64 lines
2 KiB
TypeScript
64 lines
2 KiB
TypeScript
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/**
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* BrainyDataInterface
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*
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* This interface defines the methods from BrainyData that are used by serverSearchAugmentations.ts.
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* It's used to break the circular dependency between brainyData.ts and serverSearchAugmentations.ts.
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*/
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import { Vector } from '../coreTypes.js'
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export interface BrainyDataInterface<T = unknown> {
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/**
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* Initialize the database
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*/
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init(): Promise<void>
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/**
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* Get a noun by ID
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* @param id The ID of the noun to get
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*/
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getNoun(id: string): Promise<unknown>
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/**
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* Add a noun (entity with vector and metadata) to the database
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* @param data Text string or vector representation (will auto-embed strings)
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* @param metadata Optional metadata to associate with the noun
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* @param options Optional configuration including custom ID
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* @returns The ID of the added noun
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*/
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addNoun(data: string | Vector, metadata?: T, options?: { id?: string; [key: string]: any }): Promise<string>
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/**
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* Search for text in the database
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* @param text The text to search for
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* @param limit Maximum number of results to return
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* @returns Search results
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*/
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searchText(text: string, limit?: number): Promise<unknown[]>
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/**
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* Create a relationship (verb) between two entities
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* @param sourceId The ID of the source entity
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* @param targetId The ID of the target entity
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* @param verbType The type of relationship
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* @param metadata Optional metadata about the relationship
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* @returns The ID of the created verb
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*/
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addVerb(sourceId: string, targetId: string, verbType: string, metadata?: unknown): Promise<string>
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/**
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* Find entities similar to a given entity ID
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* @param id ID of the entity to find similar entities for
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* @param options Additional options
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* @returns Array of search results with similarity scores
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*/
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findSimilar(id: string, options?: { limit?: number }): Promise<unknown[]>
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/**
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* Generate embedding vector from text
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* @param text The text to embed
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* @returns Vector representation of the text
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*/
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embed(text: string): Promise<Vector>
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}
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