By name: graphify description: "Knowledge graph hybride Neo4j + Qdrant. Stocker et retrouver concepts, decisions et entites ; traversee de graphe et recherche semantique ; indexer la sortie de recherche ; seed depuis le vault Obsidian. Declencheurs : stocke dans le graphe, qu'est-ce qui est relie a X, retrouve les concepts lies, graphify_store, graphify_retrieve."
SKILL -- graphify
Trigger conditions
Use this skill when the conversation involves:
- Storing knowledge, concepts, decisions, or entities in the graph
- Retrieving related concepts via graph traversal or semantic search
- Querying "what is connected to X", "what decisions relate to Y"
- Indexing research output from deep-research-core
- Seeding the graph from Obsidian vault notes
- Running a LangGraph research -> extract -> store -> reflect loop
What graphify does
graphify is a hybrid knowledge graph combining Neo4j (structural) and Qdrant (semantic).
Write path:
- Receive content (text, metadata, node type)
- Create/update Neo4j node with relationships
- Embed content -> Qdrant vector (same ID as Neo4j node)
Read path:
- Query arrives (natural language or structured)
- Parallel: Neo4j graph traversal + Qdrant vector search
- Merge + rank results
- Return enriched context to caller
Node types
Concept | Entity | Task | Skill | Agent | Source | Session | Decision
MCP tools (when graph-server is active)
- graphify_store(node_type, title, content, relationships[])
- graphify_retrieve(query, mode="hybrid", limit=10)
- graphify_relate(from_id, rel_type, to_id)
- graphify_ping()
LangGraph state mapping
Concept -> state["knowledge"] Task -> state["tasks"] Decision -> state["context"] Source -> state["sources"]
Dependencies
- Neo4j port 7687 : docker compose up -d neo4j
- Qdrant port 6333 : docker compose up -d qdrant
- NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD, QDRANT_URL in .env
Status
v0.1 scaffold -- planning only. Implementation in Phase 3. Use validate_repo.py --show-stubs to track progress.