Claude Code Guide
Set up Flaq AI Claude models and explore Claude Code skills
Try GPT 6.1 Sol API by OpenAI for web search, online research, and answers using current information through Flaq AI's stable, affordable API with streaming.
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: 'gpt-6.1-sol-web-search',
messages: [
{
role: 'user',
content: 'Find the latest public information about Flaq AI and summarize it for a product brief.'
}
],
stream: true,
max_tokens: 2048
})
});
if (!response.ok) {
const errorBody = await response.json().catch(() => null);
throw new Error(errorBody?.error?.message ?? errorBody?.message ?? `HTTP ${response.status}`);
}
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(/\r?\n\r?\n/);
buffer = frames.pop() || '';
for (const frame of frames) {
const lines = frame.split(/\r?\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': 'gpt-6.1-sol-web-search',
'messages': [
{
'role': 'user',
'content': 'Find the latest public information about Flaq AI and summarize it for a product brief.',
}
],
'stream': True,
'max_tokens': 2048,
},
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": "gpt-6.1-sol-web-search",
"messages": [
{
"role": "user",
"content": "Find the latest public information about Flaq AI and summarize it for a product brief."
}
],
"stream": true,
"max_tokens": 2048
}'
| Parameters | Price | Original Price | Discount |
|---|
GPT 6.1 Sol Web Search API supports search-enabled questions and research workflows on Flaq AI. Provide the topic, the information you need, and any relevant context to request a written answer with access to web search. This route combines message-based interaction with optional supporting attachments, streaming responses, and adjustable output length for applications that need to work with online information.
Web Search Capability: Use the search-enabled route for questions that require looking beyond the context already supplied in the conversation.
Prompt-Directed Research: Specify the subject, time period, and desired scope so the request is focused on the information your application needs.
Supporting Reference Inputs: Attach supported files or an image alongside a text question to give the research task additional context.
Conversational Refinement: Ask follow-up questions that narrow the topic or request a clearer explanation of an earlier answer.
Streaming Research Responses: Return generated text progressively for an interactive research experience.
Adjustable Answer Length: Set an output token limit and describe whether you want a concise overview or a more developed response.
Input: A text research question with relevant conversation context and optional supported image or file attachments.
Output: A written response produced through the web-search-enabled model route.
Controls: Research scope in the prompt, an optional output token limit, and streaming selection.
Capabilities: Search-enabled question answering, online research summaries, and follow-up exploration of a topic.
Research Assistants: Help users investigate a topic and refine the questions they want to explore.
Product Research: Collect information for a product comparison brief and check the underlying sources before making a decision.
Industry Briefs: Request a focused overview of developments within a named topic and time period.
Documentation Discovery: Find information relevant to a technical question and verify it against the original documentation.
Contextual Investigation: Combine an uploaded reference with a question that calls for additional online information.
Note State the relevant date range and check source dates and original pages before relying on a research answer. Web access does not guarantee that every relevant page is available or that all returned statements are current.
GPT 6.1 Sol Web Search vs. GPT 6.1 Sol Text-to-Text Text-to-Text handles supplied messages without web search. The Web Search route is configured for questions that need online information.
GPT 6.1 Sol Web Search vs. GPT 6.1 Sol File Analysis File Analysis centers on material the user uploads. Web Search supports research beyond those attachments while retaining optional reference inputs.
GPT 6.1 Sol Web Search vs. GPT 6.1 Sol Image-to-Text Image-to-Text focuses on describing or answering questions about a supplied image. Web Search addresses a text research question and can use an image as supporting context.
GPT 6.1 Sol Web Search vs. Claude Sonnet 5.5 File Analysis Claude's File Analysis configuration accepts documents and images without enabling web search. This GPT route provides the search-enabled option when the task also depends on online material.
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