pdf-autofillr-chatbot 0.2.2

Conversational investor onboarding chatbot — PDF form-filling via LLM extraction

chatbot Module

Conversational investor onboarding chatbot — collects investor data through natural language and fills PDF subscription forms.

🚀 Quick Start

1. Configure

cd modules/chatbot
cp .env.example .env
nano .env          # add OPENAI_API_KEY at minimum

See SETUP_GUIDE.md for full configuration.

2. Install Dependencies

pip install -r requirements.txt
pip install -r requirements-api.txt

3. Run

# API server (recommended)
python api_server.py

# Interactive CLI
python -m entrypoints.local

# Or interactively via CLI tool
python -m entrypoints.cli

Server: http://localhost:8001
API Docs: http://localhost:8001/docs

---

📁 Structure

modules/chatbot/
├── .env.example              ← Copy to .env — add your API keys
├── api_server.py             ← FastAPI server (run this!)
├── requirements.txt          ← Core dependencies
├── requirements-api.txt      ← FastAPI + uvicorn
├── requirements-mapper.txt   ← PDF mapper connector (optional)
├── requirements-s3.txt       ← AWS S3 storage (optional)
├── requirements-full.txt     ← Everything (used by Docker)
├── pyproject.toml            ← Package metadata
├── Dockerfile                ← Container build
├── SETUP_GUIDE.md            ← Detailed setup
├── API_SERVER.md             ← API endpoint reference
├── entrypoints/
│   ├── local.py              ← Interactive CLI / Python-callable
│   ├── cli.py                ← Command-line interface
│   ├── fastapi_app.py        ← Bare FastAPI app (no /chatbot prefix)
│   └── aws_lambda.py         ← AWS Lambda handler
├── src/chatbot/              ← Core SDK source
│   ├── client.py             ← chatbotClient — main entry point
│   ├── config/               ← Settings, FormConfig
│   ├── core/                 ← Engine, router, session, states (13-state machine)
│   ├── extraction/           ← LLM extractor, fallback, prompt builder
│   ├── handlers/             ← One handler per conversation state
│   ├── limits/               ← Rate limiter
│   ├── logging/              ← Debug logger
│   ├── managed/              ← Stub for private managed PDF service
│   ├── pdf/                  ← PDFFillerInterface, MapperPDFFiller, workflow
│   ├── storage/              ← LocalStorage, S3Storage, StorageBackend
│   ├── telemetry/            ← Opt-in telemetry collector
│   ├── utils/                ← Field utils, address utils, intent detection
│   └── validation/           ← Field + phone validators
├── config_samples/           ← Form config JSON files (10 investor types)
├── tests/
│   ├── conftest.py           ← Shared fixtures
│   ├── unit/                 ← Fast, no I/O tests
│   └── integration/          ← Full-stack tests (TestClient)
└── data/
    ├── input/                ← Place blank PDFs here
    ├── output/               ← Filled PDFs and session data written here
    └── cache/                ← Optional: session cache

---

🎯 What This Module Does

A 13-state conversation engine that:

1. Greets the investor and checks for existing saved data
2. Asks the investor to select their type (Individual, Corporation, LLC, Trust, etc.)
3. Collects all mandatory fields through natural conversation using GPT-4o-mini extraction
4. Validates fields (email, phone format, boolean checks)
5. Handles address copy (mailing = registered), boolean groups, sequential fill for stubborn fields
6. Fills the blank PDF via the mapper module (optional)
7. Completes the session and saves structured JSON output

Conversation states

State	Description
INIT	Greeting, check for existing profile
UPDATE_EXISTING_PROMPT	Offer to pre-fill from previous session
INVESTOR_TYPE_SELECT	Choose from 10 investor types
DATA_COLLECTION	Main loop — LLM extraction per turn
MISSING_FIELDS_PROMPT	Re-ask skipped mandatory fields
BOOLEAN_GROUP_SELECT	Handle yes/no checkbox groups
SEQUENTIAL_FILL	One field at a time for stubborn fields
MAILING_ADDRESS_CHECK	Is mailing same as registered?
CONTINUE_PROMPT	Mid-session checkpoint
OPTIONAL_FIELDS_PROMPT	Offer non-mandatory fields
ANOTHER_INFO_PROMPT	Any corrections before submit?
CONFIRM_AND_SUBMIT	Final confirmation
COMPLETE	Session done, outputs saved

---

🔌 PDF Filling

Three modes controlled by chatbot_PDF_FILLER env var:

Mode	Description
none (default)	Data-only — no PDF filling
mapper	Connect to the mapper module (modules/mapper/) via its API
managed	Private Auth0+Lambda service (requires chatbot-managed package)

For mapper mode, start the mapper API server first:

cd ../mapper
python api_server.py     # runs on port 8000

# Then in modules/chatbot:
chatbot_PDF_FILLER=mapper
MAPPER_API_URL=http://localhost:8000
MAPPER_URL_PREFIX=/mapper

---

🌐 API Endpoints

Endpoint	Method	Description
/	GET	API info
/health	GET	Health check
/chatbot/chat	POST	Send a message
/chatbot/session/{user_id}/{session_id}	GET	Get completed session data
/chatbot/session/{user_id}/{session_id}/fill-report	GET	Fill statistics report
/chatbot/session/{user_id}/{session_id}	DELETE	Delete session

See API_SERVER.md for full request/response schemas.

---

📦 Using as a Python Library

from src.chatbot import chatbotClient, LocalStorage, FormConfig

client = chatbotClient(
    openai_api_key="sk-...",
    storage=LocalStorage("./chatbot_data", "./config_samples"),
    form_config=FormConfig.from_directory("./config_samples"),
    pdf_filler=None,
)

# Send messages
response, complete, data = client.send_message(
    user_id="investor_123",
    session_id="session_abc",
    message="",
)
print(response)   # → "Hi! I am here to help you fill out..."

---

🧪 Testing

# Run all tests
pytest

# Unit tests only (fast, no network)
pytest tests/unit/ -v

# Integration tests
pytest tests/integration/ -v

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

# A specific test
pytest tests/unit/test_rate_limiter.py -v

---

🐳 Docker

docker build -t chatbot-module .
docker run -p 8001:8001 --env-file .env chatbot-module

---

🔗 Integration with mapper module

rv1 repo/
├── modules/
│   ├── mapper/          ← PDF extraction + mapping + filling engine
│   │   └── api_server.py  runs on :8000
│   └── chatbot/         ← This module
│       └── api_server.py  runs on :8001
│           └── MAPPER_API_URL=http://localhost:8000

---

📚 Documentation

• SETUP_GUIDE.md — Configuration reference
• API_SERVER.md — API endpoint documentation
• config_samples/README.md — Form config format

---

Quick Command Reference

# Setup
cp .env.example .env && nano .env
pip install -r requirements.txt requirements-api.txt

# Run
python api_server.py

# Test
curl http://localhost:8001/health
pytest tests/unit/

# Interactive CLI
python -m entrypoints.local