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POST
/
v1
/
chat
/
completions
cURL
curl --request POST \
  --url https://api.perceptron.inc/v1/chat/completions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "messages": [
    {
      "content": "<string>",
      "role": "system"
    }
  ],
  "model": "<string>",
  "frequency_penalty": 0,
  "max_completion_tokens": 1,
  "presence_penalty": 0,
  "regex": "<string>",
  "response_format": {
    "type": "text"
  },
  "stream": false,
  "stream_options": {
    "include_usage": true
  },
  "temperature": 1,
  "top_k": 1,
  "top_p": 0,
  "vision_config": {
    "enable_thinking": true,
    "internal_tools": {
      "focus": true
    }
  }
}
'
import requests

url = "https://api.perceptron.inc/v1/chat/completions"

payload = {
"messages": [
{
"content": "<string>",
"role": "system"
}
],
"model": "<string>",
"frequency_penalty": 0,
"max_completion_tokens": 1,
"presence_penalty": 0,
"regex": "<string>",
"response_format": { "type": "text" },
"stream": False,
"stream_options": { "include_usage": True },
"temperature": 1,
"top_k": 1,
"top_p": 0,
"vision_config": {
"enable_thinking": True,
"internal_tools": { "focus": True }
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)
const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
messages: [{content: '<string>', role: 'system'}],
model: '<string>',
frequency_penalty: 0,
max_completion_tokens: 1,
presence_penalty: 0,
regex: '<string>',
response_format: {type: 'text'},
stream: false,
stream_options: {include_usage: true},
temperature: 1,
top_k: 1,
top_p: 0,
vision_config: {enable_thinking: true, internal_tools: {focus: true}}
})
};

fetch('https://api.perceptron.inc/v1/chat/completions', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));
<?php

$curl = curl_init();

curl_setopt_array($curl, [
CURLOPT_URL => "https://api.perceptron.inc/v1/chat/completions",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'messages' => [
[
'content' => '<string>',
'role' => 'system'
]
],
'model' => '<string>',
'frequency_penalty' => 0,
'max_completion_tokens' => 1,
'presence_penalty' => 0,
'regex' => '<string>',
'response_format' => [
'type' => 'text'
],
'stream' => false,
'stream_options' => [
'include_usage' => true
],
'temperature' => 1,
'top_k' => 1,
'top_p' => 0,
'vision_config' => [
'enable_thinking' => true,
'internal_tools' => [
'focus' => true
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}
package main

import (
"fmt"
"strings"
"net/http"
"io"
)

func main() {

url := "https://api.perceptron.inc/v1/chat/completions"

payload := strings.NewReader("{\n \"messages\": [\n {\n \"content\": \"<string>\",\n \"role\": \"system\"\n }\n ],\n \"model\": \"<string>\",\n \"frequency_penalty\": 0,\n \"max_completion_tokens\": 1,\n \"presence_penalty\": 0,\n \"regex\": \"<string>\",\n \"response_format\": {\n \"type\": \"text\"\n },\n \"stream\": false,\n \"stream_options\": {\n \"include_usage\": true\n },\n \"temperature\": 1,\n \"top_k\": 1,\n \"top_p\": 0,\n \"vision_config\": {\n \"enable_thinking\": true,\n \"internal_tools\": {\n \"focus\": true\n }\n }\n}")

req, _ := http.NewRequest("POST", url, payload)

req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")

res, _ := http.DefaultClient.Do(req)

defer res.Body.Close()
body, _ := io.ReadAll(res.Body)

fmt.Println(string(body))

}
HttpResponse<String> response = Unirest.post("https://api.perceptron.inc/v1/chat/completions")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"messages\": [\n {\n \"content\": \"<string>\",\n \"role\": \"system\"\n }\n ],\n \"model\": \"<string>\",\n \"frequency_penalty\": 0,\n \"max_completion_tokens\": 1,\n \"presence_penalty\": 0,\n \"regex\": \"<string>\",\n \"response_format\": {\n \"type\": \"text\"\n },\n \"stream\": false,\n \"stream_options\": {\n \"include_usage\": true\n },\n \"temperature\": 1,\n \"top_k\": 1,\n \"top_p\": 0,\n \"vision_config\": {\n \"enable_thinking\": true,\n \"internal_tools\": {\n \"focus\": true\n }\n }\n}")
.asString();
require 'uri'
require 'net/http'

url = URI("https://api.perceptron.inc/v1/chat/completions")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"messages\": [\n {\n \"content\": \"<string>\",\n \"role\": \"system\"\n }\n ],\n \"model\": \"<string>\",\n \"frequency_penalty\": 0,\n \"max_completion_tokens\": 1,\n \"presence_penalty\": 0,\n \"regex\": \"<string>\",\n \"response_format\": {\n \"type\": \"text\"\n },\n \"stream\": false,\n \"stream_options\": {\n \"include_usage\": true\n },\n \"temperature\": 1,\n \"top_k\": 1,\n \"top_p\": 0,\n \"vision_config\": {\n \"enable_thinking\": true,\n \"internal_tools\": {\n \"focus\": true\n }\n }\n}"

response = http.request(request)
puts response.read_body
{
  "choices": [
    {
      "index": 1,
      "message": {
        "content": "<string>",
        "reasoning_content": "<string>"
      }
    }
  ],
  "created": 1,
  "id": "<string>",
  "model": "<string>",
  "object": "<string>",
  "usage": {
    "completion_tokens": 1,
    "prompt_tokens": 1,
    "total_tokens": 1
  }
}
{
"error": {
"code": null,
"message": "Model 'test' does not support video input",
"param": null,
"type": "invalid_request_error"
}
}
{
"error": {
"code": null,
"message": "Invalid API key",
"param": null,
"type": "authentication_error"
}
}
{
"error": {
"code": "rate_limit_exceeded",
"message": "Organization rate limit exceeded (300 requests/minute). Please retry after 30 seconds.",
"param": null,
"type": "rate_limit_error"
}
}
{
"error": {
"code": null,
"message": "The server had an error while processing your request.",
"param": null,
"type": "server_error"
}
}

Overview

The Chat Completions API is fully compatible with OpenAI’s chat completions specification, supporting both text-only and multimodal (image) requests. Use it to generate responses from Isaac 0.2. Isaac 0.2 triggers thinking and structured grounding through <hint>...</hint> tags inside a system-role message.

<hint> system messages

Place hint values inside a system-role message. Multiple hints can share one <hint> tag, separated by spaces.
HintOutput
<hint>BOX</hint>Bounding boxes
<hint>POINT</hint>Points / keypoints
<hint>POLYGON</hint>Polygon masks
<hint>THINK</hint>Chain-of-thought reasoning
<hint>FOCUS</hint>Enable internal focus tool

Example: Grounded detection

curl https://api.perceptron.inc/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
    "model": "isaac-0.2-2b-preview",
    "messages": [
      { "role": "system", "content": "<hint>BOX</hint>" },
      { "role": "user",
        "content": [
          { "type": "image_url",
            "image_url": { "url": "<image-url>" } },
          { "type": "text",
            "text": "Find every worker wearing PPE." }
        ]
      }
    ]
  }'

Example: Counting with grounding

For counting tasks or multi-step spatial reasoning, combining THINK with BOX (or POINT) is helpful on Isaac 0.2. For pure detection without counting, use the spatial hint alone.
curl https://api.perceptron.inc/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
    "model": "isaac-0.2-2b-preview",
    "messages": [
      { "role": "system", "content": "<hint>BOX THINK</hint>" },
      { "role": "user",
        "content": [
          { "type": "image_url",
            "image_url": { "url": "<image-url>" } },
          { "type": "text",
            "text": "Count the safety violations and box each one. Explain your reasoning." }
        ]
      }
    ]
  }'

Example: OCR without hints

For free-form text tasks like OCR, no hint is needed — just send your prompt.
curl https://api.perceptron.inc/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
    "model": "isaac-0.2-2b-preview",
    "messages": [
      { "role": "user",
        "content": [
          { "type": "image_url",
            "image_url": { "url": "<image-url>" } },
          { "type": "text",
            "text": "Extract each produce label along with its listed price." }
        ]
      }
    ]
  }'

Streaming

Set "stream": true to receive Server-Sent Events (SSE). To get token usage, also set stream_options.include_usage: true — when enabled, usage is attached to the final chunk (the one with finish_reason: "stop"), immediately before data: [DONE].
curl https://api.perceptron.inc/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $PERCEPTRON_API_KEY" \
  -d '{
    "model": "isaac-0.2-2b-preview",
    "messages": [
      { "role": "user",
        "content": [
          { "type": "image_url",
            "image_url": { "url": "<image-url>" } },
          { "type": "text", "text": "Describe this scene in detail." }
        ]
      }
    ],
    "stream": true,
    "stream_options": { "include_usage": true }
  }'

Best Practices

  1. Combining THINK with BOX/POINT is helpful for counting. Use the spatial hint alone for pure detection; add THINK when you need step-by-step reasoning alongside the bounding boxes.
  2. Leave temperature unset. The default is 0.0 (deterministic). Only set a non-zero value if you want more varied outputs.
  3. Image format: HTTP(S) URLs and base64 data URLs are both supported. MIME types: image/png, image/jpeg, image/webp.
  4. Token limits: 8K context.

Limits

LimitValue
Requests300/min
Request body size20 MB
Media upload20 GB per 48 hours
For large images, resize client-side before uploading. See the Tokenization guide for optimization tips.

Authorizations

Authorization
string
header
required

Bearer token authentication using your Perceptron API key

Body

application/json
messages
object[]
required

Conversation history listed in order. Supported roles: system, user, assistant.

Author role of the message as defined by the OpenAI Chat Completions spec.

model
string
required

The model to invoke. Available options: isaac-0.1, isaac-0.2-1b, isaac-0.2-2b-preview, perceptron-mk1.

frequency_penalty
number<float> | null

Positive values discourage the model from repeating previously used tokens.

Required range: -2 <= x <= 2
max_completion_tokens
integer<int32> | null

Maximum number of completion tokens to generate.

Model-specific limits:

  • Isaac 0.1: The combined total of input tokens and output tokens must not exceed 8192 tokens.
Required range: x >= 0
presence_penalty
number<float> | null

Positive values encourage the model to introduce new concepts.

Required range: -2 <= x <= 2
regex
string | null

Regex pattern for constrained generation.

response_format
object

An object specifying the format that the model must output. Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which ensures the model will match your supplied JSON schema.

stream
boolean | null
default:false

Set to true for SSE streaming. When omitted, the API returns a single JSON response.

stream_options
null | object

Optional streaming flags. Token usage is always reported in the final chunk of a streaming response.

temperature
number<float> | null

Sampling temperature. Lower values yield deterministic replies; higher values explore more creative outputs.

Model-specific recommendations:

  • Isaac 0.1: Default and recommended value is 0.0.
Required range: 0 <= x <= 2
top_k
integer<int32> | null

Top-k sampling. The model samples from the top k most likely tokens.

Required range: x >= 0
top_p
number<float> | null

Nucleus sampling probability. The model samples from the smallest token set whose cumulative probability exceeds this threshold.

Required range: x <= 1
vision_config
null | object

Perceptron vision-model controls (thinking, spatial output format, internal-tool toggles). Only supported on Perceptron-owned models.

Response

Chat completion generated successfully.

Non-streaming response body when stream=false.

choices
object[]
required
created
integer<int64>
required
Required range: x >= 0
id
string
required
model
string
required
object
string
required
usage
null | object

Token accounting emitted with every completion.