openai.embeddings.create(input=text, model="text-embedding-3-large").data[0].embedding
this returns a vector of len 3072, if the dimension is not defined.
opeani filesearch uses by default a text-embedding-3-large at 256 dimensions.
why? what is best, 256 or 3072? how to choose?
I asked chatgpt about it, but the answer does not help much.
Larger Vectors (e.g., 3072 dimensions):
- Pros: Can capture more intricate details and nuances about the input text. This is generally beneficial if you need higher accuracy for tasks such as semantic search or nuanced recommendations.
- Cons: More computationally expensive to process and store. It may also lead to diminishing returns in certain applications where additional detail does not provide significant extra value.
Smaller Vectors (e.g., 256 dimensions):
- Pros: More efficient in terms of storage and computation. Often suitable for applications where approximate similarity is sufficient.
- Cons: Might miss out on some of the depth and nuances captured in larger embeddings.
When OpenAI or any other provider offers a specific default embedding dimension, like 256, in a product feature (e.g., file search), it is generally because this dimension size balances well among various factors like performance, speed, and storage efficiency for the typical use cases of that service.
What’s best for you depends on your specific use case. If computational resources and speed are a priority, and if you find that 256 dimensions suffice for your accuracy needs, the smaller embeddings are preferable. However, if accuracy is paramount and you’re dealing with complex language tasks, you may opt for a larger embedding size, if available.