Opensolr Ships Native LangChain, LlamaIndex, Haystack, MCP and Laravel Scout Integrations, All at v0.2.0 With Grounded RAG Answers

Opensolr now plugs directly into the tools people actually build AI search with: LangChain, LlamaIndex, Haystack, Laravel Scout and any MCP-speaking agent. All of them run hybrid BM25 + kNN search on a managed Solr 9 index, with embeddings computed server-side on our GPU infrastructure — no embedding model to configure and no third-party API keys.

01 The five integrations

• LangChain. Opensolr is 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 multilingual 1024-dimension server-side embeddings and true hybrid BM25 + kNN search with a tunable semantic-lexical balance. See the LangChain integration page, the PyPI package, and the source on GitHub.
• LlamaIndex. pip install llama-index-opensolr gives you OpensolrVectorStore and OpensolrEmbedding — server-side GPU embeddings, hybrid BM25 + kNN query mode, and standard metadata filters. See the PyPI package and the source on GitHub.
• Haystack. pip install opensolr-haystack adds Opensolr to Haystack pipelines as a DocumentStore with a hybrid retriever. No embedder components are 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.
• Laravel Scout. 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.
• MCP Registry. Opensolr is now listed in the official MCP Registry, so any AI agent that speaks the Model Context Protocol — Claude Desktop, Claude Code, Cursor, Windsurf and more — can 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.

02 Everything upgraded to v0.2.0

All five integrations are at v0.2.0. Writes in every one of them — LangChain, MCP, LlamaIndex, Haystack and Laravel Scout — now go through the Data Ingestion API: documents are queued and enriched server-side with embeddings, sentiment, language and all derived fields, identical to the Web Crawler, with progress visible in your Control Panel.

Each integration also gains a lexical-only mode: pure keyword search with zero AI quota, which works on any Opensolr index, including non-vector ones. v0.2.0 adds delete-by-query support and server-side PDF/DOCX text extraction via rtf:true.

03 Grounded RAG answers in one call

LangChain, MCP, LlamaIndex, Haystack and Laravel Scout all 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.

It is configurable: how many documents and how many words feed the LLM, plus an optional custom instruction for things like answer language, citation style or extraction tasks. The AI-Hints API guide now documents this two-step retrieve-then-summarize pattern with full cURL, PHP and Python examples.

04 Finding vector-enabled regions automatically

A new vector_regions API endpoint returns the live list of vector-enabled environments — Solr 9.x with a dense-vector schema and the hybrid query parser. Currently that is Chicago, Germany and Finland. Tools such as the LangChain package call it to discover valid locations, so new regions start working without any client update.