feat: add create extension with predefined type docs

docs/SUMMARY.md

@@ -14,8 +14,8 @@
 * [Setup VSCode Inside Container](ten_agent/setting_up_vscode_for_development_inside_container.md)
 * [How does interrupt work in TEN-Agent](ten_agent/how_does_interrupt_work.md)
 * [Create an Extension with Predefined Type](ten_agent/create_an_extension_with_predefined_type/overview.md)
-  * [LLM Extension](ten_agent/create_an_extension_with_predefined_type/llm_extension.md)
-  * [LLM Tool Extension](ten_agent/create_an_extension_with_predefined_type/llm_tool_extension.md)
+  * [LLM Extension](ten_agent/create_an_extension_with_predefined_type/type_llm_extension.md)
+  * [LLM Tool Extension](ten_agent/create_an_extension_with_predefined_type/type_llm_tool_extension.md)
 
 ## TEN framework
 

docs/ten_agent/create_an_extension_with_predefined_type/overview.md

@@ -1,17 +1,3 @@
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 # Overview
 
 ## Extension types
@@ -20,8 +6,8 @@ When developing Extensions, we often notice that implementations for Extensions
 
 Currently, TEN Agent supports the following Extension types:
 
-- AsyncLLMBaseExtension: Designed for implementing large language model Extensions, such as those similar to OpenAI.
-- AsyncLLMToolBaseExtension: Used to implement tool Extensions for large language models. These are Extensions that provide tool capabilities based on Function Call mechanisms.
+- `AsyncLLMBaseExtension`: Designed for implementing large language model Extensions, such as those similar to OpenAI.
+- `AsyncLLMToolBaseExtension`: Used to implement tool Extensions for large language models. These are Extensions that provide tool capabilities based on Function Call mechanisms.
 
 This abstraction helps standardize development while reducing repetitive work.
 

docs/ten_agent/create_an_extension_with_predefined_type/type_llm_extension.md

@@ -0,0 +1,35 @@
+# Create a LLM Extension
+
+## Creating AsyncLLMBaseExtension by using tman
+
+Run the following command,
+
+```bash
+tman install extension default_async_llm_extension_python --template-mode --template-data package_name=llm_extension --template-data class_name_prefix=LLMExtension
+```
+
+## Abstract APIs to implement
+
+### `on_data_chat_completion(self, ten_env: TenEnv, **kargs: LLMDataCompletionArgs) -> None`
+
+This method is called when the LLM Extension receives a data completion request. It's used when data is passed in via data protocol in streaming mode.
+
+### `on_call_chat_completion(self, ten_env: TenEnv, **kargs: LLMCallCompletionArgs) -> any`
+
+This method is called when the LLM Extension receives a call completion request. It's used when data is passed in via call protocol in non-streaming mode.
+
+This method is called when the LLM Extension receives a call completion request.
+
+### `on_tools_update(self, ten_env: TenEnv, tool: LLMToolMetadata) -> None`
+
+This method is called when the LLM Extension receives a tool update request.
+
+## APIs
+
+### cmd_in: `tool_register`
+
+This API is used to consume the tool registration request. An array of LLMToolMetadata will be received as input. The tools will be appended to `self.available_tools` for future use.
+
+### cmd_out: `tool_call`
+
+This API is used to send the tool call request. You can connect this API to any LLMTool extension destination to get the tool call result.

docs/ten_agent/create_an_extension_with_predefined_type/type_llm_tool_extension.md

@@ -0,0 +1,29 @@
+# Create a LLM Tool Extension
+
+## Creating AsyncLLMToolBaseExtension by using tman
+
+Run the following command,
+
+```bash
+tman install extension default_async_llm_tool_extension_python --template-mode --template-data package_name=llm_tool_extension --template-data class_name_prefix=LLMToolExtension
+```
+
+## Abstract APIs to implement
+
+### `get_tool_metadata(self, ten_env: TenEnv) -> list[LLMToolMetadata]`
+
+This method is called when the LLM Extension is going to register itself to any connected LLM. It should return a list of LLMToolMetadata.
+
+### `run_tool(self, ten_env: AsyncTenEnv, name: str, args: dict) -> LLMToolResult`
+
+This method is called when the LLM Extension receives a tool call request. It should execute the tool function and return the result.
+
+## APIs
+
+### cmd_out: `tool_register`
+
+This API is used to send the tool registration request. An array of LLMToolMetadata returned from `get_tool_metadata` will be sent as output.
+
+### cmd_in: `tool_call`
+
+This API is used to consume the tool call request. The `run_tool` will be executed when the cmd_in is received.