Migrate data from relational, document, graph, and vector sources into Neumann.
npx skills add https://github.com/Shadylukin/Neumann --skill neumann-migrate
Please help me install this Agent Skill. Skill: neumann-migrate Source: https://github.com/Shadylukin/Neumann/tree/main/.claude/skills/neumann-migrate Command: npx skills add https://github.com/Shadylukin/Neumann --skill neumann-migrate
Neumann migration guide removes the complexity and risk of moving relational tables, document collections, graph data, and vector indexes into a single unified Neumann instance by providing type mappings, batch import patterns, and verification steps to preserve data integrity.
Migrate my PostgreSQL users table and associated embeddings into Neumann by mapping column types, running a small test batch, and then performing bulk inserts and embed batch imports while verifying counts.
High-intent search queries and answers about installing and using this skill.
Database migration into Neumann maps relational tables and graph relationships using schema mapping and batch inserts. It converts nested documents into nodes and edges while preserving type semantics during the transfer.
Yes, database migration supports bulk embedding imports from vector indexes like Pinecone, Weaviate, and Qdrant. It utilizes EMBED BATCH strategies to efficiently load embeddings and rebuild vector indexes after the data transfer.
The best way to ensure data integrity during database migration is to use transactional checkpoints, count verification queries, and index building after bulk loads. These steps confirm counts and maintain consistency across imported datasets.
Schema mapping translates document and SQL types into corresponding graph database types to preserve semantics. It maps nested document structures into graph nodes and edges, ensuring structural fidelity during the migration process.
Limitations during large dataset migration are mitigated by using multi-row INSERT, ENTITY BATCH, and GRAPH BATCH patterns for efficient loading. You must perform index building only after bulk loads to avoid performance degradation.
Yes, you need to rebuild indexes after a bulk embedding import to ensure query performance and data integrity. Database migration workflows recommend building indexes post-load and running verification queries to confirm successful imports.
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