chore: add llm config example to docs (#884)

## Description

This PR updates docs with full LLM configuration example.

### Introduces a breaking change?

- [ ] Yes
- [x] No

### Type of change

- [ ] Bug fix (change which fixes an issue)
- [ ] New feature (change which adds functionality)
- [x] Documentation update (improves or adds clarity to existing
documentation)
- [ ] Other (chores, tests, code style improvements etc.)

### Tested on

- [ ] iOS
- [ ] Android

### Testing instructions

Check out docs and check out `useLLM` section (there are no examples for
`LLMModule` at all, so I did not add this there)

### Screenshots

<!-- Add screenshots here, if applicable -->

### Related issues

<!-- Link related issues here using #issue-number -->

### Checklist

- [ ] I have performed a self-review of my code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [x] I have updated the documentation accordingly
- [ ] My changes generate no new warnings

### Additional notes

<!-- Include any additional information, assumptions, or context that
reviewers might need to understand this PR. -->

Author: mateuszlampert

docs/docs/03-hooks/01-natural-language-processing/useLLM.md

@@ -210,6 +210,81 @@ To configure model (i.e. change system prompt, load initial conversation history
 
   - [`topp`](../../06-api-reference/interfaces/GenerationConfig.md#topp) - Only samples from the smallest set of tokens whose cumulative probability exceeds topp.
 
+### Model configuration example
+
+```tsx
+import { useEffect } from 'react';
+import {
+  MessageCountContextStrategy,
+  DEFAULT_SYSTEM_PROMPT,
+  ToolCall,
+  useLLM,
+  LLAMA3_2_1B_SPINQUANT,
+} from 'react-native-executorch';
+
+const TOOL_DEFINITIONS: LLMTool[] = [
+  {
+    name: 'get_weather',
+    description: 'Get/check weather in given location.',
+    parameters: {
+      type: 'dict',
+      properties: {
+        location: {
+          type: 'string',
+          description: 'Location where user wants to check weather',
+        },
+      },
+      required: ['location'],
+    },
+  },
+];
+
+const getWeather = async (_call: ToolCall) => {
+  return 'The weather is great!';
+};
+
+const executeTool: (call: ToolCall) => Promise<string | null> = async (
+  call
+) => {
+  switch (call.toolName) {
+    case 'get_weather':
+      return await getWeather(call);
+    default:
+      console.error(`Wrong function! We don't handle it!`);
+      return null;
+  }
+};
+
+const llm = useLLM({ model: LLAMA3_2_1B_SPINQUANT });
+
+const { configure } = llm;
+useEffect(() => {
+  configure({
+    chatConfig: {
+      systemPrompt: `${DEFAULT_SYSTEM_PROMPT} Current time and date: ${new Date().toString()}`,
+      initialMessageHistory: [
+        {
+          role: 'user',
+          content: 'What is the current time and date?',
+        },
+      ],
+      contextStrategy: new MessageCountContextStrategy(6),
+    },
+    toolsConfig: {
+      tools: TOOL_DEFINITIONS,
+      executeToolCallback: executeTool,
+      displayToolCalls: true,
+    },
+    generationConfig: {
+      outputTokenBatchSize: 15,
+      batchTimeInterval: 100,
+      temperature: 0.7,
+      topp: 0.9,
+    },
+  });
+}, [configure]);
+```
+
 ### Sending a message
 
 In order to send a message to the model, one can use the following code:

docs/docs/06-api-reference/interfaces/LLMConfig.md

@@ -18,7 +18,7 @@ Object configuring chat management, contains following properties:
 
 `initialMessageHistory` - An array of `Message` objects that represent the conversation history. This can be used to provide initial context to the model.
 
-`contextWindowLength` - The number of messages from the current conversation that the model will use to generate a response. The higher the number, the more context the model will have. Keep in mind that using larger context windows will result in longer inference time and higher memory usage.
+`contextStrategy` - Defines a strategy for managing the conversation context window and message history
 
 ---
 

packages/react-native-executorch/src/types/llm.ts

@@ -138,7 +138,7 @@ export interface LLMConfig {
    *
    * `initialMessageHistory` - An array of `Message` objects that represent the conversation history. This can be used to provide initial context to the model.
    *
-   * `contextWindowLength` - The number of messages from the current conversation that the model will use to generate a response. The higher the number, the more context the model will have. Keep in mind that using larger context windows will result in longer inference time and higher memory usage.
+   * `contextStrategy` - Defines a strategy for managing the conversation context window and message history
    */
   chatConfig?: Partial<ChatConfig>;
 

skills/canary/react-native-executorch/references/reference-llms.md

@@ -51,7 +51,6 @@ useEffect(() => {
   llm.configure({
     chatConfig: {
       systemPrompt: 'You are a helpful assistant',
-      contextWindowLength: 10,
     },
     generationConfig: {
       temperature: 0.7,
@@ -67,6 +66,81 @@ llm.sendMessage('Hello!');
 console.log(llm.messageHistory);
 ```
 
+## Model configuration
+
+```tsx
+import { useEffect } from 'react';
+import {
+  MessageCountContextStrategy,
+  DEFAULT_SYSTEM_PROMPT,
+  ToolCall,
+  useLLM,
+  LLAMA3_2_1B_SPINQUANT,
+} from 'react-native-executorch';
+
+const TOOL_DEFINITIONS: LLMTool[] = [
+  {
+    name: 'get_weather',
+    description: 'Get/check weather in given location.',
+    parameters: {
+      type: 'dict',
+      properties: {
+        location: {
+          type: 'string',
+          description: 'Location where user wants to check weather',
+        },
+      },
+      required: ['location'],
+    },
+  },
+];
+
+const getWeather = async (_call: ToolCall) => {
+  return 'The weather is great!';
+};
+
+const executeTool: (call: ToolCall) => Promise<string | null> = async (
+  call
+) => {
+  switch (call.toolName) {
+    case 'get_weather':
+      return await getWeather(call);
+    default:
+      console.error(`Wrong function! We don't handle it!`);
+      return null;
+  }
+};
+
+const llm = useLLM({ model: LLAMA3_2_1B_SPINQUANT });
+
+const { configure } = llm;
+useEffect(() => {
+  configure({
+    chatConfig: {
+      systemPrompt: `${DEFAULT_SYSTEM_PROMPT} Current time and date: ${new Date().toString()}`,
+      initialMessageHistory: [
+        {
+          role: 'user',
+          content: 'What is the current time and date?',
+        },
+      ],
+      contextStrategy: new MessageCountContextStrategy(6),
+    },
+    toolsConfig: {
+      tools: TOOL_DEFINITIONS,
+      executeToolCallback: executeTool,
+      displayToolCalls: true,
+    },
+    generationConfig: {
+      outputTokenBatchSize: 15,
+      batchTimeInterval: 100,
+      temperature: 0.7,
+      topp: 0.9,
+    },
+  });
+}, [configure]);
+```
+
 ## Tool Calling
 
 ```tsx