opensolr-haystack

Haystack integration for Opensolr — managed Apache Solr as a DocumentStore, with server-side embeddings and native hybrid (BM25 + kNN) retrieval.

No embedder components needed in your pipeline — texts and queries are embedded on Opensolr's GPU infrastructure (multilingual E5-large-instruct, 1024 dimensions, cosine).

Product page: opensolr.com/langchain · free 15-day trial, no card, at opensolr.com

pip install opensolr-haystack

Quickstart

from haystack import Document, Pipeline
from haystack_integrations.document_stores.opensolr import OpensolrDocumentStore
from haystack_integrations.components.retrievers.opensolr import OpensolrHybridRetriever

# credentials default to OPENSOLR_EMAIL / OPENSOLR_API_KEY env vars
store = OpensolrDocumentStore(index="mysite__dense", create_if_missing=True)

store.write_documents([
    Document(content="Hybrid search fuses BM25 with vector similarity"),
    Document(content="Cats sleep sixteen hours a day"),
])

pipe = Pipeline()
pipe.add_component("retriever", OpensolrHybridRetriever(document_store=store))
result = pipe.run({"retriever": {"query": "how do keyword and semantic search combine?"}})
print(result["retriever"]["documents"])

Note there is no embedder in the pipeline — not for documents, not for the query. The store embeds server-side at both index and query time.

Hybrid retrieval

OpensolrHybridRetriever fuses BM25 and kNN scores per document via Opensolr's native {!hybrid} Solr query parser:

OpensolrHybridRetriever(
    document_store=store,
    top_k=10,
    hybrid=True,     # False = pure semantic kNN
    alpha=0.5,       # 0 = all semantic … 1 = all lexical
)

Standard Haystack filters are supported and map to Solr fq:

pipe.run({"retriever": {
    "query": "search engines",
    "filters": {"field": "meta.category", "operator": "==", "value": "docs"},
}})

Notes

• Vector-enabled indexes run on Opensolr's Solr 9.x environments — currently us (Chicago), de (Germany), fi (Finland). Additional dedicated regions can be deployed on request (paid add-on): support@opensolr.com.
• Every index is also plain Apache Solr with the native /select API — facets, highlighting, spellcheck included.
• Siblings: langchain-opensolr · llama-index-opensolr · opensolr-mcp

How writing works (Data Ingestion API)

Writes go through Opensolr's Data Ingestion API — the same pipeline the Drupal and WordPress connectors use. It is asynchronous: documents are queued, then embeddings, sentiment, language and all crawler-identical derived fields are computed server-side, and documents become searchable within about a minute. Progress is visible in the Opensolr Control Panel and via the ingest_status API. Each document's identity is its uri (the Solr id is md5(uri)): pass a real URL in metadata ({"uri": "https://..."}), or a deterministic one is synthesized from your id. Re-submitting the same uri updates the document. Pass {"rtf": True, "uri": "https://.../file.pdf"} and the server extracts the text from PDF/DOCX/XLSX for you.

Lexical-only mode

Don't need vectors? Pure keyword search skips the embedding call entirely — zero AI quota, and it works on any Opensolr index, including non-vector ones and older Solr versions.

Your index schema

Documents follow the Opensolr document model (title, description, text, meta_* custom fields). To see the full schema: Control Panel → click your index → Configuration → Edit File → schema.xml. Prefer zero-effort data entry? Configure the Web Crawler in the Control Panel (Index Tools → WebCrawler): add your site URL, validate it, and Opensolr indexes the whole site for you.

Grounded RAG answers

One call: hybrid retrieval picks the top hits, whose content becomes the LLM context, and Opensolr's server-side LLM answers — no generator component, no LLM key:

answer = store.ai_answer(
    "what does the refund policy say?",
    rag_docs=3,        # how many hybrid hits feed the LLM (default 3)
    rag_words=1500,    # words of text taken from each hit (default 1500)
    # instruction="Answer in German, cite the exact titles you used",  # optional
)

How it's tested

Every release is validated against live Opensolr infrastructure — no mocks:

• Unit tests (offline): location aliases, filter→fq mapping, query building, escaping.
• End-to-end suite: the full write path through the async Data Ingestion queue (queued → server-side enrichment → searchable), semantic / hybrid / lexical retrieval, metadata round-trip, filters, id round-trip (your ids and the Solr md5(uri) ids), deletes by id and by query.
• Real-corpus validation: searches run against a 340-document replica of opensolr.com's own production search index. Verified: pure-semantic hits with zero keyword overlap ("how do I get my data back after a disaster" → backup & restore docs), cross-lingual queries (Romanian query → English content), exact-term surfacing in hybrid mode, all four hybrid modes, and the full alpha range 0 → 1.
• PDF ingestion: a real PDF ingested via rtf:true — server-side text extraction (13k+ chars), automatic content-type detection, then retrieved with a purely semantic query against its contents.
• Grounded RAG answers: ai_answer verified end-to-end — a question answerable only from the ingested PDF returns the correct answer, sourced from the PDF's extracted text via hybrid retrieval.

The store is exercised live (write via ingestion, DuplicatePolicy SKIP/FAIL, hybrid + lexical retrieval, filters, serde round-trip) before every release.

MIT license.