OpenAI's API platform offers multiple APIs to interact with the large language models. The Embedding API is used to measure the relatedness of text strings. These floating-point measurements, or embeddings, can then be used as input to other machine learning models for further data analysis.
Microsoft's Azure OpenAI Service provides REST API access to OpenAI's language models.
The Teradata API Integration enables Teradata customers to obtain OpenAI text embeddings from Teradata Vantage data, using a large language model deployed in Azure OpenAI or a model provided directly from OpenAI. Users can request text embeddings from an OpenAI or Azure OpenAI model using Teradata input tables or queries, and benefit from the integration of embeddings with Teradata in-database analytics functions.
- Text Classification
- Recommendation
- Information Retrieval
- Clustering
- Anomaly detection
- Text Search
- Translation
Users can choose from several language models provided by OpenAI that are tailored for the computation of embeddings. Alternatively, embeddings can be requested from an endpoint of a language model deployed in Azure OpenAI.
- API_Request In-database Function
This in-database function enables Vantage users to connect with an endpoint for scoring using a Vantage query (SQL).
- teradataml Extension Library (tdapiclient)
This client package provides Python interface to execute API_Request in-database function.
API_Request In-database Function
- Data cleaning, feature engineering inside Teradata database
- Exporting data for requesting embeddings to OpenAI or Azure OpenAI
- Performing in-database analytics on the embeddings obtained
teradataml Extension Library ()
The tdapiclient package provides Python interface to execute API_Request in-database function.