Delete a memory by id.

Recall memories semantically similar to a query (vector), most similar first. Optionally narrow to exact-match metadata via filter (ColumnStore), e.g. {"project":"veles","status":"resolved"}.

Fused vector + graph recall: like recall, but also walks the graph from the top vector hit and folds any connected fact into the ranking — the tri-engine ranking (vector similarity + ColumnStore filter + graph reach) measured on multi-hop and temporal benchmarks. Reach for this when an answer needs a fact the query doesn't mention directly but a stored relate/extracted link connects (multi-hop reasoning, temporal chains). hops/graph_boost tune the graph reach; omit them for the proven defaults. Optionally narrow with an exact-match filter. Set date_field (the metadata key holding a YYYYMMDD date) to also get a dated_context timeline and a now anchor for temporal questions. Most relevant first.

Fused recall: semantically similar memories (vector) constrained by structured ColumnStore predicates over metadata — ranges and comparisons, not just equality. Each filter is {field, op (eq/ne/lt/le/gt/ge), value}, ANDed. Use for time-windowed or numeric-scoped recall, e.g. facts about a topic with ts in a date range. Most similar first.

Create a typed link from one memory to another. Returns the edge id.

Store a fact in durable local memory. Optionally link it to existing memories (graph) and tag it with structured metadata like project/author/type/status/date (ColumnStore) for later filtering. Set ttl_seconds to make the fact expire after a delay (a durable TTL that survives restarts); omit it for a permanent memory. Returns the fact's stable id.

Store a passage of raw text by extracting its atomic facts and auto-building the fact↔topic graph, so why can later connect them with no manual links. Requires the server to be started with an extraction backend (set VELESDB_MEMORY_EXTRACTOR; build with --features extract). Returns the stored facts' ids.

Explain a decision: find the best-matching memory (optionally scoped by a metadata filter, e.g. the current project) and return the connected subgraph of related memories reachable through typed links — fusing vector, ColumnStore, and graph to surface context a plain similarity search misses.