✨ feat: Desenvolvendo os serviços relacionados a acessar o Vertex AI, se comunicar com o modelo, registrar as Tools do sistema e gerenciar as mesmas conforme a solicitação do modelo.

app/services/llm_service.py

@@ -0,0 +1,67 @@
+from typing import Dict, Any, List
+import vertexai
+from vertexai.generative_models import GenerativeModel, Tool, FunctionDeclaration
+from app.core.settings import settings
+from app.models.tool_model import ToolDefinition
+
+
+class LLMService:
+
+    def __init__(self):
+        vertexai.init(
+            project=settings.google_project_id,
+            location=settings.google_location
+        )
+
+        self.model = GenerativeModel("gemini-1.5-pro")
+
+    def build_vertex_tools(self, tools: List[ToolDefinition]): #  Converte as Tools internas (ToolDefinition) para o formato que o Vertex AI entende.
+
+        vertex_tools = []
+
+        for tool in tools:
+            vertex_tools.append(
+                Tool(
+                    function_declarations=[
+                        FunctionDeclaration(
+                            name=tool.name,
+                            description=tool.description,
+                            parameters=tool.parameters
+                        )
+                    ]
+                )
+            )
+
+        return vertex_tools
+
+    async def generate_response(
+        self,
+        message: str,
+        tools: List[ToolDefinition],
+        history: List[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+
+        vertex_tools = self.build_vertex_tools(tools) # Convertendo tools para formato do Vertex
+
+        chat = self.model.start_chat(
+            history=history or [],
+            tools=vertex_tools
+        )
+
+        response = chat.send_message(message)
+
+        part = response.candidates[0].content.parts[0]
+
+        if part.function_call:
+            return {
+                "response": None,
+                "tool_call": {
+                    "name": part.function_call.name,
+                    "arguments": dict(part.function_call.args)
+                }
+            }
+
+        return {
+            "response": response.text,
+            "tool_call": None
+        }

app/services/tool_registry.py

@@ -0,0 +1,49 @@
+from typing import List
+from app.models.tool_model import ToolDefinition
+from app.integrations.estoque_service import consultar_estoque
+
+
+class ToolRegistry:
+
+    def __init__(self):
+        self._tools = []
+
+        self.register_tool(
+            name="consultar_estoque",
+            description="Consulta veículos disponíveis até determinado preço", # Descrição que ajuda o modelo Gemini a entender quado selecionar essa Tool.
+            parameters={
+                "type": "object",
+                "properties": {
+                    "preco_max": {
+                        "type": "number",
+                        "description": "Preço máximo do veículo"
+                    }
+                },
+                "required": ["preco_max"]
+            },
+            handler=consultar_estoque
+        )
+
+
+    def register_tool(self, name, description, parameters, handler):
+        self._tools.append(
+            ToolDefinition(
+                name=name,
+                description=description,
+                parameters=parameters,
+                handler=handler
+            )
+        )
+
+
+    def get_tools(self) -> List[ToolDefinition]:
+        return self._tools
+
+
+    async def execute(self, name: str, arguments: dict):
+        tool = next((t for t in self._tools if t.name == name), None) # Procura dentro da lista de tools aquela que tem o mesmo nome
+
+        if not tool:
+            raise Exception(f"Tool {name} não encontrada.")
+
+        return await tool.handler(**arguments)