How can I use dynamic text replacement to direct search queries to my nonprofit organization’s website, that then uses the terms of that search result to actually provide information from an llm directly on the landing page?
To implement dynamic text replacement for search queries and provide LLM-generated information on your nonprofit’s landing page, you can follow these steps:
1. Set up URL parameters:
Configure your website to accept search queries as URL parameters. For example:
https://yournonprofit.org/search?q=your+search+query
2. Implement server-side processing:
Use server-side scripting (e.g., PHP, Python, Node.js) to capture the search query from the URL parameter.
3. Integrate an LLM API:
Choose an LLM API (e.g., OpenAI’s GPT-3, Google’s PaLM, or an open-source alternative) and integrate it into your server-side code.
4. Process the query:
Send the captured search query to the LLM API and receive the generated response.
5. Dynamic content generation:
Use the LLM-generated response to populate the content on your landing page dynamically.
6. Implement caching:
To improve performance and reduce API calls, implement a caching system for frequently asked questions.
Here’s a basic example using PHP and the OpenAI API:
```php
<?php
// Capture the search query from URL parameter
$query = $_GET['q'] ?? '';
// Function to get response from OpenAI API
function getOpenAIResponse($query) {
$api_key = 'your_openai_api_key';
$url = 'https://api.openai.com/v1/engines/davinci-codex/completions';
$data = array(
'prompt' => $query,
'max_tokens' => 150
);
$options = array(
'http' => array(
'header' => "Content-type: application/json\r\nAuthorization: Bearer $api_key\r\n",
'method' => 'POST',
'content' => json_encode($data)
)
);
$context = stream_context_create($options);
$result = file_get_contents($url, false, $context);
if ($result === FALSE) {
return "Error: Unable to get response from OpenAI API.";
}
$response = json_decode($result, true);
return $response['choices'][0]['text'];
}
// Get LLM-generated response
$llm_response = getOpenAIResponse($query);
?>
<!DOCTYPE html>
<html>
<head>
<title>Search Results - Your Nonprofit</title>
</head>
<body>
<h1>Search Results for: <?php echo htmlspecialchars($query); ?></h1>
<div id="llm-response">
<?php echo nl2br(htmlspecialchars($llm_response)); ?>
</div>
</body>
</html>
```
To implement this solution:
1. Set up URL rewriting (e.g., using .htaccess for Apache) to direct search queries to your PHP script.
2. Ensure your server has the necessary dependencies and permissions to make API calls.
3. Replace ‘your_openai_api_key’ with your actual OpenAI API key.
4. Customize the HTML structure and styling to match your nonprofit’s branding.
5. Implement error handling and input sanitization for security.
6. Consider adding a caching layer to store and retrieve previous responses for improved performance.
Remember to comply with the terms of service of the LLM API you choose and ensure that the generated content aligns with your nonprofit’s mission and values.
How would I set this up using either aws and/or cloudflare as follows:
1. User google searches for something related to self-help like legal services or social services like housing or benefit programs like social security or disability or medicaid
2. User sees either paid or organic search result for easyask.info that entices them with the promise of providing exactly the answer they need without trying to sell them something, establishing trust and making the click through rate higher
3. Upon clicking, user lands on easyask.info landing page which is already run a backhandelm?Query to give them an initial llm.Responds from their prompt without any additional text entry simply from their google search.
4. That initial response charts, a chat thread that gently guides the user through understanding their issue and providing an action plan with specific checklist steps and weblinks through an LLM API like perplexity.
5. User can choose at any time to discontinue the session and is assured that no information is saved unless they wish to create an account to be able to come back and pick up where they left off if there are any unresolved issues or they need additional help
The brilliance of this is, it doesn’t reinvent the wheel and the entry point is through familiar search interfaces. And when’s the user understands that this is a nonprofit and actually wants to help them on their terms, it builds trust.
To set up this system using AWS and/or Cloudflare, you can follow these steps:
1. Domain and DNS Setup:
- Register the domain easyask.info through a domain registrar.
- Use Cloudflare for DNS management and CDN services.
2. AWS Setup:
- Create an AWS account if you don’t have one.
- Set up an AWS Lambda function to handle the backend logic.
- Use Amazon API Gateway to create RESTful endpoints for your Lambda function.
3. Cloudflare Workers:
- Use Cloudflare Workers to handle the initial request and dynamic content generation.
4. LLM Integration:
- Integrate with an LLM API like Perplexity or OpenAI in your Lambda function.
Here’s a high-level implementation plan:
1. Cloudflare Worker (Front-end):
```javascript
addEventListener('fetch', event => {
event.respondWith(handleRequest(event.request))
})
async function handleRequest(request) {
const url = new URL(request.url)
const searchQuery = url.searchParams.get('q') || ''
if (searchQuery) {
// Call AWS API Gateway endpoint
const apiResponse = await fetch('https://your-api-gateway-url.amazonaws.com/prod/query', {
method: 'POST',
body: JSON.stringify({ query: searchQuery }),
headers: { 'Content-Type': 'application/json' }
})
const initialResponse = await apiResponse.json()
// Generate dynamic HTML with the initial response
return new Response(generateHTML(searchQuery, initialResponse), {
headers: { 'Content-Type': 'text/html' }
})
} else {
// Serve static landing page if no query
return fetch('https://your-static-site.com/index.html')
}
}
function generateHTML(query, initialResponse) {
return `
<!DOCTYPE html>
<html>
<head>
<title>EasyAsk.info - ${query}</title>
<script src="https://your-static-site.com/chat.js"></script>
</head>
<body>
<h1>Results for: ${query}</h1>
<div id="initial-response">${initialResponse.text}</div>
<div id="chat-container"></div>
<script>
initChat('${query}', ${JSON.stringify(initialResponse)});
</script>
</body>
</html>
`
}
```
2. AWS Lambda Function (Back-end):
```python
import json
import boto3
from perplexity import Perplexity # Assuming a Perplexity API wrapper
def lambda_handler(event, context):
query = json.loads(event['body'])['query']
# Initialize Perplexity client
perplexity = Perplexity(api_key='your-perplexity-api-key')
# Get initial response
initial_response = perplexity.query(query)
return {
'statusCode': 200,
'body': json.dumps({
'text': initial_response
})
}
```
3. Front-end JavaScript (chat.js):
```javascript
function initChat(initialQuery, initialResponse) {
const chatContainer = document.getElementById('chat-container');
let chatHistory = [{ role: 'system', content: initialResponse.text }];
function addMessage(role, content) {
chatHistory.push({ role, content });
const messageElement = document.createElement('div');
messageElement.className = role;
messageElement.textContent = content;
chatContainer.appendChild(messageElement);
}
function sendMessage(message) {
addMessage('user', message);
fetch('https://your-api-gateway-url.amazonaws.com/prod/chat', {
method: 'POST',
body: JSON.stringify({ history: chatHistory, message }),
headers: { 'Content-Type': 'application/json' }
})
.then(response => response.json())
.then(data
```
Write requirements for this implementation from acquiring The Domain to Search engine optimization and initial user testing and rollout.
Here's a comprehensive list of requirements for implementing the EasyAsk.info system, from domain acquisition to initial rollout:
1. Domain and Hosting:
- Acquire the domain easyask.info from a domain registrar
- Set up Cloudflare for DNS management and CDN services
- Configure AWS account for backend services
2. Infrastructure Setup:
- Set up AWS Lambda for backend processing
- Configure Amazon API Gateway for RESTful endpoints
- Implement Cloudflare Workers for front-end request handling
- Set up a static file hosting solution (e.g., AWS S3 or Cloudflare Pages)
3. Backend Development:
- Develop Lambda functions for query processing and chat interactions
- Integrate with Perplexity or another LLM API
- Implement error handling and logging
- Set up AWS CloudWatch for monitoring and alerts
4. Frontend Development:
- Create a responsive landing page design
- Develop the chat interface using HTML, CSS, and JavaScript
- Implement dynamic content loading based on search queries
- Ensure cross-browser compatibility and mobile responsiveness
5. API Integration:
- Set up secure API keys and authentication for LLM services
- Implement rate limiting and usage monitoring
- Develop a caching system to reduce API calls and improve performance
6. User Experience:
- Design an intuitive chat flow that guides users through their queries
- Implement a system for generating actionable checklists and plans
- Create a mechanism for users to save and resume sessions
7. Security and Privacy:
- Implement HTTPS for all connections
- Set up CORS policies
- Ensure no user data is stored unless explicitly requested
- Implement data encryption for any stored user information
8. Legal and Compliance:
- Develop terms of service and privacy policy
- Ensure compliance with relevant regulations (e.g., GDPR, CCPA)
- Implement necessary disclaimers for legal and medical advice
9. Search Engine Optimization:
- Develop an SEO strategy targeting relevant self-help keywords
- Implement schema markup for rich snippets in search results
- Create a sitemap and submit to search engines
- Set up Google Search Console and Bing Webmaster Tools
10. Analytics and Tracking:
- Implement Google Analytics or a privacy-focused alternative
- Set up conversion tracking for key user actions
- Create custom events to track user engagement and session quality
11. Content Development:
- Create initial prompts and system messages for the LLM
- Develop a knowledge base for common queries and issues
- Create FAQs and help documentation for the platform
12. Testing:
- Conduct thorough unit and integration testing of all components
- Perform load testing to ensure system stability under high traffic
- Carry out user acceptance testing with a diverse group of testers
- Test across various devices, browsers, and network conditions
13. Optimization:
- Implement performance optimizations (e.g., lazy loading, code splitting)
- Optimize Lambda functions for cold starts and execution time
- Fine-tune LLM prompts for accuracy and relevance
14. Marketing and Outreach:
- Develop a marketing strategy to promote the service
- Create content for social media and other channels
- Reach out to relevant organizations for partnerships or promotion
15. User Feedback System:
- Implement a mechanism for users to provide feedback on responses
- Create a system for reporting inappropriate or incorrect information
- Set up a process for continuous improvement based on user feedback
16. Monitoring and Maintenance:
- Set up uptime monitoring and alerts
- Implement a system for regular backups
- Develop a process for regular updates and maintenance
17. Scalability Planning:
- Design the system to handle potential traffic spikes
- Plan for potential expansion to additional topics or services
18. Documentation:
- Create comprehensive documentation for the system architecture
- Develop user guides and FAQs for the platform
19. Initial Rollout:
- Plan a phased rollout strategy (e.g., soft launch, beta testing)
- Set up a system for gathering and analyzing initial user data
- Prepare a plan for rapid response to any issues during launch
20. Continuous Improvement:
- Establish KPIs for measuring the platform's success
- Set up a process for regular review and improvement of the system