Opensolr Changelog

Recent updates and improvements to the Opensolr platform.

API × Aug 2026 ×

API Aug 15, 2026

  • Improved embed_and_search now matches your hosted search page exactly. The one-shot hybrid search endpoint runs the platform’s full tuned pipeline: your index’s saved Search Tuning applies automatically, natural-language questions are processed the same way as on your search page (stopwords cleaned for keyword matching, while the full phrase goes to the semantic embedder), and every tuning knob (fw_*, mm, search_mode, vector_topk, quality_boost, and more) accepts per-request overrides. Results returned via the API are now identical to your search page. Full parameter reference in the AI-Hints API guide.
  • Improved Your Search Tuning now applies everywhere — including all AI integrations. Everything you configure in Index Settings → Search Tuning (semantic↔lexical balance, field weights, minimum match, search mode, vector candidate pool, content quality boost) now applies automatically to every search and every RAG answer made through the LangChain, LlamaIndex, Haystack, Laravel Scout, and MCP integrations — the same tuned pipeline as your hosted search page. Each knob can also be overridden per call from code via the new tuning parameter. Packages updated: LangChain, LlamaIndex, Haystack (v0.2.5), Laravel Scout (v0.2.5), MCP (v0.2.6).

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.
  • Improved All AI integrations upgraded to v0.2.0. Writes in every integration — LangChain, MCP, LlamaIndex, Haystack, Laravel Scout — now go through the Data Ingestion API: documents are queued and enriched server-side (embeddings, sentiment, language, and all derived fields — identical to the Web Crawler), with progress visible in your Control Panel. Every integration also gains a lexical-only mode: pure keyword search with zero AI quota that works on any Opensolr index, including non-vector ones. Plus delete-by-query support and server-side PDF/DOCX text extraction via rtf:true.
  • 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.