feat: added Nexus Architect

.gitignore

@@ -4,6 +4,7 @@ logs*
 nexus_workflows
 chat.rapidgpt
 .vscode
+optimized_workflows/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

examples/create_workflow_using_architect.py

@@ -0,0 +1,36 @@
+import os, sys
+from dotenv import load_dotenv
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from primisai.nexus.architect import Architect
+
+load_dotenv()
+
+llm_config = {'model': os.getenv('LLM_MODEL'), 'api_key': os.getenv('LLM_API_KEY'), 'base_url': os.getenv('LLM_BASE_URL')}
+
+user_query = """Design a workflow to solve GRE/SAT-style riddle questions and math problems. Use 1 supervisor and 1 Question Ansering Agent"""
+
+"""JSON object containing the keys "question" and "answer".
+Example line: {"question": "What is 2+2?", "answer": "4"}"""
+
+benchmark_path = "path_to_the_data.jsonl"
+
+architect = Architect(user_query=user_query,
+                      benchmark_path=benchmark_path,
+                      llm_config=llm_config,
+                      workflow_name="math_riddle_solver",
+                      subset_size=5,
+                      max_iterations=2)
+
+results = architect.build_and_optimize()
+
+# --- 3. Review the Results ---
+print("\n\n" + "=" * 25 + " FINAL SUMMARY " + "=" * 25)
+print(f"✅ Process Completed!")
+print(f"Final Accuracy: {results['final_accuracy']:.2%}")
+print(f"Final optimized workflow file saved to: {results['output_path']}")
+print("\n--- Performance History ---")
+for record in results['history']:
+    print(f"  - Iteration {record['iteration']}: Accuracy={record['accuracy']:.2%}, Saved to -> {record['saved_path']}")
+print("=" * 67)