KnowledgeGraph

SQLite-backed knowledge graph with nodes, edges, and optional embeddings.

Usage

Source

KnowledgeGraph()

Stores documents, entities, and topics as nodes connected by typed, weighted edges. Supports text search, neighbor traversal, and optional vector embeddings for similarity search.

Parameters

path: str | Path = ":memory:"

Path to the SQLite database file. Use ":memory:" for an in-memory graph (useful for testing).

Examples

Create a graph and add nodes:

import talk_box as tb

kg = tb.KnowledgeGraph(":memory:")

kg.add_node(tb.Node(
    id="doc-1",
    node_type=tb.NodeType.DOCUMENT,
    name="README.md",
    content="# Talk Box\nAI assistant framework.",
))

kg.add_node(tb.Node(
    id="entity-tb",
    node_type=tb.NodeType.ENTITY,
    name="Talk Box",
))

kg.add_edge(tb.Edge(
    source="doc-1",
    target="entity-tb",
    relation="mentions",
))

neighbors = kg.neighbors("doc-1")
neighbors[0].name  # "Talk Box"

Search nodes by text:

results = kg.search("Talk Box")
results[0].name  # "Talk Box"