base on OpenDeepWiki is the open-source version of the DeepWiki project, aiming to provide a powerful knowledge management and collaboration platform. The project is mainly developed using C# and TypeScript, supporting modular design, and is easy to expand and customize. # OpenDeepWiki [中文](README.zh-CN.md) | [English](README.md) <div align="center"> <img src="/img/favicon.png" alt="OpenDeepWiki Logo" width="220" /> <h3>AI-Driven Code Knowledge Base</h3> </div> --- # enterprise service [Pricing of enterprise services](https://docs.opendeep.wiki/pricing) Our enterprise service offers comprehensive support and flexibility for businesses seeking professional AI solutions. --- # Features - **Quick Conversion**: Supports converting all GitHub, GitLab, Gitee, Gitea and other code repositories into knowledge bases within minutes. - **Multi-language Support**: Supports code analysis and documentation generation for all programming languages. - **Code Structure Diagrams**: Automatically generates Mermaid diagrams to help understand code structure. - **Custom Model Support**: Supports custom models and custom APIs for flexible extension. - **AI Intelligent Analysis**: AI-based code analysis and code relationship understanding. - **SEO Friendly**: Generates SEO-friendly documentation and knowledge bases based on Next.js for easy search engine crawling. - **Conversational Interaction**: Supports conversations with AI to obtain detailed code information and usage methods for deep code understanding. --- # Feature List - [x] Support multiple code repositories (GitHub, GitLab, Gitee, Gitea, etc.) - [x] Support multiple programming languages (Python, Java, C#, JavaScript, etc.) - [x] Support repository management (CRUD operations on repositories) - [x] Support multiple AI providers (OpenAI, AzureOpenAI, Anthropic, etc.) - [x] Support multiple databases (SQLite, PostgreSQL, SqlServer, MySQL, etc.) - [x] Support multiple languages (Chinese, English, French, etc.) - [x] Support uploading ZIP files and local files - [x] Provide data fine-tuning platform to generate fine-tuning datasets - [x] Support directory-level repository management with dynamic directory and document generation - [x] Support repository directory modification management - [x] Support user management (CRUD operations on users) - [x] Support user permission management - [x] Support repository-level generation of different fine-tuning framework datasets --- # Project Introduction OpenDeepWiki is an open-source project inspired by [DeepWiki](https://deepwiki.com/), developed based on .NET 9 and Semantic Kernel. It aims to help developers better understand and utilize code repositories, providing features such as code analysis, documentation generation, and knowledge graph construction. Main Features: - Analyze code structure - Understand repository core concepts - Generate code documentation - Automatically generate README.md for code - Support MCP (Model Context Protocol) --- # MCP Support OpenDeepWiki supports the MCP protocol: - Can serve as a single repository MCPServer for repository analysis. Example configuration: ```json { "mcpServers": { "OpenDeepWiki":{ "url": "http://Your OpenDeepWiki service IP:port/sse?owner=AIDotNet&name=OpenDeepWiki" } } } ``` - owner: Repository organization or owner name - name: Repository name After adding the repository, you can test by asking questions like "What is OpenDeepWiki?", with effects as shown below:  This way, OpenDeepWiki can serve as an MCPServer for other AI models to call, facilitating analysis and understanding of open-source projects. --- # 🚀 Quick Start 1. Clone the repository ```bash git clone https://github.com/AIDotNet/OpenDeepWiki.git cd OpenDeepWiki ``` 2. Modify environment variable configuration in `docker-compose.yml`: - OpenAI example: ```yaml services: koalawiki: environment: - KOALAWIKI_REPOSITORIES=/repositories - TASK_MAX_SIZE_PER_USER=5 # Maximum parallel document generation tasks per user for AI - CHAT_MODEL=DeepSeek-V3 # Model must support function calling - ANALYSIS_MODEL= # Analysis model for generating repository directory structure - CHAT_API_KEY= # Your API Key - LANGUAGE= # Default generation language, e.g., "Chinese" - ENDPOINT=https://api.token-ai.cn/v1 - DB_TYPE=sqlite - MODEL_PROVIDER=OpenAI # Model provider, supports OpenAI, AzureOpenAI, Anthropic - DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db - EnableSmartFilter=true # Whether to enable smart filtering, affects AI's ability to get repository file directories - UPDATE_INTERVAL # Repository incremental update interval in days - MAX_FILE_LIMIT=100 # Maximum upload file limit in MB - DEEP_RESEARCH_MODEL= # Deep research model, if empty uses CHAT_MODEL - ENABLE_INCREMENTAL_UPDATE=true # Whether to enable incremental updates - ENABLE_CODED_DEPENDENCY_ANALYSIS=false # Whether to enable code dependency analysis, may affect code quality - ENABLE_WAREHOUSE_COMMIT=true # Whether to enable warehouse commit - ENABLE_FILE_COMMIT=true # Whether to enable file commit - REFINE_AND_ENHANCE_QUALITY=false # Whether to refine and enhance quality - ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK=true # Whether to enable warehouse function prompt task - ENABLE_WAREHOUSE_DESCRIPTION_TASK=true # Whether to enable warehouse description task - CATALOGUE_FORMAT=compact # Directory structure format (compact, json, pathlist, unix) - ENABLE_CODE_COMPRESSION=false # Whether to enable code compression - READ_MAX_TOKENS=100000 # The maximum token limit for reading files in AI is set to prevent unlimited file reading. It is recommended to fill in 70% of the model's maximum token. # Feishu Bot configuration (optional) - FeishuAppId= - FeishuAppSecret= - FeishuBotName=KoalaWiki ``` - AzureOpenAI and Anthropic configurations are similar, only need to adjust `ENDPOINT` and `MODEL_PROVIDER`. ## Database Configuration ### SQLite (Default) ```yaml - DB_TYPE=sqlite - DB_CONNECTION_STRING=Data Source=/data/KoalaWiki.db ``` ### PostgreSQL ```yaml - DB_TYPE=postgres - DB_CONNECTION_STRING=Host=localhost;Database=KoalaWiki;Username=postgres;Password=password ``` ### SQL Server ```yaml - DB_TYPE=sqlserver - DB_CONNECTION_STRING=Server=localhost;Database=KoalaWiki;Trusted_Connection=true; ``` ### MySQL ```yaml - DB_TYPE=mysql - DB_CONNECTION_STRING=Server=localhost;Database=KoalaWiki;Uid=root;Pwd=password; ``` 3. Start services Using Makefile commands: ```bash # Build all Docker images make build # Start all services in background make up # Start in development mode (with visible logs) make dev ``` Visit http://localhost:8090 to access the knowledge base page. For Windows users without make environment, use Docker Compose directly: ```bash docker-compose build docker-compose up -d docker-compose up docker-compose down docker-compose logs -f ``` --- # Deployment Recommendations - Build for specific architecture: ```bash docker-compose build --build-arg ARCH=arm64 docker-compose build --build-arg ARCH=amd64 ``` - Build only backend or frontend: ```bash docker-compose build koalawiki docker-compose build koalawiki-web ``` - One-click deployment to Sealos (supports public network access): [](https://bja.sealos.run/?openapp=system-template%3FtemplateName%3DOpenDeepWiki) For detailed steps, please refer to: [One-click Sealos Deployment of OpenDeepWiki](scripts/sealos/README.zh-CN.md) --- # 🔍 How It Works OpenDeepWiki leverages AI to achieve: - Clone code repository locally - Read .gitignore configuration to ignore irrelevant files - Recursively scan directories to get all files and directories - Determine if file count exceeds threshold; if so, call AI model for intelligent directory filtering - Parse AI-returned directory JSON data - Generate or update README.md - Call AI model to generate repository classification information and project overview - Clean project analysis tag content and save project overview to database - Call AI to generate thinking directory (task list) - Recursively process directory tasks to generate document directory structure - Save directory structure to database - Process incomplete document tasks - If Git repository, clean old commit records, call AI to generate update log and save --- # OpenDeepWiki Repository Parsing to Documentation Detailed Flow Chart ```mermaid graph TD A[Clone code repository] --> B[Read .gitignore configuration to ignore files] B --> C[Recursively scan directories to get all files and directories] C --> D{Does file count exceed threshold?} D -- No --> E[Directly return directory structure] D -- Yes --> F[Call AI model for intelligent directory structure filtering] F --> G[Parse AI-returned directory JSON data] E --> G G --> H[Generate or update README.md] H --> I[Call AI model to generate repository classification information] I --> J[Call AI model to generate project overview information] J --> K[Clean project analysis tag content] K --> L[Save project overview to database] L --> M[Call AI to generate thinking directory task list] M --> N[Recursively process directory tasks to generate DocumentCatalog] N --> O[Save directory structure to database] O --> P[Process incomplete document tasks] P --> Q{Is repository type Git?} Q -- Yes --> R[Clean old commit records] R --> S[Call AI to generate update log] S --> T[Save update log to database] Q -- No --> T ``` --- # Advanced Configuration ## Environment Variables - `KOALAWIKI_REPOSITORIES`: Repository storage path - `TASK_MAX_SIZE_PER_USER`: Maximum parallel document generation tasks per user for AI - `CHAT_MODEL`: Chat model (must support function calling) - `ENDPOINT`: API endpoint - `ANALYSIS_MODEL`: Analysis model for generating repository directory structure - `CHAT_API_KEY`: API key - `LANGUAGE`: Document generation language - `DB_TYPE`: Database type, supports sqlite, postgres, sqlserver, mysql (default: sqlite) - `MODEL_PROVIDER`: Model provider, default OpenAI, supports AzureOpenAI, Anthropic - `DB_CONNECTION_STRING`: Database connection string - `EnableSmartFilter`: Whether to enable smart filtering, affects AI's ability to get repository directories - `UPDATE_INTERVAL`: Repository incremental update interval (days) - `MAX_FILE_LIMIT`: Maximum upload file limit (MB) - `DEEP_RESEARCH_MODEL`: Deep research model, if empty uses CHAT_MODEL - `ENABLE_INCREMENTAL_UPDATE`: Whether to enable incremental updates - `ENABLE_CODED_DEPENDENCY_ANALYSIS`: Whether to enable code dependency analysis, may affect code quality - `ENABLE_WAREHOUSE_COMMIT`: Whether to enable warehouse commit - `ENABLE_FILE_COMMIT`: Whether to enable file commit - `REFINE_AND_ENHANCE_QUALITY`: Whether to refine and enhance quality - `ENABLE_WAREHOUSE_FUNCTION_PROMPT_TASK`: Whether to enable warehouse function prompt task - `ENABLE_WAREHOUSE_DESCRIPTION_TASK`: Whether to enable warehouse description task - `CATALOGUE_FORMAT`: Directory structure format (compact, json, pathlist, unix) - `ENABLE_CODE_COMPRESSION`: Whether to enable code compression - `MAX_FILE_READ_COUNT`: Maximum file read count limit for AI, prevents unlimited file reading and improves processing efficiency (default: 10, 0 = no limit) - `FeishuAppId`: Feishu App ID (required if enabling Feishu Bot) - `FeishuAppSecret`: Feishu App Secret (required if enabling Feishu Bot) - `FeishuBotName`: Feishu bot display name (optional) --- # Feishu Bot Integration - Purpose: Connect the current repository to Feishu group/DM as a knowledge bot for Q&A and content delivery. - Callback route: `/api/feishu-bot/{owner}/{name}` (copy the full URL from the "Feishu Bot" button on the repository page). - Requirement: Service must be publicly accessible; message encryption (Encrypt Key) is not supported yet. ## 1) Create an App in Feishu Open Platform - Type: Internal App (for your organization). - Capability: Enable "Bot". Publish to the organization and install it. - Permissions (at minimum, per platform guidance): - Message send/read related scopes (e.g., `im:message`, `im:message:send_as_bot`). - Event subscription related scopes (for receiving message events). ## 2) Configure Event Subscriptions (Important) - Open "Event Subscriptions" and disable "Encrypt Key". - Subscribe to event: `im.message.receive_v1`. - Request URL: `https://your-domain/api/feishu-bot/{owner}/{name}`. - `{owner}` is the repository organization or owner, e.g., `AIDotNet`. - `{name}` is the repository name, e.g., `OpenDeepWiki`. - Save to complete the URL verification (backend already handles the challenge response). Tip: You can also click the "Feishu Bot" button on the repository page to copy the dedicated callback URL. ## 3) Configure Server Environment Variables Set the following in your backend service (see docker-compose example below): - `FeishuAppId`: Feishu App ID - `FeishuAppSecret`: Feishu App Secret - `FeishuBotName`: Bot display name (optional) ## 4) Add the Bot to a Group and Use It - After installing the app, add the bot to the target group. - Group: @bot + your question (answers using the current repository's knowledge). - Direct Message: send your question directly. - Supports text and image replies (e.g., mind map images). ## Feishu FAQ - No response/callback failed: ensure the Request URL is publicly reachable and that Nginx proxies `/api/` to the backend. - "Encryption enabled" message: disable Encrypt Key (current version doesn't support encrypted messages). - 403/insufficient permissions: make sure the app is installed in the org and required scopes/events are granted. ## Build for Different Architectures Makefile commands: ```bash make build-arm # ARM architecture make build-amd # AMD architecture make build-backend-arm # Backend only ARM make build-frontend-amd # Frontend only AMD ``` --- # Community - Discord: [join us](https://discord.gg/Y3fvpnGVwt) - Feishu QR Code:   --- # 📄 License This project is licensed under the MIT License. See [LICENSE](./LICENSE) for details. --- # ⭐ Star History [](https://www.star-history.com/#AIDotNet/OpenDeepWiki&Date) ", Assign "at most 3 tags" to the expected json: {"id":"14428","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"