Upload services/core_engine/agent_orchestrator.py with huggingface_hub

services/core_engine/agent_orchestrator.py

@@ -0,0 +1,86 @@
+import concurrent.futures
+import time
+import json
+from typing import Dict, List, Any, Optional
+
+# Mock definitions for the 4 parallel agents (DeepSeek-Math-7B, Llama-3.1-8B, etc.)
+AGENT_PROFILES = [
+    {"id": "agent_1", "name": "GPT-4"},
+    {"id": "agent_2", "name": "Llama 3"},
+    {"id": "agent_3", "name": "Gemini 1.5 Pro"},
+    {"id": "agent_4", "name": "Qwen-2.5-Math-7B"}
+]
+
+def format_prompt(problem: str) -> str:
+    return (
+        f"Solve the following mathematical problem step-by-step:\\n{problem}\\n\\n"
+        "Return your response explicitly wrapped in this JSON triplet schema:\\n"
+        "{\\n"
+        "  \\'Answer\\': \\'<Final Exact Canonical Answer>\\',\\n"
+        "  \\'Reasoning Trace\\': [\\'step 1\\', \\'step 2\\', ...],\\n"
+        "  \\'Confidence Explanation\\': \\'<Brief justification of strategy>\\'\\n"
+        "}"
+    )
+
+def simulate_agent_execution(agent: Dict[str, str], problem: str, steps: Optional[List[str]] = None) -> Dict[str, Any]:
+    """
+    Simulates external API calls to the quantized LLM weights.
+    In production, this would hit VLLM / Hugging Face Inference endpoints.
+    """
+    # Simulate network/compute latency (0.5s to 2.5s)
+    time.sleep(1.2)
+    
+    # If steps are provided, we are in "Verification Mode"
+    if steps and len(steps) > 0:
+        # Simulate logic for verifying provided steps
+        has_error = any("6 apples" in s for s in steps) # Janet placeholder error detection
+        ans = "5" if not has_error else "ERROR"
+        trace = [f"Checking: {s}" for s in steps]
+        conf = "Steps verified against symbolic constraints."
+    # Mocking appropriate divergent responses for the Fresnel Integral targeting
+    elif "Calculus" in problem or "Integral" in problem or "int" in problem:
+        if "Llama" in agent["name"]:
+            # Llama might hallucinate the constant or format
+            ans = "1/2" 
+            trace = ["Recognize Fresnel integral form", "Integrate sin(x^2)", "Evaluate from 0 to inf is sqrt(pi)/2", "This is bounded at pi so approximation is 0.43"]
+            conf = "Used Taylor expansion approximation."
+        else:
+            # DeepSeek Math usually nails it
+            ans = "0.438"
+            trace = ["Use Taylor series expansion over sin(x^2)", "Integrate term by term", "Evaluate at bounds 0 to pi", "Result is approximately 0.438"]
+            conf = "Taylor series provides guaranteed convergence bounds."
+    else:
+        ans = "42"
+        trace = ["Read the problem", "Compute 6 * 7", "Determine answer is 42"]
+        conf = "Basic arithmetic operation."
+
+    return {
+        "agent": agent["name"],
+        "response": {
+            "Answer": ans,
+            "Reasoning Trace": trace,
+            "Confidence Explanation": conf
+        }
+    }
+
+def run_agent_orchestrator(problem: str) -> List[Dict[str, Any]]:
+    """
+    Sequentially dispatches the math problem to 4 heterogeneous LLM agents.
+    Enforces the Triplet Schema as strictly mapped in MVM2.
+    """
+    print(f"[Orchestrator] Dispatching '{problem}' to {len(AGENT_PROFILES)} Parallel Models...")
+    results = []
+    
+    for agent in AGENT_PROFILES:
+        try:
+            res = simulate_agent_execution(agent, problem)
+            results.append(res)
+            print(f"[OK] {agent['name']} completed reasoning.")
+        except Exception as exc:
+            print(f"[ERROR] {agent['name']} generated an exception: {exc}")
+                
+    return results
+
+if __name__ == "__main__":
+    test_out = run_agent_orchestrator("\\int_{0}^{\\pi} \\sin(x^{2}) \\, dx")
+    print(json.dumps(test_out, indent=2))