genai-toolbox/docs/en/samples/bigquery/local_quickstart.md

title, type, weight, description

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Quickstart (Local with BigQuery)	docs	1	How to get started running Toolbox locally with Python, BigQuery, and LangGraph, LlamaIndex, or ADK.

Before you begin

This guide assumes you have already done the following:

1. Installed Python 3.9+ (including pip and your preferred virtual environment tool for managing dependencies e.g. venv).

2. Installed and configured the Google Cloud SDK (gcloud CLI).

3. Authenticated with Google Cloud for Application Default Credentials (ADC):

   gcloud auth login --update-adc
   

4. Set your default Google Cloud project (replace YOUR_PROJECT_ID with your actual project ID):

   gcloud config set project YOUR_PROJECT_ID
   export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_ID
   

   Toolbox and the client libraries will use this project for BigQuery, unless overridden in configurations.

5. Enabled the BigQuery API in your Google Cloud project.

6. Installed the BigQuery client library for Python:

   pip install google-cloud-bigquery
   

7. Completed setup for usage with an LLM model such as {{< tabpane text=true persist=header >}} {{% tab header="Core" lang="en" %}}

• langchain-vertexai package.

• langchain-google-genai package.

• langchain-anthropic package. {{% /tab %}} {{% tab header="LangChain" lang="en" %}}

• langchain-vertexai package.

• langchain-google-genai package.

• langchain-anthropic package. {{% /tab %}} {{% tab header="LlamaIndex" lang="en" %}}

• llama-index-llms-google-genai package.

• llama-index-llms-anthropic package. {{% /tab %}} {{% tab header="ADK" lang="en" %}}

• google-adk package. {{% /tab %}} {{< /tabpane >}}

Step 1: Set up your BigQuery Dataset and Table

In this section, we will create a BigQuery dataset and a table, then insert some data that needs to be accessed by our agent. BigQuery operations are performed against your configured Google Cloud project.

1. Create a new BigQuery dataset (replace YOUR_DATASET_NAME with your desired dataset name, e.g., toolbox_ds, and optionally specify a location like US or EU):

   export BQ_DATASET_NAME="YOUR_DATASET_NAME" # e.g., toolbox_ds
   export BQ_LOCATION="US" # e.g., US, EU, asia-northeast1
   
   bq --location=$BQ_LOCATION mk $BQ_DATASET_NAME
   

   You can also do this through the Google Cloud Console.

   {{< notice tip >}} For a real application, ensure that the service account or user running Toolbox has the necessary IAM permissions (e.g., BigQuery Data Editor, BigQuery User) on the dataset or project. For this local quickstart with user credentials, your own permissions will apply. {{< /notice >}}

2. The hotels table needs to be defined in your new dataset for use with the bq query command. First, create a file named create_hotels_table.sql with the following content:

   CREATE TABLE IF NOT EXISTS `YOUR_PROJECT_ID.YOUR_DATASET_NAME.hotels` (
     id            INT64 NOT NULL,
     name          STRING NOT NULL,
     location      STRING NOT NULL,
     price_tier    STRING NOT NULL,
     checkin_date  DATE NOT NULL,
     checkout_date DATE NOT NULL,
     booked        BOOLEAN NOT NULL
   );
   

   Note: Replace YOUR_PROJECT_ID and YOUR_DATASET_NAME in the SQL with your actual project ID and dataset name.

   Then run the command below to execute the sql query:

   bq query --project_id=$GOOGLE_CLOUD_PROJECT --dataset_id=$BQ_DATASET_NAME --use_legacy_sql=false < create_hotels_table.sql
   

3. Next, populate the hotels table with some initial data. To do this, create a file named insert_hotels_data.sql and add the following SQL INSERT statement to it.

   INSERT INTO `YOUR_PROJECT_ID.YOUR_DATASET_NAME.hotels` (id, name, location, price_tier, checkin_date, checkout_date, booked)
   VALUES
     (1, 'Hilton Basel', 'Basel', 'Luxury', '2024-04-20', '2024-04-22', FALSE),
     (2, 'Marriott Zurich', 'Zurich', 'Upscale', '2024-04-14', '2024-04-21', FALSE),
     (3, 'Hyatt Regency Basel', 'Basel', 'Upper Upscale', '2024-04-02', '2024-04-20', FALSE),
     (4, 'Radisson Blu Lucerne', 'Lucerne', 'Midscale', '2024-04-05', '2024-04-24', FALSE),
     (5, 'Best Western Bern', 'Bern', 'Upper Midscale', '2024-04-01', '2024-04-23', FALSE),
     (6, 'InterContinental Geneva', 'Geneva', 'Luxury', '2024-04-23', '2024-04-28', FALSE),
     (7, 'Sheraton Zurich', 'Zurich', 'Upper Upscale', '2024-04-02', '2024-04-27', FALSE),
     (8, 'Holiday Inn Basel', 'Basel', 'Upper Midscale', '2024-04-09', '2024-04-24', FALSE),
     (9, 'Courtyard Zurich', 'Zurich', 'Upscale', '2024-04-03', '2024-04-13', FALSE),
     (10, 'Comfort Inn Bern', 'Bern', 'Midscale', '2024-04-04', '2024-04-16', FALSE);
   

   Note: Replace YOUR_PROJECT_ID and YOUR_DATASET_NAME in the SQL with your actual project ID and dataset name.

   Then run the command below to execute the sql query:

   bq query --project_id=$GOOGLE_CLOUD_PROJECT --dataset_id=$BQ_DATASET_NAME --use_legacy_sql=false < insert_hotels_data.sql
   

Step 2: Install and configure Toolbox

In this section, we will download Toolbox, configure our tools in a tools.yaml to use BigQuery, and then run the Toolbox server.

1. Download the latest version of Toolbox as a binary:

   {{< notice tip >}} Select the correct binary corresponding to your OS and CPU architecture. {{< /notice >}}

   export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64
   curl -O https://storage.googleapis.com/genai-toolbox/v0.8.0/$OS/toolbox
   

2. Make the binary executable:

   chmod +x toolbox
   

3. Write the following into a tools.yaml file. You must replace the YOUR_PROJECT_ID and YOUR_DATASET_NAME placeholder in the config with your actual BigQuery project and dataset name. The location field is optional; if not specified, it defaults to 'us'. The table name hotels is used directly in the statements.

   {{< notice tip >}} Authentication with BigQuery is handled via Application Default Credentials (ADC). Ensure you have run gcloud auth application-default login. {{< /notice >}}

   sources:
     my-bigquery-source:
       kind: bigquery
       project: YOUR_PROJECT_ID
       location: us
   tools:
     search-hotels-by-name:
       kind: bigquery-sql
       source: my-bigquery-source
       description: Search for hotels based on name.
       parameters:
         - name: name
           type: string
           description: The name of the hotel.
       statement: SELECT * FROM `YOUR_DATASET_NAME.hotels` WHERE LOWER(name) LIKE LOWER(CONCAT('%', @name, '%'));
     search-hotels-by-location:
       kind: bigquery-sql
       source: my-bigquery-source
       description: Search for hotels based on location.
       parameters:
         - name: location
           type: string
           description: The location of the hotel.
       statement: SELECT * FROM `YOUR_DATASET_NAME.hotels` WHERE LOWER(location) LIKE LOWER(CONCAT('%', @location, '%'));
     book-hotel:
       kind: bigquery-sql
       source: my-bigquery-source
       description: >-
          Book a hotel by its ID. If the hotel is successfully booked, returns a NULL, raises an error if not.
       parameters:
         - name: hotel_id
           type: integer
           description: The ID of the hotel to book.
       statement: UPDATE `YOUR_DATASET_NAME.hotels` SET booked = TRUE WHERE id = @hotel_id;
     update-hotel:
       kind: bigquery-sql
       source: my-bigquery-source
       description: >-
         Update a hotel's check-in and check-out dates by its ID. Returns a message indicating whether the hotel was successfully updated or not.
       parameters:
         - name: checkin_date
           type: string
           description: The new check-in date of the hotel.
         - name: checkout_date
           type: string
           description: The new check-out date of the hotel.
         - name: hotel_id
           type: integer
           description: The ID of the hotel to update.
       statement: >-
         UPDATE `YOUR_DATASET_NAME.hotels` SET checkin_date = PARSE_DATE('%Y-%m-%d', @checkin_date), checkout_date = PARSE_DATE('%Y-%m-%d', @checkout_date) WHERE id = @hotel_id;
     cancel-hotel:
       kind: bigquery-sql
       source: my-bigquery-source
       description: Cancel a hotel by its ID.
       parameters:
         - name: hotel_id
           type: integer
           description: The ID of the hotel to cancel.
       statement: UPDATE `YOUR_DATASET_NAME.hotels` SET booked = FALSE WHERE id = @hotel_id;
   

   Important Note on toolsets: The tools.yaml content above does not include a toolsets section. The Python agent examples in Step 3 (e.g., await toolbox_client.load_toolset("my-toolset")) rely on a toolset named my-toolset. To make those examples work, you will need to add a toolsets section to your tools.yaml file, for example:

   # Add this to your tools.yaml if using load_toolset("my-toolset")
   # Ensure it's at the same indentation level as 'sources:' and 'tools:'
   toolsets:
     my-toolset:
       - search-hotels-by-name
       - search-hotels-by-location
       - book-hotel
       - update-hotel
       - cancel-hotel
   

   Alternatively, you can modify the agent code to load tools individually (e.g., using await toolbox_client.load_tool("search-hotels-by-name")).

   For more info on tools, check out the Resources section of the docs.

4. Run the Toolbox server, pointing to the tools.yaml file created earlier:

   ./toolbox --tools-file "tools.yaml"
   

Step 3: Connect your agent to Toolbox

In this section, we will write and run an agent that will load the Tools from Toolbox.

{{< notice tip>}} If you prefer to experiment within a Google Colab environment, you can connect to a local runtime. {{< /notice >}}

1. In a new terminal, install the SDK package.

   {{< tabpane persist=header >}} {{< tab header="Core" lang="bash" >}}

pip install toolbox-core {{< /tab >}} {{< tab header="Langchain" lang="bash" >}}

pip install toolbox-langchain {{< /tab >}} {{< tab header="LlamaIndex" lang="bash" >}}

pip install toolbox-llamaindex {{< /tab >}} {{< tab header="ADK" lang="bash" >}}

pip install google-adk {{< /tab >}}

{{< /tabpane >}}

1. Install other required dependencies:

   {{< tabpane persist=header >}} {{< tab header="Core" lang="bash" >}}

TODO(developer): replace with correct package if needed

pip install langgraph langchain-google-vertexai

pip install langchain-google-genai

pip install langchain-anthropic

{{< /tab >}} {{< tab header="Langchain" lang="bash" >}}

TODO(developer): replace with correct package if needed

pip install langgraph langchain-google-vertexai

pip install langchain-google-genai

pip install langchain-anthropic

{{< /tab >}} {{< tab header="LlamaIndex" lang="bash" >}}

TODO(developer): replace with correct package if needed

pip install llama-index-llms-google-genai

pip install llama-index-llms-anthropic

{{< /tab >}} {{< tab header="ADK" lang="bash" >}} pip install toolbox-core {{< /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="LangChain" lang="python" >}}

import asyncio from langgraph.prebuilt import create_react_agent

TODO(developer): replace this with another import if needed

from langchain_google_vertexai import ChatVertexAI

from langchain_google_genai import ChatGoogleGenerativeAI

from langchain_anthropic import ChatAnthropic

from langgraph.checkpoint.memory import MemorySaver

from toolbox_langchain 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 ids 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.", "Can you book the Hilton Basel for me?", "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.", "My check in dates would be from April 10, 2024 to April 19, 2024.", ]

async def main(): # TODO(developer): replace this with another model if needed model = ChatVertexAI(model_name="gemini-2.0-flash-001") # model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001") # model = ChatAnthropic(model="claude-3-5-sonnet-20240620")

# Load the tools from the Toolbox server
client = ToolboxClient("http://127.0.0.1:5000")
tools = await client.aload_toolset()

agent = create_react_agent(model, tools, checkpointer=MemorySaver())

config = {"configurable": {"thread_id": "thread-1"}}
for query in queries:
    inputs = {"messages": [("user", prompt + query)]}
    response = await agent.ainvoke(inputs, stream_mode="values", config=config)
    print(response["messages"][-1].content)

asyncio.run(main()) {{< /tab >}} {{< tab header="LlamaIndex" lang="python" >}} import asyncio import os

from llama_index.core.agent.workflow import AgentWorkflow

from llama_index.core.workflow import Context

TODO(developer): replace this with another import if needed

from llama_index.llms.google_genai import GoogleGenAI

from llama_index.llms.anthropic import Anthropic

from toolbox_llamaindex 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 ids 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.", "Can you book the Hilton Basel for me?", "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.", "My check in dates would be from April 10, 2024 to April 19, 2024.", ]

async def main(): # TODO(developer): replace this with another model if needed llm = GoogleGenAI( model="gemini-2.0-flash-001", vertexai_config={"location": "us-central1"}, ) # llm = GoogleGenAI( # api_key=os.getenv("GOOGLE_API_KEY"), # model="gemini-2.0-flash-001", # ) # llm = Anthropic( # model="claude-3-7-sonnet-latest", # api_key=os.getenv("ANTHROPIC_API_KEY") # )

# Load the tools from the Toolbox server
client = ToolboxClient("http://127.0.0.1:5000")
tools = await client.aload_toolset()

agent = AgentWorkflow.from_tools_or_functions(
    tools,
    llm=llm,
    system_prompt=prompt,
)
ctx = Context(agent)
for query in queries:
    response = await agent.arun(user_msg=query, ctx=ctx)
    print(f"---- {query} ----")
    print(str(response))

asyncio.run(main()) {{< /tab >}} {{< tab header="ADK" lang="python" >}} from google.adk.agents import Agent from google.adk.runners import Runner from google.adk.sessions import InMemorySessionService from google.adk.artifacts.in_memory_artifact_service import InMemoryArtifactService from google.genai import types # For constructing message content from toolbox_core import ToolboxSyncClient

import os os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'

TODO(developer): Replace 'YOUR_PROJECT_ID' with your Google Cloud Project ID

os.environ['GOOGLE_CLOUD_PROJECT'] = 'YOUR_PROJECT_ID'

TODO(developer): Replace 'us-central1' with your Google Cloud Location (region)

os.environ['GOOGLE_CLOUD_LOCATION'] = 'us-central1'

--- Load Tools from Toolbox ---

TODO(developer): Ensure the Toolbox server is running at http://127.0.0.1:5000

with ToolboxSyncClient("http://127.0.0.1:5000") as toolbox_client: # TODO(developer): Replace "my-toolset" with the actual ID of your toolset as configured in your MCP Toolbox server. agent_toolset = toolbox_client.load_toolset("my-toolset")

# --- Define the Agent's Prompt ---
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 ids 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.
"""

# --- Configure the Agent ---

root_agent = Agent(
    model='gemini-2.0-flash-001',
    name='hotel_agent',
    description='A helpful AI assistant that can search and book hotels.',
    instruction=prompt,
    tools=agent_toolset, # Pass the loaded toolset
)

# --- Initialize Services for Running the Agent ---
session_service = InMemorySessionService()
artifacts_service = InMemoryArtifactService()
# Create a new session for the interaction.
session = session_service.create_session(
    state={}, app_name='hotel_agent', user_id='123'
)

runner = Runner(
    app_name='hotel_agent',
    agent=root_agent,
    artifact_service=artifacts_service,
    session_service=session_service,
)

# --- Define Queries and Run the Agent ---
queries = [
    "Find hotels in Basel with Basel in it's name.",
    "Can you book the Hilton Basel for me?",
    "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.",
    "My check in dates would be from April 10, 2024 to April 19, 2024.",
]

for query in queries:
    content = types.Content(role='user', parts=[types.Part(text=query)])
    events = runner.run(session_id=session.id,
                        user_id='123', new_message=content)

    responses = (
      part.text
      for event in events
      for part in event.content.parts
      if part.text is not None
    )

    for text in responses:
      print(text)

{{< /tab >}} {{< /tabpane >}}

{{< tabpane text=true persist=header >}}

{{% tab header="Core" lang="en" %}} To learn more about the Core SDK, check out the Toolbox Core SDK documentation. {{% /tab %}} {{% tab header="Langchain" lang="en" %}} To learn more about Agents in LangChain, check out the LangGraph Agent documentation. {{% /tab %}} {{% tab header="LlamaIndex" lang="en" %}} To learn more about Agents in LlamaIndex, check out the LlamaIndex AgentWorkflow documentation. {{% /tab %}} {{% tab header="ADK" lang="en" %}} To learn more about Agents in ADK, check out the ADK documentation. {{% /tab %}} {{< /tabpane >}}

1. Run your agent, and observe the results:

   python hotel_agent.py