Opensolr Changelog — Recent Updates & Improvements

Website Aug 25, 2026

New RAG in 60 seconds — a live sandbox, no signup required. The new Try it live page creates a real Apache Solr index for you in one click, lets you push your own JSON or a document URL, and then search it with hybrid search and ask questions answered by an LLM from your own content, with sources cited. You also get the raw Solr index credentials, so you can browse the documents and see the vector embeddings for yourself. Sandbox indexes delete themselves after 24 hours.

Security Aug 25, 2026

New Scoped API keys — stop sharing your master key. You can now create additional API keys from Account › API Keys, each restricted to exactly the endpoints you tick and, optionally, to a chosen subset of your indexes. Give a developer search-only access to one index, give your CI pipeline Data Ingestion rights and nothing else, or hand a contractor a key that expires on a date you choose. Keys can be revoked or regenerated instantly, and your master key — which is root-level and doubles as the initial index password — never has to leave your hands. Details in the Security & Trust Center.

API Aug 14, 2026

New Grounded RAG answers in every AI integration. LangChain, MCP, LlamaIndex, Haystack, and Laravel Scout now include a one-call ai_answer: hybrid (BM25 + kNN) retrieval picks your top documents, and their content becomes the LLM context — the same pipeline that powers the AI answers on Opensolr hosted search pages. Configurable: how many documents and how many words feed the LLM, plus an optional custom instruction (answer language, citation style, extraction tasks). The AI-Hints API guide now documents this two-step retrieve-then-summarize pattern with full cURL, PHP, and Python examples.

Website Aug 14, 2026

New New: dedicated pages, docs, and FAQs for every AI integration. Each channel now has its own product page — LangChain, MCP for AI agents, LlamaIndex, Haystack, and Laravel Scout — plus a new AI Integrations chapter in the platform documentation, five new FAQ guides in the AI Services section, and a redesigned homepage overview of all six ways in.

API Aug 14, 2026

New Laravel Scout driver released. Add semantic + keyword search to any Laravel app: composer require opensolr/laravel-scout-opensolr, set SCOUT_DRIVER=opensolr, and Model::search() runs hybrid BM25 + kNN on a managed Solr index — with embeddings computed server-side. One index serves all your models. See the source on GitHub.
New Haystack integration released. Opensolr now plugs into Haystack pipelines as a DocumentStore with a hybrid retriever: pip install opensolr-haystack. No embedder components needed — documents and queries are embedded server-side on our GPU infrastructure, and retrieval fuses BM25 + kNN scores natively. See the PyPI package and the source on GitHub.
New LlamaIndex integration released. Opensolr is now a native LlamaIndex vector store: pip install llama-index-opensolr gives you OpensolrVectorStore and OpensolrEmbedding — server-side GPU embeddings, hybrid BM25 + kNN query mode, and standard metadata filters, on a managed Solr 9 index. See the PyPI package and the source on GitHub.
New Opensolr is now in the official MCP Registry. Any AI agent that speaks the Model Context Protocol — Claude Desktop, Claude Code, Cursor, Windsurf and more — can now use your Opensolr indexes as native tools: hybrid BM25 + kNN search, document indexing with server-side embeddings, and grounded RAG answers. One config entry with uvx opensolr-mcp and your API key. See the registry listing, the PyPI package, and the source on GitHub.
New New API endpoint: vector_regions. Returns the live list of vector-enabled environments (Solr 9.x with dense-vector schema and the hybrid query parser) — currently Chicago, Germany, and Finland. Tools like the LangChain package use it to discover valid locations automatically, so new regions work without any client updates. Need a dedicated vector-enabled environment in another region? We deploy them on request.
New Opensolr is now a native LangChain vector store. Install with pip install langchain-opensolr and every LangChain chain, agent, and RAG tutorial runs on a managed Solr 9 index — with embeddings computed server-side on our GPU infrastructure (multilingual, 1024 dimensions) and true hybrid BM25 + kNN search with a tunable semantic-lexical balance. No embedding model to configure, no third-party API keys. See the LangChain integration page, the PyPI package, and the source on GitHub.