Text Embedding | OpenAI | Page 1

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Hi @onyx drum. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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OpenAI's text embeddings, such as text-embedding-3-small and text-embedding-3-large, are models that take a text string as input and produce an embedding vector as output. An embedding is a vector representation of a piece of data (in this case, some text) that is meant to preserve aspects of its content and/or its meaning. Chunks of data that are similar in some way will tend to have embeddings that are closer together than unrelated data. This is useful for tasks like search, clustering, recommendations, anomaly detection, classification, and more (source (https://platform.openai.com/docs/introduction)).

The specific model you mentioned, text-embedding-ada-002, is not mentioned in the provided sources, so I can't provide specific details about it. However, OpenAI's text embeddings generally measure the relatedness of text strings. The distance between two vectors measures their relatedness, with small distances suggesting high relatedness and large distances suggesting low relatedness (source (https://platform.openai.com/docs/guides/embeddings)).

As for how these embeddings are generated, the sources don't provide specific details about the underlying architecture or whether transformers are used. However, it's common in the field of NLP to use transformer-based models for generating embeddings, as they are capable of capturing complex patterns in the text.

Regarding your question about tiktoken, it's not mentioned in the provided sources in relation to text embeddings. tiktoken is a Python library developed by OpenAI that can be used to count the number of tokens in a text string without making an API call. It's often used in conjunction with OpenAI's language models, but its relationship to text embeddings isn't specified in the sources provided.

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To get the embedding vectors for a list of input texts, you can use the embeddings.create method from the OpenAI API. Here's an example:

client = openai.OpenAI(api_key=OPENAI_API_KEY)
embedding_model_name = "text-embedding-3-small"

result = client.embeddings.create(
 input=[
 "This is a sentence",
 "A second sentence"
 ],
 model=embedding_model_name,
)

(source (https://cookbook.openai.com/examples/vector_databases/cassandra_astradb/philosophical_quotes_cassio#a-test-call-for-embeddings))

This will return a list of embedding vectors, one for each input text. The length of each vector depends on the specific embedding model used. For example, in the case of `text-embedding-3-small`, the length of the embedding vector is 1536.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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#Text Embedding

This will return a list of embedding vectors, one for each input text. The length of each vector depends on the specific embedding model used. For example, in the case of text-embedding-3-small, the length of the embedding vector is 1536.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

This will return a list of embedding vectors, one for each input text. The length of each vector depends on the specific embedding model used. For example, in the case of `text-embedding-3-small`, the length of the embedding vector is 1536.