Knowledge Foundations

Overview

Knowledge representation in quantsmind.knowledge: value types, provenance records, validation helpers, and a concrete KnowledgeGraph with neighbors, degrees, and shortest paths.

Purpose

Let users model entities and relationships as inspectable graph data with provenance attached, without any external store.

Concept

Graphs hold node/edge dicts; neighbors(), degree(), and shortest_path() traverse them; SearchResult and KnowledgeValidator cover retrieval vocabulary and strictness-gated validation.

API

KnowledgeGraph (add/remove nodes and edges, traversal), SearchResult, KnowledgeValidator, package enums, exceptions, and constants. Abstract engines (SearchEngine, repositories) define extension seams and are not directly instantiable.

Input / Processing / Output

Input: node ids, edge pairs, strictness levels. Processing: dict traversal. Output: neighbor lists, paths, validation verdicts.

Example

from quantsmind.knowledge.graph.knowledge_graph import KnowledgeGraph

graph = KnowledgeGraph("g1")
graph.add_node("a")
graph.add_node("b")
graph.add_edge("a", "b")
assert graph.shortest_path("a", "b") == ["a", "b"]

Limitations

In-memory dict storage only; no persistent backend; search backends beyond the vocabulary types are extension seams, not implementations.