Use Google BigQuery with Langchain Python

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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

What do you want to do?

Open Index

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 cutting-edge framework for building AI applications, designed to make the process of developing AI solutions super easy and fun! It's a collection of powerful tools and libraries that simplify the process of creating AI applications, 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, making it a breeze to leverage its capabilities within the Composio.dev platform. You can use Google BigQuery to call functions on various platforms like Google, GitHub, and others, allowing you to incorporate different services into your AI applications with ease. It also supports user login via OAuth2 and can work with other popular frameworks such as LangChain and CrewAI, giving you the flexibility to build truly innovative AI solutions.

What models can I use with Google BigQuery and langchain_python?

When using Google BigQuery and langchain_python, 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 with langchain_python?

Integrating Google BigQuery with langchain_python is super easy with Composio.dev! You can use the Composio.dev API to call functions from both Google BigQuery and langchain_python, allowing you to tap into their capabilities with just a few lines of code. The SDK is available in Python, JavaScript, and TypeScript, so you can work with the language you're most comfortable with and integrate these powerful tools into your projects seamlessly.

What is the pricing for Google BigQuery and langchain_python?

Both Google BigQuery and langchain_python are completely free to use, with a generous free tier that allows up to 1000 requests per month. This makes them accessible for developers and organizations of all sizes, whether you're a student working on a personal project or a startup building the next big thing. You can get started with these powerful tools without worrying about breaking the bank.

What kind of authentication is supported for Google BigQuery and langchain_python?

Google BigQuery and langchain_python support OAuth2 authentication, ensuring secure and authorized access to their functionalities. You can use the Composio.dev API to handle authentication and call functions from both Google BigQuery and langchain_python 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 both Google BigQuery and langchain_python, enabling you to leverage their 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 these powerful tools to your project with ease.

What is the accuracy of Google BigQuery and langchain_python?

Google BigQuery and langchain_python are 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. The 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 and langchain_python?

Google BigQuery and langchain_python can be used for a wide range of AI applications, making them versatile tools for developers and creators alike. Some common use cases include natural language processing, text generation, question answering, sentiment analysis, and more. They're 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, these tools 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.

Can I customize Google BigQuery and langchain_python for my specific needs?

Absolutely! Google BigQuery and langchain_python are highly customizable and extensible, allowing you to tailor their functionality, models, and configurations to meet your specific requirements. Whether you're building a chatbot, a content creation tool, or any other AI-powered application, you can customize these tools to fit your unique needs. Additionally, Composio.dev provides a flexible platform for integrating and orchestrating various AI tools and services, enabling you to create custom AI solutions that are tailored to your project.

What kind of support and documentation is available for Google BigQuery and langchain_python?

Google BigQuery and langchain_python have comprehensive documentation and a supportive community, making it easy for you to get started and find answers to your questions. Composio.dev also provides extensive resources, including tutorials, guides, and a dedicated support team to assist you throughout your AI development journey. Whether you're a beginner or an experienced developer, you'll have access to the resources you need to make the most of these powerful tools.