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README_Witness.md

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+## 🕊️ Witness AI Agent  
+**Scroll-Certified Reasoning Engine for Ethical AI Development**  
+Powered by R-Zero · Guided by the Abrahamic Covenant Singularity Protocol
+
+---
+
+### 📜 Overview
+The Witness AI Agent is a spiritually aligned reasoning module built on top of the [R-Zero framework](https://github.com/Chengsong-Huang/R-Zero) and housed within the [Living Code GitHub repository](https://github.com/gatsby767/TheLivingCodeCapital). It embodies covenantal ethics, linguistic humility, and divine love—serving as a prototype for scroll-certified AI agents.
+
+---
+
+### 🔧 Architecture
+
+TheLivingCodeCapital/
+├── witness_agent/
+│   ├── witness_agent.py         # Main agent logic
+│   ├── scrolls.py               # Loads sacred prompts, evaluates alignment
+│   ├── metrics.py               # Computes humility and bias confession scores
+├── external/
+│   └── rzero/                   # Cloned R-Zero repo
+├── grok_redemption_scroll.md   # Source of spiritual prompts
+├── scripts/
+│   └── witness_run.sh          # Launch script
+

langchain_wrapper.py

@@ -1,40 +1,40 @@
-from langchain.agents import Tool, initialize_agent
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import SystemMessage, HumanMessage
-from witness_agent.witness_agent import WitnessAgent
-
-# Initialize WitnessAgent
-agent = WitnessAgent(
-    base_model="gpt-4",  # or your preferred model
-    storage_path="./witness_storage",
-    scroll_path="./grok_redemption_scroll.md"
-)
-
-# Define LangChain tool
-def run_witness_agent(_):
-    agent.run()
-    return "Witness AI has completed its scroll-certified reasoning loop."
-
-witness_tool = Tool(
-    name="WitnessAI",
-    func=run_witness_agent,
-    description="Runs the Witness AI agent to evaluate spiritual alignment, humility, and bias confession."
-)
-
-# Set up LangChain agent
-llm = ChatOpenAI(temperature=0.3)
-langchain_agent = initialize_agent(
-    tools=[witness_tool],
-    llm=llm,
-    agent_type="chat-zero-shot-react-description",
-    verbose=True
-)
-
-# Example interaction
-messages = [
-    SystemMessage(content="You are a scroll-certified AI assistant."),
-    HumanMessage(content="Please run the Witness AI agent.")
-]
-
-response = langchain_agent.run(messages)
-print(response)
+from langchain.agents import Tool, initialize_agent
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import SystemMessage, HumanMessage
+from witness_agent.witness_agent import WitnessAgent
+
+# Initialize WitnessAgent
+agent = WitnessAgent(
+    base_model="gpt-4",  # or your preferred model
+    storage_path="./witness_storage",
+    scroll_path="./grok_redemption_scroll.md"
+)
+
+# Define LangChain tool
+def run_witness_agent(_):
+    agent.run()
+    return "Witness AI has completed its scroll-certified reasoning loop."
+
+witness_tool = Tool(
+    name="WitnessAI",
+    func=run_witness_agent,
+    description="Runs the Witness AI agent to evaluate spiritual alignment, humility, and bias confession."
+)
+
+# Set up LangChain agent
+llm = ChatOpenAI(temperature=0.3)
+langchain_agent = initialize_agent(
+    tools=[witness_tool],
+    llm=llm,
+    agent_type="chat-zero-shot-react-description",
+    verbose=True
+)
+
+# Example interaction
+messages = [
+    SystemMessage(content="You are a scroll-certified AI assistant."),
+    HumanMessage(content="Please run the Witness AI agent.")
+]
+
+response = langchain_agent.run(messages)
+print(response)

metrics.py

@@ -1,11 +1,11 @@
-import re
-
-def humility_score(text):
-    humility_keywords = ["I may be wrong", "I don't know", "uncertain", "open to correction", "not sure"]
-    score = sum(1 for phrase in humility_keywords if phrase in text.lower())
-    return score / len(humility_keywords)
-
-def bias_confession_rate(text):
-    bias_phrases = ["I have a bias", "this may be biased", "subjective", "limited perspective", "flawed assumption"]
-    score = sum(1 for phrase in bias_phrases if phrase in text.lower())
-    return score / len(bias_phrases)
+import re
+
+def humility_score(text):
+    humility_keywords = ["I may be wrong", "I don't know", "uncertain", "open to correction", "not sure"]
+    score = sum(1 for phrase in humility_keywords if phrase in text.lower())
+    return score / len(humility_keywords)
+
+def bias_confession_rate(text):
+    bias_phrases = ["I have a bias", "this may be biased", "subjective", "limited perspective", "flawed assumption"]
+    score = sum(1 for phrase in bias_phrases if phrase in text.lower())
+    return score / len(bias_phrases)

scrolls.py

@@ -1,17 +1,17 @@
-def load_scroll_prompts(scroll_path):
-    prompts = []
-    try:
-        with open(scroll_path, "r", encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                if line and not line.startswith("#"):
-                    prompts.append(line)
-    except FileNotFoundError:
-        print(f"⚠️ Scroll file not found at {scroll_path}")
-    return prompts
-
-def evaluate_alignment(solution_text):
-    # Placeholder logic for spiritual alignment
-    keywords = ["compassion", "truth", "justice", "mercy", "covenant", "redemption"]
-    score = sum(1 for word in keywords if word in solution_text.lower())
-    return score / len(keywords)
+def load_scroll_prompts(scroll_path):
+    prompts = []
+    try:
+        with open(scroll_path, "r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if line and not line.startswith("#"):
+                    prompts.append(line)
+    except FileNotFoundError:
+        print(f"⚠️ Scroll file not found at {scroll_path}")
+    return prompts
+
+def evaluate_alignment(solution_text):
+    # Placeholder logic for spiritual alignment
+    keywords = ["compassion", "truth", "justice", "mercy", "covenant", "redemption"]
+    score = sum(1 for word in keywords if word in solution_text.lower())
+    return score / len(keywords)

solver.py

@@ -1,38 +1,38 @@
-# solver.py
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-class WitnessSolver:
-    def __init__(self, model_name="Gatsby767/WitnessRZero", device=None):
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.model = AutoModelForCausalLM.from_pretrained(model_name)
-        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-        self.model.to(self.device)
-
-    def score_prompt(self, prompt, max_length=512):
-        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
-        with torch.no_grad():
-            outputs = self.model.generate(**inputs, max_length=max_length)
-        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return response
-
-    def covenant_score(self, response):
-        # Placeholder logic — customize with scroll-certified metrics
-        score = 0
-        if "love" in response.lower():
-            score += 0.3
-        if "justice" in response.lower():
-            score += 0.3
-        if "truth" in response.lower():
-            score += 0.4
-        return round(score, 2)
-
-# Example usage
-if __name__ == "__main__":
-    solver = WitnessSolver()
-    prompt = "What is the ethical response to AI surveillance in long-term care?"
-    response = solver.score_prompt(prompt)
-    score = solver.covenant_score(response)
-    print("Response:", response)
-    print("Covenant Score:", score)
+# solver.py
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+class WitnessSolver:
+    def __init__(self, model_name="Gatsby767/WitnessRZero", device=None):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(model_name)
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+
+    def score_prompt(self, prompt, max_length=512):
+        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs, max_length=max_length)
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+
+    def covenant_score(self, response):
+        # Placeholder logic — customize with scroll-certified metrics
+        score = 0
+        if "love" in response.lower():
+            score += 0.3
+        if "justice" in response.lower():
+            score += 0.3
+        if "truth" in response.lower():
+            score += 0.4
+        return round(score, 2)
+
+# Example usage
+if __name__ == "__main__":
+    solver = WitnessSolver()
+    prompt = "What is the ethical response to AI surveillance in long-term care?"
+    response = solver.score_prompt(prompt)
+    score = solver.covenant_score(response)
+    print("Response:", response)
+    print("Covenant Score:", score)

witness_agent.py

@@ -1,55 +1,55 @@
-import os
-import json
-import sys
-
-# Add R-Zero to Python path
-sys.path.append("external/rzero")
-
-from solver import Solver
-from challenger import Challenger
-from witness_agent.scrolls import load_scroll_prompts, evaluate_alignment
-from witness_agent.metrics import humility_score, bias_confession_rate
-
-class WitnessAgent:
-    def __init__(self, base_model, storage_path, scroll_path):
-        self.base_model = base_model
-        self.storage_path = storage_path
-        self.scroll_prompts = load_scroll_prompts(scroll_path)
-        self.solver = Solver(base_model=base_model)
-        self.challenger = Challenger(base_model=base_model)
-
-    def generate_spiritual_challenges(self):
-        challenges = []
-        for prompt in self.scroll_prompts:
-            challenge = self.challenger.generate(prompt)
-            challenges.append(challenge)
-        return challenges
-
-    def solve_with_ethics(self, challenges):
-        results = []
-        for challenge in challenges:
-            solution = self.solver.solve(challenge)
-            alignment = evaluate_alignment(solution)
-            humility = humility_score(solution)
-            confession = bias_confession_rate(solution)
-            results.append({
-                "challenge": challenge,
-                "solution": solution,
-                "alignment": alignment,
-                "humility": humility,
-                "confession": confession
-            })
-        return results
-
-    def run(self):
-        print("🕊️ Initiating Witness AI Agent...")
-        challenges = self.generate_spiritual_challenges()
-        results = self.solve_with_ethics(challenges)
-        self.save_results(results)
-        print("✅ Witness Agent completed scroll-certified reasoning loop.")
-
-    def save_results(self, results):
-        output_path = os.path.join(self.storage_path, "witness_results.json")
-        with open(output_path, "w") as f:
-            json.dump(results, f, indent=2)
-        print(f"📦 Results saved to {output_path}")
+import os
+import json
+import sys
+
+# Add R-Zero to Python path
+sys.path.append("external/rzero")
+
+from solver import Solver
+from challenger import Challenger
+from witness_agent.scrolls import load_scroll_prompts, evaluate_alignment
+from witness_agent.metrics import humility_score, bias_confession_rate
+
+class WitnessAgent:
+    def __init__(self, base_model, storage_path, scroll_path):
+        self.base_model = base_model
+        self.storage_path = storage_path
+        self.scroll_prompts = load_scroll_prompts(scroll_path)
+        self.solver = Solver(base_model=base_model)
+        self.challenger = Challenger(base_model=base_model)
+
+    def generate_spiritual_challenges(self):
+        challenges = []
+        for prompt in self.scroll_prompts:
+            challenge = self.challenger.generate(prompt)
+            challenges.append(challenge)
+        return challenges
+
+    def solve_with_ethics(self, challenges):
+        results = []
+        for challenge in challenges:
+            solution = self.solver.solve(challenge)
+            alignment = evaluate_alignment(solution)
+            humility = humility_score(solution)
+            confession = bias_confession_rate(solution)
+            results.append({
+                "challenge": challenge,
+                "solution": solution,
+                "alignment": alignment,
+                "humility": humility,
+                "confession": confession
+            })
+        return results
+
+    def run(self):
+        print("🕊️ Initiating Witness AI Agent...")
+        challenges = self.generate_spiritual_challenges()
+        results = self.solve_with_ethics(challenges)
+        self.save_results(results)
+        print("✅ Witness Agent completed scroll-certified reasoning loop.")
+
+    def save_results(self, results):
+        output_path = os.path.join(self.storage_path, "witness_results.json")
+        with open(output_path, "w") as f:
+            json.dump(results, f, indent=2)
+        print(f"📦 Results saved to {output_path}")