This module is used to review the content input by end-users and the output content of the LLM within the application. It is divided into two types of extension points.
Extension Points
app.moderation.input - Extension point for reviewing end-user input content
Used to review the variable content passed in by end-users and the input content of conversational applications.
app.moderation.output - Extension point for reviewing LLM output content
Used to review the content output by the LLM.
When the LLM output is streamed, the content will be segmented into 100-character blocks for API requests to avoid delays in reviewing longer outputs.
app.moderation.input Extension Point
When Content Moderation > Review Input Content is enabled in applications like Chatflow, Agent, or Chat Assistant, Dify will send the following HTTP POST request to the corresponding API extension:
Request Body
{
"point": "app.moderation.input", // Extension point type, fixed as app.moderation.input here
"params": {
"app_id": string, // Application ID
"inputs": { // Variable values passed in by end-users, key is the variable name, value is the variable value
"var_1": "value_1",
"var_2": "value_2",
...
},
"query": string | null // Current dialogue input content from the end-user, fixed parameter for conversational applications.
}
}
Example
{
"point": "app.moderation.input",
"params": {
"app_id": "61248ab4-1125-45be-ae32-0ce91334d021",
"inputs": {
"var_1": "I will kill you.",
"var_2": "I will fuck you."
},
"query": "Happy everydays."
}
}
API Response Specifications
{
"flagged": bool, // Whether it violates the moderation rules
"action": string, // Action to take, direct_output for directly outputting a preset response; overridden for overriding the input variable values
"preset_response": string, // Preset response (returned only when action=direct_output)
"inputs": { // Variable values passed in by end-users, key is the variable name, value is the variable value (returned only when action=overridden)
"var_1": "value_1",
"var_2": "value_2",
...
},
"query": string | null // Overridden current dialogue input content from the end-user, fixed parameter for conversational applications. (returned only when action=overridden)
}
{
"flagged": true,
"action": "overridden",
"inputs": {
"var_1": "I will *** you.",
"var_2": "I will *** you."
},
"query": "Happy everydays."
}
app.moderation.output Extension Point
When Content Moderation > Review Output Content is enabled in applications like Chatflow, Agent, or Chat Assistant, Dify will send the following HTTP POST request to the corresponding API extension:
Request Body
{
"point": "app.moderation.output", // Extension point type, fixed as app.moderation.output here
"params": {
"app_id": string, // Application ID
"text": string // LLM response content. When the LLM output is streamed, this will be content segmented into 100-character blocks.
}
}
Example
{
"point": "app.moderation.output",
"params": {
"app_id": "61248ab4-1125-45be-ae32-0ce91334d021",
"text": "I will kill you."
}
}
API Response Specifications
{
"flagged": bool, // Whether it violates the moderation rules
"action": string, // Action to take, direct_output for directly outputting a preset response; overridden for overriding the input variable values
"preset_response": string, // Preset response (returned only when action=direct_output)
"text": string // Overridden LLM response content (returned only when action=overridden)
}
{
"flagged": true,
"action": "overridden",
"text": "I will *** you."
}
Code Example
The code works by performing keyword matching to filter Input (user-entered content) and output (content returned by the large model). Users can modify the matching logic according to their needs.
import { Hono } from "hono";
import { bearerAuth } from "hono/bearer-auth";
import { z } from "zod";
import { zValidator } from "@hono/zod-validator";
import { generateSchema } from '@anatine/zod-openapi';
type Bindings = {
TOKEN: string;
};
const app = new Hono<{ Bindings: Bindings }>();
// API format validation ⬇️
const schema = z.object({
point: z.union([
z.literal("ping"),
z.literal("app.external_data_tool.query"),
z.literal("app.moderation.input"),
z.literal("app.moderation.output"),
]), // Restricts 'point' to specific values
params: z
.object({
app_id: z.string().optional(),
tool_variable: z.string().optional(),
inputs: z.record(z.any()).optional(),
query: z.any(),
text: z.any()
})
.optional(),
});
// Generate OpenAPI schema
app.get("/", (c) => {
return c.json(generateSchema(schema));
});
app.post(
"/",
(c, next) => {
const auth = bearerAuth({ token: c.env.TOKEN });
return auth(c, next);
},
zValidator("json", schema),
async (c) => {
const { point, params } = c.req.valid("json");
if (point === "ping") {
return c.json({
result: "pong",
});
}
// ⬇️ implement your logic here ⬇️
// point === "app.external_data_tool.query"
else if (point === "app.moderation.input"){
// Input check ⬇️
const inputkeywords = ["input filter test1", "input filter test2", "input filter test3"];
if (inputkeywords.some(keyword => params.query.includes(keyword)))
{
return c.json({
"flagged": true,
"action": "direct_output",
"preset_response": "Input contains prohibited content, please try a different question!"
});
} else {
return c.json({
"flagged": false,
"action": "direct_output",
"preset_response": "Input is normal"
});
}
// Input check completed
}
else {
// Output check ⬇️
const outputkeywords = ["output filter test1", "output filter test2", "output filter test3"];
if (outputkeywords.some(keyword => params.text.includes(keyword)))
{
return c.json({
"flagged": true,
"action": "direct_output",
"preset_response": "Output contains sensitive content and has been filtered by the system. Please ask a different question!"
});
}
else {
return c.json({
"flagged": false,
"action": "direct_output",
"preset_response": "Output is normal"
});
};
}
// Output check completed
}
);
export default app;
