OpenAI.Embeddings API Endpoint
(Redirected from OpenAI.Embeddings)
Jump to navigation
Jump to search
An OpenAI.Embeddings API Endpoint is a text-item embedding web service endpoint that is an OpenAI API endpoint.
- Context:
- It can offer new models, including text-embedding-3-small and text-embedding-3-large, featuring improved performance, lower costs, and enhanced multilingual capabilities.
- It can allow for adjusting the dimensionality of the embedding vectors to suit various use cases without losing conceptual information.
- ...
- Example(s):
curl https://api.openai.com/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer $OPENAI_API_KEY" -d '{"input": "Your text string goes here", "model": "text-embedding-3-small"}'
.- ...
- Counter-Example(s):
- See: Multilingual Embedding, Dimensionality Reduction Technique, Text-Item Embedding Encoder.
References
2024
- https://platform.openai.com/docs/guides/embeddings
- It provides a way to convert text strings into numerical vectors, enabling various applications like search, clustering, and recommendation systems.
- It includes new models, text-embedding-3-small and text-embedding-3-large, which offer improved performance, lower costs, and enhanced multilingual capabilities.
- It measures the relatedness of text by generating embeddings, which are vectors of floating point numbers where distances between vectors indicate relatedness.
- It requires sending text strings to the embeddings API endpoint with a specified model name to receive an embedding vector in response.
- It allows for the adjustment of the dimensionality of the embedding vectors to suit different use cases without losing conceptual information.
- It supports a wide range of use cases, including but not limited to, question answering, text and code search, recommendations, and as feature encoders for machine learning algorithms.
- It answers frequently asked questions regarding the use of embeddings, including token counting, vector database retrieval, distance functions, sharing of embeddings, and the knowledge cutoff for the models.
- Example requests:
curl https://api.openai.com/v1/embeddings \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "input": "Your text string goes here", "model": "text-embedding-3-small" }'
2023
- chat
# Create an embedding for a text string response = openai.Embedding.create( input = "canine companions say", model = "text-embedding-ada-002") # Get the embedding vector embedding = response["data"][0]["embedding"] # Print the size of the embedding vector print("The size of the embedding vector is:", len(embedding)) >> The size of the embedding vector is: 1536
2023
- https://platform.openai.com/docs/guides/embeddings
- QUOTE: ... To get an embedding, send your text string to the embeddings API endpoint along with a choice of embedding model ID (e.g., text-embedding-ada-002). The response will contain an embedding, which you can extract, save, and use.
- Example requests:
curl https://api.openai.com/v1/embeddings \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "input": "Your text string goes here", "model": "text-embedding-ada-002" }'