Metadata-Version: 2.4
Name: pyomnibrain
Version: 0.1.0
Summary: Python bindings and SDK for OmniBrain: Embedded Local-First Temporal Semantic Knowledge Graph & Reader Engine
Author-email: "Shah Md. Mahi" <shahmdmahi15@gmail.com>
Project-URL: Homepage, https://github.com/shahmdmahi15/omnibrain
Project-URL: Bug Tracker, https://github.com/shahmdmahi15/omnibrain/issues
Project-URL: Website, https://shahmdmahi.dpdns.org
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Database :: Database Engines/Servers
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: requests>=2.28.0

# pyomnibrain

**Python SDK & Client for OmniBrain (ChronoCache)**
*Embedded Local-First Temporal Semantic Knowledge Graph & Reader Engine*

## Installation

```bash
pip install pyomnibrain
```

## Quickstart

```python
from pyomnibrain import OmniBrainClient

# Connect to local OmniBrain daemon
client = OmniBrainClient(host="127.0.0.1", port=4821)

# Check daemon health
health = client.health()
print(f"Connected to OmniBrain v{health['version']}")

# Snapshot an article into offline memory
capture_result = client.capture(
    url="https://example.com/rust-architecture",
    title="High-Performance Rust Architectures",
    content="""
    <article>
        <h1>High-Performance Rust Architectures</h1>
        <p>Rust empowers developers to build reliable, high-throughput systems with zero-cost abstractions.</p>
    </article>
    """
)
print(f"Captured: {capture_result['title']} | Compression: {capture_result['compression']['compression_ratio']}x")

# Search offline memory with hybrid fusion
results = client.search("high-performance reliable systems", limit=5, mode="hybrid")
for r in results:
    print(f"[{r['match_type'].upper()}] {r['title']} (Score: {r['score']:.4f})")
    print(f"  Snippet: {r['snippet']}")
```
