AI Integrations

Opensolr plugs natively into the frameworks and agent ecosystems people use to build RAG and AI search in 2026. Underneath every integration it is the same platform: a managed, vector-enabled Apache Solr 9 index, server-side GPU embeddings (multilingual E5, 1024 dimensions), and the {!hybrid} query parser that fuses BM25 and kNN scores per document. You never run an embedding model, and you never send us vectors — just text.

Writes in every integration go through the Data Ingestion API — the same pipeline the Drupal and WordPress connectors use. Ingestion is asynchronous: documents are queued, enriched server-side (embeddings, sentiment, language, derived fields), and become searchable within about a minute; progress is visible in Control Panel → Data Ingestion — a per-job status board with detailed document counts (queued / processing / completed / failed) — and via the ingest_status API. Every integration also offers a lexical-only mode (pure keyword search, zero AI quota) that works on any Opensolr index, including non-vector ones.

One account, every integration

All integrations authenticate with the same two values: your account email and your API key (Account → API in the control panel). Vector-enabled indexes are created in the us (Chicago), de (Germany) or fi (Finland) regions — the live list is available programmatically, and additional dedicated regions can be deployed on request (paid add-on).

LangChain (Python)

Opensolr is a native LangChain vector store, listed in the official LangChain integrations directory with its own provider card.

pip install langchain-opensolr

You get OpensolrVectorStore (add_texts, similarity_search with hybrid=True, metadata filters, auto index provisioning via create_if_missing=True) and OpensolrEmbeddings. vs.as_retriever() drops straight into any chain, agent, or RAG tutorial.

Links: product page · PyPI · GitHub · FAQ

MCP — AI Agents (Claude, Cursor, and any MCP client)

The official Opensolr MCP server gives any Model Context Protocol agent your search as native tools: hybrid retrieval, document indexing with automatic embeddings, grounded RAG answers, index management, and ingestion job status — 9 tools in total. It is published in the official MCP Registry under our domain-verified com.opensolr namespace.

{
  "mcpServers": {
    "opensolr": {
      "command": "uvx",
      "args": ["opensolr-mcp"],
      "env": {
        "OPENSOLR_EMAIL": "you@example.com",
        "OPENSOLR_API_KEY": "YOUR_OPENSOLR_API_KEY"
      }
    }
  }
}

Credential tip

The agent gains the powers of the API key you give it. Use a dedicated Opensolr account (or at least a non-critical one) for agent configurations, exactly as you would with any automation credential.

Links: product page · MCP Registry · PyPI · GitHub · FAQ

LlamaIndex (Python)

Native LlamaIndex integration under the standard namespaces llama_index.vector_stores.opensolr and llama_index.embeddings.opensolr.

pip install llama-index-opensolr

OpensolrVectorStore supports VectorStoreQueryMode.HYBRID with a tunable alpha (semantic↔lexical balance) and maps standard MetadataFilters (EQ, NE, IN, NIN, ranges) to Solr filters. Your VectorStoreIndex, retriever, and query engine work unchanged.

Links: product page · PyPI · GitHub · FAQ

Haystack (Python)

An Opensolr DocumentStore plus a hybrid retriever for Haystack pipelines — with zero embedder components. A typical Haystack pipeline needs one embedder for documents and another for queries; with Opensolr both are unnecessary, because everything embeds server-side.

pip install opensolr-haystack

OpensolrDocumentStore implements the full protocol (DuplicatePolicy, standard filter dicts, Secret-based credentials, serialization for saved pipelines) and OpensolrHybridRetriever exposes hybrid/alpha with per-run overrides.

Links: product page · PyPI · GitHub · FAQ

Laravel Scout (PHP)

An official Laravel Scout engine: set SCOUT_DRIVER=opensolr and every Searchable model gets hybrid semantic search through the standard Model::search() API.

composer require opensolr/laravel-scout-opensolr

Scout's where() / whereIn() map to Solr filters with full operator support, pagination returns real totals, and one Opensolr index serves all your models — documents are scoped per model automatically, so a single plan covers the whole application. The package auto-updates on Packagist with every release.

Links: product page · Packagist · GitHub · FAQ

REST API (any language)

Every capability above is plain HTTPS underneath — usable from any language. The management API lives on opensolr.com, the AI endpoints on api.opensolr.com:

Endpoint	What it does
`embed` / `batch_embed`	1024-dim multilingual embeddings for a query or up to 50 documents per call
`embed_and_search`	One-shot: embed the query, run the platform's tuned hybrid search, return ranked results. The index's saved Search Tuning applies automatically; per-request overrides: `fw_*`, `lexical_weight`, `vector_weight`, `vector_topk`, `search_mode`, `quality_boost`, `min_score`, `mm`
`ai_summary`	Streaming AI answer/summary of a provided context — pair with `embed_and_search` (search first, summarize the top results) for grounded RAG
`vector_regions`	Live list of vector-enabled environments

Links: AI & Vector Search docs · full API reference · Embed API guide