pdf-autofillr-cli 0.1.1

Unified CLI for all pdf-autofillr modules

pdf-autofillr-cli

Unified command-line interface for all pdf-autofillr modules.

One install. One command. All modules.

pip install "pdf-autofillr-cli[all]"
pdf-autofillr status

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What is this?

pdf-autofillr-cli wraps the five pdf-autofillr packages — chatbot, mapper, rag, doc-upload, and plugins — under a single pdf-autofillr terminal command. Instead of learning four separate CLIs, you get one consistent interface.

Without this package	With this package
ragpdf predict ...	pdf-autofillr rag predict ...
chatbot-cli ...	pdf-autofillr chatbot session ...
pdf-mapper-server	pdf-autofillr mapper start
No unified status check	pdf-autofillr status

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Install

# Minimal — CLI only (modules installed separately)
pip install pdf-autofillr-cli

# CLI + all modules
pip install "pdf-autofillr-cli[all]"

# CLI + specific modules
pip install "pdf-autofillr-cli[rag]"
pip install "pdf-autofillr-cli[chatbot]"
pip install "pdf-autofillr-cli[mapper]"
pip install "pdf-autofillr-cli[doc-upload]"

The CLI loads instantly regardless of which modules are installed. If you run a command for a module that isn't installed, you get a friendly hint — not a crash.

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Quick start

# 1. First-time setup
pdf-autofillr setup

# 2. Add API keys to .env
cp .env.example .env

# 3. Check everything is configured
pdf-autofillr status

→ See quickstart.md for a 3-step walkthrough.

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Commands

pdf-autofillr status

Shows which modules are installed, config files present, env vars, and inter-module connections.

pdf-autofillr status
pdf-autofillr status --path /path/to/project

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pdf-autofillr setup

First-time setup — creates .env, configs/, and data/ skeleton.

pdf-autofillr setup                    # all installed modules
pdf-autofillr setup --module rag       # rag only
pdf-autofillr setup --force            # overwrite existing files

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pdf-autofillr rag

RAG field prediction commands. Requires pip install "pdf-autofillr-cli[rag]".

# Generate vector embeddings (run once after setup)
pdf-autofillr rag init-vectors
pdf-autofillr rag init-vectors --backend openai --force

# Predict field mappings
pdf-autofillr rag predict \
  --user u1 --session s1 --pdf p1 \
  --fields fields.json \
  --hash abc123 \
  --category pdf_category.json

# Submit corrections (triggers re-learning)
pdf-autofillr rag feedback \
  --user u1 --session s1 --pdf p1 \
  --errors errors.json

# Accuracy and coverage metrics
pdf-autofillr rag metrics --type global
pdf-autofillr rag metrics --type pdf --user u1 --session s1 --pdf p1
pdf-autofillr rag metrics --type category --category "Private Markets"

# Vector DB overview
pdf-autofillr rag system-info

# Error analytics with date filters
pdf-autofillr rag error-analytics --from 2026-01-01T00:00:00Z

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pdf-autofillr chatbot

Conversational form-filling commands. Requires pip install "pdf-autofillr-cli[chatbot]".

# Start the REST API server (default port 8001)
pdf-autofillr chatbot start
pdf-autofillr chatbot start --port 9000 --reload

# Run an interactive session in the terminal
pdf-autofillr chatbot session \
  --pdf data/input/blank_form.pdf \
  --user user_001

# Resume an existing session
pdf-autofillr chatbot session \
  --pdf data/input/blank_form.pdf \
  --user user_001 \
  --session existing-session-id

# List all active sessions
pdf-autofillr chatbot sessions

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pdf-autofillr mapper

PDF template embedding and filling. Requires pip install "pdf-autofillr-cli[mapper]".

Two-step workflow: embed once per blank form template, then fill many times.

# Step 1 — embed the blank template (run once per form)
pdf-autofillr mapper embed \
  --pdf data/input/blank_form.pdf \
  --user user_001 \
  --id lp_sub_v1

# Step 2 — fill with user data (JSON file)
pdf-autofillr mapper fill \
  --pdf data/input/blank_form.pdf \
  --user user_001 \
  --id lp_sub_v1 \
  --data user_data.json

# Step 2 — fill with inline JSON (Linux/Mac)
pdf-autofillr mapper fill \
  --pdf data/input/blank_form.pdf \
  --user user_001 \
  --id lp_sub_v1 \
  --data '{"investor_name": "Jane Smith", "commitment_amount": "500000"}'

# Start the mapper API server (default port 8002)
pdf-autofillr mapper start
pdf-autofillr mapper start --port 8002 --reload

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pdf-autofillr doc-upload

Extract data from uploaded documents (PDF, DOCX, XLSX, CSV, JSON, TXT, MD) and fill a PDF form. Requires pip install "pdf-autofillr-cli[doc-upload]".

# Extract from document and fill PDF in one step
pdf-autofillr doc-upload process \
  --doc investor_data.pdf \
  --pdf data/input/blank_form.pdf \
  --schema configs/form_keys.json \
  --user user_001 \
  --id lp_sub_v1

# Supported source formats: PDF, DOCX, XLSX, CSV, JSON, TXT, MD
pdf-autofillr doc-upload process --doc investor.xlsx ...
pdf-autofillr doc-upload process --doc investor.csv  ...

# Start the doc-upload API server (default port 8003)
pdf-autofillr doc-upload start

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pdf-autofillr plugins

Inspect installed plugins. Requires pip install pdf-autofillr-plugins.

# List all discovered plugins
pdf-autofillr plugins list

# Filter by category
pdf-autofillr plugins list --category validator
pdf-autofillr plugins list --category extractor

# Scan a specific directory
pdf-autofillr plugins list --path ./my_plugins/

# Output as JSON (useful for scripting)
pdf-autofillr plugins list --json

# Show detailed info for one plugin
pdf-autofillr plugins info email-validator
pdf-autofillr plugins info invoice-extractor --category extractor

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Configuration

Copy .env.example to .env and fill in the values for your setup:

cp .env.example .env

Key variables:

Variable	Default	Description
OPENAI_API_KEY	—	OpenAI API key (chatbot, mapper, rag corrector)
ANTHROPIC_API_KEY	—	Anthropic API key (alternative LLM)
RAGPDF_STORAGE	local	RAG storage: local, s3, azure, gcs
RAGPDF_EMBEDDING_BACKEND	sentence_transformer	sentence_transformer, openai, litellm
RAGPDF_VECTOR_STORE	local	local, pinecone, chroma, weaviate
RAGPDF_CORRECTOR_BACKEND	noop	noop, openai, anthropic
RAGPDF_API_KEY	dev-key	Auth key for the RAG server

Full reference in .env.example.

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For developers

Run from source

git clone https://github.com/Engineersmind/pdf-autofillr.git
cd pdf-autofillr/packages/cli

python -m venv .venv
source .venv/bin/activate       # Windows: .venv\Scripts\activate

pip install -e ".[dev]"

# Verify the entry point works
pdf-autofillr --version
pdf-autofillr --help

Project layout

packages/cli/
├── src/
│   └── pdf_autofillr_cli/
│       ├── __init__.py          # version
│       ├── main.py              # entry point — builds parser, dispatches
│       ├── utils.py             # module availability checks
│       ├── cmd_status.py        # pdf-autofillr status
│       ├── cmd_setup.py         # pdf-autofillr setup
│       ├── cmd_rag.py           # pdf-autofillr rag *
│       ├── cmd_chatbot.py       # pdf-autofillr chatbot *
│       ├── cmd_mapper.py        # pdf-autofillr mapper *
│       ├── cmd_doc_upload.py    # pdf-autofillr doc-upload *
│       └── cmd_plugins.py       # pdf-autofillr plugins *
├── tests/
│   ├── conftest.py
│   ├── unit/                    # mocked, no modules needed
│   └── integration/             # full parser wiring tests
├── requirements/
│   ├── base.txt
│   ├── dev.txt
│   └── all.txt
├── .env.example
├── CHANGELOG.md
├── LICENSE
├── MANIFEST.in
├── README.md
├── USAGE.md
├── quickstart.md
└── pyproject.toml

Adding a new subcommand

1. Create src/pdf_autofillr_cli/cmd_mymodule.py with add_parser(subparsers) and run(args) -> int
2. Import and register it in main.py alongside the existing commands
3. Add tests under tests/unit/ and tests/integration/

Run tests

pip install -e ".[dev]"

# All tests
pytest tests/ -v

# Unit only (no modules needed — everything mocked)
pytest tests/unit/ -v

# Integration only (parser wiring)
pytest tests/integration/ -v

# With coverage
pytest tests/ --cov=src/pdf_autofillr_cli --cov-report=term-missing

Publish a new version

# 1. Bump version in pyproject.toml and src/pdf_autofillr_cli/__init__.py
# 2. Add entry to CHANGELOG.md
# 3. Build
pip install build
python -m build
# 4. Upload
pip install twine
twine upload dist/*

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Related packages

Package	PyPI	Description
pdf-autofillr		Umbrella package
pdf-autofillr-rag		RAG prediction engine
pdf-autofillr-chatbot		Conversational onboarding
pdf-autofillr-mapper		Semantic PDF field mapper
pdf-autofillr-doc-upload		Document extraction
pdf-autofillr-plugins		Plugin framework

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License

MIT — see LICENSE.