Use Google BigQuery with AI Agents

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Google BigQuery is a fully managed enterprise data warehouse that enables serverless, highly scalable data analysis. It allows for querying large datasets using SQL, with automatic scaling and no infrastructure to manage.

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🔗 Connect and Use Google BigQuery

1. 🔑 Connect your Google BigQuery

2. ✅ Select an action

3. 🚀 Go live with the agent

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API actions for Google BigQuery for AI assitants/agents

Language

PYTHON

Framework

Run Query

Execute a SQL query on BigQuery and return the results.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.RUN_QUERY])

Create Dataset

Create a new dataset in BigQuery to organize and contain tables.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.CREATE_DATASET])

Create Table

Create a new table in a specified dataset with defined schema.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.CREATE_TABLE])

Delete Dataset

Remove a dataset and all its contents from BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.DELETE_DATASET])

Delete Table

Remove a specific table from a dataset in BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.DELETE_TABLE])

Insert Rows

Insert new rows of data into a specified BigQuery table.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.INSERT_ROWS])

Update Table Schema

Modify the schema of an existing table in BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.UPDATE_TABLE_SCHEMA])

Export Table

Export a BigQuery table to a specified destination like Google Cloud Storage.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.EXPORT_TABLE])

Import Data

Import data from external sources into a BigQuery table.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.IMPORT_DATA])

Create View

Create a new view based on a SQL query in BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.CREATE_VIEW])

Grant Access

Grant access permissions to a user or service account for a dataset or table.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.GRANT_ACCESS])

Revoke Access

Revoke access permissions from a user or service account for a dataset or table.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.REVOKE_ACCESS])

Schedule Query

Set up a scheduled query to run automatically at specified intervals.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.SCHEDULE_QUERY])

Cancel Job

Cancel a running BigQuery job, such as a long-running query.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.CANCEL_JOB])

Create Partition

Create a new partition in a partitioned table for improved query performance.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.CREATE_PARTITION])

New Row Inserted

Triggered when a new row is inserted into a specified BigQuery table.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.NEW_ROW_INSERTED])

Query Completed

Triggered when a BigQuery job (typically a query) completes execution.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.QUERY_COMPLETED])

Dataset Created

Triggered when a new dataset is created in BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.DATASET_CREATED])

Table Created

Triggered when a new table is created in a BigQuery dataset.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.TABLE_CREATED])

Table Updated

Triggered when a table's schema or metadata is updated in BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.TABLE_UPDATED])

Query Error

Triggered when a BigQuery query fails or encounters an error during execution.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.QUERY_ERROR])

Storage Threshold Reached

Triggered when storage usage for a dataset or project reaches a specified threshold.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.STORAGE_THRESHOLD_REACHED])

Scheduled Query Started

Triggered when a scheduled query begins its execution.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.SCHEDULED_QUERY_STARTED])

Data Export Completed

Triggered when a data export job from BigQuery to external storage completes.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.DATA_EXPORT_COMPLETED])

Access Changed

Triggered when access permissions are modified for a dataset or table.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.ACCESS_CHANGED])

Table Deleted

Triggered when a table is deleted from a BigQuery dataset.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.TABLE_DELETED])

Dataset Deleted

Triggered when a dataset is deleted from BigQuery.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.DATASET_DELETED])

Query Timeout

Triggered when a BigQuery query exceeds its specified timeout duration.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.QUERY_TIMEOUT])

Data Staleness Alert

Triggered when data in a table hasn't been updated within a specified timeframe.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.DATA_STALENESS_ALERT])

Quota Exceeded

Triggered when a BigQuery quota (e.g., query bytes processed) is exceeded.

from composio_langchain import ComposioToolSet, Action tool_set = ComposioToolSet() tools = tool_set.get_tools(actions=[Action.QUOTA_EXCEEDED])

Frequently asked questions

What is Composio.dev?

Composio.dev is a platform for building AI applications, designed to make the process of developing AI solutions super easy and fun! It provides a comprehensive set of tools and libraries that simplify the process of developing AI solutions, allowing you to focus on the creative aspects of your project without getting bogged down by the technical details.

How does Composio.dev support Google BigQuery?

Composio.dev seamlessly integrates with Google BigQuery, allowing you to leverage its capabilities within the Composio.dev platform. You can utilize Google BigQuery to call functions across various platforms, including Google, GitHub, and others, making it a breeze to incorporate different services into your AI applications. Additionally, it supports user authentication via OAuth2 and can work in conjunction with other popular frameworks like LangChain and CrewAI, giving you the flexibility to build truly innovative AI solutions.

What models can I use with Google BigQuery?

With Google BigQuery, you have access to a wide range of state-of-the-art language models, including GPT-4o (OpenAI), GPT-3.5 (OpenAI), GPT-4 (OpenAI), Claude (Anthropic), PaLM (Google), LLaMA and LLaMA 2 (Meta), Gemini, and many others. This flexibility allows you to choose the model that best suits your specific use case, whether you're building a chatbot, a content creation tool, or any other AI-powered application. You can experiment with different models and find the one that delivers the best performance for your project.

How can I integrate Google BigQuery into my project?

Composio.dev provides a seamless integration for Google BigQuery, making it super easy to incorporate this powerful framework into your projects. You can leverage the Composio.dev API to call functions from Google BigQuery, allowing you to tap into its capabilities with just a few lines of code. The SDK is available in Python, JavaScript, and TypeScript, so you can work with your preferred programming language and integrate Google BigQuery into your projects seamlessly.

What is the pricing for Google BigQuery?

Google BigQuery is completely free to use, with a generous free tier that allows up to 1000 requests per month. This makes it accessible for developers and organizations of all sizes to explore and experiment with this powerful tool without any upfront costs. Whether you're a student working on a personal project or a startup building the next big thing, you can get started with Google BigQuery without worrying about breaking the bank.

What kind of authentication is supported for Google BigQuery?

Google BigQuery supports OAuth2 authentication, ensuring secure and authorized access to its functionalities. You can leverage the Composio.dev API to handle authentication and call functions from Google BigQuery seamlessly. The SDK is available in Python, JavaScript, and TypeScript for your convenience, making it easy to integrate authentication into your projects and keep your users' data safe and secure.

Can I add Google BigQuery to my project?

Absolutely! You can easily incorporate Google BigQuery into your project by utilizing the Composio.dev API. This API allows you to call functions from Google BigQuery, enabling you to leverage its capabilities within your application. The SDK is available in Python, JavaScript, and TypeScript to facilitate integration, so you can work with the language you're most comfortable with and add Google BigQuery to your project with ease.

What is the accuracy of Google BigQuery?

Google BigQuery is designed to provide highly accurate and reliable results, ensuring that your AI applications perform at their best. The integration with Composio.dev ensures precise function calls, enabling you to build robust and powerful AI applications with confidence. Google BigQuery's comprehensive framework and the ability to leverage state-of-the-art models ensure reliable and accurate outcomes for your AI development needs, whether you're working on a chatbot, a content creation tool, or any other AI-powered project.

What are some common use cases for Google BigQuery?

Google BigQuery can be used for a wide range of AI applications, making it a versatile tool for developers and creators alike. Some common use cases include natural language processing, text generation, question answering, sentiment analysis, and more. It's particularly useful for building chatbots, virtual assistants, content creation tools, and other AI-powered applications that can help you automate tasks, engage with users, and create compelling content. Whether you're working on a personal project or building a product for your startup, Google BigQuery can help you bring your ideas to life.

How does Google BigQuery handle data privacy and security?

Data privacy and security are crucial considerations when working with AI systems, and Google BigQuery takes these issues seriously. It follows industry best practices and adheres to strict data protection regulations, ensuring that your data is kept safe and secure. Google BigQuery provides robust security measures, such as encryption and access controls, to ensure the confidentiality and integrity of your data. You can rest assured that your sensitive information is protected when using Google BigQuery for your AI development needs.