ATLAS - AI Knowledge Management & Retrieval Platform

ATLAS - Advanced Technology for Learning and Social engagement - is a production-grade AI-powered knowledge management and community platform that revolutionizes how individuals and organizations discover, share, and collaborate on knowledge. Part of the MTM-CE (Maximize The Magic - Cascade Edition) ecosystem.

🌟 Overview

ATLAS is an advanced AI system designed to help individuals and organizations manage, organize, and retrieve knowledge efficiently. It combines intelligent search capabilities, knowledge graph visualization, automated content organization, and community-driven collaboration features to create a comprehensive knowledge ecosystem.

Why ATLAS?

Intelligent Knowledge Discovery: AI-powered search and recommendation engine that understands context and relationships
Community-Driven Learning: Connect with experts and learners in your field
Automated Organization: Smart categorization and tagging powered by machine learning
Scalable Architecture: Built for enterprise-scale deployments
Privacy-First Design: Your knowledge remains secure and under your control

🚀 Key Features

🧠 Intelligent Document Processing

Multi-Format Support: Process PDFs, Word documents, presentations, code files, and more
Smart Extraction: Extract key concepts, entities, and relationships automatically
Semantic Indexing: Index content based on meaning, not just keywords
Version Control: Track changes and maintain document history
Batch Processing: Handle thousands of documents efficiently

🔍 Advanced Search Capabilities

Natural Language Queries: Search using everyday language
Semantic Understanding: Find content based on concepts, not just exact matches
Contextual Results: Results ranked by relevance and context
Search Analytics: Learn from search patterns to improve results
Custom Search Filters: Filter by date, author, tags, and custom metadata

📊 Knowledge Graph Visualization

Interactive Graphs: Explore relationships between concepts visually
Dynamic Updates: Graphs update in real-time as new knowledge is added
Path Finding: Discover connections between seemingly unrelated topics
Export Capabilities: Export graphs for presentations and reports
3D Visualization: Advanced 3D graph rendering for complex relationships

🏷️ Automated Tagging & Categorization

ML-Powered Tagging: Automatic tag generation using NLP
Hierarchical Categories: Multi-level categorization system
Custom Taxonomies: Define your own classification schemes
Tag Suggestions: AI suggests relevant tags as you work
Bulk Operations: Apply tags and categories to multiple items

💡 Personalized Recommendations

Content Discovery: Find relevant content you didn't know existed
Learning Paths: AI-generated learning sequences
Expert Matching: Connect with subject matter experts
Interest Tracking: System learns your preferences over time
Cross-Domain Suggestions: Discover connections across different fields

🤝 Collaborative Features

Team Workspaces: Shared knowledge bases for teams
Access Control: Granular permissions and sharing settings
Collaborative Editing: Real-time collaboration on documents
Discussion Threads: Contextual discussions on any content
Knowledge Validation: Community-driven quality assurance

📈 Analytics & Insights

Usage Analytics: Track how knowledge is being used
Knowledge Gaps: Identify missing information in your knowledge base
Trend Analysis: Discover emerging topics and trends
Performance Metrics: Monitor search effectiveness and user satisfaction
Custom Reports: Generate reports for stakeholders

🏗️ Architecture

System Architecture

┌─────────────────────────────────────────────────────────────┐
│                      Load Balancer                          │
└─────────────────────────────┬───────────────────────────────┘
                              │
┌─────────────────────────────┴───────────────────────────────┐
│                    API Gateway (FastAPI)                     │
├─────────────────────────────────────────────────────────────┤
│  Auth │  Rate Limiting │  Request Routing │  Response Cache │
└─────────────┬──────────────────────────────┬────────────────┘
              │                              │
┌─────────────┴────────────┐    ┌───────────┴────────────────┐
│    Service Layer         │    │     ML Pipeline            │
├──────────────────────────┤    ├────────────────────────────┤
│ • Document Service       │    │ • NLP Engine               │
│ • Search Service         │    │ • Recommendation Engine    │
│ • Knowledge Graph        │    │ • Classification Models    │
│ • Collaboration Service  │    │ • Embedding Generator      │
│ • Analytics Service      │    │ • Graph Algorithms         │
└──────────────────────────┘    └────────────────────────────┘
              │                              │
┌─────────────┴────────────────────────────┴─────────────────┐
│                      Data Layer                             │
├─────────────────────────────────────────────────────────────┤
│  PostgreSQL  │  Redis  │  Elasticsearch  │  Vector DB      │
└─────────────────────────────────────────────────────────────┘

Directory Structure

ATLAS/
├── app/
│   ├── api/                    # API endpoints
│   │   ├── auth.py            # Authentication endpoints
│   │   ├── documents.py       # Document management
│   │   ├── search.py          # Search functionality
│   │   ├── knowledge_graph.py # Graph operations
│   │   ├── collaboration.py   # Team features
│   │   └── analytics.py       # Analytics endpoints
│   ├── core/                  # Core functionality
│   │   ├── config.py         # Configuration
│   │   ├── security.py       # Security utilities
│   │   └── dependencies.py   # Dependency injection
│   ├── models/               # Database models
│   │   ├── user.py          # User model
│   │   ├── document.py      # Document model
│   │   ├── knowledge.py     # Knowledge entities
│   │   └── collaboration.py # Collaboration models
│   ├── schemas/              # Pydantic schemas
│   │   ├── user.py          # User schemas
│   │   ├── document.py      # Document schemas
│   │   └── search.py        # Search schemas
│   └── services/             # Business logic
│       ├── document_processor.py
│       ├── search_engine.py
│       └── knowledge_builder.py
├── ml/                       # Machine learning modules
│   ├── nlp/                 # NLP models
│   ├── embeddings/          # Embedding generation
│   ├── classification/      # Document classification
│   └── recommendations/     # Recommendation algorithms
├── tests/                   # Test suite
│   ├── unit/               # Unit tests
│   ├── integration/        # Integration tests
│   └── e2e/               # End-to-end tests
├── migrations/             # Database migrations
├── scripts/               # Utility scripts
└── docs/                  # Documentation

📦 Installation

Prerequisites

Python 3.8+
PostgreSQL 12+
Redis 6+
Elasticsearch 7+ (optional, for advanced search)
Docker & Docker Compose (for containerized deployment)

Quick Start

Clone the repository

git clone https://github.com/mtm-ce/atlas.git
cd atlas

Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies

pip install -r requirements.txt
pip install -r requirements-dev.txt  # For development

Set up environment variables

cp .env.example .env
# Edit .env with your configuration

Initialize database

# Create database
createdb atlas_db

# Run migrations
alembic upgrade head

# Seed initial data (optional)
python scripts/seed_data.py

Start the application

# Development mode
uvicorn main:app --reload --host 0.0.0.0 --port 8000

# Production mode
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker

Docker Installation

# Build and run with Docker Compose
docker-compose up -d

# View logs
docker-compose logs -f atlas

# Stop services
docker-compose down

💻 Usage

Basic Usage Example

import requests
from atlas_client import ATLASClient

# Initialize client
client = ATLASClient(
    base_url="http://localhost:8000",
    api_key="your-api-key"
)

# Upload and process a document
with open("research_paper.pdf", "rb") as f:
    document = client.documents.upload(
        file=f,
        metadata={
            "title": "AI in Healthcare",
            "author": "Dr. Smith",
            "tags": ["AI", "Healthcare", "Research"]
        }
    )

# Search for related content
results = client.search.query(
    query="machine learning applications in medical diagnosis",
    filters={"tags": ["Healthcare"]},
    limit=10
)

# Build knowledge graph
graph = client.knowledge_graph.build(
    document_ids=[doc.id for doc in results],
    depth=2,
    min_similarity=0.7
)

# Get recommendations
recommendations = client.recommendations.get(
    based_on=document.id,
    recommendation_type="similar_content",
    limit=5
)

Advanced Features

Custom Document Processing Pipeline

from atlas.ml import DocumentProcessor, CustomExtractor

# Create custom extractor
class MedicalTermExtractor(CustomExtractor):
    def extract(self, text):
        # Custom logic to extract medical terms
        return medical_terms

# Configure processing pipeline
processor = DocumentProcessor()
processor.add_extractor(MedicalTermExtractor())
processor.add_classifier("medical_specialty")

# Process document with custom pipeline
processed = processor.process(document_path, pipeline_config)

Knowledge Graph Queries

# Find shortest path between concepts
path = client.knowledge_graph.find_path(
    start_concept="Deep Learning",
    end_concept="Drug Discovery",
    max_depth=5
)

# Get concept neighborhood
neighborhood = client.knowledge_graph.get_neighborhood(
    concept="Artificial Intelligence",
    radius=2,
    min_edge_weight=0.5
)

# Discover concept clusters
clusters = client.knowledge_graph.find_clusters(
    min_cluster_size=5,
    similarity_threshold=0.8
)

📚 API Documentation

Authentication

All API endpoints require authentication via JWT tokens or API keys.

Authorization: Bearer <token>
# or
X-API-Key: <api-key>

Core Endpoints

Documents

Upload Document

POST /api/v1/documents
Content-Type: multipart/form-data

{
  "file": <file>,
  "metadata": {
    "title": "Document Title",
    "tags": ["tag1", "tag2"]
  }
}

Search Documents

GET /api/v1/search?q=<query>&filters=<filters>&limit=20&offset=0

Get Document

GET /api/v1/documents/{document_id}

Knowledge Graph

Build Graph

POST /api/v1/knowledge-graph/build
Content-Type: application/json

{
  "document_ids": ["doc1", "doc2"],
  "options": {
    "depth": 2,
    "min_similarity": 0.7
  }
}

Query Graph

POST /api/v1/knowledge-graph/query
Content-Type: application/json

{
  "cypher": "MATCH (n:Concept)-[r]->(m:Concept) WHERE n.name = 'AI' RETURN n, r, m"
}

Collaboration

Create Workspace

POST /api/v1/workspaces
Content-Type: application/json

{
  "name": "Research Team",
  "description": "Collaborative research workspace",
  "members": ["[email protected]", "[email protected]"]
}

Share Document

POST /api/v1/documents/{document_id}/share
Content-Type: application/json

{
  "workspace_id": "workspace123",
  "permissions": ["read", "comment"]
}

WebSocket Endpoints

For real-time features:

const ws = new WebSocket('ws://localhost:8000/ws');

// Subscribe to document updates
ws.send(JSON.stringify({
  action: 'subscribe',
  channel: 'document_updates',
  document_id: 'doc123'
}));

// Receive real-time updates
ws.onmessage = (event) => {
  const update = JSON.parse(event.data);
  console.log('Document updated:', update);
};

🤖 Machine Learning

ML Capabilities

1. Natural Language Processing

Named Entity Recognition: Extract people, places, organizations
Keyword Extraction: Identify important terms and phrases
Text Summarization: Generate concise summaries
Language Detection: Support for 50+ languages
Sentiment Analysis: Understand document tone and sentiment

2. Document Classification

Hierarchical Classification: Multi-level categorization
Multi-Label Classification: Documents can belong to multiple categories
Custom Classifiers: Train models on your data
Active Learning: Improve classification with user feedback

3. Semantic Search

Dense Retrieval: Vector-based semantic search
Query Understanding: Intent recognition and expansion
Re-ranking: ML-based result re-ranking
Personalization: User-specific result ordering

4. Knowledge Graph ML

Entity Linking: Connect mentions to knowledge base entities
Relation Extraction: Identify relationships between entities
Graph Embeddings: Learn vector representations of graph nodes
Link Prediction: Suggest missing connections

5. Recommendation Systems

Content-Based Filtering: Recommend based on document similarity
Collaborative Filtering: Leverage community interactions
Hybrid Approaches: Combine multiple recommendation strategies
Explanation Generation: Explain why items are recommended

Training Custom Models

from atlas.ml import ModelTrainer, DatasetBuilder

# Build training dataset
dataset = DatasetBuilder()
dataset.add_documents(document_ids)
dataset.add_labels(labels)

# Configure trainer
trainer = ModelTrainer(
    model_type="document_classifier",
    base_model="bert-base-uncased",
    config={
        "num_epochs": 10,
        "batch_size": 32,
        "learning_rate": 2e-5
    }
)

# Train model
model = trainer.train(dataset)

# Deploy model
client.models.deploy(
    model_id=model.id,
    endpoint_name="custom-classifier",
    min_replicas=2
)

⚙️ Configuration

Environment Variables

# Application
APP_NAME=ATLAS
APP_ENV=production
APP_PORT=8000
APP_WORKERS=4

# Database
DATABASE_URL=postgresql://user:password@localhost:5432/atlas_db
DATABASE_POOL_SIZE=20
DATABASE_POOL_OVERFLOW=0

# Redis
REDIS_URL=redis://localhost:6379/0
REDIS_MAX_CONNECTIONS=50

# Elasticsearch (optional)
ELASTICSEARCH_URL=http://localhost:9200
ELASTICSEARCH_INDEX_PREFIX=atlas_

# ML Configuration
ML_MODEL_PATH=/models
ML_BATCH_SIZE=32
ML_MAX_SEQUENCE_LENGTH=512
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2

# Security
JWT_SECRET_KEY=your-secret-key-here
JWT_ALGORITHM=HS256
JWT_EXPIRATION_HOURS=24
API_KEY_HEADER=X-API-Key

# Storage
UPLOAD_PATH=/uploads
MAX_UPLOAD_SIZE=104857600  # 100MB
ALLOWED_EXTENSIONS=pdf,docx,txt,md,py,java,cpp

# Features
ENABLE_ML_FEATURES=true
ENABLE_GRAPH_VISUALIZATION=true
ENABLE_COLLABORATION=true
ENABLE_ANALYTICS=true

# External Services
OPENAI_API_KEY=your-openai-key  # For advanced NLP features
AWS_ACCESS_KEY_ID=your-aws-key
AWS_SECRET_ACCESS_KEY=your-aws-secret
S3_BUCKET_NAME=atlas-documents

Configuration File (config.yaml)

atlas:
  version: 1.0.0
  environment: production
  
server:
  host: 0.0.0.0
  port: 8000
  workers: 4
  reload: false
  
database:
  url: ${DATABASE_URL}
  echo: false
  pool_size: 20
  pool_recycle: 3600
  
redis:
  url: ${REDIS_URL}
  decode_responses: true
  
ml:
  models:
    nlp:
      model_name: en_core_web_lg
      gpu: false
    embeddings:
      model_name: sentence-transformers/all-MiniLM-L6-v2
      dimension: 384
    classification:
      model_name: atlas-doc-classifier
      threshold: 0.7
      
search:
  engine: elasticsearch  # or 'postgres' for simpler setup
  min_score: 0.5
  max_results: 100
  
features:
    ml_processing: true
    real_time_collaboration: true
    advanced_analytics: true
    graph_visualization: true

🔧 Development

Setting Up Development Environment

Install development dependencies

pip install -r requirements-dev.txt
pre-commit install

Configure IDE

# VS Code settings
cp .vscode/settings.json.example .vscode/settings.json

# PyCharm
# Import project settings from .idea/

Run development services

# Start all services
docker-compose -f docker-compose.dev.yml up -d

# Watch logs
docker-compose -f docker-compose.dev.yml logs -f

Code Style & Quality

Formatting: Black (line length: 88)
Linting: Flake8, Pylint
Type Checking: MyPy
Import Sorting: isort
Documentation: Google-style docstrings

# Format code
black .

# Run linters
flake8
pylint atlas/

# Type checking
mypy atlas/

# All checks
pre-commit run --all-files

Git Workflow

Feature Development

git checkout -b feature/your-feature-name
# Make changes
git commit -m "feat: add new feature"
git push origin feature/your-feature-name

Commit Convention

feat: New feature
fix: Bug fix
docs: Documentation
style: Code style changes
refactor: Code refactoring
test: Test changes
chore: Build/aux changes

🧪 Testing

Test Structure

tests/
├── unit/              # Unit tests
│   ├── test_models.py
│   ├── test_services.py
│   └── test_ml/
├── integration/       # Integration tests
│   ├── test_api.py
│   ├── test_database.py
│   └── test_ml_pipeline.py
├── e2e/              # End-to-end tests
│   └── test_workflows.py
└── fixtures/         # Test data

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=atlas --cov-report=html

# Run specific test file
pytest tests/unit/test_models.py

# Run specific test
pytest tests/unit/test_models.py::test_document_creation

# Run by marker
pytest -m "not slow"

# Run in parallel
pytest -n 4

Test Examples

import pytest
from atlas.models import Document
from atlas.services import SearchService

@pytest.fixture
def search_service():
    return SearchService()

@pytest.fixture
def sample_documents(db_session):
    docs = [
        Document(title="AI Basics", content="Introduction to AI..."),
        Document(title="ML Advanced", content="Deep learning techniques...")
    ]
    db_session.add_all(docs)
    db_session.commit()
    return docs

def test_semantic_search(search_service, sample_documents):
    results = search_service.search(
        query="artificial intelligence fundamentals",
        limit=10
    )
    
    assert len(results) > 0
    assert results[0].title == "AI Basics"
    assert results[0].score > 0.7

@pytest.mark.slow
def test_knowledge_graph_building(kg_service, large_dataset):
    graph = kg_service.build_graph(
        documents=large_dataset,
        min_similarity=0.6
    )
    
    assert graph.num_nodes() > 100
    assert graph.num_edges() > 500

Performance Testing

# Load testing with Locust
locust -f tests/performance/locustfile.py --host=http://localhost:8000

# Benchmark ML models
python tests/performance/benchmark_ml.py

🚀 Deployment

Docker Deployment

# Dockerfile
FROM python:3.9-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    build-essential \
    postgresql-client \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Run migrations and start server
CMD ["sh", "-c", "alembic upgrade head && uvicorn main:app --host 0.0.0.0 --port 8000"]

Kubernetes Deployment

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: atlas
spec:
  replicas: 3
  selector:
    matchLabels:
      app: atlas
  template:
    metadata:
      labels:
        app: atlas
    spec:
      containers:
      - name: atlas
        image: atlas:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: atlas-secrets
              key: database-url
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10

Production Checklist

📊 Performance

Benchmarks

Operation	Average Time	Throughput
Document Upload	250ms	4 docs/sec
Text Extraction	500ms	2 docs/sec
Semantic Search	50ms	20 queries/sec
Graph Building	2s	0.5 graphs/sec
ML Inference	100ms	10 docs/sec

Optimization Tips

Database Optimization
- Create appropriate indexes
- Use connection pooling
- Enable query caching
- Regular VACUUM operations
Caching Strategy
- Cache search results
- Cache ML model outputs
- Use Redis for session storage
- Implement CDN for static assets
ML Optimization
- Batch processing for inference
- Model quantization
- GPU acceleration
- Model caching
Scaling Strategies
- Horizontal scaling for API servers
- Read replicas for database
- Distributed ML inference
- Queue-based document processing

🔐 Security

Security Features

Authentication: JWT-based auth with refresh tokens
Authorization: Role-based access control (RBAC)
Encryption: TLS 1.3 for transport, AES-256 for storage
Input Validation: Comprehensive request validation
Rate Limiting: Configurable per-endpoint limits
Audit Logging: Complete audit trail of all actions

Security Best Practices

API Security

# Rate limiting
@app.get("/api/v1/search")
@limiter.limit("100/hour")
async def search(query: str = Query(..., max_length=1000)):
    # Validate and sanitize input
    clean_query = sanitize_input(query)
    return await search_service.search(clean_query)

Data Protection
- Encrypt sensitive data at rest
- Use secure communication channels
- Implement data retention policies
- Regular security audits

Access Control

@require_permissions(["documents:read"])
async def get_document(document_id: str, user: User = Depends(get_current_user)):
    # Check document access
    if not await has_document_access(user, document_id):
        raise HTTPException(403, "Access denied")
    return await get_document_by_id(document_id)

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines.

How to Contribute

Fork the repository
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

Development Process

Issue Creation: Create an issue describing the feature/bug
Discussion: Discuss the implementation approach
Implementation: Write code following our guidelines
Testing: Add tests for new functionality
Documentation: Update documentation as needed
Review: Submit PR for review

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Built with ❤️ by the MTM-CE team
Powered by FastAPI, PostgreSQL, and cutting-edge ML
Special thanks to all contributors and the open-source community

📞 Support

Documentation: https://docs.atlas.mtm-ce.com
Issues: GitHub Issues
Discussions: GitHub Discussions
Email: [email protected]
Discord: Join our community

ATLAS - Empowering knowledge discovery through intelligent AI
Part of the MTM-CE Ecosystem

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.github/workflows		.github/workflows
ml		ml
tests		tests
.gitignore		.gitignore
README.md		README.md
community_session.py		community_session.py
config.py		config.py
config.yaml		config.yaml
health_check.py		health_check.py
logger.py		logger.py
main.py		main.py
models.py		models.py
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
schemas.py		schemas.py
service.py		service.py
service_methods.py		service_methods.py

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