Metadata-Version: 2.4
Name: sara_engine
Version: 0.2.3
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Summary: A bio-inspired Spiking Neural Network engine with Hugging Face-like pipeline APIs (CPU-only, Backprop-free).
Author-email: Your Name <your.email@example.com>
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Homepage, https://github.com/yourusername/sara_engine

# **SARA Engine**

**SARA (Spiking Architecture for Reasoning and Adaptation) Engine** is a cutting-edge AI framework that bridges the gap between biological intelligence and modern artificial neural networks.

It provides a highly efficient, event-driven Spiking Neural Network (SNN) core accelerated by Rust, combined with an intuitive PyTorch-like API. SARA goes beyond standard deep learning by natively supporting biological mechanisms such as **NeuroFEM**, **Predictive Coding**, and **Hippocampal-inspired memory systems**.
  
  
## **🧠 Key Features**

* **High-Performance Event-Driven Core:** Rust-based SNN simulation engine that minimizes computational overhead and maximizes simulation speed.  
* **PyTorch-like API (sara\_engine.nn):** Build, train, and deploy complex spiking networks using familiar, modular, and declarative syntax.  
* **Advanced Biologically-Plausible Mechanisms:**  
  * **NeuroFEM:** Neuro-Finite Element Method for modeling spatial neural dynamics in 2D/3D spaces.  
  * **Predictive Coding:** Cortex-inspired architecture supporting top-down predictions and bottom-up error processing.  
  * **Hippocampal Memory System:** Long-Term (LTM) and Short-Term (STM) memory supporting **Million-Token contexts** and SDR (Sparse Distributed Representations).  
  * **Synaptic Plasticity:** Native support for STDP (Spike-Timing-Dependent Plasticity) and Reward-Modulated STDP (R-STDP).  
* **Spiking LLMs & Transformers:** Innovative spike-based attention mechanisms and fully operational Spiking Language Models.  
* **Multimodal Integration:** Built-in encoders and pipelines for Vision, Audio, Physical, and Textual data.  
* **Hardware & Edge Ready:** Includes a Hardware Abstraction Layer (HAL) and exporters for edge deployment (e.g., SARA Board).
  
  
## **🚀 Installation**

Ensure you have Python 3.10 or higher and a working Rust toolchain installed.

\# Clone the repository  
git clone \[https://github.com/matsushibadenki/sara-engine-project.git\](https://github.com/matsushibadenki/sara-engine-project.git)  
cd sara-engine-project

\# Install the package in editable mode (compiles the Rust core automatically)  
pip install \-e .

*(Note: If changes to the core are not reflecting, ensure you re-run pip install \-e . to rebuild the Rust extensions.)*
  
  
## **💡 Quick Start**

Here is a simple example of building and running a Spiking Neural Network using the SARA Engine:

import numpy as np  
from sara\_engine import nn

\# Define a simple SNN model  
class SimpleSNN(nn.Module):  
    def \_\_init\_\_(self):  
        super().\_\_init\_\_()  
        self.fc1 \= nn.LinearSpike(in\_features=784, out\_features=256)  
        self.fc2 \= nn.LinearSpike(in\_features=256, out\_features=10)

    def forward(self, spikes):  
        x \= self.fc1(spikes)  
        x \= self.fc2(x)  
        return x

\# Initialize model  
model \= SimpleSNN()

\# Create dummy input spikes (Batch Size: 1, Features: 784\)  
input\_spikes \= np.random.rand(1, 784\) \> 0.8 

\# Forward pass  
output\_spikes \= model(input\_spikes)  
print("Output Spikes Shape:", output\_spikes.shape)
  
  
## **🛠️ Examples and Tools**

The repository contains a massive collection of 70+ scripts covering demos, interactive tools, benchmarks, and unit tests.
  
  
### **🌟 Demos (examples/)**

Explore over 50 demonstration scripts showing SARA's capabilities:

* **Spiking LLMs & Transformers:** demo\_spiking\_llm.py, demo\_bio\_transformer.py  
* **Agent Frameworks:** demo\_agent\_chat.py, demo\_million\_token\_agent.py  
* **Multimodal Pipelines:** demo\_multimodal\_pipeline.py, demo\_crossmodal\_recall.py  
* **Learning & Plasticity:** demo\_rl\_training.py, demo\_snn\_learning.py  
* **Advanced Bio-Mechanisms:** demo\_predictive\_coding.py, demo\_semantic\_spike\_routing.py
  
  
### **📊 Benchmarks (examples/)**

Measure performance and scaling:

* benchmark\_rust\_acceleration.py: Compare Python vs. Rust core speeds.  
* benchmark\_long\_context.py: Evaluate memory usage over massive contexts.
  
  
### **🧪 Tests (tests/)**

Comprehensive test suite ensuring stability:

* test\_neurofem.py, test\_hippocampal\_system.py, test\_event\_driven\_snn.py

👉 **For a complete and detailed list of all available scripts, please refer to [doc/About-Tools-EN.md](https://www.google.com/search?q=doc/About-Tools-EN.md).**
  
  
## **🏗️ Architecture & Modules**

The project is structured to provide both high-level usability and low-level performance:

* sara\_engine.core: The fundamental building blocks, interfacing with the Rust backend.  
* sara\_engine.nn: High-level PyTorch-like API for model construction.  
* sara\_engine.models: Pre-built architectures (e.g., SpikingImageClassifier, BioTransformer, SpikingLLM).  
* sara\_engine.pipelines: End-to-end inference pipelines (Text, Vision, Audio).  
* sara\_engine.memory: Implementations of SDR, Hippocampus, and Vector Stores.  
* sara\_engine.edge: Exporters and runtime utilities for hardware deployment.
  
  
## **🗺️ Roadmap & Documentation**

To understand the future direction and deep theoretical background of the SARA Engine, check the following documents:

* [ROADMAP.md](https://www.google.com/search?q=doc/ROADMAP.md) \- Short-term development goals.  
* [SARA\_EVOLUTION\_ROADMAP.md](https://www.google.com/search?q=doc/SARA_EVOLUTION_ROADMAP.md) \- Long-term evolutionary roadmap.  
* [stateful\_snn\_theory.md](https://www.google.com/search?q=doc/stateful_snn_theory.md) \- Theoretical background on Stateful SNNs and NeuroFEM.
  
  
## **🤝 Contributing**

We welcome contributions\! Please review our [policy.md](https://www.google.com/search?q=doc/policy.md) for coding standards and contribution guidelines. When developing, remember our core philosophy: avoid relying on backpropagation or dense matrix multiplications where biological spike-driven mechanisms (like STDP) are intended, and ensure hardware agnosticism (no hard GPU dependencies).
  
  
## **📄 License**

This project is licensed under the MIT License.
