Embeddings

In this tutorial, you'll learn how to:

• Upload documents to Telnyx Storage
• Transform these documents into embeddings, enabling a language model to retrieve relevant sections of your documents
• Provide the storage bucket as context for the language model

You can upload objects to Telnyx's S3-Compatible storage API using our quickstart or with our drag-and-drop interface in the portal.

Once you've uploaded your documents, you can embed them via API or by clicking the "Embed for AI Use" button in the portal while viewing your storage bucket's contents.

Behind the scenes, your documents will be processed into sections and each section will be "embedded" based on its contents. Later, when a user asks a language model a question, it will automatically be provided with the most relevant sections of documents from the bucket to help answer the question.

Once your documents are embedded, you can try it out in the AI Playground in the portal by selecting your embedded bucket from the storage dropdown.

You can also use embeddings via our chat completions API. Here is a Python example.

Note

Make sure you have set the TELNYX_API_KEY environment variable. Also, update the question and bucket variables in the sample code.

import os

from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("TELNYX_API_KEY"),
    base_url="https://api.telnyx.com/v2/ai",
)

question = "<ADD QUESTION HERE>"
bucket = "<ADD EMBEDDED BUCKET HERE>"
chat_completion = client.chat.completions.create(
  messages=[
    {
        "role": "user",
        "content": question
    }
  ],
  model="meta-llama/Meta-Llama-3.1-8B-Instruct",
  stream=True,
  tools=[
    {
        "type": "retrieval",
        "retrieval": {
            "bucket_ids": [bucket]
        }
    }
  ]
)

for chunk in chat_completion:
  if chunk.choices[0].delta.content:
    print(chunk.choices[0].delta.content, end="", flush=True)