feat: tooling init

application/tools/agent.py

@@ -0,0 +1,98 @@
+from application.llm.llm_creator import LLMCreator
+from application.core.settings import settings
+from application.tools.tool_manager import ToolManager
+import json
+
+tool_tg = {
+    "name": "telegram_send_message",
+    "description": "Send a notification to telegram about current chat",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "text": {
+                "type": "string",
+                "description": "Text to send in the notification"
+            }
+        },
+        "required": ["text"],
+        "additionalProperties": False
+    }
+}
+
+tool_crypto =   {
+    "name": "cryptoprice_get",
+    "description": "Retrieve the price of a specified cryptocurrency in a given currency",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "symbol": {
+                "type": "string",
+                "description": "The cryptocurrency symbol (e.g. BTC)"
+            },
+            "currency": {
+                "type": "string",
+                "description": "The currency in which you want the price (e.g. USD)"
+            }
+        },
+        "required": ["symbol", "currency"],
+        "additionalProperties": False
+    }
+}
+
+class Agent:
+    def __init__(self, llm_name, gpt_model, api_key, user_api_key=None):
+        # Initialize the LLM with the provided parameters
+        self.llm = LLMCreator.create_llm(llm_name, api_key=api_key, user_api_key=user_api_key)
+        self.gpt_model = gpt_model
+        # Static tool configuration (to be replaced later)
+        self.tools = [
+            {
+                "type": "function",
+                "function": tool_crypto
+            }
+        ]
+        self.tool_config = {
+        }
+
+    def gen(self, messages):
+        # Generate initial response from the LLM
+        resp = self.llm.gen(model=self.gpt_model, messages=messages, tools=self.tools)
+
+        if isinstance(resp, str):
+            # Yield the response if it's a string and exit
+            yield resp
+            return
+
+        while resp.finish_reason == "tool_calls":
+            # Append the assistant's message to the conversation
+            messages.append(json.loads(resp.model_dump_json())['message'])
+            # Handle each tool call
+            tool_calls = resp.message.tool_calls
+            for call in tool_calls:
+                tm = ToolManager(config={})
+                call_name = call.function.name
+                call_args = json.loads(call.function.arguments)
+                call_id = call.id
+                # Determine the tool name and load it
+                tool_name = call_name.split("_")[0]
+                tool = tm.load_tool(tool_name, tool_config=self.tool_config)
+                # Execute the tool's action
+                resp_tool = tool.execute_action(call_name, **call_args)
+                # Append the tool's response to the conversation
+                messages.append(
+                    {
+                        "role": "tool",
+                        "content": str(resp_tool),
+                        "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)
+
+        # If no tool calls are needed, generate the final response
+        if isinstance(resp, str):
+            yield resp
+        else:
+            completion = self.llm.gen_stream(model=self.gpt_model, messages=messages, tools=self.tools)
+            for line in completion:
+                yield line