snipcontext 0.2.3

AI-powered code snippet & context manager with local semantic search

SnipContext

AI-powered code snippet & context manager.

Save, search, tag, and instantly inject your best boilerplate, patterns, and context into any LLM (Claude, Cursor, Grok, Windsurf, etc.).

Local-first — Open source — Built for humans + AI agents

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Why SnipContext?

• Stop rewriting the same auth flows, component patterns, or utility functions
• Stop feeding LLMs messy or outdated code from your clipboard history
• Build your personal/team "second brain" of high-quality, reusable code
• Semantic search finds code by meaning, not just keywords
• LLM-optimized exports format your snippets for maximum comprehension

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Key Features

Feature	Status
Rich snippet saving with tags, metadata, and versioning	✅
Semantic search with local embeddings (sentence-transformers + FAISS)	✅
Hybrid search — semantic + keyword with configurable weights	✅
One-command export optimized for major LLMs	✅
CLI + Library support (Python)	✅
Plugin system for new providers and exporters	✅
Git-friendly, local-first storage	✅
Import/export for backup and sharing	✅

Supported LLM Providers

Provider	Format	Best For
Generic	Markdown	Universal compatibility
Claude	XML documents	Anthropic Claude
Cursor	File-style headers	Cursor IDE
OpenAI	Delineated sections	ChatGPT / GPT-4

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Quick Start

Installation

# From PyPI (recommended)
pip install snipcontext

# Or with uv
uv tool install snipcontext

# From source (after cloning)
pip install -e ".[dev]"

# Or install directly from GitHub
pip install git+https://github.com/billybox1926-jpg/snipcontext.git

Windows Users: Use snipcontext instead of sc

Windows has a built-in sc.exe (Service Control) that shadows the sc CLI entry point. Use the full command name instead:

snipcontext add "print('hello')" --title "Hello" --tag python
snipcontext search "hello world"
snipcontext list
snipcontext stats

Or run via module:

python -m snipcontext add "print('hello')" --title "Hello" --tag python

Verify Installation

snipcontext --help          # or: python -m snipcontext --help
snipcontext providers       # List available export providers

CLI Usage

Note: On Windows, use snipcontext instead of sc (see Installation).

# Add a snippet
snipcontext add "def authenticate(token):\n    return jwt.decode(token, SECRET)" \
  --title "JWT Authentication" \
  --desc "Decode and verify JWT tokens" \
  --lang python \
  --tag auth --tag jwt --tag security

# Search semantically
snipcontext search "how to validate auth tokens"

# Search by tag
snipcontext search "auth" --mode tag

# Export for Claude
snipcontext search "authentication" --provider claude --output context.xml

# List all snippets
snipcontext list

# Show stats
snipcontext stats

Library Usage

from snipcontext.core.models import Snippet, SnippetMetadata, Language
from snipcontext.core.storage import StorageEngine
from snipcontext.core.search import HybridSearch
from snipcontext.config.settings import get_config

# Initialize
config = get_config()
storage = StorageEngine(config)

# Create and save a snippet
snippet = Snippet(
    content="def memoize(fn):\n    cache = {}\n    ...",
    metadata=SnippetMetadata(
        title="Memoization Decorator",
        description="Cache function results",
        language=Language.PYTHON,
    ),
    tags=["python", "decorator", "performance"],
)
storage.save(snippet)

# Search with semantic understanding
searcher = HybridSearch(config)
searcher.index_snippets(storage.list_all())
results = searcher.search("cache function results decorator")

for r in results:
    print(f"{r.score:.3f} | {r.snippet.metadata.title}")

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🔐 Encryption at Rest

SnipContext supports Fernet (AES-128) encryption for sensitive snippets. When enabled, snippet content is encrypted at rest using a key derived from a passphrase via PBKDF2 (100k iterations).

Enable Encryption

# Enable encryption (required)
export SNIPCONTEXT_ENCRYPT_ENABLED=true

# Set passphrase (used for key derivation)
export SNIPCONTEXT_ENCRYPTION_PASSPHRASE="your-secure-passphrase"

# Optional: persist salt to config (auto-generated if omitted)
export SNIPCONTEXT_ENCRYPT_KEY_SALT="base64-encoded-salt"

Encrypt Snippets

# Encrypt a new snippet
snipcontext add "api_key = 'sk-12345'" \
  --title "API Key" \
  --tag secret \
  --encrypt

# Mark as sensitive (auto-enables encryption)
snipcontext add "password = 'secret123'" \
  --title "DB Password" \
  --sensitive

Decrypt for Viewing/Editing

# Decrypt for viewing
snipcontext decrypt <snippet-id>

# Encrypt an existing snippet
snipcontext encrypt <snippet-id>

Note: When encrypted, the plaintext content is cleared from storage. The encrypted_content field stores the encrypted data. Use sc decrypt <id> to restore plaintext for editing.

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🔄 Index Rebuild & Resilience

SnipContext automatically detects and recovers from index corruption. The HybridSearch engine validates index integrity on load and rebuilds automatically when needed.

Manual Rebuild

# Check if rebuild is needed (skips if index is valid)
snipcontext rebuild-index

# Force rebuild (useful after corruption, dependency changes, or mode switches)
snipcontext rebuild-index --force

Auto-Recovery

The search engine automatically:

1. Validates index integrity on load (checks ID map lengths, matrix dimensions)
2. Cleans up corrupted files (deletes mismatched/corrupted index files)
3. Falls back gracefully — if semantic index unavailable, runs keyword-only search
4. Rebuilds on demand — index_snippets() auto-loads existing indices before rebuilding

Watchdog / Real-time Indexing

Run sc watch to monitor the snippets directory and automatically reindex when files change:

sc watch

Disable via config if you prefer manual rebuilds only:

export SNIPCONTEXT_WATCHDOG_ENABLED=false

---

Architecture

CLI (Typer + Rich)
  │
├── Providers (Claude XML / Cursor / OpenAI / Generic Markdown)
│
├── Search Engine
│   ├── Semantic: sentence-transformers + FAISS
│   ├── Keyword: TF-IDF (scikit-learn)
│   └── Hybrid: configurable weighted fusion
│
├── Storage Engine
│   └── Git-friendly JSON files per snippet
│
└── Data Models (Pydantic v2)
    └── Snippet / SnippetMetadata / SnippetVersion

See docs/ARCHITECTURE.md for detailed design documentation.

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Configuration

SnipContext uses environment variables and a YAML config file:

# Use GPU for embeddings
export SNIPCONTEXT_EMBED_DEVICE="cuda"

# Change embedding model
export SNIPCONTEXT_EMBED_MODEL_NAME="all-mpnet-base-v2"

# Adjust search weights
export SNIPCONTEXT_SEARCH_SEMANTIC_WEIGHT="0.8"

Or edit ~/.config/SnipContext/snipcontext.yaml:

embedding:
  model_name: "all-MiniLM-L6-v2"
  device: "cpu"

search:
  default_mode: "hybrid"
  semantic_weight: 0.7
  keyword_weight: 0.3
  top_k: 10

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Development

# Clone
git clone https://github.com/billybox1926-jpg/snipcontext.git
cd snipcontext

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=snipcontext

# Linting
ruff check .
mypy .

# Install pre-commit hooks
pre-commit install

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Roadmap

• Core snippet CRUD with git-friendly storage
• Semantic + hybrid search with local embeddings
• LLM-optimized export providers (Claude, Cursor, OpenAI, Generic)
• Rich CLI with Typer
• Plugin system with entry points
• Python library distribution (PyPI)
• Real-time index updates (currently requires rebuild)
• Import from GitHub Gists
• Import from Git repositories
• Snippet templates and scaffolding
• Team sharing via git-sync
• VS Code extension

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Project Structure

snipcontext/
├── snipcontext/              # Python package
│   ├── __init__.py           # Package exports
│   ├── __main__.py           # python -m snipcontext
│   ├── core/                 # Core engine (models, storage, search)
│   │   ├── models.py         # Pydantic data models
│   │   ├── storage.py        # Git-friendly JSON storage
│   │   └── search.py         # Semantic + hybrid search
│   ├── providers/            # LLM export providers
│   │   ├── base.py           # Provider interface
│   │   ├── claude.py         # Anthropic Claude XML
│   │   ├── cursor.py         # Cursor IDE format
│   │   ├── openai.py         # OpenAI format
│   │   └── generic.py        # Universal Markdown
│   ├── plugins/              # Plugin system
│   │   └── base.py           # Plugin base + manager
│   ├── config/               # Configuration
│   │   └── settings.py       # Pydantic Settings
│   └── cli/                  # Command-line interface
│       └── main.py           # Typer CLI commands
├── tests/                    # Comprehensive test suite
├── docs/                     # Documentation
│   ├── ARCHITECTURE.md       # Design docs
│   ├── API.md                # Python API reference
│   └── MAINTAINER.md         # Maintainer guide
├── pyproject.toml            # Modern Python packaging
└── README.md                 # This file

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License

MIT License — see LICENSE for details.

Contributing

Contributions are welcome! Please read CONTRIBUTING.md and CODE_OF_CONDUCT.md first. New contributors should check out our Good First Issues.