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174 lines
7.7 KiB
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174 lines
7.7 KiB
Plaintext
---
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title: "Code Execution"
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---
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## Table of Contents
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- [Introduction](#introduction)
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- [Usage Scenarios](#usage-scenarios)
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- [Local Deployment](#local-deployment)
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- [Security Policies](#security-policies)
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## Introduction
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The code node supports running Python/NodeJS code to perform data transformations within a workflow. It can simplify your workflow and is suitable for scenarios such as arithmetic operations, JSON transformations, text processing, and more.
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This node significantly enhances the flexibility for developers, allowing them to embed custom Python or JavaScript scripts within the workflow and manipulate variables in ways that preset nodes cannot achieve. Through configuration options, you can specify the required input and output variables and write the corresponding execution code:
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<img
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src="https://assets-docs.dify.ai/dify-enterprise-mintlify/en/guides/workflow/node/9969aa1bc1912aebe366f5d8f5dde296.png"
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width="400"
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/>
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## Configuration
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If you need to use variables from other nodes in the code node, you must define the variable names in the `input variables` and reference these variables. You can refer to [Variable References](/en/guides/workflow/variables).
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## Usage Scenarios
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Using the code node, you can perform the following common operations:
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### Structured Data Processing
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In workflows, you often have to deal with unstructured data processing, such as parsing, extracting, and transforming JSON strings. A typical example is data processing from an HTTP node. In common API return structures, data may be nested within multiple layers of JSON objects, and you need to extract certain fields. The code node can help you perform these operations. Here is a simple example that extracts the `data.name` field from a JSON string returned by an HTTP node:
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```python
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def main(http_response: str) -> dict:
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import json
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data = json.loads(http_response)
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return {
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# Note to declare 'result' in the output variables
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'result': data['data']['name']
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}
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```
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### Mathematical Calculations
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When you need to perform complex mathematical calculations in a workflow, you can also use the code node. For example, calculating a complex mathematical formula or performing some statistical analysis on data. Here is a simple example that calculates the variance of an array:
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```python
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def main(x: list) -> dict:
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return {
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# Note to declare 'result' in the output variables
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'result': sum([(i - sum(x) / len(x)) ** 2 for i in x]) / len(x)
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}
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```
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### Data Concatenation
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Sometimes, you may need to concatenate multiple data sources, such as multiple knowledge retrievals, data searches, API calls, etc. The code node can help you integrate these data sources together. Here is a simple example that merges data from two knowledge bases:
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```python
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def main(knowledge1: list, knowledge2: list) -> dict:
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return {
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# Note to declare 'result' in the output variables
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'result': knowledge1 + knowledge2
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}
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```
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## Local Deployment
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If you are a local deployment user, you need to start a sandbox service to ensure that malicious code is not executed. This service requires the use of Docker. You can find specific information about the sandbox service [here](https://github.com/langgenius/dify/tree/main/docker/docker-compose.middleware.yaml). You can also start the service directly via `docker-compose`:
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```bash
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docker-compose -f docker-compose.middleware.yaml up -d
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```
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## Security Policies
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Both Python and JavaScript execution environments are strictly isolated (sandboxed) to ensure security. This means that developers cannot use functions that consume large amounts of system resources or may pose security risks, such as direct file system access, making network requests, or executing operating system-level commands. These limitations ensure the safe execution of the code while avoiding excessive consumption of system resources.
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### Advanced Features
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**Retry on Failure**
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For some exceptions that occur in the node, it is usually sufficient to retry the node again. When the error retry function is enabled, the node will automatically retry according to the preset strategy when an error occurs. You can adjust the maximum number of retries and the interval between each retry to set the retry strategy.
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- The maximum number of retries is 10
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- The maximum retry interval is 5000 ms
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**Error Handling**
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When processing information, code nodes may encounter code execution exceptions. Developers can follow these steps to configure fail branches, enabling contingency plans when nodes encounter exceptions, thus avoiding workflow interruptions.
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1. Enable "Error Handling" in the code node
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2. Select and configure an error handling strategy
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For more information about exception handling approaches, please refer to [Error Handling](/en/guides/workflow/error-handling).
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### FAQ
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**Why can't I save the code it in the code node?**
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Please check if the code contains potentially dangerous behaviors. For example:
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```python
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def main() -> dict:
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return {
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"result": open("/etc/passwd").read(),
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}
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```
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This code snippet has the following issues:
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- **Unauthorized file access:** The code attempts to read the "/etc/passwd" file, which is a critical system file in Unix/Linux systems that stores user account information.
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- **Sensitive information disclosure:** The "/etc/passwd" file contains important information about system users, such as usernames, user IDs, group IDs, home directory paths, etc. Direct access could lead to information leakage.
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Dangerous code will be automatically blocked by Cloudflare WAF. You can check if it's been blocked by looking at the "Network" tab in your browser's "Web Developer Tools".
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The **Code Fix** feature enables **automatic code correction** by leveraging the previous run’s `current_code` and `error_message` variables.
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# When a Code Node fails:
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- The system captures the code and error message.
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- These are passed into the prompt as context variables.
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- A new version of the code is generated for review and retry.
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**Configuration:**
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1. **Write Repair Prompt**:
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In the prompt editor, use the variable insertion menu (`/` or `{`) to insert variables. You may customize a prompt like:
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`Fix the following code based on this error message: Code: {{current_code}} Error: {{error_message}}`
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2. **Using Context Variables (if needed later in workflow)**
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To enable automatic code repair, reference the following **context variables** in your prompt:
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- `{{current_code}}`: The code from the last run of this node.
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- `{{error_message}}`: The error message if the last run failed.
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You can also reference output variables from any predecessor nodes.
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These variables are automatically available when the Code Node is run and allow the model to use prior run information for iterative correction.
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3. **Use Version Management**:
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1. Each correction attempt is saved as a separate version (e.g., Version 1, Version 2).
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2. Users can **switch between versions** via the dropdown in the result display area.
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**Notes:**
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- `error_message` is empty if the last run succeeds.
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- `last_run` can be used to reference previous input/output.
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This reduces manual copy-paste and allows iterative debugging directly within the workflow.
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---
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{/*
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Contributing Section
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DO NOT edit this section!
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It will be automatically generated by the script.
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*/}
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[Edit this page](https://github.com/langgenius/dify-docs/edit/main/en/guides/workflow/node/code.mdx) | [Report an issue](https://github.com/langgenius/dify-docs/issues/new?template=docs.yml)
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