Insert vectors with metadata into EmergentDB via official SDKs.
npx skills add https://github.com/justrach/emergent-sdk --skill emdb-insert
Please help me install this Agent Skill. Skill: emdb-insert Source: https://github.com/justrach/emergent-sdk/tree/main/skills/emdb-insert Command: npx skills add https://github.com/justrach/emergent-sdk --skill emdb-insert
Insert vectors into EmergentDB using the official SDKs to store embeddings, index documents, or batch upload vectors, enabling scalable similarity search and retrieval.
Choose your preferred SDK (TypeScript or Python) and perform a single vector insert or a batch upload to EmergentDB.
High-intent search queries and answers about installing and using this skill.
Insert embeddings into EmergentDB using official TypeScript or Python SDKs by providing vectors, optional metadata like title or source_url, and a valid API key. The SDK handles dimension checks and namespace routing automatically.
Batch upload vectors to EmergentDB using the batchInsert or batch_insert SDK methods. Each batch insert call supports a maximum of 1000 vectors and enforces strict dimension checks against the index before persisting the embeddings.
Upserting a vector with an existing ID in EmergentDB replaces the previous vector and its metadata within the same namespace. Re-inserting an existing ID functions as an update rather than generating a duplicate record.
Yes, EmergentDB supports optional namespaces during vector insertion with a default namespace fallback. If a specified namespace does not exist, the SDK automatically creates it before persisting the embeddings and metadata.
When storing vectors in EmergentDB, you can attach optional metadata fields including title, content, source_url, and tags. These metadata fields are persisted alongside the embeddings to support filtered similarity search.
Vector insertion fails when embeddings do not match the index dimension, which defaults to 1536. Ensure your generated embeddings align with the configured dimension constraints before attempting to batch insert or upsert.
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