doclens

简体中文

Structure-aware document retrieval — FTS5/BM25 keyword search over document trees, a PWA Web UI as the default interface, and a built-in harness agent for knowledge-base Q&A and vibe coding.

doclens parses documents into tree structures (headings, classes, functions…) and searches them with FTS5/BM25 keyword matching — no embeddings, no chunking, no vector DB required. Works entirely offline.

Features

Core


Structure-aware search	Results anchored to document headings, code classes, or function definitions — not orphaned line fragments
Multi-format	Markdown, PDF, DOCX, PPTX, Excel, HTML, JSON, CSV, PST email archives, EPUB, images (vision-parsed), code (Python AST + tree-sitter)
Zero embeddings	FTS5/BM25 over document trees — no vector DB, fully offline
CJK support	Chinese/Japanese/Korean tokenization via jieba
Web UI	Lit + Shoelace PWA — the default interface
Structured queries	AND / OR / NOT / PHRASE operators (`search_v2`)
Regex fallback	Ripgrep-style regex `grep`; ripgrep fallback when FTS returns nothing

Additional


Built-in AI agent	A full harness agent (planify): tools, sub-agents, teammates, skills, context compaction — powers both KB Q&A and vibe coding. See below
Rewind	Every chat turn snapshots tracked files and each write is backed up before it happens — restore any earlier state from the session history
Slash skills	Invoke a skill directly in chat with `/skill-name question`, with a guided dropdown in the UI
Background watching	Auto-reindexes changed files via `watchdog`
Git sync	Auto commit → pull → merge → push for the knowledge directory (diary directory merges with `union`)
Diary	Daily fragments → deterministic next-day summary; image compression; weather lookup
Web file manager	Upload / rename / move / preview with TOC drawer
Access PIN	6-digit password gate for non-loopback access; `doclens auth reset` to clear
Web fetch	Extract public web pages as markdown before searching

AI Agent: Knowledge-Base Q&A + Vibe Coding

The chat is not a thin "search + LLM" wrapper — it is a full harness agent built on planify, doclens' in-repo AI agent framework. The same agent loop serves two modes that blend into each other: answering questions grounded in your knowledge base, and vibe coding — editing the knowledge base (or any working directory) through natural language.

Why a harness agent

Capability	What it does
Tool loop	The agent plans, calls tools, reads results, and iterates — multi-turn retrieval and code edits happen autonomously, not in a single fixed pipeline
Sub-agents	`task` spawns one-shot sub-agents (e.g. `Explore`) that read big files in parallel and return condensed summaries — large-document Q&A without blowing the context window
Teammates	`spawn_teammate` starts persistent autonomous teammates with their own agent loops; coordinate via `send_message` / `read_inbox` / file-based message bus
Skills	`load_skill` injects domain playbooks (KB retrieval strategy, citation rules, ignore-rule editing, README regeneration…) at call time; in chat, `/skill-name question` invokes one directly. Users can add their own SKILL.md files; skills hot-reload on change
Context compaction	Two-level automatic compaction (microcompact / auto-compact) keeps long sessions inside the model's context window
Prompt caching	Anthropic prompt caching with ephemeral breakpoints — system prompt and tools hit cache across turns
Ask the user	`ask_user_question` pauses for structured user input mid-task; chat-mode answers also work headless / mobile
Rewind	Turn-start snapshots plus pre-write backups make every AI edit reversible — pick a point in the session and restore the files as they were then
Safety guard	Outside-workdir guard: tool reads/writes outside the working directory are confirmed / allowed / blocked per `PLANIFY_OUTSIDE_WORKDIR`

Knowledge-base Q&A mode

The system prompt enforces a knowledge-base-first policy: factual questions must go through load_skill("knowledge-base") → search_kb (multi-query, synonyms, EN↔CN) before anything else; only when the KB truly has nothing may the agent fall back to web search — and it must say so. Citations are validated mechanically against the workdir (paths must actually exist), so answers cite what you really have.

A guidance file at the KB root (CLAUDE.md or AGENTS.md, up to 32 KB) is auto-injected into every system prompt — instructions you wrote for coding tools also steer this agent.

Vibe coding mode

The same agent carries a full editing toolkit for the working directory:

File tools — read_file / write_file / edit_file (edit_file requires unique old_text, so edits can't land in the wrong place)
Shell tools — bash (Git Bash on Windows) and powershell, plus background_run / check_background for long jobs
Search tools — grep / glob / search_kb / read_document over the indexed tree
Project tracking — persistent task board (task_create / task_update / claim_task), in-memory todos, background shells — so multi-step refactors survive across turns

Open the chat, describe the change ("add a settings toggle for X", "summarize these three files into one"), and the agent searches, edits, runs builds, and reports — your knowledge base is the repo it works on. If an edit goes wrong, rewind restores the files to any earlier state captured in that session.

Model presets

Switch LLM identity without touching .env by hand: named presets bundle protocol + base URL + model ID + API key (+ context window), managed in Settings → Model and applied hot (no restart). Anthropic and OpenAI-compatible endpoints are both supported; separate presets cover vision (image parsing) and search tuning.

Installation

pip install doclens

Requires Python ≥ 3.10.

Quick Start

# 1. Index your documents (current directory)
doclens index --force
# ...or point at a specific directory
doclens index --workdir /path/to/kb

# 2. Search from the CLI
doclens search "authentication flow"
doclens search "量子 计算"          # Chinese via jieba
doclens search_v2 '{"type": "and", "terms": ["auth", "token"]}'  # structured query

# 3. Launch the Web UI (opens the browser automatically)
doclens gui
# INFO: Uvicorn running on http://127.0.0.1:7860

# 4. Ask the AI a grounded question
doclens ai "How does the authentication system work?"

doclens automatically discovers supported files (.md, .py, .pdf, .docx, .xlsx, …) and skips common ignore patterns (.git, node_modules, __pycache__, .venv). The knowledge base root can also carry ignore rules in its .gitignore.

CLI Reference

doclens <command> [--workdir DIR]

Command	Description
`doclens search <query…>`	Keyword search across indexed documents
`doclens search_v2 '<json>'`	Structured search: AND / OR / NOT / PHRASE operators
`doclens ai <message…>`	Send a message to the AI agent
`doclens index [--force]`	Build or update the document index
`doclens status`	Show index statistics and system status
`doclens gui [--port PORT]`	Launch the Web UI (PWA)
`doclens read_document --path <path>`	Read a document with structure info
`doclens search_kb <query…>`	KB search in the AI-tool output format (same engine as `search`)
`doclens webfetch <url>`	Extract a web page as markdown
`doclens grep <pattern>`	Ripgrep-style regex search
`doclens auth reset`	Clear the Web UI access password

The bare doclens command launches the Web GUI by default.

Web UI

The default interface is a PWA built with Lit + Shoelace, served by FastAPI on http://127.0.0.1:7860 (port auto-increments on conflict — check the startup log).

Search — keyword search with result cards and snippets
Chat — the harness agent with SSE streaming, tool traces, and session history (see AI Agent)
Files — knowledge-base file tree with upload / rename / move / preview (Markdown, PDF, DOCX, PPTX, Excel, images, PST email lists) and a TOC drawer for long documents
Diary — daily fragments with images and weather, summarized into finished entries the next day
Settings — model presets (LLM / vision / search), access PIN, skills, MCP servers, Git sync

Access control: loopback (127.0.0.1) access never requires login. A 6-digit PIN can be set in Settings → Network; non-loopback requests then require it. Forgot the PIN? Run doclens auth reset.

Claude Code Integration (MCP KB Q&A)

doclens auto-starts an MCP server (Streamable HTTP) inside the GUI, exposing the knowledge base as two MCP tools — search_kb and read_document. Any MCP-compatible client (Claude Code, Cursor, Cline, …) can connect and answer questions grounded in your indexed documents, with zero embedding / vector DB.

The MCP HTTP server runs in a background thread in-process, sharing the same IndexManager as the GUI — live reindexing via the file watcher applies to MCP queries too.
It listens at http://<host>:<port>/mcp. The URL is printed in the startup log on every launch.
Loopback (127.0.0.1) by default, no auth. Binding to a non-loopback address requires a bearer token (CORTEX_MCP_TOKEN) — the server refuses to start otherwise.

Setup

1. Start doclens (this also starts the MCP server):

doclens gui

Read the MCP URL from the startup log:

MCP server: http://127.0.0.1:7880/mcp

2. Register it in Claude Code (once per project):

claude mcp add --transport http doclens http://127.0.0.1:7880/mcp --scope local
claude mcp list      # expect: doclens: ... ✔ Connected

Scopes: local (default — this project + you, not committed), user (global), project (.mcp.json, committed).

3. Restart your Claude Code session if it was already running — MCP servers load only at session start.

4. Ask. In Claude Code, ask anything about your indexed docs; it will call search_kb / read_document automatically. For a focused, KB-only answer, restrict the session to the two tools:

claude -p "量子密钥分发 QKD 的基本原理是什么？" \
  --allowedTools "mcp__doclens__search_kb" "mcp__doclens__read_document"

Bundled skill: `kb-ask`

doclens ships a Claude Code skill (source: doclens/claude_code_skills/kb-ask/skill.md) that codifies the full KB Q&A workflow: MCP-connected prerequisite check, FTS multi-query strategy, read_document deep-read, source-citation rules, and a no-fabrication constraint.

On GUI startup, doclens checks ~/.claude/skills/kb-ask/ and, if the skill is missing or out of date, prompts to install/overwrite it (skipped silently in non-interactive terminals). After the first install, restart your Claude Code session and invoke it anywhere:

/kb-ask 新能源汽车技术有哪些

Configuration

MCP behavior is controlled by these env vars (same .env as the rest of doclens):

Variable	Default	Description
`CORTEX_MCP_ENABLED`	`false`	Auto-start the MCP server with the GUI. Set `true` to enable.
`CORTEX_MCP_PORT`	`7880`	MCP HTTP port.
`CORTEX_MCP_HOST`	`127.0.0.1`	Bind address. Non-loopback requires `CORTEX_MCP_TOKEN`.
`CORTEX_MCP_TOKEN`	—	Bearer token enforced when host is non-loopback.

Configuration

doclens reads .env from the project root. Copy and customize the template:

cp doclens/.env.example .env

Key variables (see doclens/.env.example for the full annotated list):

Web / MCP

Variable	Default	Description
`CORTEX_WEB_HOST`	`127.0.0.1`	Web UI bind address
`CORTEX_WEB_PORT`	`7860`	Web UI port
`CORTEX_WEB_PASSWORD_HASH`	—	Access PIN hash (auto-written by the settings page; empty = no PIN)
`CORTEX_MCP_*`	—	MCP server toggle / host / port / token (see above)
`CORTEX_CHAT_DISCONNECT_CONTINUE`	`true`	SSE disconnect keeps the generation running; reconnect to resume viewing

LLM (AI chat)

Variable	Default	Description
`CORTEX_ACTIVE_LLM_PRESET`	—	Name of the active model preset
`PLANIFY_PROTOCOL`	—	`anthropic` or empty (OpenAI-compatible)
`PLANIFY_BASE_URL` / `PLANIFY_MODEL_ID` / `PLANIFY_API_KEY`	—	LLM endpoint, model, and key
`PLANIFY_CONTEXT_WINDOW`	`200000`	Context window in tokens; auto-compact at 0.8×
`PLANIFY_MAX_TOKENS`	`8000`	Max output tokens per response
`PLANIFY_ASK_MODE`	`interactive`	How `ask_user_question` asks: interactive card or chat message

Vision (image parsing)

Variable	Default	Description
`CORTEX_ACTIVE_VISION_PRESET`	—	Name of the active vision preset
`VISION_BASE_URL`	dashscope compatible-mode	OpenAI-compatible vision endpoint
`VISION_MODEL`	`qwen-vl-max`	Vision model
`VISION_API_KEY`	—	Required for image parsing
`VISION_AUTO_ROTATE`	`false`	Auto-rotate uploaded images via vision

Search tuning

Variable	Default	Description
`CORTEX_MAX_RESULTS`	`50`	Max search results returned
`CORTEX_MIN_SCORE_THRESHOLD`	`0.3`	Results below this score are filtered
`CORTEX_MAX_SPAN`	`50`	Keyword proximity window (chars)
`CORTEX_WEIGHT_*`	4.0 / 2.0 / 1.0 / 2.0 / 1.0	Scoring weights: keyword / file-name / FTS / title / proximity
`CORTEX_GREP_MAX_RESULTS`	`50`	Max grep results

Indexing / watching / Git sync

Variable	Default	Description
`CORTEX_WATCH_ENABLED`	`true`	Background file watcher
`CORTEX_WATCH_DEBOUNCE`	`5.0`	Watcher debounce (seconds)
`CORTEX_SYNC_ENABLED`	`false`	Git auto-sync (commit → pull → merge → push)
`CORTEX_SYNC_INTERVAL_MINUTES`	`5.0`	Sync interval
`CORTEX_CJK_TOKENIZER`	`jieba`	CJK tokenizer: jieba / bigram / char / auto
`TREESEARCH_INDEX_CHUNK_SIZE`	`500`	Chunked indexing block size (files)

Development

# 1. Create a virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1        # Windows (use pwsh 7)
# source .venv/bin/activate       # macOS / Linux

# 2. Install doclens in editable mode with dev dependencies
pip install -e ".[dev]"

# 3. Override treesearch / planify with local sources (editable, changes take effect immediately)
pip install -e ./treesearch
pip install -e ./planify

# 4. Playwright browsers for webfetch's JS-rendering fallback and E2E tests
python -m playwright install chromium --only-shell

Daily driving (repo root, PowerShell 7):

./start-app.ps1 gui              # backend + built frontend
./start-app.ps1 gui -C D:\kb     # use a specific knowledge directory
./start-app.ps1 search "量子 计算"

Frontend (doclens/web_v2/frontend):

npm install
npm run build      # tsc --noEmit && vite build → outputs to doclens/web_v2/static/
npm test           # vitest unit tests

Backend tests:

.venv/Scripts/python.exe -m pytest

Architecture

┌─────────────────────────────────────────────────┐
│            Web UI (Lit + Shoelace PWA)          │
│           FastAPI + SSE streaming               │
└───────────────────────┬─────────────────────────┘
                        │
┌───────────────────────▼─────────────────────────┐
│                 CLI (argparse)                  │
│   search / index / status / gui / ai / …        │
└───────────────────────┬─────────────────────────┘
                        │
┌───────────────────────▼─────────────────────────┐
│      IndexManager │ CortexAgent │ EventBus      │
│         Scoring │ FileWatcher │ MCP server      │
└───────────────────────┬─────────────────────────┘
                        │
┌───────────────────────▼─────────────────────────┐
│   treesearch/  —  parsers, indexer, FTS5/BM25  │
│   planify/     —  AI agent runner + tools       │
└─────────────────────────────────────────────────┘

doclens — the host: CLI, Web UI, MCP server, event bus, file watcher, diary, Git sync
treesearch — indexing and retrieval engine (FTS5/BM25 over document trees; distributed as treesearchlib on PyPI)
planify — AI agent framework: LLM providers (Anthropic / OpenAI-compatible), streaming runner, tool registry, skills, teammates (distributed as planify on PyPI)

Layering rule: doclens may depend on planify and treesearch; the two libraries never import each other or the host, and private (underscore) members are never imported across module boundaries.

License

Apache License 2.0 — see LICENSE.

Contact

Lianghao — zhlhao@163.com

Metadata

Release files for doclens 1.2.49

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Table of built distributions (wheels) for doclens 1.2.49
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