doc: doc the --json option

.editorconfig

@@ -7,4 +7,4 @@ charset = utf-8
 
 [*.py]
 indent_style = space
-indent_size = 2
+indent_size = 4

.gitignore

@@ -8,3 +8,6 @@ wheels/
 
 # Virtual environments
 .venv
+
+# test dir
+.tmp

AGENTS.md

@@ -4,631 +4,81 @@
 
 `paperlib` is a local-first paper library engine with a CLI.
 
-It is designed to:
+**Key point**: `paperlib` is **not** primarily an AI app. AI summarization is optional enrichment. The project must remain useful without LLM configuration.
 
-- import PDF papers into a structured local library
-- convert PDFs into Markdown using external converters such as MinerU
-- maintain stable per-paper metadata files and a searchable index database
-- optionally generate AI-based structured summaries
-- expose a clean CLI that is useful both for humans and for higher-level automation tools such as an arXiv daily digest workflow
+## Critical design principles
 
-`paperlib` is **not** primarily an AI app. AI summarization is an optional enrichment layer, not the core of the system.
+1. **Local-first**: User data lives locally. Prefer plain files + SQLite over opaque state.
+2. **CLI-first**: The CLI is the primary interface. Python API is secondary.
+3. **JSON files are source of truth**: Per-paper JSON files are durable truth. SQLite is rebuildable index/cache.
+4. **AI is optional**: Core workflows (import/convert/index/list/show/search) work without AI.
+5. **Machine-readable**: Commands support `--json` output for automation.
 
-The project should remain useful even when:
+## Development commands
 
-- no LLM API key is configured
-- no summarization is enabled
-- only import / convert / index / search features are used
+- **Testing**: `uv run pytest` (specific: `uv run pytest tests/test_models.py`)
+- **Linting**: `uv run ruff check src/` 
+- **Formatting**: `uv run ruff format`
+- **CLI testing**: `uv run paperlib --help` or `uv run paperlib init .tmp/test-lib`
 
----
+**Always use `uv run` for Python commands. Use `./.tmp` for test libraries (it's tmpfs).**
 
-## Core design principles
+## Current CLI commands
 
-### 1. Local-first
+**Implemented**:
+- `init` - Initialize library
+- `status` - Show library config
+- `list` - List papers  
+- `show` - Show paper details
+- `search` - Search papers
+- `import` - Import papers (PDF/arXiv)
+- `convert` - Convert PDFs to Markdown (MinerU)
+- `reindex` - Rebuild search index
 
-User data lives locally in the paper library directory.
+**Planned**: `import-dir`, `watch`, `doctor`, `open`, `print-path`, `summarize`, `render-summary`, `export`
 
-The library must remain usable without a server, web app, or remote database.
+## Critical constraints
 
-Prefer plain files plus SQLite over opaque internal state.
+### What paperlib IS
+- PDF import and local storage
+- PDF → Markdown conversion  
+- Metadata files and search indexing
+- CLI for all operations
+- Optional AI summarization
 
-### 2. CLI-first
+### What paperlib is NOT
+- Web UI or daemon
+- Multi-user service
+- Cloud-first design
+- Vector database requirement
+- Autonomous research assistant
 
-The CLI is the primary interface.
-
-All important workflows should be accessible from the CLI.
-
-The Python API is useful, but secondary.
-
-### 3. JSON files are the source of truth
-
-Per-paper JSON files in the library are the durable source of truth.
-
-Examples:
-
-- `meta.json`
-- `summary.json`
-
-SQLite is an index/cache layer, not the canonical data store.
-
-This means:
-
-- the index must be rebuildable from files
-- `reindex` should be able to repair the database from on-disk records
-- code must not assume the database alone is authoritative
-
-### 4. AI is optional enrichment
-
-Importing, converting, indexing, listing, showing, and searching papers must work without AI.
-
-AI summarization should be isolated behind a clean interface.
-
-Do not make core workflows depend on an LLM provider.
-
-### 5. Stable machine-readable interfaces
-
-Important commands should support `--json` output so that other tools can consume them.
-
-Examples:
-
-- `paperlib import ... --json`
-- `paperlib summarize ... --json`
-- `paperlib show ... --json`
-- `paperlib export ... --format json`
-
-### 6. Small, explicit, inspectable components
-
-Prefer simple and explicit logic over large hidden frameworks.
-
-Keep components understandable:
-
-- importer
-- converter
-- renderer
-- summarizer
-- search
-- reindex
-- doctor
-
-Avoid unnecessary abstraction until there is a real need.
-
----
-
-## Non-goals
-
-The following are currently out of scope unless explicitly planned later:
-
-- mandatory daemon architecture
-- web UI
-- multi-user remote service
-- cloud-first design
-- vector database as a required dependency
-- opaque agent framework controlling the core library
-- “fully autonomous research assistant” behavior
-
----
-
-## Library data layout
-
-The paper library on disk should be human-browsable.
-
-A typical layout looks like:
-
-```text
-library_root/
-  config/
-    config.toml
-    vocab.yaml
-    prompts/
-      summarize_paper.md
-
-  inbox/
-  papers/
-    arxiv/
-      2026/
-        2604.12345/
-          meta.json
-          source.pdf
-          paper.md
-          summary.json
-          summary.md
-          ref.bib
-          assets/
-          logs/
-            mineru.log
-    local/
-      sha256-.../
-        meta.json
-        source.pdf
-        paper.md
-        summary.json
-        summary.md
-
-  db/
-    paperlib.sqlite3
-
-  cache/
-```
-
-Conventions:
-
-- `meta.json` contains stable metadata and processing status
-- `summary.json` contains structured AI-generated enrichment
-- `summary.md` is rendered from `summary.json`
-- `paper.md` is generated from the PDF by an external converter such as MinerU
-- the database is rebuildable from the files above
-
----
-
-## Data model boundaries
-
-### `meta.json`
-
-`meta.json` should contain deterministic or near-deterministic information, mostly from:
-
-- import process
-- file system state
-- external paper metadata sources
-
-Typical fields include:
-
-- `paper_id`
-- `source_type`
-- `source_id`
-- `title`
-- `authors`
-- `published_date`
-- `updated_date`
-- `categories`
-- `pdf_path`
-- `paper_md_path`
-- `summary_json_path`
-- `summary_md_path`
-- `imported_at`
-- `conversion_status`
-- `summary_status`
-
-Avoid putting speculative AI content into `meta.json`.
-
-### `summary.json`
-
-`summary.json` is optional enrichment and may be regenerated.
-
-It should contain structured fields such as:
-
-- one-sentence summary
-- problem statement
-- method overview
-- main results
-- claimed contributions
-- assumptions
-- limitations
-- problem tags
-- technique tags
-- entities
-- relevance-to-user fields
-- recommended sections
-
-`summary.json` must include a schema version.
-
-### SQLite
-
-SQLite stores searchable/indexed state and job-independent status.
-
-It should help with:
-
-- listing papers
-- filtering and search
-- path lookup
-- tag lookup
-- status overview
-
-But it should never be treated as the only durable source of paper metadata.
-
----
-
-## CLI philosophy
-
-The CLI should be easy for humans and predictable for scripts.
-
-### Important CLI expectations
-
-- human-readable by default
-- machine-readable with `--json`
-- clear exit codes
-- no hidden background magic
-- no required daemon
-- stable command names
-- idempotent operations when possible
-
-### Expected command families
-
-Core commands include:
-
-- `init`
-- `import`
-- `import-dir`
-- `watch`
-- `convert`
-- `reindex`
-- `doctor`
-- `status`
-- `list`
-- `show`
-- `search`
-- `open`
-- `print-path`
-- `summarize`
-- `render-summary`
-- `export`
-
-When implementing commands, preserve a clear separation between:
-
-- mutation commands
-- read/query commands
-
----
-
-## Architecture guidelines
-
-The codebase should be organized around a few clear layers.
-
-### 1. Core domain logic
-
-Pure Python logic for:
-
-- identifying papers
-- computing paths
-- importing PDFs
-- updating metadata
-- converting PDFs to Markdown
-- rendering summaries
-- rebuilding the index
-
-This layer should be testable without the CLI.
-
-### 2. CLI layer
-
-Thin wrappers around the core domain logic.
-
-The CLI should:
-
-- parse arguments
-- call core functions
-- format output
-- handle exit codes
-
-The CLI should not contain deep business logic.
-
-### 3. Optional integrations
-
-External systems should live in integration modules, for example:
-
-- MinerU wrapper
-- filesystem watch integration
-- ripgrep integration
-- LLM provider integration
-
-Keep these adapters isolated.
-
-### 4. Optional AI layer
-
-The AI summarization layer should be behind a stable abstraction.
-
-For example:
-
-- load prompt template
-- load paper markdown
-- load optional profile / vocabulary
-- call provider
-- validate structured output
-- write `summary.json`
-- render `summary.md`
-
-Avoid leaking provider-specific behavior into unrelated modules.
-
----
-
-## AI collaboration guidelines
-
-When using AI to help develop this project, the AI should follow these rules.
-
-### 1. Respect the project boundaries
-
-Do not redesign `paperlib` into:
-
-- a web app
-- a required daemon
-- a monolithic agent system
-- a chat-first interface
-
-Unless explicitly asked, keep the project aligned with:
-
-- local-first
-- CLI-first
-- JSON/SQLite-based architecture
-- AI-optional enrichment
-
-### 2. Prefer incremental changes
-
-Make small, reviewable changes.
-
-When implementing a feature:
-
-- first clarify which module owns it
-- avoid broad refactors unless necessary
-- preserve existing CLI semantics unless intentionally changing them
-
-### 3. Keep file formats stable
-
-Changes to `meta.json` or `summary.json` are important.
-
-If changing schemas:
-
-- update the schema version
-- update documentation
-- consider migration or backward compatibility
-- do not silently break existing libraries
-
-### 4. Avoid hidden coupling
-
-Do not make unrelated modules depend on each other unnecessarily.
-
-For example:
+### File format stability
+Changes to `meta.json` or `summary.json` schemas are breaking changes. Must update schema version and consider migration.
 
+### Module boundaries
 - `search` should not depend on LLM code
-- `import` should not require summarization
-- `reindex` should not assume a specific converter
-- `render-summary` should not require calling AI again
+- `import` should not require summarization  
+- `reindex` should work from files alone
+- Keep AI behind clean interfaces
 
-### 5. Prefer explicit data flow
+## Git commits
+Format: `"<scope>: <subject>"` where scope is `feat|fix|docs|style|refactor|test|perf|update`
+First line ≤88 chars, second line empty.
 
-When adding features, keep data flow obvious.
+## When you need details
 
-For example:
-
-- `import` creates or updates metadata
-- `convert` creates `paper.md`
-- `summarize` creates `summary.json`
-- `render-summary` creates `summary.md`
-- `reindex` rebuilds SQLite from files
-
-### 6. Do not invent capabilities
-
-If a feature is not implemented yet, do not pretend it exists.
-
-Examples:
-
-- do not write code that assumes a daemon exists
-- do not assume remote sync exists
-- do not assume vector search exists
-- do not assume arXiv-specific logic belongs in the core library
-
-### 7. Prefer durable outputs over polished prose
-
-When designing AI summarization outputs, favor:
-
-- structured JSON
-- stable field names
-- grep-friendly rendered Markdown
-- concise, reusable information
-
-over:
-
-- highly polished review prose
-- flashy but unstable output formats
-
----
-
-## Coding guidelines
-
-### General style
-
-- Prefer straightforward Python.
-- Use type hints.
-- Keep functions small and focused.
-- Add docstrings to public functions and classes.
-- Avoid overengineering.
-- Prefer composition over deep inheritance.
-
-### Error handling
-
-- Fail clearly.
-- Provide helpful error messages.
-- Distinguish user-facing CLI errors from internal exceptions.
-- Avoid silently swallowing errors.
-
-### Logging
-
-- Use structured and informative logging where useful.
-- Avoid noisy logs in normal CLI output.
-- Keep machine-readable command output clean when `--json` is used.
-
-### File operations
-
-- Be careful with moves, copies, and overwrites.
-- Prefer atomic writes for JSON files when possible.
-- Never corrupt existing metadata due to partial writes.
-
-### Idempotence
-
-Where possible, commands should behave safely when run multiple times.
-
-Examples:
-
-- re-importing the same file should detect duplicates
-- `render-summary` should be repeatable
-- `reindex` should be safe to rerun
-
-### Testing
-
-Add tests for:
-
-- path layout logic
-- metadata read/write behavior
-- duplicate detection
-- reindex behavior
-- summary rendering
-- search behavior
-- CLI output contracts for core commands
-
-Prefer unit tests for core logic and targeted integration tests for CLI behavior.
-
----
-
-## Search design guidelines
-
-Search should support at least two useful modes:
-
-### 1. Field-aware structured search
-
-Examples:
-
-- tags
-- authors
-- categories
-- titles
-- summary fields
-
-### 2. Full-text-friendly search
-
-Support grep-like workflows and integration with tools such as `ripgrep`.
-
-Do not require semantic/vector search as a baseline feature.
-
-If semantic search is ever added later, it should be optional and must not displace simple grep/database search.
-
----
-
-## Summarization design guidelines
-
-Summarization should produce reusable structured outputs.
-
-### Summarization goals
-
-A summary should be useful for:
-
-- later human review
-- grep-style reverse lookup
-- building daily/weekly reports
-- indexing by problem/method/result
-- personal research triage
-
-### Summarization output
-
-Prefer generating:
-
-- `summary.json` as the canonical structured output
-- `summary.md` rendered from JSON
-
-Do not make free-form Markdown the only output.
-
-### Prompting guidelines
-
-Prompts should instruct the model to:
-
-- extract factual information
-- avoid unsupported claims
-- use concise and stable language
-- prefer controlled vocabulary when available
-- return structured JSON only
-- use `null` or empty lists for unclear fields rather than hallucinating
-
-### Provider abstraction
-
-The summarizer should not be tightly coupled to a single LLM provider.
-
-Use a provider abstraction so the project can support:
-
-- OpenAI-compatible APIs
-- local models later if desired
-- different prompt templates and vocabularies
-
----
-
-## What belongs in `paperlib` vs higher-level tools
-
-`paperlib` is the base library engine.
-
-It should own:
-
-- PDF import
-- local storage layout
-- conversion to Markdown
-- metadata files
-- summary files
-- index maintenance
-- CLI access to those capabilities
-
-It should not own high-level discovery workflows such as:
-
-- arXiv daily fetching
-- personalized new-paper ranking
-- daily digest generation
-- automated paper downloading from external feeds
-
-Those belong in higher-level tools that consume `paperlib`.
-
----
-
-## Expected development workflow
-
-When implementing a new feature, the preferred order is:
-
-1. identify the owning module
-2. define or update the data contract
-3. implement the core logic
-4. add tests
-5. expose it through the CLI if appropriate
-6. update docs and examples
-
-If a change affects on-disk formats or CLI behavior, document it clearly.
-
----
+- **Architecture**: See `dev-docs/architecture.md`
+- **Data model**: See `dev-docs/data-model.md` 
+- **AI integration**: See `dev-docs/ai-guidelines.md`
+- **Code style**: See `dev-docs/coding-guidelines.md`
 
 ## Decision heuristics
 
 When uncertain, prefer the option that is:
-
 - more local-first
-- more inspectable
+- more inspectable  
 - easier to test
-- easier to recover from
 - less coupled to AI
 - more stable for scripts
-- less magical
-
-Examples:
-
-- prefer JSON + Markdown over opaque internal blobs
-- prefer explicit CLI commands over hidden automation
-- prefer rebuildable indexes over fragile single-source databases
-- prefer optional AI enrichment over mandatory AI workflows
-
----
-
-## Documentation expectations
-
-Important features should be documented in:
-
-- `README.md` for user-facing overview
-- `docs/cli.md` for command behavior
-- `docs/storage-layout.md` for on-disk structure
-- `docs/summary-schema.md` for `summary.json`
-- `docs/integration-guide.md` for higher-level tool integration
-
-Keep docs aligned with actual behavior.
-
----
-
-## If you are an AI agent contributing code
-
-Before making a change, ask:
-
-1. Does this belong in `paperlib`, or in a higher-level workflow project?
-2. Does this preserve local-first and CLI-first design?
-3. Does this make AI optional, not mandatory?
-4. Does this keep JSON files as the durable source of truth?
-5. Does this keep the system understandable to a developer reading the code later?
-
-If the answer to any of these is no, reconsider the approach.
+- less magical

README.md

@@ -1,19 +1,301 @@
-# `paperlib`: a CLI tool to manage paper library
+# paperlib
 
-This project use `mineru` to convert PDF to markdown, and establish a markdown paper library.
+A local-first paper library engine with a CLI for managing academic papers.
 
-## usage
+**paperlib** is designed to import PDF papers into a structured local library, convert PDFs into Markdown using external converters, maintain stable per-paper metadata files, and provide a searchable index database. It offers optional AI-based structured summaries while remaining useful even without AI features.
+
+## Key Features
+
+- **Local-first**: All data lives locally in the paper library directory
+- **CLI-first**: All important workflows accessible from the command line
+- **JSON source of truth**: Per-paper metadata files with rebuildable SQLite index
+- **AI-optional**: Core workflows work without LLM configuration
+- **Machine-readable**: `--json` output for automation and integration
+- **Stable interfaces**: Designed for scripts and higher-level tools
+
+## Installation
+
+### System Dependencies
+
+For PDF conversion functionality, paperlib requires OpenGL support through MinerU. If you are inside a graphical everionment, you are likely fine. On headless systems, install:
 
 ```bash
-# init a library in current directory
+# Debian based
+sudo apt-get install libglvnd0
+
+# Fedora  
+sudo dnf install libglvnd-glx
+
+# Arch Linux
+sudo pacman -S libglvnd
+
+# Gentoo
+sudo emerge -av media-libs/libglvnd
+# or just add media-libs/libglvnd to your @world or some set
+```
+
+### Python Package
+
+```bash
+# Install with uv (recommended)
+uv add paperlib
+
+# Or with pip
+pip install paperlib
+```
+
+## Quick Start
+
+```bash
+# Initialize a paper library
 paperlib init
 
-# manually import a PDF
-paperlib import --pdf <path to pdf> [--arxiv-id xxxx.xxxxx]
+# Import a local PDF
+paperlib import --pdf paper.pdf --title "My Research Paper"
 
-# import an arXiv paper
-paperlib import --arxiv xxxx.xxxxx
+# Import from arXiv
+paperlib import --arxiv 2212.06340
 
-# place holder
-...
+# List all papers
+paperlib list
+
+# Show paper details
+paperlib show <paper-id>
+
+# Convert PDFs to Markdown (requires MinerU)
+paperlib convert
+
+# Search papers
+paperlib search "machine learning"
+
+# Rebuild search index
+paperlib reindex
 ```
+
+## Core Commands
+
+### Library Management
+- `paperlib init [path]` - Initialize a paper library directory
+- `paperlib status` - Show library configuration and layout
+- `paperlib reindex` - Rebuild search index from stored papers
+
+### Paper Import
+- `paperlib import --pdf <path>` - Import a local PDF file
+- `paperlib import --arxiv <id>` - Import paper from arXiv
+- Options: `--title`, `--notes`, `--tags`, `--library`
+
+### Paper Management
+- `paperlib list` - List all imported papers with status
+- `paperlib show <paper-id>` - Show detailed paper information
+- `paperlib convert` - Convert pending papers to Markdown using MinerU
+
+### Search (Future)
+- `paperlib search <query>` - Search papers by content and metadata
+
+## Library Structure
+
+A paperlib library is organized as follows:
+
+```
+library_root/
+├── config/
+│   ├── config.toml
+│   └── prompts/
+├── papers/
+│   ├── arxiv/
+│   │   └── 2026/
+│   │       └── arxiv-2212_06340/
+│   │           ├── meta.json          # Paper metadata
+│   │           ├── source.pdf         # Original PDF
+│   │           ├── paper.md           # Converted markdown
+│   │           ├── summary.json       # AI summary (optional)
+│   │           ├── summary.md         # Rendered summary
+│   │           ├── assets/            # Images, figures
+│   │           └── logs/              # Conversion logs
+│   └── local/
+│       └── <hash>/
+│           └── ...
+├── db/
+│   └── paperlib.sqlite3              # Search index (rebuildable)
+├── inbox/                             # Temporary imports
+└── cache/                            # Processing cache
+```
+
+## Data Model
+
+### Paper Metadata (`meta.json`)
+Each paper has a `meta.json` file containing:
+- Core identifiers: `paper_id`, `source_type`, `source_id`
+- Bibliographic info: `title`, `authors`, `published_date`, `categories`
+- File paths: `pdf_path`, `paper_md_path`, `summary_json_path`
+- Processing status: `conversion_status`, `summary_status`
+- User data: `tags`, `notes`
+
+### Summary Data (`summary.json`)
+Optional AI-generated summaries with:
+- Structured fields: problem statement, method overview, results
+- Categorization: problem tags, technique tags
+- Relevance scoring and recommended sections
+
+## PDF Conversion
+
+paperlib integrates with [MinerU](https://github.com/opendatalab/MinerU) for high-quality PDF to Markdown conversion:
+
+```bash
+# Install MinerU (optional)
+pip install mineru[core]
+
+# Convert all pending papers
+paperlib convert
+
+# Retry failed conversions (useful after fixing system dependencies)
+paperlib convert --retry-failed
+
+# Force reconvert all papers
+paperlib convert --force
+
+# Convert specific paper
+paperlib convert --paper-id <paper-id>
+```
+
+### Troubleshooting PDF Conversion
+
+If conversion fails with OpenGL/display errors on headless systems:
+
+```bash
+# Check if MinerU is properly installed
+uv run mineru --version
+
+# If you get "libxcb.so.1" or similar errors, install OpenGL support:
+sudo apt-get install libglvnd0  # Ubuntu/Debian
+sudo pacman -S libglvnd         # Arch Linux
+sudo dnf install libglvnd-glx   # Fedora
+
+# Test conversion manually
+mineru -p example.pdf -o /tmp/test_output -b pipeline
+
+# Check paperlib conversion logs
+cat path/to/library/papers/.../logs/mineru.log
+```
+
+## Machine-Readable Output
+
+Most commands support `--json` output for automation and integration:
+
+```bash
+# Get library configuration in JSON
+paperlib status --json
+
+# List all papers with metadata
+paperlib list --json
+
+# Get detailed paper information  
+paperlib show <paper-id> --json
+
+# Get import results
+paperlib import --arxiv 2212.06340 --json
+
+# Get conversion status and results
+paperlib convert --json
+paperlib convert --paper-id <paper-id> --json
+
+# Get reindexing statistics
+paperlib reindex --json
+```
+
+### JSON Output Format
+
+All JSON responses follow a consistent envelope format:
+
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "data": { /* command-specific data */ }
+}
+```
+
+For errors:
+```json
+{
+  "success": false, 
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "error": "Error message here",
+  "error_code": 1
+}
+```
+
+This structured output enables reliable automation, scripting, and integration with other tools. The JSON format is stable across paperlib versions.
+
+## Development
+
+paperlib is designed for extensibility and integration with higher-level tools.
+
+### Running Tests
+
+```bash
+# Run all tests
+uv run pytest
+
+# Run specific test module
+uv run pytest tests/test_models.py
+
+# Run with coverage
+uv run pytest --cov=paperlib
+```
+
+### Code Quality
+
+```bash
+# Format code
+uv run ruff format
+
+# Check linting
+uv run ruff check
+
+# Type checking
+uv run mypy src/
+```
+
+## Architecture
+
+paperlib follows clean architecture principles:
+
+- **Models**: Data structures for papers and summaries
+- **Storage**: File-based metadata and PDF management  
+- **Index**: SQLite search and retrieval layer
+- **Importers**: PDF and arXiv import workflows
+- **Converters**: PDF to Markdown transformation
+- **CLI**: Command-line interface and argument parsing
+
+## Roadmap
+
+- [x] Core paper import (local PDF, arXiv)
+- [x] PDF to Markdown conversion (MinerU integration)*
+- [x] Metadata management and search indexing
+- [x] CLI with all basic commands
+- [x] Comprehensive test suite
+- [ ] Search command implementation
+- [ ] AI summarization with provider abstraction
+- [x] JSON output for core commands
+- [ ] Configuration file support
+- [ ] Advanced arXiv workflows
+
+**Note**: PDF conversion requires `libglvnd` system dependency for OpenGL support on headless systems.
+
+## Non-Goals
+
+paperlib is intentionally focused and does NOT include:
+- Web UI or GUI applications
+- Multi-user or cloud-first features
+- Mandatory daemon or background services
+- Vector database requirements
+- Fully autonomous research assistant behavior
+
+## License
+
+MIT License - see LICENSE file for details.
+
+## Contributing
+
+Contributions welcome! Please read the development guidelines in AGENTS.md and ensure all tests pass before submitting PRs.

dev-docs/ai-guidelines.md

@@ -0,0 +1,55 @@
+# AI Integration Guidelines
+
+## Search design
+
+Search should support at least two useful modes:
+
+### 1. Field-aware structured search
+Examples: tags, authors, categories, titles, summary fields
+
+### 2. Full-text-friendly search
+Support grep-like workflows and integration with tools such as `ripgrep`.
+
+Do not require semantic/vector search as a baseline feature.
+
+If semantic search is ever added later, it should be optional and must not displace simple grep/database search.
+
+## Summarization design
+
+Summarization should produce reusable structured outputs.
+
+### Summarization goals
+
+A summary should be useful for:
+- later human review
+- grep-style reverse lookup
+- building daily/weekly reports
+- indexing by problem/method/result
+- personal research triage
+
+### Summarization output
+
+Prefer generating:
+- `summary.json` as the canonical structured output
+- `summary.md` rendered from JSON
+
+Do not make free-form Markdown the only output.
+
+### Prompting guidelines
+
+Prompts should instruct the model to:
+- extract factual information
+- avoid unsupported claims
+- use concise and stable language
+- prefer controlled vocabulary when available
+- return structured JSON only
+- use `null` or empty lists for unclear fields rather than hallucinating
+
+### Provider abstraction
+
+The summarizer should not be tightly coupled to a single LLM provider.
+
+Use a provider abstraction so the project can support:
+- OpenAI-compatible APIs
+- local models later if desired
+- different prompt templates and vocabularies

dev-docs/architecture.md

@@ -0,0 +1,74 @@
+# Architecture Guidelines
+
+The codebase should be organized around a few clear layers.
+
+## 1. Core domain logic
+
+Pure Python logic for:
+
+- identifying papers
+- computing paths
+- importing PDFs
+- updating metadata
+- converting PDFs to Markdown
+- rendering summaries
+- rebuilding the index
+
+This layer should be testable without the CLI.
+
+## 2. CLI layer
+
+Thin wrappers around the core domain logic.
+
+The CLI should:
+
+- parse arguments
+- call core functions
+- format output
+- handle exit codes
+
+The CLI should not contain deep business logic.
+
+## 3. Optional integrations
+
+External systems should live in integration modules, for example:
+
+- MinerU wrapper
+- filesystem watch integration
+- ripgrep integration
+- LLM provider integration
+
+Keep these adapters isolated.
+
+## 4. Optional AI layer
+
+The AI summarization layer should be behind a stable abstraction.
+
+For example:
+
+- load prompt template
+- load paper markdown
+- load optional profile / vocabulary
+- call provider
+- validate structured output
+- write `summary.json`
+- render `summary.md`
+
+Avoid leaking provider-specific behavior into unrelated modules.
+
+## Component boundaries
+
+Avoid hidden coupling:
+
+- `search` should not depend on LLM code
+- `import` should not require summarization
+- `reindex` should not assume a specific converter
+- `render-summary` should not require calling AI again
+
+Prefer explicit data flow:
+
+- `import` creates or updates metadata
+- `convert` creates `paper.md`
+- `summarize` creates `summary.json`
+- `render-summary` creates `summary.md`
+- `reindex` rebuilds SQLite from files

dev-docs/coding-guidelines.md

@@ -0,0 +1,51 @@
+# Coding Guidelines
+
+## General style
+
+- Prefer straightforward Python
+- Use type hints
+- Keep functions small and focused
+- Add docstrings to public functions and classes
+- Avoid overengineering
+- Prefer composition over deep inheritance
+
+## Error handling
+
+- Fail clearly
+- Provide helpful error messages
+- Distinguish user-facing CLI errors from internal exceptions
+- Avoid silently swallowing errors
+
+## Logging
+
+- Use structured and informative logging where useful
+- Avoid noisy logs in normal CLI output
+- Keep machine-readable command output clean when `--json` is used
+
+## File operations
+
+- Be careful with moves, copies, and overwrites
+- Prefer atomic writes for JSON files when possible
+- Never corrupt existing metadata due to partial writes
+
+## Idempotence
+
+Where possible, commands should behave safely when run multiple times.
+
+Examples:
+- re-importing the same file should detect duplicates
+- `render-summary` should be repeatable
+- `reindex` should be safe to rerun
+
+## Testing
+
+Add tests for:
+- path layout logic
+- metadata read/write behavior
+- duplicate detection
+- reindex behavior
+- summary rendering
+- search behavior
+- CLI output contracts for core commands
+
+Prefer unit tests for core logic and targeted integration tests for CLI behavior.

dev-docs/data-model.md

@@ -0,0 +1,92 @@
+# Data Model
+
+## Library data layout
+
+The paper library on disk should be human-browsable.
+
+A typical layout looks like:
+
+```text
+library_root/
+  config/
+    config.toml
+    vocab.yaml
+    prompts/
+      summarize_paper.md
+
+  inbox/
+  papers/
+    arxiv/
+      2026/
+        2604.12345/
+          meta.json
+          source.pdf
+          paper.md
+          summary.json
+          summary.md
+          ref.bib
+          assets/
+          logs/
+            mineru.log
+    local/
+      sha256-.../
+        meta.json
+        source.pdf
+        paper.md
+        summary.json
+        summary.md
+
+  db/
+    paperlib.sqlite3
+
+  cache/
+```
+
+## Data boundaries
+
+### `meta.json`
+
+`meta.json` should contain deterministic or near-deterministic information, mostly from:
+
+- import process
+- file system state
+- external paper metadata sources
+
+Typical fields include:
+
+- `paper_id`, `source_type`, `source_id`
+- `title`, `authors`, `published_date`, `updated_date`, `categories`
+- `pdf_path`, `paper_md_path`, `summary_json_path`, `summary_md_path`
+- `imported_at`, `conversion_status`, `summary_status`
+
+Avoid putting speculative AI content into `meta.json`.
+
+### `summary.json`
+
+`summary.json` is optional enrichment and may be regenerated.
+
+It should contain structured fields such as:
+
+- one-sentence summary, problem statement, method overview
+- main results, claimed contributions, assumptions, limitations
+- problem tags, technique tags, entities
+- relevance-to-user fields, recommended sections
+
+`summary.json` must include a schema version.
+
+### SQLite
+
+SQLite stores searchable/indexed state and job-independent status.
+
+It should help with:
+- listing papers, filtering and search, path lookup, tag lookup, status overview
+
+But it should never be treated as the only durable source of paper metadata.
+
+## Key conventions
+
+- `meta.json` contains stable metadata and processing status
+- `summary.json` contains structured AI-generated enrichment
+- `summary.md` is rendered from `summary.json`
+- `paper.md` is generated from the PDF by an external converter such as MinerU
+- the database is rebuildable from the files above

docs/cli.md

@@ -0,0 +1,463 @@
+# CLI Reference
+
+This document describes all available commands in the paperlib CLI.
+
+## Global Options
+
+All commands support these global options:
+
+- `--help`, `-h`: Show help message
+- `--version`: Show version information
+
+Many commands also support:
+- `--library`, `-L`: Specify library root directory (default: current directory)
+- `--json`: Output machine-readable JSON instead of human-readable format
+
+## Commands
+
+### `paperlib init [PATH]`
+
+Initialize a paper library directory structure.
+
+**Arguments:**
+- `PATH`: Directory to initialize (default: current directory)
+
+**Examples:**
+```bash
+# Initialize library in current directory
+paperlib init
+
+# Initialize library in specific directory
+paperlib init /path/to/my/papers
+
+# Initialize and create parent directories
+paperlib init ~/Documents/research/papers
+```
+
+**Behavior:**
+- Creates standard directory structure (config/, papers/, db/, etc.)
+- Safe to run multiple times (idempotent)
+- Creates parent directories if they don't exist
+
+---
+
+### `paperlib import`
+
+Import papers into the library from various sources.
+
+**Required (one of):**
+- `--pdf PATH`: Import a local PDF file
+- `--arxiv ID`: Import paper from arXiv by ID or URL
+
+**Options:**
+- `--title TEXT`: Override paper title (for local PDFs)
+- `--notes TEXT`: Add notes about the paper
+- `--tags TAG1 TAG2`: Add tags to the paper
+- `--library PATH`: Specify library directory
+- `--json`: Output import results in JSON format for automation
+
+**Examples:**
+```bash
+# Import local PDF
+paperlib import --pdf paper.pdf --title "My Research" --tags ml ai
+
+# Import from arXiv
+paperlib import --arxiv 2212.06340
+
+# Import with arXiv URL
+paperlib import --arxiv https://arxiv.org/abs/2212.06340
+
+# Import to specific library
+paperlib import --pdf paper.pdf --library ~/research
+
+# Import with JSON output for automation
+paperlib import --arxiv 2212.06340 --json
+```
+
+**Behavior:**
+- Generates stable paper ID based on content (local) or arXiv ID
+- Copies PDF to structured storage location
+- Creates meta.json with paper metadata
+- Prevents duplicate imports (same content/ID)
+- Indexes paper in search database
+
+---
+
+### `paperlib list`
+
+List all papers in the library with their current status.
+
+**Options:**
+- `--library PATH`: Specify library directory
+- `--json`: Output in JSON format
+
+**Examples:**
+```bash
+# List all papers
+paperlib list
+
+# List papers in specific library
+paperlib list --library ~/research
+
+# Get machine-readable output
+paperlib list --json
+```
+
+**Output Format:**
+```
+Found 3 papers:
+
+📄 arxiv-2212_06340
+   The new discontinuous Galerkin methods based numerical relativity program Nmesh
+   By: Wolfgang Tichy, Liwei Ji, Ananya Adhikari (+2 more)
+   Categories: gr-qc
+
+⏳ local-a1b2c3d4e5f6
+   Machine Learning Applications in Physics
+   Categories: cs.AI, physics.comp-ph
+```
+
+**Status Indicators:**
+- ⏳ Paper imported, conversion pending
+- 📄 PDF converted to Markdown
+- 📝 AI summary generated
+- ❌ Conversion or processing failed
+
+---
+
+### `paperlib show PAPER_ID`
+
+Show detailed information about a specific paper.
+
+**Arguments:**
+- `PAPER_ID`: The unique paper identifier
+
+**Options:**
+- `--library PATH`: Specify library directory
+- `--json`: Output in JSON format
+
+**Examples:**
+```bash
+# Show paper details
+paperlib show arxiv-2212_06340
+
+# Show with JSON output
+paperlib show local-a1b2c3d4 --json
+```
+
+**Output includes:**
+- All metadata fields
+- Processing status
+- File locations and existence
+- Import timestamp
+- Tags and notes
+
+---
+
+### `paperlib convert`
+
+Convert papers from PDF to Markdown using MinerU.
+
+**Options:**
+- `--library PATH`: Specify library directory
+- `--paper-id ID`: Convert specific paper only
+- `--retry-failed`: Retry papers with failed conversion status
+- `--force`: Force reconvert all papers (including successful ones)
+- `--no-ui`: Disable rich UI display (useful for scripting)
+- `--json`: Output conversion results in JSON format (automatically disables UI)
+
+**Examples:**
+```bash
+# Convert all pending papers (with rich UI)
+paperlib convert
+
+# Retry failed conversions
+paperlib convert --retry-failed
+
+# Force reconvert all papers
+paperlib convert --force
+
+# Convert specific paper
+paperlib convert --paper-id arxiv-2212_06340
+
+# Convert without UI (for scripts)
+paperlib convert --no-ui
+
+# Convert in specific library
+paperlib convert --library ~/research
+
+# Get JSON output for automation (disables UI automatically)
+paperlib convert --json
+paperlib convert --paper-id arxiv-2212_06340 --json
+```
+
+**Behavior:**
+- Processes papers with `conversion_status: pending` (or failed with `--retry-failed`)
+- Uses MinerU for PDF to Markdown conversion with CPU pipeline backend
+- Shows rich UI with progress bar and live MinerU output (unless `--no-ui`)
+- Updates metadata with conversion status
+- Creates conversion logs in `logs/` directory
+- Post-processes markdown to fix image references (`images/` → `assets/`)
+- Handles conversion failures gracefully
+
+**Rich UI Features:**
+- Progress bar showing papers converted
+- Live streaming of MinerU output
+- Current paper being processed
+- Color-coded output (errors in red, progress in blue, etc.)
+
+---
+
+### `paperlib reindex`
+
+Rebuild the search index from stored paper metadata.
+
+**Options:**
+- `--library PATH`: Specify library directory
+- `--json`: Output reindexing results and statistics in JSON format
+
+**Examples:**
+```bash
+# Rebuild index
+paperlib reindex
+
+# Rebuild index for specific library
+paperlib reindex --library ~/research
+
+# Get JSON output with statistics
+paperlib reindex --json
+```
+
+**Behavior:**
+- Clears existing SQLite database
+- Scans all meta.json files in papers/ directory
+- Rebuilds full-text search index
+- Reports statistics on completion
+- Safe to run anytime (repairs corrupted index)
+
+---
+
+### `paperlib status`
+
+Show library configuration and layout information.
+
+**Options:**
+- `--library PATH`: Specify library directory
+- `--json`: Output in JSON format
+
+**Examples:**
+```bash
+# Show current library status
+paperlib status
+
+# Show specific library status
+paperlib status --library ~/research
+
+# Get JSON output for automation
+paperlib status --json
+```
+
+**Output:**
+```
+root: /home/user/papers
+config: /home/user/papers/config/config.toml
+database: /home/user/papers/db/paperlib.sqlite3
+papers: /home/user/papers/papers
+inbox: /home/user/papers/inbox
+cache: /home/user/papers/cache
+```
+
+---
+
+## Future Commands
+
+These commands are planned but not yet implemented:
+
+### `paperlib search QUERY`
+Search papers by content and metadata.
+
+### `paperlib summarize [PAPER_ID]`
+Generate AI summaries for papers.
+
+### `paperlib export FORMAT`
+Export papers in various formats.
+
+### `paperlib doctor`
+Diagnose and repair library issues.
+
+---
+
+## Exit Codes
+
+paperlib commands return standard exit codes:
+
+- `0`: Success
+- `1`: General error (file not found, invalid arguments, etc.)
+- `2`: Command line argument error
+
+## Configuration
+
+paperlib looks for configuration in these locations (in order):
+1. `$LIBRARY_ROOT/config/config.toml`
+2. `~/.config/paperlib/config.toml`
+3. Built-in defaults
+
+## JSON Output Format
+
+When using `--json`, commands output structured data suitable for programmatic consumption. All JSON responses follow a consistent envelope format with standard fields:
+
+### Standard Response Envelope
+
+**Success Response:**
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  // Command-specific data fields below
+}
+```
+
+**Error Response:**
+```json
+{
+  "success": false,
+  "timestamp": "2024-01-15T10:30:00.000Z", 
+  "error": "Error message here",
+  "error_code": 1
+}
+```
+
+### Command-Specific JSON Formats
+
+#### `paperlib status --json`
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "library_root": "/home/user/papers",
+  "config_path": "/home/user/papers/config/config.toml",
+  "database_path": "/home/user/papers/db/paperlib.sqlite3",
+  "papers_dir": "/home/user/papers/papers",
+  "inbox_dir": "/home/user/papers/inbox",
+  "cache_dir": "/home/user/papers/cache"
+}
+```
+
+#### `paperlib list --json`
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "papers": [
+    {
+      "paper_id": "arxiv-2212_06340",
+      "source_type": "arxiv",
+      "source_id": "2212.06340", 
+      "title": "Example Paper",
+      "authors": ["Alice Smith", "Bob Jones"],
+      "published_date": "2022-12-06T00:00:00.000Z",
+      "categories": ["cs.AI"],
+      "conversion_status": "success",
+      "summary_status": "pending",
+      "imported_at": "2024-01-15T10:30:00.000Z",
+      "tags": [],
+      "notes": ""
+    }
+  ],
+  "total": 1
+}
+```
+
+#### `paperlib show <paper_id> --json`
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "paper": {
+    "paper_id": "arxiv-2212_06340",
+    "source_type": "arxiv",
+    "source_id": "2212.06340",
+    "title": "Example Paper",
+    "authors": ["Alice Smith", "Bob Jones"],
+    "conversion_status": "success",
+    "summary_status": "pending",
+    "pdf_path": "papers/arxiv/2022/arxiv-2212_06340.pdf",
+    "paper_md_path": "papers/arxiv/2022/arxiv-2212_06340.md",
+    "files_status": {
+      "pdf_exists": true,
+      "markdown_exists": true,
+      "summary_exists": false
+    }
+  }
+}
+```
+
+#### `paperlib import --json`
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "paper_id": "arxiv-2212_06340",
+  "title": "Example Paper Title",
+  "source_type": "arxiv",
+  "source_id": "2212.06340",
+  "authors": ["Alice Smith", "Bob Jones"],
+  "message": "Successfully imported arXiv paper",
+  "paper": {
+    // Full paper metadata object
+  }
+}
+```
+
+#### `paperlib convert --json`
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "action": "convert_pending",
+  "success_count": 5,
+  "failure_count": 1,
+  "total_attempted": 6
+}
+```
+
+For single paper conversion (`--paper-id`):
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "paper_id": "arxiv-2212_06340",
+  "conversion_success": true,
+  "conversion_status": "success",
+  "message": "Successfully converted paper"
+}
+```
+
+#### `paperlib reindex --json`
+```json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "reindex_complete": true,
+  "papers_indexed": 42,
+  "errors": 1,
+  "statistics": {
+    "total_papers": 42,
+    "by_source_type": {
+      "arxiv": 38,
+      "local": 4
+    }
+  }
+}
+```
+
+### JSON Data Types
+
+- **Timestamps**: Always in ISO 8601 format (`YYYY-MM-DDTHH:mm:ss.sssZ`)
+- **Paper IDs**: String identifiers (e.g., `"arxiv-2212_06340"`, `"local-a1b2c3d4"`)
+- **Status Fields**: String enums (`"pending"`, `"success"`, `"failed"`)
+- **Authors**: Array of strings
+- **Categories/Tags**: Array of strings
+- **File Paths**: Relative to library root
+
+This JSON format is stable across paperlib versions for reliable automation and scripting.

docs/integration-guide.md

@@ -0,0 +1,686 @@
+# Integration Guide
+
+This document describes how to integrate paperlib with higher-level tools and automation workflows.
+
+## Overview
+
+paperlib is designed as a **library engine** that higher-level tools can build upon. It provides:
+
+- **Stable CLI interface** with machine-readable JSON output
+- **File-based storage** that external tools can read directly
+- **Python API** for programmatic access
+- **Event hooks** for workflow integration (future)
+
+## CLI Integration
+
+### Machine-Readable Output
+
+Most paperlib commands support `--json` output for automation:
+
+```bash
+# Get library configuration
+paperlib status --json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "library_root": "/home/user/papers",
+  "config_path": "/home/user/papers/config/config.toml",
+  "database_path": "/home/user/papers/db/paperlib.sqlite3",
+  "papers_dir": "/home/user/papers/papers",
+  "inbox_dir": "/home/user/papers/inbox",
+  "cache_dir": "/home/user/papers/cache"
+}
+
+# List papers with metadata
+paperlib list --json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "papers": [
+    {
+      "paper_id": "arxiv-2212_06340",
+      "source_type": "arxiv",
+      "source_id": "2212.06340",
+      "title": "Example Paper",
+      "authors": ["Alice Smith", "Bob Jones"],
+      "published_date": "2022-12-06T00:00:00.000Z",
+      "categories": ["cs.AI"],
+      "conversion_status": "success",
+      "summary_status": "pending",
+      "imported_at": "2024-01-15T10:30:00.000Z",
+      "tags": [],
+      "notes": ""
+    }
+  ],
+  "total": 1
+}
+
+# Import with JSON response
+paperlib import --arxiv 2212.06340 --json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "paper_id": "arxiv-2212_06340",
+  "title": "Example Paper Title",
+  "source_type": "arxiv", 
+  "source_id": "2212.06340",
+  "authors": ["Alice Smith", "Bob Jones"],
+  "message": "Successfully imported arXiv paper",
+    "paper": {
+      // Full paper metadata object
+    }
+  }
+}
+
+# Convert papers with JSON output
+paperlib convert --json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "action": "convert_pending",
+  "success_count": 5,
+  "failure_count": 1,
+  "total_attempted": 6
+}
+
+# Reindex with JSON output
+paperlib reindex --json
+{
+  "success": true,
+  "timestamp": "2024-01-15T10:30:00.000Z",
+  "reindex_complete": true,
+  "papers_indexed": 42,
+  "errors": 1,
+  "statistics": {
+    "total_papers": 42,
+    "by_source_type": {
+      "arxiv": 38,
+      "local": 4
+    }
+  }
+}
+}
+```
+
+### Exit Codes
+
+paperlib commands follow standard Unix exit code conventions:
+
+```bash
+paperlib import --arxiv 2212.06340
+echo $?  # 0 for success, 1 for error
+
+# Check if paper exists before processing
+if paperlib show "$paper_id" --json >/dev/null 2>&1; then
+    echo "Paper exists"
+else
+    echo "Paper not found"
+fi
+```
+
+### Scripting Examples
+
+#### Daily arXiv Import
+
+```bash
+#!/bin/bash
+# daily-arxiv.sh - Import papers from daily arXiv feed
+
+LIBRARY="$HOME/research"
+ARXIV_FEED_URL="http://export.arxiv.org/rss/cs.AI"
+
+# Parse RSS feed and extract arXiv IDs
+curl -s "$ARXIV_FEED_URL" | \
+grep -oP 'arxiv\.org/abs/\K[0-9]{4}\.[0-9]{4,5}' | \
+while read arxiv_id; do
+    echo "Importing $arxiv_id..."
+    paperlib import --arxiv "$arxiv_id" --library "$LIBRARY" --json
+done
+
+# Convert newly imported papers with JSON output
+paperlib convert --library "$LIBRARY" --json
+
+# Generate daily report
+paperlib list --library "$LIBRARY" --json | \
+jq '.papers | map(select(.imported_at | startswith(now | strftime("%Y-%m-%d"))))'
+```
+
+#### Batch Processing
+
+```bash
+#!/bin/bash
+# batch-process.sh - Process multiple papers from a list
+
+LIBRARY="$HOME/research"
+PAPER_LIST="papers.txt"
+
+while IFS= read -r pdf_path; do
+    if [[ -f "$pdf_path" ]]; then
+        echo "Importing $pdf_path..."
+        result=$(paperlib import --pdf "$pdf_path" --library "$LIBRARY" --json)
+        
+        if [[ $? -eq 0 ]]; then
+            paper_id=$(echo "$result" | jq -r '.paper_id')
+            echo "Successfully imported as $paper_id"
+        else
+            echo "Failed to import $pdf_path"
+        fi
+    fi
+done < "$PAPER_LIST"
+
+# Convert all pending papers with JSON output
+paperlib convert --library "$LIBRARY" --json
+```
+
+## Python API
+
+### Direct Library Access
+
+```python
+from paperlib.config import LibraryPaths
+from paperlib.storage import PaperStorageManager
+from paperlib.index import DatabaseManager
+from paperlib.importer import ArxivImporter, LocalImporter
+
+# Initialize library components
+library_paths = LibraryPaths.from_root("/path/to/library")
+storage = PaperStorageManager(library_paths)
+database = DatabaseManager(library_paths)
+database.initialize_database()
+
+# Import paper programmatically
+arxiv_importer = ArxivImporter(storage)
+metadata = arxiv_importer.import_arxiv_paper("2212.06340")
+database.index_paper(metadata)
+
+# Search and retrieve
+results = list(database.search_papers("neural networks"))
+for result in results:
+    paper = storage.load_paper_metadata(result["paper_id"], result["source_type"])
+    print(f"{paper.title} by {', '.join(paper.authors)}")
+
+# Get statistics
+stats = database.get_statistics()
+print(f"Total papers: {stats['total_papers']}")
+```
+
+### Metadata Processing
+
+```python
+import json
+from pathlib import Path
+from paperlib.models import PaperMetadata, PaperSummary
+
+# Process all papers in library
+papers_dir = Path("/home/user/papers/papers")
+
+for meta_file in papers_dir.rglob("meta.json"):
+    # Load metadata
+    metadata = PaperMetadata.load_from_file(meta_file)
+    
+    # Check for summary
+    summary_path = meta_file.parent / "summary.json"
+    if summary_path.exists():
+        summary = PaperSummary.load_from_file(summary_path)
+        
+        # Extract key information
+        tags = summary.problem_tags + summary.technique_tags
+        entities = summary.entities
+        
+        print(f"Paper: {metadata.title}")
+        print(f"Tags: {', '.join(tags)}")
+        print(f"Entities: {', '.join(entities)}")
+```
+
+## File System Integration
+
+### Direct File Access
+
+Since paperlib uses a documented file layout, tools can read data directly:
+
+```python
+import json
+from pathlib import Path
+
+def scan_library(library_root: Path):
+    """Scan library and extract metadata."""
+    papers = []
+    
+    for meta_file in library_root.glob("papers/**/meta.json"):
+        with meta_file.open() as f:
+            metadata = json.load(f)
+        papers.append(metadata)
+    
+    return papers
+
+def find_papers_by_category(library_root: Path, category: str):
+    """Find papers in a specific category."""
+    matching_papers = []
+    
+    for meta_file in library_root.glob("papers/**/meta.json"):
+        with meta_file.open() as f:
+            metadata = json.load(f)
+        
+        if category in metadata.get("categories", []):
+            matching_papers.append(metadata)
+    
+    return matching_papers
+```
+
+### Watch for Changes
+
+```python
+import time
+from pathlib import Path
+from watchdog.observers import Observer
+from watchdog.events import FileSystemEventHandler
+
+class PaperLibraryHandler(FileSystemEventHandler):
+    def __init__(self, library_root):
+        self.library_root = Path(library_root)
+    
+    def on_created(self, event):
+        if event.src_path.endswith("meta.json"):
+            print(f"New paper imported: {event.src_path}")
+            # Trigger processing workflow
+            self.process_new_paper(event.src_path)
+    
+    def on_modified(self, event):
+        if event.src_path.endswith("summary.json"):
+            print(f"Summary updated: {event.src_path}")
+            # Update downstream systems
+    
+    def process_new_paper(self, meta_path):
+        """Handle newly imported paper."""
+        # Load metadata
+        with open(meta_path) as f:
+            metadata = json.load(f)
+        
+        # Trigger downstream processing
+        # - Send to processing queue
+        # - Update knowledge base
+        # - Generate notifications
+
+# Watch library for changes
+observer = Observer()
+handler = PaperLibraryHandler("/home/user/papers")
+observer.schedule(handler, "/home/user/papers/papers", recursive=True)
+observer.start()
+```
+
+## Higher-Level Tool Examples
+
+### Research Dashboard
+
+```python
+"""research_dashboard.py - Web dashboard for research library"""
+
+from flask import Flask, jsonify, render_template
+from paperlib.config import LibraryPaths
+from paperlib.storage import PaperStorageManager
+from paperlib.index import DatabaseManager
+
+app = Flask(__name__)
+
+# Initialize paperlib components
+library_paths = LibraryPaths.from_root("/home/user/research")
+storage = PaperStorageManager(library_paths)
+database = DatabaseManager(library_paths)
+
+@app.route('/api/papers')
+def list_papers():
+    """List all papers with metadata."""
+    papers = list(database.list_papers(limit=50))
+    return jsonify(papers)
+
+@app.route('/api/search/<query>')
+def search_papers(query):
+    """Search papers by query."""
+    results = list(database.search_papers(query, limit=20))
+    return jsonify(results)
+
+@app.route('/api/stats')
+def library_stats():
+    """Get library statistics."""
+    stats = database.get_statistics()
+    return jsonify(stats)
+
+@app.route('/')
+def dashboard():
+    """Main dashboard page."""
+    return render_template('dashboard.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)
+```
+
+### Daily Digest Generator
+
+```python
+"""daily_digest.py - Generate daily research digest"""
+
+import json
+from datetime import datetime, timedelta
+from pathlib import Path
+from paperlib.config import LibraryPaths
+from paperlib.index import DatabaseManager
+
+def generate_daily_digest(library_root: str, output_file: str):
+    """Generate digest of recently imported papers."""
+    
+    # Initialize database
+    library_paths = LibraryPaths.from_root(library_root)
+    database = DatabaseManager(library_paths)
+    
+    # Get papers from last 24 hours
+    yesterday = datetime.now() - timedelta(days=1)
+    yesterday_iso = yesterday.isoformat()
+    
+    recent_papers = []
+    for paper in database.list_papers():
+        if paper["imported_at"] >= yesterday_iso:
+            recent_papers.append(paper)
+    
+    if not recent_papers:
+        print("No new papers imported yesterday.")
+        return
+    
+    # Group by category
+    by_category = {}
+    for paper in recent_papers:
+        categories = json.loads(paper["categories_json"])
+        for category in categories:
+            if category not in by_category:
+                by_category[category] = []
+            by_category[category].append(paper)
+    
+    # Generate HTML digest
+    html_content = f"""
+    <html>
+    <head><title>Daily Research Digest - {datetime.now().strftime('%Y-%m-%d')}</title></head>
+    <body>
+    <h1>Daily Research Digest</h1>
+    <p>Found {len(recent_papers)} new papers</p>
+    """
+    
+    for category, papers in by_category.items():
+        html_content += f"<h2>{category}</h2><ul>"
+        for paper in papers:
+            title = paper["title"]
+            paper_id = paper["paper_id"]
+            html_content += f'<li><strong>{title}</strong> ({paper_id})</li>'
+        html_content += "</ul>"
+    
+    html_content += "</body></html>"
+    
+    # Write output
+    Path(output_file).write_text(html_content)
+    print(f"Digest written to {output_file}")
+
+if __name__ == "__main__":
+    generate_daily_digest("/home/user/research", "digest.html")
+```
+
+### Literature Review Assistant
+
+```python
+"""review_assistant.py - AI-powered literature review helper"""
+
+from paperlib.config import LibraryPaths
+from paperlib.index import DatabaseManager
+from paperlib.models import PaperSummary
+
+class ReviewAssistant:
+    def __init__(self, library_root: str):
+        self.library_paths = LibraryPaths.from_root(library_root)
+        self.database = DatabaseManager(self.library_paths)
+    
+    def find_related_papers(self, paper_id: str, max_results: int = 10):
+        """Find papers related to the given paper."""
+        
+        # Get source paper metadata
+        source_paper = self.database.get_paper(paper_id)
+        if not source_paper:
+            return []
+        
+        # Extract search terms from title and categories
+        title_words = source_paper["title"].lower().split()
+        categories = json.loads(source_paper["categories_json"])
+        
+        # Search for papers with similar keywords
+        search_terms = title_words + categories
+        related_papers = []
+        
+        for term in search_terms:
+            results = list(self.database.search_papers(term, limit=5))
+            for result in results:
+                if result["paper_id"] != paper_id:
+                    related_papers.append(result)
+        
+        # Remove duplicates and return top results
+        seen_ids = set()
+        unique_papers = []
+        for paper in related_papers:
+            if paper["paper_id"] not in seen_ids:
+                seen_ids.add(paper["paper_id"])
+                unique_papers.append(paper)
+                if len(unique_papers) >= max_results:
+                    break
+        
+        return unique_papers
+    
+    def generate_topic_overview(self, topic: str):
+        """Generate overview of papers on a specific topic."""
+        
+        # Search for papers on topic
+        papers = list(self.database.search_papers(topic, limit=50))
+        
+        if not papers:
+            return f"No papers found for topic: {topic}"
+        
+        # Analyze summaries if available
+        key_entities = set()
+        techniques = set()
+        
+        for paper in papers:
+            summary_path = Path(paper["summary_json_path"])
+            if summary_path.exists():
+                summary = PaperSummary.load_from_file(summary_path)
+                key_entities.update(summary.entities)
+                techniques.update(summary.technique_tags)
+        
+        # Generate overview
+        overview = f"""
+        Topic: {topic}
+        
+        Papers found: {len(papers)}
+        
+        Key entities mentioned:
+        {', '.join(sorted(key_entities)[:10])}
+        
+        Common techniques:
+        {', '.join(sorted(techniques)[:10])}
+        
+        Recent papers:
+        """
+        
+        # Add recent papers
+        recent_papers = sorted(papers, key=lambda x: x["imported_at"], reverse=True)[:5]
+        for paper in recent_papers:
+            overview += f"\n- {paper['title']} ({paper['paper_id']})"
+        
+        return overview
+
+# Usage
+assistant = ReviewAssistant("/home/user/research")
+overview = assistant.generate_topic_overview("transformer architecture")
+print(overview)
+```
+
+## Integration Patterns
+
+### Pipeline Processing
+
+```bash
+# Multi-stage processing pipeline
+paperlib import --arxiv 2212.06340 --json > import_result.json
+paper_id=$(jq -r '.paper_id' import_result.json)
+
+# Convert to markdown
+paperlib convert --paper-id "$paper_id"
+
+# Generate summary (when available)
+# paperlib summarize --paper-id "$paper_id"
+
+# Update downstream systems
+curl -X POST "http://research-db/api/papers" \
+     -H "Content-Type: application/json" \
+     -d @import_result.json
+```
+
+### Event-Driven Architecture
+
+```python
+"""event_handler.py - Process paperlib events"""
+
+import json
+from pathlib import Path
+import pika  # RabbitMQ client
+
+class PaperLibraryEventHandler:
+    def __init__(self, rabbitmq_url: str):
+        self.connection = pika.BlockingConnection(pika.URLParameters(rabbitmq_url))
+        self.channel = self.connection.channel()
+    
+    def on_paper_imported(self, paper_metadata: dict):
+        """Handle new paper import."""
+        message = {
+            "event": "paper_imported",
+            "paper_id": paper_metadata["paper_id"],
+            "title": paper_metadata["title"],
+            "categories": paper_metadata["categories"],
+            "timestamp": paper_metadata["imported_at"]
+        }
+        
+        # Send to processing queue
+        self.channel.basic_publish(
+            exchange='',
+            routing_key='paper_processing',
+            body=json.dumps(message)
+        )
+    
+    def on_summary_generated(self, paper_id: str, summary_path: Path):
+        """Handle summary generation."""
+        with summary_path.open() as f:
+            summary = json.load(f)
+        
+        message = {
+            "event": "summary_generated",
+            "paper_id": paper_id,
+            "tags": summary["problem_tags"] + summary["technique_tags"],
+            "entities": summary["entities"]
+        }
+        
+        # Send to indexing service
+        self.channel.basic_publish(
+            exchange='',
+            routing_key='summary_indexing',
+            body=json.dumps(message)
+        )
+```
+
+## Best Practices
+
+### Error Handling
+
+```python
+import subprocess
+import json
+
+def safe_paperlib_command(command: list[str]) -> dict:
+    """Execute paperlib command with proper error handling."""
+    try:
+        result = subprocess.run(
+            ["paperlib"] + command + ["--json"],
+            capture_output=True,
+            text=True,
+            check=True
+        )
+        return json.loads(result.stdout)
+    
+    except subprocess.CalledProcessError as e:
+        return {
+            "success": False,
+            "error": e.stderr,
+            "exit_code": e.returncode
+        }
+    
+    except json.JSONDecodeError as e:
+        return {
+            "success": False,
+            "error": f"Invalid JSON response: {e}",
+            "raw_output": result.stdout
+        }
+
+# Usage
+result = safe_paperlib_command(["import", "--arxiv", "2212.06340"])
+if result.get("success", True):  # Assume success if no "success" field
+    print(f"Imported paper: {result['paper_id']}")
+else:
+    print(f"Import failed: {result['error']}")
+```
+
+### Performance Optimization
+
+```python
+# Batch operations for better performance
+from paperlib.index import DatabaseManager
+
+def batch_index_papers(library_root: str, paper_ids: list[str]):
+    """Index multiple papers efficiently."""
+    database = DatabaseManager(LibraryPaths.from_root(library_root))
+    storage = PaperStorageManager(LibraryPaths.from_root(library_root))
+    
+    # Begin transaction for batch insert
+    with database._get_connection() as conn:
+        for paper_id in paper_ids:
+            metadata = storage.load_paper_metadata(paper_id, source_type)
+            if metadata:
+                database.index_paper(metadata)
+        # Automatic commit on context exit
+```
+
+### Configuration Management
+
+```python
+# config_manager.py - Centralized configuration
+import os
+from pathlib import Path
+
+class ConfigManager:
+    def __init__(self):
+        self.library_root = os.getenv("PAPERLIB_ROOT", Path.home() / "research")
+        self.api_keys = {
+            "openai": os.getenv("OPENAI_API_KEY"),
+            "anthropic": os.getenv("ANTHROPIC_API_KEY")
+        }
+    
+    def get_library_path(self, name: str = "default") -> str:
+        """Get library path by name."""
+        if name == "default":
+            return str(self.library_root)
+        return str(Path.home() / f"research-{name}")
+    
+    def paperlib_command_base(self, library_name: str = "default") -> list[str]:
+        """Get base command for paperlib with library."""
+        return ["paperlib", "--library", self.get_library_path(library_name)]
+
+config = ConfigManager()
+
+# Usage in scripts
+import subprocess
+cmd = config.paperlib_command_base("arxiv") + ["list", "--json"]
+result = subprocess.run(cmd, capture_output=True, text=True)
+```
+
+This integration guide provides the foundation for building sophisticated research workflows on top of paperlib's stable, local-first architecture.

docs/storage-layout.md

@@ -0,0 +1,264 @@
+# Storage Layout
+
+This document describes the on-disk structure and organization of a paperlib library.
+
+## Overview
+
+A paperlib library is a directory containing all papers, metadata, configuration, and index data. The layout is designed to be:
+
+- **Human-readable**: Directory structure is intuitive and browsable
+- **Stable**: File locations don't change unexpectedly
+- **Rebuildable**: Index can be reconstructed from source files
+- **Portable**: Entire library can be moved or backed up as a unit
+
+## Directory Structure
+
+```
+library_root/
+├── config/                    # Library configuration
+│   ├── config.toml           # Main configuration file
+│   ├── vocab.yaml            # Controlled vocabulary (future)
+│   └── prompts/              # AI prompt templates (future)
+│       └── summarize_paper.md
+├── papers/                   # Paper storage (source of truth)
+│   ├── arxiv/               # arXiv papers organized by year
+│   │   └── 2026/
+│   │       └── arxiv-2212_06340/
+│   │           ├── meta.json         # Paper metadata
+│   │           ├── source.pdf        # Original PDF
+│   │           ├── paper.md          # Converted markdown
+│   │           ├── summary.json      # AI-generated summary
+│   │           ├── summary.md        # Rendered summary
+│   │           ├── ref.bib          # Bibliography (future)
+│   │           ├── assets/          # Images, figures
+│   │           └── logs/            # Processing logs
+│   │               └── mineru.log
+│   └── local/               # Local PDF imports by hash
+│       └── a1b2c3d4e5f6/
+│           └── ... (same structure)
+├── inbox/                   # Temporary import staging (future)
+├── db/                      # Search index (rebuildable)
+│   └── paperlib.sqlite3
+└── cache/                   # Processing cache (safe to delete)
+```
+
+## Paper Directory Organization
+
+### arXiv Papers
+
+arXiv papers are organized by year and paper ID:
+
+```
+papers/arxiv/YEAR/arxiv-NORMALIZED_ID/
+```
+
+Where:
+- `YEAR` is extracted from the arXiv ID (e.g., `2212.06340` → `2022`, `0001.12345` → `2000`)
+- `NORMALIZED_ID` replaces dots and version numbers with underscores
+  - `2212.06340` → `arxiv-2212_06340`
+  - `2212.06340v2` → `arxiv-2212_06340v2`
+
+The year extraction follows arXiv's YYMM.NNNNN format:
+- Years 00-89 map to 2000-2089
+- Years 90-99 map to 1990-1999
+
+**Examples:**
+```
+papers/arxiv/2022/arxiv-2212_06340/    # 2212.06340 -> year 2022
+papers/arxiv/2023/arxiv-2301_12345v1/  # 2301.12345v1 -> year 2023  
+papers/arxiv/2000/arxiv-0001_98765/    # 0001.98765 -> year 2000
+papers/arxiv/1999/arxiv-9912_12345/    # 9912.12345 -> year 1999
+```
+
+### Local Papers
+
+Local papers are organized by content hash:
+
+```
+papers/local/HASH_PREFIX/
+```
+
+Where `HASH_PREFIX` is the first 16 characters of the SHA256 hash of the PDF file.
+
+**Examples:**
+```
+papers/local/a1b2c3d4e5f67890/
+papers/local/fedcba9876543210/
+```
+
+## File Types
+
+### Required Files
+
+Every paper directory contains:
+
+#### `meta.json`
+The canonical metadata file (JSON format):
+```json
+{
+  "paper_id": "arxiv-2212_06340",
+  "source_type": "arxiv",
+  "source_id": "2212.06340",
+  "title": "Example Paper Title",
+  "authors": ["Alice Smith", "Bob Jones"],
+  "published_date": "2022-12-13T02:46:55",
+  "categories": ["cs.AI", "stat.ML"],
+  "pdf_path": "papers/arxiv/2022/arxiv-2212_06340/source.pdf",
+  "paper_md_path": "papers/arxiv/2022/arxiv-2212_06340/paper.md",
+  "imported_at": "2024-01-15T10:30:00",
+  "conversion_status": "success",
+  "summary_status": "not_requested",
+  "tags": ["machine-learning"],
+  "notes": "Important paper on neural networks"
+}
+```
+
+#### `source.pdf`
+The original PDF file, exactly as imported.
+
+### Generated Files
+
+These files are created by paperlib processing:
+
+#### `paper.md`
+Markdown conversion of the PDF, generated by MinerU or other converters.
+
+#### `summary.json` (optional)
+AI-generated structured summary:
+```json
+{
+  "schema_version": "1.0",
+  "one_sentence_summary": "This paper introduces...",
+  "problem_statement": "Current methods have limitations...",
+  "method_overview": "We propose a novel approach...",
+  "main_results": "Experiments show 95% accuracy...",
+  "claimed_contributions": ["Novel architecture", "Improved performance"],
+  "problem_tags": ["classification", "optimization"],
+  "technique_tags": ["neural-networks", "transformers"],
+  "entities": ["BERT", "ImageNet", "ResNet"],
+  "relevance_to_user": 0.85
+}
+```
+
+#### `summary.md` (optional)
+Human-readable summary rendered from `summary.json`.
+
+### Supporting Directories
+
+#### `assets/`
+Contains extracted images, figures, and other media from the PDF conversion process.
+
+#### `logs/`
+Processing logs for debugging and audit trails:
+- `mineru.log` - PDF conversion logs
+- `summary.log` - AI summarization logs (future)
+
+## Index Database
+
+The SQLite database at `db/paperlib.sqlite3` contains:
+
+### Tables
+
+#### `papers`
+Main paper index with searchable fields:
+- Metadata from all `meta.json` files
+- Computed search fields (full-text, author lists, etc.)
+- Processing status tracking
+
+#### `papers_fts`
+Full-text search virtual table (SQLite FTS5) for content search.
+
+### Rebuilding
+
+The database is **always rebuildable** from the source files:
+```bash
+paperlib reindex
+```
+
+This design ensures the JSON files remain the authoritative source of truth.
+
+## Path Conventions
+
+### Relative Paths
+All paths in `meta.json` are relative to the library root:
+```json
+{
+  "pdf_path": "papers/local/a1b2c3d4e5f6/source.pdf",
+  "paper_md_path": "papers/local/a1b2c3d4e5f6/paper.md"
+}
+```
+
+### Cross-Platform Compatibility
+All paths use forward slashes (`/`) regardless of operating system.
+
+## Backup and Portability
+
+### What to Backup
+For complete library backup, include:
+- `config/` directory (configuration)
+- `papers/` directory (source of truth)
+
+### What NOT to Backup
+These can be regenerated:
+- `db/` directory (rebuildable index)
+- `cache/` directory (temporary files)
+
+### Moving Libraries
+To move a library:
+1. Copy the entire directory structure
+2. Run `paperlib reindex` to rebuild the database
+3. Update any absolute paths in configuration
+
+## Storage Efficiency
+
+### Deduplication
+Papers are naturally deduplicated:
+- arXiv papers by normalized arXiv ID
+- Local papers by SHA256 content hash
+
+### Large Files
+For papers with large asset directories:
+- Assets are stored alongside papers for locality
+- Consider using file system compression or deduplication if needed
+
+## File System Requirements
+
+### Permissions
+paperlib requires:
+- Read/write access to library directory
+- Ability to create subdirectories
+- Atomic file operations for metadata updates
+
+### File System Features
+Recommended:
+- Case-sensitive file system (avoids conflicts)
+- Support for Unicode filenames
+- Journaling (protects against corruption)
+
+### Disk Space
+Typical storage requirements:
+- PDF files: 1-10 MB each
+- Markdown conversions: 10-100 KB each
+- Metadata: ~1-5 KB per paper
+- Database index: ~1-10 KB per paper
+- Assets: Varies (0-50 MB for image-heavy papers)
+
+## Migration and Versioning
+
+### Schema Evolution
+When paperlib updates its storage format:
+- Metadata schema versions are tracked in each file
+- Migration tools handle format upgrades
+- Backward compatibility is maintained when possible
+
+### Validation
+paperlib provides tools to validate library integrity:
+```bash
+paperlib doctor  # (future command)
+```
+
+This will check:
+- All referenced files exist
+- Metadata format is valid
+- Database consistency with files
+- No orphaned or corrupted data

docs/summary-schema.md

@@ -0,0 +1,289 @@
+# Summary Schema
+
+This document defines the structure and semantics of the `summary.json` files that contain AI-generated paper summaries.
+
+## Overview
+
+The `summary.json` file contains structured, AI-generated analysis of a paper. It is designed to:
+
+- Provide consistent, machine-readable summaries
+- Support research triage and discovery workflows
+- Enable automated categorization and search
+- Remain stable across different AI providers
+- Use controlled vocabulary when available
+
+## Schema Version 1.0
+
+### File Structure
+
+```json
+{
+  "schema_version": "1.0",
+  "one_sentence_summary": "This paper introduces a novel neural architecture for...",
+  "problem_statement": "Current approaches to X suffer from limitations...",
+  "method_overview": "The authors propose a hybrid approach combining...",
+  "main_results": "Experiments show 15% improvement over baselines...",
+  "claimed_contributions": [
+    "Novel attention mechanism design",
+    "State-of-the-art results on ImageNet",
+    "Theoretical analysis of convergence properties"
+  ],
+  "assumptions": [
+    "Data is independently distributed",
+    "Computational budget allows for large models"
+  ],
+  "limitations": [
+    "Only evaluated on English text",
+    "Requires significant computational resources",
+    "Limited theoretical justification for design choices"
+  ],
+  "problem_tags": ["classification", "computer-vision", "optimization"],
+  "technique_tags": ["neural-networks", "attention", "transformers"],
+  "entities": ["ImageNet", "BERT", "ResNet", "CIFAR-10"],
+  "relevance_to_user": 0.75,
+  "recommended_sections": ["Section 3.2", "Algorithm 1", "Table 2"]
+}
+```
+
+## Field Definitions
+
+### Required Fields
+
+#### `schema_version` (string)
+- **Purpose**: Track format version for migration
+- **Format**: Semantic version string (e.g., "1.0")
+- **Required**: Yes
+
+#### `one_sentence_summary` (string)
+- **Purpose**: Concise paper overview for quick scanning
+- **Guidelines**: 
+  - One complete sentence, under 200 characters
+  - Focus on the main contribution or finding
+  - Avoid technical jargon when possible
+- **Example**: "This paper introduces a new attention mechanism that improves transformer efficiency by 40% while maintaining accuracy."
+
+### Core Content Fields
+
+#### `problem_statement` (string)
+- **Purpose**: What problem does this paper address?
+- **Guidelines**:
+  - 2-3 sentences maximum
+  - Focus on the gap or limitation being addressed
+  - Explain why this problem matters
+
+#### `method_overview` (string)
+- **Purpose**: High-level description of the approach
+- **Guidelines**:
+  - 3-4 sentences maximum
+  - Focus on the key innovation or insight
+  - Avoid detailed algorithmic descriptions
+
+#### `main_results` (string)
+- **Purpose**: Key empirical findings or theoretical results
+- **Guidelines**:
+  - Quantitative results when available
+  - Highlight significance of improvements
+  - Note any surprising or counterintuitive findings
+
+### Structured Lists
+
+#### `claimed_contributions` (array of strings)
+- **Purpose**: Authors' stated contributions
+- **Guidelines**:
+  - Extract from paper's contribution list
+  - Preserve authors' framing and claims
+  - 3-6 items typically
+
+#### `assumptions` (array of strings)
+- **Purpose**: Key assumptions underlying the work
+- **Guidelines**:
+  - Mathematical, methodological, or data assumptions
+  - Critical for understanding applicability
+  - Often unstated but important
+
+#### `limitations` (array of strings)
+- **Purpose**: Acknowledged or apparent limitations
+- **Guidelines**:
+  - From authors' discussion or limitations section
+  - Obvious limitations not acknowledged by authors
+  - Important for understanding scope
+
+### Categorization
+
+#### `problem_tags` (array of strings)
+- **Purpose**: Categorize the problem domain
+- **Controlled vocabulary** (preferred values):
+  - `classification`, `regression`, `clustering`
+  - `optimization`, `search`, `planning`
+  - `generation`, `translation`, `summarization`
+  - `detection`, `segmentation`, `tracking`
+  - `compression`, `encoding`, `decoding`
+  - `privacy`, `security`, `robustness`
+  - `interpretability`, `fairness`, `ethics`
+  - `efficiency`, `scalability`, `deployment`
+
+#### `technique_tags` (array of strings)  
+- **Purpose**: Categorize the technical approaches
+- **Controlled vocabulary** (preferred values):
+  - `neural-networks`, `deep-learning`, `transformers`
+  - `cnn`, `rnn`, `lstm`, `gru`, `attention`
+  - `reinforcement-learning`, `supervised-learning`, `unsupervised-learning`
+  - `bayesian`, `probabilistic`, `statistical`
+  - `graph-neural-networks`, `graph-algorithms`
+  - `computer-vision`, `natural-language-processing`
+  - `federated-learning`, `transfer-learning`, `meta-learning`
+  - `adversarial`, `generative-models`, `vae`, `gan`
+
+### Entities and References
+
+#### `entities` (array of strings)
+- **Purpose**: Important datasets, models, algorithms, or systems mentioned
+- **Guidelines**:
+  - Proper names: "ImageNet", "BERT", "ResNet"
+  - Algorithms: "SGD", "Adam", "RANSAC"  
+  - Benchmarks: "GLUE", "COCO", "WMT"
+  - Avoid generic terms like "neural network"
+
+### User Relevance
+
+#### `relevance_to_user` (number, optional)
+- **Purpose**: Estimated relevance score for the user
+- **Format**: Float between 0.0 and 1.0
+- **Guidelines**:
+  - Based on user's research interests (if known)
+  - `null` if user preferences unavailable
+  - Higher scores = more relevant
+
+#### `recommended_sections` (array of strings, optional)
+- **Purpose**: Specific sections worth reading in detail
+- **Format**: Section references as they appear in paper
+- **Examples**: ["Section 3.2", "Algorithm 1", "Table 2", "Appendix A"]
+
+## Generation Guidelines
+
+### AI Provider Instructions
+
+When generating summaries, AI models should:
+
+1. **Read for understanding**: Focus on the paper's core contributions
+2. **Use structured thinking**: Work through each field systematically  
+3. **Prefer facts over interpretation**: Extract what authors claim, not opinions
+4. **Use controlled vocabulary**: Select from predefined tag lists when possible
+5. **Be concise**: Optimize for quick scanning and search
+6. **Handle uncertainty**: Use `null` or empty arrays for unclear fields
+
+### Quality Criteria
+
+Good summaries exhibit:
+- **Accuracy**: Faithful to the paper's content
+- **Completeness**: Cover all major aspects  
+- **Consistency**: Similar papers get similar treatment
+- **Searchability**: Use terms that aid discovery
+- **Brevity**: Information density over verbosity
+
+### Common Issues to Avoid
+
+- **Hallucination**: Never invent facts not in the paper
+- **Editorializing**: Don't add opinions about paper quality
+- **Inconsistent terminology**: Use standard field names
+- **Over-abstraction**: Keep concrete details when useful
+- **Under-specification**: Provide enough detail for usefulness
+
+## Schema Evolution
+
+### Version History
+
+- **v1.0** (current): Initial schema with core fields
+
+### Migration Strategy
+
+When the schema evolves:
+1. New versions increment the `schema_version` field
+2. Migration tools handle format upgrades automatically  
+3. Backward compatibility maintained when possible
+4. Deprecated fields are marked but preserved
+
+### Extensibility
+
+Future versions may add:
+- Additional structured fields
+- Hierarchical tag taxonomies
+- Multi-lingual support
+- Citation relationship mapping
+- Experimental reproducibility metadata
+
+## Integration with paperlib
+
+### File Lifecycle
+
+1. **Generation**: AI provider creates `summary.json`
+2. **Validation**: paperlib validates against schema
+3. **Indexing**: Content indexed for search
+4. **Rendering**: Human-readable `summary.md` generated
+5. **Updates**: Summaries can be regenerated with new models
+
+### Search Integration
+
+Summary fields are indexed for search:
+- Full-text search includes all text fields
+- Tag-based search uses `problem_tags` and `technique_tags`  
+- Entity search uses the `entities` field
+- Relevance ranking can use `relevance_to_user` scores
+
+### API Integration
+
+Higher-level tools can consume summaries programmatically:
+
+```python
+import json
+from pathlib import Path
+
+# Load summary
+summary_path = Path("papers/arxiv/2022/arxiv-2212_06340/summary.json")
+with summary_path.open() as f:
+    summary = json.load(f)
+
+# Extract key information  
+tags = summary["problem_tags"] + summary["technique_tags"]
+relevance = summary.get("relevance_to_user", 0.0)
+entities = summary["entities"]
+```
+
+This enables automated workflows like:
+- Daily digest generation
+- Research recommendation systems  
+- Literature review automation
+- Cross-reference discovery
+
+## Examples
+
+### Machine Learning Paper
+```json
+{
+  "schema_version": "1.0",
+  "one_sentence_summary": "Introduces EfficientNet, a family of convolutional neural networks that achieve better accuracy and efficiency than previous models through compound scaling.",
+  "problem_statement": "Existing ConvNet scaling methods arbitrarily scale network dimensions, leading to suboptimal accuracy and efficiency trade-offs.",
+  "method_overview": "The paper proposes compound scaling that uniformly scales network width, depth, and resolution with a fixed ratio, guided by neural architecture search to find optimal scaling coefficients.",
+  "main_results": "EfficientNet-B7 achieves 84.3% top-1 accuracy on ImageNet while being 8.4x smaller and 6.1x faster than the best existing ConvNet.",
+  "claimed_contributions": [
+    "Novel compound scaling method for ConvNets",
+    "EfficientNet family with state-of-the-art accuracy/efficiency",
+    "Systematic study of scaling dimensions"
+  ],
+  "assumptions": [
+    "ImageNet classification transfers to other vision tasks",
+    "Compound scaling works across different architectures"
+  ],
+  "limitations": [
+    "Limited evaluation on tasks beyond image classification",
+    "Scaling coefficients may not generalize to all architectures"
+  ],
+  "problem_tags": ["classification", "computer-vision", "efficiency"],
+  "technique_tags": ["cnn", "neural-architecture-search", "model-scaling"],
+  "entities": ["ImageNet", "MobileNet", "ResNet", "NASNet"],
+  "relevance_to_user": null,
+  "recommended_sections": ["Section 3.1", "Table 2", "Figure 2"]
+}
+```
+
+This schema provides a foundation for consistent, structured paper analysis while remaining flexible enough to evolve with new research needs and AI capabilities.

pyproject.toml

@@ -5,6 +5,7 @@ description = "Local-first CLI toolkit for managing a paper library"
 readme = "README.md"
 requires-python = ">=3.13,<3.14"
 dependencies = [
+    "arxiv>=2.0.0",
     "mineru[core]>=3.0.9",
     "rich>=15.0.0",
     "typer>=0.24.1",
@@ -30,3 +31,8 @@ select = ["E", "F", "I", "B", "UP"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
+
+[dependency-groups]
+dev = [
+    "pytest>=9.0.3",
+]

src/paperlib/__main__.py

@@ -2,6 +2,5 @@
 
 from paperlib.cli import main
 
-
 if __name__ == "__main__":
-  main()
+    main()

src/paperlib/cli.py

@@ -7,120 +7,556 @@ from pathlib import Path
 
 from paperlib import __version__
 from paperlib.config import LibraryPaths
+from paperlib.converter import MinerUConverter
+from paperlib.importer import ArxivImporter, LocalImporter
+from paperlib.index import DatabaseManager
+from paperlib.storage import PaperStorageManager
+from paperlib.utils import JSONOutputMixin
 
 
 def _resolve_library_root(path: Path | None) -> Path:
-  """Resolve the target library root, defaulting to the current directory."""
-  return (path or Path.cwd()).expanduser()
+    """Resolve the target library root, defaulting to the current directory."""
+    return (path or Path.cwd()).expanduser()
 
 
 def _build_parser() -> argparse.ArgumentParser:
-  """Create the top-level argument parser."""
-  parser = argparse.ArgumentParser(
-    prog="paperlib",
-    description="Local-first paper library engine with a CLI.",
-  )
-  parser.add_argument(
-    "--version",
-    action="version",
-    version=f"%(prog)s {__version__}",
-  )
-  subparsers = parser.add_subparsers(dest="command", metavar="COMMAND")
+    """Create the top-level argument parser."""
+    parser = argparse.ArgumentParser(
+        prog="paperlib",
+        description="Local-first paper library engine with a CLI.",
+    )
+    parser.add_argument(
+        "--version",
+        action="version",
+        version=f"%(prog)s {__version__}",
+    )
+    subparsers = parser.add_subparsers(dest="command", metavar="COMMAND")
 
-  init_parser = subparsers.add_parser(
-    "init",
-    help="Initialize a paper library directory.",
-  )
-  init_parser.add_argument(
-    "path",
-    nargs="?",
-    default=".",
-    help="Directory where the library should be initialized.",
-  )
-  init_parser.set_defaults(handler=_handle_init)
+    init_parser = subparsers.add_parser(
+        "init",
+        help="Initialize a paper library directory.",
+    )
+    init_parser.add_argument(
+        "path",
+        nargs="?",
+        default=".",
+        help="Directory where the library should be initialized.",
+    )
+    init_parser.set_defaults(handler=_handle_init)
 
-  status_parser = subparsers.add_parser(
-    "status",
-    help="Show the resolved library layout for the selected root.",
-  )
-  status_parser.add_argument(
-    "--library",
-    "-L",
-    default=".",
-    help="Library root to inspect. Defaults to the current directory.",
-  )
-  status_parser.set_defaults(handler=_handle_status)
+    status_parser = subparsers.add_parser(
+        "status",
+        help="Show the resolved library layout for the selected root.",
+    )
+    status_parser.add_argument(
+        "--library",
+        "-L",
+        default=".",
+        help="Library root to inspect. Defaults to the current directory.",
+    )
+    status_parser.add_argument(
+        "--json", action="store_true", help="Output in JSON format"
+    )
+    status_parser.set_defaults(handler=_handle_status)
 
-  list_parser = subparsers.add_parser("list", help="List imported papers.")
-  list_parser.set_defaults(handler=_handle_list)
+    list_parser = subparsers.add_parser("list", help="List imported papers.")
+    list_parser.add_argument("--library", "-L", default=".", help="Library root")
+    list_parser.add_argument(
+        "--json", action="store_true", help="Output in JSON format"
+    )
+    list_parser.set_defaults(handler=_handle_list)
 
-  show_parser = subparsers.add_parser(
-    "show",
-    help="Show detailed information for a paper.",
-  )
-  show_parser.set_defaults(handler=_handle_show)
+    show_parser = subparsers.add_parser(
+        "show",
+        help="Show detailed information for a paper.",
+    )
+    show_parser.add_argument("paper_id", help="Paper ID to show")
+    show_parser.add_argument("--library", "-L", default=".", help="Library root")
+    show_parser.add_argument(
+        "--json", action="store_true", help="Output in JSON format"
+    )
+    show_parser.set_defaults(handler=_handle_show)
 
-  search_parser = subparsers.add_parser(
-    "search",
-    help="Search the paper library.",
-  )
-  search_parser.set_defaults(handler=_handle_search)
+    search_parser = subparsers.add_parser(
+        "search",
+        help="Search the paper library.",
+    )
+    search_parser.set_defaults(handler=_handle_search)
 
-  return parser
+    # Import command
+    import_parser = subparsers.add_parser(
+        "import",
+        help="Import a paper into the library.",
+    )
+    import_group = import_parser.add_mutually_exclusive_group(required=True)
+    import_group.add_argument("--pdf", type=Path, help="Path to a local PDF file")
+    import_group.add_argument("--arxiv", type=str, help="arXiv ID or URL")
+    import_parser.add_argument("--title", type=str, help="Title for local PDFs")
+    import_parser.add_argument("--notes", type=str, default="", help="Notes")
+    import_parser.add_argument("--tags", nargs="*", default=[], help="Tags")
+    import_parser.add_argument("--library", "-L", default=".", help="Library root")
+    import_parser.add_argument(
+        "--json", action="store_true", help="Output in JSON format"
+    )
+    import_parser.set_defaults(handler=_handle_import)
+
+    # Convert command
+    convert_parser = subparsers.add_parser(
+        "convert",
+        help="Convert papers to Markdown.",
+    )
+    convert_parser.add_argument("--library", "-L", default=".", help="Library root")
+    convert_parser.add_argument("--paper-id", help="Convert specific paper by ID")
+    convert_parser.add_argument(
+        "--retry-failed", action="store_true", help="Retry failed conversions"
+    )
+    convert_parser.add_argument(
+        "--force", action="store_true", help="Force reconvert successful papers"
+    )
+    convert_parser.add_argument(
+        "--no-ui", action="store_true", help="Disable rich UI (useful for scripting)"
+    )
+    convert_parser.add_argument(
+        "--json", action="store_true", help="Output in JSON format"
+    )
+    convert_parser.set_defaults(handler=_handle_convert)
+
+    # Reindex command
+    reindex_parser = subparsers.add_parser(
+        "reindex",
+        help="Rebuild the search index from stored papers.",
+    )
+    reindex_parser.add_argument("--library", "-L", default=".", help="Library root")
+    reindex_parser.add_argument(
+        "--json", action="store_true", help="Output in JSON format"
+    )
+    reindex_parser.set_defaults(handler=_handle_reindex)
+
+    return parser
 
 
 def _format_paths(paths: LibraryPaths) -> str:
-  """Render library paths in a simple, grep-friendly format."""
-  lines = [
-    f"root: {paths.root}",
-    f"config: {paths.config_path}",
-    f"database: {paths.db_path}",
-    f"papers: {paths.papers_dir}",
-    f"inbox: {paths.inbox_dir}",
-    f"cache: {paths.cache_dir}",
-  ]
-  return "\n".join(lines)
+    """Render library paths in a simple, grep-friendly format."""
+    lines = [
+        f"root: {paths.root}",
+        f"config: {paths.config_path}",
+        f"database: {paths.db_path}",
+        f"papers: {paths.papers_dir}",
+        f"inbox: {paths.inbox_dir}",
+        f"cache: {paths.cache_dir}",
+    ]
+    return "\n".join(lines)
 
 
 def _handle_init(args: argparse.Namespace) -> int:
-  """Initialize a paper library directory."""
-  paths = LibraryPaths.from_root(Path(args.path))
-  paths.create_directories()
-  print(f"Initialized paper library at {paths.root}")
-  print(_format_paths(paths))
-  return 0
+    """Initialize a paper library directory."""
+    paths = LibraryPaths.from_root(Path(args.path))
+    paths.create_directories()
+    print(f"Initialized paper library at {paths.root}")
+    print(_format_paths(paths))
+    return 0
 
 
 def _handle_status(args: argparse.Namespace) -> int:
-  """Show the resolved library layout for a selected root."""
-  paths = LibraryPaths.from_root(_resolve_library_root(Path(args.library)))
-  print(_format_paths(paths))
-  return 0
+    """Show the resolved library layout for a selected root."""
+    library_root = _resolve_library_root(Path(args.library))
+    paths = LibraryPaths.from_root(library_root)
+
+    if args.json:
+        JSONOutputMixin.output_json(
+            {
+                "library_root": str(paths.root),
+                "config_path": str(paths.config_path),
+                "database_path": str(paths.db_path),
+                "papers_dir": str(paths.papers_dir),
+                "inbox_dir": str(paths.inbox_dir),
+                "cache_dir": str(paths.cache_dir),
+            }
+        )
+    else:
+        print(_format_paths(paths))
+    return 0
 
 
-def _handle_list(_: argparse.Namespace) -> int:
-  """Placeholder for listing imported papers."""
-  print("Listing papers is not implemented yet.")
-  return 0
+def _handle_list(args: argparse.Namespace) -> int:
+    """List imported papers."""
+    try:
+        paths = LibraryPaths.from_root(
+            _resolve_library_root(
+                Path(args.library if hasattr(args, "library") else ".")
+            )
+        )
+        storage_manager = PaperStorageManager(paths)
+        db_manager = DatabaseManager(paths)
+
+        # Initialize database if it doesn't exist
+        db_manager.initialize_database()
+
+        # List all papers from storage (more reliable than index)
+        papers = list(storage_manager.list_all_papers())
+
+        if args.json:
+            JSONOutputMixin.output_json(
+                JSONOutputMixin.format_papers_list_for_json(papers)
+            )
+            return 0
+
+        if not papers:
+            print("No papers found in library.")
+            return 0
+
+        print(f"Found {len(papers)} papers:")
+        print()
+
+        for metadata in papers:
+            status_indicators = []
+            if metadata.conversion_status.value == "success":
+                status_indicators.append("📄")  # Converted
+            if metadata.summary_status.value == "success":
+                status_indicators.append("📝")  # Summarized
+
+            status_str = "".join(status_indicators) if status_indicators else "⏳"
+
+            print(f"{status_str} {metadata.paper_id}")
+            print(f"   {metadata.title}")
+            if metadata.authors:
+                authors_str = ", ".join(metadata.authors[:3])
+                if len(metadata.authors) > 3:
+                    authors_str += f" (+{len(metadata.authors) - 3} more)"
+                print(f"   By: {authors_str}")
+            if metadata.categories:
+                print(f"   Categories: {', '.join(metadata.categories)}")
+            print()
+
+        return 0
+
+    except Exception as e:
+        print(f"Error listing papers: {e}")
+        return 1
 
 
-def _handle_show(_: argparse.Namespace) -> int:
-  """Placeholder for showing paper details."""
-  print("Showing paper details is not implemented yet.")
-  return 0
+def _handle_show(args: argparse.Namespace) -> int:
+    """Show detailed information for a paper."""
+    if not hasattr(args, "paper_id") or not args.paper_id:
+        print("Please specify a paper ID with --paper-id")
+        return 1
+
+    try:
+        paths = LibraryPaths.from_root(
+            _resolve_library_root(
+                Path(args.library if hasattr(args, "library") else ".")
+            )
+        )
+        storage_manager = PaperStorageManager(paths)
+
+        # Find paper by ID
+        for metadata in storage_manager.list_all_papers():
+            if metadata.paper_id == args.paper_id:
+                if args.json:
+                    # Add file existence information
+                    paper_data = JSONOutputMixin.format_metadata_for_json(metadata)
+
+                    # Add file status information
+                    files_status = {}
+                    if metadata.pdf_path:
+                        pdf_path = paths.root / metadata.pdf_path
+                        files_status["pdf_exists"] = pdf_path.exists()
+                    if metadata.paper_md_path:
+                        md_path = paths.root / metadata.paper_md_path
+                        files_status["markdown_exists"] = md_path.exists()
+                    if metadata.summary_json_path:
+                        summary_path = paths.root / metadata.summary_json_path
+                        files_status["summary_exists"] = summary_path.exists()
+
+                    paper_data["files_status"] = files_status
+                    JSONOutputMixin.output_json({"paper": paper_data})
+                else:
+                    print(f"Paper ID: {metadata.paper_id}")
+                    print(f"Source: {metadata.source_type.value}")
+                    if metadata.source_id:
+                        print(f"Source ID: {metadata.source_id}")
+                    print(f"Title: {metadata.title}")
+                    if metadata.authors:
+                        print(f"Authors: {', '.join(metadata.authors)}")
+                    if metadata.published_date:
+                        print(
+                            f"Published: {metadata.published_date.strftime('%Y-%m-%d')}"
+                        )
+                    if metadata.categories:
+                        print(f"Categories: {', '.join(metadata.categories)}")
+                    if metadata.tags:
+                        print(f"Tags: {', '.join(metadata.tags)}")
+                    imported_str = metadata.imported_at.strftime("%Y-%m-%d %H:%M:%S")
+                    print(f"Imported: {imported_str}")
+                    print(f"Conversion Status: {metadata.conversion_status.value}")
+                    print(f"Summary Status: {metadata.summary_status.value}")
+                    if metadata.notes:
+                        print(f"Notes: {metadata.notes}")
+
+                    # Show file paths
+                    print("\nFiles:")
+                    if metadata.pdf_path:
+                        pdf_path = paths.root / metadata.pdf_path
+                        exists = "✓" if pdf_path.exists() else "✗"
+                        print(f"  PDF: {exists} {metadata.pdf_path}")
+                    if metadata.paper_md_path:
+                        md_path = paths.root / metadata.paper_md_path
+                        exists = "✓" if md_path.exists() else "✗"
+                        print(f"  Markdown: {exists} {metadata.paper_md_path}")
+                    if metadata.summary_json_path:
+                        summary_path = paths.root / metadata.summary_json_path
+                        exists = "✓" if summary_path.exists() else "✗"
+                        print(f"  Summary: {exists} {metadata.summary_json_path}")
+
+                return 0
+
+        if args.json:
+            JSONOutputMixin.output_json_error(f"Paper not found: {args.paper_id}")
+        else:
+            print(f"Paper not found: {args.paper_id}")
+        return 1
+
+    except Exception as e:
+        if args.json:
+            JSONOutputMixin.output_json_error(f"Error showing paper: {e}")
+        else:
+            print(f"Error showing paper: {e}")
+        return 1
 
 
 def _handle_search(_: argparse.Namespace) -> int:
-  """Placeholder for searching the paper library."""
-  print("Search is not implemented yet.")
-  return 0
+    """Placeholder for searching the paper library."""
+    print("Search is not implemented yet.")
+    return 0
+
+
+def _handle_import(args: argparse.Namespace) -> int:
+    """Handle importing a paper into the library."""
+    try:
+        # Set up library paths and managers
+        paths = LibraryPaths.from_root(_resolve_library_root(Path(args.library)))
+        storage_manager = PaperStorageManager(paths)
+        db_manager = DatabaseManager(paths)
+
+        # Initialize database
+        db_manager.initialize_database()
+
+        if args.pdf:
+            # Import local PDF
+            local_importer = LocalImporter(storage_manager)
+            metadata = local_importer.import_pdf(
+                pdf_path=args.pdf,
+                title=args.title or "",
+                notes=args.notes,
+                tags=args.tags,
+            )
+            # Index the paper
+            db_manager.index_paper(metadata)
+
+            if args.json:
+                JSONOutputMixin.output_json(
+                    {
+                        "paper_id": metadata.paper_id,
+                        "title": metadata.title,
+                        "source_type": metadata.source_type.value,
+                        "message": "Successfully imported local PDF",
+                        "paper": JSONOutputMixin.format_metadata_for_json(metadata),
+                    }
+                )
+            else:
+                print(f"Successfully imported local PDF: {metadata.paper_id}")
+                print(f"Title: {metadata.title}")
+
+        elif args.arxiv:
+            # Import from arXiv
+            arxiv_importer = ArxivImporter(storage_manager)
+            metadata = arxiv_importer.import_arxiv_paper(
+                arxiv_input=args.arxiv,
+                notes=args.notes,
+                tags=args.tags,
+            )
+            # Index the paper
+            db_manager.index_paper(metadata)
+
+            if args.json:
+                JSONOutputMixin.output_json(
+                    {
+                        "paper_id": metadata.paper_id,
+                        "title": metadata.title,
+                        "source_type": metadata.source_type.value,
+                        "source_id": metadata.source_id,
+                        "authors": metadata.authors,
+                        "message": "Successfully imported arXiv paper",
+                        "paper": JSONOutputMixin.format_metadata_for_json(metadata),
+                    }
+                )
+            else:
+                print(f"Successfully imported arXiv paper: {metadata.paper_id}")
+                print(f"Title: {metadata.title}")
+                print(f"Authors: {', '.join(metadata.authors)}")
+
+        return 0
+
+    except Exception as e:
+        if args.json:
+            JSONOutputMixin.output_json_error(f"Error importing paper: {e}")
+        else:
+            print(f"Error importing paper: {e}")
+        return 1
+
+
+def _handle_convert(args: argparse.Namespace) -> int:
+    """Handle converting papers to Markdown."""
+    try:
+        # Set up library paths and components
+        paths = LibraryPaths.from_root(_resolve_library_root(Path(args.library)))
+        storage_manager = PaperStorageManager(paths)
+        converter = MinerUConverter(storage_manager)
+
+        if args.paper_id:
+            # Convert specific paper
+            for metadata in storage_manager.list_all_papers():
+                if metadata.paper_id == args.paper_id:
+                    conversion_success = converter.convert_paper(metadata)
+
+                    if args.json:
+                        # Get updated metadata after conversion
+                        updated_metadata = storage_manager.load_paper_metadata(
+                            metadata.paper_id, metadata.source_type
+                        )
+                        status_val = (
+                            updated_metadata.conversion_status.value
+                            if updated_metadata
+                            else "unknown"
+                        )
+                        msg = (
+                            "Successfully converted paper"
+                            if conversion_success
+                            else "Failed to convert paper"
+                        )
+                        JSONOutputMixin.output_json(
+                            {
+                                "paper_id": metadata.paper_id,
+                                "conversion_success": conversion_success,
+                                "conversion_status": status_val,
+                                "message": msg,
+                            }
+                        )
+                    else:
+                        if conversion_success:
+                            print(f"Successfully converted paper: {metadata.paper_id}")
+                        else:
+                            print(f"Failed to convert paper: {metadata.paper_id}")
+                    return 0 if conversion_success else 1
+
+            if args.json:
+                JSONOutputMixin.output_json_error(f"Paper not found: {args.paper_id}")
+            else:
+                print(f"Paper not found: {args.paper_id}")
+            return 1
+        else:
+            # Convert papers based on flags
+            use_ui = not (args.no_ui or args.json)  # Disable UI for JSON output
+            success_count, failure_count = converter.convert_all_pending(
+                retry_failed=args.retry_failed, force=args.force, use_ui=use_ui
+            )
+
+            if args.json:
+                # Determine action type
+                if args.force:
+                    action_type = "force_convert"
+                elif args.retry_failed:
+                    action_type = "convert_with_retry"
+                else:
+                    action_type = "convert_pending"
+
+                JSONOutputMixin.output_json(
+                    {
+                        "action": action_type,
+                        "success_count": success_count,
+                        "failure_count": failure_count,
+                        "total_attempted": success_count + failure_count,
+                    }
+                )
+            else:
+                # Show what was attempted (if not using UI)
+                if args.no_ui or (success_count == 0 and failure_count == 0):
+                    if args.force:
+                        action = "Force converted"
+                    elif args.retry_failed:
+                        action = "Converted pending and retried failed"
+                    else:
+                        action = "Converted pending"
+
+                    msg = (
+                        f"{action}: {success_count} successful, {failure_count} failed"
+                    )
+                    print(msg)
+
+            return 0 if failure_count == 0 else 1
+    except Exception as e:
+        if args.json:
+            JSONOutputMixin.output_json_error(f"Error during conversion: {e}")
+        else:
+            print(f"Error during conversion: {e}")
+        return 1
+
+
+def _handle_reindex(args: argparse.Namespace) -> int:
+    """Rebuild the search index from stored papers."""
+    try:
+        paths = LibraryPaths.from_root(_resolve_library_root(Path(args.library)))
+        storage_manager = PaperStorageManager(paths)
+        db_manager = DatabaseManager(paths)
+
+        if not args.json:
+            print("Rebuilding search index...")
+
+        # Initialize database schema
+        db_manager.initialize_database()
+
+        # Rebuild index from storage
+        success_count, error_count = db_manager.reindex_from_storage(storage_manager)
+
+        # Show statistics
+        stats = db_manager.get_statistics()
+
+        if args.json:
+            JSONOutputMixin.output_json(
+                {
+                    "reindex_complete": True,
+                    "papers_indexed": success_count,
+                    "errors": error_count,
+                    "statistics": stats,
+                }
+            )
+        else:
+            reindex_msg = (
+                f"Complete: {success_count} papers indexed, {error_count} errors"
+            )
+            print(reindex_msg)
+            print(f"Total papers: {stats['total_papers']}")
+            if stats.get("by_source_type"):
+                by_source = ", ".join(
+                    f"{k}: {v}" for k, v in stats["by_source_type"].items()
+                )
+                print(f"By source: {by_source}")
+
+        return 0 if error_count == 0 else 1
+
+    except Exception as e:
+        if args.json:
+            JSONOutputMixin.output_json_error(f"Error during reindex: {e}")
+        else:
+            print(f"Error during reindex: {e}")
+        return 1
 
 
 def main() -> None:
-  """Console script entrypoint."""
-  parser = _build_parser()
-  args = parser.parse_args()
-  if not hasattr(args, "handler"):
-    parser.print_help()
-    raise SystemExit(0)
-  raise SystemExit(args.handler(args))
+    """Console script entrypoint."""
+    parser = _build_parser()
+    args = parser.parse_args()
+    if not hasattr(args, "handler"):
+        parser.print_help()
+        raise SystemExit(0)
+    raise SystemExit(args.handler(args))

src/paperlib/config.py

@@ -5,7 +5,6 @@ from __future__ import annotations
 from dataclasses import dataclass
 from pathlib import Path
 
-
 DEFAULT_CONFIG_DIRNAME = "config"
 DEFAULT_DB_DIRNAME = "db"
 DEFAULT_CACHE_DIRNAME = "cache"
@@ -17,42 +16,42 @@ DEFAULT_CONFIG_FILENAME = "config.toml"
 
 @dataclass(frozen=True, slots=True)
 class LibraryPaths:
-  """Resolved filesystem layout for a paper library."""
+    """Resolved filesystem layout for a paper library."""
 
-  root: Path
-  config_dir: Path
-  papers_dir: Path
-  inbox_dir: Path
-  db_dir: Path
-  cache_dir: Path
-  db_path: Path
-  config_path: Path
+    root: Path
+    config_dir: Path
+    papers_dir: Path
+    inbox_dir: Path
+    db_dir: Path
+    cache_dir: Path
+    db_path: Path
+    config_path: Path
 
-  @classmethod
-  def from_root(cls, root: Path) -> "LibraryPaths":
-    """Build a standard library layout from a root directory."""
-    resolved_root = root.expanduser().resolve()
-    config_dir = resolved_root / DEFAULT_CONFIG_DIRNAME
-    db_dir = resolved_root / DEFAULT_DB_DIRNAME
-    return cls(
-      root=resolved_root,
-      config_dir=config_dir,
-      papers_dir=resolved_root / DEFAULT_PAPERS_DIRNAME,
-      inbox_dir=resolved_root / DEFAULT_INBOX_DIRNAME,
-      db_dir=db_dir,
-      cache_dir=resolved_root / DEFAULT_CACHE_DIRNAME,
-      db_path=db_dir / DEFAULT_DB_FILENAME,
-      config_path=config_dir / DEFAULT_CONFIG_FILENAME,
-    )
+    @classmethod
+    def from_root(cls, root: Path) -> LibraryPaths:
+        """Build a standard library layout from a root directory."""
+        resolved_root = root.expanduser().resolve()
+        config_dir = resolved_root / DEFAULT_CONFIG_DIRNAME
+        db_dir = resolved_root / DEFAULT_DB_DIRNAME
+        return cls(
+            root=resolved_root,
+            config_dir=config_dir,
+            papers_dir=resolved_root / DEFAULT_PAPERS_DIRNAME,
+            inbox_dir=resolved_root / DEFAULT_INBOX_DIRNAME,
+            db_dir=db_dir,
+            cache_dir=resolved_root / DEFAULT_CACHE_DIRNAME,
+            db_path=db_dir / DEFAULT_DB_FILENAME,
+            config_path=config_dir / DEFAULT_CONFIG_FILENAME,
+        )
 
-  def create_directories(self) -> None:
-    """Create the standard library directories if they do not exist."""
-    for path in (
-      self.root,
-      self.config_dir,
-      self.papers_dir,
-      self.inbox_dir,
-      self.db_dir,
-      self.cache_dir,
-    ):
-      path.mkdir(parents=True, exist_ok=True)
+    def create_directories(self) -> None:
+        """Create the standard library directories if they do not exist."""
+        for path in (
+            self.root,
+            self.config_dir,
+            self.papers_dir,
+            self.inbox_dir,
+            self.db_dir,
+            self.cache_dir,
+        ):
+            path.mkdir(parents=True, exist_ok=True)

src/paperlib/converter/__init__.py

@@ -0,0 +1,5 @@
+"""PDF conversion functionality for paperlib."""
+
+from .mineru_converter import MinerUConverter
+
+__all__ = ["MinerUConverter"]

src/paperlib/converter/mineru_converter.py

@@ -0,0 +1,268 @@
+"""PDF to Markdown conversion using MinerU."""
+
+from __future__ import annotations
+
+import logging
+import subprocess
+import sys
+from pathlib import Path
+
+from paperlib.models import ConversionStatus, PaperMetadata
+from paperlib.storage import PaperStorageManager
+from paperlib.ui import ConversionUI
+
+
+class MinerUConverter:
+    """Handles PDF to Markdown conversion using MinerU."""
+
+    def __init__(self, storage_manager: PaperStorageManager) -> None:
+        self.storage_manager = storage_manager
+        self.logger = logging.getLogger(__name__)
+
+    def is_mineru_available(self) -> bool:
+        """Check if MinerU CLI is available in the environment."""
+        try:
+            # Check if mineru command is available
+            result = subprocess.run(
+                ["mineru", "--version"],
+                capture_output=True,
+                check=False,
+            )
+            return result.returncode == 0
+        except (subprocess.SubprocessError, FileNotFoundError):
+            # Fallback: check if mineru module is importable
+            try:
+                result = subprocess.run(
+                    [sys.executable, "-c", "import mineru"],
+                    capture_output=True,
+                    check=False,
+                )
+                return result.returncode == 0
+            except (subprocess.SubprocessError, FileNotFoundError):
+                return False
+
+    def convert_paper(self, metadata: PaperMetadata) -> bool:
+        """Convert a paper's PDF to Markdown using MinerU."""
+        if not self.is_mineru_available():
+            self.logger.error("MinerU is not available")
+            return False
+
+        # Get paper paths
+        paths = self.storage_manager.get_paper_paths(
+            metadata.paper_id, metadata.source_type
+        )
+        pdf_path = self.storage_manager.library_paths.root / metadata.pdf_path
+        markdown_path = paths["markdown"]
+        logs_dir = paths["logs"]
+
+        if not pdf_path.exists():
+            self.logger.error(f"PDF file not found: {pdf_path}")
+            return False
+
+        # Update status to processing
+        metadata.conversion_status = ConversionStatus.PROCESSING
+        self.storage_manager.update_paper_metadata(metadata)
+
+        try:
+            # Create temporary output directory in cache
+            cache_dir = self.storage_manager.library_paths.cache_dir
+            temp_output_dir = cache_dir / f"mineru_temp_{metadata.paper_id}"
+            temp_output_dir.mkdir(exist_ok=True)
+
+            # Clear/create log file to start fresh
+            log_file = logs_dir / "mineru.log"
+            log_file.write_text("")  # Clear existing log content
+
+            # Correct MinerU command
+            cmd = [
+                "mineru",
+                "-p",
+                str(pdf_path),
+                "-o",
+                str(temp_output_dir),
+                "-b",
+                "pipeline",  # CPU-only mode for compatibility
+            ]
+
+            self.logger.info(f"Running MinerU conversion: {' '.join(cmd)}")
+
+            with log_file.open("w") as log:
+                result = subprocess.run(
+                    cmd,
+                    stdout=log,
+                    stderr=subprocess.STDOUT,
+                    check=False,
+                )
+
+            # Check if conversion was successful
+            if result.returncode == 0:
+                # MinerU outputs to <output_dir>/<filename>/auto/
+                pdf_stem = pdf_path.stem  # Get filename without .pdf extension
+                mineru_output_dir = temp_output_dir / pdf_stem / "auto"
+                expected_markdown = mineru_output_dir / f"{pdf_stem}.md"
+                expected_images = mineru_output_dir / "images"
+
+                if expected_markdown.exists():
+                    # Post-process markdown file before moving
+                    self._post_process_markdown(expected_markdown)
+
+                    # Move markdown file to paper directory
+                    expected_markdown.rename(markdown_path)
+
+                    # Move images directory if it exists
+                    if expected_images.exists():
+                        assets_target = paths["assets"]
+                        if assets_target.exists():
+                            # Remove existing assets directory
+                            import shutil
+
+                            shutil.rmtree(assets_target)
+                        expected_images.rename(assets_target)
+
+                    # Update metadata
+                    metadata.conversion_status = ConversionStatus.SUCCESS
+                    self.storage_manager.update_paper_metadata(metadata)
+
+                    self.logger.info(
+                        f"Successfully converted {pdf_path} to {markdown_path}"
+                    )
+
+                    # Clean up temporary directory
+                    import shutil
+
+                    shutil.rmtree(temp_output_dir)
+                    return True
+                else:
+                    self.logger.error(
+                        f"Expected markdown file not found: {expected_markdown}"
+                    )
+                    # For debugging, list what files were actually created
+                    if temp_output_dir.exists():
+                        created_files = list(temp_output_dir.rglob("*"))
+                        files_str = [str(f) for f in created_files]
+                        self.logger.error(f"Files created by MinerU: {files_str}")
+                    metadata.conversion_status = ConversionStatus.FAILED
+                    self.storage_manager.update_paper_metadata(metadata)
+                    return False
+            else:
+                self.logger.error(
+                    f"MinerU conversion failed with return code {result.returncode}"
+                )
+                metadata.conversion_status = ConversionStatus.FAILED
+                self.storage_manager.update_paper_metadata(metadata)
+                return False
+
+        except Exception as e:
+            self.logger.error(f"Exception during conversion: {e}")
+            metadata.conversion_status = ConversionStatus.FAILED
+            self.storage_manager.update_paper_metadata(metadata)
+            return False
+        finally:
+            # Ensure cleanup of temp directory
+            if "temp_output_dir" in locals() and temp_output_dir.exists():
+                import shutil
+
+                shutil.rmtree(temp_output_dir, ignore_errors=True)
+
+    def convert_all_pending(
+        self, retry_failed: bool = False, force: bool = False, use_ui: bool = True
+    ) -> tuple[int, int]:
+        """Convert papers based on their conversion status."""
+        # Find papers to convert
+        papers_to_convert = []
+        for metadata in self.storage_manager.list_all_papers():
+            should_convert = False
+
+            if force:
+                # Force convert all papers
+                should_convert = True
+            elif metadata.conversion_status == ConversionStatus.PENDING:
+                # Convert pending papers
+                should_convert = True
+            elif retry_failed and metadata.conversion_status == ConversionStatus.FAILED:
+                # Retry failed conversions if requested
+                should_convert = True
+
+            if should_convert:
+                papers_to_convert.append(metadata)
+
+        if not papers_to_convert:
+            return 0, 0
+
+        # Use rich UI for multiple papers or when explicitly requested
+        if use_ui and len(papers_to_convert) > 0:
+            conversion_ui = ConversionUI()
+            return conversion_ui.run_conversion_with_ui(
+                papers_to_convert, self.convert_paper, self.storage_manager
+            )
+        else:
+            # Fallback to simple conversion without UI
+            success_count = 0
+            failure_count = 0
+
+            for metadata in papers_to_convert:
+                if self.convert_paper(metadata):
+                    success_count += 1
+                else:
+                    failure_count += 1
+
+            return success_count, failure_count
+
+    def _post_process_markdown(self, markdown_path: Path) -> None:
+        """Post-process the markdown file to fix image references and other issues."""
+        try:
+            # Read the original markdown content
+            content = markdown_path.read_text(encoding="utf-8")
+
+            # Fix image references: images/ -> assets/
+            # This handles both ![](images/...) and ![alt text](images/...)
+            import re
+
+            content = re.sub(
+                r"!\[([^\]]*)\]\(images/",  # Match ![...](images/
+                r"![\1](assets/",  # Replace with ![...](assets/
+                content,
+            )
+
+            # Also handle standalone image references without alt text
+            content = re.sub(
+                r"!\[\]\(images/",  # Match ![](images/
+                r"![](assets/",  # Replace with ![](assets/
+                content,
+            )
+
+            # Apply additional cleanup
+            content = self._clean_markdown_content(content)
+
+            # Write the modified content back
+            markdown_path.write_text(content, encoding="utf-8")
+
+            self.logger.info("Post-processed markdown file: fixed image references")
+
+        except Exception as e:
+            # Don't fail conversion if post-processing fails
+            self.logger.warning(f"Failed to post-process markdown: {e}")
+
+    def _clean_markdown_content(self, content: str) -> str:
+        """Additional markdown cleanup (extensible for future needs)."""
+        # Remove or fix common MinerU artifacts
+        lines = content.split("\n")
+        cleaned_lines = []
+
+        for line in lines:
+            # Skip empty lines with just whitespace
+            if line.strip() == "":
+                cleaned_lines.append("")
+                continue
+
+            # Remove excessive whitespace
+            line = " ".join(line.split())
+
+            # TODO: Add more cleanup rules here as needed
+            # - Fix table formatting
+            # - Clean up figure captions
+            # - Remove processing artifacts
+
+            cleaned_lines.append(line)
+
+        return "\n".join(cleaned_lines)

src/paperlib/importer/__init__.py

@@ -0,0 +1,6 @@
+"""Import functionality for paperlib."""
+
+from .arxiv_importer import ArxivImporter
+from .local_importer import LocalImporter
+
+__all__ = ["ArxivImporter", "LocalImporter"]

src/paperlib/importer/arxiv_importer.py

@@ -0,0 +1,112 @@
+"""arXiv import functionality."""
+
+from __future__ import annotations
+
+import re
+import tempfile
+from pathlib import Path
+
+import arxiv
+
+from paperlib.models import PaperMetadata, SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class ArxivImporter:
+    """Handles importing papers from arXiv."""
+
+    def __init__(self, storage_manager: PaperStorageManager) -> None:
+        self.storage_manager = storage_manager
+        # Create arXiv client with reasonable defaults
+        self.client = arxiv.Client(page_size=10, delay_seconds=3.0, num_retries=3)
+
+    def extract_arxiv_id(self, input_string: str) -> str:
+        """Extract arXiv ID from various input formats."""
+        # Clean input
+        input_string = input_string.strip()
+
+        # Pattern for arXiv ID (both old and new formats)
+        # New format: YYMM.NNNNN[vN]
+        # Old format: subject-class/YYMMnnn
+        patterns = [
+            r"(?:arxiv:)?(\d{4}\.\d{4,5}(?:v\d+)?)",  # New format
+            r"(?:arxiv:)?([a-z-]+/\d{7})",  # Old format
+        ]
+
+        for pattern in patterns:
+            match = re.search(pattern, input_string, re.IGNORECASE)
+            if match:
+                return match.group(1)
+
+        # If no pattern matches, assume it's already a clean arXiv ID
+        return input_string
+
+    def fetch_paper_metadata(self, arxiv_id: str) -> arxiv.Result:
+        """Fetch paper metadata from arXiv API."""
+        search = arxiv.Search(id_list=[arxiv_id])
+
+        results = list(self.client.results(search))
+        if not results:
+            msg = f"Paper not found on arXiv: {arxiv_id}"
+            raise ValueError(msg)
+
+        return results[0]
+
+    def download_pdf(self, result: arxiv.Result) -> Path:
+        """Download PDF from arXiv to a temporary location."""
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
+            tmp_path = Path(tmp_file.name)
+
+        # Download PDF
+        result.download_pdf(filename=str(tmp_path))
+
+        return tmp_path
+
+    def import_arxiv_paper(
+        self, arxiv_input: str, notes: str = "", tags: list[str] | None = None
+    ) -> PaperMetadata:
+        """Import a paper from arXiv."""
+        # Extract clean arXiv ID
+        arxiv_id = self.extract_arxiv_id(arxiv_input)
+
+        # Check if already imported
+        paper_id = self.storage_manager.generate_paper_id(SourceType.ARXIV, arxiv_id)
+        if self.storage_manager.paper_exists(paper_id, SourceType.ARXIV):
+            msg = f"Paper already imported: {arxiv_id}"
+            raise ValueError(msg)
+
+        # Fetch metadata from arXiv
+        result = self.fetch_paper_metadata(arxiv_id)
+
+        # Download PDF
+        pdf_path = self.download_pdf(result)
+
+        try:
+            # Convert arXiv result to our metadata format
+            published_date = (
+                result.published.replace(tzinfo=None) if result.published else None
+            )
+            updated_date = (
+                result.updated.replace(tzinfo=None) if result.updated else None
+            )
+
+            # Store the paper
+            metadata = self.storage_manager.store_paper(
+                pdf_path=pdf_path,
+                source_type=SourceType.ARXIV,
+                source_id=arxiv_id,
+                title=result.title,
+                authors=[author.name for author in result.authors],
+                published_date=published_date,
+                updated_date=updated_date,
+                categories=[cat for cat in result.categories],
+                notes=notes,
+                tags=tags or [],
+            )
+
+            return metadata
+
+        finally:
+            # Clean up temporary PDF file
+            if pdf_path.exists():
+                pdf_path.unlink()

src/paperlib/importer/local_importer.py

@@ -0,0 +1,56 @@
+"""Local PDF import functionality."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from paperlib.models import PaperMetadata, SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class LocalImporter:
+    """Handles importing local PDF files."""
+
+    def __init__(self, storage_manager: PaperStorageManager) -> None:
+        self.storage_manager = storage_manager
+
+    def import_pdf(
+        self,
+        pdf_path: Path,
+        title: str = "",
+        notes: str = "",
+        tags: list[str] | None = None,
+    ) -> PaperMetadata:
+        """Import a local PDF file."""
+        if not pdf_path.exists():
+            msg = f"PDF file not found: {pdf_path}"
+            raise FileNotFoundError(msg)
+
+        if not pdf_path.suffix.lower() == ".pdf":
+            msg = f"File is not a PDF: {pdf_path}"
+            raise ValueError(msg)
+
+        # Generate paper ID and check for duplicates
+        paper_id = self.storage_manager.generate_paper_id(
+            SourceType.LOCAL, pdf_path=pdf_path
+        )
+
+        if self.storage_manager.paper_exists(paper_id, SourceType.LOCAL):
+            msg = f"Paper already imported: {paper_id}"
+            raise ValueError(msg)
+
+        # Extract title from filename if not provided
+        if not title:
+            title = pdf_path.stem.replace("_", " ").replace("-", " ").title()
+
+        # Store the paper
+        metadata = self.storage_manager.store_paper(
+            pdf_path=pdf_path,
+            source_type=SourceType.LOCAL,
+            source_id=None,
+            title=title,
+            notes=notes,
+            tags=tags or [],
+        )
+
+        return metadata

src/paperlib/index/__init__.py

@@ -0,0 +1,5 @@
+"""SQLite index layer for paperlib."""
+
+from .database import DatabaseManager
+
+__all__ = ["DatabaseManager"]

src/paperlib/index/database.py

@@ -0,0 +1,324 @@
+"""SQLite database manager for indexing papers."""
+
+from __future__ import annotations
+
+import sqlite3
+from collections.abc import Iterator
+
+from paperlib.config import LibraryPaths
+from paperlib.models import ConversionStatus, PaperMetadata, SourceType, SummaryStatus
+
+
+class DatabaseManager:
+    """Manages SQLite database for indexing papers."""
+
+    def __init__(self, library_paths: LibraryPaths) -> None:
+        self.library_paths = library_paths
+        self.db_path = library_paths.db_path
+
+    def _get_connection(self) -> sqlite3.Connection:
+        """Get a database connection with proper settings."""
+        # Ensure database directory exists
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row  # Enable dict-like access to rows
+        conn.execute("PRAGMA foreign_keys = ON")  # Enable foreign keys
+        return conn
+
+    def initialize_database(self) -> None:
+        """Initialize the database schema."""
+        with self._get_connection() as conn:
+            # Main papers table
+            conn.execute("""
+                CREATE TABLE IF NOT EXISTS papers (
+                    paper_id TEXT PRIMARY KEY,
+                    source_type TEXT NOT NULL,
+                    source_id TEXT,
+                    title TEXT NOT NULL,
+                    authors_json TEXT NOT NULL,  -- JSON array of authors
+                    published_date TEXT,         -- ISO format
+                    updated_date TEXT,           -- ISO format
+                    categories_json TEXT NOT NULL,  -- JSON array of categories
+                    pdf_path TEXT,
+                    paper_md_path TEXT,
+                    summary_json_path TEXT,
+                    summary_md_path TEXT,
+                    imported_at TEXT NOT NULL,   -- ISO format
+                    conversion_status TEXT NOT NULL,
+                    summary_status TEXT NOT NULL,
+                    tags_json TEXT NOT NULL,     -- JSON array of tags
+                    notes TEXT NOT NULL,
+
+                    -- Computed fields for search
+                    search_text TEXT,            -- Full-text search content
+                    author_list TEXT,            -- Space-separated authors for search
+                    category_list TEXT           -- Space-separated categories
+                )
+            """)
+
+            # Create indexes for common queries
+            conn.execute(
+                "CREATE INDEX IF NOT EXISTS idx_papers_source_type "
+                "ON papers(source_type)"
+            )
+            conn.execute(
+                "CREATE INDEX IF NOT EXISTS idx_papers_source_id ON papers(source_id)"
+            )
+            conn.execute(
+                "CREATE INDEX IF NOT EXISTS idx_papers_conversion_status "
+                "ON papers(conversion_status)"
+            )
+            conn.execute(
+                "CREATE INDEX IF NOT EXISTS idx_papers_summary_status "
+                "ON papers(summary_status)"
+            )
+            conn.execute(
+                "CREATE INDEX IF NOT EXISTS idx_papers_imported_at "
+                "ON papers(imported_at)"
+            )
+
+            # Full-text search virtual table
+            conn.execute("""
+                CREATE VIRTUAL TABLE IF NOT EXISTS papers_fts USING fts5(
+                    paper_id UNINDEXED,
+                    title,
+                    authors,
+                    search_text,
+                    categories,
+                    tags,
+                    notes
+                )
+            """)
+
+    def index_paper(self, metadata: PaperMetadata) -> None:
+        """Index a paper in the database."""
+        import json
+
+        with self._get_connection() as conn:
+            # Prepare data for insertion
+            parts = [
+                metadata.title,
+                " ".join(metadata.authors),
+                " ".join(metadata.categories),
+                " ".join(metadata.tags),
+                metadata.notes,
+            ]
+            search_text = " ".join(parts)
+            author_list = " ".join(metadata.authors)
+            category_list = " ".join(metadata.categories)
+
+            # Insert or replace in main table
+            conn.execute(
+                """
+                INSERT OR REPLACE INTO papers (
+                    paper_id, source_type, source_id, title, authors_json,
+                    published_date, updated_date, categories_json, pdf_path,
+                    paper_md_path, summary_json_path, summary_md_path,
+                    imported_at, conversion_status, summary_status,
+                    tags_json, notes, search_text, author_list, category_list
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """,
+                (
+                    metadata.paper_id,
+                    metadata.source_type.value,
+                    metadata.source_id,
+                    metadata.title,
+                    json.dumps(metadata.authors),
+                    metadata.published_date.isoformat()
+                    if metadata.published_date
+                    else None,
+                    metadata.updated_date.isoformat()
+                    if metadata.updated_date
+                    else None,
+                    json.dumps(metadata.categories),
+                    metadata.pdf_path,
+                    metadata.paper_md_path,
+                    metadata.summary_json_path,
+                    metadata.summary_md_path,
+                    metadata.imported_at.isoformat(),
+                    metadata.conversion_status.value,
+                    metadata.summary_status.value,
+                    json.dumps(metadata.tags),
+                    metadata.notes,
+                    search_text,
+                    author_list,
+                    category_list,
+                ),
+            )
+
+            # Update FTS table
+            conn.execute(
+                """
+                INSERT OR REPLACE INTO papers_fts (
+                    paper_id, title, authors, search_text, categories, tags, notes
+                ) VALUES (?, ?, ?, ?, ?, ?, ?)
+            """,
+                (
+                    metadata.paper_id,
+                    metadata.title,
+                    " ".join(metadata.authors),
+                    search_text,
+                    " ".join(metadata.categories),
+                    " ".join(metadata.tags),
+                    metadata.notes,
+                ),
+            )
+
+    def remove_paper(self, paper_id: str) -> bool:
+        """Remove a paper from the index."""
+        with self._get_connection() as conn:
+            cursor = conn.execute("DELETE FROM papers WHERE paper_id = ?", (paper_id,))
+            conn.execute("DELETE FROM papers_fts WHERE paper_id = ?", (paper_id,))
+            return cursor.rowcount > 0
+
+    def get_paper(self, paper_id: str) -> dict | None:
+        """Get a paper by ID from the index."""
+        with self._get_connection() as conn:
+            cursor = conn.execute(
+                "SELECT * FROM papers WHERE paper_id = ?", (paper_id,)
+            )
+            row = cursor.fetchone()
+            return dict(row) if row else None
+
+    def list_papers(
+        self,
+        source_type: SourceType | None = None,
+        conversion_status: ConversionStatus | None = None,
+        summary_status: SummaryStatus | None = None,
+        limit: int | None = None,
+        offset: int = 0,
+    ) -> Iterator[dict]:
+        """List papers with optional filtering."""
+        conditions = []
+        params = []
+
+        if source_type:
+            conditions.append("source_type = ?")
+            params.append(source_type.value)
+
+        if conversion_status:
+            conditions.append("conversion_status = ?")
+            params.append(conversion_status.value)
+
+        if summary_status:
+            conditions.append("summary_status = ?")
+            params.append(summary_status.value)
+
+        where_clause = ""
+        if conditions:
+            where_clause = "WHERE " + " AND ".join(conditions)
+
+        query = f"SELECT * FROM papers {where_clause} ORDER BY imported_at DESC"
+
+        if limit:
+            query += " LIMIT ? OFFSET ?"
+            params.extend([limit, offset])
+
+        with self._get_connection() as conn:
+            cursor = conn.execute(query, params)
+            for row in cursor:
+                yield dict(row)
+
+    def search_papers(self, query: str, limit: int = 50) -> Iterator[dict]:
+        """Search papers using full-text search."""
+        with self._get_connection() as conn:
+            # Use FTS for full-text search
+            cursor = conn.execute(
+                """
+                SELECT papers.* FROM papers_fts 
+                JOIN papers ON papers.paper_id = papers_fts.paper_id
+                WHERE papers_fts MATCH ?
+                ORDER BY rank
+                LIMIT ?
+            """,
+                (query, limit),
+            )
+
+            for row in cursor:
+                yield dict(row)
+
+    def search_by_field(
+        self,
+        field: str,
+        value: str,
+        exact_match: bool = False,
+        limit: int = 50,
+    ) -> Iterator[dict]:
+        """Search papers by specific field."""
+        if field not in ["title", "author_list", "category_list", "notes"]:
+            msg = f"Invalid field for search: {field}"
+            raise ValueError(msg)
+
+        if exact_match:
+            where_clause = f"{field} = ?"
+            params = [value]
+        else:
+            where_clause = f"{field} LIKE ?"
+            params = [f"%{value}%"]
+
+        order_by = "ORDER BY imported_at DESC LIMIT ?"
+        query = f"SELECT * FROM papers WHERE {where_clause} {order_by}"
+        params.append(limit)
+
+        with self._get_connection() as conn:
+            cursor = conn.execute(query, params)
+            for row in cursor:
+                yield dict(row)
+
+    def get_statistics(self) -> dict:
+        """Get library statistics."""
+        with self._get_connection() as conn:
+            stats = {}
+
+            # Total papers
+            cursor = conn.execute("SELECT COUNT(*) as count FROM papers")
+            stats["total_papers"] = cursor.fetchone()["count"]
+
+            # By source type
+            cursor = conn.execute(
+                "SELECT source_type, COUNT(*) as count FROM papers GROUP BY source_type"
+            )
+            stats["by_source_type"] = {
+                row["source_type"]: row["count"] for row in cursor
+            }
+
+            # By conversion status
+            cursor = conn.execute(
+                "SELECT conversion_status, COUNT(*) as count FROM papers "
+                "GROUP BY conversion_status"
+            )
+            stats["by_conversion_status"] = {
+                row["conversion_status"]: row["count"] for row in cursor
+            }
+
+            # By summary status
+            cursor = conn.execute(
+                "SELECT summary_status, COUNT(*) as count FROM papers "
+                "GROUP BY summary_status"
+            )
+            stats["by_summary_status"] = {
+                row["summary_status"]: row["count"] for row in cursor
+            }
+
+            return stats
+
+    def reindex_from_storage(self, storage_manager) -> tuple[int, int]:
+        """Rebuild the index from storage files."""
+        success_count = 0
+        error_count = 0
+
+        # Clear existing index
+        with self._get_connection() as conn:
+            conn.execute("DELETE FROM papers")
+            conn.execute("DELETE FROM papers_fts")
+
+        # Reindex all papers from storage
+        for metadata in storage_manager.list_all_papers():
+            try:
+                self.index_paper(metadata)
+                success_count += 1
+            except Exception:
+                error_count += 1
+
+        return success_count, error_count

src/paperlib/models/__init__.py

@@ -0,0 +1,17 @@
+"""Data models for paperlib."""
+
+from .paper import (
+    ConversionStatus,
+    PaperMetadata,
+    PaperSummary,
+    SourceType,
+    SummaryStatus,
+)
+
+__all__ = [
+    "ConversionStatus",
+    "PaperMetadata",
+    "PaperSummary",
+    "SourceType",
+    "SummaryStatus",
+]

src/paperlib/models/paper.py

@@ -0,0 +1,164 @@
+"""Data models for paper metadata and summaries."""
+
+from __future__ import annotations
+
+import json
+from dataclasses import asdict, dataclass, field
+from datetime import datetime
+from enum import StrEnum
+from pathlib import Path
+from typing import Any
+
+
+class ConversionStatus(StrEnum):
+    """Status of PDF to Markdown conversion."""
+
+    PENDING = "pending"
+    PROCESSING = "processing"
+    SUCCESS = "success"
+    FAILED = "failed"
+
+
+class SummaryStatus(StrEnum):
+    """Status of AI summarization."""
+
+    PENDING = "pending"
+    PROCESSING = "processing"
+    SUCCESS = "success"
+    FAILED = "failed"
+    NOT_REQUESTED = "not_requested"
+
+
+class SourceType(StrEnum):
+    """Type of paper source."""
+
+    LOCAL = "local"
+    ARXIV = "arxiv"
+
+
+@dataclass
+class PaperMetadata:
+    """Metadata for a paper (stored in meta.json)."""
+
+    # Core identifiers
+    paper_id: str
+    source_type: SourceType
+    source_id: str | None = None  # arXiv ID or local file hash
+
+    # Bibliographic information
+    title: str = ""
+    authors: list[str] = field(default_factory=list)
+    published_date: datetime | None = None
+    updated_date: datetime | None = None
+    categories: list[str] = field(default_factory=list)
+
+    # File paths (relative to library root)
+    pdf_path: str | None = None
+    paper_md_path: str | None = None
+    summary_json_path: str | None = None
+    summary_md_path: str | None = None
+
+    # Processing status
+    imported_at: datetime = field(default_factory=datetime.now)
+    conversion_status: ConversionStatus = ConversionStatus.PENDING
+    summary_status: SummaryStatus = SummaryStatus.NOT_REQUESTED
+
+    # Additional metadata
+    tags: list[str] = field(default_factory=list)
+    notes: str = ""
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        data = asdict(self)
+        # Convert datetime objects to ISO format strings
+        for field_name in ["published_date", "updated_date", "imported_at"]:
+            if data[field_name] is not None:
+                data[field_name] = data[field_name].isoformat()
+        # Convert enums to strings
+        data["source_type"] = self.source_type.value
+        data["conversion_status"] = self.conversion_status.value
+        data["summary_status"] = self.summary_status.value
+        return data
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> PaperMetadata:
+        """Create from dictionary (JSON deserialization)."""
+        # Convert ISO format strings back to datetime objects
+        for field_name in ["published_date", "updated_date", "imported_at"]:
+            if data.get(field_name):
+                data[field_name] = datetime.fromisoformat(data[field_name])
+        # Convert strings back to enums
+        if "source_type" in data:
+            data["source_type"] = SourceType(data["source_type"])
+        if "conversion_status" in data:
+            data["conversion_status"] = ConversionStatus(data["conversion_status"])
+        if "summary_status" in data:
+            data["summary_status"] = SummaryStatus(data["summary_status"])
+        return cls(**data)
+
+    def save_to_file(self, file_path: Path) -> None:
+        """Save metadata to a JSON file atomically."""
+        # Write to temporary file first, then move (atomic operation)
+        temp_path = file_path.with_suffix(".tmp")
+        with temp_path.open("w") as f:
+            json.dump(self.to_dict(), f, indent=2)
+        temp_path.rename(file_path)
+
+    @classmethod
+    def load_from_file(cls, file_path: Path) -> PaperMetadata:
+        """Load metadata from a JSON file."""
+        with file_path.open() as f:
+            data = json.load(f)
+        return cls.from_dict(data)
+
+
+@dataclass
+class PaperSummary:
+    """Structured summary for a paper (stored in summary.json)."""
+
+    # Schema version for migration
+    schema_version: str = "1.0"
+
+    # Core summary fields
+    one_sentence_summary: str = ""
+    problem_statement: str = ""
+    method_overview: str = ""
+    main_results: str = ""
+    claimed_contributions: list[str] = field(default_factory=list)
+    assumptions: list[str] = field(default_factory=list)
+    limitations: list[str] = field(default_factory=list)
+
+    # Categorization
+    problem_tags: list[str] = field(default_factory=list)
+    technique_tags: list[str] = field(default_factory=list)
+
+    # Entities mentioned
+    entities: list[str] = field(default_factory=list)
+
+    # Relevance scoring (optional)
+    relevance_to_user: float | None = None
+    recommended_sections: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        return asdict(self)
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> PaperSummary:
+        """Create from dictionary (JSON deserialization)."""
+        return cls(**data)
+
+    def save_to_file(self, file_path: Path) -> None:
+        """Save summary to a JSON file atomically."""
+        # Write to temporary file first, then move (atomic operation)
+        temp_path = file_path.with_suffix(".tmp")
+        with temp_path.open("w") as f:
+            json.dump(self.to_dict(), f, indent=2)
+        temp_path.rename(file_path)
+
+    @classmethod
+    def load_from_file(cls, file_path: Path) -> PaperSummary:
+        """Load summary from a JSON file."""
+        with file_path.open() as f:
+            data = json.load(f)
+        return cls.from_dict(data)

src/paperlib/storage/__init__.py

@@ -0,0 +1,5 @@
+"""Storage layer for paperlib."""
+
+from .manager import PaperStorageManager
+
+__all__ = ["PaperStorageManager"]

src/paperlib/storage/manager.py

@@ -0,0 +1,188 @@
+"""Paper storage manager for CRUD operations on metadata files."""
+
+from __future__ import annotations
+
+import hashlib
+import shutil
+from collections.abc import Iterator
+from datetime import datetime
+from pathlib import Path
+
+from paperlib.config import LibraryPaths
+from paperlib.models import PaperMetadata, PaperSummary, SourceType
+
+
+class PaperStorageManager:
+    """Manages storage and retrieval of papers and their metadata."""
+
+    def __init__(self, library_paths: LibraryPaths) -> None:
+        self.library_paths = library_paths
+
+    def generate_paper_id(
+        self,
+        source_type: SourceType,
+        source_id: str | None = None,
+        pdf_path: Path | None = None,
+    ) -> str:
+        """Generate a stable paper ID based on source type and content."""
+        if source_type == SourceType.ARXIV and source_id:
+            # Use arXiv ID directly (normalized)
+            return f"arxiv-{source_id.replace('.', '_').replace('v', '_v')}"
+        elif source_type == SourceType.LOCAL and pdf_path:
+            # Use SHA256 hash of PDF file content
+            with pdf_path.open("rb") as f:
+                content = f.read()
+            hash_hex = hashlib.sha256(content).hexdigest()
+            return f"local-{hash_hex[:16]}"  # Use first 16 chars of hash
+        else:
+            msg = "Cannot generate paper ID without proper source information"
+            raise ValueError(msg)
+
+    def get_paper_directory(self, paper_id: str, source_type: SourceType) -> Path:
+        """Get the directory path for storing a paper's files."""
+        if source_type == SourceType.ARXIV:
+            # Extract year from arXiv ID pattern (e.g., "2212.06340" -> "2022")
+            arxiv_id = paper_id.replace("arxiv-", "").replace("_", ".")
+            year_part = arxiv_id[:2]  # Get YY part
+            # Modern arXiv format: YYMM.NNNNN
+            if len(year_part) == 2 and year_part.isdigit():
+                # Convert 2-digit year to 4-digit year
+                yy = int(year_part)
+                if yy >= 90:  # 90-99 maps to 1990-1999
+                    year = str(1900 + yy)
+                else:  # 00-89 maps to 2000-2089
+                    year = str(2000 + yy)
+            else:
+                # Fallback to current year for older formats
+                year = str(datetime.now().year)
+            return self.library_paths.papers_dir / "arxiv" / year / paper_id
+        else:
+            # Local papers go under papers/local/{hash-prefix}/
+            hash_part = paper_id.replace("local-", "")
+            return self.library_paths.papers_dir / "local" / hash_part
+
+    def get_paper_paths(
+        self, paper_id: str, source_type: SourceType
+    ) -> dict[str, Path]:
+        """Get all expected file paths for a paper."""
+        paper_dir = self.get_paper_directory(paper_id, source_type)
+        return {
+            "directory": paper_dir,
+            "meta": paper_dir / "meta.json",
+            "pdf": paper_dir / "source.pdf",
+            "markdown": paper_dir / "paper.md",
+            "summary_json": paper_dir / "summary.json",
+            "summary_md": paper_dir / "summary.md",
+            "assets": paper_dir / "assets",
+            "logs": paper_dir / "logs",
+        }
+
+    def store_paper(
+        self,
+        pdf_path: Path,
+        source_type: SourceType,
+        source_id: str | None = None,
+        **metadata_kwargs,
+    ) -> PaperMetadata:
+        """Store a paper and create its metadata."""
+        # Generate paper ID
+        paper_id = self.generate_paper_id(source_type, source_id, pdf_path)
+
+        # Get storage paths
+        paths = self.get_paper_paths(paper_id, source_type)
+
+        # Create directory structure
+        paths["directory"].mkdir(parents=True, exist_ok=True)
+        paths["assets"].mkdir(exist_ok=True)
+        paths["logs"].mkdir(exist_ok=True)
+
+        # Copy PDF to storage
+        shutil.copy2(pdf_path, paths["pdf"])
+
+        # Create metadata
+        metadata = PaperMetadata(
+            paper_id=paper_id,
+            source_type=source_type,
+            source_id=source_id,
+            pdf_path=str(paths["pdf"].relative_to(self.library_paths.root)),
+            paper_md_path=str(paths["markdown"].relative_to(self.library_paths.root)),
+            summary_json_path=str(
+                paths["summary_json"].relative_to(self.library_paths.root)
+            ),
+            summary_md_path=str(
+                paths["summary_md"].relative_to(self.library_paths.root)
+            ),
+            **metadata_kwargs,
+        )
+
+        # Save metadata
+        metadata.save_to_file(paths["meta"])
+
+        return metadata
+
+    def load_paper_metadata(
+        self, paper_id: str, source_type: SourceType
+    ) -> PaperMetadata | None:
+        """Load paper metadata from storage."""
+        paths = self.get_paper_paths(paper_id, source_type)
+        if not paths["meta"].exists():
+            return None
+
+        try:
+            return PaperMetadata.load_from_file(paths["meta"])
+        except (FileNotFoundError, ValueError):
+            return None
+
+    def update_paper_metadata(self, metadata: PaperMetadata) -> None:
+        """Update paper metadata in storage."""
+        paths = self.get_paper_paths(metadata.paper_id, metadata.source_type)
+        metadata.save_to_file(paths["meta"])
+
+    def load_paper_summary(
+        self, paper_id: str, source_type: SourceType
+    ) -> PaperSummary | None:
+        """Load paper summary from storage."""
+        paths = self.get_paper_paths(paper_id, source_type)
+        if not paths["summary_json"].exists():
+            return None
+
+        try:
+            return PaperSummary.load_from_file(paths["summary_json"])
+        except (FileNotFoundError, ValueError):
+            return None
+
+    def save_paper_summary(
+        self, paper_id: str, source_type: SourceType, summary: PaperSummary
+    ) -> None:
+        """Save paper summary to storage."""
+        paths = self.get_paper_paths(paper_id, source_type)
+        summary.save_to_file(paths["summary_json"])
+
+    def list_all_papers(self) -> Iterator[PaperMetadata]:
+        """Iterate over all papers in the library."""
+        papers_dir = self.library_paths.papers_dir
+        if not papers_dir.exists():
+            return
+
+        # Look for meta.json files in the papers directory structure
+        for meta_file in papers_dir.rglob("meta.json"):
+            try:
+                yield PaperMetadata.load_from_file(meta_file)
+            except (ValueError, FileNotFoundError):
+                # Skip corrupted metadata files
+                continue
+
+    def paper_exists(self, paper_id: str, source_type: SourceType) -> bool:
+        """Check if a paper already exists in storage."""
+        paths = self.get_paper_paths(paper_id, source_type)
+        return paths["meta"].exists()
+
+    def delete_paper(self, paper_id: str, source_type: SourceType) -> bool:
+        """Delete a paper and all its files."""
+        paths = self.get_paper_paths(paper_id, source_type)
+        if not paths["directory"].exists():
+            return False
+
+        # Remove entire paper directory
+        shutil.rmtree(paths["directory"])
+        return True

src/paperlib/ui/__init__.py

@@ -0,0 +1,5 @@
+"""Rich UI components for paperlib."""
+
+from .converter_ui import ConversionUI
+
+__all__ = ["ConversionUI"]

src/paperlib/ui/converter_ui.py

@@ -0,0 +1,234 @@
+"""Rich UI for PDF conversion progress."""
+
+from __future__ import annotations
+
+import threading
+import time
+from queue import Empty, Queue
+
+from rich.console import Console
+from rich.live import Live
+from rich.panel import Panel
+from rich.progress import BarColumn, Progress, TaskID, TextColumn, TimeRemainingColumn
+from rich.table import Table
+
+
+class ConversionUI:
+    """Rich UI for displaying conversion progress and MinerU output."""
+
+    def __init__(self, console: Console | None = None):
+        self.console = console or Console()
+        self.progress = Progress(
+            TextColumn("[bold blue]{task.description}"),
+            BarColumn(bar_width=40),
+            "[progress.percentage]{task.percentage:>3.0f}%",
+            "•",
+            TextColumn("{task.completed}/{task.total} papers"),
+            "•",
+            TimeRemainingColumn(),
+            console=self.console,
+        )
+        self.output_lines = []
+        self.max_output_lines = 15  # Show last 15 lines of output
+
+    def create_display_table(self, task_id: TaskID, current_paper: str = "") -> Table:
+        """Create the main display table with progress and output."""
+        table = Table.grid()
+
+        # Progress section
+        progress_panel = Panel(
+            self.progress, title="[bold green]Conversion Progress", border_style="green"
+        )
+        table.add_row(progress_panel)
+
+        # Current paper info
+        if current_paper:
+            current_panel = Panel(
+                f"[bold yellow]Converting: {current_paper}", border_style="yellow"
+            )
+            table.add_row(current_panel)
+
+        # MinerU output section
+        output_text = (
+            "\n".join(self.output_lines[-self.max_output_lines :])
+            or "[dim]Waiting for output..."
+        )
+        output_panel = Panel(
+            output_text,
+            title="[bold cyan]MinerU Output",
+            border_style="cyan",
+            height=self.max_output_lines + 2,  # +2 for border
+        )
+        table.add_row(output_panel)
+
+        return table
+
+    def run_conversion_with_ui(
+        self, papers_to_convert: list, convert_func, storage_manager=None
+    ):
+        """Run conversion with rich UI display."""
+        if not papers_to_convert:
+            self.console.print("[yellow]No papers to convert.")
+            return 0, 0
+
+        # Get storage manager from converter or use passed one
+        if storage_manager is None:
+            try:
+                storage_manager = convert_func.__self__.storage_manager
+            except AttributeError:
+                # Fallback for mocked functions
+                storage_manager = None
+
+        # Initialize progress
+        task_id = self.progress.add_task(
+            "Converting papers...", total=len(papers_to_convert)
+        )
+
+        success_count = 0
+        failure_count = 0
+
+        with Live(
+            self.create_display_table(task_id),
+            console=self.console,
+            refresh_per_second=4,
+            vertical_overflow="visible",
+        ) as live:
+            for _i, metadata in enumerate(papers_to_convert):
+                # Update current paper info
+                current_paper = f"{metadata.paper_id} - {metadata.title[:50]}..."
+
+                # Clear previous output for new paper
+                self.output_lines = [f"Starting conversion of {metadata.paper_id}..."]
+
+                # Update display
+                live.update(self.create_display_table(task_id, current_paper))
+
+                # Run conversion with output streaming
+                if self._convert_with_streaming_output(
+                    metadata,
+                    convert_func,
+                    storage_manager,
+                    live,
+                    task_id,
+                    current_paper,
+                ):
+                    success_count += 1
+                    self.output_lines.append(
+                        "[bold green]✓ Conversion completed successfully"
+                    )
+                else:
+                    failure_count += 1
+                    self.output_lines.append("[bold red]✗ Conversion failed")
+
+                # Update progress
+                self.progress.update(task_id, advance=1)
+                live.update(self.create_display_table(task_id, current_paper))
+
+                # Brief pause to show result
+                time.sleep(0.5)
+
+        return success_count, failure_count
+
+    def _convert_with_streaming_output(
+        self, metadata, convert_func, storage_manager, live, task_id, current_paper
+    ):
+        """Convert a single paper with streaming output."""
+        # Get paper paths for log streaming
+        if storage_manager:
+            paths = storage_manager.get_paper_paths(
+                metadata.paper_id, metadata.source_type
+            )
+            log_file = paths["logs"] / "mineru.log"
+        else:
+            # Fallback when storage manager not available (testing)
+            log_file = None
+
+        # Start conversion in background thread
+        result_queue = Queue()
+
+        def run_conversion():
+            try:
+                result = convert_func(metadata)
+                result_queue.put(result)
+            except Exception:
+                result_queue.put(False)
+
+        # Start conversion thread
+        conversion_thread = threading.Thread(target=run_conversion)
+        conversion_thread.start()
+
+        # Stream output while conversion runs
+        last_size = 0
+        while conversion_thread.is_alive():
+            if log_file and log_file.exists():
+                try:
+                    # Read new content from log file
+                    current_content = log_file.read_text(
+                        encoding="utf-8", errors="ignore"
+                    )
+
+                    if len(current_content) > last_size:
+                        # Get new lines
+                        new_content = current_content[last_size:]
+                        new_lines = new_content.strip().split("\n")
+
+                        for line in new_lines:
+                            if line.strip():
+                                # Add line with some formatting
+                                formatted_line = self._format_mineru_output_line(line)
+                                self.output_lines.append(formatted_line)
+
+                                # Keep only recent lines
+                                if len(self.output_lines) > 50:
+                                    self.output_lines = self.output_lines[-30:]
+
+                        last_size = len(current_content)
+
+                        # Update display
+                        live.update(self.create_display_table(task_id, current_paper))
+
+                except Exception:
+                    # Ignore file read errors (file might be locked)
+                    pass
+
+            time.sleep(0.2)  # Check for updates 5 times per second
+
+        # Wait for thread to complete and get result
+        conversion_thread.join()
+
+        try:
+            return result_queue.get_nowait()
+        except Empty:
+            return False
+
+    def _format_mineru_output_line(self, line: str) -> str:
+        """Format a line of MinerU output for display."""
+        line = line.strip()
+
+        # Color code different types of output
+        if "INFO" in line:
+            return f"[dim]{line}"
+        elif "ERROR" in line or "Failed" in line:
+            return f"[red]{line}"
+        elif "WARNING" in line or "WARN" in line:
+            return f"[yellow]{line}"
+        elif "%" in line or "it/s" in line:
+            # Progress indicators
+            return f"[blue]{line}"
+        elif "Fetching" in line:
+            return f"[cyan]{line}"
+        else:
+            return line
+
+    def show_simple_progress(self, message: str, total: int) -> tuple[TaskID, Live]:
+        """Show a simple progress bar for operations without streaming output."""
+        task_id = self.progress.add_task(message, total=total)
+
+        display = Panel(
+            self.progress, title="[bold green]paperlib", border_style="green"
+        )
+
+        live = Live(display, console=self.console, refresh_per_second=10)
+        live.start()
+
+        return task_id, live

src/paperlib/utils/__init__.py

@@ -0,0 +1,5 @@
+"""Utility functions for paperlib."""
+
+from .json_output import JSONOutputMixin
+
+__all__ = ["JSONOutputMixin"]

src/paperlib/utils/json_output.py

@@ -0,0 +1,60 @@
+"""JSON output utilities for CLI commands."""
+
+import json
+from datetime import datetime
+from typing import Any
+
+
+class JSONOutputMixin:
+    """Mixin class for commands that support JSON output."""
+
+    @staticmethod
+    def output_json(data: dict[str, Any], success: bool = True) -> None:
+        """Output JSON data to stdout."""
+        output = {
+            "success": success,
+            "timestamp": datetime.now().isoformat(),
+            **data,
+        }
+
+        print(json.dumps(output, indent=2, ensure_ascii=False))
+
+    @staticmethod
+    def output_json_error(error_message: str, error_code: int = 1) -> None:
+        """Output JSON error to stdout."""
+        output = {
+            "success": False,
+            "timestamp": datetime.now().isoformat(),
+            "error": error_message,
+            "error_code": error_code,
+        }
+
+        print(json.dumps(output, indent=2, ensure_ascii=False))
+
+    @staticmethod
+    def format_metadata_for_json(metadata) -> dict[str, Any]:
+        """Convert PaperMetadata to JSON-serializable dict."""
+        from paperlib.models import PaperMetadata
+
+        if isinstance(metadata, PaperMetadata):
+            return metadata.to_dict()
+        elif isinstance(metadata, dict):
+            # Already a dict (from database query)
+            return metadata
+        else:
+            # Fallback for other types
+            return {"error": "Unknown metadata format"}
+
+    @staticmethod
+    def format_papers_list_for_json(papers: list) -> dict[str, Any]:
+        """Format a list of papers for JSON output."""
+        formatted_papers = []
+
+        for paper in papers:
+            formatted_paper = JSONOutputMixin.format_metadata_for_json(paper)
+            formatted_papers.append(formatted_paper)
+
+        return {
+            "papers": formatted_papers,
+            "total": len(formatted_papers),
+        }

tests/__init__.py

@@ -0,0 +1 @@
+"""Test package for paperlib."""

tests/test_arxiv_year_fix.py

@@ -0,0 +1,139 @@
+"""Test for arXiv year extraction bug fix."""
+
+import shutil
+from pathlib import Path
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.models import SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class TestArxivYearFix:
+    """Test the arXiv year extraction fix."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_arxiv_year_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def storage_manager(self, temp_library):
+        """Create a storage manager for testing."""
+        return PaperStorageManager(temp_library)
+
+    def test_arxiv_year_extraction_2022(self, storage_manager):
+        """Test year extraction for 2022 paper (2212.06340)."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-2212_06340", SourceType.ARXIV
+        )
+
+        # Should extract year 2022 from 2212.06340
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2022"
+            / "arxiv-2212_06340"
+        )
+        assert paper_dir == expected
+
+    def test_arxiv_year_extraction_2023(self, storage_manager):
+        """Test year extraction for 2023 paper (2301.12345)."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-2301_12345", SourceType.ARXIV
+        )
+
+        # Should extract year 2023 from 2301.12345
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2023"
+            / "arxiv-2301_12345"
+        )
+        assert paper_dir == expected
+
+    def test_arxiv_year_extraction_2020(self, storage_manager):
+        """Test year extraction for 2020 paper (2005.67890)."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-2005_67890", SourceType.ARXIV
+        )
+
+        # Should extract year 2020 from 2005.67890
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2020"
+            / "arxiv-2005_67890"
+        )
+        assert paper_dir == expected
+
+    def test_arxiv_year_extraction_1999(self, storage_manager):
+        """Test year extraction for 1999 paper (9912.12345)."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-9912_12345", SourceType.ARXIV
+        )
+
+        # Should extract year 1999 from 9912.12345 (99 -> 1999)
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "1999"
+            / "arxiv-9912_12345"
+        )
+        assert paper_dir == expected
+
+    def test_arxiv_year_extraction_2000(self, storage_manager):
+        """Test year extraction for 2000 paper (0001.12345)."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-0001_12345", SourceType.ARXIV
+        )
+
+        # Should extract year 2000 from 0001.12345 (00 -> 2000)
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2000"
+            / "arxiv-0001_12345"
+        )
+        assert paper_dir == expected
+
+    def test_arxiv_id_with_version(self, storage_manager):
+        """Test year extraction with version number."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-2212_06340v1", SourceType.ARXIV
+        )
+
+        # Should extract year 2022 from 2212.06340v1
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2022"
+            / "arxiv-2212_06340v1"
+        )
+        assert paper_dir == expected
+
+    def test_existing_storage_test_still_passes(self, storage_manager):
+        """Ensure we didn't break the existing test case."""
+        # This matches the test case in test_storage.py
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-2212_06340", SourceType.ARXIV
+        )
+
+        # The old test expected papers/arxiv/2212/ but should now be papers/arxiv/2022/
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2022"
+            / "arxiv-2212_06340"
+        )
+        assert paper_dir == expected

tests/test_cli.py

@@ -0,0 +1,247 @@
+"""Tests for paperlib CLI functionality."""
+
+import shutil
+import subprocess
+from pathlib import Path
+
+import pytest
+
+
+class TestCLI:
+    """Test CLI functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_cli_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+
+        yield temp_dir
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def sample_pdf(self):
+        """Create a sample PDF file for testing."""
+        pdf_file = Path("./.tmp") / f"cli_test_{hash(self)}.pdf"
+        with pdf_file.open("wb") as f:
+            # Minimal PDF content
+            f.write(b"%PDF-1.4\n")
+            f.write(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
+            f.write(b"%%EOF\n")
+
+        yield pdf_file
+
+        # Cleanup
+        if pdf_file.exists():
+            pdf_file.unlink()
+
+    def run_paperlib_cmd(self, *args):
+        """Helper to run paperlib commands."""
+        cmd = ["uv", "run", "paperlib"] + list(args)
+        result = subprocess.run(cmd, capture_output=True, text=True, cwd=Path.cwd())
+        return result
+
+    def test_cli_help(self):
+        """Test CLI help output."""
+        result = self.run_paperlib_cmd("--help")
+
+        assert result.returncode == 0
+        assert "paperlib" in result.stdout
+        assert "Local-first paper library engine" in result.stdout
+        assert "init" in result.stdout
+        assert "import" in result.stdout
+        assert "convert" in result.stdout
+
+    def test_cli_version(self):
+        """Test CLI version output."""
+        result = self.run_paperlib_cmd("--version")
+
+        assert result.returncode == 0
+        assert "paperlib" in result.stdout
+        assert "0.1.0" in result.stdout
+
+    def test_init_command(self, temp_library):
+        """Test library initialization command."""
+        result = self.run_paperlib_cmd("init", str(temp_library))
+
+        assert result.returncode == 0
+        assert "Initialized paper library" in result.stdout
+
+        # Check directory structure was created
+        assert (temp_library / "config").exists()
+        assert (temp_library / "papers").exists()
+        assert (temp_library / "inbox").exists()
+        assert (temp_library / "db").exists()
+        assert (temp_library / "cache").exists()
+
+    def test_status_command(self, temp_library):
+        """Test status command."""
+        # Initialize library first
+        self.run_paperlib_cmd("init", str(temp_library))
+
+        result = self.run_paperlib_cmd("status", "--library", str(temp_library))
+
+        assert result.returncode == 0
+        # Check for absolute path since that's what we get
+        assert str(temp_library.resolve()) in result.stdout
+        assert "config:" in result.stdout
+        assert "database:" in result.stdout
+        assert "papers:" in result.stdout
+
+    def test_import_local_pdf_command(self, temp_library, sample_pdf):
+        """Test importing local PDF via CLI."""
+        # Initialize library
+        self.run_paperlib_cmd("init", str(temp_library))
+
+        # Import PDF
+        result = self.run_paperlib_cmd(
+            "import",
+            "--pdf",
+            str(sample_pdf),
+            "--title",
+            "Test CLI Paper",
+            "--tags",
+            "test",
+            "cli",
+            "--library",
+            str(temp_library),
+        )
+
+        assert result.returncode == 0
+        assert "Successfully imported local PDF" in result.stdout
+        assert "Test CLI Paper" in result.stdout
+
+    def test_list_command_empty(self, temp_library):
+        """Test list command with empty library."""
+        self.run_paperlib_cmd("init", str(temp_library))
+
+        result = self.run_paperlib_cmd("list", "--library", str(temp_library))
+
+        assert result.returncode == 0
+        assert "No papers found" in result.stdout
+
+    def test_list_command_with_papers(self, temp_library, sample_pdf):
+        """Test list command with papers."""
+        # Initialize and import
+        self.run_paperlib_cmd("init", str(temp_library))
+        self.run_paperlib_cmd(
+            "import",
+            "--pdf",
+            str(sample_pdf),
+            "--title",
+            "Test Paper for List",
+            "--library",
+            str(temp_library),
+        )
+
+        result = self.run_paperlib_cmd("list", "--library", str(temp_library))
+
+        assert result.returncode == 0
+        assert "Found 1 papers" in result.stdout
+        assert "Test Paper for List" in result.stdout
+
+    def test_show_command(self, temp_library, sample_pdf):
+        """Test show command."""
+        # Initialize and import
+        self.run_paperlib_cmd("init", str(temp_library))
+        import_result = self.run_paperlib_cmd(
+            "import",
+            "--pdf",
+            str(sample_pdf),
+            "--title",
+            "Test Paper for Show",
+            "--library",
+            str(temp_library),
+        )
+
+        # Extract paper ID from import output
+        paper_id = None
+        for line in import_result.stdout.split("\n"):
+            if "Successfully imported local PDF:" in line:
+                paper_id = line.split(":")[-1].strip()
+                break
+
+        assert paper_id is not None
+
+        # Show paper details
+        result = self.run_paperlib_cmd("show", paper_id, "--library", str(temp_library))
+
+        assert result.returncode == 0
+        assert f"Paper ID: {paper_id}" in result.stdout
+        assert "Test Paper for Show" in result.stdout
+        assert "Source: local" in result.stdout
+
+    def test_show_nonexistent_paper(self, temp_library):
+        """Test show command with nonexistent paper."""
+        self.run_paperlib_cmd("init", str(temp_library))
+
+        result = self.run_paperlib_cmd(
+            "show", "nonexistent", "--library", str(temp_library)
+        )
+
+        assert result.returncode == 1
+        assert "Paper not found" in result.stdout
+
+    def test_reindex_command(self, temp_library, sample_pdf):
+        """Test reindex command."""
+        # Initialize and import
+        self.run_paperlib_cmd("init", str(temp_library))
+        self.run_paperlib_cmd(
+            "import", "--pdf", str(sample_pdf), "--library", str(temp_library)
+        )
+
+        # Reindex
+        result = self.run_paperlib_cmd("reindex", "--library", str(temp_library))
+
+        assert result.returncode == 0
+        assert "Rebuilding search index" in result.stdout
+        assert "papers indexed" in result.stdout
+        assert "Total papers: 1" in result.stdout
+
+    def test_convert_command_no_papers(self, temp_library):
+        """Test convert command with no papers."""
+        self.run_paperlib_cmd("init", str(temp_library))
+
+        result = self.run_paperlib_cmd(
+            "convert", "--no-ui", "--library", str(temp_library)
+        )
+
+        assert result.returncode == 0
+        assert "Converted pending: 0 successful, 0 failed" in result.stdout
+
+    def test_convert_command_with_papers_no_mineru(self, temp_library, sample_pdf):
+        """Test convert command with papers when MinerU is not available."""
+        # Initialize and import
+        self.run_paperlib_cmd("init", str(temp_library))
+        self.run_paperlib_cmd(
+            "import", "--pdf", str(sample_pdf), "--library", str(temp_library)
+        )
+
+        # Convert without UI (will fail because MinerU command may not be properly set up)
+        result = self.run_paperlib_cmd(
+            "convert", "--no-ui", "--library", str(temp_library)
+        )
+
+        # Should complete but may have failures due to MinerU setup
+        assert ("Converted pending:" in result.stdout) or (
+            "Converting papers" in result.stdout
+        )
+
+    def test_invalid_command(self):
+        """Test invalid command."""
+        result = self.run_paperlib_cmd("invalid-command")
+
+        assert result.returncode != 0
+
+    def test_missing_required_arguments(self):
+        """Test commands with missing required arguments."""
+        # Import without PDF or arXiv
+        result = self.run_paperlib_cmd("import")
+        assert result.returncode != 0
+
+        # Show without paper ID
+        result = self.run_paperlib_cmd("show")
+        assert result.returncode != 0

tests/test_config.py

@@ -0,0 +1,73 @@
+"""Tests for paperlib configuration."""
+
+import shutil
+from pathlib import Path
+
+from paperlib.config import LibraryPaths
+
+
+class TestLibraryPaths:
+    """Test LibraryPaths configuration."""
+
+    def test_from_root(self):
+        """Test creating LibraryPaths from root directory."""
+        root = Path("./.tmp/test_config")
+        paths = LibraryPaths.from_root(root)
+
+        # Check root path
+        assert paths.root == root.resolve()
+
+        # Check default subdirectories
+        assert paths.config_dir == root.resolve() / "config"
+        assert paths.papers_dir == root.resolve() / "papers"
+        assert paths.inbox_dir == root.resolve() / "inbox"
+        assert paths.db_dir == root.resolve() / "db"
+        assert paths.cache_dir == root.resolve() / "cache"
+
+        # Check specific files
+        assert paths.db_path == root.resolve() / "db" / "paperlib.sqlite3"
+        assert paths.config_path == root.resolve() / "config" / "config.toml"
+
+    def test_create_directories(self):
+        """Test creating library directory structure."""
+        root = Path("./.tmp/test_create_dirs")
+
+        try:
+            paths = LibraryPaths.from_root(root)
+
+            # Directories shouldn't exist initially
+            assert not paths.root.exists()
+
+            # Create directories
+            paths.create_directories()
+
+            # All directories should now exist
+            assert paths.root.exists()
+            assert paths.config_dir.exists()
+            assert paths.papers_dir.exists()
+            assert paths.inbox_dir.exists()
+            assert paths.db_dir.exists()
+            assert paths.cache_dir.exists()
+
+        finally:
+            # Cleanup
+            if root.exists():
+                shutil.rmtree(root)
+
+    def test_expanduser(self):
+        """Test that ~ is expanded in paths."""
+        # Test with tilde path
+        paths = LibraryPaths.from_root(Path("~/.tmp/test_tilde"))
+
+        # Root should be expanded
+        assert "~" not in str(paths.root)
+        assert paths.root.is_absolute()
+
+    def test_resolve_relative_paths(self):
+        """Test that relative paths are resolved."""
+        # Use relative path
+        paths = LibraryPaths.from_root(Path("./relative/path"))
+
+        # Should be absolute
+        assert paths.root.is_absolute()
+        assert "relative/path" in str(paths.root)

tests/test_converter.py

@@ -0,0 +1,233 @@
+"""Tests for paperlib PDF converter."""
+
+import shutil
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.converter import MinerUConverter
+from paperlib.models import ConversionStatus, PaperMetadata, SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class TestMinerUConverter:
+    """Test MinerUConverter functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_converter_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def storage_manager(self, temp_library):
+        """Create a storage manager for testing."""
+        return PaperStorageManager(temp_library)
+
+    @pytest.fixture
+    def converter(self, storage_manager):
+        """Create a MinerUConverter for testing."""
+        return MinerUConverter(storage_manager)
+
+    @pytest.fixture
+    def sample_metadata(self, storage_manager):
+        """Create sample paper metadata for testing."""
+        # Create a sample PDF file
+        pdf_file = Path("./.tmp") / f"test_convert_{hash(self)}.pdf"
+        with pdf_file.open("wb") as f:
+            f.write(b"%PDF-1.4\n")
+            f.write(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
+            f.write(b"%%EOF\n")
+
+        # Store the paper
+        metadata = storage_manager.store_paper(
+            pdf_path=pdf_file,
+            source_type=SourceType.LOCAL,
+            title="Test Paper for Conversion",
+        )
+
+        return metadata
+
+    @patch("subprocess.run")
+    def test_is_mineru_available_cli(self, mock_run, converter):
+        """Test MinerU availability check using CLI."""
+        # Mock successful mineru --version command
+        mock_run.return_value.returncode = 0
+
+        assert converter.is_mineru_available() is True
+        mock_run.assert_called_with(
+            ["mineru", "--version"],
+            capture_output=True,
+            check=False,
+        )
+
+    @patch("subprocess.run")
+    def test_is_mineru_available_fallback(self, mock_run, converter):
+        """Test MinerU availability fallback to import check."""
+        # Mock mineru command not found, but module available
+        mock_run.side_effect = [
+            Mock(returncode=1),  # mineru --version fails
+            Mock(returncode=0),  # import mineru succeeds
+        ]
+
+        assert converter.is_mineru_available() is True
+        assert mock_run.call_count == 2
+
+    @patch("subprocess.run")
+    def test_is_mineru_unavailable(self, mock_run, converter):
+        """Test when MinerU is completely unavailable."""
+        # Mock both command and import failing
+        mock_run.side_effect = [
+            Mock(returncode=1),  # mineru --version fails
+            Mock(returncode=1),  # import mineru fails
+        ]
+
+        assert converter.is_mineru_available() is False
+
+    @patch("subprocess.run")
+    def test_convert_paper_success(self, mock_run, converter, sample_metadata):
+        """Test successful paper conversion."""
+        # Mock successful mineru command
+        mock_run.return_value.returncode = 0
+
+        # Create expected output structure in temp cache
+        cache_dir = converter.storage_manager.library_paths.cache_dir
+        temp_output_dir = cache_dir / f"mineru_temp_{sample_metadata.paper_id}"
+        pdf_stem = "test_convert_" + str(hash(sample_metadata))
+        mineru_output_dir = temp_output_dir / pdf_stem
+        mineru_output_dir.mkdir(parents=True, exist_ok=True)
+
+        # Create expected output files
+        markdown_file = mineru_output_dir / f"{pdf_stem}.md"
+        images_dir = mineru_output_dir / "images"
+
+        markdown_file.write_text(
+            "# Test Markdown Content\n\nThis is converted content."
+        )
+        images_dir.mkdir(exist_ok=True)
+        (images_dir / "figure1.png").write_bytes(b"fake image data")
+
+        try:
+            # Run conversion
+            result = converter.convert_paper(sample_metadata)
+
+            # Verify command was called correctly
+            expected_cmd = [
+                "mineru",
+                "-p",
+                mock_run.call_args[0][0][2],  # PDF path
+                "-o",
+                mock_run.call_args[0][0][4],  # Output dir
+                "-b",
+                "pipeline",
+            ]
+
+            # Check that mineru was called with correct arguments
+            actual_cmd = mock_run.call_args[0][0]
+            assert actual_cmd[0] == "mineru"
+            assert "-p" in actual_cmd
+            assert "-o" in actual_cmd
+            assert "-b" in actual_cmd
+            assert "pipeline" in actual_cmd
+
+            # Verify conversion was successful
+            assert result is True
+
+            # Reload metadata and check status
+            updated_metadata = converter.storage_manager.load_paper_metadata(
+                sample_metadata.paper_id, sample_metadata.source_type
+            )
+            assert updated_metadata.conversion_status == ConversionStatus.SUCCESS
+
+        finally:
+            # Cleanup
+            if temp_output_dir.exists():
+                shutil.rmtree(temp_output_dir, ignore_errors=True)
+
+    @patch("subprocess.run")
+    def test_convert_paper_command_failure(self, mock_run, converter, sample_metadata):
+        """Test conversion when mineru command fails."""
+        # Mock failed mineru command
+        mock_run.return_value.returncode = 1
+
+        result = converter.convert_paper(sample_metadata)
+
+        # Verify conversion failed
+        assert result is False
+
+        # Check metadata was updated with failure status
+        updated_metadata = converter.storage_manager.load_paper_metadata(
+            sample_metadata.paper_id, sample_metadata.source_type
+        )
+        assert updated_metadata.conversion_status == ConversionStatus.FAILED
+
+    def test_convert_paper_mineru_unavailable(self, converter, sample_metadata):
+        """Test conversion when MinerU is not available."""
+        # Mock MinerU as unavailable
+        with patch.object(converter, "is_mineru_available", return_value=False):
+            result = converter.convert_paper(sample_metadata)
+
+            assert result is False
+
+    def test_convert_paper_missing_pdf(self, converter, storage_manager):
+        """Test conversion when PDF file is missing."""
+        # Create metadata pointing to non-existent PDF
+        metadata = PaperMetadata(
+            paper_id="missing-pdf-test",
+            source_type=SourceType.LOCAL,
+            title="Missing PDF Test",
+            pdf_path="nonexistent/path.pdf",
+            conversion_status=ConversionStatus.PENDING,
+        )
+
+        result = converter.convert_paper(metadata)
+        assert result is False
+
+    def test_convert_all_pending(self, converter, storage_manager):
+        """Test converting all papers with pending status."""
+        # Create sample PDF
+        pdf_file = Path("./.tmp") / f"batch_test_{hash(self)}.pdf"
+        with pdf_file.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            # Store multiple papers
+            papers = []
+            for i in range(3):
+                unique_pdf = Path("./.tmp") / f"batch_{i}_{hash(self)}.pdf"
+                shutil.copy2(pdf_file, unique_pdf)
+
+                try:
+                    metadata = storage_manager.store_paper(
+                        pdf_path=unique_pdf,
+                        source_type=SourceType.LOCAL,
+                        title=f"Batch Paper {i}",
+                    )
+                    papers.append(metadata)
+                finally:
+                    if unique_pdf.exists():
+                        unique_pdf.unlink()
+
+            # Mock conversions: 2 succeed, 1 fails
+            with patch.object(converter, "convert_paper") as mock_convert:
+                mock_convert.side_effect = [True, False, True]
+
+                success_count, failure_count = converter.convert_all_pending()
+
+                assert success_count == 2
+                assert failure_count == 1
+                assert mock_convert.call_count == 3
+
+        finally:
+            if pdf_file.exists():
+                pdf_file.unlink()

tests/test_converter_ui.py

@@ -0,0 +1,92 @@
+"""Tests for converter UI functionality."""
+
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import pytest
+from rich.console import Console
+
+from paperlib.ui import ConversionUI
+
+
+class TestConversionUI:
+    """Test ConversionUI functionality."""
+
+    @pytest.fixture
+    def ui(self):
+        """Create a ConversionUI instance for testing."""
+        # Use a console that doesn't output to terminal during tests
+        console = Console(file=open("/dev/null", "w"), force_terminal=True)
+        return ConversionUI(console=console)
+
+    @pytest.fixture
+    def mock_papers(self):
+        """Create mock paper metadata for testing."""
+        papers = []
+        for i in range(3):
+            paper = Mock()
+            paper.paper_id = f"test-paper-{i + 1}"
+            paper.title = f"Test Paper Title {i + 1}"
+            papers.append(paper)
+        return papers
+
+    def test_format_mineru_output_line(self, ui):
+        """Test formatting of MinerU output lines."""
+        # Test INFO line
+        info_line = "2026-04-17 17:46:01.450 | INFO | Processing started"
+        formatted = ui._format_mineru_output_line(info_line)
+        assert "[dim]" in formatted
+
+        # Test ERROR line
+        error_line = "ERROR: Conversion failed"
+        formatted = ui._format_mineru_output_line(error_line)
+        assert "[red]" in formatted
+
+        # Test WARNING line
+        warning_line = "WARNING: Low memory"
+        formatted = ui._format_mineru_output_line(warning_line)
+        assert "[yellow]" in formatted
+
+        # Test progress line
+        progress_line = "Layout Predict: 50%|█████     | 22/44 [00:15<00:15, 1.44it/s]"
+        formatted = ui._format_mineru_output_line(progress_line)
+        assert "[blue]" in formatted
+
+        # Test fetching line (may be colored blue due to % character)
+        fetch_line = "Fetching 7 files: 100%|██████████| 7/7"
+        formatted = ui._format_mineru_output_line(fetch_line)
+        assert ("[cyan]" in formatted) or (
+            "[blue]" in formatted
+        )  # Either color is fine
+
+    @patch("threading.Thread")
+    @patch("time.sleep")
+    def test_run_conversion_with_ui_empty(self, mock_sleep, mock_thread, ui):
+        """Test UI with no papers to convert."""
+        result = ui.run_conversion_with_ui([], lambda x: True)
+        assert result == (0, 0)
+
+    def test_create_display_table(self, ui):
+        """Test creating the display table."""
+        task_id = ui.progress.add_task("test", total=1)
+
+        # Test without current paper
+        table = ui.create_display_table(task_id)
+        assert table is not None
+
+        # Test with current paper
+        table = ui.create_display_table(task_id, "test-paper-1 - Sample Title")
+        assert table is not None
+
+    def test_output_line_management(self, ui):
+        """Test that output lines are properly managed."""
+        # Add many lines
+        for i in range(60):
+            ui.output_lines.append(f"Line {i}")
+
+        # The list can grow beyond 50, but display is limited to last 15 lines
+        assert len(ui.output_lines) == 60
+
+        # Check that display shows only recent lines
+        recent_lines = ui.output_lines[-ui.max_output_lines :]
+        assert len(recent_lines) == ui.max_output_lines

tests/test_database.py

@@ -0,0 +1,312 @@
+"""Tests for paperlib database manager."""
+
+import shutil
+from pathlib import Path
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.index import DatabaseManager
+from paperlib.models import ConversionStatus, PaperMetadata, SourceType, SummaryStatus
+
+
+class TestDatabaseManager:
+    """Test DatabaseManager functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_db_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def db_manager(self, temp_library):
+        """Create a database manager for testing."""
+        manager = DatabaseManager(temp_library)
+        manager.initialize_database()
+        return manager
+
+    @pytest.fixture
+    def sample_metadata(self):
+        """Create sample paper metadata for testing."""
+        return PaperMetadata(
+            paper_id="test-paper-1",
+            source_type=SourceType.LOCAL,
+            source_id=None,
+            title="A Test Paper on Machine Learning",
+            authors=["Alice Smith", "Bob Jones", "Charlie Brown"],
+            categories=["cs.AI", "stat.ML"],
+            tags=["machine-learning", "neural-networks", "test"],
+            notes="This is a test paper for unit testing.",
+            pdf_path="papers/local/test-paper-1/source.pdf",
+            paper_md_path="papers/local/test-paper-1/paper.md",
+            summary_json_path="papers/local/test-paper-1/summary.json",
+            summary_md_path="papers/local/test-paper-1/summary.md",
+        )
+
+    def test_initialize_database(self, temp_library):
+        """Test database initialization."""
+        db_manager = DatabaseManager(temp_library)
+
+        # Database file shouldn't exist initially
+        assert not db_manager.db_path.exists()
+
+        # Initialize database
+        db_manager.initialize_database()
+
+        # Database file should now exist
+        assert db_manager.db_path.exists()
+
+        # Should be able to connect and query
+        with db_manager._get_connection() as conn:
+            cursor = conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
+            tables = [row[0] for row in cursor.fetchall()]
+            assert "papers" in tables
+            assert "papers_fts" in tables
+
+    def test_index_paper(self, db_manager, sample_metadata):
+        """Test indexing a paper."""
+        # Index the paper
+        db_manager.index_paper(sample_metadata)
+
+        # Verify it was indexed
+        paper = db_manager.get_paper(sample_metadata.paper_id)
+        assert paper is not None
+        assert paper["paper_id"] == "test-paper-1"
+        assert paper["title"] == "A Test Paper on Machine Learning"
+        assert paper["source_type"] == "local"
+
+    def test_get_paper(self, db_manager, sample_metadata):
+        """Test getting a paper by ID."""
+        # Initially not found
+        paper = db_manager.get_paper("nonexistent")
+        assert paper is None
+
+        # Index a paper
+        db_manager.index_paper(sample_metadata)
+
+        # Now it should be found
+        paper = db_manager.get_paper(sample_metadata.paper_id)
+        assert paper is not None
+        assert paper["paper_id"] == sample_metadata.paper_id
+        assert paper["title"] == sample_metadata.title
+
+    def test_remove_paper(self, db_manager, sample_metadata):
+        """Test removing a paper from index."""
+        # Index a paper
+        db_manager.index_paper(sample_metadata)
+        assert db_manager.get_paper(sample_metadata.paper_id) is not None
+
+        # Remove it
+        result = db_manager.remove_paper(sample_metadata.paper_id)
+        assert result is True
+
+        # Verify it's gone
+        assert db_manager.get_paper(sample_metadata.paper_id) is None
+
+        # Removing again should return False
+        result = db_manager.remove_paper(sample_metadata.paper_id)
+        assert result is False
+
+    def test_list_papers(self, db_manager):
+        """Test listing papers with filtering."""
+        # Create multiple test papers
+        paper1 = PaperMetadata(
+            paper_id="paper-1",
+            source_type=SourceType.LOCAL,
+            title="Local Paper",
+            conversion_status=ConversionStatus.PENDING,
+            summary_status=SummaryStatus.NOT_REQUESTED,
+        )
+
+        paper2 = PaperMetadata(
+            paper_id="paper-2",
+            source_type=SourceType.ARXIV,
+            title="ArXiv Paper",
+            conversion_status=ConversionStatus.SUCCESS,
+            summary_status=SummaryStatus.PENDING,
+        )
+
+        # Index papers
+        db_manager.index_paper(paper1)
+        db_manager.index_paper(paper2)
+
+        # List all papers
+        all_papers = list(db_manager.list_papers())
+        assert len(all_papers) == 2
+
+        # Filter by source type
+        local_papers = list(db_manager.list_papers(source_type=SourceType.LOCAL))
+        assert len(local_papers) == 1
+        assert local_papers[0]["source_type"] == "local"
+
+        arxiv_papers = list(db_manager.list_papers(source_type=SourceType.ARXIV))
+        assert len(arxiv_papers) == 1
+        assert arxiv_papers[0]["source_type"] == "arxiv"
+
+        # Filter by conversion status
+        pending_papers = list(
+            db_manager.list_papers(conversion_status=ConversionStatus.PENDING)
+        )
+        assert len(pending_papers) == 1
+        assert pending_papers[0]["conversion_status"] == "pending"
+
+        # Test limit and offset
+        limited_papers = list(db_manager.list_papers(limit=1))
+        assert len(limited_papers) == 1
+
+    def test_search_papers_fts(self, db_manager, sample_metadata):
+        """Test full-text search."""
+        # Index a paper
+        db_manager.index_paper(sample_metadata)
+
+        # Search by title words
+        results = list(db_manager.search_papers("Machine Learning"))
+        assert len(results) == 1
+        assert results[0]["paper_id"] == sample_metadata.paper_id
+
+        # Search by author
+        results = list(db_manager.search_papers("Alice Smith"))
+        assert len(results) == 1
+
+        # Search by tag (quoted for FTS)
+        results = list(db_manager.search_papers('"neural-networks"'))
+        assert len(results) == 1
+
+        # Search for non-existent term
+        results = list(db_manager.search_papers("nonexistent"))
+        assert len(results) == 0
+
+    def test_search_by_field(self, db_manager, sample_metadata):
+        """Test searching by specific field."""
+        # Index a paper
+        db_manager.index_paper(sample_metadata)
+
+        # Search by title
+        results = list(db_manager.search_by_field("title", "Machine Learning"))
+        assert len(results) == 1
+
+        # Search by author list
+        results = list(db_manager.search_by_field("author_list", "Alice"))
+        assert len(results) == 1
+
+        # Exact match
+        results = list(
+            db_manager.search_by_field(
+                "title", "A Test Paper on Machine Learning", exact_match=True
+            )
+        )
+        assert len(results) == 1
+
+        results = list(
+            db_manager.search_by_field("title", "Partial Title", exact_match=True)
+        )
+        assert len(results) == 0
+
+        # Invalid field should raise error
+        with pytest.raises(ValueError):
+            list(db_manager.search_by_field("invalid_field", "test"))
+
+    def test_get_statistics(self, db_manager):
+        """Test getting library statistics."""
+        # Initially empty
+        stats = db_manager.get_statistics()
+        assert stats["total_papers"] == 0
+        assert stats["by_source_type"] == {}
+
+        # Add some papers
+        paper1 = PaperMetadata(
+            paper_id="paper-1",
+            source_type=SourceType.LOCAL,
+            title="Local Paper",
+            conversion_status=ConversionStatus.PENDING,
+        )
+
+        paper2 = PaperMetadata(
+            paper_id="paper-2",
+            source_type=SourceType.ARXIV,
+            title="ArXiv Paper 1",
+            conversion_status=ConversionStatus.SUCCESS,
+        )
+
+        paper3 = PaperMetadata(
+            paper_id="paper-3",
+            source_type=SourceType.ARXIV,
+            title="ArXiv Paper 2",
+            conversion_status=ConversionStatus.FAILED,
+        )
+
+        db_manager.index_paper(paper1)
+        db_manager.index_paper(paper2)
+        db_manager.index_paper(paper3)
+
+        # Check updated statistics
+        stats = db_manager.get_statistics()
+        assert stats["total_papers"] == 3
+        assert stats["by_source_type"]["local"] == 1
+        assert stats["by_source_type"]["arxiv"] == 2
+        assert stats["by_conversion_status"]["pending"] == 1
+        assert stats["by_conversion_status"]["success"] == 1
+        assert stats["by_conversion_status"]["failed"] == 1
+
+    def test_reindex_from_storage(self, db_manager, temp_library):
+        """Test reindexing from storage files."""
+        from paperlib.storage import PaperStorageManager
+
+        # Create storage manager and add some papers
+        storage_manager = PaperStorageManager(temp_library)
+
+        # Create a mock PDF file
+        pdf_file = Path("./.tmp") / "test.pdf"
+        with pdf_file.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            # Store papers in storage
+            metadata1 = storage_manager.store_paper(
+                pdf_path=pdf_file, source_type=SourceType.LOCAL, title="Paper 1"
+            )
+
+            metadata2 = storage_manager.store_paper(
+                pdf_path=pdf_file,
+                source_type=SourceType.ARXIV,
+                source_id="2212.06340",
+                title="Paper 2",
+            )
+
+            # Database should initially be empty
+            stats = db_manager.get_statistics()
+            assert stats["total_papers"] == 0
+
+            # Reindex from storage
+            success_count, error_count = db_manager.reindex_from_storage(
+                storage_manager
+            )
+
+            # Check results
+            assert success_count == 2
+            assert error_count == 0
+
+            # Verify papers are now in database
+            stats = db_manager.get_statistics()
+            assert stats["total_papers"] == 2
+
+            paper1 = db_manager.get_paper(metadata1.paper_id)
+            assert paper1 is not None
+            assert paper1["title"] == "Paper 1"
+
+            paper2 = db_manager.get_paper(metadata2.paper_id)
+            assert paper2 is not None
+            assert paper2["title"] == "Paper 2"
+
+        finally:
+            if pdf_file.exists():
+                pdf_file.unlink()

tests/test_importer.py

@@ -0,0 +1,273 @@
+"""Tests for paperlib import functionality."""
+
+import shutil
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.importer import ArxivImporter, LocalImporter
+from paperlib.models import SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class TestLocalImporter:
+    """Test LocalImporter functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_import_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def local_importer(self, temp_library):
+        """Create a LocalImporter for testing."""
+        storage_manager = PaperStorageManager(temp_library)
+        return LocalImporter(storage_manager)
+
+    @pytest.fixture
+    def sample_pdf(self):
+        """Create a sample PDF file for testing."""
+        pdf_file = Path("./.tmp") / f"sample_{hash(self)}.pdf"
+        with pdf_file.open("wb") as f:
+            # Minimal PDF content
+            f.write(b"%PDF-1.4\n")
+            f.write(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
+            f.write(b"%%EOF\n")
+
+        yield pdf_file
+
+        # Cleanup
+        if pdf_file.exists():
+            pdf_file.unlink()
+
+    def test_import_pdf_success(self, local_importer, sample_pdf):
+        """Test successful PDF import."""
+        metadata = local_importer.import_pdf(
+            pdf_path=sample_pdf,
+            title="Test Paper",
+            notes="Test notes",
+            tags=["test", "sample"],
+        )
+
+        # Check metadata
+        assert metadata.source_type == SourceType.LOCAL
+        assert metadata.title == "Test Paper"
+        assert metadata.notes == "Test notes"
+        assert metadata.tags == ["test", "sample"]
+        assert metadata.paper_id.startswith("local-")
+
+    def test_import_pdf_auto_title(self, local_importer, sample_pdf):
+        """Test PDF import with auto-generated title."""
+        # Rename PDF to have a meaningful name
+        meaningful_pdf = sample_pdf.parent / "Machine_Learning-Paper.pdf"
+        sample_pdf.rename(meaningful_pdf)
+
+        try:
+            metadata = local_importer.import_pdf(pdf_path=meaningful_pdf)
+
+            # Title should be auto-generated from filename
+            assert metadata.title == "Machine Learning Paper"
+
+        finally:
+            if meaningful_pdf.exists():
+                meaningful_pdf.unlink()
+
+    def test_import_nonexistent_pdf(self, local_importer):
+        """Test importing non-existent PDF file."""
+        nonexistent = Path("./.tmp/nonexistent.pdf")
+
+        with pytest.raises(FileNotFoundError):
+            local_importer.import_pdf(pdf_path=nonexistent)
+
+    def test_import_non_pdf_file(self, local_importer):
+        """Test importing non-PDF file."""
+        text_file = Path("./.tmp") / "not_a_pdf.txt"
+        with text_file.open("w") as f:
+            f.write("This is not a PDF")
+
+        try:
+            with pytest.raises(ValueError, match="File is not a PDF"):
+                local_importer.import_pdf(pdf_path=text_file)
+        finally:
+            if text_file.exists():
+                text_file.unlink()
+
+    def test_import_duplicate_pdf(self, local_importer, sample_pdf):
+        """Test importing the same PDF twice."""
+        # Import once
+        local_importer.import_pdf(pdf_path=sample_pdf)
+
+        # Try to import again
+        with pytest.raises(ValueError, match="Paper already imported"):
+            local_importer.import_pdf(pdf_path=sample_pdf)
+
+
+class TestArxivImporter:
+    """Test ArxivImporter functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_arxiv_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def arxiv_importer(self, temp_library):
+        """Create an ArxivImporter for testing."""
+        storage_manager = PaperStorageManager(temp_library)
+        return ArxivImporter(storage_manager)
+
+    def test_extract_arxiv_id_clean(self, arxiv_importer):
+        """Test extracting clean arXiv ID."""
+        # Test various formats
+        assert arxiv_importer.extract_arxiv_id("2212.06340") == "2212.06340"
+        assert arxiv_importer.extract_arxiv_id("arxiv:2212.06340") == "2212.06340"
+        assert arxiv_importer.extract_arxiv_id("2212.06340v1") == "2212.06340v1"
+        assert arxiv_importer.extract_arxiv_id("math-ph/0701002") == "math-ph/0701002"
+
+    def test_extract_arxiv_id_from_url(self, arxiv_importer):
+        """Test extracting arXiv ID from URLs."""
+        url = "https://arxiv.org/abs/2212.06340"
+        extracted = arxiv_importer.extract_arxiv_id(url)
+        assert extracted == "2212.06340"
+
+    def test_fetch_paper_metadata_success(self, arxiv_importer):
+        """Test successful metadata fetching from arXiv."""
+        # Mock arXiv result
+        mock_result = Mock()
+        mock_result.title = "Test Paper"
+        mock_result.authors = [Mock(name="Alice Smith"), Mock(name="Bob Jones")]
+        mock_result.published = Mock()
+        mock_result.updated = Mock()
+        mock_result.categories = ["cs.AI", "stat.ML"]
+
+        # Mock the client's results method directly
+        arxiv_importer.client.results = Mock(return_value=[mock_result])
+
+        # Test
+        result = arxiv_importer.fetch_paper_metadata("2212.06340")
+        assert result == mock_result
+
+    def test_fetch_paper_metadata_not_found(self, arxiv_importer):
+        """Test fetching metadata for non-existent paper."""
+        # Mock empty results
+        arxiv_importer.client.results = Mock(return_value=[])
+
+        with pytest.raises(ValueError, match="Paper not found on arXiv"):
+            arxiv_importer.fetch_paper_metadata("9999.99999")
+
+    @patch("paperlib.importer.arxiv_importer.tempfile.NamedTemporaryFile")
+    def test_download_pdf(self, mock_tempfile, arxiv_importer):
+        """Test PDF downloading."""
+        # Mock temporary file
+        mock_temp_path = Path("./.tmp/mock_temp.pdf")
+        mock_tempfile.return_value.__enter__.return_value.name = str(mock_temp_path)
+
+        # Mock arXiv result
+        mock_result = Mock()
+
+        # Create actual temp file for test
+        with mock_temp_path.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            pdf_path = arxiv_importer.download_pdf(mock_result)
+            assert pdf_path == mock_temp_path
+            mock_result.download_pdf.assert_called_once_with(
+                filename=str(mock_temp_path)
+            )
+        finally:
+            if mock_temp_path.exists():
+                mock_temp_path.unlink()
+
+    @patch.object(ArxivImporter, "download_pdf")
+    @patch.object(ArxivImporter, "fetch_paper_metadata")
+    def test_import_arxiv_paper_success(
+        self, mock_fetch, mock_download, arxiv_importer
+    ):
+        """Test successful arXiv paper import."""
+        # Mock PDF file
+        pdf_file = Path("./.tmp") / "test_arxiv.pdf"
+        with pdf_file.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            # Mock arXiv result with proper string values
+            mock_author = Mock()
+            mock_author.name = "Alice Smith"
+
+            mock_result = Mock()
+            mock_result.title = "Test ArXiv Paper"
+            mock_result.authors = [mock_author]
+            mock_result.published = None
+            mock_result.updated = None
+            mock_result.categories = ["cs.AI"]
+
+            mock_fetch.return_value = mock_result
+            mock_download.return_value = pdf_file
+
+            # Test import
+            metadata = arxiv_importer.import_arxiv_paper(
+                arxiv_input="2212.06340", notes="Test notes", tags=["test"]
+            )
+
+            # Check results
+            assert metadata.source_type == SourceType.ARXIV
+            assert metadata.source_id == "2212.06340"
+            assert metadata.title == "Test ArXiv Paper"
+            assert metadata.authors == ["Alice Smith"]
+            assert metadata.categories == ["cs.AI"]
+            assert metadata.notes == "Test notes"
+            assert metadata.tags == ["test"]
+
+        finally:
+            if pdf_file.exists():
+                pdf_file.unlink()
+
+    @patch.object(ArxivImporter, "fetch_paper_metadata")
+    def test_import_duplicate_arxiv_paper(self, mock_fetch, arxiv_importer):
+        """Test importing the same arXiv paper twice."""
+        # Mock first import
+        pdf_file = Path("./.tmp") / "test_arxiv_dup.pdf"
+        with pdf_file.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            with patch.object(ArxivImporter, "download_pdf", return_value=pdf_file):
+                mock_result = Mock()
+                mock_result.title = "Test Paper"
+                mock_result.authors = []
+                mock_result.published = None
+                mock_result.updated = None
+                mock_result.categories = []
+                mock_fetch.return_value = mock_result
+
+                # First import should succeed
+                arxiv_importer.import_arxiv_paper("2212.06340")
+
+                # Second import should fail
+                with pytest.raises(ValueError, match="Paper already imported"):
+                    arxiv_importer.import_arxiv_paper("2212.06340")
+
+        finally:
+            if pdf_file.exists():
+                pdf_file.unlink()

tests/test_integration.py

@@ -0,0 +1,219 @@
+"""Integration tests for paperlib."""
+
+import shutil
+from pathlib import Path
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.importer import LocalImporter
+from paperlib.index import DatabaseManager
+from paperlib.models import SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class TestIntegration:
+    """Test full integration workflows."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_integration_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def sample_pdf(self):
+        """Create a sample PDF file for testing."""
+        pdf_file = Path("./.tmp") / f"integration_test_{hash(self)}.pdf"
+        with pdf_file.open("wb") as f:
+            # Minimal PDF content
+            f.write(b"%PDF-1.4\n")
+            f.write(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
+            f.write(b"2 0 obj\n<< /Type /Pages /Kids [3 0 R] /Count 1 >>\nendobj\n")
+            f.write(b"3 0 obj\n<< /Type /Page /Parent 2 0 R >>\nendobj\n")
+            f.write(b"%%EOF\n")
+
+        yield pdf_file
+
+        # Cleanup
+        if pdf_file.exists():
+            pdf_file.unlink()
+
+    def test_complete_local_import_workflow(self, temp_library, sample_pdf):
+        """Test complete workflow for importing and managing a local PDF."""
+        # Set up components
+        storage_manager = PaperStorageManager(temp_library)
+        db_manager = DatabaseManager(temp_library)
+        local_importer = LocalImporter(storage_manager)
+
+        # Initialize database
+        db_manager.initialize_database()
+
+        # Import PDF
+        metadata = local_importer.import_pdf(
+            pdf_path=sample_pdf,
+            title="Integration Test Paper",
+            tags=["integration", "test"],
+            notes="This is an integration test paper",
+        )
+
+        # Update metadata with authors after import
+        metadata.authors = ["Test Author"]
+        storage_manager.update_paper_metadata(metadata)
+
+        # Verify metadata
+        assert metadata.source_type == SourceType.LOCAL
+        assert metadata.title == "Integration Test Paper"
+        assert metadata.authors == ["Test Author"]
+        assert metadata.tags == ["integration", "test"]
+
+        # Index in database
+        db_manager.index_paper(metadata)
+
+        # Test retrieval from database
+        retrieved_paper = db_manager.get_paper(metadata.paper_id)
+        assert retrieved_paper is not None
+        assert retrieved_paper["title"] == "Integration Test Paper"
+
+        # Test search functionality
+        search_results = list(db_manager.search_papers("Integration Test"))
+        assert len(search_results) == 1
+        assert search_results[0]["paper_id"] == metadata.paper_id
+
+        # Test field search
+        author_results = list(db_manager.search_by_field("author_list", "Test Author"))
+        assert len(author_results) == 1
+
+        # Test listing papers
+        all_papers = list(db_manager.list_papers())
+        assert len(all_papers) == 1
+        assert all_papers[0]["paper_id"] == metadata.paper_id
+
+        # Test statistics
+        stats = db_manager.get_statistics()
+        assert stats["total_papers"] == 1
+        assert stats["by_source_type"]["local"] == 1
+
+        # Test updating metadata
+        metadata.notes = "Updated notes"
+        storage_manager.update_paper_metadata(metadata)
+
+        # Re-index and verify update
+        db_manager.index_paper(metadata)
+        updated_paper = db_manager.get_paper(metadata.paper_id)
+        assert "Updated notes" in updated_paper["search_text"]
+
+    def test_multiple_papers_workflow(self, temp_library, sample_pdf):
+        """Test workflow with multiple papers."""
+        # Set up components
+        storage_manager = PaperStorageManager(temp_library)
+        db_manager = DatabaseManager(temp_library)
+        local_importer = LocalImporter(storage_manager)
+
+        # Initialize database
+        db_manager.initialize_database()
+
+        # Import multiple papers (create unique PDFs)
+        papers = []
+        for i in range(3):
+            # Create unique PDF for each import
+            unique_pdf = Path("./.tmp") / f"unique_paper_{i}_{hash(self)}.pdf"
+            with unique_pdf.open("wb") as f:
+                f.write(b"%PDF-1.4\n")
+                f.write(f"% Unique content {i}\n".encode())
+                f.write(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
+                f.write(b"%%EOF\n")
+
+            try:
+                metadata = local_importer.import_pdf(
+                    pdf_path=unique_pdf,
+                    title=f"Test Paper {i + 1}",
+                    tags=[f"tag{i + 1}", "common"],
+                    notes=f"Notes for paper {i + 1}",
+                )
+                # Update metadata with authors after import
+                metadata.authors = [f"Author {i + 1}"]
+                storage_manager.update_paper_metadata(metadata)
+
+                papers.append(metadata)
+                db_manager.index_paper(metadata)
+
+            finally:
+                if unique_pdf.exists():
+                    unique_pdf.unlink()
+
+        # Test listing all papers
+        all_papers = list(db_manager.list_papers())
+        assert len(all_papers) == 3
+
+        # Test search across papers
+        common_tag_results = list(db_manager.search_papers("common"))
+        assert len(common_tag_results) == 3
+
+        # Test filtering
+        filtered_results = list(db_manager.list_papers(limit=2))
+        assert len(filtered_results) == 2
+
+        # Test reindexing
+        success_count, error_count = db_manager.reindex_from_storage(storage_manager)
+        assert success_count == 3
+        assert error_count == 0
+
+        # Verify papers still exist after reindex
+        stats = db_manager.get_statistics()
+        assert stats["total_papers"] == 3
+
+    def test_storage_and_database_consistency(self, temp_library, sample_pdf):
+        """Test consistency between storage and database."""
+        # Set up components
+        storage_manager = PaperStorageManager(temp_library)
+        db_manager = DatabaseManager(temp_library)
+        local_importer = LocalImporter(storage_manager)
+
+        # Initialize database
+        db_manager.initialize_database()
+
+        # Import paper
+        metadata = local_importer.import_pdf(
+            pdf_path=sample_pdf,
+            title="Consistency Test Paper",
+        )
+
+        # Index in database
+        db_manager.index_paper(metadata)
+
+        # Verify file exists in storage
+        assert storage_manager.paper_exists(metadata.paper_id, metadata.source_type)
+
+        # Verify paper exists in database
+        db_paper = db_manager.get_paper(metadata.paper_id)
+        assert db_paper is not None
+
+        # Load from storage and compare
+        storage_metadata = storage_manager.load_paper_metadata(
+            metadata.paper_id, metadata.source_type
+        )
+        assert storage_metadata.title == db_paper["title"]
+        assert storage_metadata.paper_id == db_paper["paper_id"]
+
+        # Test reindexing maintains consistency
+        db_manager.remove_paper(metadata.paper_id)
+        assert db_manager.get_paper(metadata.paper_id) is None
+
+        # Reindex from storage
+        success_count, error_count = db_manager.reindex_from_storage(storage_manager)
+        assert success_count == 1
+        assert error_count == 0
+
+        # Verify paper is back in database
+        restored_paper = db_manager.get_paper(metadata.paper_id)
+        assert restored_paper is not None
+        assert restored_paper["title"] == "Consistency Test Paper"

tests/test_json_output.py

@@ -0,0 +1,290 @@
+"""Tests for JSON output functionality."""
+
+import json
+import subprocess
+from pathlib import Path
+
+import pytest
+
+from paperlib.models import PaperMetadata, SourceType
+from paperlib.utils import JSONOutputMixin
+
+
+class TestJSONOutputMixin:
+    """Test JSONOutputMixin utility functions."""
+
+    def test_format_metadata_for_json(self):
+        """Test formatting PaperMetadata for JSON output."""
+        metadata = PaperMetadata(
+            paper_id="test-paper-1",
+            source_type=SourceType.ARXIV,
+            source_id="2212.06340",
+            title="Test Paper",
+            authors=["Alice Smith", "Bob Jones"],
+            categories=["cs.AI"],
+        )
+
+        result = JSONOutputMixin.format_metadata_for_json(metadata)
+
+        assert result["paper_id"] == "test-paper-1"
+        assert result["source_type"] == "arxiv"
+        assert result["source_id"] == "2212.06340"
+        assert result["title"] == "Test Paper"
+        assert result["authors"] == ["Alice Smith", "Bob Jones"]
+        assert result["categories"] == ["cs.AI"]
+
+    def test_format_metadata_for_json_dict(self):
+        """Test formatting dict metadata for JSON output."""
+        metadata_dict = {
+            "paper_id": "test-paper-1",
+            "title": "Test Paper",
+            "source_type": "local",
+        }
+
+        result = JSONOutputMixin.format_metadata_for_json(metadata_dict)
+        assert result == metadata_dict
+
+    def test_format_papers_list_for_json(self):
+        """Test formatting a list of papers for JSON output."""
+        papers = [
+            PaperMetadata(
+                paper_id="paper-1",
+                source_type=SourceType.LOCAL,
+                title="Paper 1",
+            ),
+            PaperMetadata(
+                paper_id="paper-2",
+                source_type=SourceType.ARXIV,
+                title="Paper 2",
+            ),
+        ]
+
+        result = JSONOutputMixin.format_papers_list_for_json(papers)
+
+        assert "papers" in result
+        assert "total" in result
+        assert result["total"] == 2
+        assert len(result["papers"]) == 2
+        assert result["papers"][0]["paper_id"] == "paper-1"
+        assert result["papers"][1]["paper_id"] == "paper-2"
+
+
+class TestCLIJSONOutput:
+    """Test CLI commands with JSON output."""
+
+    def run_paperlib_cmd(self, *args):
+        """Helper to run paperlib commands and parse JSON output."""
+        cmd = ["uv", "run", "paperlib"] + list(args)
+        result = subprocess.run(cmd, capture_output=True, text=True, cwd=Path.cwd())
+
+        if "--json" in args:
+            try:
+                output_data = json.loads(result.stdout)
+                return result.returncode, output_data, result.stderr
+            except json.JSONDecodeError as e:
+                pytest.fail(f"Invalid JSON output: {e}\nOutput: {result.stdout}")
+
+        return result.returncode, result.stdout, result.stderr
+
+    def test_status_json_output(self):
+        """Test status command with JSON output."""
+        # Create temporary library
+        temp_lib = Path("./.tmp") / f"test_status_json_{hash(self)}"
+        temp_lib.mkdir(parents=True, exist_ok=True)
+
+        try:
+            # Initialize library
+            self.run_paperlib_cmd("init", str(temp_lib))
+
+            # Test status with JSON
+            returncode, output_data, stderr = self.run_paperlib_cmd(
+                "status", "--library", str(temp_lib), "--json"
+            )
+
+            assert returncode == 0
+            assert isinstance(output_data, dict)
+            assert output_data["success"] is True
+            assert "timestamp" in output_data
+            assert "library_root" in output_data
+            assert "config_path" in output_data
+            assert "database_path" in output_data
+            assert str(temp_lib.resolve()) in output_data["library_root"]
+
+        finally:
+            if temp_lib.exists():
+                import shutil
+
+                shutil.rmtree(temp_lib)
+
+    def test_list_json_output_empty(self):
+        """Test list command with JSON output for empty library."""
+        temp_lib = Path("./.tmp") / f"test_list_json_{hash(self)}"
+        temp_lib.mkdir(parents=True, exist_ok=True)
+
+        try:
+            # Initialize library
+            self.run_paperlib_cmd("init", str(temp_lib))
+
+            # Test list with JSON
+            returncode, output_data, stderr = self.run_paperlib_cmd(
+                "list", "--library", str(temp_lib), "--json"
+            )
+
+            assert returncode == 0
+            assert isinstance(output_data, dict)
+            assert output_data["success"] is True
+            assert output_data["papers"] == []
+            assert output_data["total"] == 0
+
+        finally:
+            if temp_lib.exists():
+                import shutil
+
+                shutil.rmtree(temp_lib)
+
+    def test_import_json_output(self):
+        """Test import command with JSON output."""
+        temp_lib = Path("./.tmp") / f"test_import_json_{hash(self)}"
+        temp_lib.mkdir(parents=True, exist_ok=True)
+
+        # Create sample PDF
+        sample_pdf = Path("./.tmp") / f"test_import_json_{hash(self)}.pdf"
+        with sample_pdf.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            # Initialize library
+            self.run_paperlib_cmd("init", str(temp_lib))
+
+            # Test import with JSON
+            returncode, output_data, stderr = self.run_paperlib_cmd(
+                "import",
+                "--pdf",
+                str(sample_pdf),
+                "--title",
+                "Test JSON Import",
+                "--library",
+                str(temp_lib),
+                "--json",
+            )
+
+            assert returncode == 0
+            assert isinstance(output_data, dict)
+            assert output_data["success"] is True
+            assert "paper_id" in output_data
+            assert output_data["title"] == "Test JSON Import"
+            assert output_data["source_type"] == "local"
+            assert "Successfully imported local PDF" in output_data["message"]
+            assert "paper" in output_data
+            assert isinstance(output_data["paper"], dict)
+
+        finally:
+            if temp_lib.exists():
+                import shutil
+
+                shutil.rmtree(temp_lib)
+            if sample_pdf.exists():
+                sample_pdf.unlink()
+
+    def test_show_json_output(self):
+        """Test show command with JSON output."""
+        temp_lib = Path("./.tmp") / f"test_show_json_{hash(self)}"
+        temp_lib.mkdir(parents=True, exist_ok=True)
+
+        # Create sample PDF
+        sample_pdf = Path("./.tmp") / f"test_show_json_{hash(self)}.pdf"
+        with sample_pdf.open("wb") as f:
+            f.write(b"%PDF-1.4\n%%EOF\n")
+
+        try:
+            # Initialize and import
+            self.run_paperlib_cmd("init", str(temp_lib))
+            import_returncode, import_data, _ = self.run_paperlib_cmd(
+                "import",
+                "--pdf",
+                str(sample_pdf),
+                "--title",
+                "Test JSON Show",
+                "--library",
+                str(temp_lib),
+                "--json",
+            )
+
+            assert import_returncode == 0
+            paper_id = import_data["paper_id"]
+
+            # Test show with JSON
+            returncode, output_data, stderr = self.run_paperlib_cmd(
+                "show", paper_id, "--library", str(temp_lib), "--json"
+            )
+
+            assert returncode == 0
+            assert isinstance(output_data, dict)
+            assert output_data["success"] is True
+            assert "paper" in output_data
+            assert output_data["paper"]["paper_id"] == paper_id
+            assert output_data["paper"]["title"] == "Test JSON Show"
+            assert "files_status" in output_data["paper"]
+            assert "pdf_exists" in output_data["paper"]["files_status"]
+
+        finally:
+            if temp_lib.exists():
+                import shutil
+
+                shutil.rmtree(temp_lib)
+            if sample_pdf.exists():
+                sample_pdf.unlink()
+
+    def test_show_json_not_found(self):
+        """Test show command with JSON output for non-existent paper."""
+        temp_lib = Path("./.tmp") / f"test_show_json_nf_{hash(self)}"
+        temp_lib.mkdir(parents=True, exist_ok=True)
+
+        try:
+            # Initialize library
+            self.run_paperlib_cmd("init", str(temp_lib))
+
+            # Test show non-existent paper
+            returncode, output_data, stderr = self.run_paperlib_cmd(
+                "show", "nonexistent", "--library", str(temp_lib), "--json"
+            )
+
+            assert returncode == 1
+            assert isinstance(output_data, dict)
+            assert output_data["success"] is False
+            assert "error" in output_data
+            assert "Paper not found" in output_data["error"]
+
+        finally:
+            if temp_lib.exists():
+                import shutil
+
+                shutil.rmtree(temp_lib)
+
+    def test_convert_json_output(self):
+        """Test convert command with JSON output."""
+        temp_lib = Path("./.tmp") / f"test_convert_json_{hash(self)}"
+        temp_lib.mkdir(parents=True, exist_ok=True)
+
+        try:
+            # Initialize library
+            self.run_paperlib_cmd("init", str(temp_lib))
+
+            # Test convert with no papers (JSON)
+            returncode, output_data, stderr = self.run_paperlib_cmd(
+                "convert", "--library", str(temp_lib), "--json"
+            )
+
+            assert returncode == 0
+            assert isinstance(output_data, dict)
+            assert output_data["success"] is True
+            assert output_data["action"] == "convert_pending"
+            assert output_data["success_count"] == 0
+            assert output_data["failure_count"] == 0
+            assert output_data["total_attempted"] == 0
+
+        finally:
+            if temp_lib.exists():
+                import shutil
+
+                shutil.rmtree(temp_lib)

tests/test_mineru_postprocess.py

@@ -0,0 +1,219 @@
+"""Tests for MinerU markdown post-processing."""
+
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.converter import MinerUConverter
+from paperlib.storage import PaperStorageManager
+
+
+class TestMinerUPostProcess:
+    """Test MinerU markdown post-processing functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_postprocess_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+        return library_paths
+
+    @pytest.fixture
+    def converter(self, temp_library):
+        """Create a MinerUConverter for testing."""
+        storage_manager = PaperStorageManager(temp_library)
+        return MinerUConverter(storage_manager)
+
+    def test_image_reference_replacement(self, converter):
+        """Test that image references are correctly updated."""
+        # Create test markdown content with various image reference formats
+        test_content = """# Test Document
+
+Here's an image with alt text:
+![Figure 1](images/03781efbc8005e66728b733052e050ccbd581e5079942e5ab8e4c3020e53540d.jpg)
+
+Here's an image without alt text:
+![](images/another_image.png)
+
+Some text content.
+
+Here's another image:
+![Complex alt text with spaces](images/subfolder/image.svg)
+
+This should not be changed:
+![External image](https://example.com/image.jpg)
+
+And this local reference should not change:
+![Local ref](./local_images/test.png)
+"""
+
+        expected_content = """# Test Document
+
+Here's an image with alt text:
+![Figure 1](assets/03781efbc8005e66728b733052e050ccbd581e5079942e5ab8e4c3020e53540d.jpg)
+
+Here's an image without alt text:
+![](assets/another_image.png)
+
+Some text content.
+
+Here's another image:
+![Complex alt text with spaces](assets/subfolder/image.svg)
+
+This should not be changed:
+![External image](https://example.com/image.jpg)
+
+And this local reference should not change:
+![Local ref](./local_images/test.png)
+"""
+
+        # Create temporary file
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".md", delete=False, encoding="utf-8"
+        ) as tmp:
+            tmp.write(test_content)
+            tmp_path = Path(tmp.name)
+
+        try:
+            # Apply post-processing
+            converter._post_process_markdown(tmp_path)
+
+            # Read the result
+            result_content = tmp_path.read_text(encoding="utf-8")
+
+            # Verify image references were updated correctly
+            assert "![Figure 1](assets/" in result_content
+            assert "![](assets/another_image.png)" in result_content
+            assert (
+                "![Complex alt text with spaces](assets/subfolder/image.svg)"
+                in result_content
+            )
+
+            # Verify external and local references were NOT changed
+            assert "https://example.com/image.jpg" in result_content
+            assert "./local_images/test.png" in result_content
+
+            # Verify no "images/" references remain
+            assert "](images/" not in result_content
+
+        finally:
+            if tmp_path.exists():
+                tmp_path.unlink()
+
+    def test_markdown_content_cleaning(self, converter):
+        """Test markdown content cleaning functionality."""
+        test_content = """# Title with Extra    Spaces
+
+
+Here's a paragraph with     multiple    spaces.
+
+        Indented line with tabs and spaces.
+
+
+Another paragraph.
+
+
+
+Too many blank lines above.
+"""
+
+        expected_cleaned = """# Title with Extra Spaces
+
+
+Here's a paragraph with multiple spaces.
+
+Indented line with tabs and spaces.
+
+
+Another paragraph.
+
+
+
+Too many blank lines above.
+"""
+
+        result = converter._clean_markdown_content(test_content)
+
+        # Check that excessive whitespace within lines is cleaned
+        lines = result.split("\n")
+        for line in lines:
+            if line.strip():  # Non-empty lines
+                # Should not have multiple consecutive spaces
+                assert "  " not in line or line.startswith(
+                    "    "
+                )  # Except for code blocks
+
+    def test_post_process_error_handling(self, converter):
+        """Test that post-processing errors don't crash conversion."""
+        # Test with non-existent file
+        fake_path = Path("./.tmp/nonexistent.md")
+
+        # Should not raise exception
+        converter._post_process_markdown(fake_path)
+
+        # Test with unreadable file (permission issue simulation)
+        with tempfile.NamedTemporaryFile(suffix=".md", delete=False) as tmp:
+            tmp_path = Path(tmp.name)
+
+        try:
+            # Create file then make it unreadable by removing it
+            tmp_path.unlink()
+
+            # Should handle gracefully
+            converter._post_process_markdown(tmp_path)
+
+        finally:
+            # Cleanup if file somehow still exists
+            if tmp_path.exists():
+                tmp_path.unlink()
+
+    def test_complex_image_patterns(self, converter):
+        """Test complex image reference patterns."""
+        test_content = """
+Various image patterns:
+
+![](images/simple.jpg)
+![Alt](images/with-dashes.png) 
+![Alt text](images/under_scores.svg)
+![](images/path/with/subdirs.gif)
+![Caption with (parentheses)](images/weird-name(1).jpg)
+![Multi
+line alt](images/multiline.png)
+
+Non-image patterns that should not change:
+[Link text](images/not-an-image)
+`code with images/path`
+    code block with images/reference
+"""
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".md", delete=False, encoding="utf-8"
+        ) as tmp:
+            tmp.write(test_content)
+            tmp_path = Path(tmp.name)
+
+        try:
+            converter._post_process_markdown(tmp_path)
+            result = tmp_path.read_text(encoding="utf-8")
+
+            # Verify all image references were updated
+            assert "![](assets/simple.jpg)" in result
+            assert "![Alt](assets/with-dashes.png)" in result
+            assert "![Alt text](assets/under_scores.svg)" in result
+            assert "![](assets/path/with/subdirs.gif)" in result
+            assert "![Caption with (parentheses)](assets/weird-name(1).jpg)" in result
+
+            # Verify non-image patterns were preserved
+            assert "[Link text](images/not-an-image)" in result
+            assert "`code with images/path`" in result
+            assert (
+                "code block with images/reference" in result
+            )  # Leading spaces may be removed by cleaning
+
+        finally:
+            if tmp_path.exists():
+                tmp_path.unlink()

tests/test_models.py

@@ -0,0 +1,228 @@
+"""Tests for paperlib data models."""
+
+import json
+import tempfile
+from datetime import datetime
+from pathlib import Path
+
+from paperlib.models import (
+    ConversionStatus,
+    PaperMetadata,
+    PaperSummary,
+    SourceType,
+    SummaryStatus,
+)
+
+
+class TestPaperMetadata:
+    """Test PaperMetadata data model."""
+
+    def test_create_metadata(self):
+        """Test creating a PaperMetadata instance."""
+        metadata = PaperMetadata(
+            paper_id="test-paper-1",
+            source_type=SourceType.LOCAL,
+            title="Test Paper",
+            authors=["Alice Smith", "Bob Jones"],
+            categories=["cs.AI", "stat.ML"],
+            tags=["machine-learning", "ai"],
+            notes="Test notes",
+        )
+
+        assert metadata.paper_id == "test-paper-1"
+        assert metadata.source_type == SourceType.LOCAL
+        assert metadata.title == "Test Paper"
+        assert metadata.authors == ["Alice Smith", "Bob Jones"]
+        assert metadata.categories == ["cs.AI", "stat.ML"]
+        assert metadata.tags == ["machine-learning", "ai"]
+        assert metadata.notes == "Test notes"
+        assert metadata.conversion_status == ConversionStatus.PENDING
+        assert metadata.summary_status == SummaryStatus.NOT_REQUESTED
+
+    def test_to_dict(self):
+        """Test converting metadata to dictionary."""
+        metadata = PaperMetadata(
+            paper_id="test-paper-1",
+            source_type=SourceType.ARXIV,
+            source_id="2212.06340",
+            title="Test Paper",
+            published_date=datetime(2022, 12, 13, 2, 46, 55),
+        )
+
+        data = metadata.to_dict()
+
+        assert data["paper_id"] == "test-paper-1"
+        assert data["source_type"] == "arxiv"
+        assert data["source_id"] == "2212.06340"
+        assert data["title"] == "Test Paper"
+        assert data["published_date"] == "2022-12-13T02:46:55"
+
+    def test_from_dict(self):
+        """Test creating metadata from dictionary."""
+        data = {
+            "paper_id": "test-paper-1",
+            "source_type": "local",
+            "title": "Test Paper",
+            "authors": ["Alice Smith"],
+            "published_date": "2022-12-13T02:46:55",
+            "categories": ["cs.AI"],
+            "pdf_path": "papers/test.pdf",
+            "imported_at": "2022-12-13T02:46:55",
+            "conversion_status": "success",
+            "summary_status": "pending",
+            "tags": ["test"],
+            "notes": "Test notes",
+        }
+
+        metadata = PaperMetadata.from_dict(data)
+
+        assert metadata.paper_id == "test-paper-1"
+        assert metadata.source_type == SourceType.LOCAL
+        assert metadata.title == "Test Paper"
+        assert metadata.authors == ["Alice Smith"]
+        assert metadata.published_date == datetime(2022, 12, 13, 2, 46, 55)
+        assert metadata.conversion_status == ConversionStatus.SUCCESS
+        assert metadata.summary_status == SummaryStatus.PENDING
+
+    def test_save_and_load_file(self):
+        """Test saving and loading metadata from file."""
+        metadata = PaperMetadata(
+            paper_id="test-paper-1",
+            source_type=SourceType.LOCAL,
+            title="Test Paper",
+            authors=["Alice Smith"],
+        )
+
+        with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp:
+            tmp_path = Path(tmp.name)
+
+        try:
+            # Save to file
+            metadata.save_to_file(tmp_path)
+
+            # Verify file exists and contains JSON
+            assert tmp_path.exists()
+            with tmp_path.open() as f:
+                data = json.load(f)
+            assert data["paper_id"] == "test-paper-1"
+
+            # Load from file
+            loaded_metadata = PaperMetadata.load_from_file(tmp_path)
+            assert loaded_metadata.paper_id == "test-paper-1"
+            assert loaded_metadata.title == "Test Paper"
+            assert loaded_metadata.source_type == SourceType.LOCAL
+
+        finally:
+            if tmp_path.exists():
+                tmp_path.unlink()
+
+
+class TestPaperSummary:
+    """Test PaperSummary data model."""
+
+    def test_create_summary(self):
+        """Test creating a PaperSummary instance."""
+        summary = PaperSummary(
+            one_sentence_summary="This paper introduces a new method.",
+            problem_statement="Current methods are inefficient.",
+            method_overview="We propose a novel approach.",
+            main_results="Our method achieves 95% accuracy.",
+            claimed_contributions=["Novel architecture", "Improved performance"],
+            problem_tags=["classification", "optimization"],
+            technique_tags=["neural-networks", "reinforcement-learning"],
+        )
+
+        assert summary.schema_version == "1.0"
+        assert summary.one_sentence_summary == "This paper introduces a new method."
+        assert summary.problem_statement == "Current methods are inefficient."
+        assert summary.claimed_contributions == [
+            "Novel architecture",
+            "Improved performance",
+        ]
+        assert summary.problem_tags == ["classification", "optimization"]
+
+    def test_to_dict(self):
+        """Test converting summary to dictionary."""
+        summary = PaperSummary(
+            one_sentence_summary="Test summary",
+            relevance_to_user=0.85,
+        )
+
+        data = summary.to_dict()
+
+        assert data["schema_version"] == "1.0"
+        assert data["one_sentence_summary"] == "Test summary"
+        assert data["relevance_to_user"] == 0.85
+
+    def test_from_dict(self):
+        """Test creating summary from dictionary."""
+        data = {
+            "schema_version": "1.0",
+            "one_sentence_summary": "Test summary",
+            "problem_statement": "Test problem",
+            "claimed_contributions": ["Test contribution"],
+            "problem_tags": ["test"],
+            "technique_tags": ["neural-networks"],
+            "entities": ["Entity1", "Entity2"],
+        }
+
+        summary = PaperSummary.from_dict(data)
+
+        assert summary.schema_version == "1.0"
+        assert summary.one_sentence_summary == "Test summary"
+        assert summary.problem_statement == "Test problem"
+        assert summary.claimed_contributions == ["Test contribution"]
+        assert summary.entities == ["Entity1", "Entity2"]
+
+    def test_save_and_load_file(self):
+        """Test saving and loading summary from file."""
+        summary = PaperSummary(
+            one_sentence_summary="Test summary",
+            problem_tags=["tag1", "tag2"],
+        )
+
+        with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as tmp:
+            tmp_path = Path(tmp.name)
+
+        try:
+            # Save to file
+            summary.save_to_file(tmp_path)
+
+            # Verify file exists and contains JSON
+            assert tmp_path.exists()
+            with tmp_path.open() as f:
+                data = json.load(f)
+            assert data["one_sentence_summary"] == "Test summary"
+
+            # Load from file
+            loaded_summary = PaperSummary.load_from_file(tmp_path)
+            assert loaded_summary.one_sentence_summary == "Test summary"
+            assert loaded_summary.problem_tags == ["tag1", "tag2"]
+
+        finally:
+            if tmp_path.exists():
+                tmp_path.unlink()
+
+
+class TestEnums:
+    """Test enum types."""
+
+    def test_source_type_values(self):
+        """Test SourceType enum values."""
+        assert SourceType.LOCAL == "local"
+        assert SourceType.ARXIV == "arxiv"
+
+    def test_conversion_status_values(self):
+        """Test ConversionStatus enum values."""
+        assert ConversionStatus.PENDING == "pending"
+        assert ConversionStatus.PROCESSING == "processing"
+        assert ConversionStatus.SUCCESS == "success"
+        assert ConversionStatus.FAILED == "failed"
+
+    def test_summary_status_values(self):
+        """Test SummaryStatus enum values."""
+        assert SummaryStatus.PENDING == "pending"
+        assert SummaryStatus.PROCESSING == "processing"
+        assert SummaryStatus.SUCCESS == "success"
+        assert SummaryStatus.FAILED == "failed"
+        assert SummaryStatus.NOT_REQUESTED == "not_requested"

tests/test_storage.py

@@ -0,0 +1,261 @@
+"""Tests for paperlib storage manager."""
+
+import shutil
+from pathlib import Path
+
+import pytest
+
+from paperlib.config import LibraryPaths
+from paperlib.models import ConversionStatus, SourceType
+from paperlib.storage import PaperStorageManager
+
+
+class TestPaperStorageManager:
+    """Test PaperStorageManager functionality."""
+
+    @pytest.fixture
+    def temp_library(self):
+        """Create a temporary library for testing."""
+        temp_dir = Path("./.tmp") / f"test_library_{hash(self)}"
+        temp_dir.mkdir(parents=True, exist_ok=True)
+        library_paths = LibraryPaths.from_root(temp_dir)
+        library_paths.create_directories()
+
+        yield library_paths
+
+        # Cleanup
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    @pytest.fixture
+    def storage_manager(self, temp_library):
+        """Create a storage manager for testing."""
+        return PaperStorageManager(temp_library)
+
+    @pytest.fixture
+    def sample_pdf(self):
+        """Create a sample PDF file for testing."""
+        # Create a minimal PDF-like file
+        temp_file = Path("./.tmp") / f"test_paper_{hash(self)}.pdf"
+        with temp_file.open("wb") as f:
+            # Minimal PDF header
+            f.write(b"%PDF-1.4\n")
+            f.write(b"1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n")
+            f.write(b"%%EOF\n")
+
+        yield temp_file
+
+        # Cleanup
+        if temp_file.exists():
+            temp_file.unlink()
+
+    def test_generate_paper_id_local(self, storage_manager, sample_pdf):
+        """Test generating paper ID for local files."""
+        paper_id = storage_manager.generate_paper_id(
+            SourceType.LOCAL, pdf_path=sample_pdf
+        )
+
+        assert paper_id.startswith("local-")
+        assert len(paper_id) == 22  # "local-" + 16 chars hash
+
+    def test_generate_paper_id_arxiv(self, storage_manager):
+        """Test generating paper ID for arXiv papers."""
+        paper_id = storage_manager.generate_paper_id(
+            SourceType.ARXIV, source_id="2212.06340"
+        )
+
+        assert paper_id == "arxiv-2212_06340"
+
+    def test_get_paper_directory_arxiv(self, storage_manager):
+        """Test getting paper directory for arXiv papers."""
+        paper_dir = storage_manager.get_paper_directory(
+            "arxiv-2212_06340", SourceType.ARXIV
+        )
+
+        # Should extract year 2022 from 2212.06340 (22 -> 2022)
+        expected = (
+            storage_manager.library_paths.papers_dir
+            / "arxiv"
+            / "2022"
+            / "arxiv-2212_06340"
+        )
+        assert paper_dir == expected
+
+    def test_get_paper_directory_local(self, storage_manager):
+        """Test getting paper directory for local papers."""
+        paper_dir = storage_manager.get_paper_directory(
+            "local-abcd1234efgh5678", SourceType.LOCAL
+        )
+
+        expected = (
+            storage_manager.library_paths.papers_dir / "local" / "abcd1234efgh5678"
+        )
+        assert paper_dir == expected
+
+    def test_get_paper_paths(self, storage_manager):
+        """Test getting all paper paths."""
+        paths = storage_manager.get_paper_paths("arxiv-2212_06340", SourceType.ARXIV)
+
+        assert "directory" in paths
+        assert "meta" in paths
+        assert "pdf" in paths
+        assert "markdown" in paths
+        assert "summary_json" in paths
+        assert "summary_md" in paths
+        assert "assets" in paths
+        assert "logs" in paths
+
+        # Check that paths are Path objects
+        assert isinstance(paths["meta"], Path)
+        assert paths["meta"].name == "meta.json"
+        assert paths["pdf"].name == "source.pdf"
+
+    def test_store_paper_local(self, storage_manager, sample_pdf):
+        """Test storing a local PDF paper."""
+        metadata = storage_manager.store_paper(
+            pdf_path=sample_pdf,
+            source_type=SourceType.LOCAL,
+            title="Test Paper",
+            authors=["Test Author"],
+            tags=["test"],
+        )
+
+        # Check metadata
+        assert metadata.source_type == SourceType.LOCAL
+        assert metadata.title == "Test Paper"
+        assert metadata.authors == ["Test Author"]
+        assert metadata.tags == ["test"]
+        assert metadata.conversion_status == ConversionStatus.PENDING
+
+        # Check file structure was created
+        paths = storage_manager.get_paper_paths(metadata.paper_id, metadata.source_type)
+        assert paths["directory"].exists()
+        assert paths["meta"].exists()
+        assert paths["pdf"].exists()
+        assert paths["assets"].exists()
+        assert paths["logs"].exists()
+
+    def test_store_paper_arxiv(self, storage_manager, sample_pdf):
+        """Test storing an arXiv paper."""
+        metadata = storage_manager.store_paper(
+            pdf_path=sample_pdf,
+            source_type=SourceType.ARXIV,
+            source_id="2212.06340",
+            title="Test arXiv Paper",
+            authors=["Alice Smith", "Bob Jones"],
+            categories=["cs.AI"],
+        )
+
+        # Check metadata
+        assert metadata.source_type == SourceType.ARXIV
+        assert metadata.source_id == "2212.06340"
+        assert metadata.title == "Test arXiv Paper"
+        assert metadata.authors == ["Alice Smith", "Bob Jones"]
+        assert metadata.categories == ["cs.AI"]
+
+        # Check file paths are set correctly
+        assert metadata.pdf_path
+        assert metadata.paper_md_path
+        assert metadata.summary_json_path
+        assert metadata.summary_md_path
+
+    def test_load_paper_metadata(self, storage_manager, sample_pdf):
+        """Test loading paper metadata."""
+        # First store a paper
+        original_metadata = storage_manager.store_paper(
+            pdf_path=sample_pdf, source_type=SourceType.LOCAL, title="Test Paper"
+        )
+
+        # Load it back
+        loaded_metadata = storage_manager.load_paper_metadata(
+            original_metadata.paper_id, original_metadata.source_type
+        )
+
+        assert loaded_metadata is not None
+        assert loaded_metadata.paper_id == original_metadata.paper_id
+        assert loaded_metadata.title == "Test Paper"
+        assert loaded_metadata.source_type == SourceType.LOCAL
+
+    def test_load_nonexistent_paper(self, storage_manager):
+        """Test loading metadata for nonexistent paper."""
+        metadata = storage_manager.load_paper_metadata("nonexistent", SourceType.LOCAL)
+        assert metadata is None
+
+    def test_update_paper_metadata(self, storage_manager, sample_pdf):
+        """Test updating paper metadata."""
+        # Store initial paper
+        metadata = storage_manager.store_paper(
+            pdf_path=sample_pdf, source_type=SourceType.LOCAL, title="Original Title"
+        )
+
+        # Update metadata
+        metadata.title = "Updated Title"
+        metadata.conversion_status = ConversionStatus.SUCCESS
+        storage_manager.update_paper_metadata(metadata)
+
+        # Load and verify update
+        loaded_metadata = storage_manager.load_paper_metadata(
+            metadata.paper_id, metadata.source_type
+        )
+        assert loaded_metadata.title == "Updated Title"
+        assert loaded_metadata.conversion_status == ConversionStatus.SUCCESS
+
+    def test_list_all_papers(self, storage_manager, sample_pdf):
+        """Test listing all papers in library."""
+        # Initially empty
+        papers = list(storage_manager.list_all_papers())
+        assert len(papers) == 0
+
+        # Add some papers
+        metadata1 = storage_manager.store_paper(
+            pdf_path=sample_pdf, source_type=SourceType.LOCAL, title="Paper 1"
+        )
+
+        metadata2 = storage_manager.store_paper(
+            pdf_path=sample_pdf,
+            source_type=SourceType.ARXIV,
+            source_id="2212.06340",
+            title="Paper 2",
+        )
+
+        # List papers
+        papers = list(storage_manager.list_all_papers())
+        assert len(papers) == 2
+
+        paper_ids = {p.paper_id for p in papers}
+        assert metadata1.paper_id in paper_ids
+        assert metadata2.paper_id in paper_ids
+
+    def test_paper_exists(self, storage_manager, sample_pdf):
+        """Test checking if paper exists."""
+        # Initially doesn't exist
+        assert not storage_manager.paper_exists("nonexistent", SourceType.LOCAL)
+
+        # Store a paper
+        metadata = storage_manager.store_paper(
+            pdf_path=sample_pdf, source_type=SourceType.LOCAL, title="Test Paper"
+        )
+
+        # Now it exists
+        assert storage_manager.paper_exists(metadata.paper_id, metadata.source_type)
+
+    def test_delete_paper(self, storage_manager, sample_pdf):
+        """Test deleting a paper."""
+        # Store a paper
+        metadata = storage_manager.store_paper(
+            pdf_path=sample_pdf, source_type=SourceType.LOCAL, title="Test Paper"
+        )
+
+        # Verify it exists
+        assert storage_manager.paper_exists(metadata.paper_id, metadata.source_type)
+
+        # Delete it
+        result = storage_manager.delete_paper(metadata.paper_id, metadata.source_type)
+        assert result is True
+
+        # Verify it's gone
+        assert not storage_manager.paper_exists(metadata.paper_id, metadata.source_type)
+
+        # Deleting again should return False
+        result = storage_manager.delete_paper(metadata.paper_id, metadata.source_type)
+        assert result is False

uv.lock

@@ -97,6 +97,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353, upload-time = "2026-03-24T12:59:08.246Z" },
 ]
 
+[[package]]
+name = "arxiv"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "feedparser" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ff/78/1e93a001ed51b5114e1978247078fa3130cbb2794a520603949cbe9a7028/arxiv-3.0.0.tar.gz", hash = "sha256:c8cb0d31208afbc1ceb17bd3f9816c8d4c5ca1e0abf199d211e216715440498d", size = 67344, upload-time = "2026-04-12T22:48:59.623Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9d/0d/bb2ef604e5548ba73ba6326576908d8285ebf3468b02b86af83381c7c973/arxiv-3.0.0-py3-none-any.whl", hash = "sha256:8b4d4e2e336bfeb71ea653623d7dadb260f682f0475cee2aecad0560a23b34db", size = 11928, upload-time = "2026-04-12T22:48:58.44Z" },
+]
+
 [[package]]
 name = "audioop-lts"
 version = "0.2.2"
@@ -502,6 +515,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/31/fb/6d251f3fdfe3346ee60d091f55106513e509659ee005ad39c914182c96f4/fasttext_predict-0.9.2.4-cp313-cp313t-win_amd64.whl", hash = "sha256:be0933fa4af7abae09c703d28f9e17c80e7069eb6f92100b21985b777f4ea275", size = 110325, upload-time = "2024-11-23T17:24:16.984Z" },
 ]
 
+[[package]]
+name = "feedparser"
+version = "6.0.12"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sgmllib3k" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dc/79/db7edb5e77d6dfbc54d7d9df72828be4318275b2e580549ff45a962f6461/feedparser-6.0.12.tar.gz", hash = "sha256:64f76ce90ae3e8ef5d1ede0f8d3b50ce26bcce71dd8ae5e82b1cd2d4a5f94228", size = 286579, upload-time = "2025-09-10T13:33:59.486Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4e/eb/c96d64137e29ae17d83ad2552470bafe3a7a915e85434d9942077d7fd011/feedparser-6.0.12-py3-none-any.whl", hash = "sha256:6bbff10f5a52662c00a2e3f86a38928c37c48f77b3c511aedcd51de933549324", size = 81480, upload-time = "2025-09-10T13:33:58.022Z" },
+]
+
 [[package]]
 name = "ffmpy"
 version = "1.0.0"
@@ -739,6 +764,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/49/fa/391e437a34e55095173dca5f24070d89cbc233ff85bf1c29c93248c6588d/imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0", size = 317646, upload-time = "2026-03-09T11:31:10.771Z" },
 ]
 
+[[package]]
+name = "iniconfig"
+version = "2.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -1382,18 +1416,28 @@ name = "paperlib"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
+    { name = "arxiv" },
     { name = "mineru", extra = ["core"] },
     { name = "rich" },
     { name = "typer" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "pytest" },
+]
+
 [package.metadata]
 requires-dist = [
+    { name = "arxiv", specifier = ">=2.0.0" },
     { name = "mineru", extras = ["core"], specifier = ">=3.0.9" },
     { name = "rich", specifier = ">=15.0.0" },
     { name = "typer", specifier = ">=0.24.1" },
 ]
 
+[package.metadata.requires-dev]
+dev = [{ name = "pytest", specifier = ">=9.0.3" }]
+
 [[package]]
 name = "pdfminer-six"
 version = "20260107"
@@ -1455,6 +1499,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/00/a4/285f12aeacbe2d6dc36c407dfbbe9e96d4a80b0fb710a337f6d2ad978c75/pillow-12.2.0-cp313-cp313t-win_arm64.whl", hash = "sha256:2e5a76d03a6c6dcef67edabda7a52494afa4035021a79c8558e14af25313d453", size = 2465765, upload-time = "2026-04-01T14:44:45.996Z" },
 ]
 
+[[package]]
+name = "pluggy"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f9/e2/3e91f31a7d2b083fe6ef3fa267035b518369d9511ffab804f839851d2779/pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3", size = 69412, upload-time = "2025-05-15T12:30:07.975Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" },
+]
+
 [[package]]
 name = "protobuf"
 version = "7.34.1"
@@ -1638,6 +1691,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/d8/940fcaa6a1f3763d72751b6bc8054f40beeacd6e9e5b19069c6c73dab5af/pypptx_with_oxml-1.0.3-py3-none-any.whl", hash = "sha256:4b3ccf51185e0f9e60ebf2884e74153d7fcb00e7e4f0461404e96e0260d7bba1", size = 493041, upload-time = "2026-01-30T08:51:25.797Z" },
 ]
 
+[[package]]
+name = "pytest"
+version = "9.0.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+    { name = "pygments" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/7d/0d/549bd94f1a0a402dc8cf64563a117c0f3765662e2e668477624baeec44d5/pytest-9.0.3.tar.gz", hash = "sha256:b86ada508af81d19edeb213c681b1d48246c1a91d304c6c81a427674c17eb91c", size = 1572165, upload-time = "2026-04-07T17:16:18.027Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" },
+]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -1947,6 +2016,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" },
 ]
 
+[[package]]
+name = "sgmllib3k"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9e/bd/3704a8c3e0942d711c1299ebf7b9091930adae6675d7c8f476a7ce48653c/sgmllib3k-1.0.0.tar.gz", hash = "sha256:7868fb1c8bfa764c1ac563d3cf369c381d1325d36124933a726f29fcdaa812e9", size = 5750, upload-time = "2010-08-24T14:33:52.445Z" }
+
 [[package]]
 name = "shapely"
 version = "2.1.2"