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Deepwiki Open Api Readme Md At Main Github

Leo Migdal
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deepwiki open api readme md at main github

There was an error while loading. Please reload this page. This document provides a comprehensive reference for the DeepWiki backend API endpoints, request/response formats, and integration details. The API is built with FastAPI and provides streaming AI responses, wiki generation, and repository analysis capabilities. For information about API server setup and configuration, see 5.1. For details about chat completion functionality, see 5.2.

For wiki management features, see 5.3. The DeepWiki API consists of three main server components that handle different aspects of the system functionality. Sources: api/main.py api/simple_chat.py api/api.py api/config.py Returns basic API information and server status. A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools. Fully local web research and report writing assistant

Utilities intended for use with Llama models. 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. 🦜🔗 Build context-aware reasoning applications DeepWiki is my own implementation attempt of DeepWiki, automatically creates beautiful, interactive wikis for any GitHub, GitLab, or BitBucket repository! Just enter a repo name, and DeepWiki will: English | 简体中文 | 繁體中文 | 日本語 | Español | 한국어 | Tiếng Việt | Português Brasileiro | Français | Русский

For detailed instructions on using DeepWiki with Ollama and Docker, see Ollama Instructions. Create a .env file in the project root with these keys: DeepWiki now implements a flexible provider-based model selection system supporting multiple LLM providers: This document provides a comprehensive overview of DeepWiki-Open, an AI-powered automated wiki generation system for code repositories. DeepWiki-Open transforms GitHub, GitLab, and BitBucket repositories into interactive, searchable wikis using artificial intelligence for content generation and retrieval-augmented generation (RAG) for intelligent Q&A capabilities. The system consists of a multi-tier architecture with a Next.js frontend, FastAPI backend, and integrated AI services supporting multiple model providers.

For specific implementation details of individual components, see Frontend Components, Backend Services, and AI Integration. DeepWiki-Open implements a sophisticated multi-tier architecture separating presentation, business logic, and AI processing into distinct layers that communicate through well-defined APIs. Sources: README.md143-163 src/app/page.tsx api/main.py api/api.py api/rag.py api/data_pipeline.py The system processes repositories through a sophisticated pipeline that transforms raw source code into searchable, AI-powered documentation with vector embeddings for intelligent retrieval. There was an error while loading. Please reload this page.

This document provides a high-level overview of the DeepWiki system architecture, components, and workflows. DeepWiki is an AI-powered documentation generation system that automatically analyzes code repositories and creates structured wikis with visual diagrams, explanations, and interactive Q&A capabilities. For specific implementation details, see: DeepWiki follows a three-tier architecture with clear separation between presentation, application logic, and data storage. Sources: README.md1-186 src/app/[owner]/[repo]/page.tsx:1-50, src/components/Ask.tsx1-50 Sources: README.md166-186 Diagram 1 from system overview

There was an error while loading. Please reload this page. This document provides a comprehensive overview of the DeepWiki-Open system, its architecture, core components, and key capabilities. DeepWiki-Open is an AI-powered platform that automatically generates interactive wikis from code repositories hosted on GitHub, GitLab, or BitBucket. This introduction covers the high-level system architecture, data flow, and core features. For specific implementation details about individual components, see the following related pages:

DeepWiki-Open is a full-stack application that combines a Next.js frontend with a FastAPI backend to provide intelligent documentation generation and interactive Q&A capabilities for code repositories. The system leverages multiple AI providers and Retrieval Augmented Generation (RAG) to create context-aware documentation and enable natural language interactions with codebases. Sources: README.md1-558 src/app/page.tsx1-50 api/api.py1-100 The system follows a layered architecture with clear separation between frontend presentation, backend services, and external integrations:

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