Name | structifyai JSON |
Version |
0.34.0
JSON |
| download |
home_page | None |
Summary | Microsoft Structify Client Library for Python |
upload_time | 2024-04-27 19:18:20 |
maintainer | None |
docs_url | None |
author | Microsoft Corporation |
requires_python | >=3.8 |
license | MIT License |
keywords |
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
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Travis-CI |
No Travis.
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coveralls test coverage |
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# Structify
[Screenshot](assets/logo.png)
## Getting started
### Installating the package
```bash
pip install structify.ai
```
```python
# First, define the schema of your dataset as a pydantic model
class Person(BaseModel):
name: str
job_title: str
company: str
# Next, create a dataset with that schema
structify.dataset.create(name = "my_network", tables = [Person.schema()])
# Then, create an agent to index information from defined sources for your dataset
structify.agents.create(name = "my_network", source = [Source.Internet(websites = ["linkedin.com"]), Source.Document(path = "contact_cards/*")])
# Finally, let our agents structify those sources into a dataset for you
structify.it("my_network")
```
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"description": "# Structify\n[Screenshot](assets/logo.png)\n\n## Getting started\n\n### Installating the package\n\n```bash\npip install structify.ai\n```\n\n```python\n# First, define the schema of your dataset as a pydantic model\nclass Person(BaseModel):\n name: str\n job_title: str\n company: str\n\n# Next, create a dataset with that schema\nstructify.dataset.create(name = \"my_network\", tables = [Person.schema()])\n\n# Then, create an agent to index information from defined sources for your dataset\nstructify.agents.create(name = \"my_network\", source = [Source.Internet(websites = [\"linkedin.com\"]), Source.Document(path = \"contact_cards/*\")])\n\n# Finally, let our agents structify those sources into a dataset for you\nstructify.it(\"my_network\")\n```\n\n",
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