@astermind/astermind-pro
Advanced tools
Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis
Premium ML Toolkit - Advanced RAG, Reranking, Summarization, and Information Flow Analysis
Astermind Pro extends the base @astermind/astermind-elm package with premium features for production-grade machine learning applications.
All APIs are public and extensible - no private APIs. Build your own pipelines using this professional toolbox.
src/
├── math/ # Production-grade numerical methods
├── omega/ # Omega RAG system
├── retrieval/ # Hybrid retrieval system (sparse + dense)
│ ├── vectorization.ts # TF-IDF, sparse/dense operations
│ ├── index-builder.ts # Vocabulary, IDF, Nyström landmarks
│ └── hybrid-retriever.ts # Hybrid retrieval with ridge regularization
├── elm/ # Advanced ELM variants
│ ├── multi-kernel-elm.ts # Multi-Kernel ELM
│ ├── deep-elm-pro.ts # Improved Deep ELM
│ ├── online-kernel-elm.ts # Online Kernel ELM
│ ├── multi-task-elm.ts # Multi-Task ELM
│ └── sparse-elm.ts # Sparse ELM
├── reranking/ # OmegaRR reranking
├── summarization/ # OmegaSumDet summarization
├── infoflow/ # Transfer Entropy analysis
├── workers/ # Web Workers (dev & production)
├── utils/ # Utility functions
│ ├── tokenization.ts # Tokenization & stemming
│ ├── markdown.ts # Markdown parsing & chunking
│ ├── autotune.ts # Hyperparameter optimization
│ └── model-serialization.ts # Model export/import
└── types.ts # TypeScript types
Key Feature: All retrieval and utility functions are now reusable outside of workers - use them directly in your applications!
npm install @astermind/astermind-pro
Prerequisites:
@astermind/astermind-elm (peer dependency)@astermindai/license-runtime (included as dependency)Note: Astermind Pro subscription includes Astermind Synth - a synthetic data generator for bootstrapping your projects. See the Developer Guide for details.
Astermind Pro uses a centralized license configuration that automatically propagates to both Pro and Synth.
To get started with Astermind Pro, visit our getting started page:
The getting started page provides step-by-step instructions for:
Edit src/config/license-config.ts:
export const LICENSE_TOKEN: string | null = 'YOUR_LICENSE_TOKEN_HERE';
Or use environment variable:
export ASTERMIND_LICENSE_TOKEN="your-license-token-here"
Or set programmatically:
import { setLicenseTokenFromString } from '@astermind/astermind-pro';
await setLicenseTokenFromString('your-license-token-here');
See LICENSE_SETUP.md for complete license setup guide.
import {
// License Management
initializeLicense, checkLicense, setLicenseTokenFromString,
// Math utilities
cosine, l2, normalizeL2, ridgeSolvePro, OnlineRidge, buildRFF,
// Retrieval (NEW - reusable outside workers!)
tokenize, expandQuery, toTfidf, hybridRetrieve, buildIndex,
parseMarkdownToSections, flattenSections,
// Omega RAG
omegaComposeAnswer,
// Reranking
rerank, rerankAndFilter, filterMMR,
// Summarization
summarizeDeterministic,
// Information Flow
TransferEntropy, InfoFlowGraph, TEController,
// Auto-tuning (NEW - reusable!)
autoTune, sampleQueriesFromCorpus,
// Model serialization (NEW - reusable!)
exportModel, importModel,
// Advanced ELM Variants (NEW!)
MultiKernelELM, DeepELMPro, OnlineKernelELM, MultiTaskELM, SparseELM,
// Types
SerializedModel, Settings
} from '@astermind/astermind-pro';
For development and training:
// In browser context
const worker = new Worker(
new URL('@astermind/astermind-pro/workers/dev-worker', import.meta.url),
{ type: 'module' }
);
worker.postMessage({
action: 'init',
payload: {
settings: { /* ... */ },
chaptersPath: '/chapters.json'
}
});
// Training, autotune, etc. available
worker.postMessage({
action: 'autotune',
payload: { budget: 40, sampleQueries: 24 }
});
For production deployments - optimized for inference:
// In browser context
const worker = new Worker(
new URL('@astermind/astermind-pro/workers/prod-worker', import.meta.url),
{ type: 'module' }
);
// Load pre-trained model
worker.postMessage({
action: 'init',
payload: {
model: serializedModel // SerializedModel from dev-worker exportModel()
}
});
// Query only
worker.postMessage({
action: 'ask',
payload: { q: 'your query here' }
});
Complete RAG pipeline with recursive compression, query-aligned sentence selection, and personality modes (neutral, teacher, scientist).
Use Cases: Technical documentation assistants, customer support systems, knowledge base Q&A
Production-grade reranking with rich feature engineering (TF-IDF, BM25, structural signals), weak supervision, and MMR filtering.
Use Cases: Search engines, legal document retrieval, product search optimization
Intent-aware, deterministic summarization with code-aware processing and heading alignment.
Use Cases: Code explanation generation, research paper summarization, technical documentation summaries
Information flow monitoring with streaming TE estimation, PWS variant, and closed-loop adaptive control.
Use Cases: Pipeline quality assurance, automatic hyperparameter tuning, system health monitoring
Production-grade math including KRR (Cholesky + CG fallback), RFF approximation, OnlineRidge, and overflow-safe operations.
Sparse (TF-IDF) + dense (kernel) retrieval with Nyström approximation and multiple kernel types. Now available as standalone modules - use hybridRetrieve() and buildIndex() directly in your code, not just in workers!
Automated hyperparameter optimization with random search, refinement, and real-time progress reporting. Now available as standalone function - use autoTune() directly in your applications.
Every Astermind Pro subscription includes Astermind Synth - the synthetic data generator that helps you bootstrap your ML projects quickly.
Features:
See the Developer Guide for complete examples.
import {
buildIndex,
hybridRetrieve,
rerankAndFilter,
summarizeDeterministic
} from '@astermind/astermind-pro';
// Build index from your documents
const index = buildIndex({
chunks: yourDocuments,
vocab: 10000,
landmarks: 256,
headingW: 2.0,
useStem: true,
kernel: 'rbf',
sigma: 1.0
});
// Perform hybrid retrieval
const retrieved = hybridRetrieve({
query: 'your query',
chunks: yourDocuments,
vocabMap: index.vocabMap,
idf: index.idf,
tfidfDocs: index.tfidfDocs,
denseDocs: index.denseDocs,
landmarksIdx: index.landmarksIdx,
landmarkMat: index.landmarkMat,
vocabSize: index.vocabMap.size,
kernel: 'rbf',
sigma: 1.0,
alpha: 0.7,
beta: 0.1,
ridge: 0.08,
headingW: 2.0,
useStem: true,
expandQuery: false,
topK: 10
});
// Rerank and summarize
const reranked = rerankAndFilter(query, retrieved.items, {
lambdaRidge: 1e-2,
probThresh: 0.45,
useMMR: true
});
const summary = summarizeDeterministic(query, reranked, {
maxAnswerChars: 1000,
includeCitations: true
});
import {
rerankAndFilter,
summarizeDeterministic,
InfoFlowGraph
} from '@astermind/astermind-pro';
// Build your custom pipeline
const results = rerankAndFilter(query, documents, {
lambdaRidge: 1e-2,
probThresh: 0.45,
useMMR: true
});
const summary = summarizeDeterministic(query, results, {
maxAnswerChars: 1000,
includeCitations: true
});
PROPRIETARY - This is a premium package. See TERMS_OF_SERVICE.md for usage rights.
Astermind Pro - Professional ML Toolkit for Production Applications
For questions and support, contact AsterMind LLC.
FAQs
Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis
The npm package @astermind/astermind-pro receives a total of 15 weekly downloads. As such, @astermind/astermind-pro popularity was classified as not popular.
We found that @astermind/astermind-pro demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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