Integrate Neumann database using official client SDKs. Use when writing Rust, Python, or TypeScript code that connects to Neumann, executes queries, or handles results.
Deploy and operate Neumann in production. Use when setting up single-node or cluster deployments, configuring Docker Compose or Kubernetes, or planning production infrastructure.
Model and query graph data in Neumann. Use when creating nodes and edges, traversing relationships, finding paths, running graph algorithms, or using Cypher-style queries.
Migrate data to Neumann from other databases. Use when moving data from PostgreSQL, MySQL, MongoDB, Neo4j, Pinecone, Redis, or other systems to Neumann.
Write correct Neumann database queries. Use when writing NODE, EDGE, EMBED, SIMILAR, ENTITY, VAULT, CACHE, BLOB, CHECKPOINT, CHAIN, or CLUSTER commands, or any Neumann SQL query.
Design Neumann data models using relational tables, graph nodes/edges, and vector embeddings. Use when planning a database schema or deciding which Neumann engines to use for a feature.
Debug and troubleshoot Neumann issues. Use when encountering errors, performance problems, cluster issues, or unexpected query results.
Build vector search and embedding pipelines with Neumann. Use when implementing similarity search, RAG retrieval, semantic caching, or working with HNSW indexes.