Search an Opensolr index and return the k most relevant documents.

search_mode: "hybrid" (default — BM25 + semantic kNN fused per document),
"semantic" (pure kNN), or "lexical" (pure keyword edismax — no embedding
call, zero AI quota, works on ANY index including non-vector ones).
For hybrid: mode is union / keywords_required / meaning_required /
intersection, alpha balances semantic (0) vs lexical (1).
filter_query accepts a raw Solr fq expression, e.g. 'meta_category:"docs"'.
fresh_bias biases the ranking toward recent documents by multiplying each
score by a recency curve on creation_date. It re-orders and never filters:
the hit count is unchanged, nothing old becomes unreachable, and a document
with no creation_date is simply left unboosted. Works in all three search
modes. Off by default — turn it on when newer should beat older on a tie.

Search an Opensolr index with a PHOTO instead of a text query.

The image at image_path is read three ways by the Opensolr image engine —
visual labels (what it depicts), OCR text (words printed on it), and any
barcode / QR code — and turned into a text query that runs through the normal
search. No image vector is stored; the picture simply becomes words.

using selects which reading drives the search:
  "auto"    the engine's chosen text (OCR text when the picture is mostly text,
            otherwise the visual labels) — the default,
  "meaning" the visual labels (what the picture depicts),
  "text"    only the OCR text read off the picture (empty if none),
  "code"    the first barcode / QR code, matched as an exact keyword.

search_mode / mode / alpha / fresh_bias / filter_query behave exactly as in
opensolr_search. Returns {"read": {text, mode, labels, codes}, "results": [...]}
so the caller sees both what the picture was read as and the matching documents.

Ask a question and get a grounded RAG answer generated ONLY from the content already indexed in the given Opensolr index. Retrieval runs through the platform's tuned hybrid pipeline (the index's saved Search Tuning applies automatically) — the top rag_docs hits (first rag_words words of text each) become the LLM context, the same pipeline as the hosted search UI. filter_query optionally narrows retrieval with a raw Solr fq expression; instruction optionally replaces the default prompt (e.g. "Answer in German", "Extract a list of people"); tuning optionally overrides retrieval knobs per call. That list is the whole set, not a sample — an abbreviated one reads as everything that is supported, and freshness_boost was invisible to callers because of it: fw_title, fw_description, fw_uri, fw_text, fw_text_t, lexical_weight, vector_weight, vector_topk, search_mode (union / keywords_required / meaning_required / intersection), quality_boost, min_score, freshness_boost, fresh_bias, lexical_norm_k, mm (flexible / balanced / strict or raw Solr mm syntax). freshness_boost and fresh_bias are different knobs despite the names: the first is a hard window in DAYS that filters older documents out, the second only re-orders, multiplying each score by a recency curve on creation_date so recent documents win ties while nothing becomes unreachable.

List all search indexes in the connected Opensolr account.

Get connection details for an index: Solr URL, version, environment. (Credentials are intentionally not returned.)

Index plain-text documents via the Opensolr Data Ingestion API.

Ingestion is ASYNC: embeddings, sentiment, and all derived fields are
computed server-side and documents become searchable within about a
minute (progress is visible in the Opensolr Control Panel). With
wait=true (default) this blocks until the job completes. Metadata keys
become filterable meta_* fields; metadata "uri" (a real URL) is used as
the document identity — the Solr id is md5(uri). Returns job info and
the resulting Solr document ids.

Status of a Data Ingestion job (state, processed/success/failed doc counts). Also visible in the Opensolr Control Panel.

Delete documents by ids (Solr ids or your original ids) or by a raw Solr query, e.g. 'meta_category:"drafts"' or '+id:"abc123"'.

Create a new vector-enabled Opensolr index. location: us, de, fi, or any environment id from opensolr_vector_regions. Additional dedicated regions can be deployed on request (support@opensolr.com).

List the vector-enabled Opensolr environments currently available (Solr 9.x with dense vectors and the hybrid query parser).