302.AIis a pay-as-you-go, one-stop enterprise-level AI application platform. It offers an open platform and open-source ecosystem, allowing AI to find solutions for every need. Click here to get your $1 free credit!
- Quick Conversion: All Github, Gitlab, Gitee, Gitea and other code repositories can be converted into knowledge bases in just a few minutes.
- Multi-language Support: Code analysis and documentation generation are supported for all programming languages.
- Code Structure: Automatic Mermaid diagrams are generated to understand the code structure.
- Custom Models: Custom models and custom APIs are supported, allowing for expansion as needed.
- AI Intelligent Analysis: Code analysis and understanding of code relationships based on AI.
- Easy SEO: Generate SEO-friendly documents and knowledge bases using Next.js, making it easier for search engines to index.
- Dialogic Interaction: Supports dialogic interaction with AI to obtain detailed information and usage methods of the code, and to deeply understand the code.
Feature list:
- Supports multiple code repositories (Github, Gitlab, Gitee, Gitea, etc.)
- Supports multiple programming languages (Python, Java, C#, JavaScript, etc.)
- Supports repository management, providing functions for adding, deleting, modifying, and querying repositories
- Supports multiple AI providers (OpenAI, AzureOpenAI, Anthropic, etc.)
- Supports multiple databases (SQLite, PostgreSQL, SqlServer, etc.)
- Supports multiple languages (Chinese, English, French, etc.)
- Supports uploading ZIP files, and uploading local files
- provides a data fine-tuning platform to generate fine-tuning datasets
- Supports directory-level management of repositories, allowing for custom directory generation and dynamic documentation creation
- Supports repository directory management, allowing for modification of repository directories
- Supports user-level management, providing user management functions for adding, deleting, modifying, and querying users
- Supports user permission management, providing user permission management functions for adding, deleting, modifying, and querying user permissions
- Supports generating different fine-tuning framework datasets at the repository level
OpenDeepWiki is an open-source project inspired by DeepWiki, developed using .NET 9 and Semantic Kernel. It aims to help developers better understand and utilize codebases by providing features such as code analysis, documentation generation, and knowledge graph creation.
- Analyze code structure
- Understand core concepts of repositories
- Generate code documentation
- Automatically create README.md for code MCP Support
OpenDeepWiki supports MCP (Model Context Protocol)
- Supports providing an MCPServer for a single repository and conducting analysis on a single repository.
Usage: The following is the usage of cursor:
{
"mcpServers": {
"OpenDeepWiki":{
"url": "http://Your OpenDeepWiki service IP:port/sse?owner=AIDotNet&name=OpenDeepWiki"
}
}
}- owner: It is the name of the organization or owner of the repository.
- name: It is the name of the repository.
After adding the repository, test by asking a question (please note that before doing this, the repository must be processed first): What is OpenDeepWiki? The effect is as shown in the picture: !
In this way, you can use OpenDeepWiki as an MCPServer, making it available for other AI models to call upon, facilitating the analysis and understanding of an open-source project.
- Clone the repository
git clone https://github.com/AIDotNet/OpenDeepWiki.git
cd OpenDeepWiki- Open the
docker-compose.ymlfile and modify the following environment variables:
OpenAI:
services:
koalawiki:
environment:
- KOALAWIKI_REPOSITORIES=/repositories
- TASK_MAX_SIZE_PER_USER=5 # Maximum number of parallel document generation tasks per user by AI
- CHAT_MODEL=DeepSeek-V3 # Model must support functions
- ANALYSIS_MODEL= # Analysis model used for generating repository directory structure
- CHAT_API_KEY= # Your API key
- LANGUAGE= # Set the default language for generation as "Chinese"
- ENDPOINT=https://api.token-ai.cn/v1
- DB_TYPE=sqlite
- MODEL_PROVIDER=OpenAI # Model provider, default is OpenAI, supports AzureOpenAI and Anthropic
- DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db
- EnableSmartFilter=true # Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repository
- UPDATE_INTERVAL # Warehouse increment update interval, unit: days
- MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
- DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
- ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates
- ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.AzureOpenAI:
services:
koalawiki:
environment:
- KOALAWIKI_REPOSITORIES=/repositories
- TASK_MAX_SIZE_PER_USER=5 # Maximum number of parallel document generation tasks per user by AI
- CHAT_MODEL=DeepSeek-V3 # Model must support functions
- ANALYSIS_MODEL= # Analysis model used for generating repository directory structure
- CHAT_API_KEY= # Your API key
- LANGUAGE= # Set the default language for generation as "Chinese"
- ENDPOINT=https://your-azure-address.openai.azure.com/
- DB_TYPE=sqlite
- MODEL_PROVIDER=AzureOpenAI # Model provider, default is OpenAI, supports AzureOpenAI and Anthropic
- DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db
- EnableSmartFilter=true # Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repository
- UPDATE_INTERVAL # Warehouse increment update interval, unit: days
- MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
- DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
- ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates
- ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.Anthropic:
services:
koalawiki:
environment:
- KOALAWIKI_REPOSITORIES=/repositories
- TASK_MAX_SIZE_PER_USER=5 # Maximum number of parallel document generation tasks per user by AI
- CHAT_MODEL=DeepSeek-V3 # Model must support functions
- ANALYSIS_MODEL= # Analysis model used for generating repository directory structure
- CHAT_API_KEY= # Your API key
- LANGUAGE= # Set the default language for generation as "Chinese"
- ENDPOINT=https://api.anthropic.com/
- DB_TYPE=sqlite
- MODEL_PROVIDER=Anthropic # Model provider, default is OpenAI, supports AzureOpenAI and Anthropic
- DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db
- EnableSmartFilter=true # Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repository
- UPDATE_INTERVAL # Warehouse increment update interval, unit: days
- MAX_FILE_LIMIT=100 # The maximum limit for uploading files, in MB
- DEEP_RESEARCH_MODEL= # Conduct in-depth research on the model and use CHAT_MODEL for the empty
- ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates
- ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis,This might have an impact on the quality of the code.π‘ How to get an API Key:
- Get Google API key Google AI Studio
- Get OpenAI API key OpenAI Platform
- Get CoresHub CoresHub Click here for 50 million free tokens
- Get TokenAI TokenAI
- Start the service
You can use the provided Makefile commands to easily manage the application:
# Build all Docker images
make build
# Start all services in background mode
make up
# Or start in development mode (with logs visible)
make devThen visit http://localhost:8090 to access the knowledge base.
For more commands:
make helpIf you're using Windows and don't have make available, you can use these Docker Compose commands directly:
# Build all Docker images
docker-compose build
# Start all services in background mode
docker-compose up -d
# Start in development mode (with logs visible)
docker-compose up
# Stop all services
docker-compose down
# View logs
docker-compose logs -fFor building specific architectures or services, use:
# Build only backend
docker-compose build koalawiki
<
8000
span class="pl-c"># Build only frontend
docker-compose build koalawiki-web
# Build with architecture parameters
docker-compose build --build-arg ARCH=arm64
docker-compose build --build-arg ARCH=amd64
For detailed steps, refer to:One-Click Deployment of OpenDeepWiki as a Sealos Application Exposed to the Public Network Using Templates
OpenDeepWiki uses AI to:
- Clone code repository locally
- Analyze based on repository README.md
- Analyze code structure and read code files as needed, then generate directory json data
- Process tasks according to directory, each task is a document
- Read code files, analyze code files, generate code documentation, and create Mermaid charts representing code structure dependencies
- Generate the final knowledge base document
- Analyze repository through conversational interaction and respond to user inquiries
graph TD
A[Clone code repository] --> B[Analyze README.md]
B --> C[Analyze code structure]
C --> D[Generate directory json data]
D --> E[Process multiple tasks]
E --> F[Read code files]
F --> G[Analyze code files]
G --> H[Generate code documentation]
H --> I[Create Mermaid charts]
I --> J[Generate knowledge base document]
J --> K[Conversational interaction]
- KOALAWIKI_REPOSITORIES Path for storing repositories
- TASK_MAX_SIZE_PER_USER Maximum parallel tasks for AI document generation per user
- CHAT_MODEL Model must support functions
- ENDPOINT API Endpoint
- ANALYSIS_MODEL Analysis model for generating repository directory structure
- CHAT_API_KEY Your API key
- LANGUAGE Change the language of the generated documents
- DB_TYPE Database type, default is sqlite
- MODEL_PROVIDER Model provider, by default OpenAI, supports Azure, OpenAI and Anthropic
- DB_CONNECTION_STRING Database connection string
- EnableSmartFilter Whether intelligent filtering is enabled or not may affect how the AI can obtain the file directory of the repository
- UPDATE_INTERVAL Warehouse increment update interval, unit: days
- MAX_FILE_LIMIT The maximum limit for uploading files, in MB
- DEEP_RESEARCH_MODEL Conduct in-depth research on the model and use CHAT_MODEL for the empty
- ENABLE_INCREMENTAL_UPDATE Whether to enable incremental updates
- ENABLE_CODED_DEPENDENCY_ANALYSIS Whether to enable code dependency analysis,This might have an impact on the quality of the code.
The Makefile provides commands to build for different CPU architectures:
# Build for ARM architecture
make build-arm
# Build for AMD architecture
make build-amd
# Build only backend for ARM
make build-backend-arm
# Build only frontend for AMD
make build-frontend-amdThis project is licensed under the MIT License - see the LICENSE file for details.