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Documentation Index

Fetch the complete documentation index at: https://docs.ninjachat.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Request

POST https://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
  }
}
The AI answer is in answer. Sources are in sources[].

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Parameters

ParameterTypeRequiredDefaultDescription
querystringYes—Search query. Max 2,000 chars.
groupstringNowebweb, academic, or news
max_resultsintegerNo10Number of sources (1–20)
search_depthstringNobasicbasic (fast) or advanced (deeper)
topicstringNogeneralgeneral, news, or finance
include_answerbooleanNotrueGenerate an AI-synthesized answer
include_imagesbooleanNofalseInclude image results

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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://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"])
Not ready to write code? Try this in the API Playground →

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Search groups

GroupWhat it searchesUse for
webGeneral webDefault. Most use cases.
academicAcademic papers, researchRAG, research tools, citations
newsRecent news articlesNews aggregation, current events

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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://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()["message"]["content"])