diffrag 0.2.3

AI-powered git diff analysis and code review with RAG context

diffrag

AI-powered code reviewer for git repositories. Point it at two branches and get an LLM-generated review that understands the broader codebase through RAG (Retrieval-Augmented Generation).

Features

• AI code review — analyzes the diff between any two branches and reports on breaking changes, API surface changes, SOLID violations, code smells, security concerns, missing documentation, and more.
• RAG context — the full repository is indexed into a ChromaDB vector store so the LLM can reference relevant surrounding code, not just the changed lines.
• Smart index caching — the vector index is rebuilt only when the HEAD commit changes; repeated runs on the same commit reuse the cached index instantly.
• Interactive Q&A — after a review you can open an interactive REPL and ask follow-up questions about the diff or the codebase; the session remembers prior exchanges so follow-up questions work naturally.
• Flexible AI backends — works with any OpenAI-compatible endpoint (OpenWebUI, Ollama /v1, OpenAI) as well as Ollama's native API.
• Fully configurable — settings can be set once in a user-level global config or overridden per-project; sane defaults work out of the box. Indexes are stored in the OS user cache directory — your project directories stay clean.
• .gitignore-aware indexing — the repository's .gitignore is parsed automatically and its patterns are respected during indexing; extra excludes can be added via config.

Installation

uv add diffrag          # add to a project
uv tool install diffrag # install as a standalone CLI tool

Requires Python ≥ 3.12.

Setup

1. Create a config file

diffrag looks for configuration in the following order (first match wins):

1. --config <path> — explicit path passed on the command line
2. .diffrag.toml — in the current working directory (project-level override)
3. $(diffrag config-path) — user-level global config (set once, used everywhere)
4. Built-in defaults — everything works without any config file

Recommended: set up a global config once so every repository uses the same model and endpoint without needing its own .diffrag.toml:

# Create the global config (creates the directory too)
diffrag create-config

# Open it in your editor
$EDITOR "$(diffrag config-path)"

For a project-specific override (e.g. a different base_branch or verbosity):

diffrag create-config --out .diffrag.toml

Per-project .diffrag.toml files take precedence over the global config.

Minimal config — Ollama for both LLM and embeddings:

[ai]
base_url = "http://localhost:11434"
model    = "llama3.2"

[embedding]
base_url = "http://localhost:11434"
model    = "nomic-embed-text"

OpenWebUI for the LLM, Ollama for embeddings:

[ai]
base_url = "http://localhost:3000"
model    = "gpt-4o"
api_key  = "your-openwebui-token"

[embedding]
base_url = "http://localhost:11434"
model    = "nomic-embed-text"

The [ai] and [embedding] sections are independent — you can mix and match any OpenAI-compatible endpoint for each role.

---

CLI usage

review — generate a code review

# Review current branch (HEAD) against main
diffrag review --base main

# Review a specific branch against main
diffrag review --base main --head feature/my-feature

# Review against a tag or commit SHA
diffrag review --base v1.2.0 --head HEAD

# Review a repo at a different path
diffrag review --repo /path/to/repo --base main

# Use a non-default config file
diffrag review --base main --config /path/to/my.toml

Index caching: on the first run the repository is indexed and the result is persisted to the OS user cache directory (run diffrag db-path to see the exact path for a given repo). On subsequent runs the tool compares the current HEAD SHA against the stored one — if they match the index is reused immediately; if they differ (new commit or branch switch) the index is rebuilt automatically. Your project directories are never modified.

ask — interactive Q&A about the diff

diffrag ask --base main
diffrag ask --base main --head feature/my-feature

Opens an interactive prompt backed by the same RAG index. Run review at least once beforehand so the index is populated. Type exit or press Ctrl-D to quit.

Each answer is informed by the repository context and the diff. The session accumulates conversation history, so follow-up questions can refer back to earlier answers:

> What does the change in auth.py affect downstream?
> Can you give a concrete example of how that breaks existing callers?
> Which tests cover the modified functions?

show-config — inspect effective configuration

diffrag show-config
diffrag show-config --config /path/to/my.toml

Prints a table of every setting that is in effect, including defaults for keys not present in your config file.

db-path — print the database path for a repository

diffrag db-path
diffrag db-path --repo /path/to/repo

Prints the path where the vector index for that repository is (or will be) stored. Useful for inspecting or deleting a stale index.

create-config — create a starter config file

diffrag create-config                       # write to user-level global config
diffrag create-config --out .diffrag.toml  # write a project-level override
diffrag create-config --force               # overwrite if the file already exists

Creates a config file pre-populated with all available settings and their defaults. The destination directory is created automatically.

config-path — print the user-level config path

diffrag config-path

Prints the OS-appropriate path for the user-level config file (it may not exist yet). Useful for opening the config in an editor: $EDITOR $(diffrag config-path).

Global options

Option	Description
-v / --verbose	Enable debug-level logging
-c / --config PATH	Path to a .toml config file (overrides auto-discovery)

---

Configuration reference

All keys are optional. Unset keys fall back to the defaults shown below. Place the file at $(diffrag config-path) for global defaults, or at .diffrag.toml in a project root to override per-repository.

[ai]
# Base URL of the OpenAI-compatible or Ollama endpoint used for review generation.
# Do NOT include a /v1 suffix — it is appended automatically.
base_url = "http://localhost:11434"

# Model identifier sent with every completion request.
model = "llama3.2"

# Bearer token for the Authorization header. Leave empty for local servers.
api_key = ""

# Per-read timeout in seconds. Requests use streaming, so this is the maximum
# time allowed between consecutive chunks — not the total generation time.
# Increase only if the model stalls mid-generation for unusually long periods.
timeout = 120.0


[embedding]
# Endpoint used for computing text embeddings. Can be the same server as [ai]
# or a different one (e.g. a dedicated embedding service).
base_url = "http://localhost:11434"

# Embedding model. nomic-embed-text works well with Ollama.
model = "nomic-embed-text"

api_key = ""
timeout = 60.0


[indexing]
# The index is always stored in the OS user cache directory, keyed by repo.
# Run "diffrag db-path" to see the exact path. This cannot be configured.

# Maximum number of characters per text chunk when splitting files.
chunk_size = 1024

# Character overlap between consecutive chunks (helps preserve context at boundaries).
chunk_overlap = 128

# The repository's .gitignore is parsed automatically and its patterns are
# applied first. The list below provides additional excludes on top of .gitignore.
# Any path component matching one of these strings is also skipped.
excluded_patterns = [
    ".git", ".venv", "__pycache__", "node_modules",
    ".idea", ".mypy_cache", ".ruff_cache", "dist", "build"
]

# Files larger than this (in bytes) are silently skipped.
# Binary files are always skipped regardless of this setting.
max_file_size = 1000000


[review]
# Default base branch when --base is not specified on the command line.
base_branch = "main"

# Number of context chunks retrieved from the vector index per diff chunk.
# Higher values give the LLM more surrounding context but increase prompt size.
similarity_top_k = 5

# Approximate token budget per diff chunk before it is split into individual hunks.
# Reduce this if your model has a small context window.
max_prompt_tokens = 6000

# Detail level of generated reviews. Can be overridden per-run with --verbosity.
# "brief"    — top 3 critical issues only, under 200 words
# "standard" — balanced default
# "detailed" — exhaustive, includes minor nits and suggested fixes per issue
verbosity = "standard"

---

Library usage

diffrag is also usable as a Python library:

import asyncio
from pathlib import Path
from diffrag import (
    Settings,
    GitRepository,
    OpenAICompatClient,
    RepoIndexer,
    CodeReviewer,
)

settings = Settings.from_toml(Path(".diffrag.toml"))

ai_client = OpenAICompatClient(
    base_url=settings.ai.base_url,
    model=settings.ai.model,
    api_key=settings.ai.api_key,
)
embed_client = OpenAICompatClient(
    base_url=settings.embedding.base_url,
    model=settings.embedding.model,
    api_key=settings.embedding.api_key,
)
indexer = RepoIndexer(
    db_path=settings.indexing.db_path,
    embedding_client=embed_client,
)
reviewer = CodeReviewer(ai_client=ai_client, indexer=indexer)
repo = GitRepository(Path("/path/to/repo"))


async def main() -> None:
    # review() checks the HEAD commit hash and only rebuilds the index when needed
    result = await reviewer.review(repo, base="main", head="feature/my-feature")
    print(result.summary)

    # ask follow-up questions about the same diff; pass history for multi-turn
    diff = repo.get_diff("main", "feature/my-feature")
    history: list[tuple[str, str]] = []
    q1 = "Are there any breaking API changes?"
    a1 = await reviewer.ask(q1, diff)
    print(a1)
    history.append((q1, a1))
    a2 = await reviewer.ask("Which callers are affected?", diff, history=history)
    print(a2)


asyncio.run(main())

For Ollama's native API (supports num_ctx and other Ollama-specific options):

from diffrag import OllamaClient

embed_client = OllamaClient(
    base_url="http://localhost:11434",
    model="nomic-embed-text",
)

---

How the review pipeline works

1. Diff — git diff base...head is run for each changed file; results are parsed into typed FileDiff / Hunk objects.
2. Index — every non-binary file in the repository is split into overlapping character chunks and embedded into ChromaDB. The HEAD commit hash is saved alongside the index; on future runs this hash is compared and the rebuild is skipped if nothing has changed.
3. Per-file review — for each changed file:
   • The LLM summarises the diff in plain English (used as a targeted search query).
   • The summary is embedded and the top-k nearest chunks are retrieved from the index.
   • The LLM reviews the raw diff in light of the retrieved context.
   • Large diffs are split into individual git hunks and reviewed separately.
4. Consolidation — all per-file reviews are sent to the LLM in a final prompt that produces a single, cross-cutting summary.

---

Development

# Install with dev dependencies
uv sync --group dev

# Run tests
uv run pytest

# Lint and format checks
uv run ruff check src tests
uv run ruff format --check src tests

# Type checking
uv run ty check src

# Run all checks via tox
uv run tox

# Build Sphinx docs
uv run sphinx-build docs docs/_build/html

Project structure

src/diffrag/
├── config/       # Settings dataclasses + TOML loading (settings.py)
├── diff/         # GitRepository, FileDiff, Hunk models (git.py, models.py)
├── ai/           # AIClient / EmbeddingClient protocols, OpenAI-compat + Ollama clients
├── indexing/     # ChromaDB-backed RepoIndexer with staleness detection
├── review/       # CodeReviewer orchestrator + prompt templates
└── cli/          # Click CLI: review / ask / show-config / create-config / db-path / config-path