langchain-opensolr

LangChain integration for Opensolr — managed Apache Solr with server-side embeddings and native hybrid (BM25 + kNN) search.

Product page: opensolr.com/langchain · Platform: opensolr.com (managed Solr hosting since 2011 — free 15-day trial, no card)

No local embedding model. No third-party embedding API key. One set of credentials, and the vectors are computed on Opensolr's GPU infrastructure (multilingual E5-large-instruct, 1024 dimensions, cosine).

pip install langchain-opensolr

The whole tutorial

from langchain_opensolr import OpensolrVectorStore

vs = OpensolrVectorStore(
    index="mysite__dense",          # vector-enabled Opensolr index
    email="you@example.com",
    api_key="YOUR_OPENSOLR_API_KEY",
    create_if_missing=True,          # provisions the index on first use
)

vs.add_texts(
    ["Hybrid search fuses BM25 with vector similarity",
     "Cats sleep sixteen hours a day"],
    metadatas=[{"category": "search"}, {"category": "animals"}],
)

docs = vs.similarity_search("how do lexical and semantic search combine?", k=1)
print(docs[0].page_content)

That's it — no embedding model was configured, because embedding happens on the server at both index and query time.

Hybrid search

Pure vector search fails on exact identifiers; pure BM25 fails on meaning. Opensolr's {!hybrid} query parser fuses both scores per document:

docs = vs.similarity_search(
    "affordable restaurants",
    k=5,
    hybrid=True,
    mode="union",     # union | keywords_required | meaning_required | intersection
    alpha=0.5,        # 0 = all semantic … 1 = all lexical
)

Metadata filters

vs.similarity_search("search engines", k=5, filter={"category": "search"})
vs.similarity_search("anything", k=5, filter='meta_rank:[2 TO *]')   # raw Solr fq

Metadata round-trips losslessly (stored as JSON alongside filterable meta_* fields).

As a retriever, in any chain

retriever = vs.as_retriever(search_kwargs={"k": 5, "hybrid": True})

Standalone embeddings

Use Opensolr's embedding endpoint with any other LangChain component:

from langchain_opensolr import OpensolrEmbeddings

emb = OpensolrEmbeddings(email="you@example.com", api_key="...", index="mysite__dense")
emb.embed_query("budget-friendly dining")   # -> 1024 floats

Notes

• Vector-enabled indexes run on Opensolr's Solr 9.x environments — currently us (Chicago), de (Germany), fi (Finland). Pass location= to choose. The list is fetched live from the platform, so new regions work without a package upgrade — and additional dedicated regions can be deployed on request (paid add-on): support@opensolr.com.
• A free Opensolr account (15-day trial, no card) includes an AI quota that comfortably covers this README end to end: opensolr.com.
• Full platform docs: AI & Vector Search.

Development

pip install -e . pytest
pytest tests/unit_tests
OPENSOLR_EMAIL=... OPENSOLR_API_KEY=... OPENSOLR_INDEX=... pytest tests/integration_tests

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 — the same pipeline that powers the AI answers on our hosted search pages. No LLM key, no chain to assemble:

answer = vs.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
)

Search tuning

Retrieval (search and RAG grounding) runs through the platform's tuned pipeline: global defaults → your index's saved Search Tuning (Control Panel → Index Settings → Search Tuning: semantic↔lexical balance, field weights, minimum match, search mode, vector candidate pool, content quality boost) → optional per-call overrides via tuning:

tuning={"search_mode": "keywords_required", "fw_title": 0.2,
        "mm": "strict", "vector_topk": 500, "quality_boost": 0.3}

Defaults match the platform's PHP configuration exactly — customize in the Control Panel once, or per call from code.

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.

pytest tests/unit_tests
OPENSOLR_EMAIL=... OPENSOLR_API_KEY=... OPENSOLR_INDEX=... pytest tests/integration_tests

MIT license.