{
  "opencollection": "1.0.0",
  "info": {
    "name": "Telnyx Access Tokens Embeddings API",
    "version": "2.0.0"
  },
  "request": {
    "auth": {
      "type": "bearer",
      "token": "{{bearerToken}}"
    }
  },
  "items": [
    {
      "info": {
        "name": "Embeddings",
        "type": "folder"
      },
      "items": [
        {
          "info": {
            "name": "Get Tasks by Status",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://api.telnyx.com/v2/ai/embeddings",
            "params": [
              {
                "name": "status",
                "value": "",
                "type": "query",
                "description": "List of task statuses i.e. `status=queued&status=processing`"
              }
            ]
          },
          "docs": "Retrieve tasks for the user that are either `queued`, `processing`, `failed`, `success` or `partial_success` based on the query string. Defaults to `queued` and `processing`."
        },
        {
          "info": {
            "name": "Embed documents",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://api.telnyx.com/v2/ai/embeddings",
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Perform embedding on a Telnyx Storage Bucket using the a embedding model.\nThe current supported file types are:\n- PDF\n- HTML\n- txt/unstructured text files\n- json\n- csv\n- audio / video (mp3, mp4, mpeg, mpga, m4a, wav, or webm ) - Max of 100mb file size.\n\nAny files not matching the above types will be attempted to be embedded as unstructured text.\n\nThis process can be slow, so it runs in the background and the user can check\nthe status of the task using the endpoint `/ai/embeddings/{task_id}`.\n\n *"
        },
        {
          "info": {
            "name": "List embedded buckets",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://api.telnyx.com/v2/ai/embeddings/buckets"
          },
          "docs": "Get all embedding buckets for a user."
        },
        {
          "info": {
            "name": "Get file-level embedding statuses for a bucket",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://api.telnyx.com/v2/ai/embeddings/buckets/:bucket_name",
            "params": [
              {
                "name": "bucket_name",
                "value": "",
                "type": "path"
              }
            ]
          },
          "docs": "Get all embedded files for a given user bucket, including their processing status."
        },
        {
          "info": {
            "name": "Disable AI for an Embedded Bucket",
            "type": "http"
          },
          "http": {
            "method": "DELETE",
            "url": "https://api.telnyx.com/v2/ai/embeddings/buckets/:bucket_name",
            "params": [
              {
                "name": "bucket_name",
                "value": "",
                "type": "path"
              }
            ]
          },
          "docs": "Deletes an entire bucket's embeddings and disables the bucket for AI-use, returning it to normal storage pricing."
        },
        {
          "info": {
            "name": "Search for documents",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://api.telnyx.com/v2/ai/embeddings/similarity-search",
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Perform a similarity search on a Telnyx Storage Bucket, returning the most similar `num_docs` document chunks to the query.\n\nCurrently the only available distance metric is cosine similarity which will return a `distance` between 0 and 1.\nThe lower the distance, the more similar the returned document chunks are to the query.\nA `certainty` will also be returned, which is a value between 0 and 1 where the higher the certainty, the more similar the document.\nYou can read more about Weaviate distanc"
        },
        {
          "info": {
            "name": "Embed URL content",
            "type": "http"
          },
          "http": {
            "method": "POST",
            "url": "https://api.telnyx.com/v2/ai/embeddings/url",
            "body": {
              "type": "json",
              "data": "{}"
            }
          },
          "docs": "Embed website content from a specified URL, including child pages up to 5 levels deep within the same domain. The process crawls and loads content from the main URL and its linked pages into a Telnyx Cloud Storage bucket. As soon as each webpage is added to the bucket, its content is immediately processed for embeddings, that can be used for [similarity search](https://developers.telnyx.com/api-reference/embeddings/search-for-documents) and [clustering](https://developers.telnyx.com/docs/inferen"
        },
        {
          "info": {
            "name": "Get an embedding task's status",
            "type": "http"
          },
          "http": {
            "method": "GET",
            "url": "https://api.telnyx.com/v2/ai/embeddings/:task_id",
            "params": [
              {
                "name": "task_id",
                "value": "",
                "type": "path"
              }
            ]
          },
          "docs": "Check the status of a current embedding task. Will be one of the following:\n- `queued` - Task is waiting to be picked up by a worker\n- `processing` - The embedding task is running\n- `success` - Task completed successfully and the bucket is embedded\n- `failure` - Task failed and no files were embedded successfully\n- `partial_success` - Some files were embedded successfully, but at least one failed"
        }
      ]
    }
  ],
  "bundled": true
}