mirror of
https://github.com/googleapis/genai-toolbox.git
synced 2026-05-02 03:00:36 -04:00
chore: reorder quickstart (#740)
Reorder quickstart. Order from `GoogleGenAI -> ADK -> Langchain -> Llamaindex` to `ADK -> Langchain -> Llamaindex -> GoogleGenAI`
This commit is contained in:
Twisha Bansal
@@ -474,165 +474,12 @@
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"source": [
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"> You can either use LangGraph or LlamaIndex to develop a Toolbox based\n",
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"> application. Run one of the sections below\n",
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"> - [Connect using Google GenAI](#scrollTo=Rwgv1LDdNKSn)\n",
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"> - [Connect using Google GenAI](#scrollTo=Fv2-uT4mvYtp)\n",
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"> - [Connect using ADK](#scrollTo=QqRlWqvYNKSo)\n",
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"> - [Connect Using LangGraph](#scrollTo=pbapNMhhL33S)\n",
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"> - [Connect using LlamaIndex](#scrollTo=04iysrm_L_7v)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Rwgv1LDdNKSn"
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},
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"source": [
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"### Connect Using Google GenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "HY23RMk4NKSn"
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},
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"outputs": [],
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"source": [
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"# Install the Toolbox Core package\n",
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"!pip install toolbox-core --quiet\n",
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"\n",
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"# Install the Google GenAI package\n",
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"!pip install google-genai --quiet"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "9F1u566sNKSn"
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},
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"source": [
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"Create a Google GenAI Application which can Search, Book and Cancel hotels."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "LAuBIOXvNKSn"
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},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"\n",
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"from google import genai\n",
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"from google.genai.types import (\n",
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" Content,\n",
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" FunctionDeclaration,\n",
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" GenerateContentConfig,\n",
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" Part,\n",
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" Tool,\n",
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")\n",
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"\n",
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"from toolbox_core import ToolboxClient\n",
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"\n",
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"prompt = \"\"\"\n",
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" You're a helpful hotel assistant. You handle hotel searching, booking and\n",
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" cancellations. When the user searches for a hotel, mention it's name, id,\n",
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" location and price tier. Always mention hotel id while performing any\n",
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" searches. This is very important for any operations. For any bookings or\n",
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" cancellations, please provide the appropriate confirmation. Be sure to\n",
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" update checkin or checkout dates if mentioned by the user.\n",
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" Don't ask for confirmations from the user.\n",
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"\"\"\"\n",
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"\n",
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"queries = [\n",
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" \"Find hotels in Basel with Basel in it's name.\",\n",
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" \"Please book the hotel Hilton Basel for me.\",\n",
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" \"This is too expensive. Please cancel it.\",\n",
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" \"Please book Hyatt Regency for me\",\n",
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" \"My check in dates for my booking would be from April 10, 2024 to April 19, 2024.\",\n",
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"]\n",
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"\n",
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"\n",
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"async def run_application():\n",
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" toolbox_client = ToolboxClient(\"http://127.0.0.1:5000\")\n",
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"\n",
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" # The toolbox_tools list contains Python callables (functions/methods) designed for LLM tool-use\n",
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" # integration. While this example uses Google's genai client, these callables can be adapted for\n",
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" # various function-calling or agent frameworks. For easier integration with supported frameworks\n",
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" # (https://github.com/googleapis/mcp-toolbox-python-sdk/tree/main/packages), use the\n",
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" # provided wrapper packages, which handle framework-specific boilerplate.\n",
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" toolbox_tools = await toolbox_client.load_toolset(\"my-toolset\")\n",
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" genai_client = genai.Client(\n",
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" vertexai=True, project=project_id, location=\"us-central1\"\n",
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" )\n",
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"\n",
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" genai_tools = [\n",
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" Tool(\n",
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" function_declarations=[\n",
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" FunctionDeclaration.from_callable_with_api_option(callable=tool)\n",
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" ]\n",
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" )\n",
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" for tool in toolbox_tools\n",
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" ]\n",
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" history = []\n",
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" for query in queries:\n",
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" user_prompt_content = Content(\n",
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" role=\"user\",\n",
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" parts=[Part.from_text(text=query)],\n",
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" )\n",
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" history.append(user_prompt_content)\n",
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"\n",
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" response = genai_client.models.generate_content(\n",
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" model=\"gemini-2.0-flash-001\",\n",
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" contents=history,\n",
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" config=GenerateContentConfig(\n",
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" system_instruction=prompt,\n",
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" tools=genai_tools,\n",
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" ),\n",
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" )\n",
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" history.append(response.candidates[0].content)\n",
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" function_response_parts = []\n",
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" for function_call in response.function_calls:\n",
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" fn_name = function_call.name\n",
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" # The tools are sorted alphabetically\n",
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" if fn_name == \"search-hotels-by-name\":\n",
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" function_result = await toolbox_tools[3](**function_call.args)\n",
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" elif fn_name == \"search-hotels-by-location\":\n",
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" function_result = await toolbox_tools[2](**function_call.args)\n",
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" elif fn_name == \"book-hotel\":\n",
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" function_result = await toolbox_tools[0](**function_call.args)\n",
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" elif fn_name == \"update-hotel\":\n",
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" function_result = await toolbox_tools[4](**function_call.args)\n",
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" elif fn_name == \"cancel-hotel\":\n",
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" function_result = await toolbox_tools[1](**function_call.args)\n",
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" else:\n",
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" raise ValueError(\"Function name not present.\")\n",
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" function_response = {\"result\": function_result}\n",
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" function_response_part = Part.from_function_response(\n",
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" name=function_call.name,\n",
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" response=function_response,\n",
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" )\n",
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" function_response_parts.append(function_response_part)\n",
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"\n",
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" if function_response_parts:\n",
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" tool_response_content = Content(role=\"tool\", parts=function_response_parts)\n",
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" history.append(tool_response_content)\n",
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"\n",
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" response2 = genai_client.models.generate_content(\n",
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" model=\"gemini-2.0-flash-001\",\n",
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" contents=history,\n",
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" config=GenerateContentConfig(\n",
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" tools=genai_tools,\n",
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" ),\n",
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" )\n",
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" final_model_response_content = response2.candidates[0].content\n",
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" history.append(final_model_response_content)\n",
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" print(response2.text)\n",
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"\n",
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"\n",
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"asyncio.run(run_application())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -931,6 +778,159 @@
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"await run_application()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Fv2-uT4mvYtp"
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},
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"source": [
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"### Connect Using Google GenAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "mHSvk5_AvYtu"
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},
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"outputs": [],
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"source": [
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"# Install the Toolbox Core package\n",
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"!pip install toolbox-core --quiet\n",
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"\n",
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"# Install the Google GenAI package\n",
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"!pip install google-genai --quiet"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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||||
"id": "sO_7FGSYvYtu"
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},
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"source": [
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"Create a Google GenAI Application which can Search, Book and Cancel hotels."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "-NVVBiLnvYtu"
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||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"import asyncio\n",
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"\n",
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"from google import genai\n",
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"from google.genai.types import (\n",
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" Content,\n",
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" FunctionDeclaration,\n",
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" GenerateContentConfig,\n",
|
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" Part,\n",
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" Tool,\n",
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")\n",
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"\n",
|
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"from toolbox_core import ToolboxClient\n",
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"\n",
|
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"prompt = \"\"\"\n",
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" You're a helpful hotel assistant. You handle hotel searching, booking and\n",
|
||||
" cancellations. When the user searches for a hotel, mention it's name, id,\n",
|
||||
" location and price tier. Always mention hotel id while performing any\n",
|
||||
" searches. This is very important for any operations. For any bookings or\n",
|
||||
" cancellations, please provide the appropriate confirmation. Be sure to\n",
|
||||
" update checkin or checkout dates if mentioned by the user.\n",
|
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" Don't ask for confirmations from the user.\n",
|
||||
"\"\"\"\n",
|
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"\n",
|
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"queries = [\n",
|
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" \"Find hotels in Basel with Basel in it's name.\",\n",
|
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" \"Please book the hotel Hilton Basel for me.\",\n",
|
||||
" \"This is too expensive. Please cancel it.\",\n",
|
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" \"Please book Hyatt Regency for me\",\n",
|
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" \"My check in dates for my booking would be from April 10, 2024 to April 19, 2024.\",\n",
|
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"]\n",
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"\n",
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"\n",
|
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"async def run_application():\n",
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" toolbox_client = ToolboxClient(\"http://127.0.0.1:5000\")\n",
|
||||
"\n",
|
||||
" # The toolbox_tools list contains Python callables (functions/methods) designed for LLM tool-use\n",
|
||||
" # integration. While this example uses Google's genai client, these callables can be adapted for\n",
|
||||
" # various function-calling or agent frameworks. For easier integration with supported frameworks\n",
|
||||
" # (https://github.com/googleapis/mcp-toolbox-python-sdk/tree/main/packages), use the\n",
|
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" # provided wrapper packages, which handle framework-specific boilerplate.\n",
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" toolbox_tools = await toolbox_client.load_toolset(\"my-toolset\")\n",
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" genai_client = genai.Client(\n",
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" vertexai=True, project=project_id, location=\"us-central1\"\n",
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||||
" )\n",
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"\n",
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" genai_tools = [\n",
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||||
" Tool(\n",
|
||||
" function_declarations=[\n",
|
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" FunctionDeclaration.from_callable_with_api_option(callable=tool)\n",
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||||
" ]\n",
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" )\n",
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" for tool in toolbox_tools\n",
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" ]\n",
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" history = []\n",
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" for query in queries:\n",
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" user_prompt_content = Content(\n",
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||||
" role=\"user\",\n",
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" parts=[Part.from_text(text=query)],\n",
|
||||
" )\n",
|
||||
" history.append(user_prompt_content)\n",
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"\n",
|
||||
" response = genai_client.models.generate_content(\n",
|
||||
" model=\"gemini-2.0-flash-001\",\n",
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||||
" contents=history,\n",
|
||||
" config=GenerateContentConfig(\n",
|
||||
" system_instruction=prompt,\n",
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||||
" tools=genai_tools,\n",
|
||||
" ),\n",
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||||
" )\n",
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" history.append(response.candidates[0].content)\n",
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" function_response_parts = []\n",
|
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" for function_call in response.function_calls:\n",
|
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" fn_name = function_call.name\n",
|
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" # The tools are sorted alphabetically\n",
|
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" if fn_name == \"search-hotels-by-name\":\n",
|
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" function_result = await toolbox_tools[3](**function_call.args)\n",
|
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" elif fn_name == \"search-hotels-by-location\":\n",
|
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" function_result = await toolbox_tools[2](**function_call.args)\n",
|
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" elif fn_name == \"book-hotel\":\n",
|
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" function_result = await toolbox_tools[0](**function_call.args)\n",
|
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" elif fn_name == \"update-hotel\":\n",
|
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" function_result = await toolbox_tools[4](**function_call.args)\n",
|
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" elif fn_name == \"cancel-hotel\":\n",
|
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" function_result = await toolbox_tools[1](**function_call.args)\n",
|
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" else:\n",
|
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" raise ValueError(\"Function name not present.\")\n",
|
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" function_response = {\"result\": function_result}\n",
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" function_response_part = Part.from_function_response(\n",
|
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" name=function_call.name,\n",
|
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" response=function_response,\n",
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" )\n",
|
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" function_response_parts.append(function_response_part)\n",
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"\n",
|
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" if function_response_parts:\n",
|
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" tool_response_content = Content(role=\"tool\", parts=function_response_parts)\n",
|
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" history.append(tool_response_content)\n",
|
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"\n",
|
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" response2 = genai_client.models.generate_content(\n",
|
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" model=\"gemini-2.0-flash-001\",\n",
|
||||
" contents=history,\n",
|
||||
" config=GenerateContentConfig(\n",
|
||||
" tools=genai_tools,\n",
|
||||
" ),\n",
|
||||
" )\n",
|
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" final_model_response_content = response2.candidates[0].content\n",
|
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" history.append(final_model_response_content)\n",
|
||||
" print(response2.text)\n",
|
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"\n",
|
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"\n",
|
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"asyncio.run(run_application())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
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@@ -3,9 +3,8 @@ title: "Quickstart (Local)"
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type: docs
|
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weight: 2
|
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description: >
|
||||
How to get started running Toolbox locally with Python, PostgreSQL, and
|
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[GoogleGenAI](https://pypi.org/project/google-genai/),
|
||||
[LangGraph](https://www.langchain.com/langgraph), [LlamaIndex](https://www.llamaindex.ai/) or [Agent Development Kit](https://google.github.io/adk-docs/).
|
||||
How to get started running Toolbox locally with Python, PostgreSQL, and [Agent Development Kit](https://google.github.io/adk-docs/),
|
||||
[LangGraph](https://www.langchain.com/langgraph), [LlamaIndex](https://www.llamaindex.ai/) or [GoogleGenAI](https://pypi.org/project/google-genai/).
|
||||
---
|
||||
|
||||
[](https://colab.research.google.com/github/googleapis/genai-toolbox/blob/main/docs/en/getting-started/colab_quickstart.ipynb)
|
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@@ -253,10 +252,6 @@ you can connect to a
|
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1. In a new terminal, install the SDK package.
|
||||
|
||||
{{< tabpane persist=header >}}
|
||||
{{< tab header="Core" lang="bash" >}}
|
||||
|
||||
pip install toolbox-core
|
||||
{{< /tab >}}
|
||||
{{< tab header="ADK" lang="bash" >}}
|
||||
|
||||
pip install toolbox-core
|
||||
@@ -269,15 +264,15 @@ pip install toolbox-langchain
|
||||
|
||||
pip install toolbox-llamaindex
|
||||
{{< /tab >}}
|
||||
{{< tab header="Core" lang="bash" >}}
|
||||
|
||||
pip install toolbox-core
|
||||
{{< /tab >}}
|
||||
{{< /tabpane >}}
|
||||
|
||||
1. Install other required dependencies:
|
||||
|
||||
{{< tabpane persist=header >}}
|
||||
{{< tab header="Core" lang="bash" >}}
|
||||
|
||||
pip install google-genai
|
||||
{{< /tab >}}
|
||||
{{< tab header="ADK" lang="bash" >}}
|
||||
|
||||
pip install google-adk
|
||||
@@ -301,123 +296,16 @@ pip install llama-index-llms-google-genai
|
||||
|
||||
# pip install llama-index-llms-anthropic
|
||||
|
||||
{{< /tab >}}
|
||||
{{< tab header="Core" lang="bash" >}}
|
||||
|
||||
pip install google-genai
|
||||
{{< /tab >}}
|
||||
{{< /tabpane >}}
|
||||
|
||||
1. Create a new file named `hotel_agent.py` and copy the following
|
||||
code to create an agent:
|
||||
{{< tabpane persist=header >}}
|
||||
{{< tab header="Core" lang="python" >}}
|
||||
import asyncio
|
||||
|
||||
from google import genai
|
||||
from google.genai.types import (
|
||||
Content,
|
||||
FunctionDeclaration,
|
||||
GenerateContentConfig,
|
||||
Part,
|
||||
Tool,
|
||||
)
|
||||
|
||||
from toolbox_core import ToolboxClient
|
||||
|
||||
prompt = """
|
||||
You're a helpful hotel assistant. You handle hotel searching, booking and
|
||||
cancellations. When the user searches for a hotel, mention it's name, id,
|
||||
location and price tier. Always mention hotel id while performing any
|
||||
searches. This is very important for any operations. For any bookings or
|
||||
cancellations, please provide the appropriate confirmation. Be sure to
|
||||
update checkin or checkout dates if mentioned by the user.
|
||||
Don't ask for confirmations from the user.
|
||||
"""
|
||||
|
||||
queries = [
|
||||
"Find hotels in Basel with Basel in it's name.",
|
||||
"Please book the hotel Hilton Basel for me.",
|
||||
"This is too expensive. Please cancel it.",
|
||||
"Please book Hyatt Regency for me",
|
||||
"My check in dates for my booking would be from April 10, 2024 to April 19, 2024.",
|
||||
]
|
||||
|
||||
async def run_application():
|
||||
async with ToolboxClient("http://127.0.0.1:5000") as toolbox_client:
|
||||
|
||||
# The toolbox_tools list contains Python callables (functions/methods) designed for LLM tool-use
|
||||
# integration. While this example uses Google's genai client, these callables can be adapted for
|
||||
# various function-calling or agent frameworks. For easier integration with supported frameworks
|
||||
# (https://github.com/googleapis/mcp-toolbox-python-sdk/tree/main/packages), use the
|
||||
# provided wrapper packages, which handle framework-specific boilerplate.
|
||||
toolbox_tools = await toolbox_client.load_toolset("my-toolset")
|
||||
genai_client = genai.Client(
|
||||
vertexai=True, project="project-id", location="us-central1"
|
||||
)
|
||||
|
||||
genai_tools = [
|
||||
Tool(
|
||||
function_declarations=[
|
||||
FunctionDeclaration.from_callable_with_api_option(callable=tool)
|
||||
]
|
||||
)
|
||||
for tool in toolbox_tools
|
||||
]
|
||||
history = []
|
||||
for query in queries:
|
||||
user_prompt_content = Content(
|
||||
role="user",
|
||||
parts=[Part.from_text(text=query)],
|
||||
)
|
||||
history.append(user_prompt_content)
|
||||
|
||||
response = genai_client.models.generate_content(
|
||||
model="gemini-2.0-flash-001",
|
||||
contents=history,
|
||||
config=GenerateContentConfig(
|
||||
system_instruction=prompt,
|
||||
tools=genai_tools,
|
||||
),
|
||||
)
|
||||
history.append(response.candidates[0].content)
|
||||
function_response_parts = []
|
||||
for function_call in response.function_calls:
|
||||
fn_name = function_call.name
|
||||
# The tools are sorted alphabetically
|
||||
if fn_name == "search-hotels-by-name":
|
||||
function_result = await toolbox_tools[3](**function_call.args)
|
||||
elif fn_name == "search-hotels-by-location":
|
||||
function_result = await toolbox_tools[2](**function_call.args)
|
||||
elif fn_name == "book-hotel":
|
||||
function_result = await toolbox_tools[0](**function_call.args)
|
||||
elif fn_name == "update-hotel":
|
||||
function_result = await toolbox_tools[4](**function_call.args)
|
||||
elif fn_name == "cancel-hotel":
|
||||
function_result = await toolbox_tools[1](**function_call.args)
|
||||
else:
|
||||
raise ValueError("Function name not present.")
|
||||
function_response = {"result": function_result}
|
||||
function_response_part = Part.from_function_response(
|
||||
name=function_call.name,
|
||||
response=function_response,
|
||||
)
|
||||
function_response_parts.append(function_response_part)
|
||||
|
||||
if function_response_parts:
|
||||
tool_response_content = Content(role="tool", parts=function_response_parts)
|
||||
history.append(tool_response_content)
|
||||
|
||||
response2 = genai_client.models.generate_content(
|
||||
model="gemini-2.0-flash-001",
|
||||
contents=history,
|
||||
config=GenerateContentConfig(
|
||||
tools=genai_tools,
|
||||
),
|
||||
)
|
||||
final_model_response_content = response2.candidates[0].content
|
||||
history.append(final_model_response_content)
|
||||
print(response2.text)
|
||||
|
||||
asyncio.run(run_application())
|
||||
|
||||
{{< /tab >}}
|
||||
{{< tab header="ADK" lang="python" >}}
|
||||
from google.adk.agents import Agent
|
||||
from google.adk.runners import Runner
|
||||
@@ -604,14 +492,121 @@ async def run_application():
|
||||
print(str(response))
|
||||
|
||||
asyncio.run(run_application())
|
||||
{{< /tab >}}
|
||||
{{< tab header="Core" lang="python" >}}
|
||||
import asyncio
|
||||
|
||||
from google import genai
|
||||
from google.genai.types import (
|
||||
Content,
|
||||
FunctionDeclaration,
|
||||
GenerateContentConfig,
|
||||
Part,
|
||||
Tool,
|
||||
)
|
||||
|
||||
from toolbox_core import ToolboxClient
|
||||
|
||||
prompt = """
|
||||
You're a helpful hotel assistant. You handle hotel searching, booking and
|
||||
cancellations. When the user searches for a hotel, mention it's name, id,
|
||||
location and price tier. Always mention hotel id while performing any
|
||||
searches. This is very important for any operations. For any bookings or
|
||||
cancellations, please provide the appropriate confirmation. Be sure to
|
||||
update checkin or checkout dates if mentioned by the user.
|
||||
Don't ask for confirmations from the user.
|
||||
"""
|
||||
|
||||
queries = [
|
||||
"Find hotels in Basel with Basel in it's name.",
|
||||
"Please book the hotel Hilton Basel for me.",
|
||||
"This is too expensive. Please cancel it.",
|
||||
"Please book Hyatt Regency for me",
|
||||
"My check in dates for my booking would be from April 10, 2024 to April 19, 2024.",
|
||||
]
|
||||
|
||||
async def run_application():
|
||||
async with ToolboxClient("<http://127.0.0.1:5000>") as toolbox_client:
|
||||
|
||||
# The toolbox_tools list contains Python callables (functions/methods) designed for LLM tool-use
|
||||
# integration. While this example uses Google's genai client, these callables can be adapted for
|
||||
# various function-calling or agent frameworks. For easier integration with supported frameworks
|
||||
# (https://github.com/googleapis/mcp-toolbox-python-sdk/tree/main/packages), use the
|
||||
# provided wrapper packages, which handle framework-specific boilerplate.
|
||||
toolbox_tools = await toolbox_client.load_toolset("my-toolset")
|
||||
genai_client = genai.Client(
|
||||
vertexai=True, project="project-id", location="us-central1"
|
||||
)
|
||||
|
||||
genai_tools = [
|
||||
Tool(
|
||||
function_declarations=[
|
||||
FunctionDeclaration.from_callable_with_api_option(callable=tool)
|
||||
]
|
||||
)
|
||||
for tool in toolbox_tools
|
||||
]
|
||||
history = []
|
||||
for query in queries:
|
||||
user_prompt_content = Content(
|
||||
role="user",
|
||||
parts=[Part.from_text(text=query)],
|
||||
)
|
||||
history.append(user_prompt_content)
|
||||
|
||||
response = genai_client.models.generate_content(
|
||||
model="gemini-2.0-flash-001",
|
||||
contents=history,
|
||||
config=GenerateContentConfig(
|
||||
system_instruction=prompt,
|
||||
tools=genai_tools,
|
||||
),
|
||||
)
|
||||
history.append(response.candidates[0].content)
|
||||
function_response_parts = []
|
||||
for function_call in response.function_calls:
|
||||
fn_name = function_call.name
|
||||
# The tools are sorted alphabetically
|
||||
if fn_name == "search-hotels-by-name":
|
||||
function_result = await toolbox_tools[3](**function_call.args)
|
||||
elif fn_name == "search-hotels-by-location":
|
||||
function_result = await toolbox_tools[2](**function_call.args)
|
||||
elif fn_name == "book-hotel":
|
||||
function_result = await toolbox_tools[0](**function_call.args)
|
||||
elif fn_name == "update-hotel":
|
||||
function_result = await toolbox_tools[4](**function_call.args)
|
||||
elif fn_name == "cancel-hotel":
|
||||
function_result = await toolbox_tools[1](**function_call.args)
|
||||
else:
|
||||
raise ValueError("Function name not present.")
|
||||
function_response = {"result": function_result}
|
||||
function_response_part = Part.from_function_response(
|
||||
name=function_call.name,
|
||||
response=function_response,
|
||||
)
|
||||
function_response_parts.append(function_response_part)
|
||||
|
||||
if function_response_parts:
|
||||
tool_response_content = Content(role="tool", parts=function_response_parts)
|
||||
history.append(tool_response_content)
|
||||
|
||||
response2 = genai_client.models.generate_content(
|
||||
model="gemini-2.0-flash-001",
|
||||
contents=history,
|
||||
config=GenerateContentConfig(
|
||||
tools=genai_tools,
|
||||
),
|
||||
)
|
||||
final_model_response_content = response2.candidates[0].content
|
||||
history.append(final_model_response_content)
|
||||
print(response2.text)
|
||||
|
||||
asyncio.run(run_application())
|
||||
|
||||
{{< /tab >}}
|
||||
{{< /tabpane >}}
|
||||
|
||||
{{< tabpane text=true persist=header >}}
|
||||
{{% tab header="Core" lang="en" %}}
|
||||
To learn more about tool calling with Google GenAI, check out the
|
||||
[Google GenAI Documentation](https://github.com/googleapis/python-genai?tab=readme-ov-file#manually-declare-and-invoke-a-function-for-function-calling).
|
||||
{{% /tab %}}
|
||||
{{< tabpane text=true persist=header >}}
|
||||
{{% tab header="ADK" lang="en" %}}
|
||||
To learn more about Agent Development Kit, check out the [ADK documentation.](https://google.github.io/adk-docs/)
|
||||
{{% /tab %}}
|
||||
@@ -622,6 +617,10 @@ To learn more about Agents in LangChain, check out the [LangGraph Agent document
|
||||
To learn more about Agents in LlamaIndex, check out the
|
||||
[LlamaIndex AgentWorkflow documentation.](https://docs.llamaindex.ai/en/stable/examples/agent/agent_workflow_basic/)
|
||||
{{% /tab %}}
|
||||
{{% tab header="Core" lang="en" %}}
|
||||
To learn more about tool calling with Google GenAI, check out the
|
||||
[Google GenAI Documentation](https://github.com/googleapis/python-genai?tab=readme-ov-file#manually-declare-and-invoke-a-function-for-function-calling).
|
||||
{{% /tab %}}
|
||||
{{< /tabpane >}}
|
||||
|
||||
1. Run your agent, and observe the results:
|
||||
|
||||
Reference in New Issue
Block a user