refactor: tool agent for action parser and handlers

application/llm/openai.py

@@ -1,6 +1,5 @@
-from application.llm.base import BaseLLM
 from application.core.settings import settings
-
+from application.llm.base import BaseLLM
 
 
 class OpenAILLM(BaseLLM):
@@ -10,10 +9,7 @@ class OpenAILLM(BaseLLM):
 
         super().__init__(*args, **kwargs)
         if settings.OPENAI_BASE_URL:
-            self.client = OpenAI(
-                api_key=api_key,
-                base_url=settings.OPENAI_BASE_URL
-            )
+            self.client = OpenAI(api_key=api_key, base_url=settings.OPENAI_BASE_URL)
         else:
             self.client = OpenAI(api_key=api_key)
         self.api_key = api_key
@@ -27,7 +23,7 @@ class OpenAILLM(BaseLLM):
         stream=False,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
-        **kwargs
+        **kwargs,
     ):
         if tools:
             response = self.client.chat.completions.create(
@@ -48,15 +44,13 @@ class OpenAILLM(BaseLLM):
         stream=True,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
-        **kwargs
+        **kwargs,
     ):
         response = self.client.chat.completions.create(
             model=model, messages=messages, stream=stream, **kwargs
         )
 
         for line in response:
-            # import sys
-            # print(line.choices[0].delta.content, file=sys.stderr)
             if line.choices[0].delta.content is not None:
                 yield line.choices[0].delta.content
 

application/tools/agent.py

@@ -1,8 +1,7 @@
-import json
-import logging
-
 from application.core.mongo_db import MongoDB
 from application.llm.llm_creator import LLMCreator
+from application.tools.llm_handler import get_llm_handler
+from application.tools.tool_action_parser import ToolActionParser
 from application.tools.tool_manager import ToolManager
 
 
@@ -12,6 +11,7 @@ class Agent:
         self.llm = LLMCreator.create_llm(
             llm_name, api_key=api_key, user_api_key=user_api_key
         )
+        self.llm_handler = get_llm_handler(llm_name)
         self.gpt_model = gpt_model
         # Static tool configuration (to be replaced later)
         self.tools = []
@@ -61,10 +61,8 @@ class Agent:
         ]
 
     def _execute_tool_action(self, tools_dict, call):
-        call_id = call.id
-        call_args = json.loads(call.function.arguments)
-        tool_id = call.function.name.split("_")[-1]
-        action_name = call.function.name.rsplit("_", 1)[0]
+        parser = ToolActionParser(self.llm.__class__.__name__)
+        tool_id, action_name, call_args = parser.parse_args(call)
 
         tool_data = tools_dict[tool_id]
         action_data = next(
@@ -78,26 +76,9 @@ class Agent:
         tm = ToolManager(config={})
         tool = tm.load_tool(tool_data["name"], tool_config=tool_data["config"])
         print(f"Executing tool: {action_name} with args: {call_args}")
-        return tool.execute_action(action_name, **call_args), call_id
-
-    def _execute_tool_action_google(self, tools_dict, call):
-        call_args = json.loads(call.args)
-        tool_id = call.name.split("_")[-1]
-        action_name = call.name.rsplit("_", 1)[0]
-
-        tool_data = tools_dict[tool_id]
-        action_data = next(
-            action for action in tool_data["actions"] if action["name"] == action_name
-        )
-
-        for param, details in action_data["parameters"]["properties"].items():
-            if param not in call_args and "value" in details:
-                call_args[param] = details["value"]
-
-        tm = ToolManager(config={})
-        tool = tm.load_tool(tool_data["name"], tool_config=tool_data["config"])
-        print(f"Executing tool: {action_name} with args: {call_args}")
-        return tool.execute_action(action_name, **call_args)
+        result = tool.execute_action(action_name, **call_args)
+        call_id = getattr(call, "id", None)
+        return result, call_id
 
     def _simple_tool_agent(self, messages):
         tools_dict = self._get_user_tools()
@@ -111,47 +92,8 @@ class Agent:
         if resp.message.content:
             yield resp.message.content
             return
-        # check if self.llm class is GoogleLLM
-        while self.llm.__class__.__name__ == "GoogleLLM" and resp.content.parts[0].function_call:
-            from google.genai import types
 
-            function_call_part = resp.candidates[0].content.parts[0]
-            tool_response = self._execute_tool_action_google(tools_dict, function_call_part.function_call)
-            function_response_part = types.Part.from_function_response(
-                name=function_call_part.function_call.name,
-                response=tool_response
-            )
-
-        while self.llm.__class__.__name__ == "OpenAILLM" and resp.finish_reason == "tool_calls":
-            message = json.loads(resp.model_dump_json())["message"]
-            keys_to_remove = {"audio", "function_call", "refusal"}
-            filtered_data = {
-                k: v for k, v in message.items() if k not in keys_to_remove
-            }
-            messages.append(filtered_data)
-            tool_calls = resp.message.tool_calls
-            for call in tool_calls:
-                try:
-                    tool_response, call_id = self._execute_tool_action(tools_dict, call)
-                    messages.append(
-                        {
-                            "role": "tool",
-                            "content": str(tool_response),
-                            "tool_call_id": call_id,
-                        }
-                    )
-                except Exception as e:
-                    messages.append(
-                        {
-                            "role": "tool",
-                            "content": f"Error executing tool: {str(e)}",
-                            "tool_call_id": call.id,
-                        }
-                    )
-            # Generate a new response from the LLM after processing tools
-            resp = self.llm.gen(
-                model=self.gpt_model, messages=messages, tools=self.tools
-            )
+        resp = self.llm_handler.handle_response(self, resp, tools_dict, messages)
 
         # If no tool calls are needed, generate the final response
         if isinstance(resp, str):

application/tools/llm_handler.py

@@ -0,0 +1,74 @@
+import json
+from abc import ABC, abstractmethod
+
+
+class LLMHandler(ABC):
+    @abstractmethod
+    def handle_response(self, agent, resp, tools_dict, messages, **kwargs):
+        pass
+
+
+class OpenAILLMHandler(LLMHandler):
+    def handle_response(self, agent, resp, tools_dict, messages):
+        while resp.finish_reason == "tool_calls":
+            message = json.loads(resp.model_dump_json())["message"]
+            keys_to_remove = {"audio", "function_call", "refusal"}
+            filtered_data = {
+                k: v for k, v in message.items() if k not in keys_to_remove
+            }
+            messages.append(filtered_data)
+
+            tool_calls = resp.message.tool_calls
+            for call in tool_calls:
+                try:
+                    tool_response, call_id = agent._execute_tool_action(
+                        tools_dict, call
+                    )
+                    messages.append(
+                        {
+                            "role": "tool",
+                            "content": str(tool_response),
+                            "tool_call_id": call_id,
+                        }
+                    )
+                except Exception as e:
+                    messages.append(
+                        {
+                            "role": "tool",
+                            "content": f"Error executing tool: {str(e)}",
+                            "tool_call_id": call_id,
+                        }
+                    )
+            resp = agent.llm.gen(
+                model=agent.gpt_model, messages=messages, tools=agent.tools
+            )
+        return resp
+
+
+class GoogleLLMHandler(LLMHandler):
+    def handle_response(self, agent, resp, tools_dict, messages):
+        from google.genai import types
+
+        while resp.content.parts[0].function_call:
+            function_call_part = resp.candidates[0].content.parts[0]
+            tool_response, call_id = agent._execute_tool_action(
+                tools_dict, function_call_part.function_call
+            )
+            function_response_part = types.Part.from_function_response(
+                name=function_call_part.function_call.name, response=tool_response
+            )
+
+            messages.append(function_call_part, function_response_part)
+            resp = agent.llm.gen(
+                model=agent.gpt_model, messages=messages, tools=agent.tools
+            )
+
+        return resp
+
+
+def get_llm_handler(llm_type):
+    handlers = {
+        "openai": OpenAILLMHandler(),
+        "google": GoogleLLMHandler(),
+    }
+    return handlers.get(llm_type, OpenAILLMHandler())

application/tools/tool_action_parser.py

@@ -0,0 +1,26 @@
+import json
+
+
+class ToolActionParser:
+    def __init__(self, llm_type):
+        self.llm_type = llm_type
+        self.parsers = {
+            "OpenAILLM": self._parse_openai_llm,
+            "GoogleLLM": self._parse_google_llm,
+        }
+
+    def parse_args(self, call):
+        parser = self.parsers.get(self.llm_type, self._parse_openai_llm)
+        return parser(call)
+
+    def _parse_openai_llm(self, call):
+        call_args = json.loads(call.function.arguments)
+        tool_id = call.function.name.split("_")[-1]
+        action_name = call.function.name.rsplit("_", 1)[0]
+        return tool_id, action_name, call_args
+
+    def _parse_google_llm(self, call):
+        call_args = json.loads(call.args)
+        tool_id = call.name.split("_")[-1]
+        action_name = call.name.rsplit("_", 1)[0]
+        return tool_id, action_name, call_args