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repo-to-wiki: scan any repository into a portable SQLite knowledge store with wiki pages, diagrams, and grounded Q&A.","maintainers":[{"name":"caffeinecoder","email":"anserbridge@gmail.com"}],"readme":"# rekipedia\n\n**[English](README.md) | [繁體中文](README.zh-TW.md) | [简体中文](README.zh-CN.md)**\n\n> 您的 AI 技術領導——隨時待命，永遠最新。\n\nrekipedia 能掃描任何程式碼庫，將其轉化為可攜式的 SQLite 知識庫，並為團隊中每位開發者提供一位由 LLM 驅動的技術領導，讓他們隨時提問：_「驗證流程是如何運作的？」、「新增 API 端點最快的方法是什麼？」、「上週付款服務是什麼出了問題？」_\n\n不會產生幻覺，不靠猜測——每個答案都紮根於您實際的程式碼庫。\n\n### 主要功能\n- **關係可信度評分**：每個提取的關係都標記為 EXTRACTED/INFERRED/AMBIGUOUS，並附有可信度分數\n- **設計原理提取**：`# NOTE:`、`# HACK:`、`# WHY:` 注釋會被提取為知識節點\n- **核心節點（God nodes）**：連結度最高的符號會顯示於 index.md 並在圖表介面中高亮標示\n- **互動式依賴關係圖**：`rekipedia serve` 現在包含一個使用 D3.js 力導向視覺化的 `/graph` 路由\n- **Git hooks**：`rekipedia hook install` 會在每次提交時觸發自動重建\n- **代理式 wiki 協作**：`PlannerAgent` 根據您的程式碼庫動態設計 wiki 結構\n- **頁面重要性評分**：規劃器為每個頁面分配重要性分數（0–100）；導航側邊欄依優先級排序\n- **DeepWiki 式章節**：頁面分組至邏輯章節（`getting-started`、`architecture`、`core-components` 等）\n- **Wiki 側邊欄分類**：`reki serve` 側邊欄依 `section` 欄位將頁面分組，並支援可折疊標頭\n- **即時搜尋**：在側邊欄搜尋框中輸入，即可立即按標題或分類篩選 wiki 頁面\n- **重構分析**：`reki refactor` 偵測程式碼異味（神類、循環依賴、死碼、高耦合），並提供 LLM 增強的建議——輸出 `REFACTOR.md` + `refactor_report.json`\n- **上下文切片**：每個頁面只接收其所需的資料（相較固定佈局方式減少約 40–60% 的 token 用量）\n- **混合式 RAG 問答**：FAISS 索引的程式碼片段 + wiki 頁面，讓 LLM 在回答問題時擁有完整的程式碼庫上下文\n- **嵌入提供者選擇**：`--embed-provider openai|ollama|azure|...`——支援任何 litellm 相容的嵌入模型\n- **Wiki 匯出**：打包為單一 Markdown 檔案、ZIP 壓縮包或結構化 JSON（`rekipedia export`）\n- **增量更新**：初次掃描後僅重新處理已變更的檔案\n- **有依據的問答**：答案引用真實的檔案路徑和行號——不產生幻覺\n- **程式碼庫樹狀索引**——每次掃描都會在 SQLite 中建立階層式目錄/檔案樹，支援結構化導航與未來基於推理的檢索。\n\n## 快速開始\n\n### 透過 npm / npx（無需安裝）\n\n```bash\nnpx rekipedia init .\nnpx rekipedia scan .\n```\n\n### 透過 uv / uvx（無需安裝）\n\n```bash\nuvx rekipedia init .\nuvx rekipedia scan .\n```\n\n### 永久安裝\n\n```bash\n# Core (scan + serve + ask)\npip install rekipedia\n# or\nuv tool install rekipedia\n\n# With RAG support (semantic embed + search — needs faiss-cpu + numpy ~100MB)\npip install \"rekipedia[rag]\"\n\n# Homebrew (Go single binary — no Python needed)\nbrew tap unrealandychan/tap\nbrew install rekipedia\n```\n\n---\n\n## 快速開始——無需 API 金鑰\n\n無需任何 LLM API 金鑰即可執行完整靜態分析：\n\n```bash\npip install rekipedia\nreki scan . --no-llm   # ~5-10 秒，零 API 呼叫\nreki onboard .         # 架構總覽\nreki tour .            # 依賴深度導覽\nreki domain .          # 業務領域層次圖\nreki diff .            # 變更影響分析\nreki export . --format md  # 匯出完整 wiki 為 Markdown\n```\n\n> **注意：** `reki ask`（AI 問答）需要 LLM API 金鑰。請參閱下方 [LLM 設定](#llm-設定)。\n\n---\n\n## Python API\n\n在 Jupyter 筆記本、CI 流水線或任何 Python 應用程式中以程式化方式使用 rekipedia：\n\n```python\nimport rekipedia\n\n# Scan a local repo\nresult = rekipedia.scan(\"/path/to/repo\")\nprint(result.page_count)   # number of wiki pages generated\nprint(result.symbol_count) # number of code symbols extracted\nprint(result.token_count)  # estimated token count of the wiki\n\n# Ask a question — grounded answer with file:line citations\nanswer = rekipedia.ask(\"/path/to/repo\", \"How does the auth flow work?\")\nprint(answer.text)\nfor citation in answer.citations:\n    print(f\"  {citation.file}:{citation.line}\")\n\n# Async variants (Jupyter-friendly)\nresult = await rekipedia.scan_async(\"/path/to/repo\")\nanswer = await rekipedia.ask_async(\"/path/to/repo\", \"What is the entry point?\")\n```\n\n**回傳型別：**\n\n| Type | Key fields |\n|---|---|\n| `ScanResult` | `page_count`, `symbol_count`, `token_count`, `wiki_pages`, `db_path`, `wiki_dir` |\n| `AskResult` | `text`, `citations: list[Citation]`, `model_used` |\n| `Citation` | `file`, `line`, `snippet` |\n\n---\n\n## 指令\n\n| Command | Description |\n|---|---|\n| `rekipedia init [REPO]` | Scaffold `.rekipedia/` with `config.yml` and update `.gitignore` |\n| `rekipedia scan [REPO]` | Full analysis — extracts symbols, synthesises wiki pages, exports JSON |\n| `rekipedia update [REPO]` | Incremental refresh — re-extracts only changed files, keeps the rest |\n| `rekipedia ask [QUESTION]` | Interactive Q&A REPL — streaming answers, Ctrl+C to quit |\n| `rekipedia serve [REPO]` | Start a local web UI to browse wiki pages and ask questions |\n| `rekipedia embed [REPO]` | Build (or rebuild) the FAISS semantic search index for hybrid RAG Q&A |\n| `rekipedia export [REPO]` | Bundle the wiki to a single file (`--format md\\|zip\\|json`) |\n| `rekipedia hook install/uninstall/status` | Manage git post-commit hook for auto wiki rebuild |\n| `rekipedia diff [A] [B]` | Compare two graph snapshots (defaults to last two) |\n| `rekipedia impact <file>` | Show blast-radius — all affected files, symbols, tests for a changed file |\n| `rekipedia search <query>` | Search symbols (`--all-repos` for cross-repo parallel search) |\n| `rekipedia export --format graphml\\|cypher\\|obsidian` | Export graph to GraphML / Neo4j Cypher / Obsidian wikilinks |\n| `rekipedia mcp` | Start JSON-RPC 2.0 MCP stdio server (6 tools for AI coding assistants) |\n| `rekipedia watch add\\|start\\|list\\|remove` | Watch repos and auto-index on file change |\n| `rekipedia refactor [REPO]` | Detect code smells + generate `REFACTOR.md` and `refactor_report.json` (use `--no-llm` for static only) |\n| `rekipedia note add\\|list\\|remove\\|edit\\|import` | Manage persistent tech lead notes — injected into `reki ask` context automatically |\n\n---\n\n## LLM 設定\n\nrekipedia 使用 [litellm](https://github.com/BerriAI/litellm)，支援任何提供者：\n\n| 提供者 | 範例 |\n|---|---|\n| OpenAI | `OPENAI_API_KEY=sk-... reki scan .` |\n| Anthropic Claude | `REKIPEDIA_MODEL=claude-3-5-sonnet-20241022 REKIPEDIA_API_KEY=sk-ant-... reki scan .` |\n| Google Gemini | `REKIPEDIA_MODEL=gemini/gemini-2.0-flash REKIPEDIA_API_KEY=AIza... reki scan .` |\n| OpenRouter | `REKIPEDIA_MODEL=openrouter/anthropic/claude-3.5-sonnet REKIPEDIA_API_KEY=sk-or-... reki scan .` |\n| 本地 Ollama（預設） | `REKIPEDIA_MODEL=ollama/llama4 reki scan .` |\n| Azure OpenAI | `REKIPEDIA_MODEL=azure/gpt-4o REKIPEDIA_BASE_URL=https://your-resource.openai.azure.com REKIPEDIA_API_KEY=... reki scan .` |\n\n環境變數：\n- `REKIPEDIA_MODEL` — litellm 模型字串（預設：`ollama/llama4`）\n- `REKIPEDIA_API_KEY` — 所選提供者的 API 金鑰\n- `REKIPEDIA_BASE_URL` — 自訂基礎 URL（用於 Azure、Ollama、代理）\n- `REKIPEDIA_TIMEOUT` — LLM 呼叫逾時秒數（預設：180）\n\n執行 `rekipedia init` 後，編輯 `.rekipedia/config.yml`：\n\n```yaml\nversion: 1\nignore:\n  - .git\n  - node_modules\n  - __pycache__\n  - .rekipedia\nlanguages:\n  - python\n  - typescript\nllm:\n  model: ollama/llama4        # any litellm model string\n  api_key: \"\"                 # or set REKIPEDIA_API_KEY env var\n  base_url: \"\"                # for local / self-hosted endpoints\n  temperature: 0.2\n```\n\n### 支援的提供者（透過 [litellm](https://docs.litellm.ai)）\n\n| Provider | Example model string |\n|---|---|\n| Ollama (local, free) | `ollama/llama4` |\n| OpenAI | `gpt-5.5` |\n| Anthropic | `claude-opus-4-6` |\n| Google Gemini | `gemini/gemini-3.0-pro` |\n| Any OpenAI-compatible | set `base_url` in config |\n\n### 執行時覆寫（環境變數）\n\n```bash\nexport REKIPEDIA_MODEL=gpt-5.5\nexport REKIPEDIA_API_KEY=***\nexport REKIPEDIA_BASE_URL=https://my-proxy/v1\nexport REKIPEDIA_SHARD_TOKEN_BUDGET=***\n```\n\n| Variable | Description |\n|---|---|\n| `REKIPEDIA_MODEL` | LLM model name to use |\n| `REKIPEDIA_API_KEY` | API key for the LLM provider |\n| `REKIPEDIA_BASE_URL` | Base URL for OpenAI-compatible endpoints |\n| `REKIPEDIA_SHARD_TOKEN_BUDGET` | Max tokens per shard group (default: 40000) |\n| `REKIPEDIA_AGENT_ASK` | Set to `1` to enable agentic ReAct ask loop (default: `0` — single-shot) |\n| `REKIPEDIA_ASK_MAX_ITER` | Max tool-call iterations for agentic ask (default: `5`) |\n| `REKIPEDIA_AGENT_PLANNER` | Set to `1` to enable tool-calling wiki planner (default: `0`) |\n\n---\n\n## 輸出\n\n`rekipedia scan` 會將所有內容寫入您程式碼庫內的 `.rekipedia/`：\n\n```\n.rekipedia/\n├── config.yml              # your settings (committed)\n├── store.db                # SQLite knowledge store (git-ignored)\n├── scan_meta.json          # last scan metadata (model, timestamp, file count)\n├── wiki/                   # generated Markdown pages (3–15 pages, dynamically planned)\n│   ├── index.md\n│   ├── architecture-overview.md\n│   ├── repository-structure.md\n│   └── ... (pages vary by repo)\n├── rag/                    # RAG index (git-ignored)\n│   ├── index.faiss         # FAISS flat L2 index\n│   └── chunks.json         # source code chunks + metadata\n├── diagrams/               # Mermaid diagram files\n│   ├── module-graph.md\n│   └── class-hierarchy.md\n└── exports/                # JSON exports\n    ├── symbols.json\n    ├── relationships.json\n    └── manifest.json       # run summary + metadata + page importance scores\n```\n\n根據程式碼庫複雜度動態生成 3–15 個 wiki 頁面（由 PlannerAgent 驅動）。\n\nWiki 結構由 `PlannerAgent` 根據您程式碼庫的實際內容動態設計：\n\n| Section | Example pages | When generated |\n|---|---|---|\n| Getting Started | index, installation, quick-start | Always |\n| Architecture | architecture-overview, data-flow, repository-structure | ≥3 modules |\n| Core Components | One page per major module | ≥2 modules |\n| API Reference | cli-reference, python-api, rest-api | CLI/HTTP handlers found |\n| Development | testing, contributing, ci-cd | Test files found |\n| Ecosystem | integrations, deployment | ≥3 external deps |\n\n### 掃描選項\n\n```bash\n# Use a specific LLM model\nrekipedia scan . --model gpt-5.5\n\n# Skip Docker (run extractors in-process)\nrekipedia scan . --no-docker\n\n# Write output to a custom directory\nrekipedia scan . --output-dir /tmp/wiki-output\n\n# Enable debug logging (litellm, HTTP, full tracebacks)\nrekipedia scan . --verbose\n\n# Auto-embed for RAG after scan\nrekipedia scan . --embed-model text-embedding-3-small --embed-provider openai\n```\n\n### RAG / 語義搜尋\n\n`rekipedia ask` 使用**混合式檢索**——wiki 頁面 + FAISS 索引的程式碼片段——以完整的程式碼庫上下文回答問題。\n\n```bash\n# Build or rebuild the FAISS index\nrekipedia embed .\n\n# Custom embedding model + provider\nrekipedia embed . --model text-embedding-3-small --provider openai\nrekipedia embed . --model nomic-embed-text --provider ollama\n\n# If your embed provider uses a DIFFERENT API key from your main LLM:\nrekipedia embed . --model text-embedding-3-small --provider openai\n# set embed_api_key in config.yml, or:\nexport REKIPEDIA_EMBED_API_KEY=***\n\n# Or configure everything in .rekipedia/config.yml:\n# llm:\n#   model: ollama/llama4          # main LLM (local)\n#   embed_model: text-embedding-3-small\n#   embed_provider: openai\n#   embed_api_key: sk-xxx         # separate key for embed provider\n#   embed_base_url: \"\"            # optional: custom endpoint\n\n# Env var overrides (all optional):\nexport REKIPEDIA_EMBED_MODEL=nomic-embed-text\nexport REKIPEDIA_EMBED_PROVIDER=ollama\nexport REKIPEDIA_EMBED_API_KEY=***\nexport REKIPEDIA_EMBED_BASE_URL=https://my-proxy.example.com/v1\n```\n\nFAISS 索引儲存於 `.rekipedia/rag/index.faiss`，分塊的原始碼儲存於 `.rekipedia/rag/chunks.json`。\n\n### 匯出 wiki\n\n```bash\n# Single combined Markdown file (default)\nrekipedia export . --format md --output ./wiki-export.md\n\n# ZIP archive (one .md per page + manifest.json)\nrekipedia export . --format zip --output ./wiki.zip\n\n# Structured JSON (all pages + metadata)\nrekipedia export . --format json --output ./wiki.json\n```\n\n### 增量更新\n\n初次掃描後，`rekipedia update` 只會重新處理 SHA-256 已變更的檔案。未變更的符號和關係會從上次執行中延續——wiki 可在幾秒內完成更新。\n\n```bash\nrekipedia update .                    # auto-detect changed files\nrekipedia update . --no-docker        # skip Docker\n```\n\n若找不到先前的掃描記錄，`update` 會自動回退至完整掃描。\n\n### 向 wiki 提問\n\n```bash\n# Start interactive Q&A session (streams answers, Ctrl+C to quit)\nrekipedia ask\nrekipedia ask --repo ./my-project\nrekipedia ask --model gpt-4o\n\n# Single-shot mode (backward compat)\nrekipedia ask -q \"How does the auth flow work?\"\n```\n\n答案**完全**以您的 wiki 頁面和符號索引為依據——LLM 無法捏造不存在於已掃描知識庫中的細節。答案以逐 token 串流方式輸出，等待時會顯示進度指示器。\n\n對某個生成頁面不滿意？請參閱 **[docs/customizing.md](docs/customizing.md)**——您可以釘選頁面、覆寫提示詞、更改寫作風格，或新增掃描時永遠不會觸及的自訂頁面。\n\n### 啟動 wiki 伺服器\n\n```bash\nrekipedia serve .                     # opens browser at http://127.0.0.1:7070\nrekipedia serve . --port 8080         # custom port\nrekipedia serve . --no-browser        # don't auto-open browser\n```\n\n- 在深色主題的網頁介面中瀏覽生成的 wiki 頁面\n- 使用相同的有依據問答功能提問（答案透過網頁串流）\n- 問答歷史記錄儲存於 SQLite\n\n---\n\n## 環境需求\n\n- **Python ≥ 3.11**（或使用 `uv`，它自行管理 Python 版本）\n- **Docker**——可選；用於隔離式提取。若 Docker 不可用，會自動回退至行程內執行模式（`--no-docker` 強制使用行程內模式）\n\n---\n\n## 搭配 AI 程式碼代理使用 rekipedia\n\nrekipedia 附帶一個 **Hermes agent skill**（`rekipedia-agent-skill.md`），可教導 AI 助理（Copilot、Claude Code、Codex）將 rekipedia 作為其程式碼庫智慧層：\n\n1. 將 `rekipedia-agent-skill.md` 複製到您的 Hermes skills 目錄\n2. 任何已載入該技能的代理，都會在深入原始碼檔案前自動掃描並查詢 rekipedia\n3. 大幅減少大型程式碼庫的上下文視窗用量\n\n---\n\n## 代理模式\n\nrekipedia 支援實驗性的代理模式，其中 LLM 呼叫使用工具調用（ReAct）而非單次大型上下文傾倒。\n\n### 代理式提問\n\n設定 `REKIPEDIA_AGENT_ASK=1` 以啟用：\n\n```bash\nREKIPEDIA_AGENT_ASK=1 reki ask \"How does authentication work?\"\n```\n\nLLM 會發出工具調用以按需取得資訊：\n- `search_code(query)` — 對原始碼進行語義搜尋\n- `get_symbol(name)` — 查詢符號的位置和簽名\n- `get_page(slug)` — 按需取得 wiki 頁面\n- `get_relationships(target)` — 取得符號/檔案的依賴關係圖\n- `finish(answer)` — 提供最終答案\n\n最大迭代次數可透過 `REKIPEDIA_ASK_MAX_ITER` 設定（預設值：5）。\n\n### 代理式規劃器\n\n設定 `REKIPEDIA_AGENT_PLANNER=1` 以啟用工具調用式 wiki 結構規劃：\n\n```bash\nREKIPEDIA_AGENT_PLANNER=1 reki scan .\n```\n\n規劃器使用工具調用增量建構 wiki 結構，而非生成單一大型 JSON 回應。\n\n## 開發\n\n```bash\n# Install all deps\nmake dev\n\n# Run tests\nmake test\n\n# Lint\nmake lint\n\n# Build wheel + npm tarball\nmake build\n```\n\n### 發佈\n\n```bash\nPYPI_TOKEN=*** NPM_TOKEN=*** make release\n\n# Full release: build + tag + push + PyPI + npm\nmake release-all PYPI_TOKEN=*** NPM_TOKEN=***\n# With version bump\nmake release-all PYPI_TOKEN=*** NPM_TOKEN=*** VERSION=0.5.0\n```\n\n---\n\n## 授權條款\n\n專有且機密——版權所有 © 2026 Eddie Chan。保留所有權利。\n\n嚴禁未經授權複製、散布或修改本軟體。\n詳情請參閱 [LICENSE](LICENSE)。\n","readmeFilename":"README.zh-TW.md"}