If you’ve worked with RAG (Retrieval-Augmented Generation) models before, you know they act like expert reporters! They don’t just rely on their own “knowledge” but retrieve relevant information to craft more accurate responses. However, for this process to work well, choosing the right embedding is vital. Here are the key considerations for selecting the best embedding:
Context Window
This refers to the maximum number of tokens the model can process simultaneously. Models like text-embedding-ada-002 with an 8192-token window and Cohere with a 4096-token window are great for long documents.
The larger the window, the deeper and more continuous the text analysis.
Tokenization Method
Tokens are the units that the model analyzes the text by.
The most common methods are: • Subword methods like BPE: Great for rare or specialized words
- WordPiece: For models like BERT
- Word-level: Simple but less accurate for complex languages
Tokenization method significantly impacts the accuracy of indexing and semantic search, especially in specialized domains.
Dimensionality
Embedding dimensions represent the number of features each text vector has.
Higher dimensions (e.g., 3072 in OpenAI) store more semantic information but require more computation.
In contrast, lower dimensions like 1024 in Jina are faster and more cost-effective but may lose some details.
Vocabulary Size
Vocabulary size refers to the number of unique tokens the model can recognize.
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