Question:
In OpenAI Embedding, what if the language thousands of text entries being converted to Embedding vector is not english, and the base text used to compare to all other Embedding vectors is English, will Embedding vector still find the relevant texts?
Answer:
OpenAI Embedding is a language model that is capable of converting text into high-dimensional vector representations, regardless of the language of the text. However, the effectiveness of the embedding vectors in finding relevant texts would depend on various factors, such as the quality and quantity of the text data, the similarity of the language to English, and the task or application for which the embeddings are being used.
If the text entries being converted to embedding vectors are not in English, but the base text used to compare to all other embedding vectors is in English, the embeddings may still be able to find relevant texts, but the effectiveness would likely be reduced compared to using a base text that is more similar in language to the text entries being compared.
This is because the embedding vectors are trained on a large corpus of text data, and the representations are optimized to capture the semantic and syntactic relationships between words and phrases in that corpus. If the text data being used to create the embeddings is significantly different from the training data used to create the embeddings, the effectiveness of the embeddings in capturing relevant relationships may be reduced.
In summary, while OpenAI Embedding can be used to create embeddings for text data in any language, the effectiveness of the embeddings in finding relevant texts would depend on various factors, including the quality and quantity of the text data, the similarity of the language to English, and the task or application for which the embeddings are being used.