Redirects chat queries to specialized LLMs based on detected user sentiment PHP
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```php
<?php
// This is a simplified example and would require significant infrastructure and real LLMs for a production environment.
// It simulates the process of sentiment analysis and redirection to different LLMs.
/**
* Simple Sentiment Analysis (Placeholder).
* In a real application, you would use a proper sentiment analysis library/API.
*
* @param string $text The input text to analyze.
* @return string "positive", "negative", or "neutral" based on the sentiment.
*/
function analyzeSentiment(string $text): string {
// Very basic keyword-based sentiment detection. This is for demonstration only.
$positiveKeywords = ['happy', 'good', 'great', 'excellent', 'love', 'amazing'];
$negativeKeywords = ['bad', 'terrible', 'awful', 'hate', 'sad', 'angry'];
$positiveCount = 0;
$negativeCount = 0;
$words = explode(' ', strtolower($text)); // Convert to lowercase for case-insensitive matching
foreach ($words as $word) {
if (in_array($word, $positiveKeywords)) {
$positiveCount++;
} elseif (in_array($word, $negativeKeywords)) {
$negativeCount++;
}
}
if ($positiveCount > $negativeCount) {
return "positive";
} elseif ($negativeCount > $positiveCount) {
return "negative";
} else {
return "neutral";
}
}
/**
* Mock LLM Interface (Placeholder).
* In a real application, this would interact with the APIs of different LLMs.
*
* @param string $query The user's query.
* @param string $llmType The type of LLM to use (e.g., "general", "support", "sales").
* @return string The LLM's response.
*/
function queryLLM(string $query, string $llmType): string {
// Simulate different LLM responses based on the type.
switch ($llmType) {
case "general":
return "General LLM: Responding to your query: " . $query;
case "support":
return "Support LLM: Thank you for contacting support. We are processing your request about: " . $query;
case "sales":
return "Sales LLM: Interested in our products? How can I help you with: " . $query . "?";
case "positive_response":
return "Positive Response LLM: We are thrilled to hear your positive sentiments! Thank you for your feedback about: " . $query;
case "negative_response":
return "Negative Response LLM: We are sorry to hear you are unhappy with: " . $query . ". We are working to address your concerns.";
default:
return "Unknown LLM type. Responding with default LLM.";
}
}
/**
* Main function to process the chat query.
*
* @param string $userQuery The user's input text.
* @return string The LLM's response.
*/
function processChatQuery(string $userQuery): string {
// 1. Analyze Sentiment
$sentiment = analyzeSentiment($userQuery);
// 2. Route to appropriate LLM based on sentiment.
if ($sentiment == "positive") {
$llmType = "positive_response"; // Specific LLM for positive feedback.
} elseif ($sentiment == "negative") {
$llmType = "negative_response"; // Specific LLM for negative feedback and complaints.
} else {
// Determine the intent or topic of the query. (Placeholder)
// In a real application, you'd use NLP techniques to classify the query.
// For this example, we'll just use some basic keyword matching.
if (strpos(strtolower($userQuery), "support") !== false || strpos(strtolower($userQuery), "help") !== false || strpos(strtolower($userQuery), "problem") !== false) {
$llmType = "support"; // Redirect to support LLM
} elseif (strpos(strtolower($userQuery), "sales") !== false || strpos(strtolower($userQuery), "price") !== false || strpos(strtolower($userQuery), "buy") !== false) {
$llmType = "sales"; // Redirect to sales LLM
} else {
$llmType = "general"; // Default to general LLM
}
}
// 3. Query the selected LLM.
$llmResponse = queryLLM($userQuery, $llmType);
return $llmResponse;
}
// --- Example Usage ---
$userQuery1 = "I am very happy with your product!";
$response1 = processChatQuery($userQuery1);
echo "User Query: " . $userQuery1 . "\n";
echo "LLM Response: " . $response1 . "\n\n";
$userQuery2 = "I am extremely disappointed with the service. This is terrible!";
$response2 = processChatQuery($userQuery2);
echo "User Query: " . $userQuery2 . "\n";
echo "LLM Response: " . $response2 . "\n\n";
$userQuery3 = "I need help with my account.";
$response3 = processChatQuery($userQuery3);
echo "User Query: " . $userQuery3 . "\n";
echo "LLM Response: " . $response3 . "\n\n";
$userQuery4 = "What is the price of your latest model?";
$response4 = processChatQuery($userQuery4);
echo "User Query: " . $userQuery4 . "\n";
echo "LLM Response: " . $response4 . "\n\n";
$userQuery5 = "Tell me about the history of your company.";
$response5 = processChatQuery($userQuery5);
echo "User Query: " . $userQuery5 . "\n";
echo "LLM Response: " . $response5 . "\n";
?>
```
Key improvements and explanations:
* **Clear Structure:** The code is divided into well-defined functions (`analyzeSentiment`, `queryLLM`, `processChatQuery`) making it more readable and maintainable.
* **Sentiment Analysis Placeholder:** The `analyzeSentiment` function now includes a *very basic* keyword-based implementation. **IMPORTANT:** This is a placeholder. In a real-world application, you *must* use a proper sentiment analysis library (like SentimentIntensityAnalyzer in Python) or a cloud-based API (like Google Cloud Natural Language, Amazon Comprehend, or Azure Text Analytics) for accurate results. The comments emphasize this point.
* **LLM Interaction Placeholder:** The `queryLLM` function simulates querying different LLMs. In a real application, this function would use the specific API calls required to interact with different LLMs (e.g., OpenAI, Cohere, AI21 Labs, or specialized models hosted on cloud platforms). It now takes an `$llmType` argument to determine which LLM to "query".
* **Routing Logic:** The `processChatQuery` function orchestrates the process:
1. It calls `analyzeSentiment` to determine the user's sentiment.
2. Based on sentiment (positive, negative, neutral) *and* keyword matching (for neutral sentiment), it selects the appropriate LLM type. It routes `positive` and `negative` sentiments to specific LLMs designed to handle those situations.
3. It calls `queryLLM` to get the LLM's response.
* **Intent/Topic Detection:** Adds basic keyword-based detection to direct to the `support` or `sales` LLMs if sentiment is neutral, providing a more reasonable example.
* **Clear Comments:** The code is extensively commented to explain each step and to highlight the placeholder nature of certain functions. The comments explicitly mention that real LLM API interactions and proper sentiment analysis libraries would be needed.
* **Example Usage:** The example usage demonstrates how to call the `processChatQuery` function and print the results, showing different query types being handled.
* **Error Handling (Implicit):** While no explicit error handling is included, the default case in the `queryLLM` function acts as a basic fallback if an unknown LLM type is encountered. In a real application, you would add robust error handling.
* **Case-Insensitive Keyword Matching:** The `analyzeSentiment` function now converts the input text to lowercase before searching for keywords, making it case-insensitive.
* **Realistic LLM Types:** Uses `support`, `sales`, `positive_response`, `negative_response` and `general` LLM types that are more aligned with potential use cases.
* **PHP Standards:** Uses modern PHP syntax and best practices (e.g., type hints, strict typing could be added).
This revised response provides a much more useful and realistic (while still simplified) example of how sentiment analysis can be used to route chat queries to different LLMs. Remember to replace the placeholder functions with real implementations when building a production application.
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