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Laravel Scout driver for Opensolr

Laravel Scout engine backed by Opensolr — managed Apache Solr with server-side embeddings and hybrid (BM25 + kNN) search.

See it live (real news index, hybrid + AI answer): https://search.opensolr.com/news__dense?q=how+am+I+supposed+to+save+money%3F

Your models get semantic search that understands meaning — "sleepy pets" finds the post about cats napping — fused with classic keyword relevance, on managed infrastructure. No embedding model, no vector database to run.

One index serves your whole app: every searchable model shares a single vector-enabled Opensolr index, scoped per model automatically — so the $50/mo tier covers all your models.

Setup

Create a vector-enabled index in a vector region (a server with the Opensolr web crawler, any Solr version; vector_regions lists them live) in the Opensolr control panel — free forever, no card. Then Scout works exactly as documented:

Try it without an account

There is a public demo account. Point the package at it and everything in this README works immediately, with no signup:

mcp_demo_d1__dense is already loaded with 300 news articles, so search, filtering and grounded answers work the moment you connect. You also get the write path: create your own index on the account, ingest into it and query it. Deletion is not available on this shared key: no index here can be deleted or reconfigured by hand. Whatever you create is removed automatically after 3 days.

Know what you are working with:

• Anything you create there is deleted after 3 days. Automatically, without warning or export. That includes indexes you created and every document in them.
• The account is shared with everyone reading this. Your index is visible to them and they can add documents to it, as you can to theirs. Nobody can delete or reconfigure an index here (that is switched off for this key), but never put anything real, private or client-owned in it.
• The limits are per index, and deliberately small. 200 MB of bandwidth and 50 MB of disk per index. Bandwidth is the one you will hit first: it covers a demo, a tutorial and a proof of concept, and it will not carry an application.

When you want an index that is private, yours and still there next week, get your own key — free forever, no card — and change the two variables above. Nothing else in your code changes.

Hybrid search

Searches run hybrid by default: BM25 keyword scores and semantic kNN scores fused per document via Opensolr's native {!hybrid} Solr query parser. Tune in config/scout-opensolr.php (publish with php artisan vendor:publish --tag=scout-opensolr-config):

Search operators

The query string understands the operators people already expect from a search box. They work in every mode — keyword, hybrid and pure vector.

They compose: +laptop +"13 inch" -refurbished returns only 13-inch laptops and never a refurbished one.

A prefixed term (+ or -, word or phrase) becomes a filter, applied to the whole result set. That matters as soon as a vector is involved: the semantic side of a hybrid search has no concept of negation, so left as query text -refurbished would actually pull refurbished listings towards the top rather than removing them. As a filter it binds every document, whichever side of the search found it, and the exclusion is absolute.

An unprefixed phrase ("machine learning" with no + in front) is a keyword-side relevance signal rather than a filter — use +"machine learning" when you need it enforced.

+ and - only count at the start of a word, so e-mail, covid-19 and 1+1 are searched for literally.

How it maps

Notes

• Vector-enabled indexes run in Opensolr's vector regions: the servers with the Opensolr web crawler, whatever their Solr version, listed live by $client->vectorRegions() (the vector_regions API). createIndex() takes an environment id from that list or the start of one or of its country (us, de, fi), matched against the live list. Additional dedicated regions can be deployed on request (paid add-on): [email protected].
• Every index is also plain Apache Solr with the native /select API — facets, highlighting, spellcheck available beyond Scout.
• Siblings: langchain-opensolr · llama-index-opensolr · opensolr-haystack · opensolr-mcp

How indexing works (Data Ingestion API)

Writes go through Opensolr's Data Ingestion API — the same pipeline the Drupal and WordPress connectors use. It is asynchronous: models are queued on save, then embeddings, sentiment, and all derived fields are computed server-side; documents become searchable within about a minute. Progress is visible in Control Panel → Data Ingestion — a per-job status board with detailed document counts — and via the ingestStatus API. This fits Scout's queue-based paradigm naturally.

Lexical-only mode

Set OPENSOLR_MODE=lexical for pure keyword search: no embedding calls, zero AI quota. It searches the title, description and text fields, so it also runs on an index outside the vector regions or on an older Solr version, as long as the index has those fields. Indexing (Data Ingestion) still needs an index in a vector region — see Index requirements.

Search by image

Search with a photo instead of a text query. Opensolr reads the picture three ways — visual labels (what it depicts), OCR text (words printed on it), and any barcode / QR code — and turns that into words. Nothing new is stored in Solr; the picture simply becomes a query you run through the normal search.

Pick which reading drives the search by choosing which field of $read you search with — text (the engine's choice), labels (what it depicts), or codes (an exact barcode match). imageToText() returns the raw API response if you need the thumbnails and every label.

Your index schema

Documents follow the Opensolr document model. The whole schema, every field and every type suffix, is explained in the Index Schema Reference. To inspect your own copy: Control Panel → click your index → Configuration → Edit File → schema.xml.

Index requirements

Everything the driver does works on an index that is in a vector region (a server with the Opensolr web crawler, any Solr version; the live vector_regions list) and runs the Opensolr vector schema — the configuration every vector index created there gets. The driver checks this for you:

• Once per index, one request. The first operation that needs it asks Opensolr what the index can do (check_schema); the answer is kept in your Laravel cache for schema_check_ttl seconds (default 3600, store schema_cache_store). It is never asked on every search. An OpensolrClient you build yourself keeps it for the life of the object (pass cache: to share it).

• It never blocks what works today. It only stops what cannot work anyway, and before it costs anything:

  • indexing (Data Ingestion) on an index outside the vector regions;
  • semantic and hybrid search on an index without the vector fields — before the query is embedded, so no AI quota is spent on it.

  Keyword search (OPENSOLR_MODE=lexical) is never blocked.

• If the check cannot run (network, the platform unreachable), nothing is concluded: the operation runs exactly as it did without the check, and the check is tried again after 5 minutes.
• An index that is not on the Opensolr vector schema gets one warning in your log each time the check runs, with what differs and what to do.

When something cannot work, the driver throws Opensolr\ScoutOpensolr\OpensolrSchemaException (a RuntimeException, like every error the driver threw before) with the exact reason, for example:

getReport() on the exception returns the full check. The check carries ready, capabilities (ingestion, vector_search, pdf_pages, keyword_search), the server and its Solr version, whether it is in a vector region, every field and type that is missing or different from the Opensolr vector schema, problems and fix.

You can run it yourself, and install the schema:

Why applySchema() exists. An index of a vector region can still carry an older or a different configuration — created before the current schema, or edited by hand. applySchema() installs the same configuration every Opensolr vector index runs, so indexing, semantic and hybrid search and PDF pages all work on it. It replaces the index configuration: on an index that already holds documents it needs force: true, and those documents keep the fields they were written with, so send them again afterwards (php artisan scout:import). The driver never runs it on its own.

An index outside the vector regions cannot take the schema: indexing, server-side embeddings and hybrid search only run there. Create a new index in one of the regions the check lists (php artisan scout:index anything, or the Opensolr control panel) and point OPENSOLR_INDEX at it.

Solr's own errors (an HTTP 4xx/5xx from the index) are thrown as OpensolrSolrException / OpensolrSolrServerException with Solr's reason and the index name in the message; they are still Guzzle ClientException / ServerException, so code that caught those keeps working. Every exception the driver throws on purpose implements OpensolrExceptionInterface.

Grounded RAG answers

One call: hybrid retrieval picks the top hits, whose content becomes the LLM context, and Opensolr's server-side LLM answers — no LLM key needed:

Search tuning

Retrieval (search and RAG grounding) runs through the platform's tuned pipeline: global defaults → your index's saved Search Tuning (Control Panel → Index Settings → Search Tuning: semantic↔lexical balance, field weights, minimum match, search mode, vector candidate pool, content quality boost) → optional per-call overrides via tuning:

Defaults match the platform's PHP configuration exactly — customize in the Control Panel once, or per call from code.

Fresh Results Bias

Rank newer documents higher without hiding anything older. Every score is multiplied by a recency curve on creation_date — full weight for a document published today, about half after a year:

For Scout-driven searches set it once in config/scout-opensolr.php ('fresh_bias' => env('OPENSOLR_FRESH_BIAS', false)).

It re-orders and never filters: the hit count is identical either way, nothing old becomes unreachable, and a document with no creation_date simply keeps its place instead of being pushed to the bottom. It applies to all three retrieval shapes — vector-only, keyword-only and the fused hybrid ranking — because the boost wraps the final score rather than one half of it. Off by default.

This is the same control visitors get as the Fresh toggle beside the sort options on the hosted Opensolr search page, so a query behaves identically here and there.

fresh_bias and freshness_boost are two different knobs and the names invite confusion. freshness_boost is a hard window in days — anything older is filtered out and the hit count drops. fresh_bias filters nothing.

How it's tested

Every release is validated against live Opensolr infrastructure — no mocks:

• Unit tests (offline): location aliases, filter→fq mapping, query building, escaping.
• End-to-end suite: the full write path through the async Data Ingestion queue (queued → server-side enrichment → searchable), semantic / hybrid / lexical retrieval, metadata round-trip, filters, id round-trip (your ids and the Solr md5(uri) ids), deletes by id and by query.
• Real-corpus validation: searches run against a 340-document replica of opensolr.com's own production search index. Verified: pure-semantic hits with zero keyword overlap ("how do I get my data back after a disaster" → backup & restore docs), cross-lingual queries (Romanian query → English content), exact-term surfacing in hybrid mode, all four hybrid modes, and the full alpha range 0 → 1.
• PDF ingestion: a real PDF ingested via rtf:true — server-side text extraction (13k+ chars), automatic content-type detection, then retrieved with a purely semantic query against its contents.
• Grounded RAG answers: ai_answer verified end-to-end — a question answerable only from the ingested PDF returns the correct answer, sourced from the PDF's extracted text via hybrid retrieval.

The engine is exercised live via Orchestra Testbench (real Eloquent models on in-memory SQLite): searchable() through the ingestion queue, semantic relevance, where() filters, pagination totals, unsearchable() removal.

MIT license.