Embed documents

Perform embedding on a Telnyx Storage Bucket using the a embedding model.
The current supported file types are:

PDF

HTML

txt/unstructured text files

json

csv

audio / video (mp3, mp4, mpeg, mpga, m4a, wav, or webm ) - Max of 100mb file size.

Any files not matching the above types will be attempted to be embedded as unstructured text.

This process can be slow, so it runs in the background and the user can check
the status of the task using the endpoint /ai/embeddings/{task_id}.

Important Note: When you update documents in a Telnyx Storage bucket, their associated embeddings are automatically kept up to date. If you add or update a file, it is automatically embedded. If you delete a file, the embeddings are deleted for that particular file.

You can also specify a custom loader param. Currently the only supported loader value is
intercom which loads Intercom article jsons as specified by the Intercom article API
This loader will split each article into paragraphs and save additional parameters relevant to Intercom docs, such as
article_url and heading. These values will be returned by the /v2/ai/embeddings/similarity-search endpoint in the loader_metadata field.

Provide your bearer token in the

Authorization

header when making requests to protected resources.

Example:

Authorization: Bearer ********************

curl --location --request POST '/ai/embeddings' \ --header 'Authorization: Bearer <token>' \ --header 'Content-Type: application/json' \ --data-raw '{ "bucket_name": "string", "document_chunk_overlap_size": 512, "document_chunk_size": 1024, "embedding_model": "thenlper/gte-large", "loader": "default" }'

{ "data": { "created_at": "string", "finished_at": "string", "status": "string", "task_id": "736fde4d-9029-4915-8189-01353d6982cb", "task_name": "string", "user_id": "a169451c-8525-4352-b8ca-070dd449a1a5" } }

Request

Responses