openai-embedder

Embed text with OpenAI. Over 16 models available for text search, text similarity and code search.

Using

Tagger Plugins add annotations to text that can be queried and composed later.

Blockifiers convert data into Steamship’s native Block format.

Importer Plugins add annotations to text that can be queried and composed later.

Use them when writing Packages to help you work with data of different types.

Links

from steamship import Steamship, File

client = Steamship(workspace="my-workspace-handle")

# Import a file to Steamship
with open("file.ext") as f:
  file = File.create(content=file.read())

# Create an instance of this blockifier
blockifier = client.use_plugin(
  'openai-embedder'
)

# Blockify the file
task = file.blockify()
task.wait()
from steamship import Steamship, File

client = Steamship(workspace="my-workspace-handle")

# Import a file to Steamship
with open("file.ext") as f:
  file = File.create(content=file.read())

# Create a blockifier. We'll assume Markdown here.
blockifier = client.use_plugin(
  'markdown-blockifier-default'
)

# Blockify the file
task = file.blockify()

# Create an instance of this tagger.
tagger = client.use_plugin(
  'openai-embedder'
)

# Tag the file
task = file.tag()

task.wait()
Pulled from the GitHub repository.
# OpenAI Embedder Plugin - Steamship

This project contains a Steamship Tagger plugin that enables embedding with OpenAI's models.

## Configuration

This plugin must be configured with the following fields:

* `model` - The model, listed in the [OpenAI Documentation](https://studio.oneai.com/docs?api=Pipeline+API&item=Expected+Input+Format&accordion=Introduction%2CPipeline+API%2CNode.js+SDK+Reference%2CClustering+API). The default is `text-similarity-curie-001`

OpenAI supports four families of embedding models for different functionalities: text search, text similarity and code search. 
Each family includes up to four models on a spectrum of capability:

* Ada (1024 dimensions)
* Babbage (2048 dimensions)
* Curie (4096 dimensions)
* Davinci (12288 dimensions)

Within those model families you can select:

* `text-similarity-ada-001`
* `text-similarity-babbage-001`
* `text-similarity-curie-001`
* `text-similarity-davinci-001`
* `text-search-ada-doc-001`
* `text-search-ada-query-001`
* `text-search-babbage-doc-001`
* `text-search-babbage-query-001`
* `text-search-curie-doc-001`
* `text-search-curie-query-001`
* `text-search-davinci-doc-001`
* `text-search-davinci-query-001`
* `code-search-ada-code-001`
* `code-search-ada-text-001`
* `code-search-babbage-code-001`
* `code-search-babbage-text-001`

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