Testing Opensolr AI Search — Vector Search, AI Hints & Document Reader

Documentation > AI SERVICES-Hybrid Search > Testing Opensolr AI Search — Vector Search, AI Hints & Document Reader
Step-by-Step Guide
Testing Your Opensolr AI Search Engine
Three AI-powered features ship with every Opensolr Web Crawler index — intent-based Vector Search, instant AI Hints, and one-click Document Reader.
CrawlIndexEmbedSolrSearch
Your complete AI search pipeline — fully managed, out of the box
Intent-Based Vector Search
Instead of matching exact keywords, vector search understands what you mean. A query like "winter hat" finds wool beanies, fleece earflap caps, and knit headwear — even when those exact words aren't on the page. Opensolr uses BGE-m3 embeddings (1024 dimensions) combined with traditional BM25 scoring for the best of both worlds: semantic understanding plus keyword precision.
winter hatAIBGE-m31024-dimensional vector embeddings98%Wool Winter Cap94%Knit Beanie Set89%Fleece Earflap Hat
Hybrid Scoring (BM25 + Vectors)BGE-m3 1024-dimMultilingual
AI Hints — Instant Answers from Your Content
Before your users even scroll through results, AI Hints delivers a concise, AI-generated answer right at the top of the page. It uses RAG (Retrieval-Augmented Generation) — the AI retrieves the most relevant passages from YOUR indexed content, then generates a focused answer. No hallucinations, no external data — every hint is grounded in your actual pages.
best pellet heater for garage?RAG: retrieves from YOUR indexed contentAI HintLook for 40,000+ BTU models with thermostatVentilation required for enclosed spacesSee top-rated pellet heaters in results below
RAG-PoweredGrounded in Your DataZero Hallucinations
Document Reader — Summarize Any Search Result
Every search result includes a "Read" button. Click it, and the AI reads the entire web page, extracts the key information, and generates a clean summary — in seconds. You can then download the summary as a PDF. No need to visit the page, skim through ads, or parse dense content yourself.
Best Pellet Heaters 2026 — Expert ReviewsComplete guide to choosing the right pellet heater...heatersguide.com/pellet-heaters-2026ReadAIReaderPage SummaryTop 5 pellet heaters ranked by efficiency, noise level,and value. Castle 12327 rated best overall at $1,299...Download PDF
One-Click SummariesPDF ExportKey Feature Extraction

Try It Live

Test these demo search engines with real vector search. Use conceptual, intent-based queries:

Try these conceptual queries to see how vector similarity goes beyond keyword matching:

  • climate disasters hurricanes floods wildfires
  • space exploration mars colonization economy
  • ancient microbes life beyond earth

Every demo page includes built-in dev tools — query parameter inspector, full Solr debugQuery output, crawl statistics, and search analytics.

Using the Solr API Directly

Direct API access for advanced users — learn more about hybrid search.

Example Solr endpoints (credentials: 123 / 123):

https://de9.solrcluster.com/solr/vector/select?wt=json&indent=true&q=*:*&rows=2
https://fi.solrcluster.com/solr/rueb/select?wt=json&indent=true&q=*:*&rows=2
https://chicago96.solrcluster.com/solr/peilishop/select?wt=json&indent=true&q=*:*&rows=2

Simple Lexical Query

curl -u 123:123 "https://de9.solrcluster.com/solr/vector/select?q=climate+change&rows=5&wt=json"

Pure Vector Query (KNN)

curl -u 123:123 "https://de9.solrcluster.com/solr/vector/select?q={!knn%20f=embeddings%20topK=50}[0.123,0.432,0.556,...]&wt=json"

Replace the vector array with your own embedding from the OpenSolr AI NLP API.

Hybrid Query (Lexical + Vector)

curl -u 123:123 "https://de9.solrcluster.com/solr/vector/select?q={!bool%20should=$lexicalQuery%20should=$vectorQuery}&lexicalQuery={!edismax%20qf=content}climate+change&vectorQuery={!knn%20f=embeddings%20topK=50}[0.12,0.43,0.66,...]&wt=json"

Combines traditional keyword scoring with semantic vector similarity — best of both worlds.

Getting Embeddings via OpenSolr API

Generate vector embeddings for any text using these endpoints:

function postEmbeddingRequest($email, $api_key, $core_name, $payload) {
    $apiUrl = "https://api.opensolr.com/solr_manager/api/embed";
    $postFields = http_build_query([
        'email'      => $email,
        'api_key'    => $api_key,
        'index_name' => $core_name,
        'payload'    => is_array($payload) ? json_encode($payload) : $payload
    ]);

    $ch = curl_init($apiUrl);
    curl_setopt_array($ch, [
        CURLOPT_RETURNTRANSFER => true,
        CURLOPT_POST           => true,
        CURLOPT_POSTFIELDS     => $postFields,
        CURLOPT_HTTPHEADER     => ['Content-Type: application/x-www-form-urlencoded'],
        CURLOPT_TIMEOUT        => 30,
    ]);

    $response = curl_exec($ch);
    curl_close($ch);
    return json_decode($response, true);
}

The response includes the vector embedding array you can pass directly to Solr.

Code Examples

PHP

<?php
$url = 'https://de9.solrcluster.com/solr/vector/select?wt=json';
$params = [
    'q'            => '{!bool should=$lexicalQuery should=$vectorQuery}',
    'lexicalQuery' => '{!edismax qf=content}climate disasters',
    'vectorQuery'  => '{!knn f=embeddings topK=50}[0.12,0.43,0.56,0.77]'
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_USERPWD, '123:123');
curl_setopt($ch, CURLOPT_POSTFIELDS, http_build_query($params));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);

echo $response;

Python

import requests
from requests.auth import HTTPBasicAuth

url = "https://de9.solrcluster.com/solr/vector/select"
params = {
    'q': '{!bool should=$lexicalQuery should=$vectorQuery}',
    'lexicalQuery': '{!edismax qf=content}climate disasters',
    'vectorQuery': '{!knn f=embeddings topK=50}[0.12,0.43,0.56,0.77]',
    'wt': 'json'
}

response = requests.post(url, data=params, auth=HTTPBasicAuth('123', '123'))
print(response.json())

JavaScript (AJAX)

<script>
fetch('https://de9.solrcluster.com/solr/vector/select?wt=json&q={!knn%20f=embeddings%20topK=10}[0.11,0.22,0.33]', {
    headers: { 'Authorization': 'Basic ' + btoa('123:123') }
})
.then(r => r.json())
.then(console.log);
</script>

Quick Reference

  • Adjust topK to control how many similar results to retrieve (usually 20-100).
  • Use {!bool should=...} for softer relevance mixing — vector similarity has more influence on ranking.
  • For best hybrid results, always combine both lexical and vector queries.
  • All demo search pages include built-in query inspector, debugQuery, crawl stats, and search analytics.
Ready to Add AI Search to Your Site?
Get a fully managed vector search engine with AI Hints and Document Reader — set up in minutes.