DialogChain - Flexible Dialog Processing Framework

🚀 DialogChain is a powerful and extensible framework for building, managing, and deploying dialog systems and conversational AI applications. It supports multiple programming languages and integrates with various NLP and ML models.

📖 Table of Contents

• Features
• Installation
• Quick Start
• Documentation
• Examples
• Architecture
• Contributing
• License

✨ Features

• Multi-language Support: Write processors in Python, JavaScript, or any language with gRPC support
• Extensible Architecture: Easily add new input sources, processors, and output destinations
• Asynchronous Processing: Built on asyncio for high-performance dialog processing
• YAML Configuration: Define dialog flows and processing pipelines with simple YAML files
• Built-in Processors: Includes common NLP and ML model integrations
• Monitoring & Logging: Comprehensive logging and metrics out of the box
• REST & gRPC APIs: Easy integration with other services
• Docker Support: Containerized deployment options

🚀 Installation

Prerequisites

• Python 3.8+
• Poetry (for development)
• Docker (optional, for containerized deployment)

Using pip

pip install dialogchain

From Source

git clone https://github.com/dialogchain/python
cd python
poetry install

🚀 Quick Start

1. Create a simple dialog configuration in config.yaml:

version: "1.0"pipeline:
- name: greetingtype: pythonmodule: dialogchain.processors.basicclass: GreetingProcessorconfig:
default_name: "User"

2. Run the dialog server:

dialogchain serve config.yaml

3. Send a request:

curl -X POST http://localhost:8000/process -H "Content-Type: application/json" -d '{"text": "Hello!"}'

📚 Documentation

For detailed documentation, please visit our documentation site.

📝 Logging

DialogChain includes a robust logging system with the following features:

Features

• Multiple Handlers: Console and file logging out of the box
• Structured Logs: JSON-formatted logs for easy parsing
• SQLite Storage: Logs are stored in a searchable database
• Log Rotation: Automatic log rotation to prevent disk space issues
• Thread-Safe: Safe for use in multi-threaded applications

Basic Usage

fromdialogchain.utils.loggerimportsetup_logger, get_logs# Get a logger instancelogger=setup_logger(__name__, log_level='DEBUG')
# Log messages with different levelslogger.debug('Debug message')
logger.info('Information message')
logger.warning('Warning message')
logger.error('Error message', extra={'error_code': 500})
# Get recent logs from databaserecent_logs=get_logs(limit=10)

Logging Commands

DialogChain provides several make commands for log management:

# View recent logs (default: 50 lines)
make logs
# View specific number of log lines
make logs LINES=100
# Set log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
make log-level LEVEL=DEBUG
# View database logs
make log-db LIMIT=50
# Follow log file in real-time
make log-tail
# Clear log files
make log-clear

Configuration

Logging can be configured via environment variables:

# Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
LOG_LEVEL=DEBUG
# Log file path
LOG_FILE=logs/dialogchain.log
# Database log file path
DB_LOG_FILE=logs/dialogchain.db

Log Rotation

Log files are automatically rotated when they reach 10MB, keeping up to 5 backup files.

📦 Project Structure

dialogchain/
├── src/
│ └── dialogchain/
│ ├── __init__.py
│ ├── engine.py # Core processing engine
│ ├── processors/ # Built-in processors
│ ├── connectors/ # I/O connectors
│ └── utils/ # Utility functions
├── tests/ # Test suite
├── examples/ # Example configurations
└── docs/ # Documentation

🧪 Testing

Run the complete test suite:

make test

Run specific test types:

# Unit tests
make test-unit
# Integration tests
make test-integration
# End-to-end tests
make test-e2e

Generate test coverage report:

make coverage

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

📞 Support

For support, please open an issue in the GitHub repository.

🧪 Testing DialogChain

DialogChain includes a comprehensive test suite to ensure code quality and functionality. Here's how to run the tests and view logs:

Running Tests

Run the complete test suite:

make test

Or run specific test types:

# Unit tests
make test-unit
# Integration tests
make test-integration
# End-to-end tests
make test-e2e

Viewing Test Coverage

Generate a coverage report to see which parts of your code are being tested:

make coverage

This will generate an HTML report in the htmlcov directory.

Viewing Logs

View the most recent logs from your application:

# Show last 50 lines from all log files
make logs
# Show a different number of lines
make logs LINES=100
# Specify a custom log directory
make logs LOG_DIR=/path/to/logs

Linting and Code Style

Ensure your code follows the project's style guidelines:

# Run linters
make lint
# Automatically format your code
make format
# Check types
make typecheck

Running in Docker

You can also run tests in a Docker container:

# Build the Docker image
docker build -t dialogchain .# Run tests in the container
docker run --rm dialogchain make test

🔍 Network Scanning & Printing

DialogChain includes powerful network scanning capabilities to discover devices like cameras and printers on your local network.

Scan for Network Devices

Scan your local network for various devices and services:

make scan-network

Discover Cameras

Find RTSP cameras on your network:

make scan-cameras

Discover Printers

List all available printers on your system:

make scan-printers

Print a Test Page

Send a test page to your default printer:

make print-test

Using the Network Scanner in Python

You can also use the network scanner directly in your Python code:

fromdialogchain.scannerimportNetworkScannerimportasyncioasyncdefscan_network():
scanner=NetworkScanner()
# Scan for all servicesservices=awaitscanner.scan_network()
# Or scan for specific service typescameras=awaitscanner.scan_network(service_types=['rtsp'])
forserviceinservices:
print(f"{service.ip}:{service.port} - {service.service} ({service.banner})")
# Run the scanasyncio.run(scan_network())

🖨️ Printing Support

DialogChain includes basic printing capabilities using the CUPS (Common Unix Printing System) interface.

Print Text

importcupsdefprint_text(text, printer_name=None):
conn=cups.Connection()
printers=conn.getPrinters()
ifnotprinters:
print("No printers available")
returnprinter=printer_nameorlist(printers.keys())[0]
job_id=conn.printFile(printer, "/dev/stdin", "DialogChain Print", {"raw": "True"}, text)
print(f"Sent print job {job_id} to {printer}")
# Example usageprint_text("Hello from DialogChain!")

Print from File

defprint_file(file_path, printer_name=None):
conn=cups.Connection()
printers=conn.getPrinters()
ifnotprinters:
print("No printers available")
returnprinter=printer_nameorlist(printers.keys())[0]
job_id=conn.printFile(printer, file_path, "Document Print", {})
print(f"Sent print job {job_id} to {printer}")
# Example usageprint_file("document.pdf")

📦 Installation

Prerequisites

• Python 3.8+
• Poetry

Install with Poetry

1. Clone the repository:

   git clone https://github.com/dialogchain/python.git
   cd python

2. Install dependencies:

   poetry install

3. Activate the virtual environment:

   poetry shell

Development Setup

1. Install development and test dependencies:

   poetry install --with dev,test

2. Set up pre-commit hooks:

   pre-commit install

3. Run tests:

   make test

   Or with coverage report:

   make coverage

🏗️ Project Structure

dialogchain/
├── src/
│ └── dialogchain/ # Main package
│ ├── __init__.py
│ ├── cli.py # Command-line interface
│ ├── config.py # Configuration handling
│ ├── connectors/ # Connector implementations
│ ├── engine.py # Core engine
│ ├── exceptions.py # Custom exceptions
│ ├── processors/ # Processor implementations
│ └── utils.py # Utility functions
├── tests/ # Test files
│ ├── unit/ # Unit tests
│ │ ├── core/ # Core functionality tests
│ │ ├── connectors/ # Connector tests
│ │ └── ...
│ └── integration/ # Integration tests
├── .github/ # GitHub workflows
├── docs/ # Documentation
├── .gitignore
├── .pre-commit-config.yaml
├── Makefile # Common development commands
├── pyproject.toml # Project metadata and dependencies
└── README.md

🧪 Testing

Run the full test suite:

make test

Run specific test categories:

# Unit tests
make test-unit
# Integration tests
make test-integration
# With coverage report
make coverage

🧹 Code Quality

Format and check code style:

make format # Auto-format code
make lint # Run linters
make typecheck # Run type checking
make check-all # Run all checks

🚀 Quick Start

1. Create a configuration file config.yaml:

   version: 1.0pipelines:
   - name: basic_dialogsteps:
   - type: inputname: user_inputsource: console
   - type: processorname: nlp_processormodule: dialogchain.processors.nlpfunction: process_text
   - type: outputname: responsetarget: console

2. Run the dialog chain:

   poetry run dialogchain -c config.yaml

✨ Features

• 💬 Dialog Management: Stateful conversation handling and context management
• 🤖 Multi-Language Support: Python, Go, Rust, C++, Node.js processors
• 🔌 Flexible Connectors: REST APIs, WebSockets, gRPC, MQTT, and more
• 🧠 ML/NLP Integration: Built-in support for popular NLP libraries and models
• ⚙️ Simple Configuration: YAML/JSON configuration with environment variables
• 🐳 Cloud Native: Docker, Kubernetes, and serverless deployment ready
• 📊 Production Ready: Monitoring, logging, and error handling
• 🧪 Comprehensive Testing: Unit, integration, and end-to-end tests
• 🔍 Code Quality: Type hints, linting, and code formatting
• 📈 Scalable: Horizontal scaling for high-throughput applications

🛠️ Development

Code Style

This project uses:

• Black for code formatting
• isort for import sorting
• Flake8 for linting
• mypy for static type checking

Development Commands

# Run tests with coverage
poetry run pytest --cov=dialogchain --cov-report=term-missing
# Format code
poetry run black .
poetry run isort .# Lint code
poetry run flake8
# Type checking
poetry run mypy dialogchain

🏗️ Architecture

┌─────────────┐ ┌──────────────────┐ ┌─────────────┐
│ Inputs │ │ Processors │ │ Outputs │
├─────────────┤ ├──────────────────┤ ├─────────────┤
│ HTTP API │───►│ NLP Processing │───►│ REST API │
│ WebSocket │ │ Intent Detection │ │ WebSocket │
│ gRPC │ │ Entity Extraction│ │ gRPC │
│ CLI │ │ Dialog Management│ │ Message Bus │
│ Message Bus │ │ Response Gen │ │ Logging │
└─────────────┘ └──────────────────┘ └─────────────┘

🚀 Quick Start

1. Installation

# Clone repository
git clone https://github.com/dialogchain/python
cd python
# Install dependencies
poetry install
# Run the application
poetry run dialogchain --help

2. Configuration

Create your .env file:

# Copy template and edit
cp .env.example .env

Example .env:

CAMERA_USER=admin
CAMERA_PASS=your_password
CAMERA_IP=192.168.1.100
SMTP_USER=alerts@company.com
SMTP_PASS=app_password
SECURITY_EMAIL=security@company.com

3. Create Routes

Generate a configuration template:

dialogchain init --template camera --output my_config.yaml

Example route (simplified YAML):

routes:
- name: "smart_security_camera"from: "rtsp://{{CAMERA_USER}}:{{CAMERA_PASS}}@{{CAMERA_IP}}/stream1"processors:
# Python: Object detection
- type: "external"command: "python scripts/detect_objects.py"config:
confidence_threshold: 0.6target_objects: ["person", "car"]# Filter high-risk only
- type: "filter"condition: "{{threat_level}} == 'high'"to:
- "smtp://{{SMTP_SERVER}}:{{SMTP_PORT}}?user={{SMTP_USER}}&password={{SMTP_PASS}}&to={{SECURITY_EMAIL}}"
- "http://webhook.company.com/security-alert"

4. Run

Run all routes

dialogchain run -c my_config.yaml

Run specific route

dialogchain run -c my_config.yaml --route smart_dialog_flow

Dry run to see what would execute

dialogchain run -c my_config.yaml --dry-run

 dialogchain run -c my_config.yaml --dry-run
🔍 DRY RUN - Configuration Analysis:
==================================================
📍 Route: front_door_camera
From: rtsp://:@/stream1
Processors:
1. external
Command: python -m ultralytics_processor
2. filter
3. transform
To:
• smtp://:?user=&password=&to=

📖 Detailed Usage

Sources (Input)

Source	Example URL	Description
RTSP Camera	rtsp://user:pass@ip/stream1	Live video streams
Timer	timer://5m	Scheduled execution
File	file:///path/to/watch	File monitoring
gRPC	grpc://localhost:50051/Service/Method	gRPC endpoints
MQTT	mqtt://broker:1883/topic	MQTT messages

Processors (Transform)

External Processors

Delegate to any programming language:

processors:
# Python ML inference
- type: "external"command: "python scripts/detect_objects.py"input_format: "json"output_format: "json"config:
model: "yolov8n.pt"confidence_threshold: 0.6# Go image processing
- type: "external"command: "go run scripts/image_processor.go"config:
thread_count: 4optimization: "speed"# Rust performance-critical tasks
- type: "external"command: "cargo run --bin data_processor"config:
batch_size: 32simd_enabled: true# C++ optimized algorithms
- type: "external"command: "./bin/cpp_postprocessor"config:
algorithm: "fast_nms"threshold: 0.85# Node.js business logic
- type: "external"command: "node scripts/business_rules.js"config:
rules_file: "security_rules.json"

Built-in Processors

processors:
# Filter messages
- type: "filter"condition: "{{confidence}} > 0.7"# Transform output
- type: "transform"template: "Alert: {{object_type}} detected at {{position}}"# Aggregate over time
- type: "aggregate"strategy: "collect"timeout: "5m"max_size: 100

Destinations (Output)

Destination	Example URL	Description
Email	smtp://smtp.gmail.com:587?user={{USER}}&password={{PASS}}&to={{EMAILS}}	SMTP alerts
HTTP	http://api.company.com/webhook	REST API calls
MQTT	mqtt://broker:1883/alerts/camera	MQTT publishing
File	file:///logs/alerts.log	File logging
gRPC	grpc://service:50051/AlertService/Send	gRPC calls

🛠️ Development

Project Structure

dialogchain/
├── dialogchain/ # Python package
│ ├── cli.py # Command line interface
│ ├── engine.py # Main routing engine
│ ├── processors.py # Processing components
│ └── connectors.py # Input/output connectors
├── scripts/ # External processors
│ ├── detect_objects.py # Python: YOLO detection
│ ├── health_check.go # Go: Health monitoring
│ └── business_rules.js # Node.js: Business logic
├── examples/ # Configuration examples
│ └── simple_routes.yaml # Sample routes
├── k8s/ # Kubernetes deployment
│ └── deployment.yaml # K8s manifests
├── Dockerfile # Container definition
├── Makefile # Build automation
└── README.md # This file

Building External Processors

# Build all processors
make build-all
# Build specific language
make build-go
make build-rust
make build-cpp
# Install dependencies
make install-deps

Development Workflow

# Development environment
make dev
# Run tests
make test# Lint code
make lint
# Build distribution
make build

🐳 Docker Deployment

Build and Run

# Build image
make docker
# Run with Docker
docker run -it --rm \
-v $(PWD)/examples:/app/examples \
-v $(PWD)/.env:/app/.env \
dialogchain:latest
# Or use Make
make docker-run

Docker Compose (with dependencies)

version: "3.8"services:
dialogchain:
build: .environment:
- CAMERA_IP=192.168.1.100
- MQTT_BROKER=mqttvolumes:
- ./examples:/app/examples
- ./logs:/app/logsdepends_on:
- mqtt
- redismqtt:
image: eclipse-mosquitto:2ports:
- "1883:1883"redis:
image: redis:7-alpineports:
- "6379:6379"

☸️ Kubernetes Deployment

# Deploy to Kubernetes
make deploy-k8s
# Or manually
kubectl apply -f k8s/
# Check status
kubectl get pods -n dialogchain
# View logs
kubectl logs -f deployment/dialogchain -n dialogchain

Features in Kubernetes:

• Horizontal Pod Autoscaling: Auto-scale based on CPU/memory/custom metrics
• Resource Management: CPU/memory limits and requests
• Health Checks: Liveness and readiness probes
• Persistent Storage: Shared volumes for model files and logs
• Service Discovery: Internal service communication
• Monitoring: Prometheus metrics integration

📊 Monitoring and Observability

Built-in Metrics

# Health check endpoint
curl http://localhost:8080/health
# Metrics endpoint (Prometheus format)
curl http://localhost:8080/metrics
# Runtime statistics
curl http://localhost:8080/stats

Logging

# View real-time logs
make logs
# Start monitoring dashboard
make monitor

Performance Benchmarking

# Run benchmarks
make benchmark

🔧 Configuration Reference

Environment Variables

# Camera settings
CAMERA_USER=admin
CAMERA_PASS=password
CAMERA_IP=192.168.1.100
CAMERA_NAME=front_door
# Email settings
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
SMTP_USER=alerts@company.com
SMTP_PASS=app_password
SECURITY_EMAIL=security@company.com
# Service URLs
WEBHOOK_URL=https://hooks.company.com
ML_GRPC_SERVER=localhost:50051
DASHBOARD_URL=https://dashboard.company.com
# MQTT settings
MQTT_BROKER=localhost
MQTT_PORT=1883
MQTT_USER=dialogchain
MQTT_PASS=secret
# Advanced settings
MAX_CONCURRENT_ROUTES=10
DEFAULT_TIMEOUT=30
LOG_LEVEL=info
METRICS_ENABLED=true

Route Configuration Schema

routes:
- name: "route_name"# Required: Route identifierfrom: "source_uri"# Required: Input sourceprocessors: # Optional: Processing pipeline
- type: "processor_type"config: {}to: ["destination_uri"] # Required: Output destinations# Global settingssettings:
max_concurrent_routes: 10default_timeout: 30log_level: "info"metrics_enabled: truehealth_check_port: 8080# Required environment variablesenv_vars:
- CAMERA_USER
- SMTP_PASS

🎯 Use Cases

1. Smart Security System

• Input: RTSP cameras, motion sensors
• Processing: Python (YOLO), Go (risk analysis), Node.js (rules)
• Output: Email alerts, mobile push, dashboard

2. Industrial Quality Control

• Input: Factory cameras, sensor data
• Processing: Python (defect detection), C++ (performance), Rust (safety)
• Output: MQTT control, database, operator alerts

3. IoT Data Pipeline

• Input: MQTT sensors, HTTP APIs
• Processing: Go (aggregation), Python (analytics), Node.js (business logic)
• Output: Time-series DB, real-time dashboard, alerts

4. Media Processing Pipeline

• Input: File uploads, streaming video
• Processing: Python (ML inference), C++ (codec), Rust (optimization)
• Output: CDN upload, metadata database, webhooks

🔍 Troubleshooting

Common Issues

Camera Connection Failed

# Test RTSP connection
ffmpeg -i rtsp://user:pass@ip/stream1 -frames:v 1 test.jpg
# Check network connectivity
ping camera_ip
telnet camera_ip 554

External Processor Errors

# Test processor manuallyecho'{"test": "data"}'| python scripts/detect_objects.py --input /dev/stdin
# Check dependencies
which go python node cargo
# View processor logs
dialogchain run -c config.yaml --verbose

Performance Issues

# Monitor resource usage
htop
# Check route performance
make benchmark
# Optimize configuration# - Reduce frame processing rate# - Increase batch sizes# - Use async processors

Debug Mode

# Enable verbose logging
dialogchain run -c config.yaml --verbose
# Dry run to test configuration
dialogchain run -c config.yaml --dry-run
# Validate configuration
dialogchain validate -c config.yaml

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Make changes and add tests
4. Run checks: make dev-workflow
5. Commit changes: git commit -m 'Add amazing feature'
6. Push to branch: git push origin feature/amazing-feature
7. Open Pull Request

Development Setup

# Clone and setup
git clone https://github.com/dialogchain/python
cd python
make dev
# Run tests
make test
For questions or support, please open an issue in the [issue tracker](https://github.com/taskinity/dialogchain/issues).
## 🔗 Related Projects
- **[Apache Camel](https://camel.apache.org/)**: Original enterprise integration framework
- **[GStreamer](https://gstreamer.freedesktop.org/)**: Multimedia framework
- **[Apache NiFi](https://nifi.apache.org/)**: Data flow automation
- **[Kubeflow](https://kubeflow.org/)**: ML workflows on Kubernetes
- **[TensorFlow Serving](https://tensorflow.org/tfx/serving)**: ML model serving
## 💡 Roadmap
- [ ] **Web UI**: Visual route designer and monitoring dashboard
- [ ] **More Connectors**: Database, cloud storage, message queues
- [ ] **Model Registry**: Integration with MLflow, DVC
- [ ] **Stream Processing**: Apache Kafka, Apache Pulsar support
- [ ] **Auto-scaling**: Dynamic processor scaling based on load
- [ ] **Security**: End-to-end encryption, authentication, authorization
- [ ] **Templates**: Pre-built templates for common use cases
---
**Built with ❤️ for the ML and multimedia processing community**
[⭐ Star us on GitHub](https://github.com/dialogchain/python) | [📖 Documentation](https://docs.dialogchain.org) | [💬 Community](https://discord.gg/dialogchain)