Text Generation - ZnapAI Documentation

Request

curl --request POST \
  --url https://api.znapai.com/v1/messages \
  --header 'x-api-key: $ZnapAI_API_KEY' \
  --header "content-type: application/json" \
  --data '{
   "model": "claude-sonnet-4-6",
    "max_tokens": 200,
    "messages": [
      {
        "role": "user",
        "content": "What is 2+2?"
      }
    ]
}'

from anthropic import Anthropic

client = Anthropic(
    api_key="$ZnapAI_API_KEY",
    base_url="https://api.znapai.com/"
)

message = client.messages.create(
    max_tokens=200,
    messages=[
        {
            "role": "user",
            "content": "What is 2+2?"
        }
    ],
    model="claude-sonnet-4-6",
)

print(message)

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: '$ZnapAI_API_KEY',
  baseURL: 'https://api.znapai.com/',
});

const message = await anthropic.messages.create({
  max_tokens: 200,
  messages: [{ role: 'user', content: 'What is 2+2?' }],
  model: 'claude-sonnet-4-6',
});

console.log(message);

Response

{
  "model": "claude-sonnet-4-6",
  "id": "msg_bdrk_013o7o9n5vpgAoN7PwkFBcAx",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "2 + 2 = **4**"
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "stop_details": null,
  "usage": {
    "input_tokens": 14,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0,
    "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 0
    },
    "output_tokens": 14,
    "total_tokens": 28
  }
}

Vision (Image to Text)

You can also pass images to the model for image-to-text generation.

Request

curl https://api.znapai.com/v1/messages \
  --header 'x-api-key: $ZnapAI_API_KEY' \
  --header "content-type: application/json" \
  --data '{
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image",
            "source": {
              "type": "base64",
              "media_type": "image/jpeg",
              "data": "'"$(base64 -w 0 image.jpg)"'"
            }
          },
          {
            "type": "text",
            "text": "Describe this image in detail."
          }
        ]
      }
    ]
  }'

from anthropic import Anthropic
import base64

client = Anthropic(
    api_key="$ZnapAI_API_KEY",
    base_url="https://api.znapai.com/"
)

with open("path/to/image.jpg", "rb") as image_file:
    image_data = base64.b64encode(image_file.read()).decode("utf-8")

message = client.messages.create(
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/jpeg",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "Describe this image in detail."
                }
            ]
        }
    ],
    model="claude-sonnet-4-6",
)

print(message)

import Anthropic from "@anthropic-ai/sdk";
import fs from "fs";

const anthropic = new Anthropic({
  apiKey: "$ZnapAI_API_KEY",
  baseURL: "https://api.znapai.com/",
});

const image_data = fs
  .readFileSync(
    "path/to/image.jpg",
  )
  .toString("base64");

const message = await anthropic.messages.create({
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: [
        {
          type: "image",
          source: {
            type: "base64",
            media_type: "image/jpeg",
            data: image_data,
          },
        },
        {
          type: "text",
          text: "Describe this image in detail.",
        },
      ],
    },
  ],
  model: "claude-sonnet-4-6",
});

console.log(message);

Response

{
  "model": "claude-sonnet-4-6",
  "id": "msg_bdrk_01GTCduzzD8M7XfeXwx39Ltd",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "## Long Exposure Night Photography — Urban Highway\n\nThis is a **long exposure photograph** taken at night on a multi-lane urban road or highway, capturing the dynamic energy of city traffic.\n\n### Key Elements:\n\n**Light Trails**\n- Vivid **red, orange, and white streaks** streak across the road surface, created by vehicle headlights and taillights during the extended exposure\n- The trails suggest **heavy, fast-moving traffic** flowing in multiple lanes\n\n**Infrastructure**\n- A **large overhead bridge or flyover** dominates the upper right corner\n- Tall **street lamps** with warm sodium/LED lighting illuminate the scene from the left\n- **Street light poles** line the median and roadside\n\n**Vegetation**\n- **Trees illuminated in green and yellow** from artificial lighting are visible along the median/divider\n- The greenery suggests a well-maintained urban boulevard\n\n**Atmosphere**\n- The **dark night sky** contrasts dramatically with the colorful light trails\n- Blue and cyan light streaks in the distance add visual depth\n- The slight **camera movement** or zoom during exposure adds to the sense of **speed and motion**\n\n### Technical Notes:\n- Likely shot with a **slow shutter speed** (several seconds)\n- The image has a **cinematic, abstract quality** typical of intentional long-exposure urban photography"
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "stop_details": null,
  "usage": {
    "input_tokens": 1578,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0,
    "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 0
    },
    "output_tokens": 301,
    "total_tokens": 1879
  }
}

Parameters

model

string

required

The model that will complete your prompt.

messages

array

required

Input messages.

max_tokens

integer

required

The maximum number of tokens to generate before stopping.

system

string

System prompt.

temperature

number

Amount of randomness injected into the response.

top_p

number

Use nucleus sampling.

top_k

integer

Only sample from the top K options for each subsequent token.

stop_sequences

array

Custom text sequences that will cause the model to stop generating.

stream

boolean

Whether to incrementally stream the response using server-sent events.

metadata

object

An object describing metadata about the request.

tools

array

How the model should use the provided tools.

tool_choice

object

Forces the model to use a specific tool.

thinking

object

Configuration for extended thinking.

service_tier

string

The service tier for the request.

Params to Avoid

Param	Reason
`container`	Unsupported
`context_management`	Unsupported

​Request

​Response

​Vision (Image to Text)

​Request

​Response

​Parameters

​Params to Avoid

Request

Response

Vision (Image to Text)

Request

Response

Parameters

Params to Avoid