David Snelling
f8c45f2d8d
Open source vector database with HNSW indexing, graph relationships, and metadata facets. Features CLI with professional augmentation registry integration for discovering extensions and capabilities.
50 lines
1.8 KiB
TypeScript
50 lines
1.8 KiB
TypeScript
/**
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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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get(id: string): Promise<unknown>;
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/**
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* Add a vector or data to the database
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* @param vectorOrData Vector or data to add
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* @param metadata Optional metadata to associate with the vector
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* @returns The ID of the added vector
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*/
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add(vectorOrData: Vector | unknown, metadata?: T): 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 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 relationType 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 relationship
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*/
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relate(sourceId: string, targetId: string, relationType: 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?: {
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limit?: number;
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}): Promise<unknown[]>;
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}
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