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Follow-up: Hybrid Search in Apache Solr is NOW Production-Ready (with 1024D vectors!)
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. π² finds bicycles. π 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!
π οΈ Romanian Hardware Store (Dedeman)
Search a Romanian e-commerce site with emojis:
No keywords. Just an emoji. And it finds bicycle mirrors, phone holders for bikes, etc. The vector model understands that π² = bicicletΔ = bicycle-related products.
π English Jewelry Store (Rueb.co.uk)
Sterling silver, gold, gemstones - searched semantically:
βοΈ β Star-themed jewelry
𧣠Luxury Cashmere Accessories (Peilishop)
Hats, scarves, ponchos:
winter hat β Beanies, caps, cold weather gear
π° Fresh News Index
Real-time crawled news, searchable semantically:
π³ β Food/cooking articles
what do we have to eat to boost health? β 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
- 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.
- 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.
- 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.
- Use synonyms for domain-specific gaps: Even the best embedding model doesn't know that "autofiletantΔ" (Romanian) = "drill". A simple synonym file fixes what AI can't.
- Quality > Quantity: Better to return 10 excellent results than 100 mediocre ones. Use
frangeand reasonabletopKvalues.
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.
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