fix: remove dead model config code for true zero-config

- Remove unused model.type validation that caused Workshop error
- Remove model config from BrainyConfig type (never used)
- Simplify modelAutoConfig.ts (always Q8 WASM)
- Clean up zeroConfig.ts model references

This fixes the "Invalid model type: balanced" error and removes
unnecessary configuration options that did nothing.
This commit is contained in:
David Snelling 2025-12-18 10:31:02 -08:00
parent 746b1b8e24
commit e6769d6d9f
6 changed files with 37 additions and 163 deletions

View file

@ -177,7 +177,6 @@ export class Brainy<T = any> implements BrainyInterface<T> {
...this.config,
...configOverrides,
storage: { ...this.config.storage, ...configOverrides.storage },
model: { ...this.config.model, ...configOverrides.model },
index: { ...this.config.index, ...configOverrides.index },
augmentations: { ...this.config.augmentations, ...configOverrides.augmentations },
verbose: configOverrides.verbose ?? this.config.verbose,
@ -4972,11 +4971,6 @@ export class Brainy<T = any> implements BrainyInterface<T> {
// Both 'gcs' and 'gcs-native' can now use either gcsStorage or gcsNativeStorage
}
// Validate model configuration
if (config?.model?.type && !['fast', 'accurate', 'custom'].includes(config.model.type)) {
throw new Error(`Invalid model type: ${config.model.type}. Must be one of: fast, accurate, custom`)
}
// Validate numeric configurations
if (config?.index?.m && (config.index.m < 1 || config.index.m > 128)) {
throw new Error(`Invalid index m parameter: ${config.index.m}. Must be between 1 and 128`)
@ -4995,7 +4989,6 @@ export class Brainy<T = any> implements BrainyInterface<T> {
return {
storage: config?.storage || { type: 'auto' },
model: config?.model || { type: 'fast' },
index: config?.index || {},
cache: config?.cache ?? true,
augmentations: config?.augmentations || {},

View file

@ -3,19 +3,13 @@
* Main entry point for all auto-configuration features
*/
// Model configuration
export {
autoSelectModelPrecision,
ModelPrecision as ModelPrecisionType, // Avoid conflict
ModelPreset,
// Model configuration (simplified - always Q8 WASM)
export {
getModelPrecision,
shouldAutoDownloadModels,
getModelPath,
logModelConfig
getModelPath
} from './modelAutoConfig.js'
// Model precision - Always Q8 now (99% accuracy, 75% smaller)
export const getModelPrecision = () => 'q8' as const
// Storage configuration
export {
autoDetectStorage,

View file

@ -1,49 +1,24 @@
/**
* Model Configuration Auto-Selection
* Always uses Q8 for optimal size/performance balance (99% accuracy, 75% smaller)
* Model Configuration
* Brainy uses Q8 WASM embeddings - no configuration needed (zero-config)
*/
import { isBrowser, isNode } from '../utils/environment.js'
export type ModelPrecision = 'q8'
export type ModelPreset = 'small' | 'auto'
interface ModelConfigResult {
precision: ModelPrecision
precision: 'q8'
reason: string
autoSelected: boolean
}
/**
* Auto-select model precision - Always returns Q8
* Q8 provides 99% accuracy with 75% smaller size
* @param override - For backward compatibility, ignored
* Get model precision configuration
* Always returns Q8 - the optimal balance of size and accuracy
*/
export function autoSelectModelPrecision(override?: ModelPrecision | ModelPreset): ModelConfigResult {
// Always use Q8 regardless of override for simplicity
// Q8 is optimal: 33MB vs 130MB, 99% accuracy retained
// Log deprecation notice if FP32 was requested
if (typeof override === 'string' && override.toLowerCase().includes('fp32')) {
console.log('Note: FP32 precision is deprecated. Using Q8 (99% accuracy, 75% smaller).')
}
export function getModelPrecision(): ModelConfigResult {
return {
precision: 'q8',
reason: 'Q8 precision (99% accuracy, 75% smaller)',
autoSelected: true
}
}
/**
* Automatically detect the best model precision for the environment
* DEPRECATED: Always returns Q8 now
*/
function autoDetectBestPrecision(): ModelConfigResult {
// Always return Q8 - deprecated function kept for backward compatibility
return {
precision: 'q8',
reason: 'Q8 precision (99% accuracy, 75% smaller)',
reason: 'Q8 WASM (23MB bundled, no downloads)',
autoSelected: true
}
}
@ -53,7 +28,7 @@ function autoDetectBestPrecision(): ModelConfigResult {
*/
function isServerlessEnvironment(): boolean {
if (!isNode()) return false
return !!(
process.env.AWS_LAMBDA_FUNCTION_NAME ||
process.env.VERCEL ||
@ -65,104 +40,42 @@ function isServerlessEnvironment(): boolean {
}
/**
* Get available memory in MB
*/
function getAvailableMemoryMB(): number {
if (isBrowser()) {
// @ts-ignore - navigator.deviceMemory is experimental
if (navigator.deviceMemory) {
// @ts-ignore
return navigator.deviceMemory * 1024 // Device memory in GB
}
return 256 // Conservative default for browsers
}
if (isNode()) {
try {
// Try to get memory info synchronously for Node.js
// This will be available in Node.js environments
if (typeof process !== 'undefined' && process.memoryUsage) {
// Use RSS (Resident Set Size) as a proxy for available memory
const rss = process.memoryUsage().rss
// Assume we can use up to 4GB or 50% more than current usage
return Math.min(4096, Math.floor(rss / (1024 * 1024) * 1.5))
}
} catch {
// Fall through to default
}
return 1024 // Default 1GB for Node.js
}
return 512 // Conservative default
}
/**
* Convenience function to check if models need to be downloaded
* This replaces the need for BRAINY_ALLOW_REMOTE_MODELS
* Check if models need to be downloaded
* With bundled WASM model, this is rarely needed
*/
export function shouldAutoDownloadModels(): boolean {
// Always allow downloads unless explicitly disabled
// This eliminates the need for BRAINY_ALLOW_REMOTE_MODELS
// Model is bundled - no downloads needed in normal operation
// This flag exists for edge cases only
const explicitlyDisabled = process.env.BRAINY_ALLOW_REMOTE_MODELS === 'false'
if (explicitlyDisabled) {
console.warn('Model downloads disabled via BRAINY_ALLOW_REMOTE_MODELS=false')
return false
}
// In production, always allow downloads for seamless operation
if (process.env.NODE_ENV === 'production') {
return true
}
// In development, allow downloads with a one-time notice
if (process.env.NODE_ENV === 'development') {
return true
}
// Default: allow downloads
return true
return !explicitlyDisabled
}
/**
* Get the model path with intelligent defaults
* This replaces the need for BRAINY_MODELS_PATH env var
* Get the model path
* With bundled WASM model, this points to the package assets
*/
export function getModelPath(): string {
// Check if user explicitly set a path (keeping this for advanced users)
// Check if user explicitly set a path (for advanced users)
if (process.env.BRAINY_MODELS_PATH) {
return process.env.BRAINY_MODELS_PATH
}
// Browser - use cache API or IndexedDB (handled by transformers.js)
// Browser - use cache API or IndexedDB
if (isBrowser()) {
return 'browser-cache'
}
// Serverless - use /tmp for ephemeral storage
if (isServerlessEnvironment()) {
return '/tmp/.brainy/models'
}
// Node.js - use home directory for persistent storage
if (isNode()) {
// Use process.env.HOME as a fallback
const homeDir = process.env.HOME || process.env.USERPROFILE || '~'
return `${homeDir}/.brainy/models`
}
// Fallback
return './.brainy/models'
}
/**
* Log model configuration decision (only in verbose mode)
*/
export function logModelConfig(config: ModelConfigResult, verbose: boolean = false): void {
if (!verbose && process.env.NODE_ENV === 'production') {
return // Silent in production unless verbose
}
const icon = config.autoSelected ? '🤖' : '👤'
console.log(`${icon} Model: ${config.precision.toUpperCase()} - ${config.reason}`)
}

View file

@ -3,14 +3,13 @@
* Ensures configuration consistency across multiple instances using shared storage
*/
import { ModelPrecision } from './modelAutoConfig.js'
import { StorageType } from './storageAutoConfig.js'
import { getBrainyVersion } from '../utils/version.js'
export interface SharedConfig {
// Critical parameters that MUST match across instances
version: string
precision: ModelPrecision
precision: 'q8'
dimensions: number
hnswM: number
hnswEfConstruction: number

View file

@ -3,7 +3,7 @@
* Provides intelligent defaults while preserving full control
*/
import { autoSelectModelPrecision, ModelPrecision, ModelPreset, getModelPath, shouldAutoDownloadModels } from './modelAutoConfig.js'
import { getModelPrecision, getModelPath, shouldAutoDownloadModels } from './modelAutoConfig.js'
import { autoDetectStorage, StorageType, StoragePreset } from './storageAutoConfig.js'
import { AutoConfiguration } from '../utils/autoConfiguration.js'
@ -23,16 +23,6 @@ export interface BrainyZeroConfig {
*/
mode?: 'production' | 'development' | 'minimal' | 'zero' | 'writer' | 'reader'
/**
* Model precision configuration
* - 'fp32': Full precision (best quality, larger size)
* - 'q8': Quantized 8-bit (smaller size, slightly lower quality)
* - 'fast': Alias for fp32
* - 'small': Alias for q8
* - 'auto': Auto-detect based on environment (default)
*/
model?: ModelPrecision | ModelPreset
/**
* Storage configuration
* - 'memory': In-memory only (no persistence)
@ -72,31 +62,26 @@ export interface BrainyZeroConfig {
const PRESETS = {
production: {
storage: 'disk' as const,
model: 'auto' as const,
features: 'default' as const,
verbose: false
},
development: {
storage: 'memory' as const,
model: 'q8' as const, // Q8 is now the default for all presets
features: 'full' as const,
verbose: true
},
minimal: {
storage: 'memory' as const,
model: 'q8' as const,
features: 'minimal' as const,
verbose: false
},
zero: {
storage: 'auto' as const,
model: 'auto' as const,
features: 'default' as const,
verbose: false
},
writer: {
storage: 'auto' as const,
model: 'auto' as const,
features: 'minimal' as const,
verbose: false,
// Writer-specific settings
@ -107,7 +92,6 @@ const PRESETS = {
},
reader: {
storage: 'auto' as const,
model: 'auto' as const,
features: 'default' as const,
verbose: false,
// Reader-specific settings
@ -180,18 +164,17 @@ export async function processZeroConfig(input?: string | BrainyZeroConfig): Prom
...preset,
...config,
// Preserve explicit overrides
model: config.model ?? preset.model,
storage: config.storage ?? preset.storage,
features: config.features ?? preset.features,
verbose: config.verbose ?? preset.verbose
}
}
// Auto-detect environment if not in preset mode
const environment = detectEnvironmentMode()
// Process model configuration
const modelConfig = autoSelectModelPrecision(config.model)
// Get model configuration (always Q8 WASM)
const modelConfig = getModelPrecision()
// Process storage configuration
const storageConfig = await autoDetectStorage(config.storage)
@ -376,15 +359,14 @@ function logConfigurationSummary(config: any): void {
}
/**
* Create embedding function with specified precision
* This ensures the model precision is respected
* Create embedding function (always Q8 WASM)
*/
export async function createEmbeddingFunctionWithPrecision(precision: ModelPrecision): Promise<any> {
export async function createEmbeddingFunctionWithPrecision(): Promise<any> {
const { createEmbeddingFunction } = await import('../utils/embedding.js')
// Create embedding function with specified precision
// Create embedding function - always Q8 WASM
return createEmbeddingFunction({
precision: precision,
precision: 'q8',
verbose: false // Silent by default in zero-config
})
}

View file

@ -632,14 +632,7 @@ export interface BrainyConfig {
options?: any
branch?: string // COW branch name (default: 'main')
}
// Model configuration
model?: {
type: 'fast' | 'accurate' | 'balanced' | 'custom'
name?: string // Custom model name
precision?: 'q8'
}
// Index configuration
index?: {
m?: number // HNSW M parameter