Begin work on agent core

sia/agent_core.py

@@ -0,0 +1,51 @@
+from itertools import tee
+from typing import Optional, Dict, List
+
+from .docker_module import DockerModule
+from .llm_engine import LlmEngine
+from .context_template import generate_context
+from .util import get_valid_root_elements, split_response
+
+class AgentCore:
+    """
+    Core orchestration class for SIA that manages the interaction between
+    different modules and runs the main agent loop.
+    """
+    
+    def __init__(
+        self, 
+        system_prompt: str,
+        action_schema: str,
+        docker_module: DockerModule,
+        llm_engine: LlmEngine
+    ):
+        """
+        Initialize the AgentCore with required components.
+        
+        Args:
+            system_prompt: System prompt to use for the LLM
+            action_schema_path: Path to the XML schema defining valid actions
+            docker_module: DockerModule instance
+            llm_engine: LLmEngine instance
+        """
+        self.system_prompt = system_prompt
+        self.action_schema = action_schema
+        self.docker_module = docker_module
+        self.llm_engine = llm_engine
+        
+        self.valid_elements = get_valid_root_elements(self.action_schema)
+    
+    def run_iteration(self) -> None:
+        """Run a single iteration of the main agent loop."""
+        containers = self.docker_module.get_all_container_statuses()
+        context = generate_context(containers)
+        tokens = self.llm_engine.infer(
+            self.system_prompt,
+            context
+        )
+        print_tokens, response_tokens = tee(tokens)
+        for token in print_tokens:
+            print(token, end="", flush=True)
+        response_tokens = ''.join(response_tokens)
+        result = split_response(response_tokens, self.valid_elements)
+        print(f"result: {result}")