Search a collection for entries semantically similar to text (natural language query). Returns a JSON array of up to top_k results, each with id, score, and stored payload.

Advanced Optimal Transport (Wasserstein) search inside collection for text. Compares distributions/complex concept overlap. Returns a JSON array of top_k matches ranked by Wasserstein distance, each with id, score, payload.

Store a new factual claim or memory: insert text into collection under numeric id (auto-vectorized). Returns a JSON object {ok: true, id} on success or {ok: false, error} on failure.

Setup new memory spaces (collections) with specific vector dimension, metric geometry, and quantization level. Returns the created collection config as JSON result.

Deep graph exploration starting from node start_id inside collection. Finds logical paths between concept A and context B for complex reasoning/cross-referencing. Returns a JSON object {paths: [...], nodes: [...], edges: [...]} bounded by max_depth and max_nodes.

Explore local connectivity of a concept in the vector index graph. Requires collection name and id of the node/concept. Returns a list of neighboring nodes as JSON result.

Detect emergent structure/hierarchy in collection. Returns a JSON array of clusters, each {cluster_id, members: [...], centroid, size}, filtered by min_cluster_size.

Calculate Gromov Delta-hyperbolicity either for a named collection on the server or over supplied vectors (array of numeric arrays). Returns {delta, recommendation: 'lorentz'|'poincare'|'cosine'|'l2'}.

Calculate Lyapunov Convergence of a trajectory (Chain of Thought). Returns a JSON object {lyapunov_exponent, classification: 'stable'|'chaotic'} - negative result means stable/converging, positive means chaotic/hallucinating.

AI Sleep Mode: trigger Flow Matching on the server to optimize the geometric representation of concepts in collection based on usage/context. Returns a JSON object {ok, iterations, loss_before, loss_after}.

Freeze a collection to make it read-only, preventing new inserts. Returns ok/error status object.

Unfreeze a previously frozen collection to allow inserts again. Returns ok/error status object.

List all active collections. Returns a JSON array of objects, each with {name, dimension, metric, count} metadata fields.

Get detailed statistics for the named collection. Returns a JSON object with count, dimension, metric, logical_clock, and storage stats. Merges metadata, cache stats and WriteBuffer size.

Get cache statistics (hits, misses, policy, etc.) for a specific collection's L0 Hot Tier Cache. Returns stats object as JSON result.

Clear / purge all items in the L0 Cache for a specific collection. Returns ok/error status object.

Update L0 Cache configuration (eviction policy, ANN threshold) for a specific collection. Returns updated config as JSON result.

Permanently delete a collection and all of its vectors. Returns a result object with ok status or error.

Delete a single vector point from a collection by its ID. Returns a result object with ok status or error.

Retrieve vector coordinate and metadata for a list of point IDs. Requires collection name and ids array of point IDs.

Rebuild and optimize the HNSW index on the server for a specific collection. Returns rebuild status/result as JSON.

Perform vacuuming on the database to permanently purge deleted vectors and reclaim disk space. Returns vacuum status and reclaimed-space result.

Retrieve the Lorentz hierarchy subsumption tree starting from a given root_id; returns the tree as JSON in the given collection.

Retrieve parent concepts in a hierarchical collection. Requires collection and the concept id. Returns a JSON list of parent nodes.

Traverse the graph and return nodes and links in a format ready for visualization. Requires collection and start_id of the starting node.

Forecast future agent thought paths using Koopman momentum extrapolation. Requires collection and trajectory_ids array; steps sets prediction horizon. Returns predicted trajectory summary as JSON.

Evaluate stability and trust score for a given thought trajectory path. Requires collection and trajectory_ids array of trajectory node IDs.

Enterprise RAG Query Planner. Evaluates user intent before vector search: detects ambiguous/underspecified questions (requesting multiple-choice user clarification) and decomposes multi-part queries into parallel sub-queries.

Alias for hyperspace_query_planner. Evaluates intent clarity, asks clarifying questions if ambiguous, and splits multi-part questions into isolated search queries.

Create an explicit directed, labeled, weighted relationship edge between two entities in the native CSR vector-graph. Parameters: collection, source_id, target_id, optional relation (default 'OWNS'), weight (default 1.0).

Bulk-insert explicit directed edges in CSR graph atomically. Parameters: collection, edges array of {source_id, target_id, relation, weight}.

Delete an explicit directed relationship edge with zero-lag invalidation. Parameters: collection, source_id, target_id, optional relation.

Declaratively induce explicit graph edges from existing document metadata attributes (path_hierarchy, foreign_key, exact_match). Parameters: collection, rules array.

Tribunal Router (Mathematical Anti-Hallucination Guardrail): Verify whether an LLM generated answer stays within the geometric entailment cones and geodesic boundaries of retrieved context IDs.

Invoke a native HyperspaceDB PDK Extension (e.g. 'git_ast' for AST codebase indexing & blast radius, 'embed' for structure-aware Markdown ingestion, 'shield', 'chem', 'bio', 'fin', 'sec', 'legal', 'onto').