Generate a model response

Select an available model and generate a text response using the OpenAI-compatible chat-completion endpoint.

Before you start

You need curl, jq, and an API credential authorized for inference. The examples attribute the request to support-assistant; replace that reference with your project.

1. Discover model IDs

Call GET /ai/v1/models:

list-models.sh

curl --fail-with-body https://api.veryfront.com/ai/v1/models \
  -H "Authorization: Bearer $VERYFRONT_API_KEY" \
  -H "x-veryfront-project-reference: support-assistant" > models.json

jq -r '.data[].id' models.json

Choose a model that supports chat completions. The model discovery reference describes capabilities and deployment metadata. Use a returned ID rather than guessing a model or version name.

2. Generate a response

Set MODEL_ID, then call POST /ai/v1/chat/completions:

generate-response.sh

export MODEL_ID="<MODEL_ID>"

jq -n --arg model "$MODEL_ID" '{
  model: $model,
  messages: [{role: "user", content: "Explain what an API key is in one sentence."}],
  stream: false
}' | curl --fail-with-body https://api.veryfront.com/ai/v1/chat/completions \
  -H "Authorization: Bearer $VERYFRONT_API_KEY" \
  -H "x-veryfront-project-reference: support-assistant" \
  -H "Content-Type: application/json" \
  --data-binary @- > response.json

The model ID selects the deployment. Project policies, credential limits, and spending checks still apply to the request.

3. Read the answer

Inspect the response before extracting the assistant message:

read-response.sh

jq '{model, choices, usage}' response.json
jq -r '.choices[0].message.content' response.json

For this text-only example, expect an assistant answer in the first choice. Inspect the error body if the request fails, or finish_reason if generation ends unexpectedly. This is a direct inference request. To execute a configured agent and track its run history, follow Run an agent.

API references

Create a chat completion and model discovery.
List models through MCP. The available operations differ across interfaces.

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Generate a model response

Before you start

1. Discover model IDs

2. Generate a response

3. Read the answer

API references

​Before you start

​1. Discover model IDs

​2. Generate a response

​3. Read the answer

​API references

Before you start

1. Discover model IDs

2. Generate a response

3. Read the answer

API references