# pydantic-to-elastic
A simple CLI utility for converting Pydantic models to Elasticsearch mappings.
### Installation
#### From source
```bash
git clone https://github.com/malinkinsa/pydantic-to-elastic.git && cd pydantic-to-elastic
pip install .
```
### CLI options
| Prop | Description | Required | Default value |
|:----------------|:-----------------------------------------------------------------------------------------------------------------------------------|:---------|:--------------|
| --input | Path to the file containing Pydantic models. | True | |
| --output | Output type of result. Possible values: "console" or "file". | False | console |
| --output_path | Path and filename to save the output file (required if --output is set to 'file'). | False | |
| --output_format | Output format for JSON data. Use 'json' for compact single-line JSON or 'pretty' for pretty-printed JSON with 4-space indentation. | False | json |
| --submodel_type | Specifies the submodel type. Possible values: "nested" or "object" | False | nested |
| --text_fields | List of fields that must be of type 'text'. Can be specified multiple times. | False | |
### Usage
For example, you have a model `user_models.py`
```python
from pydantic import BaseModel
from typing import List
class Address(BaseModel):
street: str
city: str
zip_code: str
class User(BaseModel):
name: str
age: int
address: Address
hobbies: List[str]
```
Execute the command for converting these models into mapping json:
```bash
pydantic2es --input ./user_models.py --output_format pretty
```
And you will obtain the following result:
```json
{
"mappings": {
"properties": {
"name": {
"type": "keyword"
},
"age": {
"type": "integer"
},
"address": {
"type": "nested",
"properties": {
"street": {
"type": "keyword"
},
"city": {
"type": "keyword"
},
"zip_code": {
"type": "keyword"
}
}
},
"hobbies": {
"type": "keyword"
}
}
}
}
```
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"description": "# pydantic-to-elastic\n\nA simple CLI utility for converting Pydantic models to Elasticsearch mappings.\n\n### Installation\n\n#### From source\n```bash\ngit clone https://github.com/malinkinsa/pydantic-to-elastic.git && cd pydantic-to-elastic\npip install .\n```\n\n### CLI options\n| Prop | Description | Required | Default value |\n|:----------------|:-----------------------------------------------------------------------------------------------------------------------------------|:---------|:--------------|\n| --input | Path to the file containing Pydantic models. | True | |\n| --output | Output type of result. Possible values: \"console\" or \"file\". | False | console |\n| --output_path | Path and filename to save the output file (required if --output is set to 'file'). | False | |\n| --output_format | Output format for JSON data. Use 'json' for compact single-line JSON or 'pretty' for pretty-printed JSON with 4-space indentation. | False | json |\n| --submodel_type | Specifies the submodel type. Possible values: \"nested\" or \"object\" | False | nested |\n| --text_fields | List of fields that must be of type 'text'. Can be specified multiple times. | False | |\n\n### Usage\nFor example, you have a model `user_models.py`\n```python\nfrom pydantic import BaseModel\nfrom typing import List\n\nclass Address(BaseModel):\n street: str\n city: str\n zip_code: str\n\nclass User(BaseModel):\n name: str\n age: int\n address: Address\n hobbies: List[str]\n```\n\nExecute the command for converting these models into mapping json:\n```bash\npydantic2es --input ./user_models.py --output_format pretty\n```\n\nAnd you will obtain the following result:\n```json\n{\n \"mappings\": {\n \"properties\": {\n \"name\": {\n \"type\": \"keyword\"\n },\n \"age\": {\n \"type\": \"integer\"\n },\n \"address\": {\n \"type\": \"nested\",\n \"properties\": {\n \"street\": {\n \"type\": \"keyword\"\n },\n \"city\": {\n \"type\": \"keyword\"\n },\n \"zip_code\": {\n \"type\": \"keyword\"\n }\n }\n },\n \"hobbies\": {\n \"type\": \"keyword\"\n }\n }\n }\n}\n```\n",
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