contextlite 1.0.16

Ultra-fast context engine for retrieval and AI applications

ContextLite Python Package

A Python wrapper for ContextLite - an ultra-fast context engine for retrieval and AI applications.

🚀 Quick Start

Installation

pip install contextlite

Basic Usage

from contextlite import ContextLiteClient

# Auto-start server and add documents
with ContextLiteClient() as client:
    # Add some documents
    client.add_document("The quick brown fox jumps over the lazy dog.", doc_id="doc1")
    client.add_document("Python is a great programming language.", doc_id="doc2")
    client.add_document("Machine learning is transforming software development.", doc_id="doc3")
    
    # Query for relevant content
    results = client.query("programming language")
    print(f"Found {len(results['documents'])} relevant documents")
    
    for doc in results['documents']:
        print(f"Score: {doc['score']:.3f} - {doc['content'][:50]}...")

Command Line Usage

The package also installs a contextlite command that acts as a wrapper for the native binary:

# Start ContextLite server
contextlite --port 8080

# Get help
contextlite --help

📋 Features

• 🔥 Ultra-Fast: Native Go binary performance with Python convenience
• 🛠️ Auto-Management: Automatically detects, downloads, and manages ContextLite binary
• 🔌 Easy Integration: Simple Python API with context manager support
• 🌍 Cross-Platform: Works on Windows, macOS, and Linux (x64 and ARM64)
• ⚡ Zero Dependencies: Core functionality requires only standard library (requests for auto-download)

🏗️ Architecture

This Python package is a "shim" that provides Python bindings for the high-performance ContextLite binary:

1. Binary Detection: Automatically finds ContextLite binary in PATH or common install locations
2. Auto-Download: Downloads appropriate binary for your platform if not found
3. Server Management: Optionally manages ContextLite server lifecycle
4. Python API: Provides convenient Python interface over REST API

📖 API Reference

ContextLiteClient

The main interface for interacting with ContextLite.

Constructor

ContextLiteClient(
    host="localhost",           # Server host
    port=8080,                 # Server port  
    auto_start=True,           # Auto-start server if not running
    database_path=None,        # Optional database file path
    timeout=30.0               # Request timeout in seconds
)

Methods

• add_document(content, document_id=None, metadata=None) - Add a document
• query(query, max_results=None, min_score=None) - Search for documents
• get_document(document_id) - Retrieve specific document
• delete_document(document_id) - Delete a document
• get_stats() - Get server statistics
• is_server_running() - Check if server is responsive

Context Manager

from contextlite import contextlite_client

with contextlite_client(port=8080) as client:
    client.add_document("Hello world!")
    results = client.query("hello")

🔧 Binary Management

The package handles ContextLite binary management automatically:

Detection Strategy

1. PATH: Checks if contextlite is in system PATH
2. System Locations: Common install directories (/usr/local/bin, Program Files, etc.)
3. User Data: User-specific data directory
4. Package Data: Bundled with package (if available)

Auto-Download

If no binary is found, the package will:

1. Detect your platform and architecture
2. Download the appropriate binary from GitHub releases
3. Store it in user data directory
4. Make it executable and ready to use

Manual Installation

You can also install ContextLite binary manually:

# Download from GitHub releases
curl -L https://github.com/Michael-A-Kuykendall/contextlite/releases/latest/download/contextlite_linux_amd64 -o contextlite
chmod +x contextlite
sudo mv contextlite /usr/local/bin/

🌐 Examples

Document Management

from contextlite import ContextLiteClient

client = ContextLiteClient()

# Add documents with metadata
client.add_document(
    content="Advanced machine learning techniques for natural language processing.",
    document_id="ml-nlp-guide",
    metadata={
        "category": "machine-learning",
        "difficulty": "advanced",
        "tags": ["nlp", "deep-learning", "transformers"]
    }
)

# Query with filters
results = client.query(
    query="natural language processing",
    max_results=5,
    min_score=0.7
)

for doc in results['documents']:
    print(f"Document: {doc['id']}")
    print(f"Score: {doc['score']:.3f}")
    print(f"Content: {doc['content'][:100]}...")
    print(f"Metadata: {doc.get('metadata', {})}")
    print("-" * 50)

Batch Operations

from contextlite import ContextLiteClient

# Process multiple documents
documents = [
    "Python is a versatile programming language.",
    "JavaScript powers modern web development.", 
    "Go offers excellent performance for backend services.",
    "Rust provides memory safety without garbage collection."
]

with ContextLiteClient() as client:
    # Batch add documents
    for i, content in enumerate(documents):
        client.add_document(content, document_id=f"lang-{i}")
    
    # Search across all documents
    results = client.query("backend programming")
    
    print(f"Found {len(results['documents'])} relevant documents")
    for doc in results['documents']:
        print(f"• {doc['content']} (Score: {doc['score']:.3f})")

Custom Server Configuration

from contextlite import ContextLiteClient

# Connect to existing server
client = ContextLiteClient(
    host="remote-server.com",
    port=9090,
    auto_start=False  # Don't try to start server
)

# Use custom database location
local_client = ContextLiteClient(
    database_path="/path/to/my/database.db",
    port=8081
)

🚨 Error Handling

from contextlite import (
    ContextLiteClient, 
    BinaryNotFoundError, 
    ServerError,
    ContextLiteError
)

try:
    with ContextLiteClient() as client:
        results = client.query("test query")
        
except BinaryNotFoundError:
    print("ContextLite binary not found. Please install it manually.")
    
except ServerError as e:
    print(f"Server error: {e}")
    
except ContextLiteError as e:
    print(f"ContextLite error: {e}")

🛠️ Development

Local Development

# Clone the repository
git clone https://github.com/Michael-A-Kuykendall/contextlite.git
cd contextlite/python-wrapper

# Install in development mode
pip install -e .

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

# Run tests
pytest

# Format code
black contextlite/
isort contextlite/

# Type checking
mypy contextlite/

Testing

import pytest
from contextlite import ContextLiteClient

def test_basic_operations():
    with ContextLiteClient() as client:
        # Add document
        response = client.add_document("Test content", doc_id="test1")
        assert response['success'] == True
        
        # Query
        results = client.query("test")
        assert len(results['documents']) > 0
        
        # Cleanup
        client.delete_document("test1")

📝 Requirements

• Python: 3.8+
• Platform: Windows, macOS, Linux (x64/ARM64)
• Dependencies: requests, platformdirs
• ContextLite Binary: Auto-downloaded or manually installed

📄 License

This Python package is released under the MIT License. The ContextLite binary may have different licensing terms.

🔗 Links

• Homepage: https://contextlite.com
• Documentation: https://docs.contextlite.com
• GitHub: https://github.com/Michael-A-Kuykendall/contextlite
• PyPI: https://pypi.org/project/contextlite/
• Issues: https://github.com/Michael-A-Kuykendall/contextlite/issues

💬 Support

• GitHub Issues: For bug reports and feature requests
• Documentation: Comprehensive guides and API reference
• Community: Join our Discord server for discussions

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Built with ❤️ by the ContextLite team. Made for developers who need blazing-fast context retrieval.