RAPTOR AI Framework

Rapid AI-Powered Text and Object Recognition
Open-Source Content Insight Engine for Enterprise AI Applications

About • Features • Installation • Quick Start • Documentation • Contributing • Website

RAPTOR is an advanced AI framework developed by the DHT Taiwan Team at DHT Solutions.

🚀 Current Release

Aigle 0.1 - First Community Beta Release

This is the initial open-source release of the RAPTOR framework, codenamed "Aigle". We're excited to share this with the community and look forward to your feedback and contributions.

🧪 Aigle 0.1 - Evaluation and Testing API

To help developers get started with the RAPTOR framework quickly and easily, we've deployed a test run API on DHT's development infrastructure. This evaluation API allows developers to:

Test and evaluate RAPTOR capabilities without setting up infrastructure
Develop AI applications using the RAPTOR framework with zero deployment overhead
Utilize DHT resources for testing and development purposes
Prototype faster by accessing pre-configured AI services

This is an excellent way to explore RAPTOR's features, build proof-of-concepts, and validate your use cases before deploying your own infrastructure.

🔗 Access the Evaluation API:
http://raptor_open_0_1_api.dhtsolution.com:8012/

For detailed API documentation, usage examples, and access instructions, please visit the link above.

Note: This is a development environment intended for evaluation and testing purposes. For production deployments, please refer to the Installation and Development sections below.

📋 Table of Contents

🎯 About RAPTOR

RAPTOR (Rapid AI-Powered Text and Object Recognition) is a Content Insight Engine that represents a paradigm shift in digital asset management, transforming passive media storage into an intelligent knowledge platform. By leveraging cutting-edge AI technologies including large language models, vector search, and semantic understanding, RAPTOR enables organizations to unlock the full value of their media assets through automated analysis, intelligent search, and actionable insights.

Business Value Proposition

85% reduction in manual content tagging and metadata generation
10x faster content discovery through semantic search
60% improvement in content reuse and operational efficiency
Real-time insights from video, audio, and document content
Enterprise-grade security, scalability, and integration capabilities

Strategic Differentiators

AI-Native Architecture: Built from the ground up around LLM orchestration and vector search
Multi-Modal Understanding: Unified analysis across video, audio, image, and text
Semantic Intelligence: Context-aware search that understands intent, not just keywords
Open + Enterprise Model: Open-source core with premium enterprise features
Production-Ready: Kubernetes-native with auto-scaling, fault tolerance, and 99.9% uptime

✨ Features

Version Aigle 0.1

This first community release includes:

Core Capabilities

Multi-Modal Content Analysis: Process and understand video, audio, images, and text
Semantic Search Engine: Context-aware search using vector embeddings
AI-Powered Metadata Generation: Automated tagging and content classification
LLM Orchestration: Flexible integration with multiple language models
Vector Database Integration: High-performance similarity search and retrieval

Intelligence Features

Content Understanding: Extract insights from unstructured media
Entity Recognition: Identify people, places, objects, and concepts
Sentiment Analysis: Understand emotional context in content
Topic Modeling: Automatic categorization and clustering
Temporal Analysis: Track content evolution over time

Enterprise Ready

Scalable Architecture: Kubernetes-native deployment
API-First Design: RESTful APIs for seamless integration
Security: Enterprise-grade authentication and authorization
Monitoring: Built-in observability and logging
Extensible: Plugin architecture for custom processors

For detailed release notes, see CHANGELOG.md.

📦 Installation

# Clone the repository
git clone https://github.com/DHT-AI-Studio/RAPTOR.git
cd RAPTOR/Aigle/0.1

# Create virtual environment (recommended)
conda create -n CIE python=3.10
conda activate CIE

# Install required dependencies:
pip install -r requirements.txt

Development

Step 1.

cd raptor
chmod +x check-services.sh deploy.sh logs.sh rollback.sh stop-all.sh tag-backup.sh
./deploy.sh

Step 2.Deploy service

Check container status
```
./check-services.sh
```

Test API connectivity

# Modellifecycle 服務
curl -s http://192.168.157.165:8086/docs

# Assetmanagement
curl -s http://192.168.157.165:8010/docs

View service logs
```
./logs.sh <service_name>
```

Quick Start

Create a new user and assign a new branch to the user

curl -X 'POST' \
'http://192.168.157.165:8086/users' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"username": "user1",
"password": "dht888888",
"password_hash": "",
"branch": "",
"permissions": [
    "upload",
    "download",
    "list"
]
}'

Create a new access token for the user

curl -X 'POST' \
'http://192.168.157.165:8086/token' \
-H 'accept: application/json' \
-H 'Content-Type: application/x-www-form-urlencoded' \
-d 'grant_type=password&username=user1&password=dht888888&scope=&client_id=string&client_secret=********'

Access RedisInsight 🔗 http://192.168.157.165:5540

Add a new connection:
- Connection Type: Redis Cluster
- Host: redis1
- Port: 7000
- Name: Redis Cluster
- Authentication: dht888888
Check if the local Ollama model includes qwen2.5:7b
```
ollama list
```
If not present
```
ollama pull qwen2.5:7b
```

Register MLflow with local Ollama

curl -X 'POST' \
  'http://192.168.157.165:8010/models/register_ollama' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "local_model_name": "qwen2.5:7b",
  "model_params": 7,
  "registered_name": "qwenforsummary",
  "set_priority_to_one": false,
  "stage": "production",
  "task": "text-generation-ollama",
  "version_description": "Register qwen2.5:7b local model"
}'

Check if the registration was successful

curl -X 'GET' \
  'http://192.168.157.165:8010/models/registered_in_mlflow?show_all=false' \
  -H 'accept: application/json'

Start audio/video/image/document service
9.1 Create Kafka topics:

cd path/to/kafka
chmod +x create_topic.sh
sudo ./create_topic.sh

9.2 Starting Services

cd services
chmod +x start_services.sh
./start_services.sh

9.3 Check if all services are still running

./check_services.sh

Produce a test requests topic

cd /path/to/kafka/test_service
python test.py

View Service Logs
```
cd path/to/kafka
tail -f service_name.log
```
Available service names include: document_orchestrator_service, document_analysis_service, document_summary_service, document_save2qdrant_service, etc. You can replace document with audio, video, or image based on the required service, for example: audio_orchestrator_service, video_analysis_service, image_summary_service, etc

Check Redis Data

sudo docker exec -it redis-kafka_dev redis-cli --raw
GET "document_orchestrator:correlation_id"
GET "video_orchestrator:correlation_id"
GET "audio_orchestrator:correlation_id"
GET "image_orchestrator:correlation_id"

Note: Replace service_name and correlation_id with actual values.

Use the Qdrant Search API to query data
(8821/video_search, 8822/audio_search, 8823/document_search, 8824/image_search)

curl -X POST "http://192.168.157.165:8822/audio_search" \
  -H "Content-Type: application/json" \
  -d '{
    "query_text": "OpenAI",
    "embedding_type": "text",
    "limit": 5
  }'

📚 Documentation

Available Documentation

📖 System Design & Architecture - High-level system design
🔧 Technical Implementation Guide - Detailed implementation
📝 API Documentation - API references and examples
🚀 Quick Start Guide - Get started in minutes
📋 CHANGELOG - Version history and updates

Additional Resources

GitHub Wiki (Coming soon) - Tutorials, guides, and best practices
API Reference (Coming soon) - Complete API documentation
Video Tutorials (Coming soon) - Step-by-step video guides
Examples Repository (Coming soon) - Sample projects and use cases

🛠️ Built With

RAPTOR leverages cutting-edge technologies:

AI & Machine Learning:

🤖 Large Language Models (LLM) - Multi-provider support
🧠 LangChain - LLM orchestration framework
🔍 Qdrant - High-performance vector database
📊 MLflow - ML lifecycle management
🎯 Sentence Transformers - Text embeddings

Backend & Infrastructure:

⚡ FastAPI - Modern Python web framework
🐍 Python 3.8+ - Core programming language
🐳 Docker & Docker Compose - Containerization
☸️ Kubernetes - Container orchestration (v1.0+)
📨 Apache Kafka - Event streaming platform
💾 Redis Cluster - High-performance caching

Processing & Analysis:

🎥 FFmpeg - Video/audio processing
🔊 Whisper - Speech recognition
🖼️ OpenCV - Computer vision
📄 PyPDF2, python-docx - Document processing
🎵 Librosa - Audio analysis

Observability:

📊 Prometheus - Metrics collection
📈 Grafana - Metrics visualization
🔍 ELK Stack - Logging (roadmap)

🌐 Community & Support

We value your feedback and encourage community participation!

🐛 Reporting Issues & Feature Requests

Please use GitHub Issues to:

🐛 Report bugs
✨ Request new features
❓ Ask questions
💡 Share suggestions

Before opening an issue:

Check existing issues to avoid duplicates
Use issue templates when available
Provide detailed information and steps to reproduce

📱 Stay Connected

Join our community on multiple platforms:

Follow us for updates, announcements, and community discussions!

Coming soon: Discord server, LinkedIn group, and monthly community calls!

💬 Get Help

📖 Check our Documentation first
🔍 Browse closed issues for solutions
💭 Start a Discussion for questions
📧 Email us through dhtsolution.com

We'll post updates, respond to questions, and collaborate with users across these platforms!

🤝 Contributing

We welcome contributions from the community! Please read our CONTRIBUTING.md guide to get started.

How to Contribute

Fork the repository
Create a 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

Please read our Code of Conduct before contributing.

📄 License

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

Copyright 2025 DHT Taiwan Team

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

🙏 Acknowledgments

RAPTOR is developed and maintained by the DHT Taiwan Team.

About DHT Solutions

DHT Solutions is a technology company specializing in AI and software development solutions. Learn more at https://dhtsolution.com/.

👥 Development Team

Meet the talented developers behind RAPTOR:

_titanh	_Cing-dht	_fungdht	_GeorgeDHT	_{NelsonYou1026}
_tianyu0223	_Robertdht	_QuinnChueh	_{Matthew20040407}	_{minnie-dhtsolution}
_lunar8386	_Joe-DHT	_{benjamin-dhtsolution}

🗺️ Future Development Roadmap

The following features are planned for upcoming releases to transform RAPTOR into a production-ready, enterprise-grade platform:

1. API Gateway & Integration (v0.2 - Q4 2025)

API Gateway: Production-ready gateway with rate limiting and API documentation portal
Webhook System: Event-driven integration capability with webhook event system

2. Advanced Video Understanding (v0.3 - Q4 2025)

Implement temporal reasoning models for event sequences
Add action recognition and activity detection
Build scene relationship graphs
Create timeline-based navigation interface
Note: Docker Compose deployment won't scale to production needs

3. AI LLM Interface - MCP Integration (v0.3 - Q4 2025)

Implement Model Context Protocol (MCP) interfaces for core services:

Document Processing
Video Analysis
Audio Processing
Image Analysis
Semantic Search
Vector Database queries
Model Management
MCP Prompts

4. Custom Model Training Pipeline (v0.4 - Q1 2026)

Build fine-tuning workflows for domain-specific models
Optimized Training: Automated pipelines with DeepSpeed optimization, model evaluation framework, customer data isolation
Federated Learning: Privacy-preserving distributed training where data stays local, only model updates transferred

5. Admin Dashboard (v0.4 - Q1 2026)

Integrate Prometheus/Grafana metrics for real-time monitoring console
Create user management UI with role-based access control
Permission management and audit logging

6. OAuth2/OpenID Connect Integration (v0.4 - Q1 2026)

Migrate from basic JWT to OAuth2/OIDC standards
Enterprise SSO support (Azure AD, Google Workspace, Okta)
Multi-factor authentication

7. Content Moderation & Compliance (v0.5 - Q1 2026)

Train content moderation models (NSFW, violence, hate speech)
Implement automated flagging system
Build GDPR/CCPA compliance workflows
Create comprehensive audit reporting

8. Kubernetes Production Deployment (v1.0 - March 2026)

Production-ready Kubernetes deployment with Helm charts
Automated horizontal and vertical scaling
Service mesh integration for resilience
Multi-environment support (dev/staging/prod)
Critical: Docker Compose is not suitable for production scale

9. Centralized Logging & Observability (v1.0 - March 2026)

Deploy ELK Stack (Elasticsearch, Logstash, Kibana)
Configure log shipping from all 25+ services
Centralized log aggregation and retention
Advanced log search and analytics
Distributed tracing integration

Release Timeline

Version	Target	Focus	Key Features
v0.2	Q4 2025	API & Integration	API Gateway, Webhooks
v0.3	Q4 2025	AI Enhancement	Video Understanding, MCP Interface
v0.4	Q1 2026	Enterprise Features	Training Pipeline, Admin Dashboard, OAuth2
v0.5	Q1 2026	Compliance	Content Moderation, GDPR/CCPA
v1.0	March 2026	Production Ready	Kubernetes, ELK Stack, 99.9% SLA

Current Status: Aigle 0.1 (Community Beta) - October 2025 ✅
Next Milestone: v0.2 with API Gateway (Q4 2025)
Production Target: v1.0 by March 2026

Made with ❤️ by the DHT Taiwan Team

For business inquiries: https://dhtsolution.com/

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
.github		.github
Aigle		Aigle
COMMUNITY_GUIDELINES		COMMUNITY_GUIDELINES
MAIN_DOCUMENTATION		MAIN_DOCUMENTATION
.DS_Store		.DS_Store
FINAL_SUMMARY.md		FINAL_SUMMARY.md
GITHUB_SETUP_INFO.md		GITHUB_SETUP_INFO.md
LICENSE		LICENSE
RAPTOR_LOGO.png		RAPTOR_LOGO.png
README.md		README.md

License

DHT-AI-Studio/RAPTOR

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