Claude Code Guide
Set up Flaq AI Claude models and explore Claude Code skills
Use Kimi 2.7 Image-to-Text API for visual understanding, image Q&A, OCR-style extraction, and multimodal reasoning in stable production workflows.
const response = await fetch('https://api.flaq.ai/api/v1/chat/completions', {
method: 'POST',
headers: {
Authorization: 'Bearer YOUR_API_KEY',
Accept: 'text/event-stream',
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'kimi-2.7-image-to-text',
messages: [
{
role: 'user',
content: [
{
type: 'text',
text: 'What is roughly shown in this image?'
},
{
type: 'image_url',
image_url: {
url: 'https://example.com/sample-image.jpg'
}
}
]
}
],
stream: true,
max_tokens: 300
})
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
let assistantText = '';
while (true) {
const { done, value } = await reader.read();
if (done) break;
buffer += decoder.decode(value, { stream: true });
const frames = buffer.split('\n\n');
buffer = frames.pop() || '';
for (const frame of frames) {
const lines = frame.split('\n').filter(Boolean);
let eventName = 'message';
const dataLines = [];
for (const line of lines) {
if (line.startsWith('event:')) {
eventName = line.slice(6).trim();
} else if (line.startsWith('data:')) {
dataLines.push(line.replace(/^data:\s*/, ''));
}
}
const raw = dataLines.join('\n').trim();
if (raw === '[DONE]') {
console.log('\nFinal text:', assistantText);
continue;
}
let payload;
try {
payload = JSON.parse(raw);
} catch {
continue;
}
if (eventName === 'error' || payload.error) {
const msg = payload.error?.message ?? payload.message ?? 'Chat request failed';
throw new Error(msg);
}
const delta = payload.choices?.[0]?.delta;
if (delta?.content) {
assistantText += delta.content;
console.log(assistantText);
}
}
}
import json
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/chat/completions',
headers={
'Authorization': 'Bearer YOUR_API_KEY',
'Accept': 'text/event-stream',
'Content-Type': 'application/json',
},
json={
'model': 'kimi-2.7-image-to-text',
'messages': [
{
'role': 'user',
'content': [
{
'type': 'text',
'text': 'What is roughly shown in this image?',
},
{
'type': 'image_url',
'image_url': {
'url': 'https://example.com/sample-image.jpg',
},
},
],
}
],
'stream': True,
'max_tokens': 300,
},
stream=True,
)
response.raise_for_status()
event_name = 'message'
assistant_text = ''
for raw_line in response.iter_lines(decode_unicode=True):
if not raw_line:
event_name = 'message'
continue
if raw_line.startswith('event:'):
event_name = raw_line.replace('event:', '', 1).strip()
continue
if raw_line.startswith('data:'):
raw_data = raw_line.replace('data:', '', 1).strip()
if raw_data == '[DONE]':
print('\nFinal text:', assistant_text)
continue
payload = json.loads(raw_data)
if event_name == 'error' or payload.get('error'):
error = payload.get('error') or payload
raise RuntimeError(error.get('message', 'Chat request failed'))
choices = payload.get('choices') or []
if choices:
delta = choices[0].get('delta') or {}
content = delta.get('content')
if content:
assistant_text += content
print(content, end='', flush=True)
curl -N -X POST "https://api.flaq.ai/api/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Accept: text/event-stream" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-2.7-image-to-text",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is roughly shown in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://example.com/sample-image.jpg"
}
}
]
}
],
"stream": true,
"max_tokens": 300
}'
| Parameters | Price | Original Price | Discount |
|---|
Kimi 2.7 Image-to-Text API on Flaq AI provides Moonshot AI multimodal model access for visual understanding, image Q&A, OCR-style extraction, and image-grounded reasoning workflows. This affordable Kimi vision API integration helps developers transform image input into useful text descriptions, answers, summaries, and structured insights. It is built for teams that need stable image analysis without maintaining separate vision infrastructure.
Note Please ensure image input and prompts comply with Moonshot AI and Flaq AI safety requirements. If an error occurs, adjust the image input or prompt and try again.
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