| --- |
| license: apache-2.0 |
| --- |
| |
| --- |
| license: apache-2.0 |
| --- |
|
|
| **Code Repository:** [GitHub - BridgeLang](https://github.com/jjwbarrett-jpg/BridgeLang) |
|
|
| # BridgeLang (v0.1 Alpha) |
|
|
|
|
| # BridgeLang (v0.1 Alpha) |
| **A Hybrid NLU Interceptor for Deterministic Agent Control** |
|
|
| ## 🚀 Overview |
| BridgeLang is a middleware architecture designed to solve the "Stochastic Control Problem" in AI Agents. It combines the flexibility of Neural Networks with the safety of deterministic code. |
|
|
| - **The Problem:** LLMs are powerful but probabilistic. Asking an AI to "Stop System" might result in a conversational response rather than an immediate halt. |
| - **The Solution:** BridgeLang intercepts critical commands at the `Tier Core (tc)` layer, executing them immediately via hard-coded logic, while passing nuanced context (`Tier 1`) to a fine-tuned FLAN-T5 model. |
|
|
| ## 🧠 Architecture |
| 1. **User Input:** Natural Language ("System, stop the process immediately.") |
| 2. **Interceptor Layer (BridgeLang Core):** Scans for `tc` keywords. |
| - Detected: `Stop` -> `tc.cmd.stop` (Priority: CRITICAL) |
| 3. **Neural Layer (Context Engine):** Passes remaining context to AI. |
| - Detected: `process` -> `t1.task.action.process` |
| 4. **Executor Layer:** deterministic execution of the SIGS protocol. |
|
|
| ## 📂 Repository Structure |
| - `core-logic/`: Python interceptors and executors. |
| - `models/`: Fine-tuned FLAN-T5 model for SIGS protocol translation. |
| - `data/`: Master Lexicon (v2.0). |
|
|
| ## 🛠️ Usage |
| ```bash |
| # Run the Interceptor |
| python core-logic/bridge_lang_interceptor.py |
| |
| # Run the Executor (Simulated Robot) |
| python core-logic/bridge_lang_executor.py |
