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name: storage-backends description: "Choose, configure, validate, and troubleshoot nano-graphrag storage backends for KV data, vector indexes, graph persistence, Neo4j, and safe GraphML visualization." disable-model-invocation: true metadata: disco-role: operating license: MIT

Storage Backends

Use this sub-skill when a user needs to choose a nano-graphrag storage class, configure GraphRAG storage arguments, validate local storage persistence, adapt a third-party vector database, set up Neo4j graph storage, or inspect a GraphML knowledge graph.

Do not use this sub-skill for provider-specific LLM or embedding implementation details; storage docs only state the vector embedding_func contract and should hand off actual provider code to the sibling provider-and-model-integrations skill. Do not use it for the high-level insert/query workflow; route that to the sibling core-graphrag-workflows skill.

Runtime references and scripts

• Read references/storage-backends.md when choosing among JsonKVStorage, NanoVectorDBStorage, HNSWVectorStorage, NetworkXStorage, Neo4jStorage, or when implementing a FAISS/Milvus/Qdrant-style vector adapter.
• Read references/graphml-visualization.md when the user has a GraphML file and wants JSON export or an optional local browser preview.
• Read references/troubleshooting.md when storage initialization, persistence, HNSW capacity/search, Neo4j/GDS, empty graph clustering, or vector adapter behavior fails.
• Run scripts/storage_smoke.py to instantiate NetworkX plus local vector storage with a fake embedding function and no provider API calls.
• Run scripts/visualize_graphml.py to convert a user-provided GraphML file to node-link JSON and, only if requested, generate/serve a local HTML preview.

Fast routing

• Default local file-backed setup: keep JsonKVStorage, NanoVectorDBStorage, and NetworkXStorage; use a fresh working_dir for an independent index.
• Larger/faster local vector search: set vector_db_storage_cls=HNSWVectorStorage and tune vector_db_storage_cls_kwargs before inserting data.
• External graph service: set graph_storage_cls=Neo4jStorage and provide addon_params={"neo4j_url": ..., "neo4j_auth": (...)} after confirming Neo4j 5.x plus the GDS plugin are running.
• Third-party vector database: subclass BaseVectorStorage, preserve upsert, query, and index_done_callback, return ranked results containing source ids plus any required metadata.
• If a storage symptom is caused by LLM/entity extraction producing no graph, route to the sibling customization-and-troubleshooting skill after confirming the graph backend itself is healthy.