## EditValidation
Edit an existing validation plan with a plain-English instruction using an LLM.
Usage
``` python
EditValidation(
validation,
instruction,
model,
data=None,
api_key=None,
verify_ssl=True,
max_reprompts=1
)
```
While DraftValidation generates a validation plan from scratch by profiling a table, [EditValidation](EditValidation.md#pointblank.EditValidation) takes an *existing* plan plus a natural-language instruction (e.g., "add a freshness check on `updated_at`, tighten the email regex, and drop the `price > 0` check") and produces a revised plan. You review the change as a diff and explicitly accept() it before anything runs, keeping a human in control.
[EditValidation](EditValidation.md#pointblank.EditValidation) reuses the same provider abstraction as [DraftValidation](DraftValidation.md#pointblank.DraftValidation) (via the `chatlas` package), supporting the `"anthropic"`, `"openai"`, `"ollama"`, `"bedrock"`, and `"azure-openai"` providers. Install the optional requirements with `pip install pointblank[generate]`.
> **Warning: Warning**
>
> The [EditValidation](EditValidation.md#pointblank.EditValidation) class is experimental. Please report any issues you encounter in the [Pointblank issue tracker](https://github.com/posit-dev/pointblank/issues).
## Parameters
`validation: Any`
The existing validation plan to edit. This can be a Validate object, a Python code string (such as the output of Validate.to_code()), a YAML configuration string, or a path to a `.py` or `.yaml`/`.yml` file. All inputs are normalized to Pointblank code before being sent to the model.
`instruction: str`
A plain-English description of the changes to make to the plan.
`model: str`
The model to be used. This should be in the form of `provider:model` (e.g., `"anthropic:claude-opus-4-8"`). Supported providers are `"anthropic"`, `"openai"`, `"ollama"`, `"bedrock"`, and `"azure-openai"`.
`data: Any = None`
Optional data table. When provided, a DataScan summary of the table is included in the prompt so the model can make *informed* edits (e.g., tightening a range using observed min/max values). It is also used as the default data source when building a Validate via accept().
`api_key: str | None = None`
The API key to be used for the model.
`verify_ssl: bool = ``True`
Whether to verify SSL certificates when making requests to the LLM provider. Defaults to `True`. Use with caution as disabling SSL verification can pose security risks.
`max_reprompts: int = ``1`
The maximum number of times to automatically re-prompt the model when the returned code fails the syntax/lint check, before surfacing the result to the user. Defaults to `1`.
## Returns
`EditValidation`
An [EditValidation](EditValidation.md#pointblank.EditValidation) object exposing the revised plan through to_code(), diff(), changed_steps(), and accept().
## Notes On Data Sent To The Model Provider
As with [DraftValidation](DraftValidation.md#pointblank.DraftValidation), only a JSON summary of the table (generated by [DataScan](DataScan.md#pointblank.DataScan)) is ever sent to the model, never the underlying rows. If `data=` is omitted, no table information is sent at all; the model edits the plan using only the plan code and your instruction.
## Examples
``` python
import pointblank as pb
# An existing plan
validation = (
pb.Validate(data=pb.load_dataset("small_table"))
.col_vals_gt(columns="d", value=100)
.col_vals_regex(columns="b", pattern="[0-9]-[a-z]{3}")
)
# Edit it with a plain-English instruction
edited = pb.EditValidation(
validation=validation,
instruction="Loosen the `d` check to value=50 and add a not-null check on column `a`.",
model="anthropic:claude-opus-4-8",
data=pb.load_dataset("small_table"),
)
print(edited.diff()) # review the change
plan = edited.accept() # a Validate you can .interrogate()
```
## Methods
| Name | Description |
|----|----|
| [accept()](#accept) | Execute the revised plan and return the resulting [Validate](Validate.md#pointblank.Validate) object. |
| [changed_steps()](#changed_steps) | Return a structured, step-level list of the changes between the plans. |
| [diff()](#diff) | Return a unified textual diff of the original plan versus the revised plan. |
| [from_plans()](#from_plans) | Build an [EditValidation](EditValidation.md#pointblank.EditValidation) from two known plans, without calling a model. |
| [review()](#review) | Return a `great_tables` GT object summarizing the step-level changes. |
| [to_code()](#to_code) | Return the revised validation plan as Python code. |
| [validate_syntax()](#validate_syntax) | Return whether the revised plan parses and uses only known validation methods. |
------------------------------------------------------------------------
#### accept()
Execute the revised plan and return the resulting [Validate](Validate.md#pointblank.Validate) object.
Usage
``` python
accept(data=None)
```
The returned [Validate](Validate.md#pointblank.Validate) has NOT been interrogated; call `.interrogate()` on it to run the validation.
##### Parameters
`data: Any = None`
The data table to bind to the plan's `your_data` placeholder. If omitted, the `data=` provided to [EditValidation](EditValidation.md#pointblank.EditValidation) is used. One of the two must be present.
##### Returns
`Validate`
The revised validation plan as a [Validate](Validate.md#pointblank.Validate) object.
------------------------------------------------------------------------
#### changed_steps()
Return a structured, step-level list of the changes between the plans.
Usage
``` python
changed_steps()
```
Unlike diff(), which is a textual diff sensitive to formatting, this compares the two plans at the level of validation steps and is robust to reformatting. Each entry is a dict with an `"action"` of `"add"`, `"remove"`, or `"modify"`, a `"method"` name, and the relevant `"old"`/`"new"` step text.
##### Returns
`list[dict]`
One record per changed step, e.g. `[{"action": "modify", "method": "col_vals_gt", "old": "col_vals_gt(columns="d", " "value=100)", "new": "col_vals_gt(columns="d", value=50)"}]`.
------------------------------------------------------------------------
#### diff()
Return a unified textual diff of the original plan versus the revised plan.
Usage
``` python
diff()
```
------------------------------------------------------------------------
#### from_plans()
Build an [EditValidation](EditValidation.md#pointblank.EditValidation) from two known plans, without calling a model.
Usage
``` python
from_plans(original, revised, instruction="Manual plan comparison")
```
This is a lightweight way to compare an original plan against a revised one and review the difference with diff(), changed_steps(), and review(). It is useful for reviewing the change between two saved versions of a plan (for example, in code review) and does not require an LLM provider or any API key.
##### Parameters
`original: Any`
The original plan. Accepts anything the `validation=` argument accepts: a Validate object, a code string, a YAML string, or a file path.
`revised: Any`
The revised plan, in any of the same forms as `original`.
`instruction: str = ``"Manual plan comparison"`
An optional label describing the comparison (shown as the subtitle in review()).
##### Returns
`EditValidation`
An [EditValidation](EditValidation.md#pointblank.EditValidation) whose `original`/`revised` plans are the ones supplied, ready for diff(), changed_steps(), review(), and accept().
##### Examples
``` python
import pointblank as pb
v1 = pb.Validate(data=pb.load_dataset("small_table")).col_vals_gt(columns="d", value=100)
v2 = pb.Validate(data=pb.load_dataset("small_table")).col_vals_gt(columns="d", value=50)
comparison = pb.EditValidation.from_plans(v1, v2)
print(comparison.diff())
```
------------------------------------------------------------------------
#### review()
Return a `great_tables` GT object summarizing the step-level changes.
Usage
``` python
review()
```
This renders the added, removed, and modified steps side by side for quick human review before accepting the edit. It pairs with accept(): review the change, then accept it.
##### Returns
`GT`
A `great_tables` table with one row per changed step.
------------------------------------------------------------------------
#### to_code()
Return the revised validation plan as Python code.
Usage
``` python
to_code()
```
------------------------------------------------------------------------
#### validate_syntax()
Return whether the revised plan parses and uses only known validation methods.
Usage
``` python
validate_syntax()
```