commit 49480e694cc0e244e12c1aeff9946ee6c49bb605 Author: David Snelling Date: Fri May 23 10:55:20 2025 -0700 Initial commit of Brainy vector database v0.1.0 diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..c941a671 --- /dev/null +++ b/.gitignore @@ -0,0 +1,37 @@ +/tmp +/out-tsc +/dist + +/node_modules +npm-debug.log* +yarn-debug.log* +yarn-error.log* +/.pnp +.pnp.js + +# Coverage directory +/coverage + +# Environment files +.env +.env.local +.env.development.local +.env.test.local +.env.production.local + +# IDE files +.vscode/* +# WebStorm files to ignore +.idea/workspace.xml +.idea/usage.statistics.xml +.idea/dictionaries +.idea/shelf +# Include other WebStorm project files + +# OS files +.DS_Store +Thumbs.db + +# Data directories created by FileSystemStorage +/brainy-data +/custom-data diff --git a/.idea/brainy.iml b/.idea/brainy.iml new file mode 100644 index 00000000..53815b20 --- /dev/null +++ b/.idea/brainy.iml @@ -0,0 +1,11 @@ + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/jsLibraryMappings.xml b/.idea/jsLibraryMappings.xml new file mode 100644 index 00000000..bc5a8d4f --- /dev/null +++ b/.idea/jsLibraryMappings.xml @@ -0,0 +1,7 @@ + + + + + + + diff --git a/.idea/material_theme_project_new.xml b/.idea/material_theme_project_new.xml new file mode 100644 index 00000000..4bc91b71 --- /dev/null +++ b/.idea/material_theme_project_new.xml @@ -0,0 +1,17 @@ + + + + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 00000000..3add60e4 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,9 @@ + + + + + + + + diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 00000000..0b9a2e1f --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/typescript-compiler.xml b/.idea/typescript-compiler.xml new file mode 100644 index 00000000..4f42f93b --- /dev/null +++ b/.idea/typescript-compiler.xml @@ -0,0 +1,8 @@ + + + + + diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 00000000..94a25f7f --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..e3c7f83f --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Sodal + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 00000000..2db5c8ef --- /dev/null +++ b/README.md @@ -0,0 +1,506 @@ +# Soulcraft Brainy + +A vector database that runs in a browser or Node.js and utilizes Origin Private File System (OPFS) for storage, with HNSW (Hierarchical Navigable Small World) for efficient vector indexing. + +## Features + +- **Cross-platform**: Works in both browsers and Node.js +- **Persistent storage**: Uses Origin Private File System (OPFS) in browsers, with fallback to in-memory storage +- **Efficient vector search**: Implements HNSW (Hierarchical Navigable Small World) algorithm for fast approximate nearest neighbor search +- **Automatic embedding**: Converts text and other data to vectors using embedding models +- **TensorFlow.js integration**: Uses Universal Sentence Encoder for high-quality text embeddings +- **Metadata support**: Store and retrieve metadata alongside vectors +- **TypeScript support**: Fully typed API with generics for metadata types +- **Multiple distance functions**: Supports cosine, Euclidean, Manhattan, and dot product distance metrics +- **Augmentation system**: Extensible architecture for adding specialized capabilities +- **Graph data model**: Structured representation of entities and relationships + +## Installation + +```bash +npm install brainy +``` + +## Usage + +### Basic Example + +```typescript +import {BrainyData} from 'brainy'; + +// Create a new vector database +const db = new BrainyData(); +await db.init(); + +// Add vectors with metadata +const catId = await db.add([0.2, 0.3, 0.4, 0.1], {type: 'mammal', name: 'cat'}); +const dogId = await db.add([0.3, 0.2, 0.4, 0.2], {type: 'mammal', name: 'dog'}); +const fishId = await db.add([0.1, 0.1, 0.8, 0.2], {type: 'fish', name: 'fish'}); + +// Add text directly - it will be automatically embedded +const lionDescId = await db.add("Lions are large cats with a golden mane", {type: 'mammal', name: 'lion'}); +const tigerDescId = await db.add("Tigers are large cats with striped fur", {type: 'mammal', name: 'tiger'}); + +// Search for similar vectors +const results = await db.search([0.2, 0.3, 0.4, 0.1], 2); +console.log(results); +// [ +// { id: 'cat-id', score: 0, vector: [0.2, 0.3, 0.4, 0.1], metadata: { type: 'mammal', name: 'cat' } }, +// { id: 'dog-id', score: 0.1, vector: [0.3, 0.2, 0.4, 0.2], metadata: { type: 'mammal', name: 'dog' } } +// ] + +// Search with text directly - it will be automatically embedded +const catResults = await db.search("cat", 2); +console.log(catResults); +// Results will include vectors similar to the embedding of "cat" + +// Get a vector by ID +const cat = await db.get(catId); +console.log(cat); +// { id: 'cat-id', vector: [0.2, 0.3, 0.4, 0.1], metadata: { type: 'mammal', name: 'cat' } } + +// Update metadata +await db.updateMetadata(catId, {type: 'mammal', name: 'cat', color: 'orange'}); + +// Delete a vector +await db.delete(fishId); + +// Clear the database +await db.clear(); +``` + +### Configuration Options + +```typescript +import { + BrainyData, + euclideanDistance, + UniversalSentenceEncoder, + createEmbeddingFunction +} from 'brainy'; + +// Configure the vector database +const db = new BrainyData({ + // HNSW index configuration + hnsw: { + M: 16, // Max number of connections per node + efConstruction: 200, // Size of dynamic candidate list during construction + efSearch: 50, // Size of dynamic candidate list during search + ml: 16 // Max level + }, + + // Distance function to use (default is cosineDistance) + distanceFunction: euclideanDistance, + + // Custom embedding function (optional) + // By default, it uses the Universal Sentence Encoder for high-quality text embeddings + // You can use the SimpleEmbedding for a basic character-based embedding: + // embeddingFunction: createEmbeddingFunction(new SimpleEmbedding()), + + // Or create your own custom embedding function: + // embeddingFunction: async (data) => { + // // Convert data to a vector + // return [0.1, 0.2, 0.3, 0.4]; // Return a vector + // }, + + // Custom storage adapter (optional) + // By default, it uses OPFS in browsers, FileSystemStorage in Node.js, + // or falls back to in-memory storage if neither is available + // storageAdapter: myCustomStorageAdapter + + // You can also explicitly use the FileSystemStorage with a custom directory: + // import { FileSystemStorage } from 'brainy/storage/fileSystemStorage'; + // storageAdapter: new FileSystemStorage('/custom/path') +}); +``` + +## Contributing and Publishing + +Soulcraft Brainy is an open source project released under the MIT license. Contributions are welcome! + +### Contributing to the Project + +To contribute to the project: + +1. Fork the repository +2. Create a feature branch +3. Make your changes +4. Run tests to ensure everything works: + ```bash + npm test + ``` +5. Submit a pull request + +### Publishing a New Version + +To publish a new version to npm: + +1. Update the version in package.json following semantic versioning +2. Build the package: + ```bash + npm run build + ``` +3. Publish the package: + ```bash + npm publish + ``` + +### Installing the Package + +To install the package in your project: + +```bash +npm install brainy +``` + +### Requirements + +- Node.js >= 18.0.0 + +## Augmentation System + +Brainy includes a powerful augmentation system that allows extending its capabilities through specialized modules. Each augmentation implements a specific interface and provides additional functionality. + +### Base Augmentation Interface + +All augmentations implement the `IAugmentation` interface: + +```typescript +interface IAugmentation { + readonly name: string; // Unique identifier for the augmentation + readonly description: string; // Human-readable description + initialize(): Promise; // Called when Brainy starts up + shutDown(): Promise; // Called when shutting down + getStatus(): Promise<'active' | 'inactive' | 'error'>; // Current status +} +``` + +### WebSocket Support + +Augmentations can optionally implement WebSocket support: + +```typescript +interface IWebSocketSupport { + connectWebSocket(url: string, protocols?: string | string[]): Promise; + sendWebSocketMessage(connectionId: string, data: unknown): Promise; + onWebSocketMessage(connectionId: string, callback: DataCallback): Promise; + closeWebSocket(connectionId: string, code?: number, reason?: string): Promise; +} +``` + +### Specialized Augmentation Types + +Brainy supports several specialized augmentation types: + +#### Cognition Augmentations + +For reasoning, inference, and logical operations: + +```typescript +interface ICognitionAugmentation extends IAugmentation { + reason(query: string, context?: Record): AugmentationResponse<{ + inference: string; + confidence: number; + }>; + infer(dataSubset: Record): AugmentationResponse>; + executeLogic(ruleId: string, input: Record): AugmentationResponse; +} +``` + +#### Sense Augmentations + +For processing raw, unstructured data: + +```typescript +interface ISenseAugmentation extends IAugmentation { + processRawData(rawData: Buffer | string, dataType: string): AugmentationResponse<{ + nouns: string[]; + verbs: string[]; + }>; + listenToFeed( + feedUrl: string, + callback: DataCallback<{ nouns: string[]; verbs: string[] }> + ): Promise; +} +``` + +#### Perception Augmentations + +For interpreting and contextualizing data: + +```typescript +interface IPerceptionAugmentation extends IAugmentation { + interpret( + nouns: string[], + verbs: string[], + context?: Record + ): AugmentationResponse>; + organize( + data: Record, + criteria?: Record + ): AugmentationResponse>; + generateVisualization( + data: Record, + visualizationType: string + ): AugmentationResponse>; +} +``` + +#### Activation Augmentations + +For triggering actions and generating outputs: + +```typescript +interface IActivationAugmentation extends IAugmentation { + triggerAction( + actionName: string, + parameters?: Record + ): AugmentationResponse; + generateOutput(knowledgeId: string, format: string): AugmentationResponse>; + interactExternal(systemId: string, payload: Record): AugmentationResponse; +} +``` + +#### Dialog Augmentations + +For natural language understanding and generation: + +```typescript +interface IDialogAugmentation extends IAugmentation { + processUserInput(naturalLanguageQuery: string, sessionId?: string): AugmentationResponse<{ + intent: string; + nouns: string[]; + verbs: string[]; + context: Record; + }>; + generateResponse( + interpretedInput: Record, + knowledgeContext: Record, + sessionId?: string + ): AugmentationResponse; + manageContext(sessionId: string, contextUpdate: Record): Promise; +} +``` + +#### Conduit Augmentations + +For establishing data exchange channels: + +```typescript +interface IConduitAugmentation extends IAugmentation { + establishConnection( + targetSystemId: string, + config: Record + ): AugmentationResponse; + readData( + query: Record, + options?: Record + ): AugmentationResponse; + writeData( + data: Record, + options?: Record + ): AugmentationResponse; + monitorStream(streamId: string, callback: DataCallback): Promise; +} +``` + +## Graph Data Model + +Brainy uses a graph-based data model to represent entities and relationships. This model consists of nouns (nodes) and verbs (edges). + +### Common Types + +#### Timestamp + +Used for tracking creation and update times: + +```typescript +interface Timestamp { + seconds: number; + nanoseconds: number; +} +``` + +#### CreatorMetadata + +Tracks which augmentation and model created an element: + +```typescript +interface CreatorMetadata { + augmentation: string; // Name of the augmentation that created this element + version: string; // Version of the augmentation + model: string; // Model identifier used in creation + modelVersion: string; // Version of the model +} +``` + +### Graph Elements + +#### GraphNoun + +Base interface for nodes (entities) in the graph: + +```typescript +interface GraphNoun { + id: string; // Unique identifier for the noun + createdBy: CreatorMetadata; // Information about what created this noun + noun: NounType; // Type classification of the noun + createdAt: Timestamp; // When the noun was created + updatedAt: Timestamp; // When the noun was last updated + data?: Record; // Additional flexible data storage + embedding?: number[]; // Vector representation of the noun +} +``` + +#### GraphVerb + +Base interface for edges (relationships) in the graph: + +```typescript +interface GraphVerb { + id: string; // Unique identifier for the verb + source: string; // ID of the source noun + target: string; // ID of the target noun + label?: string; // Optional descriptive label + verb: VerbType; // Type of relationship + createdAt: Timestamp; // When the verb was created + updatedAt: Timestamp; // When the verb was last updated + data?: Record; // Additional flexible data storage + embedding?: number[]; // Vector representation of the relationship + confidence?: number; // Confidence score (0-1) + weight?: number; // Strength/importance of the relationship +} +``` + +### Noun Types + +Brainy supports the following noun types: + +- **Person**: Represents a person entity +- **Place**: Represents a physical location +- **Thing**: Represents a physical or virtual object +- **Event**: Represents an event or occurrence +- **Concept**: Represents an abstract concept or idea +- **Content**: Represents content (text, media, etc.) + +### Verb Types + +Brainy supports the following verb types: + +- **AttributedTo**: Indicates attribution or authorship +- **Controls**: Indicates control or ownership +- **Created**: Indicates creation or authorship +- **Earned**: Indicates achievement or acquisition +- **Owns**: Indicates ownership + +## Examples + +The repository includes several examples to help you get started: + +### Modern UI Demo + +A complete web application that demonstrates all the features of Soulcraft Brainy with a modern user interface: + +- Initialize the database with different distance functions +- Configure HNSW parameters +- Add sample vectors and custom vectors with metadata +- Search for similar vectors +- Get, update, and delete vectors +- View database size and clear the database + +To run the Modern UI Demo: + +1. Clone the repository +2. Build the project with `npm run build` +3. Open `examples/demo.html` in a browser + +### Node.js Examples + +The repository also includes TypeScript examples for Node.js: + +- `src/examples/basicUsage.ts`: Demonstrates basic vector operations +- `src/examples/customStorage.ts`: Shows how to use a custom storage adapter + +## How It Works + +### HNSW Indexing + +The Hierarchical Navigable Small World (HNSW) algorithm is used for efficient approximate nearest neighbor search. It creates a multi-layered graph structure that allows for logarithmic-time search complexity. + +Key features of the HNSW implementation: + +- Hierarchical graph structure for efficient navigation +- Configurable parameters for tuning performance vs. accuracy +- Support for different distance metrics + +### Origin Private File System (OPFS) Storage + +In browser environments, the database uses the Origin Private File System (OPFS) API for persistent storage. This provides: + +- Fast, local storage that persists between sessions +- Isolation from other origins for security +- Efficient file operations + +In Node.js environments, the database uses a file system-based storage adapter that stores data in JSON files. This provides: + +- Persistent storage between application restarts +- Efficient file operations using Node.js fs module +- Configurable storage location + +In environments where neither OPFS nor Node.js file system is available, the database automatically falls back to in-memory storage. + +## API Reference + +### BrainyData + +The main class for interacting with the vector database. + +#### Constructor + +```typescript +constructor(config?: BrainyDataConfig) +``` + +#### Methods + +- `init(): Promise` - Initialize the database +- `add(vectorOrData: Vector | any, metadata?: T, options?: { forceEmbed?: boolean }): Promise` - Add a vector or data to the database +- `addBatch(items: Array<{ vectorOrData: Vector | any, metadata?: T }>, options?: { forceEmbed?: boolean }): Promise` - Add multiple vectors or data items +- `search(queryVectorOrData: Vector | any, k?: number, options?: { forceEmbed?: boolean }): Promise[]>` - Search for similar vectors +- `get(id: string): Promise | null>` - Get a vector by ID +- `delete(id: string): Promise` - Delete a vector +- `updateMetadata(id: string, metadata: T): Promise` - Update metadata +- `clear(): Promise` - Clear the database +- `size(): number` - Get the number of vectors in the database + +### Distance Functions + +- `euclideanDistance(a: Vector, b: Vector): number` - Euclidean (L2) distance +- `cosineDistance(a: Vector, b: Vector): number` - Cosine distance +- `manhattanDistance(a: Vector, b: Vector): number` - Manhattan (L1) distance +- `dotProductDistance(a: Vector, b: Vector): number` - Dot product distance + +### Embedding Models + +- `SimpleEmbedding` - A simple character-based embedding model for text +- `UniversalSentenceEncoder` - TensorFlow Universal Sentence Encoder for high-quality text embeddings + +### Embedding Functions + +- `createEmbeddingFunction(model: EmbeddingModel): EmbeddingFunction` - Create an embedding function from an embedding model +- `defaultEmbeddingFunction` - Default embedding function using UniversalSentenceEncoder + +## Browser Compatibility + +The Soulcraft Brainy database works in all modern browsers that support the Origin Private File System API: + +- Chrome 86+ +- Edge 86+ +- Opera 72+ +- Chrome for Android 86+ + +For browsers without OPFS support, the database will automatically fall back to in-memory storage. + +## License + +MIT diff --git a/examples/demo.html b/examples/demo.html new file mode 100644 index 00000000..0deed905 --- /dev/null +++ b/examples/demo.html @@ -0,0 +1,1083 @@ + + + + + + Soulcraft Brainy - Demo + + + + + + + + +
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{ + "build": "tsc", + "test": "jest", + "start": "node dist/index.js" + }, + "keywords": [ + "vector-database", + "hnsw", + "opfs", + "origin-private-file-system", + "embeddings" + ], + "author": "David Snelling (david@soulcraft.com)", + "license": "MIT", + "devDependencies": { + "@types/jest": "^29.5.3", + "@types/node": "^20.4.5", + "@types/uuid": "^10.0.0", + "@typescript-eslint/eslint-plugin": "^6.0.0", + "@typescript-eslint/parser": "^6.0.0", + "eslint": "^8.45.0", + "jest": "^29.6.2", + "ts-jest": "^29.1.1", + "typescript": "^5.1.6" + }, + "dependencies": { + "@tensorflow-models/universal-sentence-encoder": "^1.3.3", + "@tensorflow/tfjs": "^4.22.0", + "@tensorflow/tfjs-backend-cpu": "^4.22.0", + "@tensorflow/tfjs-core": "^4.22.0", + "@tensorflow/tfjs-layers": "^4.22.0", + "uuid": "^9.0.0" + }, + "prettier": { + "arrowParens": "always", + "bracketSameLine": true, + "bracketSpacing": true, + "htmlWhitespaceSensitivity": "css", + "printWidth": 80, + "proseWrap": "preserve", + "semi": false, + "singleQuote": true, + "tabWidth": 2, + "trailingComma": "none", + "useTabs": false + }, + "eslintConfig": { + "root": true, + "extends": [ + "eslint:recommended", + "plugin:@typescript-eslint/recommended" + ], + "parser": "@typescript-eslint/parser", + "plugins": [ + "@typescript-eslint" + ], + "rules": { + "no-unused-vars": "off", + "@typescript-eslint/no-unused-vars": [ + "warn", + { + "args": "after-used", + "argsIgnorePattern": "^_" + } + ] + } + } +} diff --git a/src/brainyData.ts b/src/brainyData.ts new file mode 100644 index 00000000..2f56f7d6 --- /dev/null +++ b/src/brainyData.ts @@ -0,0 +1,549 @@ +/** + * BrainyData + * Main class that provides the vector database functionality + */ + +import { v4 as uuidv4 } from 'uuid' +import { HNSWIndex } from './hnsw/hnswIndex.js' +import { createStorage } from './storage/opfsStorage.js' +import { DistanceFunction, Edge, EmbeddingFunction, HNSWConfig, SearchResult, StorageAdapter, Vector, VectorDocument } from './coreTypes.js' +import { cosineDistance, defaultEmbeddingFunction, euclideanDistance } from './utils/index.js' + +export interface BrainyDataConfig { + /** + * HNSW index configuration + */ + hnsw?: Partial + + /** + * Distance function to use for similarity calculations + */ + distanceFunction?: DistanceFunction + + /** + * Custom storage adapter (if not provided, will use OPFS or memory storage) + */ + storageAdapter?: StorageAdapter + + /** + * Embedding function to convert data to vectors + */ + embeddingFunction?: EmbeddingFunction +} + +export class BrainyData { + private index: HNSWIndex + private storage: StorageAdapter | null = null + private isInitialized = false + private embeddingFunction: EmbeddingFunction + + /** + * Create a new vector database + */ + constructor(config: BrainyDataConfig = {}) { + // Initialize HNSW index + this.index = new HNSWIndex( + config.hnsw, + config.distanceFunction || cosineDistance + ) + + // Set storage if provided, otherwise it will be initialized in init() + this.storage = config.storageAdapter || null + + // Set embedding function if provided, otherwise use default + this.embeddingFunction = config.embeddingFunction || defaultEmbeddingFunction + } + + /** + * Initialize the database + * Loads existing data from storage if available + */ + public async init(): Promise { + if (this.isInitialized) { + return + } + + try { + // Initialize storage if not provided in constructor + if (!this.storage) { + this.storage = await createStorage() + } + + // Initialize storage + await this.storage!.init() + + // Load all nodes from storage + const nodes = await this.storage!.getAllNodes() + + // Clear the index and add all nodes + this.index.clear() + for (const node of nodes) { + // Add to index + this.index.addItem({ + id: node.id, + vector: node.vector + }) + } + + this.isInitialized = true + } catch (error) { + console.error('Failed to initialize vector database:', error) + throw new Error(`Failed to initialize vector database: ${error}`) + } + } + + /** + * Add a vector or data to the database + * If the input is not a vector, it will be converted using the embedding function + * @param vectorOrData Vector or data to add + * @param metadata Optional metadata to associate with the vector + * @param options Additional options + * @returns The ID of the added vector + */ + public async add( + vectorOrData: Vector | any, + metadata?: T, + options: { + forceEmbed?: boolean // Force using the embedding function even if input is a vector + } = {} + ): Promise { + await this.ensureInitialized() + + try { + let vector: Vector + + // Check if input is already a vector + if ( + Array.isArray(vectorOrData) && + vectorOrData.every((item) => typeof item === 'number') && + !options.forceEmbed + ) { + // Input is already a vector + vector = vectorOrData + } else { + // Input needs to be vectorized + try { + vector = await this.embeddingFunction(vectorOrData) + } catch (embedError) { + throw new Error(`Failed to vectorize data: ${embedError}`) + } + } + + // Check if vector is defined + if (!vector) { + throw new Error('Vector is undefined or null') + } + + // Generate ID if isn't provided + const id = uuidv4() + + // Add to index + this.index.addItem({ id, vector }) + + // Get the node from the index + const node = this.index.getNodes().get(id) + + if (!node) { + throw new Error(`Failed to retrieve newly created node with ID ${id}`) + } + + // Save node to storage + await this.storage!.saveNode(node) + + // Save metadata if provided + if (metadata !== undefined) { + await this.storage!.saveMetadata(id, metadata) + } + + return id + } catch (error) { + console.error('Failed to add vector:', error) + throw new Error(`Failed to add vector: ${error}`) + } + } + + /** + * Add multiple vectors or data items to the database + * @param items Array of items to add + * @param options Additional options + * @returns Array of IDs for the added items + */ + public async addBatch( + items: Array<{ + vectorOrData: Vector | any; + metadata?: T + }>, + options: { + forceEmbed?: boolean // Force using the embedding function even if input is a vector + } = {} + ): Promise { + await this.ensureInitialized() + + const ids: string[] = [] + + try { + for (const item of items) { + const id = await this.add(item.vectorOrData, item.metadata, options) + ids.push(id) + } + + return ids + } catch (error) { + console.error('Failed to add batch of items:', error) + throw new Error(`Failed to add batch of items: ${error}`) + } + } + + /** + * Search for similar vectors + * @param queryVectorOrData Query vector or data to search for + * @param k Number of results to return + * @param options Additional options + * @returns Array of search results + */ + public async search( + queryVectorOrData: Vector | any, + k: number = 10, + options: { + forceEmbed?: boolean // Force using the embedding function even if input is a vector + } = {} + ): Promise[]> { + await this.ensureInitialized() + + try { + let queryVector: Vector + + // Check if input is already a vector + if ( + Array.isArray(queryVectorOrData) && + queryVectorOrData.every((item) => typeof item === 'number') && + !options.forceEmbed + ) { + // Input is already a vector + queryVector = queryVectorOrData + } else { + // Input needs to be vectorized + try { + queryVector = await this.embeddingFunction(queryVectorOrData) + } catch (embedError) { + throw new Error(`Failed to vectorize query data: ${embedError}`) + } + } + + // Check if query vector is defined + if (!queryVector) { + throw new Error('Query vector is undefined or null') + } + + // Search in the index + const results = this.index.search(queryVector, k) + + // Get metadata for each result + const searchResults: SearchResult[] = [] + + for (const [id, score] of results) { + const node = this.index.getNodes().get(id) + if (!node) { + continue + } + + const metadata = await this.storage!.getMetadata(id) + + searchResults.push({ + id, + score, + vector: node.vector, + metadata + }) + } + + return searchResults + } catch (error) { + console.error('Failed to search vectors:', error) + throw new Error(`Failed to search vectors: ${error}`) + } + } + + /** + * Get a vector by ID + */ + public async get(id: string): Promise | null> { + await this.ensureInitialized() + + try { + // Get node from index + const node = this.index.getNodes().get(id) + if (!node) { + return null + } + + // Get metadata + const metadata = await this.storage!.getMetadata(id) + + return { + id, + vector: node.vector, + metadata + } + } catch (error) { + console.error(`Failed to get vector ${id}:`, error) + throw new Error(`Failed to get vector ${id}: ${error}`) + } + } + + /** + * Delete a vector by ID + */ + public async delete(id: string): Promise { + await this.ensureInitialized() + + try { + // Remove from index + const removed = this.index.removeItem(id) + if (!removed) { + return false + } + + // Remove from storage + await this.storage!.deleteNode(id) + + // Try to remove metadata (ignore errors) + try { + await this.storage!.saveMetadata(id, null) + } catch (error) { + // Ignore + } + + return true + } catch (error) { + console.error(`Failed to delete vector ${id}:`, error) + throw new Error(`Failed to delete vector ${id}: ${error}`) + } + } + + /** + * Update metadata for a vector + */ + public async updateMetadata(id: string, metadata: T): Promise { + await this.ensureInitialized() + + try { + // Check if a vector exists + const node = this.index.getNodes().get(id) + if (!node) { + return false + } + + // Update metadata + await this.storage!.saveMetadata(id, metadata) + + return true + } catch (error) { + console.error(`Failed to update metadata for vector ${id}:`, error) + throw new Error(`Failed to update metadata for vector ${id}: ${error}`) + } + } + + /** + * Add an edge between two nodes + */ + public async addEdge( + sourceId: string, + targetId: string, + vector?: Vector, + options: { + type?: string + weight?: number + metadata?: any + } = {} + ): Promise { + await this.ensureInitialized() + + try { + // Check if source and target nodes exist + const sourceNode = this.index.getNodes().get(sourceId) + const targetNode = this.index.getNodes().get(targetId) + + if (!sourceNode) { + throw new Error(`Source node with ID ${sourceId} not found`) + } + + if (!targetNode) { + throw new Error(`Target node with ID ${targetId} not found`) + } + + // Generate ID for the edge + const id = uuidv4() + + // Use a provided vector or average of source and target vectors + const edgeVector = + vector || + sourceNode.vector.map((val, i) => (val + targetNode.vector[i]) / 2) + + // Create edge + const edge: Edge = { + id, + vector: edgeVector, + connections: new Map(), + sourceId, + targetId, + type: options.type, + weight: options.weight, + metadata: options.metadata + } + + // Add to index + this.index.addItem({ id, vector: edgeVector }) + + // Get the node from the index + const indexNode = this.index.getNodes().get(id) + + if (!indexNode) { + throw new Error( + `Failed to retrieve newly created edge node with ID ${id}` + ) + } + + // Update edge connections from index + edge.connections = indexNode.connections + + // Save edge to storage + await this.storage!.saveEdge(edge) + + return id + } catch (error) { + console.error('Failed to add edge:', error) + throw new Error(`Failed to add edge: ${error}`) + } + } + + /** + * Get an edge by ID + */ + public async getEdge(id: string): Promise { + await this.ensureInitialized() + + try { + return await this.storage!.getEdge(id) + } catch (error) { + console.error(`Failed to get edge ${id}:`, error) + throw new Error(`Failed to get edge ${id}: ${error}`) + } + } + + /** + * Get all edges + */ + public async getAllEdges(): Promise { + await this.ensureInitialized() + + try { + return await this.storage!.getAllEdges() + } catch (error) { + console.error('Failed to get all edges:', error) + throw new Error(`Failed to get all edges: ${error}`) + } + } + + /** + * Get edges by source node ID + */ + public async getEdgesBySource(sourceId: string): Promise { + await this.ensureInitialized() + + try { + return await this.storage!.getEdgesBySource(sourceId) + } catch (error) { + console.error(`Failed to get edges by source ${sourceId}:`, error) + throw new Error(`Failed to get edges by source ${sourceId}: ${error}`) + } + } + + /** + * Get edges by target node ID + */ + public async getEdgesByTarget(targetId: string): Promise { + await this.ensureInitialized() + + try { + return await this.storage!.getEdgesByTarget(targetId) + } catch (error) { + console.error(`Failed to get edges by target ${targetId}:`, error) + throw new Error(`Failed to get edges by target ${targetId}: ${error}`) + } + } + + /** + * Get edges by type + */ + public async getEdgesByType(type: string): Promise { + await this.ensureInitialized() + + try { + return await this.storage!.getEdgesByType(type) + } catch (error) { + console.error(`Failed to get edges by type ${type}:`, error) + throw new Error(`Failed to get edges by type ${type}: ${error}`) + } + } + + /** + * Delete an edge + */ + public async deleteEdge(id: string): Promise { + await this.ensureInitialized() + + try { + // Remove from index + const removed = this.index.removeItem(id) + if (!removed) { + return false + } + + // Remove from storage + await this.storage!.deleteEdge(id) + + return true + } catch (error) { + console.error(`Failed to delete edge ${id}:`, error) + throw new Error(`Failed to delete edge ${id}: ${error}`) + } + } + + /** + * Clear the database + */ + public async clear(): Promise { + await this.ensureInitialized() + + try { + // Clear index + this.index.clear() + + // Clear storage + await this.storage!.clear() + } catch (error) { + console.error('Failed to clear vector database:', error) + throw new Error(`Failed to clear vector database: ${error}`) + } + } + + /** + * Get the number of vectors in the database + */ + public size(): number { + return this.index.size() + } + + /** + * Ensure the database is initialized + */ + private async ensureInitialized(): Promise { + if (!this.isInitialized) { + await this.init() + } + } +} + +// Export distance functions for convenience +export { euclideanDistance, cosineDistance, manhattanDistance, dotProductDistance } from './utils/index.js' diff --git a/src/coreTypes.ts b/src/coreTypes.ts new file mode 100644 index 00000000..8a8dae07 --- /dev/null +++ b/src/coreTypes.ts @@ -0,0 +1,123 @@ +/** + * Type definitions for the Soulcraft Brainy + */ + +/** + * Vector representation - an array of numbers + */ +export type Vector = number[]; + +/** + * A document with a vector embedding and optional metadata + */ +export interface VectorDocument { + id: string; + vector: Vector; + metadata?: T; +} + +/** + * Search result with similarity score + */ +export interface SearchResult { + id: string; + score: number; + vector: Vector; + metadata?: T; +} + +/** + * Distance function for comparing vectors + */ +export type DistanceFunction = (a: Vector, b: Vector) => number; + +/** + * Embedding function for converting data to vectors + */ +export type EmbeddingFunction = (data: any) => Promise; + +/** + * Embedding model interface + */ +export interface EmbeddingModel { + /** + * Initialize the embedding model + */ + init(): Promise; + + /** + * Embed data into a vector + */ + embed(data: any): Promise; + + /** + * Dispose of the model resources + */ + dispose(): Promise; +} + +/** + * HNSW graph node + */ +export interface HNSWNode { + id: string; + vector: Vector; + connections: Map>; // level -> set of connected node ids +} + +/** + * Edge representing a relationship between nodes + * Extends HNSWNode to allow edges to be first-class entities in the data model + */ +export interface Edge extends HNSWNode { + sourceId: string; // ID of the source node + targetId: string; // ID of the target node + type?: string; // Optional type of the relationship + weight?: number; // Optional weight of the relationship + metadata?: any; // Optional metadata for the edge +} + +/** + * HNSW index configuration + */ +export interface HNSWConfig { + M: number; // Maximum number of connections per node + efConstruction: number; // Size of the dynamic candidate list during construction + efSearch: number; // Size of the dynamic candidate list during search + ml: number; // Maximum level +} + +/** + * Storage interface for persistence + */ +export interface StorageAdapter { + init(): Promise; + + saveNode(node: HNSWNode): Promise; + + getNode(id: string): Promise; + + getAllNodes(): Promise; + + deleteNode(id: string): Promise; + + saveEdge(edge: Edge): Promise; + + getEdge(id: string): Promise; + + getAllEdges(): Promise; + + getEdgesBySource(sourceId: string): Promise; + + getEdgesByTarget(targetId: string): Promise; + + getEdgesByType(type: string): Promise; + + deleteEdge(id: string): Promise; + + saveMetadata(id: string, metadata: any): Promise; + + getMetadata(id: string): Promise; + + clear(): Promise; +} diff --git a/src/examples/basicUsage.ts b/src/examples/basicUsage.ts new file mode 100644 index 00000000..83f881c4 --- /dev/null +++ b/src/examples/basicUsage.ts @@ -0,0 +1,163 @@ +/** + * Basic usage example for the Soulcraft Brainy database + */ + +import { BrainyData } from '../brainyData.js' + +// Example data - word embeddings +const wordEmbeddings = { + cat: [0.2, 0.3, 0.4, 0.1], + dog: [0.3, 0.2, 0.4, 0.2], + fish: [0.1, 0.1, 0.8, 0.2], + bird: [0.1, 0.4, 0.2, 0.5], + tiger: [0.3, 0.4, 0.3, 0.1], + lion: [0.4, 0.3, 0.2, 0.1], + shark: [0.2, 0.1, 0.7, 0.3], + eagle: [0.2, 0.5, 0.1, 0.4] +} + +// Example metadata +const metadata = { + cat: { type: 'mammal', domesticated: true }, + dog: { type: 'mammal', domesticated: true }, + fish: { type: 'fish', domesticated: false }, + bird: { type: 'bird', domesticated: false }, + tiger: { type: 'mammal', domesticated: false }, + lion: { type: 'mammal', domesticated: false }, + shark: { type: 'fish', domesticated: false }, + eagle: { type: 'bird', domesticated: false } +} + +/** + * Run the example + */ +async function runExample() { + console.log('Initializing vector database...') + + // Create a new vector database + const db = new BrainyData() + await db.init() + + console.log('Adding vectors to the database...') + + // Add vectors to the database + const ids: Record = {} + const metadata: Record = { + cat: { type: 'mammal', domesticated: true }, + dog: { type: 'mammal', domesticated: true }, + fish: { type: 'fish', domesticated: false }, + bird: { type: 'bird', domesticated: false }, + tiger: { type: 'mammal', domesticated: false }, + lion: { type: 'mammal', domesticated: false }, + shark: { type: 'fish', domesticated: false }, + eagle: { type: 'bird', domesticated: false } + } + for (const [word, vector] of Object.entries(wordEmbeddings)) { + ids[word] = await db.add(vector, metadata[word]) + + console.log(`Added "${word}" with ID: ${ids[word]}`) + } + + console.log('\nDatabase size:', db.size()) + + // Search for similar vectors + console.log('\nSearching for vectors similar to "cat"...') + const catResults = await db.search(wordEmbeddings['cat'], 3) + console.log('Results:') + for (const result of catResults) { + const word = + Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown' + console.log( + `- ${word} (score: ${result.score.toFixed(4)}, metadata:`, + result.metadata, + ')' + ) + } + + // Search for similar vectors + console.log('\nSearching for vectors similar to "fish"...') + const fishResults = await db.search(wordEmbeddings['fish'], 3) + console.log('Results:') + for (const result of fishResults) { + const word = + Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown' + console.log( + `- ${word} (score: ${result.score.toFixed(4)}, metadata:`, + result.metadata, + ')' + ) + } + + // Update metadata + console.log('\nUpdating metadata for "bird"...') + await db.updateMetadata(ids['bird'], { + ...metadata['bird'], + notes: 'Can fly' + }) + + // Get the updated document + const birdDoc = await db.get(ids['bird']) + console.log('Updated bird document:', birdDoc) + + // Delete a vector + console.log('\nDeleting "shark"...') + await db.delete(ids['shark']) + console.log('Database size after deletion:', db.size()) + + // Search again to verify shark is gone + console.log('\nSearching for vectors similar to "fish" after deletion...') + const fishResultsAfterDeletion = await db.search(wordEmbeddings['fish'], 3) + console.log('Results:') + for (const result of fishResultsAfterDeletion) { + const word = + Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown' + console.log( + `- ${word} (score: ${result.score.toFixed(4)}, metadata:`, + result.metadata, + ')' + ) + } + + console.log('\nExample completed successfully!') +} + +// Check if we're in a browser or Node.js environment +if (typeof window !== 'undefined') { + // Browser environment + document.addEventListener('DOMContentLoaded', () => { + const button = document.createElement('button') + button.textContent = 'Run BrainyData Example' + button.addEventListener('click', async () => { + const output = document.createElement('pre') + document.body.appendChild(output) + + // Redirect console.log to the output element + const originalLog = console.log + console.log = (...args) => { + originalLog(...args) + output.textContent += + args + .map((arg) => + typeof arg === 'object' ? JSON.stringify(arg, null, 2) : arg + ) + .join(' ') + '\n' + } + + try { + await runExample() + } catch (error) { + console.error('Error running example:', error) + } + + // Restore console.log + console.log = originalLog + }) + + document.body.appendChild(button) + }) +} else { + // Node.js environment + runExample().catch((error) => { + console.error('Error running example:', error) + }) +} diff --git a/src/examples/customStorage.ts b/src/examples/customStorage.ts new file mode 100644 index 00000000..6eab4d3b --- /dev/null +++ b/src/examples/customStorage.ts @@ -0,0 +1,76 @@ +/** + * Example demonstrating how to use the FileSystemStorage adapter with a custom directory + */ + +import { BrainyData } from '../brainyData.js' +import { FileSystemStorage } from '../storage/fileSystemStorage.js' +import path from 'path' + +// Example data - word embeddings +const wordEmbeddings = { + 'cat': [0.2, 0.3, 0.4, 0.1], + 'dog': [0.3, 0.2, 0.4, 0.2], + 'fish': [0.1, 0.1, 0.8, 0.2] +} + +// Example metadata +const metadata: { + [key: string]: { type: string; [key]: boolean } | undefined | null +} = { + 'cat': { type: 'mammal', domesticated: true }, + 'dog': { type: 'mammal', domesticated: true }, + 'fish': { type: 'fish', domesticated: false } +} + +/** + * Run the example + */ +async function runExample() { + console.log('Initializing vector database with custom storage location...') + + // Create a custom storage adapter with a specific directory + // This will store data in ./custom-data directory relative to the current working directory + const customStoragePath = path.join(process.cwd(), 'custom-data') + const storageAdapter = new FileSystemStorage(customStoragePath) + + // Create a new vector database with the custom storage adapter + const db = new BrainyData({ + storageAdapter + }) + + await db.init() + + console.log(`Using custom storage location: ${customStoragePath}`) + console.log('Adding vectors to the database...') + + // Add vectors to the database + const ids: Record = {} + for (const [word, vector] of Object.entries(wordEmbeddings)) { + ids[word] = await db.add(vector, metadata[word]) + console.log(`Added "${word}" with ID: ${ids[word]}`) + } + + console.log('\nDatabase size:', db.size()) + + // Search for similar vectors + console.log('\nSearching for vectors similar to "cat"...') + const catResults = await db.search(wordEmbeddings['cat'], 2) + console.log('Results:') + for (const result of catResults) { + const word = Object.entries(ids).find(([_, id]) => id === result.id)?.[0] || 'unknown' + console.log(`- ${word} (score: ${result.score.toFixed(4)}, metadata:`, result.metadata, ')') + } + + console.log('\nExample completed successfully!') + console.log(`Data has been stored in: ${customStoragePath}`) + console.log('You can restart this example to verify that data persists between runs.') +} + +// Only run in Node.js environment +if (typeof process !== 'undefined' && process.versions && process.versions.node) { + runExample().catch(error => { + console.error('Error running example:', error) + }) +} else { + console.error('This example is designed to run in Node.js environments only.') +} diff --git a/src/hnsw/hnswIndex.ts b/src/hnsw/hnswIndex.ts new file mode 100644 index 00000000..efaf3daa --- /dev/null +++ b/src/hnsw/hnswIndex.ts @@ -0,0 +1,527 @@ +/** + * HNSW (Hierarchical Navigable Small World) Index implementation + * Based on the paper: "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs" + */ + +import { DistanceFunction, HNSWConfig, HNSWNode, Vector, VectorDocument } from '../coreTypes.js' +import { euclideanDistance } from '../utils/index.js' + +// Default HNSW parameters +const DEFAULT_CONFIG: HNSWConfig = { + M: 16, // Max number of connections per node + efConstruction: 200, // Size of a dynamic candidate list during construction + efSearch: 50, // Size of a dynamic candidate list during search + ml: 16 // Max level +} + +export class HNSWIndex { + private nodes: Map = new Map() + private entryPointId: string | null = null + private maxLevel = 0 + private config: HNSWConfig + private distanceFunction: DistanceFunction + private dimension: number | null = null + + constructor( + config: Partial = {}, + distanceFunction: DistanceFunction = euclideanDistance + ) { + this.config = { ...DEFAULT_CONFIG, ...config } + this.distanceFunction = distanceFunction + } + + /** + * Add a vector to the index + */ + public addItem(item: VectorDocument): string { + // Check if item is defined + if (!item) { + throw new Error('Item is undefined or null') + } + + const { id, vector } = item + + // Check if vector is defined + if (!vector) { + throw new Error('Vector is undefined or null') + } + + // Set dimension on first insert + if (this.dimension === null) { + this.dimension = vector.length + } else if (vector.length !== this.dimension) { + throw new Error( + `Vector dimension mismatch: expected ${this.dimension}, got ${vector.length}` + ) + } + + // Generate random level for this node + const nodeLevel = this.getRandomLevel() + + // Create new node + const node: HNSWNode = { + id, + vector, + connections: new Map() + } + + // Initialize empty connection sets for each level + for (let level = 0; level <= nodeLevel; level++) { + node.connections.set(level, new Set()) + } + + // If this is the first node, make it the entry point + if (this.nodes.size === 0) { + this.entryPointId = id + this.maxLevel = nodeLevel + this.nodes.set(id, node) + return id + } + + // Find entry point + if (!this.entryPointId) { + console.error('Entry point ID is null') + // If there's no entry point, this is the first node, so we should have returned earlier + // This is a safety check + this.entryPointId = id + this.maxLevel = nodeLevel + this.nodes.set(id, node) + return id + } + + const entryPoint = this.nodes.get(this.entryPointId) + if (!entryPoint) { + console.error(`Entry point with ID ${this.entryPointId} not found`) + // If the entry point doesn't exist, treat this as the first node + this.entryPointId = id + this.maxLevel = nodeLevel + this.nodes.set(id, node) + return id + } + + let currObj = entryPoint + let currDist = this.distanceFunction(vector, entryPoint.vector) + + // Traverse the graph from top to bottom to find the closest node + for (let level = this.maxLevel; level > nodeLevel; level--) { + let changed = true + while (changed) { + changed = false + + // Check all neighbors at current level + const connections = currObj.connections.get(level) || new Set() + + for (const neighborId of connections) { + const neighbor = this.nodes.get(neighborId) + if (!neighbor) { + console.error(`Neighbor with ID ${neighborId} not found in addItem traversal`) + continue + } + const distToNeighbor = this.distanceFunction(vector, neighbor.vector) + + if (distToNeighbor < currDist) { + currDist = distToNeighbor + currObj = neighbor + changed = true + } + } + } + } + + // For each level from nodeLevel down to 0 + for (let level = Math.min(nodeLevel, this.maxLevel); level >= 0; level--) { + // Find ef nearest elements using greedy search + const nearestNodes = this.searchLayer( + vector, + currObj, + this.config.efConstruction, + level + ) + + // Select M nearest neighbors + const neighbors = this.selectNeighbors( + vector, + nearestNodes, + this.config.M + ) + + // Add bidirectional connections + for (const [neighborId, _] of neighbors) { + const neighbor = this.nodes.get(neighborId) + if (!neighbor) { + console.error(`Neighbor with ID ${neighborId} not found`) + continue + } + + node.connections.get(level)!.add(neighborId) + + // Add reverse connection + if (!neighbor.connections.has(level)) { + neighbor.connections.set(level, new Set()) + } + neighbor.connections.get(level)!.add(id) + + // Ensure neighbor doesn't have too many connections + if (neighbor.connections.get(level)!.size > this.config.M) { + this.pruneConnections(neighbor, level) + } + } + + // Update entry point for the next level + if (nearestNodes.size > 0) { + const [nearestId, nearestDist] = [...nearestNodes][0] + if (nearestDist < currDist) { + currDist = nearestDist + const nearestNode = this.nodes.get(nearestId) + if (!nearestNode) { + console.error(`Nearest node with ID ${nearestId} not found in addItem`) + // Keep the current object as is + } else { + currObj = nearestNode + } + } + } + } + + // Update max level and entry point if needed + if (nodeLevel > this.maxLevel) { + this.maxLevel = nodeLevel + this.entryPointId = id + } + + // Add node to the index + this.nodes.set(id, node) + return id + } + + /** + * Search for nearest neighbors + */ + public search(queryVector: Vector, k: number = 10): Array<[string, number]> { + if (this.nodes.size === 0) { + return [] + } + + // Check if query vector is defined + if (!queryVector) { + throw new Error('Query vector is undefined or null') + } + + if (this.dimension !== null && queryVector.length !== this.dimension) { + throw new Error( + `Query vector dimension mismatch: expected ${this.dimension}, got ${queryVector.length}` + ) + } + + // Start from the entry point + if (!this.entryPointId) { + console.error('Entry point ID is null') + return [] + } + + const entryPoint = this.nodes.get(this.entryPointId) + if (!entryPoint) { + console.error(`Entry point with ID ${this.entryPointId} not found`) + return [] + } + + let currObj = entryPoint + let currDist = this.distanceFunction(queryVector, currObj.vector) + + // Traverse the graph from top to bottom to find the closest node + for (let level = this.maxLevel; level > 0; level--) { + let changed = true + while (changed) { + changed = false + + // Check all neighbors at current level + const connections = currObj.connections.get(level) || new Set() + + for (const neighborId of connections) { + const neighbor = this.nodes.get(neighborId) + if (!neighbor) { + console.error(`Neighbor with ID ${neighborId} not found in search`) + continue + } + const distToNeighbor = this.distanceFunction( + queryVector, + neighbor.vector + ) + + if (distToNeighbor < currDist) { + currDist = distToNeighbor + currObj = neighbor + changed = true + } + } + } + } + + // Search at level 0 with ef = k + const nearestNodes = this.searchLayer( + queryVector, + currObj, + Math.max(this.config.efSearch, k), + 0 + ) + + // Convert to array and sort by distance + return [...nearestNodes].slice(0, k) + } + + /** + * Remove an item from the index + */ + public removeItem(id: string): boolean { + if (!this.nodes.has(id)) { + return false + } + + const node = this.nodes.get(id)! + + // Remove connections to this node from all neighbors + for (const [level, connections] of node.connections.entries()) { + for (const neighborId of connections) { + const neighbor = this.nodes.get(neighborId) + if (!neighbor) { + console.error(`Neighbor with ID ${neighborId} not found in removeItem`) + continue + } + if (neighbor.connections.has(level)) { + neighbor.connections.get(level)!.delete(id) + + // Prune connections after removing this node to ensure consistency + this.pruneConnections(neighbor, level) + } + } + } + + // Also check all other nodes for references to this node and remove them + for (const [nodeId, otherNode] of this.nodes.entries()) { + if (nodeId === id) continue // Skip the node being removed + + for (const [level, connections] of otherNode.connections.entries()) { + if (connections.has(id)) { + connections.delete(id) + + // Prune connections after removing this reference + this.pruneConnections(otherNode, level) + } + } + } + + // Remove the node + this.nodes.delete(id) + + // If we removed the entry point, find a new one + if (this.entryPointId === id) { + if (this.nodes.size === 0) { + this.entryPointId = null + this.maxLevel = 0 + } else { + // Find the node with the highest level + let maxLevel = 0 + let newEntryPointId = null + + for (const [nodeId, node] of this.nodes.entries()) { + if (node.connections.size === 0) continue // Skip nodes with no connections + + const nodeLevel = Math.max(...node.connections.keys()) + if (nodeLevel >= maxLevel) { + maxLevel = nodeLevel + newEntryPointId = nodeId + } + } + + this.entryPointId = newEntryPointId + this.maxLevel = maxLevel + } + } + + return true + } + + /** + * Get all nodes in the index + */ + public getNodes(): Map { + return new Map(this.nodes) + } + + /** + * Clear the index + */ + public clear(): void { + this.nodes.clear() + this.entryPointId = null + this.maxLevel = 0 + } + + /** + * Get the size of the index + */ + public size(): number { + return this.nodes.size + } + + /** + * Search within a specific layer + * Returns a map of node IDs to distances, sorted by distance + */ + private searchLayer( + queryVector: Vector, + entryPoint: HNSWNode, + ef: number, + level: number + ): Map { + // Set of visited nodes + const visited = new Set([entryPoint.id]) + + // Priority queue of candidates (closest first) + const candidates = new Map() + candidates.set( + entryPoint.id, + this.distanceFunction(queryVector, entryPoint.vector) + ) + + // Priority queue of nearest neighbors found so far (closest first) + const nearest = new Map() + nearest.set( + entryPoint.id, + this.distanceFunction(queryVector, entryPoint.vector) + ) + + // While there are candidates to explore + while (candidates.size > 0) { + // Get closest candidate + const [closestId, closestDist] = [...candidates][0] + candidates.delete(closestId) + + // If this candidate is farther than the farthest in our result set, we're done + const farthestInNearest = [...nearest][nearest.size - 1] + if (nearest.size >= ef && closestDist > farthestInNearest[1]) { + break + } + + // Explore neighbors of the closest candidate + const node = this.nodes.get(closestId) + if (!node) { + console.error(`Node with ID ${closestId} not found in searchLayer`) + continue + } + const connections = node.connections.get(level) || new Set() + + for (const neighborId of connections) { + if (!visited.has(neighborId)) { + visited.add(neighborId) + + const neighbor = this.nodes.get(neighborId) + if (!neighbor) { + console.error(`Neighbor with ID ${neighborId} not found in searchLayer`) + continue + } + const distToNeighbor = this.distanceFunction( + queryVector, + neighbor.vector + ) + + // If we haven't found ef nearest neighbors yet, or this neighbor is closer than the farthest one we've found + if (nearest.size < ef || distToNeighbor < farthestInNearest[1]) { + candidates.set(neighborId, distToNeighbor) + nearest.set(neighborId, distToNeighbor) + + // If we have more than ef neighbors, remove the farthest one + if (nearest.size > ef) { + const sortedNearest = [...nearest].sort((a, b) => a[1] - b[1]) + nearest.clear() + for (let i = 0; i < ef; i++) { + nearest.set(sortedNearest[i][0], sortedNearest[i][1]) + } + } + } + } + } + } + + // Sort nearest by distance + return new Map([...nearest].sort((a, b) => a[1] - b[1])) + } + + /** + * Select M nearest neighbors from the candidate set + */ + private selectNeighbors( + queryVector: Vector, + candidates: Map, + M: number + ): Map { + if (candidates.size <= M) { + return candidates + } + + // Simple heuristic: just take the M closest + const sortedCandidates = [...candidates].sort((a, b) => a[1] - b[1]) + const result = new Map() + + for (let i = 0; i < Math.min(M, sortedCandidates.length); i++) { + result.set(sortedCandidates[i][0], sortedCandidates[i][1]) + } + + return result + } + + /** + * Ensure a node doesn't have too many connections at a given level + */ + private pruneConnections(node: HNSWNode, level: number): void { + const connections = node.connections.get(level)! + if (connections.size <= this.config.M) { + return + } + + // Calculate distances to all neighbors + const distances = new Map() + const validNeighborIds = new Set() + + for (const neighborId of connections) { + const neighbor = this.nodes.get(neighborId) + if (!neighbor) { + console.error(`Neighbor with ID ${neighborId} not found in pruneConnections`) + continue + } + + // Only add valid neighbors to the distances map + distances.set( + neighborId, + this.distanceFunction(node.vector, neighbor.vector) + ) + validNeighborIds.add(neighborId) + } + + // Only proceed if we have valid neighbors + if (distances.size === 0) { + // If no valid neighbors, clear connections at this level + node.connections.set(level, new Set()) + return + } + + // Select M closest neighbors from valid ones + const selectedNeighbors = this.selectNeighbors( + node.vector, + distances, + this.config.M + ) + + // Update connections with only valid neighbors + node.connections.set(level, new Set(selectedNeighbors.keys())) + } + + /** + * Generate a random level for a new node + * Uses the same distribution as in the original HNSW paper + */ + private getRandomLevel(): number { + const r = Math.random() + return Math.floor(-Math.log(r) * (1.0 / Math.log(this.config.M))) + } +} diff --git a/src/index.ts b/src/index.ts new file mode 100644 index 00000000..7b99b2bc --- /dev/null +++ b/src/index.ts @@ -0,0 +1,61 @@ +/** + * OPFS BrainyData + * A vector database using HNSW indexing with Origin Private File System storage + */ + +// Export main BrainyData class and related types +import { BrainyData, BrainyDataConfig } from './brainyData.js' +export { BrainyData } +export type { BrainyDataConfig } + +// Export distance functions for convenience +import { + euclideanDistance, + cosineDistance, + manhattanDistance, + dotProductDistance +} from './utils/index.js' +export { + euclideanDistance, + cosineDistance, + manhattanDistance, + dotProductDistance +} + +// Export storage adapters +import { + OPFSStorage, + MemoryStorage, + createStorage +} from './storage/opfsStorage.js' +export { + OPFSStorage, + MemoryStorage, + createStorage +} + +// Export types +import type { + Vector, + VectorDocument, + SearchResult, + DistanceFunction, + EmbeddingFunction, + EmbeddingModel, + HNSWNode, + Edge, + HNSWConfig, + StorageAdapter +} from './coreTypes.js' +export type { + Vector, + VectorDocument, + SearchResult, + DistanceFunction, + EmbeddingFunction, + EmbeddingModel, + HNSWNode, + Edge, + HNSWConfig, + StorageAdapter +} diff --git a/src/storage/fileSystemStorage.ts b/src/storage/fileSystemStorage.ts new file mode 100644 index 00000000..e5b20a65 --- /dev/null +++ b/src/storage/fileSystemStorage.ts @@ -0,0 +1,432 @@ +import fs from 'fs' +import path from 'path' +import { Edge, HNSWNode, StorageAdapter } from '../coreTypes.js' + +// Constants for directory and file names +const ROOT_DIR = 'brainy-data' +const NODES_DIR = 'nodes' +const EDGES_DIR = 'edges' +const METADATA_DIR = 'metadata' + +/** + * File system storage adapter for Node.js environments + */ +export class FileSystemStorage implements StorageAdapter { + private rootDir: string + private nodesDir: string + private edgesDir: string + private metadataDir: string + private isInitialized = false + + constructor(rootDirectory?: string) { + // Use the provided root directory or default to the current working directory + this.rootDir = rootDirectory ? path.resolve(rootDirectory, ROOT_DIR) : path.resolve(process.cwd(), ROOT_DIR) + this.nodesDir = path.join(this.rootDir, NODES_DIR) + this.edgesDir = path.join(this.rootDir, EDGES_DIR) + this.metadataDir = path.join(this.rootDir, METADATA_DIR) + } + + /** + * Initialize the storage adapter + */ + public async init(): Promise { + if (this.isInitialized) { + return + } + + try { + // Create directories if they don't exist + await this.ensureDirectoryExists(this.rootDir) + await this.ensureDirectoryExists(this.nodesDir) + await this.ensureDirectoryExists(this.edgesDir) + await this.ensureDirectoryExists(this.metadataDir) + + this.isInitialized = true + } catch (error) { + console.error('Failed to initialize file system storage:', error) + throw new Error(`Failed to initialize file system storage: ${error}`) + } + } + + /** + * Save a node to storage + */ + public async saveNode(node: HNSWNode): Promise { + await this.ensureInitialized() + + try { + // Convert connections Map to a serializable format + const serializableNode = { + ...node, + connections: this.mapToObject(node.connections, (set) => Array.from(set)) + } + + const filePath = path.join(this.nodesDir, `${node.id}.json`) + await fs.promises.writeFile( + filePath, + JSON.stringify(serializableNode, null, 2), + 'utf8' + ) + } catch (error) { + console.error(`Failed to save node ${node.id}:`, error) + throw new Error(`Failed to save node ${node.id}: ${error}`) + } + } + + /** + * Get a node from storage + */ + public async getNode(id: string): Promise { + await this.ensureInitialized() + + try { + const filePath = path.join(this.nodesDir, `${id}.json`) + + // Check if a file exists + try { + await fs.promises.access(filePath) + } catch { + return null // File doesn't exist + } + + const data = await fs.promises.readFile(filePath, 'utf8') + const parsedNode = JSON.parse(data) + + // Convert serialized connections back to Map> + const connections = new Map>() + for (const [level, nodeIds] of Object.entries(parsedNode.connections)) { + connections.set(Number(level), new Set(nodeIds as string[])) + } + + return { + id: parsedNode.id, + vector: parsedNode.vector, + connections + } + } catch (error) { + console.error(`Failed to get node ${id}:`, error) + return null + } + } + + /** + * Get all nodes from storage + */ + public async getAllNodes(): Promise { + await this.ensureInitialized() + + try { + const files = await fs.promises.readdir(this.nodesDir) + const nodePromises = files + .filter(file => file.endsWith('.json')) + .map(file => { + const id = path.basename(file, '.json') + return this.getNode(id) + }) + + const nodes = await Promise.all(nodePromises) + return nodes.filter((node): node is HNSWNode => node !== null) + } catch (error) { + console.error('Failed to get all nodes:', error) + throw new Error(`Failed to get all nodes: ${error}`) + } + } + + /** + * Delete a node from storage + */ + public async deleteNode(id: string): Promise { + await this.ensureInitialized() + + try { + const filePath = path.join(this.nodesDir, `${id}.json`) + + // Check if a file exists before attempting to delete + try { + await fs.promises.access(filePath) + } catch { + return // File doesn't exist, nothing to delete + } + + await fs.promises.unlink(filePath) + } catch (error) { + console.error(`Failed to delete node ${id}:`, error) + throw new Error(`Failed to delete node ${id}: ${error}`) + } + } + + /** + * Save an edge to storage + */ + public async saveEdge(edge: Edge): Promise { + await this.ensureInitialized() + + try { + // Convert connections Map to a serializable format + const serializableEdge = { + ...edge, + connections: this.mapToObject(edge.connections, (set) => Array.from(set)) + } + + const filePath = path.join(this.edgesDir, `${edge.id}.json`) + await fs.promises.writeFile( + filePath, + JSON.stringify(serializableEdge, null, 2), + 'utf8' + ) + } catch (error) { + console.error(`Failed to save edge ${edge.id}:`, error) + throw new Error(`Failed to save edge ${edge.id}: ${error}`) + } + } + + /** + * Get an edge from storage + */ + public async getEdge(id: string): Promise { + await this.ensureInitialized() + + try { + const filePath = path.join(this.edgesDir, `${id}.json`) + + // Check if a file exists + try { + await fs.promises.access(filePath) + } catch { + return null // File doesn't exist + } + + const data = await fs.promises.readFile(filePath, 'utf8') + const parsedEdge = JSON.parse(data) + + // Convert serialized connections back to Map> + const connections = new Map>() + for (const [level, nodeIds] of Object.entries(parsedEdge.connections)) { + connections.set(Number(level), new Set(nodeIds as string[])) + } + + return { + id: parsedEdge.id, + vector: parsedEdge.vector, + connections, + sourceId: parsedEdge.sourceId, + targetId: parsedEdge.targetId, + type: parsedEdge.type, + weight: parsedEdge.weight, + metadata: parsedEdge.metadata + } + } catch (error) { + console.error(`Failed to get edge ${id}:`, error) + return null + } + } + + /** + * Get all edges from storage + */ + public async getAllEdges(): Promise { + await this.ensureInitialized() + + try { + const files = await fs.promises.readdir(this.edgesDir) + const edgePromises = files + .filter(file => file.endsWith('.json')) + .map(file => { + const id = path.basename(file, '.json') + return this.getEdge(id) + }) + + const edges = await Promise.all(edgePromises) + return edges.filter((edge): edge is Edge => edge !== null) + } catch (error) { + console.error('Failed to get all edges:', error) + throw new Error(`Failed to get all edges: ${error}`) + } + } + + /** + * Get edges by source node ID + */ + public async getEdgesBySource(sourceId: string): Promise { + await this.ensureInitialized() + + try { + const allEdges = await this.getAllEdges() + return allEdges.filter(edge => edge.sourceId === sourceId) + } catch (error) { + console.error(`Failed to get edges by source ${sourceId}:`, error) + throw new Error(`Failed to get edges by source ${sourceId}: ${error}`) + } + } + + /** + * Get edges by target node ID + */ + public async getEdgesByTarget(targetId: string): Promise { + await this.ensureInitialized() + + try { + const allEdges = await this.getAllEdges() + return allEdges.filter(edge => edge.targetId === targetId) + } catch (error) { + console.error(`Failed to get edges by target ${targetId}:`, error) + throw new Error(`Failed to get edges by target ${targetId}: ${error}`) + } + } + + /** + * Get edges by type + */ + public async getEdgesByType(type: string): Promise { + await this.ensureInitialized() + + try { + const allEdges = await this.getAllEdges() + return allEdges.filter(edge => edge.type === type) + } catch (error) { + console.error(`Failed to get edges by type ${type}:`, error) + throw new Error(`Failed to get edges by type ${type}: ${error}`) + } + } + + /** + * Delete an edge from storage + */ + public async deleteEdge(id: string): Promise { + await this.ensureInitialized() + + try { + const filePath = path.join(this.edgesDir, `${id}.json`) + + // Check if a file exists before attempting to delete + try { + await fs.promises.access(filePath) + } catch { + return // File doesn't exist, nothing to delete + } + + await fs.promises.unlink(filePath) + } catch (error) { + console.error(`Failed to delete edge ${id}:`, error) + throw new Error(`Failed to delete edge ${id}: ${error}`) + } + } + + /** + * Save metadata to storage + */ + public async saveMetadata(id: string, metadata: any): Promise { + await this.ensureInitialized() + + try { + const filePath = path.join(this.metadataDir, `${id}.json`) + await fs.promises.writeFile( + filePath, + JSON.stringify(metadata, null, 2), + 'utf8' + ) + } catch (error) { + console.error(`Failed to save metadata for ${id}:`, error) + throw new Error(`Failed to save metadata for ${id}: ${error}`) + } + } + + /** + * Get metadata from storage + */ + public async getMetadata(id: string): Promise { + await this.ensureInitialized() + + try { + const filePath = path.join(this.metadataDir, `${id}.json`) + + // Check if a file exists + try { + await fs.promises.access(filePath) + } catch { + return null // File doesn't exist + } + + const data = await fs.promises.readFile(filePath, 'utf8') + return JSON.parse(data) + } catch (error) { + console.error(`Failed to get metadata for ${id}:`, error) + return null + } + } + + /** + * Clear all data from storage + */ + public async clear(): Promise { + await this.ensureInitialized() + + try { + // Delete and recreate the nodes, edges, and metadata directories + await this.deleteDirectory(this.nodesDir) + await this.deleteDirectory(this.edgesDir) + await this.deleteDirectory(this.metadataDir) + + await this.ensureDirectoryExists(this.nodesDir) + await this.ensureDirectoryExists(this.edgesDir) + await this.ensureDirectoryExists(this.metadataDir) + } catch (error) { + console.error('Failed to clear storage:', error) + throw new Error(`Failed to clear storage: ${error}`) + } + } + + /** + * Ensure the storage adapter is initialized + */ + private async ensureInitialized(): Promise { + if (!this.isInitialized) { + await this.init() + } + } + + /** + * Ensure a directory exists, creating it if necessary + */ + private async ensureDirectoryExists(dirPath: string): Promise { + try { + await fs.promises.access(dirPath) + } catch { + // Directory doesn't exist, create it + await fs.promises.mkdir(dirPath, { recursive: true }) + } + } + + /** + * Delete a directory and all its contents + */ + private async deleteDirectory(dirPath: string): Promise { + try { + const files = await fs.promises.readdir(dirPath) + + for (const file of files) { + const filePath = path.join(dirPath, file) + await fs.promises.unlink(filePath) + } + } catch (error) { + // If the directory doesn't exist, that's fine + if ((error as NodeJS.ErrnoException).code !== 'ENOENT') { + throw error + } + } + } + + /** + * Convert a Map to a plain object for serialization + */ + private mapToObject( + map: Map, + valueTransformer: (value: V) => any = (v) => v + ): Record { + const obj: Record = {} + for (const [key, value] of map.entries()) { + obj[key.toString()] = valueTransformer(value) + } + return obj + } +} diff --git a/src/storage/opfsStorage.ts b/src/storage/opfsStorage.ts new file mode 100644 index 00000000..56cf00fc --- /dev/null +++ b/src/storage/opfsStorage.ts @@ -0,0 +1,680 @@ +/** + * OPFS (Origin Private File System) Storage Adapter + * Provides persistent storage for the vector database using the Origin Private File System API + */ + +import { Edge, HNSWNode, StorageAdapter } from '../coreTypes.js' + +// Directory and file names +const ROOT_DIR = 'opfs-vector-db' +const NODES_DIR = 'nodes' +const EDGES_DIR = 'edges' +const METADATA_DIR = 'metadata' +const DB_INFO_FILE = 'db-info.json' + +export class OPFSStorage implements StorageAdapter { + private rootDir: FileSystemDirectoryHandle | null = null + private nodesDir: FileSystemDirectoryHandle | null = null + private edgesDir: FileSystemDirectoryHandle | null = null + private metadataDir: FileSystemDirectoryHandle | null = null + private isInitialized = false + private isAvailable = false + + constructor() { + // Check if OPFS is available + this.isAvailable = + typeof navigator !== 'undefined' && + 'storage' in navigator && + 'getDirectory' in navigator.storage + } + + /** + * Initialize the storage adapter + */ + public async init(): Promise { + if (this.isInitialized) { + return + } + + if (!this.isAvailable) { + throw new Error( + 'Origin Private File System is not available in this environment' + ) + } + + try { + // Get the root directory + const root = await navigator.storage.getDirectory() + + // Create or get our app's root directory + this.rootDir = await root.getDirectoryHandle(ROOT_DIR, { create: true }) + + // Create or get nodes directory + this.nodesDir = await this.rootDir.getDirectoryHandle(NODES_DIR, { + create: true + }) + + // Create or get edges directory + this.edgesDir = await this.rootDir.getDirectoryHandle(EDGES_DIR, { + create: true + }) + + // Create or get metadata directory + this.metadataDir = await this.rootDir.getDirectoryHandle(METADATA_DIR, { + create: true + }) + + this.isInitialized = true + } catch (error) { + console.error('Failed to initialize OPFS storage:', error) + throw new Error(`Failed to initialize OPFS storage: ${error}`) + } + } + + /** + * Check if OPFS is available in the current environment + */ + public isOPFSAvailable(): boolean { + return this.isAvailable + } + + /** + * Save a node to storage + */ + public async saveNode(node: HNSWNode): Promise { + await this.ensureInitialized() + + try { + // Convert connections Map to a serializable format + const serializableNode = { + ...node, + connections: this.mapToObject(node.connections, (set) => + Array.from(set) + ) + } + + // Create or get the file for this node + const fileHandle = await this.nodesDir!.getFileHandle(node.id, { + create: true + }) + + // Write the node data to the file + const writable = await fileHandle.createWritable() + await writable.write(JSON.stringify(serializableNode)) + await writable.close() + } catch (error) { + console.error(`Failed to save node ${node.id}:`, error) + throw new Error(`Failed to save node ${node.id}: ${error}`) + } + } + + /** + * Get a node from storage + */ + public async getNode(id: string): Promise { + await this.ensureInitialized() + + try { + // Get the file handle for this node + const fileHandle = await this.nodesDir!.getFileHandle(id) + + // Read the node data from the file + const file = await fileHandle.getFile() + const text = await file.text() + const data = JSON.parse(text) + + // Convert serialized connections back to Map> + const connections = new Map>() + for (const [level, nodeIds] of Object.entries(data.connections)) { + connections.set(Number(level), new Set(nodeIds as string[])) + } + + return { + id: data.id, + vector: data.vector, + connections + } + } catch (error) { + // If the file doesn't exist, return null + if ((error as any).name === 'NotFoundError') { + return null + } + + console.error(`Failed to get node ${id}:`, error) + throw new Error(`Failed to get node ${id}: ${error}`) + } + } + + /** + * Get all nodes from storage + */ + public async getAllNodes(): Promise { + await this.ensureInitialized() + + try { + const nodes: HNSWNode[] = [] + + // Get all keys (filenames) in the nodes directory + // @ts-ignore - TypeScript doesn't recognize FileSystemDirectoryHandle.keys() properly + const keys = this.nodesDir!.keys() + + // Iterate through all keys and get the corresponding nodes + for await (const name of keys) { + const node = await this.getNode(name) + if (node) { + nodes.push(node) + } + } + + return nodes + } catch (error) { + console.error('Failed to get all nodes:', error) + throw new Error(`Failed to get all nodes: ${error}`) + } + } + + /** + * Delete a node from storage + */ + public async deleteNode(id: string): Promise { + await this.ensureInitialized() + + try { + await this.nodesDir!.removeEntry(id) + } catch (error) { + // Ignore if the file doesn't exist + if ((error as any).name !== 'NotFoundError') { + console.error(`Failed to delete node ${id}:`, error) + throw new Error(`Failed to delete node ${id}: ${error}`) + } + } + } + + /** + * Save an edge to storage + */ + public async saveEdge(edge: Edge): Promise { + await this.ensureInitialized() + + try { + // Convert connections Map to a serializable format + const serializableEdge = { + ...edge, + connections: this.mapToObject(edge.connections, (set) => + Array.from(set) + ) + } + + // Create or get the file for this edge + const fileHandle = await this.edgesDir!.getFileHandle(edge.id, { + create: true + }) + + // Write the edge data to the file + const writable = await fileHandle.createWritable() + await writable.write(JSON.stringify(serializableEdge)) + await writable.close() + } catch (error) { + console.error(`Failed to save edge ${edge.id}:`, error) + throw new Error(`Failed to save edge ${edge.id}: ${error}`) + } + } + + /** + * Get an edge from storage + */ + public async getEdge(id: string): Promise { + await this.ensureInitialized() + + try { + // Get the file handle for this edge + const fileHandle = await this.edgesDir!.getFileHandle(id) + + // Read the edge data from the file + const file = await fileHandle.getFile() + const text = await file.text() + const data = JSON.parse(text) + + // Convert serialized connections back to Map> + const connections = new Map>() + for (const [level, nodeIds] of Object.entries(data.connections)) { + connections.set(Number(level), new Set(nodeIds as string[])) + } + + return { + id: data.id, + vector: data.vector, + connections, + sourceId: data.sourceId, + targetId: data.targetId, + type: data.type, + weight: data.weight, + metadata: data.metadata + } + } catch (error) { + // If the file doesn't exist, return null + if ((error as any).name === 'NotFoundError') { + return null + } + + console.error(`Failed to get edge ${id}:`, error) + throw new Error(`Failed to get edge ${id}: ${error}`) + } + } + + /** + * Get all edges from storage + */ + public async getAllEdges(): Promise { + await this.ensureInitialized() + + try { + const edges: Edge[] = [] + + // Get all keys (filenames) in the edges directory + // @ts-ignore - TypeScript doesn't recognize FileSystemDirectoryHandle.keys() properly + const keys = this.edgesDir!.keys() + + // Iterate through all keys and get the corresponding edges + for await (const name of keys) { + const edge = await this.getEdge(name) + if (edge) { + edges.push(edge) + } + } + + return edges + } catch (error) { + console.error('Failed to get all edges:', error) + throw new Error(`Failed to get all edges: ${error}`) + } + } + + /** + * Delete an edge from storage + */ + public async deleteEdge(id: string): Promise { + await this.ensureInitialized() + + try { + await this.edgesDir!.removeEntry(id) + } catch (error) { + // Ignore if the file doesn't exist + if ((error as any).name !== 'NotFoundError') { + console.error(`Failed to delete edge ${id}:`, error) + throw new Error(`Failed to delete edge ${id}: ${error}`) + } + } + } + + /** + * Get edges by source node ID + */ + public async getEdgesBySource(sourceId: string): Promise { + await this.ensureInitialized() + + try { + const allEdges = await this.getAllEdges() + return allEdges.filter(edge => edge.sourceId === sourceId) + } catch (error) { + console.error(`Failed to get edges by source ${sourceId}:`, error) + throw new Error(`Failed to get edges by source ${sourceId}: ${error}`) + } + } + + /** + * Get edges by target node ID + */ + public async getEdgesByTarget(targetId: string): Promise { + await this.ensureInitialized() + + try { + const allEdges = await this.getAllEdges() + return allEdges.filter(edge => edge.targetId === targetId) + } catch (error) { + console.error(`Failed to get edges by target ${targetId}:`, error) + throw new Error(`Failed to get edges by target ${targetId}: ${error}`) + } + } + + /** + * Get edges by type + */ + public async getEdgesByType(type: string): Promise { + await this.ensureInitialized() + + try { + const allEdges = await this.getAllEdges() + return allEdges.filter(edge => edge.type === type) + } catch (error) { + console.error(`Failed to get edges by type ${type}:`, error) + throw new Error(`Failed to get edges by type ${type}: ${error}`) + } + } + + /** + * Save metadata for a node + */ + public async saveMetadata(id: string, metadata: any): Promise { + await this.ensureInitialized() + + try { + // Create or get the file for this metadata + const fileHandle = await this.metadataDir!.getFileHandle(id, { + create: true + }) + + // Write the metadata to the file + const writable = await fileHandle.createWritable() + await writable.write(JSON.stringify(metadata)) + await writable.close() + } catch (error) { + console.error(`Failed to save metadata for ${id}:`, error) + throw new Error(`Failed to save metadata for ${id}: ${error}`) + } + } + + /** + * Get metadata for a node + */ + public async getMetadata(id: string): Promise { + await this.ensureInitialized() + + try { + // Get the file handle for this metadata + const fileHandle = await this.metadataDir!.getFileHandle(id) + + // Read the metadata from the file + const file = await fileHandle.getFile() + const text = await file.text() + return JSON.parse(text) + } catch (error) { + // If the file doesn't exist, return null + if ((error as any).name === 'NotFoundError') { + return null + } + + console.error(`Failed to get metadata for ${id}:`, error) + throw new Error(`Failed to get metadata for ${id}: ${error}`) + } + } + + /** + * Clear all data from storage + */ + public async clear(): Promise { + await this.ensureInitialized() + + try { + // Delete and recreate the nodes directory + await this.rootDir!.removeEntry(NODES_DIR, { recursive: true }) + this.nodesDir = await this.rootDir!.getDirectoryHandle(NODES_DIR, { + create: true + }) + + // Delete and recreate the edges directory + await this.rootDir!.removeEntry(EDGES_DIR, { recursive: true }) + this.edgesDir = await this.rootDir!.getDirectoryHandle(EDGES_DIR, { + create: true + }) + + // Delete and recreate the metadata directory + await this.rootDir!.removeEntry(METADATA_DIR, { recursive: true }) + this.metadataDir = await this.rootDir!.getDirectoryHandle(METADATA_DIR, { + create: true + }) + } catch (error) { + console.error('Failed to clear storage:', error) + throw new Error(`Failed to clear storage: ${error}`) + } + } + + /** + * Ensure the storage adapter is initialized + */ + private async ensureInitialized(): Promise { + if (!this.isInitialized) { + await this.init() + } + } + + /** + * Convert a Map to a plain object for serialization + */ + private mapToObject( + map: Map, + valueTransformer: (value: V) => any = (v) => v + ): Record { + const obj: Record = {} + for (const [key, value] of map.entries()) { + obj[key.toString()] = valueTransformer(value) + } + return obj + } +} + +/** + * In-memory storage adapter for environments where OPFS is not available + */ +export class MemoryStorage implements StorageAdapter { + private nodes: Map = new Map() + private edges: Map = new Map() + private metadata: Map = new Map() + + public async init(): Promise { + // Nothing to initialize for in-memory storage + } + + public async saveNode(node: HNSWNode): Promise { + // Create a deep copy to avoid reference issues + const nodeCopy: HNSWNode = { + id: node.id, + vector: [...node.vector], + connections: new Map() + } + + // Copy connections + for (const [level, connections] of node.connections.entries()) { + nodeCopy.connections.set(level, new Set(connections)) + } + + this.nodes.set(node.id, nodeCopy) + } + + public async getNode(id: string): Promise { + const node = this.nodes.get(id) + if (!node) { + return null + } + + // Return a deep copy to avoid reference issues + const nodeCopy: HNSWNode = { + id: node.id, + vector: [...node.vector], + connections: new Map() + } + + // Copy connections + for (const [level, connections] of node.connections.entries()) { + nodeCopy.connections.set(level, new Set(connections)) + } + + return nodeCopy + } + + public async getAllNodes(): Promise { + const nodes: HNSWNode[] = [] + + for (const nodeId of this.nodes.keys()) { + const node = await this.getNode(nodeId) + if (node) { + nodes.push(node) + } + } + + return nodes + } + + public async deleteNode(id: string): Promise { + this.nodes.delete(id) + } + + public async saveMetadata(id: string, metadata: any): Promise { + this.metadata.set(id, JSON.parse(JSON.stringify(metadata))) + } + + public async getMetadata(id: string): Promise { + const metadata = this.metadata.get(id) + if (!metadata) { + return null + } + + return JSON.parse(JSON.stringify(metadata)) + } + + public async saveEdge(edge: Edge): Promise { + // Create a deep copy to avoid reference issues + const edgeCopy: Edge = { + id: edge.id, + vector: [...edge.vector], + connections: new Map(), + sourceId: edge.sourceId, + targetId: edge.targetId, + type: edge.type, + weight: edge.weight, + metadata: edge.metadata ? JSON.parse(JSON.stringify(edge.metadata)) : undefined + } + + // Copy connections + for (const [level, connections] of edge.connections.entries()) { + edgeCopy.connections.set(level, new Set(connections)) + } + + this.edges.set(edge.id, edgeCopy) + } + + public async getEdge(id: string): Promise { + const edge = this.edges.get(id) + if (!edge) { + return null + } + + // Return a deep copy to avoid reference issues + const edgeCopy: Edge = { + id: edge.id, + vector: [...edge.vector], + connections: new Map(), + sourceId: edge.sourceId, + targetId: edge.targetId, + type: edge.type, + weight: edge.weight, + metadata: edge.metadata ? JSON.parse(JSON.stringify(edge.metadata)) : undefined + } + + // Copy connections + for (const [level, connections] of edge.connections.entries()) { + edgeCopy.connections.set(level, new Set(connections)) + } + + return edgeCopy + } + + public async getAllEdges(): Promise { + const edges: Edge[] = [] + + for (const edgeId of this.edges.keys()) { + const edge = await this.getEdge(edgeId) + if (edge) { + edges.push(edge) + } + } + + return edges + } + + public async getEdgesBySource(sourceId: string): Promise { + const edges: Edge[] = [] + + for (const edge of this.edges.values()) { + if (edge.sourceId === sourceId) { + const edgeCopy = await this.getEdge(edge.id) + if (edgeCopy) { + edges.push(edgeCopy) + } + } + } + + return edges + } + + public async getEdgesByTarget(targetId: string): Promise { + const edges: Edge[] = [] + + for (const edge of this.edges.values()) { + if (edge.targetId === targetId) { + const edgeCopy = await this.getEdge(edge.id) + if (edgeCopy) { + edges.push(edgeCopy) + } + } + } + + return edges + } + + public async getEdgesByType(type: string): Promise { + const edges: Edge[] = [] + + for (const edge of this.edges.values()) { + if (edge.type === type) { + const edgeCopy = await this.getEdge(edge.id) + if (edgeCopy) { + edges.push(edgeCopy) + } + } + } + + return edges + } + + public async deleteEdge(id: string): Promise { + this.edges.delete(id) + } + + public async clear(): Promise { + this.nodes.clear() + this.edges.clear() + this.metadata.clear() + } +} + +/** + * Factory function to create the appropriate storage adapter based on the environment + */ +export async function createStorage(): Promise { + // Check if we're in a Node.js environment + const isNode = typeof process !== 'undefined' && + process.versions != null && + process.versions.node != null + + if (isNode) { + // In Node.js, use FileSystemStorage + try { + const fileSystemModule = await import('./fileSystemStorage.js') + return new fileSystemModule.FileSystemStorage() + } catch (error) { + console.warn('Failed to load FileSystemStorage, falling back to in-memory storage:', error) + return new MemoryStorage() + } + } else { + // In browser, try OPFS first + const opfsStorage = new OPFSStorage() + + if (opfsStorage.isOPFSAvailable()) { + return opfsStorage + } else { + console.warn('OPFS is not available, falling back to in-memory storage') + return new MemoryStorage() + } + } +} diff --git a/src/types/augmentations.ts b/src/types/augmentations.ts new file mode 100644 index 00000000..4a3f5fe2 --- /dev/null +++ b/src/types/augmentations.ts @@ -0,0 +1,283 @@ +/** Common types for augmentation system */ +type WebSocketConnection = { + connectionId: string + url: string + status: 'connected' | 'disconnected' | 'error' +} + +type DataCallback = (data: T) => void +type AugmentationResponse = Promise<{ + success: boolean + data: T + error?: string +}> + +/** + * Base interface for all Brainy augmentations. + * All augmentations must implement these core properties. + */ +interface IAugmentation { + /** A unique identifier for the augmentation (e.g., "my-reasoner-v1") */ + readonly name: string + /** A human-readable description of the augmentation's purpose */ + readonly description: string + + /** + * Initializes the augmentation. This method is called when Brainy starts up. + * @returns A Promise that resolves when initialization is complete + */ + initialize(): Promise + + shutDown(): Promise + + getStatus(): Promise<'active' | 'inactive' | 'error'> +} + +/** + * Interface for WebSocket support. + * Augmentations that implement this interface can communicate via WebSockets. + */ +interface IWebSocketSupport { + /** + * Establishes a WebSocket connection. + * @param url The WebSocket server URL to connect to + * @param protocols Optional subprotocols + * @returns A Promise resolving to a connection handle or status + */ + connectWebSocket(url: string, protocols?: string | string[]): Promise + + /** + * Sends data through an established WebSocket connection. + * @param connectionId The identifier of the established connection + * @param data The data to send (will be serialized if not a string) + */ + sendWebSocketMessage(connectionId: string, data: unknown): Promise + + /** + * Registers a callback for incoming WebSocket messages. + * @param connectionId The identifier of the established connection + * @param callback The function to call when a message is received + */ + onWebSocketMessage(connectionId: string, callback: DataCallback): Promise + + /** + * Closes an established WebSocket connection. + * @param connectionId The identifier of the established connection + * @param code Optional close code + * @param reason Optional close reason + */ + closeWebSocket(connectionId: string, code?: number, reason?: string): Promise +} + +namespace BrainyAugmentations { + /** + * Interface for Cognitions augmentations. + * These augmentations enable advanced reasoning, inference, and logical operations. + */ + export interface ICognitionAugmentation extends IAugmentation { + /** + * Performs a reasoning operation based on current knowledge. + * @param query The specific reasoning task or question + * @param context Optional additional context for the reasoning + */ + reason(query: string, context?: Record): AugmentationResponse<{ + inference: string + confidence: number + }> + + /** + * Infers relationships or new facts from existing data. + * @param dataSubset A subset of data to infer from + */ + infer(dataSubset: Record): AugmentationResponse> + + /** + * Executes a logical operation or rule set. + * @param ruleId The identifier of the rule or logic to apply + * @param input Data to apply the logic to + */ + executeLogic(ruleId: string, input: Record): AugmentationResponse + } + + /** + * Interface for Senses augmentations. + * These augmentations ingest and process raw, unstructured data into nouns and verbs. + */ + export interface ISenseAugmentation extends IAugmentation { + /** + * Processes raw input data into structured nouns and verbs. + * @param rawData The raw, unstructured data (e.g., text, image buffer, audio stream) + * @param dataType The type of raw data (e.g., 'text', 'image', 'audio') + */ + processRawData(rawData: Buffer | string, dataType: string): AugmentationResponse<{ + nouns: string[] + verbs: string[] + }> + + /** + * Registers a listener for real-time data feeds. + * @param feedUrl The URL or identifier of the real-time feed + * @param callback A function to call with processed data + */ + listenToFeed( + feedUrl: string, + callback: DataCallback<{ nouns: string[]; verbs: string[] }> + ): Promise + } + + /** + * Interface for Perceptions augmentations. + * These augmentations interpret, contextualize, and visualize identified nouns and verbs. + */ + export interface IPerceptionAugmentation extends IAugmentation { + /** + * Interprets and contextualizes processed nouns and verbs. + * @param nouns The list of identified nouns + * @param verbs The list of identified verbs + * @param context Optional additional context for interpretation + */ + interpret( + nouns: string[], + verbs: string[], + context?: Record + ): AugmentationResponse> + + /** + * Organizes and filters information. + * @param data The data to organize (e.g., interpreted perceptions) + * @param criteria Optional criteria for filtering/prioritization + */ + organize( + data: Record, + criteria?: Record + ): AugmentationResponse> + + /** + * Generates a visualization based on the provided data. + * @param data The data to visualize (e.g., interpreted patterns) + * @param visualizationType The desired type of visualization (e.g., 'graph', 'chart') + */ + generateVisualization( + data: Record, + visualizationType: string + ): AugmentationResponse> + } + + /** + * Interface for Activations augmentations. + * These augmentations dictate how Brainy initiates actions, responses, or data manipulations. + */ + export interface IActivationAugmentation extends IAugmentation { + /** + * Triggers an action based on a processed command or internal state. + * @param actionName The name of the action to trigger + * @param parameters Optional parameters for the action + */ + triggerAction( + actionName: string, + parameters?: Record + ): AugmentationResponse + + /** + * Generates an expressive output or response from Brainy. + * @param knowledgeId The identifier of the knowledge to express + * @param format The desired output format (e.g., 'text', 'json') + */ + generateOutput(knowledgeId: string, format: string): AugmentationResponse> + + /** + * Interacts with an external system or API. + * @param systemId The identifier of the external system + * @param payload The data to send to the external system + */ + interactExternal(systemId: string, payload: Record): AugmentationResponse + } + + /** + * Interface for Dialogs augmentations. + * These augmentations facilitate natural language understanding and generation for conversational interaction. + */ + export interface IDialogAugmentation extends IAugmentation { + /** + * Processes a user's natural language input (query). + * @param naturalLanguageQuery The raw text query from the user + * @param sessionId An optional session ID for conversational context + */ + processUserInput(naturalLanguageQuery: string, sessionId?: string): AugmentationResponse<{ + intent: string + nouns: string[] + verbs: string[] + context: Record + }> + + /** + * Generates a natural language response based on Brainy's knowledge and interpreted input. + * @param interpretedInput The output from `processUserInput` or similar + * @param knowledgeContext Relevant knowledge retrieved from Brainy + * @param sessionId An optional session ID for conversational context + */ + generateResponse( + interpretedInput: Record, + knowledgeContext: Record, + sessionId?: string + ): AugmentationResponse + + /** + * Manages and updates conversational context. + * @param sessionId The session ID + * @param contextUpdate The data to update the context with + */ + manageContext(sessionId: string, contextUpdate: Record): Promise + } + + /** + * Interface for Conduits augmentations. + * These augmentations establish and manage high-bandwidth, dedicated channels for structured, programmatic two-way data exchange. + */ + export interface IConduitAugmentation extends IAugmentation { + /** + * Establishes a connection for programmatic data exchange. + * @param targetSystemId The identifier of the external system to connect to + * @param config Configuration details for the connection (e.g., API keys, endpoints) + */ + establishConnection( + targetSystemId: string, + config: Record + ): AugmentationResponse + + /** + * Reads structured data directly from Brainy's knowledge graph. + * @param query A structured query (e.g., graph query language, object path) + * @param options Optional query options (e.g., depth, filters) + */ + readData( + query: Record, + options?: Record + ): AugmentationResponse + + /** + * Writes or updates structured data directly into Brainy's knowledge graph. + * @param data The structured data to write/update + * @param options Optional write options (e.g., merge, overwrite) + */ + writeData( + data: Record, + options?: Record + ): AugmentationResponse + + /** + * Monitors a specific data stream or event within Brainy for external systems. + * @param streamId The identifier of the data stream or event + * @param callback A function to call when new data/events occur + */ + monitorStream(streamId: string, callback: DataCallback): Promise + } +} + +/** WebSocket-enabled augmentation interfaces */ +type IWebSocketCognitionAugmentation = BrainyAugmentations.ICognitionAugmentation & IWebSocketSupport +type IWebSocketSenseAugmentation = BrainyAugmentations.ISenseAugmentation & IWebSocketSupport +type IWebSocketPerceptionAugmentation = BrainyAugmentations.IPerceptionAugmentation & IWebSocketSupport +type IWebSocketActivationAugmentation = BrainyAugmentations.IActivationAugmentation & IWebSocketSupport +type IWebSocketDialogAugmentation = BrainyAugmentations.IDialogAugmentation & IWebSocketSupport +type IWebSocketConduitAugmentation = BrainyAugmentations.IConduitAugmentation & IWebSocketSupport diff --git a/src/types/fileSystemTypes.ts b/src/types/fileSystemTypes.ts new file mode 100644 index 00000000..59ff80cf --- /dev/null +++ b/src/types/fileSystemTypes.ts @@ -0,0 +1,12 @@ +/** + * Type declarations for the File System Access API + * Extends the FileSystemDirectoryHandle interface to include the [Symbol.asyncIterator] method + */ + +// Extend the FileSystemDirectoryHandle interface +interface FileSystemDirectoryHandle { + [Symbol.asyncIterator](): AsyncIterableIterator<[string, FileSystemHandle]>; +} + +// Export something to make this a module +export const fileSystemTypesLoaded = true; diff --git a/src/types/graphTypes.ts b/src/types/graphTypes.ts new file mode 100644 index 00000000..bdc2de87 --- /dev/null +++ b/src/types/graphTypes.ts @@ -0,0 +1,129 @@ +// Common metadata types +/** + * Represents a high-precision timestamp with seconds and nanoseconds + * Used for tracking creation and update times of graph elements + */ +interface Timestamp { + seconds: number + nanoseconds: number +} + +/** + * Metadata about the creator/source of a graph noun + * Tracks which augmentation and model created the element + */ +interface CreatorMetadata { + augmentation: string // Name of the augmentation that created this element + version: string // Version of the augmentation + model: string // Model identifier used in creation + modelVersion: string // Version of the model +} + +/** + * Base interface for nodes (nouns) in the graph + * Represents entities like people, places, things, etc. + */ +export interface GraphNoun { + id: string // Unique identifier for the noun + createdBy: CreatorMetadata // Information about what created this noun + noun: NounType // Type classification of the noun + createdAt: Timestamp // When the noun was created + updatedAt: Timestamp // When the noun was last updated + data?: Record // Additional flexible data storage + embedding?: number[] // Vector representation of the noun +} + +/** + * Base interface for edges (verbs) in the graph + * Represents relationships between nouns + */ +export interface GraphVerb { + id: string // Unique identifier for the verb + source: string // ID of the source noun + target: string // ID of the target noun + label?: string // Optional descriptive label + verb: VerbType // Type of relationship + createdAt: Timestamp // When the verb was created + updatedAt: Timestamp // When the verb was last updated + data?: Record // Additional flexible data storage + embedding?: number[] // Vector representation of the relationship + confidence?: number // Confidence score (0-1) + weight?: number // Strength/importance of the relationship +} + +/** + * Version of GraphVerb for embedded relationships + * Used when the source is implicit from the parent document + */ +export type EmbeddedGraphVerb = Omit + +// Proper Noun interfaces - extend GraphNoun with specific noun types + +/** + * Represents a person entity in the graph + */ +export interface Person extends GraphNoun { + noun: typeof NounType.Person +} + +/** + * Represents a physical location in the graph + */ +export interface Place extends GraphNoun { + noun: typeof NounType.Place +} + +/** + * Represents a physical or virtual object in the graph + */ +export interface Thing extends GraphNoun { + noun: typeof NounType.Thing +} + +/** + * Represents an event or occurrence in the graph + */ +export interface Event extends GraphNoun { + noun: typeof NounType.Event +} + +/** + * Represents an abstract concept or idea in the graph + */ +export interface Concept extends GraphNoun { + noun: typeof NounType.Concept +} + +/** + * Represents content (text, media, etc.) in the graph + */ +export interface Content extends GraphNoun { + noun: typeof NounType.Content +} + +/** + * Defines valid noun types for graph entities + * Used for categorizing different types of nodes + */ +export const NounType = { + Person: 'person', // Person entities + Place: 'place', // Physical locations + Thing: 'thing', // Physical or virtual objects + Event: 'event', // Events or occurrences + Concept: 'concept', // Abstract concepts or ideas + Content: 'content' // Content items +} as const +export type NounType = (typeof NounType)[keyof typeof NounType] + +/** + * Defines valid verb types for relationships + * Used for categorizing different types of connections + */ +export const VerbType = { + AttributedTo: 'attributedTo', // Indicates attribution or authorship + Controls: 'controls', // Indicates control or ownership + Created: 'created', // Indicates creation or authorship + Earned: 'earned', // Indicates achievement or acquisition + Owns: 'owns' // Indicates ownership +} as const +export type VerbType = (typeof VerbType)[keyof typeof VerbType] diff --git a/src/types/tensorflowTypes.ts b/src/types/tensorflowTypes.ts new file mode 100644 index 00000000..94a43484 --- /dev/null +++ b/src/types/tensorflowTypes.ts @@ -0,0 +1,14 @@ +// Type declarations for TensorFlow.js models + +// This file is a placeholder for TensorFlow.js model types +// We're using type assertions in the code instead of module declarations + +// Define some basic types that might be useful +export interface TensorflowModel { + load(): Promise; + embed(data: string[]): any; + dispose(): void; +} + +// Export a dummy constant to make this a proper module +export const tensorflowModelsLoaded = true; diff --git a/src/utils/distance.ts b/src/utils/distance.ts new file mode 100644 index 00000000..6cd71c81 --- /dev/null +++ b/src/utils/distance.ts @@ -0,0 +1,88 @@ +/** + * Distance functions for vector similarity calculations + */ + +import { DistanceFunction, Vector } from '../coreTypes.js' + +/** + * Calculates the Euclidean distance between two vectors + * Lower values indicate higher similarity + */ +export const euclideanDistance: DistanceFunction = (a: Vector, b: Vector): number => { + if (a.length !== b.length) { + throw new Error('Vectors must have the same dimensions') + } + + let sum = 0 + for (let i = 0; i < a.length; i++) { + const diff = a[i] - b[i] + sum += diff * diff + } + + return Math.sqrt(sum) +} + +/** + * Calculates the cosine distance between two vectors + * Lower values indicate higher similarity + * Range: 0 (identical) to 2 (opposite) + */ +export const cosineDistance: DistanceFunction = (a: Vector, b: Vector): number => { + if (a.length !== b.length) { + throw new Error('Vectors must have the same dimensions') + } + + let dotProduct = 0 + let normA = 0 + let normB = 0 + + for (let i = 0; i < a.length; i++) { + dotProduct += a[i] * b[i] + normA += a[i] * a[i] + normB += b[i] * b[i] + } + + if (normA === 0 || normB === 0) { + return 2 // Maximum distance for zero vectors + } + + const similarity = dotProduct / (Math.sqrt(normA) * Math.sqrt(normB)) + // Convert cosine similarity (-1 to 1) to distance (0 to 2) + return 1 - similarity +} + +/** + * Calculates the Manhattan (L1) distance between two vectors + * Lower values indicate higher similarity + */ +export const manhattanDistance: DistanceFunction = (a: Vector, b: Vector): number => { + if (a.length !== b.length) { + throw new Error('Vectors must have the same dimensions') + } + + let sum = 0 + for (let i = 0; i < a.length; i++) { + sum += Math.abs(a[i] - b[i]) + } + + return sum +} + +/** + * Calculates the dot product similarity between two vectors + * Higher values indicate higher similarity + * Converted to a distance metric (lower is better) + */ +export const dotProductDistance: DistanceFunction = (a: Vector, b: Vector): number => { + if (a.length !== b.length) { + throw new Error('Vectors must have the same dimensions') + } + + let dotProduct = 0 + for (let i = 0; i < a.length; i++) { + dotProduct += a[i] * b[i] + } + + // Convert to a distance metric (lower is better) + return -dotProduct +} diff --git a/src/utils/embedding.ts b/src/utils/embedding.ts new file mode 100644 index 00000000..0d145c77 --- /dev/null +++ b/src/utils/embedding.ts @@ -0,0 +1,168 @@ +/** + * Embedding functions for converting data to vectors + */ + +import { EmbeddingFunction, EmbeddingModel, Vector } from '../coreTypes.js' + +/** + * Simple character-based embedding function + * This is a very basic implementation for demo purposes + */ +export class SimpleEmbedding implements EmbeddingModel { + private initialized = false + + /** + * Initialize the embedding model + */ + public async init(): Promise { + this.initialized = true + return Promise.resolve() + } + + /** + * Embed text into a vector using character frequencies + * @param data Text to embed + */ + public async embed(data: string): Promise { + if (!this.initialized) { + await this.init() + } + + // Only handle string data + if (typeof data !== 'string') { + throw new Error('SimpleEmbedding only supports string data') + } + + // Normalize the text + const normalizedText = data.toLowerCase().trim() + + // Create a simple 4-dimensional vector based on character frequencies + const vector: Vector = [0, 0, 0, 0] + + // Count vowels, consonants, numbers, and special characters + for (let i = 0; i < normalizedText.length; i++) { + const char = normalizedText[i] + if ('aeiou'.includes(char)) { + vector[0] += 0.1 // Vowels affect first dimension + } else if ('bcdfghjklmnpqrstvwxyz'.includes(char)) { + vector[1] += 0.1 // Consonants affect second dimension + } else if ('0123456789'.includes(char)) { + vector[2] += 0.1 // Numbers affect third dimension + } else { + vector[3] += 0.1 // Special chars affect fourth dimension + } + } + + // Normalize the vector + const magnitude = Math.sqrt( + vector.reduce((sum, val) => sum + val * val, 0) + ) + if (magnitude > 0) { + return vector.map((val) => val / magnitude) + } + + return vector + } + + /** + * Dispose of the model resources + */ + public async dispose(): Promise { + this.initialized = false + return Promise.resolve() + } +} + +/** + * TensorFlow Universal Sentence Encoder embedding model + * Requires @tensorflow/tfjs and @tensorflow-models/universal-sentence-encoder to be installed + */ +export class UniversalSentenceEncoder implements EmbeddingModel { + private model: any = null + private initialized = false + private tf: any = null + private use: any = null + + /** + * Initialize the embedding model + */ + public async init(): Promise { + try { + // Dynamically import TensorFlow.js and Universal Sentence Encoder + // Use type assertions to tell TypeScript these modules exist + this.tf = await import('@tensorflow/tfjs/dist/tf.esm.js' as any) + this.use = await import('@tensorflow-models/universal-sentence-encoder/dist/universal-sentence-encoder.esm.js' as any) + + // Load the model + this.model = await this.use.load() + this.initialized = true + } catch (error) { + console.error('Failed to initialize Universal Sentence Encoder:', error) + throw new Error(`Failed to initialize Universal Sentence Encoder: ${error}`) + } + } + + /** + * Embed text into a vector using Universal Sentence Encoder + * @param data Text to embed + */ + public async embed(data: string | string[]): Promise { + if (!this.initialized) { + await this.init() + } + + try { + // Handle different input types + let textToEmbed: string[] + if (typeof data === 'string') { + textToEmbed = [data] + } else if (Array.isArray(data) && data.every(item => typeof item === 'string')) { + textToEmbed = data + } else { + throw new Error('UniversalSentenceEncoder only supports string or string[] data') + } + + // Get embeddings + const embeddings = await this.model.embed(textToEmbed) + + // Convert to array and return the first embedding + const embeddingArray = await embeddings.array() + return embeddingArray[0] + } catch (error) { + console.error('Failed to embed text with Universal Sentence Encoder:', error) + throw new Error(`Failed to embed text with Universal Sentence Encoder: ${error}`) + } + } + + /** + * Dispose of the model resources + */ + public async dispose(): Promise { + if (this.model && this.tf) { + try { + // Dispose of the model and tensors + this.model.dispose() + this.tf.disposeVariables() + this.initialized = false + } catch (error) { + console.error('Failed to dispose Universal Sentence Encoder:', error) + } + } + return Promise.resolve() + } +} + +/** + * Create an embedding function from an embedding model + * @param model Embedding model to use + */ +export function createEmbeddingFunction(model: EmbeddingModel): EmbeddingFunction { + return async (data: any): Promise => { + return await model.embed(data) + } +} + +/** + * Default embedding function using UniversalSentenceEncoder + */ +export const defaultEmbeddingFunction: EmbeddingFunction = createEmbeddingFunction(new UniversalSentenceEncoder()) diff --git a/src/utils/index.ts b/src/utils/index.ts new file mode 100644 index 00000000..11db368e --- /dev/null +++ b/src/utils/index.ts @@ -0,0 +1,2 @@ +export * from './distance.js' +export * from './embedding.js' diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 00000000..9949368e --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,30 @@ +{ + "compilerOptions": { + "target": "ES2020", + "module": "NodeNext", + "moduleResolution": "NodeNext", + "esModuleInterop": true, + "strict": true, + "declaration": true, + "outDir": "./dist", + "rootDir": "./src", + "lib": [ + "DOM", + "ESNext" + ], + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "allowImportingTsExtensions": false, + "noEmit": false, + "preserveConstEnums": true, + "sourceMap": true + }, + "include": [ + "src/**/*" + ], + "exclude": [ + "node_modules", + "dist", + "**/*.test.ts" + ] +}