Why Does text-embedding-ada-002 Always Return a 1536-Dimensional Vector?

The fixed-length output (1536 dimensions for text-embedding-ada-002) is a design choice of the model architecture rather than a reflection of the input length. Here’s why:

• Consistent Representation:
  The model is designed to encode the semantic meaning of any input text into a vector of fixed size. Whether your text is 2 words or 200 words, the result is always a vector of 1536 numbers. This consistency is crucial for comparing texts in downstream tasks like similarity search or clustering.

• Fixed Output Dimensions:
  Machine learning models, particularly those used in downstream tasks, often require inputs of the same dimension. A fixed-length embedding makes it easier to use these vectors for tasks such as nearest neighbor search, classification, or clustering.

• Architecture Design:
  The embedding model (in this case, text-embedding-ada-002) has been pre-trained to output vectors of a specific size. The neural architecture (including all layers and the final projection) is built to always produce a vector of that specific dimension regardless of input size.

• Feature Extraction Consistency:
  Even if the input text is very short, the model still extracts the most relevant features and maps them into the high-dimensional space. Longer texts might activate more features, but those are all aggregated into one fixed-size vector.

This fixed-length representation is common across many embedding models (as you might have seen with other models listed in the documentation). It ensures that every text is embedded into the same dimensional space, making various downstream operations straightforward.

I hope this clarifies the design choice behind fixed-length embeddings!