docs: rename

docs/ten_agent/how_does_interrupt_work.md

@@ -5,16 +5,16 @@
 The interrupt mechanism in
 [TEN-Agent](https://github.com/TEN-framework/TEN-Agent) consists of two main
 parts: **Interrupt Detection** and **Interrupt Response**. This document details
-both components and explains how the interrupt command propagates through the AI
+both parts and explains how the interrupt command propagates through the AI
 agent graph.
 
 ## Part 1: Interrupt Detection
 
 ### 1. Current Interrupt Detection Implementation
 
-The current interrupt detector
+The current
 ([interrupt_detector_python](https://github.com/TEN-framework/TEN-Agent/tree/main/agents/ten_packages/extension/interrupt_detector_python))
-implements a text-based interrupt detection mechanism:
+extension implements a text-based interrupt detection mechanism:
 
 ```python
 def on_data(self, ten: TenEnv, data: Data) -> None:
@@ -31,7 +31,7 @@ The interrupt detector triggers in the following cases:
 1. When receiving final text (`is_final = true`)
 2. When text length reaches a threshold (≥ 2 characters)
 
-### 2. Custom Interrupt Detection
+### 2. Customize Interrupt Detection
 
 To implement your own interrupt detection logic, you can refer to the
 implementation of
@@ -56,22 +56,22 @@ Interrupt Detector
    agora_rtc
 ```
 
-Each component in the chain follows two key steps when receiving a `flush`
+Each extension in the chain follows two key steps when receiving a `flush`
 command:
 
 1. Clean up its own resources and internal state
-2. Forward the `flush` command to downstream components
+2. Forward the `flush` command to downstream extensions
 
 This ensures that:
 
-- Components are cleaned up in the correct order
+- Extensions are cleaned up in the correct order
 - No residual data flows through the system
-- Each component returns to a clean state before the next operation
+- Each extension returns to a clean state before the next operation
 
 ## Conclusion
 
 [TEN-Agent](https://github.com/TEN-framework/TEN-Agent)'s interrupt mechanism
-uses a chain processing pattern to ensure orderly cleanup of all components in
-the AI agent graph. When an interrupt occurs, each component first cleans up its
-own state and then forwards the `flush` command to downstream components,
+uses a chain processing pattern to ensure orderly cleanup of all extensions in
+the AI agent graph. When an interrupt occurs, each extension first cleans up its
+own state and then forwards the `flush` command to downstream extensions,
 ensuring a clean system state for subsequent operations.