What are embedding models used for?
— Embedding models encode the semantic meaning of text into numerical vectors, allowing for similarity comparisons between different pieces of text based on their embeddings.
What are the key components of a good vector database for semantic search?
— Approximate search, instant vector refresh, and metadata filtering are key components of a good vector database for semantic search.
How can OpenAI's GPT-3 model be fine-tuned?
— OpenAI's GPT-3 model can be fine-tuned with custom data for specific tasks, allowing for more specialized applications.
What are the three families of embeddings used for?
— There are three families of embeddings used for search, similarity, and code, each specialized for different use cases, with the search embeddings being used for context and query embedding in the search endpoint.
How can the model handle lexical ambiguity?
— The model handles lexical ambiguity similarly to how humans handle it, but there may still be confusion within the model.
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