Documentation Index Fetch the complete documentation index at: https://docs.agentfront.dev/llms.txt
Use this file to discover all available pages before exploring further.
Add documents with a unique id, the natural-language text to vectorize, and typed metadata.
Adding Documents Single Document await toolIndex . add ( ' users:list ' , ' List all users with pagination and filtering ' , { id : ' users:list ' , toolName : ' list ' , owner : ' users ' , tags : [ ' read ' , ' user-management ' ], risk : ' safe ' , });
Batch Indexing const documents = [ { id : ' billing:charge ' , text : ' Charge a customer payment method ' , metadata : { id : ' billing:charge ' , toolName : ' charge ' , owner : ' billing ' , tags : [ ' write ' , ' payment ' ], risk : ' destructive ' , }, }, { id : ' billing:refund ' , text : ' Process a refund for a customer ' , metadata : { id : ' billing:refund ' , toolName : ' refund ' , owner : ' billing ' , tags : [ ' write ' , ' payment ' ], risk : ' destructive ' , }, }, ]; await toolIndex . addMany ( documents );
addMany validates every document, enforces maxBatchSize, and prevents duplicates.Define your metadata interface for compile-time safety:
import { VectoriaDB , DocumentMetadata } from ' vectoriadb ' ; interface ToolDocument extends DocumentMetadata { toolName : string ; owner : string ; tags : string []; risk : ' safe ' | ' destructive ' ; deprecated ?: boolean ; } const db = new VectoriaDB < ToolDocument >(); // TypeScript ensures metadata matches interface await db . add ( ' id ' , ' text ' , { id : ' id ' , toolName : ' test ' , owner : ' system ' , tags : [], risk : ' safe ' , // TypeScript error if you add wrong fields });
Updating Documents Metadata-only updates are instant and don’t trigger re-embedding:
await toolIndex . updateMetadata ( ' users:list ' , { deprecated : true , tags : [ ' read ' , ' user-management ' , ' legacy ' ], });
Update Text and Metadata When text changes, VectoriaDB re-embeds the document:
await toolIndex . update ( ' users:list ' , { text : ' Updated description for user listing ' , metadata : { id : ' users:list ' , toolName : ' list ' , owner : ' users ' , tags : [ ' read ' ], risk : ' safe ' , }, });
Batch Updates await toolIndex . updateMany ([ { id : ' users:list ' , text : ' New description ' , metadata : { /* ... */ }, }, { id : ' billing:charge ' , metadata : { deprecated : true }, // Metadata-only update }, ]);
Keep the index current with updateMetadata, update, or updateMany. Metadata-only updates never trigger re-embedding, while text changes re-embed only the affected documents.
Removing Documents // Single document await toolIndex . remove ( ' users:list ' ); // Multiple documents await toolIndex . removeMany ([ ' users:list ' , ' billing:charge ' ]); // Clear all documents await toolIndex . clear ();
Checking for Documents // Check if document exists const exists = toolIndex . has ( ' users:list ' ); // Get document by ID const doc = toolIndex . get ( ' users:list ' ); if ( doc ) { console . log ( doc . metadata . toolName ); } // Get count console . log ( ` Index contains ${ toolIndex . size () } documents ` );
Document Limits VectoriaDB enforces limits to prevent DoS attacks:
const db = new VectoriaDB ({ maxDocuments : 100000 , // Maximum documents in index maxDocumentSize : 1000000 , // Maximum text size in characters maxBatchSize : 1000 , // Maximum documents per batch operation });
Embedding Generation Embeddings are generated automatically when you add or update documents. The process:
Text is tokenized using the configured model
Embeddings are generated (~100-200 documents/second)
Embeddings are stored in memory (and optionally persisted)
For large imports, use addMany with appropriate maxBatchSize to avoid memory spikes.
Persistence Persisting embeddings
