--- title: How to Send Requests to a Model Using the System One API description: Send a state and structured questions to the TypeSafe Jev model and get typed decisions with calibrated probabilities. product: Inference url: https://docs.digitalocean.com/products/inference/how-to/use-system-one-api/ last_updated: "2026-09-28" --- > **For AI agents:** The documentation index is at [https://docs.digitalocean.com/llms.txt](https://docs.digitalocean.com/llms.txt). Markdown versions of pages use the same URL with `index.html.md` in place of the HTML page (for example, append `index.html.md` to the directory path instead of opening the HTML document). # How to Send Requests to a Model Using the System One API Inference provides a single control plane for managing inference workflows. It includes a Model Catalog where you can view available foundation models, including both DigitalOcean-hosted and third-party commercial models, compare model capabilities and pricing, use routing to match inference requests to the best-fit model, and run inference using serverless or dedicated deployments. [Jev](https://docs.typesafe.ai/models) is a TypeSafe AI System One model on DigitalOcean Serverless Inference. Instead of generating free-form text, it returns a typed decision (a choice, a numeric score, or a yes/no answer) with a calibrated probability attached to each answer, which lets you set explicit thresholds for when your application acts automatically and when it escalates to a person. Jev uses its own `/v1/systemone` endpoint rather than the [Chat Completions API](https://docs.digitalocean.com/products/inference/how-to/use-chat-completions-api/index.html.md). It takes a `state` and a `questions` object instead of a `messages` array, and it is not OpenAI compatible. It does not accept image, audio, or video input, and it does not support streaming. Create a [model access key](https://docs.digitalocean.com/products/inference/how-to/manage-model-access-keys/index.html.md) from the **Serverless Inference** tab of the DigitalOcean Control Panel and save it for use with the API. The examples below read it from a `MODEL_ACCESS_KEY` environment variable. Jev is a third-party model, so your account must be on a qualifying tier to use it. If a request returns `this model is not available for your subscription tier`, raise your tier from the Control Panel. For more information, see [Inference Limits](https://docs.digitalocean.com/products/inference/details/limits/index.html.md). Use of Jev is subject to the [TypeSafe AI Master Customer Agreement](https://typesafe.ai/legal/mca). ## Send a Decision Request Send a `POST` request to `https://inference.do-ai.run/v1/systemone` with the following fields in the body: - `model`: The model ID. Use `typesafe-jev-1.13.0`, or find it on the [available models page](https://docs.digitalocean.com/products/inference/details/models/index.html.md). - `state`: The context Jev evaluates each question against. The state can be a string, a JSON object, or an array of text values. - `questions`: An object where each key names a question. Each question sets its `type`, `instructions` that tell Jev what to decide, and the fields that the type requires. For a `choice` question, provide a `criteria` object that maps each allowed answer to a short description of when it applies. The following example asks a single `choice` question about a customer feedback string: ### cURL ```shell curl -sS -X POST https://inference.do-ai.run/v1/systemone \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "typesafe-jev-1.13.0", "state": "The customer said the delivery was late but the product quality was excellent.", "questions": { "sentiment": { "type": "choice", "instructions": "What is the overall sentiment of this feedback?", "criteria": { "positive": "Customer is happy overall", "negative": "Customer is unhappy overall", "mixed": "Customer has both good and bad things to say" } } } }' ``` ### Python ```python import os import requests response = requests.post( "https://inference.do-ai.run/v1/systemone", headers={ "Authorization": f"Bearer {os.environ['MODEL_ACCESS_KEY']}", "Content-Type": "application/json", }, json={ "model": "typesafe-jev-1.13.0", "state": "The customer said the delivery was late but the product quality was excellent.", "questions": { "sentiment": { "type": "choice", "instructions": "What is the overall sentiment of this feedback?", "criteria": { "positive": "Customer is happy overall", "negative": "Customer is unhappy overall", "mixed": "Customer has both good and bad things to say", }, } }, }, ) response.raise_for_status() print(response.json()) ``` The response returns one answer per question key. For a `choice` question, the answer includes the selected `choice`, a `probabilities` object giving the calibrated probability of each option, and an overall `confidence` score. The `usage` object reports the input and output token counts for the request. Compare `confidence`, or a specific option’s probability, against a threshold to decide whether to act on the answer automatically or route it for human review. The response is similar to the following: ```js { "model": "typesafe-jev-1.13.0", "answers": { "sentiment": { "type": "choice", "choice": "mixed", "probabilities": { "mixed": 1, "negative": 0, "positive": 0 }, "confidence": 1 } }, "usage": { "input_tokens": 349, "output_tokens": 39 } } ``` ## Ask Multiple Questions in One Request Add more keys to the `questions` object to evaluate several questions against the same `state` in a single call. Each question can use a different type, and Jev answers each one independently and returns a result per key. The following example asks a `choice` question about sentiment and a `noul` question about whether the feedback needs a follow-up: ```shell curl -sS -X POST https://inference.do-ai.run/v1/systemone \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "typesafe-jev-1.13.0", "state": "The customer said the delivery was late but the product quality was excellent.", "questions": { "sentiment": { "type": "choice", "instructions": "What is the overall sentiment of this feedback?", "criteria": { "positive": "Customer is happy overall", "negative": "Customer is unhappy overall", "mixed": "Customer has both good and bad things to say" } }, "needs_followup": { "type": "noul", "instructions": "Does this feedback require a follow-up from the support team?" } } }' ``` The response is similar to the following: ```js { "model": "typesafe-jev-1.13.0", "answers": { "needs_followup": { "type": "noul", "noul": 0.68 }, "sentiment": { "type": "choice", "choice": "mixed", "probabilities": { "mixed": 1, "negative": 0, "positive": 0 }, "confidence": 1 } }, "usage": { "input_tokens": 368, "output_tokens": 59 } } ``` ## Question Types Set each question’s `type` field to select what kind of answer Jev returns: - `choice`: Jev selects one option from the `criteria` object you define, where each key is an allowed answer and its value describes when that answer applies. The answer includes the selected `choice`, a `probabilities` object over the options, and an overall `confidence`. Use it for classification, routing, and labeling. - `score`: Jev rates the `state` against an ordered scale. Provide `criteria` as an array of levels from lowest to highest, where each level is a descriptive string or an object with a `label`. The answer includes a numeric `score` (the probability-weighted average of the level positions), a `legend` mapping each level index to your criteria, a `probabilities` object over the levels, and an overall `confidence`. Use it for ranking, rating, and grading. - `noul`: TypeSafe’s question type for yes/no decisions. Instead of a literal yes or no, Jev returns a calibrated probability between 0 and 1 that the statement in your `instructions` is true. This type does not require `criteria`. Use it for yes/no, pass/fail, and gating decisions by thresholding the probability at the level your workflow requires. The following example sends a `score` question with an ordered set of levels: ```shell curl -sS -X POST https://inference.do-ai.run/v1/systemone \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "typesafe-jev-1.13.0", "state": "The customer said the delivery was late but the product quality was excellent.", "questions": { "satisfaction": { "type": "score", "instructions": "Rate overall customer satisfaction from low to high.", "criteria": ["low", "medium", "high"] } } }' ``` The response reports the score, the per-level probabilities, and a confidence value: ```js { "model": "typesafe-jev-1.13.0", "answers": { "satisfaction": { "type": "score", "score": 1.32, "legend": { "0": "low", "1": "medium", "2": "high" }, "probabilities": { "0": 0, "1": 0.68, "2": 0.32 }, "confidence": 0.52 } }, "usage": { "input_tokens": 307, "output_tokens": 17 } } ``` ## Understand Context Limits Jev applies two separate token limits to each request: - A total limit of 64K tokens for the whole request. - A limit of 32K tokens for the `state` plus the single longest question. Requests that exceed either limit are rejected. Because Jev bills per input token and output tokens are free, your cost scales with the size of the `state` and questions you send rather than the length of the answer. For current rates, see [Inference Pricing](https://docs.digitalocean.com/products/inference/details/pricing/index.html.md). [Serverless Inference API Endpoints](https://docs.digitalocean.com/products/inference/how-to/si-endpoints/index.html.md): Synchronous and asynchronous API endpoints for serverless inference. [Available Models for Serverless Inference](https://docs.digitalocean.com/products/inference/details/models/index.html.md): DigitalOcean Inference supports 70+ models including OpenAI GPT, Claude, Llama, DeepSeek, Qwen, and Kimi through an OpenAI-compatible API.