| Name | bigquery-agent JSON |
| Version |
1.2.1
JSON |
| download |
| home_page | None |
| Summary | Library for creating agent around BigQuery |
| upload_time | 2024-12-27 17:07:49 |
| maintainer | None |
| docs_url | None |
| author | Hlib Bochkarev |
| requires_python | None |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
Below is a concise `README.md`:
---
# bigquery_agent
`bigquery_agent` creates a Gemini Agents Toolkit-based agent with built-in functions to query a specified BigQuery table.
## Requirements
- Python 3.8+
- [Gemini Agents Toolkit](https://github.com/GeminiAgentsToolkit/gemini-agents-toolkit/blob/main/README.md)
- Google Cloud credentials with BigQuery access
- Vertex AI enabled for your GCP project
## Installation
```bash
pip install bigquery_agent
```
## Usage
```python
import vertexai
from bigquery_agent import create_bigquery_agent
vertexai.init(project="YOUR_GCP_PROJECT", location="us-west1")
agent = create_bigquery_agent(
bigquery_project_id="YOUR_BQ_PROJECT",
dataset_id="YOUR_DATASET",
table_id="YOUR_TABLE",
model_name="gemini-2.0-flash-exp",
system_instruction="Query the todos table as needed."
)
response = agent.send_message("Show me all todos from the database")[0]
print(response)
```
This agent can inspect the schema (`get_schema`), run queries (`run_query`), and get table references (`get_table_ref`) using the provided BigQuery credentials and Vertex AI model.
For more details on creating and using agents, refer to the [Gemini Agents Toolkit README](https://github.com/GeminiAgentsToolkit/gemini-agents-toolkit/blob/main/README.md).
Raw data
{
"_id": null,
"home_page": null,
"name": "bigquery-agent",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": null,
"author": "Hlib Bochkarev",
"author_email": "glebuar@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/8a/1b/5816effa52ebb5a351240f368d8ee84827c6e1da4e3841303c54c31a4409/bigquery_agent-1.2.1.tar.gz",
"platform": null,
"description": "Below is a concise `README.md`:\n\n---\n\n# bigquery_agent\n\n`bigquery_agent` creates a Gemini Agents Toolkit-based agent with built-in functions to query a specified BigQuery table.\n\n## Requirements\n\n- Python 3.8+\n- [Gemini Agents Toolkit](https://github.com/GeminiAgentsToolkit/gemini-agents-toolkit/blob/main/README.md)\n- Google Cloud credentials with BigQuery access\n- Vertex AI enabled for your GCP project\n\n## Installation\n\n```bash\npip install bigquery_agent\n```\n\n## Usage\n\n```python\nimport vertexai\nfrom bigquery_agent import create_bigquery_agent\n\nvertexai.init(project=\"YOUR_GCP_PROJECT\", location=\"us-west1\")\n\nagent = create_bigquery_agent(\n bigquery_project_id=\"YOUR_BQ_PROJECT\",\n dataset_id=\"YOUR_DATASET\",\n table_id=\"YOUR_TABLE\",\n model_name=\"gemini-2.0-flash-exp\",\n system_instruction=\"Query the todos table as needed.\"\n)\n\nresponse = agent.send_message(\"Show me all todos from the database\")[0]\nprint(response)\n```\n\nThis agent can inspect the schema (`get_schema`), run queries (`run_query`), and get table references (`get_table_ref`) using the provided BigQuery credentials and Vertex AI model.\n\nFor more details on creating and using agents, refer to the [Gemini Agents Toolkit README](https://github.com/GeminiAgentsToolkit/gemini-agents-toolkit/blob/main/README.md).\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Library for creating agent around BigQuery",
"version": "1.2.1",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ea942520a0d0ed108fdeb988846f8b1fd2ab752020731c902cfcb8b212398226",
"md5": "57d4a632d5e5a1b62a47e478655b9fad",
"sha256": "371b34df652e1d74a817dd5f95d0701db8da4758b786eb72610a4a67da1ddec8"
},
"downloads": -1,
"filename": "bigquery_agent-1.2.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "57d4a632d5e5a1b62a47e478655b9fad",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 7345,
"upload_time": "2024-12-27T17:07:48",
"upload_time_iso_8601": "2024-12-27T17:07:48.757147Z",
"url": "https://files.pythonhosted.org/packages/ea/94/2520a0d0ed108fdeb988846f8b1fd2ab752020731c902cfcb8b212398226/bigquery_agent-1.2.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8a1b5816effa52ebb5a351240f368d8ee84827c6e1da4e3841303c54c31a4409",
"md5": "2ed89d0ad5e3ac11ca7e3d3f3b9d4d60",
"sha256": "03653012380d02e32212fc327f998ddd4ad9fc8469fa88d8fa8c1801869335f8"
},
"downloads": -1,
"filename": "bigquery_agent-1.2.1.tar.gz",
"has_sig": false,
"md5_digest": "2ed89d0ad5e3ac11ca7e3d3f3b9d4d60",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 4959,
"upload_time": "2024-12-27T17:07:49",
"upload_time_iso_8601": "2024-12-27T17:07:49.812027Z",
"url": "https://files.pythonhosted.org/packages/8a/1b/5816effa52ebb5a351240f368d8ee84827c6e1da4e3841303c54c31a4409/bigquery_agent-1.2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-27 17:07:49",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "bigquery-agent"
}
