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📖 [docs] 英語READMEの自動更新 #22

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81 changes: 45 additions & 36 deletions docs/README.en.md
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<p align="center">
<img src="https://raw.githubusercontent.com/Sunwood-ai-labs/AMATERASU/refs/heads/main/docs/amaterasu_main.png" width="100%">
<h1 align="center">AMATERASU v0.6.0</h1>
<h1 align="center">AMATERASU v0.6.1</h1>
</p>

<p align="center">
Expand All @@ -20,52 +20,52 @@
</h2>

>[!IMPORTANT]
>This repository leverages [SourceSage](https://github.com/Sunwood-ai-labs/SourceSage), and approximately 90% of the release notes, README, and commit messages are generated using [SourceSage](https://github.com/Sunwood-ai-labs/SourceSage) + [claude.ai](https://claude.ai/).
>This repository leverages [SourceSage](https://github.com/Sunwood-ai-labs/SourceSage). Approximately 90% of the release notes, README, and commit messages are generated using [SourceSage](https://github.com/Sunwood-ai-labs/SourceSage) and [claude.ai](https://claude.ai/).

>[!NOTE]
>AMATERASU is the successor project to [MOA](https://github.com/Sunwood-ai-labs/MOA). It has evolved to run each AI service on an independent EC2 instance using Docker Compose, enabling easy deployment with Terraform.


## 🔒 Security-Focused Design Philosophy

AMATERASU is a private AI platform specifically developed for Japanese enterprises with stringent security requirements. It enables the secure use of LLMs based on AWS Bedrock:
AMATERASU is a private AI platform foundation developed specifically for Japanese enterprises with stringent security requirements. It enables the secure use of LLMs based on AWS Bedrock:

- **Secure LLM Foundation with AWS Bedrock**:
- Supports the Claude-3 model optimized for enterprises
- AWS's enterprise-grade security
- Granular access control based on IAM roles
- Supports the Claude-3 model, optimized for enterprise use.
- Leverages AWS's enterprise-grade security.
- Fine-grained access control using IAM roles.

- **Operation in a Completely Closed Environment**:
- Operates only within the internal network
- Supports private cloud/on-premises deployments
- Operates only within the internal network.
- Supports private cloud/on-premises deployments.

- **Enterprise-Grade Security**:
- Access control via IP whitelist
- HTTPS/TLS encrypted communication
- Network segmentation using AWS Security Groups
- IAM role management based on the principle of least privilege
- Access control via IP whitelisting.
- HTTPS/TLS encrypted communication.
- Network segmentation using AWS Security Groups.
- IAM role management based on the principle of least privilege.

## 🏢 Key Features
## ✨ Key Features

### 1. Secure ChatGPT-like Interface (Open WebUI)
- Provides an internal chat UI
- Prompt template management
- Conversation history saving and search
- Provides an internal chat UI.
- Manages prompt templates.
- Saves and searches conversation history.

### 2. Secure API Proxy Server (LiteLLM)
- Secure LLM access based on AWS Bedrock
- Integrated management of the Claude-3 series (Opus/Sonnet/Haiku)
- Load balancing and rate limiting of requests
- Centralized API key management
- Secure LLM access based on AWS Bedrock.
- Integrated management of the Claude-3 series (Opus/Sonnet/Haiku).
- Load balancing and rate limiting of requests.
- Centralized API key management.

### 3. Cost Management and Monitoring Infrastructure (Langfuse)
- Visualization of token usage
- Departmental cost aggregation
- Usage analysis
### 3. Cost Management and Monitoring Foundation (Langfuse)
- Visualization of token usage.
- Departmental cost aggregation.
- Usage analysis.

## 🏗️ System Architecture

### Secure 3-Tier Architecture based on AWS Bedrock
### Secure 3-Tier Architecture Based on AWS Bedrock

```mermaid
%%{init:{'theme':'base'}}%%
Expand Down Expand Up @@ -121,17 +121,18 @@ Recommended Configuration:
1. **Development Department**
- Code review assistance
- Bug analysis efficiency improvement
- Documentation generation
- Document generation

2. **Business Departments**
- Report generation assistance
- Data analysis support
- Minutes creation
- Meeting minutes creation

3. **Customer Support**
- Inquiry response efficiency improvement
- Improved inquiry response efficiency
- Automatic FAQ generation
- Improvement of reply text quality
- Improved response quality


## 🔧 Installation and Operation

Expand All @@ -143,7 +144,7 @@ cd AMATERASU

# 2. Set environment variables
cp .env.example .env
# Edit the .env file and set credentials
# Edit the .env file and set your credentials

# 3. Deploy infrastructure
cd spellbook/base-infrastructure
Expand All @@ -153,7 +154,7 @@ cd ../open-webui/terraform/main-infrastructure
terraform init && terraform apply

# 4. Start services
# Langfuse (monitoring infrastructure)
# Langfuse (monitoring foundation)
cd ../../langfuse
docker-compose up -d

Expand All @@ -168,18 +169,26 @@ docker-compose up -d

## 📚 Detailed Documentation

- [Spellbook Infrastructure Setup Guide](spellbook/README.md)
- [Spellbook Infrastructure Construction Guide](spellbook/README.md)
- [LiteLLM Configuration Guide](spellbook/litellm/README.md)
- [Langfuse Setup Guide](spellbook/langfuse/README.md)

## 🆕 Latest Information

### v0.6.1 Update Notes

- Updated documentation and added important information to the README file.
- Updated English and Japanese READMEs.
- Added information about the development process using SourceSage and claude.ai.
- Simplified security-related descriptions.


### v0.6.0 Update Notes

- Removed unnecessary resources due to the removal of the CloudFront infrastructure.
- Simplified the code to improve maintainability.
- Added application HTTPS and HTTP URLs to the output.
- Made it easier to change the path of the environment variable file and setup script in `terraform.tfvars`.
- Made it easier to change the paths of the environment variable file and setup script in `terraform.tfvars`.
- Removed unnecessary variable definitions.
- Simplified the setup script.

Expand All @@ -193,11 +202,11 @@ Provides detailed cost analysis and management features through Langfuse:

## 👏 Acknowledgements

Thanks to Maki for their contributions.
Thanks to iris-s-coon and Maki for their contributions.

## 📄 License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## 🤝 Contributions

Expand All @@ -210,7 +219,7 @@ This project is licensed under the MIT License. See the [LICENSE](LICENSE) file
## 📧 Support

For questions or feedback, please feel free to contact us:
- Create an Issue: [GitHub Issues](https://github.com/Sunwood-ai-labs/AMATERASU/issues)
- Create an issue: [GitHub Issues](https://github.com/Sunwood-ai-labs/AMATERASU/issues)
- Email: support@sunwoodai.com

---
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