Opensolr Changelog

Every integration can now be tried without an account. The LangChain, LlamaIndex, Haystack, MCP and Laravel Scout packages all ship with the credentials of a public demo account, so the quickstart in each README runs on a fresh machine with nothing to sign up for and nothing to configure. An index preloaded with 300 news articles is there to search immediately, and you can create your own indexes on the account and push documents into them. Everything created on it is deleted after 3 days, the account is shared with everyone using it, and the limits are small and per index — 200 MB of bandwidth and 50 MB of disk — so it covers a demo and a proof of concept rather than an application.

The whole REST API is now documented — 14 new endpoint articles. Every user-facing endpoint has its own page with parameters, real responses and cURL/PHP/Python examples: Hybrid Search (embed_and_search), vector_regions, get_account_summary, elevation_toggle, add_crawl_url_signed, delete_crawler_url, crawl_schedule_status, move_done_to_todo, tail_crawl_log, log tails, live streams, restore_backup, remove_http_auth and delete_environment. The Data Ingestion Queue page now lists every queue endpoint, and endpoint aliases (create_core, delete_index, reset_core, server_country) are noted on their articles.

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.

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.

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.

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.

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 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.

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.

AI and vector features (AI Hints, AI Reader, Vector Embeddings, Hybrid Search) are now gated per package. Indexes without vector access get a friendly upgrade prompt instead of a generic error. Lexical keyword search continues to work on every plan.

JSON file upload for the Data Ingestion API. Instead of embedding your documents in the request body, save your full payload as a .json file and upload it via the payload_file field. The file can contain everything — email, api_key, core_name, and documents — or just the documents array with auth as separate form fields. Ideal for large batches generated by CMS exports or data pipelines.

Document extraction in the Data Ingestion API — add rtf:true to any document and point uri at a PDF, Word, Excel, PowerPoint, or OpenDocument file. Text is extracted automatically and indexed with full enrichment. Mix regular and RTF documents in the same batch.

Data Ingestion API — push documents directly into your Opensolr index via POST. Submit up to 50 docs per batch with automatic vector embeddings, sentiment analysis, language detection, and all derived search fields. Works alongside the Web Crawler to push content it cannot reach: gated pages, CMS exports, product feeds. Same doc ID updates existing records.

Global API rate limits are now enforced: 30 requests per minute and 500 per hour across all authenticated API endpoints. This applies to every API call including search, AI summaries, embeddings, and batch operations. If you need higher throughput for your integration, contact us and we will set custom limits for your account.

API rate limiting is now active. Every authenticated API request is tracked per user and per IP, with configurable limits per minute and per hour. If you exceed the threshold you will receive a 429 response with a Retry-After header — just back off and retry. Admin accounts are exempt. Individual users or IP addresses can also be given custom limits or blocked entirely. Monthly request counters are stored persistently so you can review your API usage over time.