Hybrid Search

Yes. Opensolr ships an official MCP (Model Context Protocol) server, listed in the official MCP Registry under our domain-verified com.opensolr namespace. Any MCP-compatible client — Claude Desktop, Claude Code, Cursor, Windsurf — gets your search as native tools with one config entry:

uvx opensolr-mcp

The agent receives 8 tools: hybrid BM25 + kNN search, document indexing with server-side embeddings, grounded RAG answers from your own content, and index management. There is nothing to build — the chat is the UI: tell your agent "index our FAQ, then find everything about refunds" and it just happens.

Learn more: MCP server page · MCP Registry listing · Source on GitHub

Yes. Opensolr plugs into Haystack pipelines as a DocumentStore with a hybrid retriever:

pip install opensolr-haystack

A normal Haystack pipeline needs two embedder components — one for documents, one for queries. With OpensolrDocumentStore it needs none: everything embeds server-side on our GPU infrastructure, and OpensolrHybridRetriever fuses BM25 + kNN scores natively on managed Apache Solr 9. The full DocumentStore protocol is supported — duplicate policies, standard filters, Secret-based credentials, and serialization for saved pipelines.

Learn more: Haystack integration page · PyPI package · Source on GitHub

Yes. Opensolr is a native LangChain vector store, listed in the official LangChain integrations directory. Install it with:

pip install langchain-opensolr

You get OpensolrVectorStore and OpensolrEmbeddings: texts and queries are embedded server-side on our GPU infrastructure (multilingual, 1024 dimensions) — no OpenAI key, no local model — and search runs hybrid BM25 + kNN, fused per document with a tunable semantic↔lexical balance. It is the shortest vector-store setup in the LangChain directory: an API key and an index name. vs.as_retriever() drops it into any chain, agent, or RAG tutorial.

Learn more: LangChain integration page · PyPI package · Source on GitHub