Build a Simple RAG App with Telnyx AI Inference

Build a retrieval-augmented generation API with Telnyx embeddings and chat completions.

Embeddings:POST /v2/ai/embeddings

• create vectors for documents and questionsAI Inference:POST /v2/ai/chat/completions

-API reference

  User question
        |
        v
  Embed question with Telnyx
        |
        v
  Compare against document embeddings
        |
        v
  Send retrieved context to Telnyx AI
        |
        v
  Grounded answer + source titles

Copy .env.example

to .env

and fill in:

AI_MODEL

string

moonshotai/Kimi-K2.6

ModelsEMBEDDING_MODEL

string

thenlper/gte-large

ModelsHOST

string

127.0.0.1

PORT

integer

5000

git clone https://github.com/team-telnyx/telnyx-code-examples.git
cd telnyx-code-examples/build-rag-with-telnyx-inference-python
cp .env.example .env
pip install -r requirements.txt
python app.py

Ask a question. The app retrieves relevant in-memory support docs and answers using only that context.

curl -X POST http://localhost:5000/rag/ask \
  -H "Content-Type: application/json" \
  -d '{
    "question": "Production signup broke after rotating an API key. Logs show 401 errors. What should we check first?"
  }'

Response:

{
  "answer": "Check that production services are using the new active API key and that the key has the required permissions. Also verify no old key is cached in deployment secrets.",
  "model": "moonshotai/Kimi-K2.6",
  "embedding_model": "thenlper/gte-large",
  "sources": [
    {"title": "API Key Authentication", "score": 0.9123},
    {"title": "Verification Message Delivery", "score": 0.7811}
  ]
}

Returns the sample knowledge base.

Returns service status, configured models, and document count.

Run LLM Inference (Python)Extract Structured JSON with AI (Python)AI Assistant Knowledge Base (Python)

Telnyx is an AI Communications Infrastructure platform - voice, messaging, SIP, AI, and IoT on one private, global network.