Infino

Use when you only need HOW MANY rows match a keyword query, not the rows themselves — a fast tally over a text column, without fetching or ranking. Cheaper than infino_keyword_search when a number is all you need (e.g. 'how many docs mention X'). For the matching rows use infino_keyword_search or infino_token_match.

Return a table's column names and types — call before searching so you know which column to target and what fields each result row carries.

Use to fetch rows whose column exactly equals a value — a tag, status, or id string. Unranked exact-equality filter over an indexed column. For ranked text relevance use infino_keyword_search; for multi-column analytical filtering use infino_sql.

Use when a query carries both specific terms and an intent — you want exact-term precision without giving up paraphrase recall. Fuses BM25 over a text column with vector similarity over the embedding column in a single ranking pass, so rows matching the literal terms AND the meaning rank highest. Embeds the query with a local model (no API key). Sits between infino_keyword_search (literal only) and infino_semantic_search (meaning only).

Use when the query is literal terms — identifiers, error codes, product names, exact phrases — and you want results ranked by relevance. BM25 full-text search over a text column: ranks rows by how well the query's tokens (and their stems) match, each with a relevance score. Matches exact tokens, not synonyms or paraphrases. Prefer this over SQL LIKE for known literal terms. For meaning-based search use infino_semantic_search; for both at once use infino_hybrid_search.

List the tables in the connected catalog. Call this first to discover what is available to search or query.

Use when searching for a concept by meaning and the exact wording is unknown — this retrieves paraphrases and synonyms, not just literal matches. Embeds the query with a local model (no API key) and ranks a table's embedding column by vector similarity, each hit with a score. Optional 'filter' restricts the ranking to rows whose keyword column matches a predicate first (a pushdown pre-filter, e.g. semantic search only within rows tagged 'billing'). For exact terms use infino_keyword_search; when the query has both literal terms and an intent use infino_hybrid_search.

Use for structural or analytical questions — counts, GROUP BY, joins, aggregates, filtering by column value — returning result rows. The engine's search functions are callable as table-valued relations, so a single query can rank AND aggregate: bm25_search('table','text_col','terms', k) — also bm25_search_prefix / token_match / exact_match — need no embedding. vector_search('table','vec_col', {{q}}, k) and hybrid_search('table','text_col','terms','vec_col', {{q}}, k) need a query vector: put a {{name}} placeholder where the vector goes and pass embed:{"name":"query text"} — the server embeds the text and substitutes the vector in. Example: SELECT path, SUM(end_line - start_line + 1) AS lines FROM bm25_search('docs','body','error timeout', 300) GROUP BY path ORDER BY lines DESC.

Use when you need the SET of rows containing a keyword, not a ranked order — a fast unranked keyword filter. Returns rows whose text column contains the token(s), matching indexed tokens and their stems. For ranked results use infino_keyword_search; for analytical filtering across columns use infino_sql.