""

[GussailRaat]

Hello,

what is the difference between both of them?

param chunk_size: int = 2048
Maximum number of texts to embed in each batch

param embedding_ctx_length: int = 8191
The maximum number of tokens to embed at once.

[cliff-4]

I have this same exact question^
If someone can provide a detailed under-the-hood answer, that would be extremely helpful.

[GussailRaat]

Are splitter-based chunking before giving documents to Azure and AzureOpenAIEmbeddings's chunk_size different??????