In the previous two articles, we gained a better understanding of Azure OpenAI, Davinci, and Codex. This module will concentrate on the deployment aspect of the Davinci Module.
- Overview Of Azure OpenAI Modules With A Focus On Davinci Module
- Exploring The Capabilities Of Codex - A Module On Azure OpenAI's AI-Powered Code Generator
When working with Davinci text modules, you may encounter a range of versions, including Text-davinci-001, Text-davinci-002, Text-davinci-003, Code-davinci-002, Text-similarity-davinci-001, Text-search-davinci-doc-001, and Text-search-davinci-query-001. Each module has its own strengths and limitations in terms of quality, speed, and availability, which depend on the specific training methods and timelines. This article aims to provide you with a better understanding of these modules, including which might best suit your particular requirements. To begin, let's examine each module in detail.
Text-davinci-001
The initial version of Text-davinci-001 is mainly intended for fine-tuning and is the oldest of the three. It can perform any task that the other models can, but its results may not be the best, and it may not adhere to standard naming conventions.
Text-davinci-002
is the subsequent version and possesses enhancements over Text-davinci-001. It can generate superior quality writing and handle more intricate instructions. Specifically, it excels at converting natural language into code.
Text-davinci-003
The most recent version is Text-davinci-003, which features numerous improvements compared to Text-davinci-002. It can produce even higher quality writing and manage even more intricate instructions. Additionally, it is proficient at inserting completions within code.
Code-davinci-002
The most proficient model from the Codex model family is Code-davinci-002, which is specifically designed to generate and complete code. It is exceptional at converting natural language into code and can insert completions within code. Its maximum input is 8,000 tokens, roughly equivalent to 3,200 words.
Text-similarity-davinci-001
Text-similarity-davinci-001 belongs to the Embeddings model family and is specialized in constructing embeddings for text. The embedding is a dense information representation of the semantic meaning of a text segment. Text-similarity-davinci-001 is formulated to create embeddings that can be employed to evaluate the similarity between two texts.
Text-search-davinci-doc-001
Text-search-davinci-doc-001 belongs to the family of Embeddings models, designed to generate embeddings for various kinds of text. Specifically, Text-search-davinci-doc-001 is intended for creating embeddings that facilitate text search, which involves identifying the most relevant texts for a given query or the most pertinent query for a given text. The purpose of Text-search-davinci-doc-001 is to generate embeddings applicable to lengthy documents such as books, articles, and reports.
Text-search-davinci-query-001 belongs to the Embeddings model category, which is specialized in generating embeddings for various types of text. Its primary function is to produce embeddings that can be used to perform a text search, i.e., to identify the most relevant text for a given query or vice versa. Text-search-davinci-query-001 is intended for creating embeddings for short queries, such as keywords, phrases, or sentences, while text-search-davinci-doc-001 is used for longer documents like articles, books, or reports.
In conclusion, understanding the different versions and capabilities of Azure OpenAI Davinci modules is crucial to choosing the suitable module for your specific requirements. The modules are designed to handle various tasks such as text generation, code generation, text similarity evaluation, and text search. Each module has its strengths and limitations regarding quality, speed, and availability. By knowing the differences between the versions, you can make a more informed decision when selecting the appropriate module for your project.