I have a client who is frustrated with their current Strapi + React setup. It seems no one explained to them that a headless approach requires more ongoing maintenance compared to a traditional CMS. Most of their content lives in Strapi and is essentially “static,” with only two pages pulling data from an external API.

They’re unhappy with their existing vendor because everything takes an unreasonable amount of time— for example, they were quoted 200+ hours just to update Strapi, which is absurd. They’re fed up and want to move in a different direction.

They want the frontend look and feel to stay the same, so I can reuse some of the existing styling. However, I’m unsure whether I should try to reuse some of the React components with minimal modifications and integrate them into Twig/Paragraphs, or simply reuse the CSS and replicate the HTML markup.

I’m an experienced Drupal themer and I know how powerful Drupal is as a CMS, so I’ve never been a fan of fully headless builds for brochure-type sites like this one.

So I see three options:

1. Reuse the CSS and some JavaScript and rebuild everything using Drupal/Twig.
2. Integrate the existing React components on top of Drupal Paragraphs.
3. Create a fully headless Drupal implementation.

My plan is to go with option #1. It may take more time upfront, but in the long run it’s the most maintainable solution.

I look forward to suggestions and hints about the following steps.

Hey everyone,

A few days back I shared my experiments with hybrid search (combining traditional lexical search with vector/semantic search). Well, I've been busy, and I'm back with some major upgrades that I think you'll find interesting.

TL;DR: We now have 1024-dimensional embeddings, blazing fast GPU inference, and you can generate embeddings via our free API endpoint. Plus: you can literally search with emojis now. Yes, really. 🚲 finds bicycles. 🐕 finds dog jewelry. Keep reading.

What Changed?

1. Upgraded from 384D to 1024D Embeddings

We switched from paraphrase-multilingual-MiniLM-L12-v2 (384 dimensions) to BAAI/bge-m3 (1024 dimensions).

Why does this matter?

Think of dimensions like pixels in an image. A 384-pixel image is blurry. A 1024-pixel image is crisp. More dimensions = the model can capture more nuance and meaning from your text.

The practical result? Searches that "kind of worked" before now work really well, especially for:

• Non-English languages (Romanian, German, French, etc.)
• Domain-specific terminology
• Conceptual/semantic queries

2. Moved Embeddings to GPU

Before: CPU embeddings taking 50-100ms per query. Now: GPU embeddings taking ~2-5ms per query.

The embedding is so fast now that even with a network round-trip from Europe to USA and back, it's still faster than local CPU embedding was. Let that sink in.

3. Optimized the Hybrid Formula

After a lot of trial and error, we settled on this normalization approach:

score = vector_score + (lexical_score / (lexical_score + k)) 

Where k is a tuning parameter (we use k=10). This gives you:

• Lexical score normalized to 0-1 range
• Vector and lexical scores that play nice together
• No division by zero issues
• Intuitive tuning (k = the score at which you get 0.5)

4. Quality Filter with frange

Here's a pro tip: use Solr's frange to filter out garbage vector matches:

fq={!frange l=0.3}query($vectorQuery) 

This says "only show me documents where the vector similarity is at least 0.3". Anything below that is typically noise anyway. This keeps your results clean and your users happy.

Live Demos (Try These!)

I've set up several demo indexes. Each one has a Debug button in the bottom-right corner - click it to see the exact Solr query parameters and full debugQuery analysis. Great for learning!

🛠️ Romanian Hardware Store (Dedeman)

Search a Romanian e-commerce site with emojis:

🚲 → Bicycle accessories

No keywords. Just an emoji. And it finds bicycle mirrors, phone holders for bikes, etc. The vector model understands that 🚲 = bicicletă = bicycle-related products.

💎 English Jewelry Store (Rueb.co.uk)

Sterling silver, gold, gemstones - searched semantically:

🐕 → Dog-themed jewelry

⭐️ → Star-themed jewelry

🧣 Luxury Cashmere Accessories (Peilishop)

Hats, scarves, ponchos:

winter hat → Beanies, caps, cold weather gear

📰 Fresh News Index

Real-time crawled news, searchable semantically:

🍳 → Food/cooking articles

what do we have to eat to boost health? → Nutrition articles

This last one is pure semantic search - there's no keyword "boost" or "health" necessarily in the results, but the meaning matches.

Free API Endpoint for 1024D Embeddings

Want to try this in your own Solr setup? We're exposing our embedding endpoint for free:

curl -X POST https://opensolr.com/api/embed \ -H "Content-Type: application/json" \ -d '{"text": "your text here"}' 

Returns a 1024-dimensional vector ready to index in Solr.

Schema setup:

<fieldType name="knn_vector" class="solr.DenseVectorField" vectorDimension="1024" similarityFunction="cosine"/> <field name="embeddings" type="knn_vector" indexed="true" stored="false"/> 

Key Learnings

1. Title repetition trick: For smaller embedding models, repeat the title 3x in your embedding text. This focuses the model's limited capacity on the most important content. Game changer for product search.
2. topK isn't "how many results": It's "how many documents the vector search considers". The rest get score=0 for the vector component. Keep it reasonable (100-500) to avoid noise.
3. Lexical search is still king for keywords: Hybrid means vector helps when lexical fails (emojis, conceptual queries), and lexical helps when you need exact matches. Best of both worlds.
4. Use synonyms for domain-specific gaps: Even the best embedding model doesn't know that "autofiletantă" (Romanian) = "drill". A simple synonym file fixes what AI can't.
5. Quality > Quantity: Better to return 10 excellent results than 100 mediocre ones. Use frange and reasonable topK values.

What's Next?

Still exploring:

• Fine-tuning embedding models for specific domains
• RRF (Reciprocal Rank Fusion) as an alternative to score-based hybrid
• More aggressive caching strategies

Happy to answer questions. And seriously, click that Debug button on the demos - seeing the actual Solr queries is super educational!

Running Apache Solr 9.x on OpenSolr.com - free hosted Solr with vector search support.

Been tasked with figuring out Acquia at work. Fwiw, I have never even touched Drupal before

Is their documentation outdated, or am I missing something? I've been trying to push my Cloud IDEs Drupal site to dev, but I am constantly running into issues.

Should this stuff not just work out of the box? I was following their "Deploying a site to Cloud Next" and couldn't get past pushing the database because their IDEs are in MySQL 8 while my Cloud environment is MySQL 5.7 apparently. Is this my failt? Or is this normal to have to fix before doing anything?

Not to mention support takes over a day to respond to each email. Bit of a rant post. Sorry

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

Do eeet! https://www.fldrupal.camp/submit-session

I'm fairly new to druapl. I'm using Druapl CMS, I've got a few questions.

1) How do I remove the black bar at the bottom, I've already remove all the footer blocks, but there is still a huge black bar at the bottom of the site.

2) On wide screens, the white background stops and there's a drupal logo wall visible 'behind' the white background on the right. How do I make the white background go all the way?

3) How do I change the background and font colors?

our internal team has been tasked with upgrading our very old, large drupal 7 website to drupal 10, we're a government-related institution, so we have strict security compliance and need guaranteed 24/7 support, we need an agency that specifically handles huge, complex enterprise projects and not just general web design. Someone in our team suggested this company’s drupal development services, but we are still open to discussion and we need some more agencies so we can compare.

does anyone here had a good experience at this level of large scale migration and long-term security maintenance with an agency?