docs: fix type errors and missing import in llm docs (#514)

## Description

Fix missing types in llm docs and errors in code

### 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

<!-- Provide step-by-step instructions on how to test your changes.
Include setup details if necessary. -->

### Screenshots

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

### Related issues

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

### Checklist

- [x] I have performed a self-review of my code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] 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: NorbertKlockiewicz

README.md

@@ -68,6 +68,7 @@ Add this to your component file:
 import {
   useLLM,
   LLAMA3_2_1B,
+  Message
 } from 'react-native-executorch';
 
 function MyComponent() {
@@ -81,7 +82,7 @@ function MyComponent() {
 
 ```tsx
 const handleGenerate = async () => {
-  const chat = [
+  const chat: Message[] = [
     { role: 'system', content: 'You are a helpful assistant' },
     { role: 'user', content: 'What is the meaning of life?' }
   ];

docs/docs/02-hooks/01-natural-language-processing/useLLM.md

@@ -165,7 +165,7 @@ To perform chat completion you can use the `generate` function. There is no retu
 const llm = useLLM({ model: LLAMA3_2_1B });
 
 const handleGenerate = () => {
-  const chat = [
+  const chat: Message[] = [
     { role: 'system', content: 'You are a helpful assistant' },
     { role: 'user', content: 'Hi!' },
     { role: 'assistant', content: 'Hi!, how can I help you?' },
@@ -220,7 +220,7 @@ const TOOL_DEFINITIONS: LLMTool[] = [
 const llm = useLLM({ model: HAMMER2_1_1_5B });
 
 const handleGenerate = () => {
-  const chat = [
+  const chat: Message[] = [
     {
       role: 'system',
       content: `You are a helpful assistant. Current time and date: ${new Date().toString()}`,
@@ -361,6 +361,8 @@ return (
 ### Structured output example
 
 ```tsx
+import { Schema } from 'jsonschema';
+
 const responseSchema: Schema = {
   properties: {
     username: {
@@ -407,7 +409,7 @@ useEffect(() => {
   // Some extra prompting to improve quality of response.
   const prompt = `Your goal is to parse user's messages and return them in JSON format. Don't respond to user. Simply return JSON with user's question parsed. \n${formattingInstructions}\n /no_think`;
 
-  configure({
+  llm.configure({
     chatConfig: {
       systemPrompt: prompt,
     },

docs/versioned_docs/version-0.4.x/natural-language-processing/useLLM.md

@@ -174,7 +174,7 @@ const llm = useLLM({
 });
 
 const handleGenerate = () => {
-  const chat = [
+  const chat: Message[] = [
     { role: 'system', content: 'You are a helpful assistant' },
     { role: 'user', content: 'Hi!' },
     { role: 'assistant', content: 'Hi!, how can I help you?'},
@@ -231,7 +231,7 @@ const llm = useLLM({
 });
 
 const handleGenerate = () => {
-  const chat = [
+  const chat: Message[] = [
     {
       role: 'system',
       content: `You are a helpful assistant. Current time and date: ${new Date().toString()}`,