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Telnyx AI: Embedding & RAG — Documentation Index

Embedding & RAG documentation within the AI section of the Telnyx developer docs (https://developers.telnyx.com). Root index: https://developers.telnyx.com/llms.txt · AI index: https://developers.telnyx.com/development/llms/ai-llms-txt.md · Full content for this subsection: https://developers.telnyx.com/development/llms/ai-embedding-rag-llms-full-txt.md

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Overview

  • Overview: Build retrieval, semantic search, and conversation memory with Telnyx.

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Conversation History

  • Overview: Search persisted conversation history across supported Telnyx communication sources.
  • Feature Coverage: Feature coverage for Conversation History.
  • Searching: Search persisted conversation records with the Conversation Histories API.
  • Retention: Retention behavior for Conversation History.
  • Pricing: Pricing information for Conversation History.

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Embedding

  • Embeddings: Generate vector embeddings with the Telnyx Inference API for semantic search, clustering, and RAG. Supports multiple embedding models and batch requests.
  • Clusters: Group similar documents and discover themes in your data with Telnyx Inference clusters. Run unsupervised clustering on embeddings to surface insights.

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API Reference (Embedding & RAG)

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Conversation Histories

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Embeddings

  • Get Tasks by Status: Retrieve tasks for the user that are either queued, processing, failed, success or partial_success based on the query string. Defaults to queued an…
  • Embed documents: Perform embedding on a Telnyx Storage Bucket using the a embedding model.
  • List embedded buckets: Get all embedding buckets for a user.
  • Get file-level embedding statuses for a bucket: Get all embedded files for a given user bucket, including their processing status.
  • Disable AI for an Embedded Bucket: Deletes an entire bucket’s embeddings and disables the bucket for AI-use, returning it to normal storage pricing.
  • Search for documents: Perform a similarity search on a Telnyx Storage Bucket, returning the most similar num_docs document chunks to the query.
  • Embed URL content: 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 mai…
  • Get an embedding task’s status: Check the status of a current embedding task. Will be one of the following:

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Clusters

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OpenAI Embeddings

  • Create embeddings: Creates an embedding vector representing the input text. This endpoint is compatible with the OpenAI Embeddings API and may be used with the OpenAI JS or Pytho…
  • List embedding models: Returns a list of available embedding models. This endpoint is compatible with the OpenAI Models API format.