Attribute & Entity Extraction
Extracts brand names, model numbers, color preferences, sizes, and technical specs from raw search text.
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Natural language query understanding API that converts buyer search strings into structured SQL/Elasticsearch filters.
https://test.rsflowhub.com/api/v1/ai/search-query-parse
E-Commerce Natural Language Search Query Parser API is a ready-to-use REST API on RSFlowHub for developers building web, mobile and AI agent applications. Send a JSON request to POST /api/v1/ai/search-query-parse with your API key; RSFlowHub runs it on a tuned AI model (with automatic fallback to a backup provider) and returns a structured JSON result, usually within 1 to 3 seconds.
X-API-Key Header
curl -X POST "https://test.rsflowhub.com/api/v1/ai/search-query-parse" \
-H "Content-Type: application/json" \
-H "X-API-Key: rfh_your_api_key" \
-d '{"query":"Samsung 5G phones under \u20b920,000 with 8GB RAM, highest rated first","schema":{"brand":{"type":"string"},"network":{"type":"string"},"price":{"type":"number"},"ram":{"type":"string"},"rating":{"type":"number"}},"context":{"domain":"ecommerce","default_currency":"INR"}}'
<?php
$client = new \GuzzleHttp\Client();
$response = $client->post('https://test.rsflowhub.com/api/v1/ai/search-query-parse', [
'headers' => [
'Content-Type' => 'application/json',
'X-API-Key' => 'rfh_your_api_key',
],
'json' => array (
'query' => 'Samsung 5G phones under ₹20,000 with 8GB RAM, highest rated first',
'schema' =>
array (
'brand' =>
array (
'type' => 'string',
),
'network' =>
array (
'type' => 'string',
),
'price' =>
array (
'type' => 'number',
),
'ram' =>
array (
'type' => 'string',
),
'rating' =>
array (
'type' => 'number',
),
),
'context' =>
array (
'domain' => 'ecommerce',
'default_currency' => 'INR',
),
),
]);
echo $response->getBody();
const response = await fetch('https://test.rsflowhub.com/api/v1/ai/search-query-parse', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-API-Key': 'rfh_your_api_key'
},
body: JSON.stringify({"query":"Samsung 5G phones under \u20b920,000 with 8GB RAM, highest rated first","schema":{"brand":{"type":"string"},"network":{"type":"string"},"price":{"type":"number"},"ram":{"type":"string"},"rating":{"type":"number"}},"context":{"domain":"ecommerce","default_currency":"INR"}})
});
const data = await response.json();
console.log(data);
import requests
url = "https://test.rsflowhub.com/api/v1/ai/search-query-parse"
headers = {
"Content-Type": "application/json",
"X-API-Key": "rfh_your_api_key"
}
payload = {"query":"Samsung 5G phones under \u20b920,000 with 8GB RAM, highest rated first","schema":{"brand":{"type":"string"},"network":{"type":"string"},"price":{"type":"number"},"ram":{"type":"string"},"rating":{"type":"number"}},"context":{"domain":"ecommerce","default_currency":"INR"}}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
Extracts brand names, model numbers, color preferences, sizes, and technical specs from raw search text.
Converts phrases like "under $50", "under ₹20,000", or "budget friendly" into exact numeric min/max price boundaries.
Detects buyer sorting intent such as "cheapest first", "highest rated", or "newest arrivals".
Automatically corrects typos and normalizes product categories prior to database querying.
See how developers integrate this API endpoint into production apps.
Parse buyer search strings like "red Nike sneakers size 10 under $100" into exact product catalog filter queries.
Convert search strings like "3 bedroom apartment in downtown Miami with pool under $3k" into DB filters.
Parse natural queries like "5-star beach resort in Bali with free breakfast" into hotel availability API parameters.
Comparing RSFlowHub's specialized API gateway vs raw multi-turn LLM prompts vs legacy regex rules.
| Evaluation Metric | RSFlowHub API | Raw LLM Prompts | Legacy Regex / Rules |
|---|---|---|---|
| Response Structure | Validated JSON envelope | Unstructured markdown or chat format | Brittle custom regex tuples |
| Setup Effort | One POST request, no prompts | Write and maintain prompts + output parsing | Fast, but rule lists miss context and sarcasm |
| Pricing Model | Flat 3 credit/request | Volatile token-based billing | High server maintenance overhead |
| Multi-Turn Hallucination | Zero (Deterministic validators) | Output format can drift between model versions | No semantic AI intelligence |
| Developer Integration | 3 lines cURL, PHP, JS, Python | Complex prompt engineering & retry loop | Fragile manual rule maintenance |
query
string
Raw natural language search query string typed by the buyer.
schema
object
Optional target catalog schema definition (e.g. {"brand": {"type": "string"}, "price": {"type": "number"}}).
context
object
Optional business domain context (e.g. {"domain": "ecommerce", "default_currency": "INR"}).
Every endpoint returns the same envelope: success, request_id, data.result and meta.
{
"success": true,
"request_id": "req_3f9a2c7e-5b1d-4e8a-9c2f-7d6e1a0b4c8d",
"data": {
"result": {
"original_query": "red running shoes under 100 dollars size 10",
"normalized_query": "red running shoes",
"intent": "product_search",
"language": "en",
"filters": [
{
"field": "color",
"value": "red"
},
{
"field": "category",
"value": "running shoes"
},
{
"field": "price_max",
"value": 100
},
{
"field": "size",
"value": "10"
}
],
"exclude": [],
"sort": {
"field": "relevance",
"direction": "desc"
},
"is_ambiguous": false
}
},
"meta": {
"version": "v1",
"timestamp": "2026-10-05T09:30:00Z"
}
}
Everything you need to know about integrating and using E-Commerce Natural Language Search Query Parser API.
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