gitview 0.1.2

Git history analyzer with LLM-powered narrative generation

GitView

Git history analyzer with LLM-powered narrative generation

GitView extracts your repository's git history and uses AI to generate compelling narratives about how your codebase evolved. Instead of manually reading through thousands of commits, get a comprehensive story of your project's journey.

Example run on this repository:

[(https://github.com/carstenbund/gitview/blob/main/output/history_story.md)]

Features

• ** Comprehensive History Extraction**: Extracts commit metadata, LOC changes, language breakdown, README evolution, comment analysis, and more
• ** Smart Chunking**: Automatically divides history into meaningful "phases" or "epochs" based on significant changes
• ** LLM-Powered Summaries**: Uses Claude to generate narrative summaries for each phase
• ** Global Story Generation**: Combines phase summaries into executive summaries, timelines, technical retrospectives, and deletion stories
• ** Multiple Output Formats**: Generates markdown reports, JSON data, and timelines

Installation

Option 1: Install with pip (recommended)

This creates a gitview command in your PATH:

# Clone the repository
git clone https://github.com/yourusername/gitview.git
cd gitview

# Install in editable mode with dependencies
pip install -e .

# The gitview command is now available system-wide
gitview --version
gitview --help

How it works: The pip install -e . command reads pyproject.toml and setup.py, which define an entry point that creates /usr/local/bin/gitview (or similar on Windows) that calls gitview.cli:main.

Option 2: Run directly from repo (no installation)

Use the executable wrapper in bin/:

# Clone the repository
git clone https://github.com/yourusername/gitview.git
cd gitview

# Install dependencies only
pip install -r requirements.txt

# Run directly from the repo
./bin/gitview --version
./bin/gitview analyze

# Or add bin/ to your PATH
export PATH="$PWD/bin:$PATH"
gitview analyze

Option 3: Run as Python module

# Install dependencies
pip install -r requirements.txt

# Run as a module
python -m gitview.cli --help
python -m gitview.cli analyze

Verify Installation

Run the verification script to check everything is set up correctly:

python verify_installation.py

This will check:

• Python version (3.8+ required)
• All required dependencies
• gitview command availability
• LLM backend configuration (API keys, Ollama server)

Troubleshooting Installation

If gitview command is not found after installation:

# Option 1: Use full path to module
python -m gitview.cli analyze

# Option 2: Reinstall in editable mode
pip uninstall gitview -y
pip install -e .

# Option 3: Check if it's in your PATH
which gitview  # Unix/Linux/Mac
where gitview  # Windows

Quick Start

# Using Anthropic Claude (default)
export ANTHROPIC_API_KEY="your-api-key-here"
gitview analyze

# Using OpenAI GPT
export OPENAI_API_KEY="your-api-key-here"
gitview analyze --backend openai

# Using local Ollama (no API key needed)
gitview analyze --backend ollama --model llama3

# Skip LLM summarization (just extract and chunk)
gitview analyze --skip-llm

Usage

Full Analysis Pipeline

The main command runs the complete pipeline: extract → chunk → summarize → story → output

gitview analyze [OPTIONS]

Options:
  -r, --repo PATH              Path to git repository (default: current directory)
  -o, --output PATH            Output directory (default: "output")
  -s, --strategy STRATEGY      Chunking strategy: fixed, time, or adaptive (default: adaptive)
  --chunk-size INTEGER         Chunk size for fixed strategy (default: 50)
  --max-commits INTEGER        Maximum commits to analyze
  --branch TEXT                Branch to analyze (default: HEAD)
  -b, --backend BACKEND        LLM backend: anthropic, openai, or ollama (auto-detected)
  -m, --model TEXT             Model identifier (uses backend defaults if not specified)
  --api-key TEXT               API key for the backend (defaults to env var)
  --ollama-url TEXT            Ollama API URL (default: http://localhost:11434)
  --repo-name TEXT             Repository name for output
  --skip-llm                   Skip LLM summarization (extract and chunk only)

Extract Only

Extract git history to JSONL file without LLM processing:

gitview extract --repo /path/to/repo --output history.jsonl

Chunk Only

Chunk an extracted JSONL file into phases:

gitview chunk history.jsonl --output ./phases --strategy adaptive

Chunking Strategies

GitView supports three chunking strategies:

1. Adaptive (Recommended)

Automatically splits history when significant changes occur:

• LOC changes by >30%
• Large deletions/additions detected
• README rewrites
• Major refactorings

gitview analyze --strategy adaptive

2. Fixed Size

Splits history into fixed-size chunks (e.g., 50 commits per phase):

gitview analyze --strategy fixed --chunk-size 50

3. Time-Based

Splits by time periods (week, month, quarter, year):

gitview analyze --strategy time --period quarter

Output Files

GitView generates several output files:

output/
├── repo_history.jsonl           # Raw commit data
├── phases/                       # Phase data
│   ├── phase_01.json
│   ├── phase_02.json
│   └── phase_index.json
├── history_story.md              # Main narrative report
├── timeline.md                   # Simple timeline
└── history_data.json             # Complete data in JSON

Main Report (history_story.md)

Contains:

• Executive Summary: High-level overview for stakeholders
• Timeline: Chronological phases with descriptive headings
• Full Narrative: Complete story of the codebase evolution
• Technical Evolution: Architectural journey and key decisions
• Story of Deletions: What was removed and why
• Phase Details: Detailed breakdown of each phase
• Statistics: Comprehensive metrics

How It Works

Phase 1: Extract Raw History

Analyzes git commits and extracts:

• Commit metadata (hash, author, date, message)
• Lines of code changes (insertions/deletions)
• File statistics
• Language breakdown
• README state and changes
• Code comments and density
• Detection of large changes, refactors, etc.

Phase 2: Chunk into Epochs

Divides history into meaningful phases based on:

• Significant LOC changes
• Large deletions or additions
• Language mix changes
• README rewrites
• Major refactorings

Phase 3: Summarize Each Phase

Uses Claude to generate narrative summaries for each phase, answering:

• What were the main activities?
• Why were changes made?
• What was deleted/added and why?
• How did documentation evolve?
• What do commit messages reveal?

Phase 4: Generate Global Story

Combines phase summaries to create:

• Executive summary for non-technical readers
• Chronological timeline with meaningful headings
• Technical retrospective
• Story of code deletions and cleanups
• Full detailed narrative

Examples

Analyze a Large Open Source Project

gitview analyze \
  --repo /path/to/large-project \
  --output ./project-analysis \
  --strategy adaptive \
  --repo-name "My Project"

Quick Analysis Without LLM

Perfect for quick exploration or when you don't have an API key:

gitview analyze --skip-llm --output ./quick-analysis

Extract and Process Later

# Extract once
gitview extract --repo /path/to/repo --output history.jsonl

# Experiment with different chunking strategies
gitview chunk history.jsonl --strategy adaptive --output ./adaptive-phases
gitview chunk history.jsonl --strategy fixed --chunk-size 25 --output ./fixed-phases

Architecture

┌─────────────────────┐
│   Git Repository    │
└──────────┬──────────┘
           │
           v
┌─────────────────────┐
│  Extractor          │  Analyzes commits, extracts metadata
│  (extractor.py)     │  Output: repo_history.jsonl
└──────────┬──────────┘
           │
           v
┌─────────────────────┐
│  Chunker            │  Splits into meaningful phases
│  (chunker.py)       │  Strategies: adaptive, fixed, time
└──────────┬──────────┘
           │
           v
┌─────────────────────┐
│  Summarizer         │  LLM summarizes each phase
│  (summarizer.py)    │  Uses Claude API
└──────────┬──────────┘
           │
           v
┌─────────────────────┐
│  StoryTeller        │  Generates global narratives
│  (storyteller.py)   │  Multiple story formats
└──────────┬──────────┘
           │
           v
┌─────────────────────┐
│  Writer             │  Outputs markdown, JSON, etc.
│  (writer.py)        │
└─────────────────────┘

Requirements

• Python 3.8+
• Git repository with commit history
• One of the following LLM backends:
  • Anthropic Claude (requires API key)
  • OpenAI GPT (requires API key)
  • Ollama (runs locally, no API key needed)
• Dependencies: gitpython, anthropic, openai, requests, click, rich, pydantic

LLM Backend Configuration

GitView supports three LLM backends with automatic detection based on environment variables:

Anthropic Claude (Default)

Get an API key from Anthropic

export ANTHROPIC_API_KEY="your-api-key-here"
gitview analyze

Default models:

• claude-sonnet-4-5-20250929 (default)
• claude-3-opus-20240229 (more powerful)
• claude-3-haiku-20240307 (faster)

OpenAI GPT

Get an API key from OpenAI

export OPENAI_API_KEY="your-api-key-here"
gitview analyze --backend openai

Default models:

• gpt-4 (default)
• gpt-4-turbo-preview (faster)
• gpt-3.5-turbo (cheaper)

Ollama (Local)

Install Ollama and pull a model:

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model
ollama pull llama3

# Start Ollama server
ollama serve

# Use with GitView (no API key needed)
gitview analyze --backend ollama --model llama3

Popular Ollama models:

• llama3 (default, balanced)
• mistral (fast, good quality)
• codellama (optimized for code)
• mixtral (large, powerful)

Custom Configuration

# Specify custom model
gitview analyze --backend anthropic --model claude-3-opus-20240229

# Use custom Ollama URL
gitview analyze --backend ollama --ollama-url http://192.168.1.100:11434

# Pass API key directly (instead of env var)
gitview analyze --backend openai --api-key "your-key"

Use Cases

• Technical Documentation: Automatically generate project history documentation
• Onboarding: Help new developers understand codebase evolution
• Retrospectives: Review what worked and what didn't
• Project Reports: Create compelling narratives for stakeholders
• Code Archaeology: Understand why code evolved the way it did
• Cleanup Planning: Identify what to remove based on deletion history

Contributing

Contributions welcome! Please open an issue or submit a pull request.

License

MIT License - see LICENSE file for details