text2report 1.0

Generate beautiful PDF reports from data using LLMs, Mermaid diagrams, and Chart.js visualizations

text2report

Generate beautiful PDF reports from data using LLMs, Mermaid diagrams, and Chart.js visualizations.

📚 Documentation

• Complete Guide - Comprehensive documentation with all functions, use cases, and examples
• Quick Reference - One-page cheat sheet for common tasks
• Custom Prompts Guide - Create custom prompt templates for reports and insights
• Anti-Hallucination Guide - Ensure data fidelity in reports
• Packaging Guide - Build and distribute the package

Features

• 📊 Automatic Visualizations: Generates Chart.js charts and Mermaid diagrams from your data
• 🤖 Multi-LLM Support: Works with Ollama (local/free), OpenAI, and Azure OpenAI
• 🎯 Behavior Customization: Tailor reports for different audiences (executives, technical, analysts)
• 📄 PDF Generation: High-quality PDF output via Playwright
• ⚡ Quick Insights: Fast summary generation for immediate feedback
• 🎨 Professional Styling: Clean, modern report layouts
• 🔧 Flexible Configuration: Presets, custom templates, or full programmatic control

Installation

1. Install the package

pip install -e .

2. Install Playwright browsers

playwright install chromium

3. Set up an LLM Provider

Choose one of the following:

Option A: Ollama (Free, runs locally)

# Install from https://ollama.ai
ollama pull gpt-oss:20b  # or your preferred model
ollama serve

Option B: OpenAI

# Get API key from https://platform.openai.com
export REPORT_LLM_PROVIDER=openai
export REPORT_LLM_API_KEY=sk-proj-your-key-here
export REPORT_LLM_MODEL=gpt-4-turbo-preview

Option C: Azure OpenAI

# Get credentials from Azure portal
export REPORT_LLM_PROVIDER=azure
export REPORT_LLM_BASE_URL=https://your-resource.openai.azure.com
export REPORT_LLM_MODEL=your-deployment-name
export REPORT_LLM_API_KEY=your-azure-key

Quick Start

from text2report import generate_report, generate_quick_insights

# Your data (can be dict, list, string, or any JSON-serializable object)
data = {
    "sales": [
        {"name": "Alice", "revenue": 50000},
        {"name": "Bob", "revenue": 75000},
        {"name": "Charlie", "revenue": 62000}
    ]
}

# Generate quick insights (fast, 3-5 bullet points)
insights = generate_quick_insights(
    data=data,
    question="Who are the top performers?"
)
print(insights)

# Generate full PDF report
pdf_path = generate_report(
    data=data,
    question="Analyze sales performance and provide recommendations",
    output_path="sales_report.pdf"  # Optional, auto-generates if not provided
)

print(f"Report generated: {pdf_path}")

Behavior Customization 🎯

NEW: Customize report output for different audiences, styles, and purposes without writing prompts!

Quick Start with Presets

Use pre-configured templates for common scenarios:

from text2report import generate_report

# Executive summary - concise, high-level, business-focused
pdf_path = generate_report(
    data=sales_data,
    question="Q4 performance analysis",
    preset="executive_summary"
)

# Technical deep dive - detailed, implementation-focused
pdf_path = generate_report(
    data=system_metrics,
    question="System performance analysis",
    preset="technical_deep_dive"
)

# Data analysis - statistical, methodology-focused
pdf_path = generate_report(
    data=experiment_results,
    question="A/B test results",
    preset="data_analysis"
)

Available Presets:

• executive_summary - High-level business summary for C-suite
• technical_deep_dive - Detailed technical analysis for engineers
• data_analysis - Statistical analysis for data scientists
• sales_presentation - Persuasive report for sales/marketing
• quick_overview - Fast, scannable summary for general audience

Fine-Grained Control

Customize individual aspects:

# Customize audience, style, and tone
pdf_path = generate_report(
    data=data,
    question="Analyze user engagement",
    audience="executives",      # executives|technical|analysts|general
    style="concise",            # concise|detailed|visual_heavy
    tone="formal",              # formal|persuasive|casual
    focus=["metrics", "recommendations"]  # What to emphasize
)

Using ReportConfig (Advanced)

For maximum control and type safety:

from text2report import (
    generate_report,
    ReportConfig,
    Audience,
    Style,
    Tone,
    FocusArea
)

# Build custom configuration
config = ReportConfig(
    audience=Audience.TECHNICAL,
    style=Style.DETAILED,
    tone=Tone.FORMAL,
    focus=[FocusArea.INSIGHTS, FocusArea.TRENDS]
)

pdf_path = generate_report(
    data=data,
    question="System performance trends",
    config=config
)

# Or use a preset and override specific values
config = ReportConfig.from_preset(
    Preset.EXECUTIVE_SUMMARY,
    tone=Tone.PERSUASIVE  # Override the preset's tone
)

Behavior Options Reference

Audiences

• executives - C-level focus: ROI, strategic implications, high-level recommendations
• technical - Engineering focus: implementation details, architecture, methodologies
• analysts - Data focus: statistical significance, methodology, data quality
• general - Accessible language, balanced depth, clear explanations

Styles

• concise - 1-2 pages, bullet points, key highlights only
• detailed - 3-5 pages, comprehensive analysis with context
• visual_heavy - Chart-first approach, minimal text, infographic style

Tones

• formal - Professional, objective, academic language
• persuasive - Action-oriented, compelling, results-focused
• casual - Friendly, conversational, accessible

Focus Areas

• metrics - Prioritize quantitative data and KPIs
• recommendations - Emphasize actionable next steps
• trends - Focus on patterns and future projections
• comparisons - Highlight comparative analysis
• insights - Uncover hidden patterns and surprises

Custom Templates (Expert)

Create your own prompt templates for complete control over report generation.

See the Custom Prompts Guide for complete documentation.

from pathlib import Path

# Option 1: Custom YAML template file (recommended)
config = ReportConfig(
    template_file=Path("./my_templates.yaml"),
    template_name="MY_CUSTOM_TEMPLATE"
)

pdf_path = generate_report(data, question="...", config=config)

# Option 2: Inline custom prompt (quick experiments)
custom_prompt = """
Create a report focusing on {question}.
Data: {answer}
Make it extremely concise with only bullet points.
"""

config = ReportConfig(custom_prompt=custom_prompt)
pdf_path = generate_report(data, question="...", config=config)

Examples:

• See examples/07_custom_prompts.py for working examples
• See examples/custom_templates.yaml for template file structure

Configuration

LLM Provider Setup

text2report supports multiple LLM providers: Ollama (local), OpenAI, and Azure OpenAI.

Option 1: Ollama (Default - Free & Local)

# Using Ollama (default configuration)
REPORT_LLM_PROVIDER=ollama
REPORT_LLM_BASE_URL=http://localhost:11434
REPORT_LLM_MODEL=gpt-oss:20b
REPORT_LLM_TEMPERATURE=0.3
REPORT_LLM_MAX_TOKENS=4096

# PDF Configuration
REPORT_PDF_OUTPUT_DIR=reports

Setup Ollama:

# Install Ollama from https://ollama.ai
ollama pull gpt-oss:20b  # or any other model
ollama serve

Option 2: OpenAI

# Using OpenAI
REPORT_LLM_PROVIDER=openai
REPORT_LLM_BASE_URL=https://api.openai.com  # Optional, this is the default
REPORT_LLM_MODEL=gpt-4-turbo-preview
REPORT_LLM_API_KEY=sk-proj-your-api-key-here
REPORT_LLM_TEMPERATURE=0.3
REPORT_LLM_MAX_TOKENS=4096

# PDF Configuration
REPORT_PDF_OUTPUT_DIR=reports

Option 3: Azure OpenAI

# Using Azure OpenAI
REPORT_LLM_PROVIDER=azure
REPORT_LLM_BASE_URL=https://your-resource.openai.azure.com
REPORT_LLM_MODEL=your-deployment-name
REPORT_LLM_API_KEY=your-azure-api-key
REPORT_LLM_TEMPERATURE=0.3
REPORT_LLM_MAX_TOKENS=4096

# PDF Configuration
REPORT_PDF_OUTPUT_DIR=reports

Programmatic Configuration

You can also configure settings in code:

from text2report.config import settings

# Configure for OpenAI
settings.llm_provider = "openai"
settings.llm_model = "gpt-4-turbo-preview"
settings.llm_api_key = "sk-proj-..."

# Or for Ollama
settings.llm_provider = "ollama"
settings.llm_model = "llama2:13b"
settings.llm_temperature = 0.5

Legacy Configuration (Deprecated)

Old REPORT_OLLAMA_* settings still work but will show deprecation warnings:

# DEPRECATED - still works but use REPORT_LLM_* instead
REPORT_OLLAMA_HOST=http://localhost:11434
REPORT_OLLAMA_MODEL=gpt-oss:20b

The system will automatically migrate these to the new settings.

API Reference

generate_report()

Generate a complete PDF report with visualizations and behavior customization.

def generate_report(
    data: Any,
    output_path: str | Path | None = None,
    question: str | None = None,
    charts: list | None = None,
    # Behavior customization (NEW)
    config: ReportConfig | None = None,
    audience: str | None = None,
    style: str | None = None,
    tone: str | None = None,
    focus: list[str] | None = None,
    preset: str | None = None,
) -> str:
    """
    Args:
        data: Input data (text, dict, list, JSON, etc.)
        output_path: Output PDF path (auto-generated if None)
        question: User's question to guide report generation
        charts: Deprecated (LLM generates visualizations automatically)

        # Behavior Customization
        config: ReportConfig object for complete control
        audience: Target audience (executives|technical|analysts|general)
        style: Report style (concise|detailed|visual_heavy)
        tone: Report tone (formal|persuasive|casual)
        focus: List of focus areas (metrics, recommendations, trends, etc.)
        preset: Use preset config (executive_summary, technical_deep_dive, etc.)

    Returns:
        Path to generated PDF file
    """

Examples:

# Basic usage
pdf_path = generate_report(
    data={"temperature": [20, 22, 25, 23, 21]},
    question="Analyze temperature trends this week"
)

# With behavior customization
pdf_path = generate_report(
    data=sales_data,
    question="Q4 analysis",
    audience="executives",
    style="concise",
    tone="formal"
)

# Using a preset
pdf_path = generate_report(
    data=data,
    question="System performance",
    preset="technical_deep_dive"
)

generate_quick_insights()

Generate quick bullet-point insights (fast, 3-5 seconds).

def generate_quick_insights(
    data: Any,
    question: str | None = None,
) -> str:
    """
    Args:
        data: Input data (text, dict, list, JSON, etc.)
        question: User's question

    Returns:
        String with 3-5 bullet point insights
    """

Example:

insights = generate_quick_insights(
    data=user_feedback_data,
    question="What are the main themes in user feedback?"
)

Report Features

The generated reports automatically include:

📊 Visualizations

The LLM intelligently chooses appropriate visualizations:

• Chart.js Charts: Bar charts, line charts, pie charts, radar charts
• Mermaid Diagrams: Flowcharts, process diagrams, relationships

📝 Report Structure

1. Title & Summary: Clear overview with key findings
2. Visualizations: Automatically generated charts and diagrams
3. Detailed Analysis: In-depth breakdown of the data
4. Tables: Raw data presented in clean tables
5. Recommendations: Actionable insights

🎨 Styling

• Professional typography and spacing
• Color-coded sections and headers
• Responsive chart sizing
• Page break optimization

Advanced Usage

Custom Output Directory

from pathlib import Path

output_dir = Path("./monthly_reports")
output_dir.mkdir(exist_ok=True)

pdf_path = generate_report(
    data=monthly_data,
    output_path=output_dir / "january_2024.pdf",
    question="Monthly performance summary"
)

Batch Report Generation

datasets = {
    "Q1": q1_data,
    "Q2": q2_data,
    "Q3": q3_data,
    "Q4": q4_data,
}

for quarter, data in datasets.items():
    pdf_path = generate_report(
        data=data,
        question=f"Analyze {quarter} performance",
        output_path=f"reports/{quarter}_report.pdf"
    )
    print(f"Generated {quarter}: {pdf_path}")

Real-World Use Cases

Board Meeting Executive Summary

# Generate high-level summary for board presentation
pdf_path = generate_report(
    data=company_metrics,
    question="Q4 2024 company performance and 2025 outlook",
    preset="executive_summary"
)

Engineering Incident Report

# Detailed technical postmortem
pdf_path = generate_report(
    data=incident_logs,
    question="Database outage root cause analysis",
    audience="technical",
    style="detailed",
    focus=["insights", "recommendations"]
)

A/B Test Results for Data Team

# Statistical analysis with methodology
pdf_path = generate_report(
    data=ab_test_results,
    question="Checkout flow A/B test analysis",
    preset="data_analysis"
)

Sales Proposal

# Persuasive presentation with visuals
pdf_path = generate_report(
    data=roi_analysis,
    question="ROI projection for enterprise plan",
    audience="general",
    style="visual_heavy",
    tone="persuasive",
    focus=["metrics", "recommendations"]
)

Requirements

• Python >= 3.10
• LLM Provider (choose one):
  • Ollama (free, local) - Download
  • OpenAI API key - Get Key
  • Azure OpenAI credentials
• Dependencies (auto-installed):
  • markdown
  • requests
  • pyyaml
  • playwright
  • pydantic >= 2.0
  • pydantic-settings

Project Structure

text2report/
├── generator.py          # Main orchestration
├── config/               # Configuration management
├── markdown_llm.py       # LLM integration
├── html_renderer.py      # Markdown → HTML conversion
├── pdf_renderer.py       # HTML → PDF conversion
├── prompts.py            # Prompt template management
├── styles.py             # CSS styling
├── serializers.py        # Data serialization
├── regex.py              # Pattern matching
└── utils/                # Utility functions

Troubleshooting

LLM Provider Issues

Ollama Connection Errors

# Check if Ollama is running
curl http://localhost:11434/api/tags

# Start Ollama (if not running)
ollama serve

# Check environment
echo $REPORT_LLM_PROVIDER  # should be "ollama" or empty

OpenAI API Errors

# Verify API key is set
echo $REPORT_LLM_API_KEY

# Test API connection
curl https://api.openai.com/v1/models \
  -H "Authorization: Bearer $REPORT_LLM_API_KEY"

Azure OpenAI Errors

# Verify all Azure settings
echo $REPORT_LLM_PROVIDER  # should be "azure"
echo $REPORT_LLM_BASE_URL
echo $REPORT_LLM_MODEL
echo $REPORT_LLM_API_KEY

Provider Configuration

# Check current provider in Python
from text2report.config import settings
print(f"Provider: {settings.llm_provider}")
print(f"Model: {settings.llm_model}")
print(f"Base URL: {settings.llm_base_url}")

Playwright Errors

# Reinstall Chromium
playwright install --force chromium

# Check Playwright installation
playwright --version

Import Errors

# Reinstall package in development mode
pip install -e .

License

MIT

Contributing

Contributions welcome! Please feel free to submit issues and pull requests.