Semantic Search API, Simplified.

Embed powerful semantic search into your app with a single JSON request. No vector databases, no model downloads—just instant meaning-based results.

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Forget Vector Databases

No Pinecone, Qdrant, or pgvector setup required. We handle all vector storage and infrastructure out of the box.

Zero Model Downloads

Stop downloading heavy model weights or managing PyTorch runtimes. Leverage state-of-the-art semantic models instantly.

Ultra-Fast & Multilingual

Sub-200ms searches powered by optimized embeddings. Seamlessly understand queries in English, Spanish, and over 50 languages.

Plug-and-Play RESTful API

Clean JSON in, clean JSON out. Integrate into any backend stack in minutes with interactive documentation.

See It in Action

A clean, streamlined example using 3 core system documents.

[01] Prepare Your JSON Payload

Use the sample payload or switch to the LLM prompt tab to format your unstructured PDFs and documents instantly.

{
  "documents": [
    {
      "id": "part-001",
      "title": "Charge System",
      "content": "solar panels energy reactor battery charging power recharge fuel cells electricity",
      "metadata": {"state": "idle"}
    },
    {
      "id": "part-002",
      "title": "Flight System",
      "content": "thruster engine propulsion fly speed boost travel navigate rocket jet turbine",
      "metadata": {"state": "active"}
    },
    {
      "id": "part-003",
      "title": "Stealth System",
      "content": "cloak invisibility hide camouflage stealth radar ghost disappear vanish disguise",
      "metadata": {"state": "hidden"}
    }
  ]
}

Act as a data conversion engine for ReadyAPI.net . Transform the following text or document into a valid JSON that strictly matches this schema structure:

{
  "documents": [
    {
      "id": "part-001",
      "title": "Descriptive title of the chunk",
      "content": "Context-rich text optimized for semantic search.",
      "keywords": ["keyword1", "keyword2"],
      "metadata": {"category": "example"}
    }
  ]
}

Strict rules:
1. If the text is long, split it into logical chunks and number the ids sequentially (part-001, part-002, etc.).
2. Do not invent information not present in the original text.
3. Return EXCLUSIVELY pure JSON code without introductory text or explanations.

Text to process:
[PASTE YOUR TEXT, ARTICLE OR PDF HERE]

[02] Natural Language Query

Query using natural human concepts directly from your terminal or script.

query.py

1import requests
2
3url = "https://readyapi.net/api/v1/search/query"
4headers = {"x-api-key": "YOUR_API_KEY", "Content-Type": "application/json"}
5payload = {"query": "I need to travel far away quickly", "top_k": 2}
6
7response = requests.post(url, json=payload, headers=headers)
8print(response.json())

[03] Instant JSON Response (~124ms)

Get ranked, highly relevant semantic matches scored from 0 to 1.

response.json

Status 200 OK

{
  "results": [
    {
      "id": "part-002",
      "title": "Flight System",
      "score": 0.54
    },
    {
      "id": "part-003",
      "title": "Stealth System",
      "score": 0.41
    }
  ],
  "count": 2,
  "processing_ms": 124
}

What is Semantic Search?

Semantic Search understands the intent and contextual meaning of a query rather than just matching literal keywords. By converting text into vector concept maps, the system identifies relationships, synonyms, and crosses languages instantly.

Feature	Traditional Search (Lexical)	Semantic Search (ReadyAPI)
Search Logic	Matches exact characters/keywords.	Understands intent and concepts.
Language	Limited to document language.	Multilingual: Search English, find Spanish.
Synonyms	Fails unless words match exactly.	Succeeds by meaning ("broken screen" = "my display is shattered").

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