> ## Documentation Index > Fetch the complete documentation index at: https://developers.telnyx.com/llms.txt > Use this file to discover all available pages before exploring further. # 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](https://developers.intercom.com/docs/references/rest-api/api.intercom.io/Articles/article/) 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. ## OpenAPI ````yaml https://telnyx-openapi-ng.s3.us-east-1.amazonaws.com/ai/analytics.yml post /ai/embeddings openapi: 3.1.0 info: title: Telnyx AI Analytics API version: 2.0.0 description: API for AI conversations, insights, embeddings, and clusters. contact: email: support@telnyx.com servers: - url: https://api.telnyx.com/v2 security: - bearerAuth: [] paths: /ai/embeddings: post: tags: - Embeddings summary: Embed documents description: >- 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](https://developers.intercom.com/docs/references/rest-api/api.intercom.io/Articles/article/) 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. operationId: PostEmbedding requestBody: content: application/json: schema: $ref: '#/components/schemas/EmbeddingBucketRequest' required: true responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/EmbeddingResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' x-codeSamples: - lang: JavaScript source: >- import Telnyx from 'telnyx'; const client = new Telnyx({ apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted }); const embeddingResponse = await client.ai.embeddings.create({ bucket_name: 'bucket_name' }); console.log(embeddingResponse.data); - lang: Python source: |- import os from telnyx import Telnyx client = Telnyx( api_key=os.environ.get("TELNYX_API_KEY"), # This is the default and can be omitted ) embedding_response = client.ai.embeddings.create( bucket_name="bucket_name", ) print(embedding_response.data) - lang: Go source: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/team-telnyx/telnyx-go\"\n\t\"github.com/team-telnyx/telnyx-go/option\"\n)\n\nfunc main() {\n\tclient := telnyx.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tembeddingResponse, err := client.AI.Embeddings.New(context.TODO(), telnyx.AIEmbeddingNewParams{\n\t\tBucketName: \"bucket_name\",\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", embeddingResponse.Data)\n}\n" - lang: Java source: |- package com.telnyx.sdk.example; import com.telnyx.sdk.client.TelnyxClient; import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient; import com.telnyx.sdk.models.ai.embeddings.EmbeddingCreateParams; import com.telnyx.sdk.models.ai.embeddings.EmbeddingResponse; public final class Main { private Main() {} public static void main(String[] args) { TelnyxClient client = TelnyxOkHttpClient.fromEnv(); EmbeddingCreateParams params = EmbeddingCreateParams.builder() .bucketName("bucket_name") .build(); EmbeddingResponse embeddingResponse = client.ai().embeddings().create(params); } } - lang: Ruby source: >- require "telnyx" telnyx = Telnyx::Client.new(api_key: "My API Key") embedding_response = telnyx.ai.embeddings.create(bucket_name: "bucket_name") puts(embedding_response) - lang: CLI source: |- telnyx ai:embeddings create \ --api-key 'My API Key' \ --bucket-name bucket_name components: schemas: EmbeddingBucketRequest: properties: bucket_name: type: string title: Bucket Name document_chunk_size: type: integer title: Document Chunk Size default: 1024 document_chunk_overlap_size: type: integer title: Document Chunk Overlap Size default: 512 embedding_model: allOf: - $ref: '#/components/schemas/SupportedEmbeddingModels' default: thenlper/gte-large loader: allOf: - $ref: '#/components/schemas/SupportedEmbeddingLoaders' default: default type: object required: - bucket_name title: EmbeddingBucketRequest EmbeddingResponse: properties: data: type: object properties: task_id: type: string format: uuid title: Task ID task_name: type: string title: Task Name status: type: string title: Status created_at: type: string title: Created At finished_at: type: - string - 'null' title: Finished At user_id: type: string format: uuid title: User ID type: object required: - data title: EmbeddingResponse HTTPValidationError: title: HTTPValidationError type: object properties: detail: title: Detail type: array items: $ref: '#/components/schemas/ValidationError' SupportedEmbeddingModels: type: string enum: - thenlper/gte-large - intfloat/multilingual-e5-large title: SupportedEmbeddingModels description: Supported models to vectorize and embed documents. SupportedEmbeddingLoaders: type: string enum: - default - intercom title: SupportedEmbeddingLoaders description: Supported types of custom document loaders for embeddings. ValidationError: title: ValidationError required: - loc - msg - type type: object properties: loc: title: Location type: array items: anyOf: - type: string - type: integer msg: title: Message type: string type: title: Error Type type: string securitySchemes: bearerAuth: scheme: bearer type: http ````