(flow-yaml-spec)=

{octicon}file-code YAML specification

To generate a YAML configuration from an Orchestration, use {meth}~jina.jaml.JAMLCompatible.save_config.

YAML completion in IDE

We provide a JSON Schema for your IDE to enable code completion, syntax validation, members listing and displaying help text.

PyCharm users

1. Click menu Preferences -> JSON Schema mappings;
2. Add a new schema, in the Schema File or URL write https://schemas.jina.ai/schemas/latest.json; select JSON Schema Version 7;
3. Add a file path pattern and link it to *.jaml or *.jina.yml or any suffix you commonly used for Jina-serve Flow's YAML.

VSCode users

1. Install the extension: YAML Language Support by Red Hat;
2. In IDE-level settings.json add:

"yaml.schemas": {
    "https://schemas.jina.ai/schemas/latest.json": ["/*.jina.yml", "/*.jaml"],
}

You can bind Schema to any file suffix you commonly used for Jina-serve Flow's YAML.

Example YAML

```yaml
jtype: Deployment
version: '1'
with:
  protocol: http
name: firstexec
uses:
  jtype: MyExec
  py_modules:
    - executor.py
```

```yaml
jtype: Flow
version: '1'
with:
  protocol: http
executors:
# inline Executor YAML
- name: firstexec
  uses:
    jtype: MyExec
    py_modules:
      - executor.py
# reference to Executor YAML
- name: secondexec
  uses: indexer.yml
  workspace: /home/my/workspace
# reference to Executor Python class
- name: thirdexec
  uses: CustomExec  # located in executor.py
```

Fields

jtype

String that is always set to either "Flow" or "Deployment", indicating the corresponding Python class.

version

String indicating the version of the Flow or Deployment.

with

Keyword arguments are passed to a Flow's __init__() method. You can set Flow-specific arguments and Gateway-specific arguments here:

Orchestration arguments

```{include} deployment-args.md
```

```{include} flow-args.md
```
##### Gateway arguments
These apply only to Flows, not Deployments

```{include} gateway-args.md
```

(executor-args)=

executors

Collection of Executors used in the Orchestration. In the case of a Deployment, this is a single Executor, while a Flow can have an arbitrary amount.

Each item in the collection specifies one Executor and can be used via:

```python
dep = Deployment(uses=MyExec, arg1="foo", arg2="bar")
```

```python
f = Flow().add(uses=MyExec, arg1="foo", arg2="bar")
```