Request
POST https://www.ninjachat.ai/api/v1/search
Authorization: Bearer nj_sk_YOUR_API_KEY
Content-Type: application/json
{
"query": "latest developments in AI safety",
"include_answer": true,
"max_results": 10
}
Response
{
"query": "latest developments in AI safety",
"answer": "Recent developments in AI safety include...",
"sources": [
{
"url": "https://example.com/article",
"title": "AI Safety Progress in 2026",
"content": "Summary of the article...",
"published_date": "2026-03-01"
}
],
"follow_up_questions": [
"What are the key AI safety organizations?",
"How does RLHF improve AI safety?"
],
"images": [],
"cost": { "this_request": "$0.05" },
"metadata": {
"group": "web",
"search_depth": "basic",
"results_count": 10,
"latency_ms": 2341
}
}
answer. Sources are in sources[].
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
query | string | Yes | — | Search query. Max 2,000 chars. |
group | string | No | web | web, academic, or news |
max_results | integer | No | 10 | Number of sources (1–20) |
search_depth | string | No | basic | basic (fast) or advanced (deeper) |
topic | string | No | general | general, news, or finance |
include_answer | boolean | No | true | Generate an AI-synthesized answer |
include_images | boolean | No | false | Include image results |
Full working example
import os, requests
HEADERS = {"Authorization": f"Bearer {os.environ['NINJACHAT_API_KEY']}"}
def search(query, group="web", depth="basic"):
r = requests.post("https://www.ninjachat.ai/api/v1/search",
headers=HEADERS,
json={
"query": query,
"group": group,
"search_depth": depth,
"include_answer": True,
}
)
r.raise_for_status()
return r.json()
# Web search
result = search("best Python web frameworks 2026")
print(result["answer"])
for src in result["sources"][:3]:
print(f" - {src['title']}: {src['url']}")
# Academic search
result = search("transformer architecture improvements", group="academic")
print(result["answer"])
# News search
result = search("AI regulation", group="news")
print(result["answer"])
async function search(query, group = "web", depth = "basic") {
const r = await fetch("https://www.ninjachat.ai/api/v1/search", {
method: "POST",
headers: {
"Authorization": `Bearer ${process.env.NINJACHAT_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
query, group, search_depth: depth, include_answer: true,
}),
});
if (!r.ok) throw new Error(`${r.status}: ${await r.text()}`);
return r.json();
}
const result = await search("best Python web frameworks 2026");
console.log(result.answer);
result.sources.slice(0, 3).forEach(s => console.log(` - ${s.title}: ${s.url}`));
Not ready to write code? Try this in the API Playground →
Search groups
| Group | What it searches | Use for |
|---|---|---|
web | General web | Default. Most use cases. |
academic | Academic papers, research | RAG, research tools, citations |
news | Recent news articles | News aggregation, current events |
Use with RAG
Combine search with chat for retrieval-augmented generation:# 1. Search for context
results = search("What is quantum computing?")
# 2. Feed to a chat model with sources as context
context = "\n".join(f"- {s['title']}: {s['content']}" for s in results["sources"][:5])
r = requests.post("https://www.ninjachat.ai/api/v1/chat",
headers=HEADERS,
json={
"model": "gpt-5",
"messages": [
{"role": "system", "content": f"Answer using these sources:\n{context}"},
{"role": "user", "content": "Explain quantum computing with citations"},
]
}
)
print(r.json()["choices"][0]["message"]["content"])