Database
knowledge_graph_skill
🌟250
Skill for searching and evolving the SQLite-backed Knowledge Graph. Use this when you need structured fact/concept/link search across one or more teams, NPCs, or directory scopes.
The Knowledge Graph (KG) is stored in the application's database (not YAML). It is scoped by (team_name, npc_name, directory_path). Facts and concepts carry generation numbers and origin tags.
Search methods (choose the right one):
1. Keyword search — fast substring match over fact statements.
`kg_search_facts(engine_or_kg, "keyword")` → List[str]
2. Embedding search — semantic cosine similarity via vector embeddings.
`kg_embedding_search(engine_or_kg, query="...", embedding_model="nomic-embed-text",
embedding_provider="ollama", similarity_threshold=0.6, max_results=20)`
→ List[dict] with 'content', 'type', 'score'
3. Link search — graph traversal (BFS/DFS) starting from keyword-matched seeds.
`kg_link_search(engine_or_kg, query="...", max_depth=2, breadth_per_step=5,
strategy="bfs", max_results=20)`
→ List[dict] with 'content', 'type', 'depth', 'path', 'score'
4. Hybrid search — combines keyword + embedding + link, boosting results
found by multiple methods.
`kg_hybrid_search(engine_or_kg, query="...", mode="all", max_depth=2,
similarity_threshold=0.6, max_results=20)`
→ List[dict] with 'content', 'type', 'score', 'source'
Graph evolution (use sparingly, usually in background): - `kg_initial(content, model, provider)` — build a new KG from text - `kg_evolve_incremental(existing_kg, new_content_text, ...)` — add content - `kg_sleep_process(existing_kg, model, provider)` — prune/deepen/consolidate - `kg_dream_process(existing_kg, model, provider, num_seeds)` — speculative synthesis
When a user asks a question that spans facts, concepts, and their relationships, prefer hybrid search. For pure semantic similarity without graph structure, use embedding search. For exploring connected neighborhoods, use link search with BFS.