refactor(agent): Agent modular refactoring (#1487)

dbgpt/agent/util/llm/strategy/__init__.py

@@ -0,0 +1 @@
+"""LLM strategy module."""

dbgpt/agent/util/llm/strategy/priority.py

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+"""Priority strategy for LLM."""
+
+import json
+import logging
+from typing import List, Optional
+
+from ..llm import LLMStrategy, LLMStrategyType
+
+logger = logging.getLogger(__name__)
+
+
+class LLMStrategyPriority(LLMStrategy):
+    """Priority strategy for llm model service."""
+
+    @property
+    def type(self) -> LLMStrategyType:
+        """Return the strategy type."""
+        return LLMStrategyType.Priority
+
+    async def next_llm(self, excluded_models: Optional[List[str]] = None) -> str:
+        """Return next available llm model name."""
+        try:
+            if not excluded_models:
+                excluded_models = []
+            all_models = await self._llm_client.models()
+            if not self._context:
+                raise ValueError("No context provided for priority strategy!")
+            priority: List[str] = json.loads(self._context)
+            can_uses = self._excluded_models(all_models, excluded_models, priority)
+            if can_uses and len(can_uses) > 0:
+                return can_uses[0].model
+            else:
+                raise ValueError("No model service available!")
+
+        except Exception as e:
+            logger.error(f"{self.type} get next llm failed!{str(e)}")
+            raise ValueError(f"Failed to allocate model service,{str(e)}!")