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

Hybrid Search
Step-by-Step Guide
Testing Your Opensolr AI Search Engine
Four powerful features ship with every Opensolr Web Crawler index — intent-based Vector Search, instant AI Hints, one-click Document Reader, and hands-on Query Elevation.
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
Query Elevation — Pin & Exclude Search Results
Take full control of what your users see. Query Elevation lets you pin important results to the top or exclude irrelevant ones — directly from the Search UI, with zero code and no reindexing required. Perfect for promoting landing pages, burying outdated content, or curating high-value queries.
Search ResultsProduct Landing Pageyoursite.com/products/best-sellerPin↑ Pinned #1— forced to top for this queryDrag to reorder when multiple results are pinnedExcluded result — hidden from this query
  • Pin — Force a specific result to the top for a given search query
  • Exclude — Hide a result completely so it never appears for that query
  • Exclude All — Apply the rule globally, across every search query
  • Drag & drop — Reorder pinned results to control exactly which one shows first
Zero Code RequiredExclude Irrelevant ResultsPin & Reorder

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

<?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;

Py 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())

JS 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.
Custom Plans Available

This is a premium feature available on custom plans tailored to your needs and budget. For small websites, we can even provide these features for free after validating your use case. Contact us at support@opensolr.com to discuss your requirements.