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‎README.md‎

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# FlowSynx CSV Plugin – FlowSynx Platform Integration
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# FlowSynx CSV Plugin
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The CSV Plugin is a pre-packaged, plug-and-play integration component for the FlowSynx engine. It enables reading from and writing to CSV files with configurable parameters such as file path, delimiter, headers, and encoding. Designed for FlowSynx’s no-code/low-code automation workflows, this plugin simplifies data extraction and transformation tasks.
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## Purpose
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This plugin allows FlowSynx users to interact with CSV data within workflows without writing code. Once installed, it appears as a connector in the platform’s workflow builder, enabling seamless integration of CSV-based data operations such as importing, exporting, and transformation into automated processes.
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The CSV Plugin allows FlowSynx users to:
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- Parse and inspect CSV structures.
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- Map CSV data to specific output fields.
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- Filter CSV data based on conditions.
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- Transform CSV data inline for downstream workflows.
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---
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## Supported Operations
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- **filter**: Filters rows in the CSV using defined `Filter` conditions. Supports logical operations (`and`, `or`) and common operators like `equals`, `contains`, `startsWith`, `endsWith`, `greaterThan`, and `lessThan`.
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- **map**: Maps existing fields in the CSV to a new subset of keys or column arrangement for simplified output.
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---
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## Input Parameters
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The plugin accepts the following parameters:
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- `Operation` (string): **Required.** The type of operation to perform. Supported values are `filter` and `map`.
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- `Data` (string/object): **Required.** The raw CSV string to process.
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- `Delimiter` (string): Optional. Defaults to `,`. The character used to separate fields in the CSV.
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- `Mappings` (list): **Required for `map` operation.** Defines which fields to include in the output.
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- `IgnoreBlankLines` (bool): Optional. Specifies whether blank lines in the CSV should be ignored (`true`) or treated as data rows (`false`). Defaults to `true`.
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- `Filters` (object): Optional. Used with the `filter` operation to define filtering criteria.
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### Example input
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```json
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{
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"Operation": "map",
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"Data": { ... },
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"Mappings": ["LastName", "Email"],
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"IgnoreBlankLines": true,
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"Delimiter": ","
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}
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```
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---
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## Operation Examples
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### map Operation
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**Input Data:**
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```csv
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CustomerID,FirstName,LastName,Email,Phone,Country
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1,John,Doe,john.doe@example.com,1234,USA
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2,Jane,Smith,jane.smith@example.com,201234,UK
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3,Raj,Patel,raj.patel@example.com,98765,India
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4,Anna,Schmidt,anna.schmidt@example.com,30234,Germany
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5,Maria,Gonzalez,maria.gonzalez@example.com,911234,Spain
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```
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**Input Parameters:**
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```json
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{
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"Operation": "map",
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"Data": { ... },
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"Mappings": ["LastName", "Email"],
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"IgnoreBlankLines": true,
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"Delimiter": ","
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}
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```
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**Output:**
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```json
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LastName,Email
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Doe,john.doe@example.com
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Smith,jane.smith@example.com
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Patel,raj.patel@example.com
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Schmidt,anna.schmidt@example.com
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Gonzalez,maria.gonzalez@example.com
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```
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---
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### filter Operation
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**Input Data:**
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```csv
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CustomerID,FirstName,LastName,Email,Phone,Country
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1,John,Doe,john.doe@example.com,1234,USA
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2,Jane,Smith,jane.smith@example.com,201234,UK
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3,Raj,Patel,raj.patel@example.com,98765,India
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4,Anna,Schmidt,anna.schmidt@example.com,30234,Germany
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5,Maria,Gonzalez,maria.gonzalez@example.com,911234,Spain
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```
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**Input Parameters:**
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```json
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{
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"Operation": "filter",
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"Data": { ... },
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"Filters": {
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"Logic": "and",
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"Filters": [
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{
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"Column": "Country",
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"Operator": "equals",
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"Value": "USA"
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},
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{
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"Column": "FirstName",
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"Operator": "startsWith",
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"Value": "J"
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}
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]
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},
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"IgnoreBlankLines": true,
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"Delimiter": ","
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}
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```
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**Output:**
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```csv
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CustomerID,FirstName,LastName,Email,Phone,Country
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1,John,Doe,john.doe@example.com,1234,USA
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```
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## Debugging Tips
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- Ensure that the `Delimiter` matches the file’s actual separator (`,` for standard CSV, `;` or `\t` for others).
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- Validate that `Mappings` and `Filters` reference columns that exist in the CSV header row.
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- If unexpected rows are excluded or included during filtering, check the logical operators (`and` / `or`) and ensure that data types align (e.g., string comparisons for string fields).
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- To troubleshoot encoding issues, verify that the CSV input uses UTF-8 or specify encoding explicitly if supported by FlowSynx.
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---
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## Notes
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## Security Notes
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- This plugin is exclusively supported on the FlowSynx platform.
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- It is installed automatically by the FlowSynx engine.
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- All operational logic is securely managed and executed by FlowSynx.
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- File access paths and related settings are configured via platform settings or workflow parameters.
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- No data is persisted unless explicitly configured.
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- All operations run in a secure sandbox within FlowSynx.
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- Only authorized platform users can view or modify configurations.
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---
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‎src/README.md‎

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## FlowSynx CSV Plugin – FlowSynx Platform Integration
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## FlowSynx CSV Plugin
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The CSV Plugin is a pre-packaged, plug-and-play integration component for the FlowSynx engine. It enables reading from and writing to CSV files with configurable parameters such as file path, delimiter, headers, and encoding. Designed for FlowSynx’s no-code/low-code automation workflows, this plugin simplifies data extraction and transformation tasks.
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The CSV Plugin is a pre-packaged, plug-and-play integration component for the FlowSynx engine. It enables reading from and writing to CSV files with configurable parameters such as file path, delimiter, headers, and encoding. Designed for FlowSynx’s no-code/low-code automation workflows, this plugin simplifies data extraction and transformation tasks.
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This plugin is automatically installed by the FlowSynx engine when selected within the platform. It is not intended for manual installation or standalone developer use outside the FlowSynx environment.
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---
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## Purpose
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This plugin allows FlowSynx users to interact with CSV data within workflows without writing code. Once installed, it appears as a connector in the platform’s workflow builder, enabling seamless integration of CSV-based data operations such as importing, exporting, and transformation into automated processes.
11+
The CSV Plugin allows FlowSynx users to:
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- Parse and inspect CSV structures.
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- Map CSV data to specific output fields.
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- Filter CSV data based on conditions.
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- Transform CSV data inline for downstream workflows.
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---
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## Supported Operations
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- **filter**: Filters rows in the CSV using defined `Filter` conditions. Supports logical operations (`and`, `or`) and common operators like `equals`, `contains`, `startsWith`, `endsWith`, `greaterThan`, and `lessThan`.
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- **map**: Maps existing fields in the CSV to a new subset of keys or column arrangement for simplified output.
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---
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## Input Parameters
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The plugin accepts the following parameters:
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- `Operation` (string): **Required.** The type of operation to perform. Supported values are `filter` and `map`.
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- `Data` (string/object): **Required.** The raw CSV string to process.
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- `Delimiter` (string): Optional. Defaults to `,`. The character used to separate fields in the CSV.
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- `Mappings` (list): **Required for `map` operation.** Defines which fields to include in the output.
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- `IgnoreBlankLines` (bool): Optional. Specifies whether blank lines in the CSV should be ignored (`true`) or treated as data rows (`false`). Defaults to `true`.
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- `Filters` (object): Optional. Used with the `filter` operation to define filtering criteria.
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### Example input
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```json
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{
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"Operation": "map",
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"Data": { ... },
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"Mappings": ["LastName", "Email"],
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"IgnoreBlankLines": true,
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"Delimiter": ","
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}
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```
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---
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## Operation Examples
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### map Operation
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**Input Data:**
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```csv
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CustomerID,FirstName,LastName,Email,Phone,Country
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1,John,Doe,john.doe@example.com,1234,USA
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2,Jane,Smith,jane.smith@example.com,201234,UK
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3,Raj,Patel,raj.patel@example.com,98765,India
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4,Anna,Schmidt,anna.schmidt@example.com,30234,Germany
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5,Maria,Gonzalez,maria.gonzalez@example.com,911234,Spain
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```
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**Input Parameters:**
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```json
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{
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"Operation": "map",
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"Data": { ... },
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"Mappings": ["LastName", "Email"],
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"IgnoreBlankLines": true,
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"Delimiter": ","
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}
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```
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**Output:**
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```json
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LastName,Email
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Doe,john.doe@example.com
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Smith,jane.smith@example.com
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Patel,raj.patel@example.com
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Schmidt,anna.schmidt@example.com
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Gonzalez,maria.gonzalez@example.com
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```
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---
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### filter Operation
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**Input Data:**
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```csv
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CustomerID,FirstName,LastName,Email,Phone,Country
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1,John,Doe,john.doe@example.com,1234,USA
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2,Jane,Smith,jane.smith@example.com,201234,UK
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3,Raj,Patel,raj.patel@example.com,98765,India
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4,Anna,Schmidt,anna.schmidt@example.com,30234,Germany
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5,Maria,Gonzalez,maria.gonzalez@example.com,911234,Spain
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```
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**Input Parameters:**
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```json
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{
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"Operation": "filter",
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"Data": { ... },
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"Filters": {
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"Logic": "and",
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"Filters": [
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{
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"Column": "Country",
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"Operator": "equals",
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"Value": "USA"
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},
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{
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"Column": "FirstName",
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"Operator": "startsWith",
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"Value": "J"
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}
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]
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},
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"IgnoreBlankLines": true,
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"Delimiter": ","
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}
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```
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**Output:**
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```csv
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CustomerID,FirstName,LastName,Email,Phone,Country
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1,John,Doe,john.doe@example.com,1234,USA
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```
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## Debugging Tips
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- Ensure that the `Delimiter` matches the file’s actual separator (`,` for standard CSV, `;` or `\t` for others).
136+
- Validate that `Mappings` and `Filters` reference columns that exist in the CSV header row.
137+
- If unexpected rows are excluded or included during filtering, check the logical operators (`and` / `or`) and ensure that data types align (e.g., string comparisons for string fields).
138+
- To troubleshoot encoding issues, verify that the CSV input uses UTF-8 or specify encoding explicitly if supported by FlowSynx.
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---
14141

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## Notes
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## Security Notes
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- This plugin is exclusively supported on the FlowSynx platform.
18-
- It is installed automatically by the FlowSynx engine.
19-
- All operational logic is securely managed and executed by FlowSynx.
20-
- File access paths and related settings are configured via platform settings or workflow parameters.
144+
- No data is persisted unless explicitly configured.
145+
- All operations run in a secure sandbox within FlowSynx.
146+
- Only authorized platform users can view or modify configurations.
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---
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## License
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© FlowSynx. All rights reserved.
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Copyright FlowSynx. All rights reserved.

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