It is feasible to build an intelligent document management system for text classification by combining AI. The core is to call external AI services through APIs to achieve automated classification; 2. Specific application scenarios include automated archiving and routing, information extraction and structure, intelligent search, compliance management and workflow automation; 3. When choosing AI services, data characteristics, cost, performance, team capabilities, privacy compliance and ease of use must be considered; 4. Technical details cover multi-format document analysis, text preprocessing, asynchronous queueing, error retry, data storage indexing, permission security and system monitoring.

It is completely feasible to use PHP combined with AI to classify text and build an intelligent document management system. Usually, this is not to let PHP perform complex machine learning operations directly, but to use PHP as a powerful backend adhesive to connect external AI services or pre-trained models to achieve automated classification and management of documents.

Solution

In my opinion, the most practical and effective way to use PHP to solve text classification is to integrate through API. PHP is a veteran in handling HTTP requests, which makes it an ideal bridge to connect various AI services.

Specifically, the process is roughly like this:

1. Text extraction: Your document management system must first be able to "dig" the content in documents in various formats (such as PDF, Word, and plain text). This may require some PHP libraries, or simply call some command line tools to assist in the completion.
2. Data cleaning and preparation: The extracted text is often not directly used. There may be a lot of noise, such as headers, advertising information, and unnecessary symbols. At this time, you need to use PHP to perform some basic preprocessing of the text, such as removing unnecessary spaces, punctuation marks, or unifying case to make it "cleaner".
3. Calling AI services: This is the core step. PHP will send the processed text content to an external AI text classification service. These services are usually provided in the form of RESTful APIs, such as Google Cloud Natural Language API, AWS Comprehend, or various model APIs provided by OpenAI and Hugging Face. Your PHP code will build an HTTP request (usually a POST request) and send the text as JSON data.
4. Receive and parse results: After the AI service has finished processing, it will return a JSON response containing the classification results. After receiving this response, PHP parses it and extracts the document's category tags (such as "contract", "invoice", "report", etc.).
5. Follow-up processing and storage: After obtaining the classification results, your PHP system can do a lot of things based on this tag. For example, automatically move the document to the corresponding folder, update the document metadata in the database, or trigger the next approval process.

I personally prefer to use AI APIs provided by cloud service providers, because they usually help you solve a series of headaches such as model training, performance optimization, and high availability. You just need to focus on how to "feed" the text to them and how to deal with the results of "spraying". Of course, if you have extremely high requirements for data privacy, or have your own machine learning team, you can also consider building an AI service yourself and then using PHP to call your internal API.

 <?php

/**
 * This is a conceptual PHP function for text classification through external AI services.
 * In actual applications, you need to replace it with specific AI service API endpoints and authentication methods.
 */
function classifyDocumentText(string $text): ?string
{
    // Suppose we use a fictitious AI classification service $aiServiceEndpoint = &#39;https://api.example-ai-classifier.com/classify&#39;;
    $apiKey = &#39;YOUR_SUPER_SECRET_AI_API_KEY&#39;; // Make sure this key is stored securely, do not hard-code in production environment $payload = [
        &#39;document_content&#39; => $text,
        &#39;model_id&#39; => &#39;your_custom_document_model&#39; // Maybe you can specify a pretrained or custom model];

    $ch = curl_init($aiServiceEndpoint);
    curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); // Get the return result curl_setopt($ch, CURLOPT_POST, true); // Use the POST method curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload)); // Send JSON data curl_setopt($ch, CURLOPT_HTTPHEADER, [
        &#39;Content-Type: application/json&#39;,
        &#39;Authorization: Bearer&#39; . $apiKey,
        &#39;Accept: application/json&#39; // explicitly accept JSON response]);
    // In production environment, you may also need to set timeout, SSL verification, etc. // curl_setopt($ch, CURLOPT_TIMEOUT, 30);
    // curl_setopt($ch, CURLOPT_SSL_VERIFYPEER, true);

    $response = curl_exec($ch);
    $httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);

    if (curl_errno($ch)) {
        error_log("AI service call failed: " . curl_error($ch));
        curl_close($ch);
        return null; // or throw an exception}

    curl_close($ch);

    if ($httpCode !== 200) {
        error_log("AI service returns a non-200 status code: " . $httpCode . " Response: " . $response);
        return null; // Handle API errors}

    $result = json_decode($response, true);

    // Assume that the structure returned by the AI service is {"classification": "Invoice", "confidence": 0.95}
    if (isset($result[&#39;classification&#39;])) {
        return $result[&#39;classification&#39;];
    }

    error_log("AI service response format is incorrect: " . $response);
    return null;
}

// Example usage:
// $documentContent = "This is a report on sales performance for the third quarter of 2023, elaborating on market performance and future forecasts.";
// $category = classifyDocumentText($documentContent);

// if ($category) {
// echo "Document is classified as: " . $category . "\n";
// // Here you can perform subsequent operations based on the classification results, such as:
// // - Store in the database and mark its category // // - Move the document to the corresponding file directory // // - Trigger email notification or workflow // } else {
// echo "Document classification failed or not recognized.\n";
// }

?>

What are the specific application scenarios of PHP combined with AI text classification in intelligent document management?

In the intelligent document management system, the application scenarios of text classification are simply too rich. It's not just labeling a file, it can really change the way we process information.

Imagine that you have to face hundreds of influx of documents every day: contracts, invoices, reports, emails, customer feedback, and more. Without automated classification, it would be a nightmare to process these documents manually. With AI text classification, the situation is very different.

• Automated archiving and routing: This is the most direct application. The system can automatically determine whether it is a "purchase order" or a "human resource file" based on the content of the document, and then automatically archive it to the corresponding folder, or even send it directly to the responsible department or personnel. For example, a document containing the keywords "contract number", "party A", and "party B" is highly likely to be a contract, and the system will directly throw it into the "contract library".
• Information extraction and structure: Classification is just the first step. After knowing the document type, we can further utilize AI (such as named entity recognition or information extraction) to extract key information from specific types of documents. For example, extract invoice numbers, amounts, and supplier information from invoices; extract names, educational backgrounds, and work experience from resumes. These structured data are crucial for subsequent statistics, analysis, and report generation.
• Intelligent search and search: When the document is correctly classified and tagged, users can not only search through keywords, but also filter more accurately through categories and topics when searching. For example, if I want to find "all reports on marketing" and the system can quickly list it. This greatly improves the efficiency and accuracy of information retrieval.
• Compliance and risk management: Some documents may contain sensitive information (such as personally identifiable information, trade secrets), or require specific regulatory requirements. AI classification can help identify these documents and automatically trigger corresponding security policies or compliance check processes to reduce potential risks.
• Workflow automation: Imagine a new customer complaint email comes in. Once AI classifies it, it is found that it is a "product quality problem". The system can automatically create a work order and assign it to the after-sales service team, and copy it to the product manager at the same time. This greatly reduces manual intervention and improves response speed.

I personally think that the core value of these applications lies in "liberating human resources". Leaving the repetitive, time-consuming classification and preliminary processing to AI allows human employees to focus on more creative and decision-making tasks. This is not only an improvement in efficiency, but also an upgrade of the enterprise's operating model.

How to choose the right AI service or library for text classification?

Choosing the right AI service or library is not something you can decide just by typing it. There are many factors that need to be considered comprehensively. After all, it is related to your system performance, cost, and even future scalability.

• Data quantity and data characteristics:
  • Small data volume and strong versatility: If your document type is relatively general and the data volume is not particularly large, then directly using APIs provided by mature cloud service providers (such as Google Cloud Natural Language API, AWS Comprehend) is usually the best choice. They have powerful pretrained models that work out of the box and are usually good.
  • Large amount of data, specialized in the field: If your documents are from a specific industry, such as legal documents, medical reports, or you have many internally unique document types, then a general model may not be enough. At this time, you may need to choose services that support custom model training (such as Google AutoML Text Classification, or fine-tuning through OpenAI/Hugging Face API) and use your own data to train a model that is more in line with your business needs.
• Cost Budget:
  • Pay per volume API: Most cloud services are charged by number of calls or processing text. This is a good deal for startup projects or small calls. But if your document volume is very large, the cost may rise rapidly and requires careful evaluation.
  • Self-built model/open source library: If you are very cost-sensitive or have a strong machine learning team, you can consider using open source machine learning libraries (such as Python's scikit-learn, TensorFlow, PyTorch) to train your own model and then deploy it into an internal API for PHP calls. But this requires a lot of manpower and material resources to develop, maintain and optimize.
• Performance requirements and latency:
  • High real-time requirements: If your system requires almost real-time classification results (such as displaying classifications immediately after a user uploads a document), then choosing a fast response and low latency API service is key.
  • Acceptable delay: If the classification can be performed asynchronously in the background (for example, after the document is uploaded, the classification results will be displayed after a few minutes), then the real-time requirements for the API can be appropriately relaxed, and batch processing can even be considered.
• Technology stack and team capabilities:
  • PHP-based: If your team is primarily good at PHP and does not have a dedicated machine learning engineer, using API services is the best path. It allows you to get started quickly and avoid in-depth in-depth complex machine learning theories and model deployments.
  • Have an ML team: If your team has a machine learning background and can handle Python, TensorFlow, etc., then building your own models and exposing the API will bring you the greatest flexibility and control, but the most complexity is also the most.
• Data Privacy and Compliance:
  • Sensitive data: If the data you process is very sensitive or has strict geographical data storage requirements (such as GDPR, domestic regulations), you need to carefully review the data processing policies of AI service providers to understand where the data is processed and stored, and how to protect privacy. In some cases, you may need to choose a model deployed locally.
• Ease of use and documentation:
  • A good AI service, in addition to its powerful functions, is more important to have clear API documentation, rich sample code and active community support. This can greatly reduce your development and debugging costs.

When I make a choice, I will start with the mature cloud service API for testing. They usually offer free credits that allow you to quickly verify results. If you find that a common API cannot meet specific needs or the cost is too high, you will consider turning to more customized solutions, such as fine-tuning existing models, or eventually becoming self-built. After all, time cost and development complexity are also very important considerations.

What technical details need to be considered when building an intelligent document management system?

Building an intelligent document management system is not just about connecting PHP and AI interfaces. It is a system project and there are many details that require us to think carefully.

• Document ingestion and analysis:
  • Multi-format support: Your system needs to be able to handle various document formats: PDF, DOCX, XLSX, TXT, JPG/PNG (the text in the picture requires OCR). This means you need to integrate different parsing libraries or services. For example, for PDF, you can use Spatie/PdfToText or call the Poppler tool; for Office documents, PHPOffice series library is the first choice. If the document is an image, OCR (optical character recognition) capabilities must be introduced, such as Tesseract or the OCR API of cloud service providers. I personally think that text extraction is the "first mile" of the entire chain. If something goes wrong here, everything behind it will be useless.
  • Text preprocessing pipeline: Extracted text is often very "dirty" and contains a lot of unstructured content. You need a robust preprocessing pipeline for cleaning and standardization. This includes removing headers and footers, advertising, navigation information, handling special characters, garbled codes, and even performing word segmentation (especially important for Chinese).
• Asynchronous processing and queueing mechanism:
  • AI classification, especially calling external APIs, is a time-consuming operation. If the user uploads a large document and the PHP scripts are waiting for the classification results synchronously, the user experience will be very bad and may even cause the request to time out.
  • Solution: Introduce message queues . When a user uploads a document, PHP responds quickly and throws the document content or storage path into a queue (such as RabbitMQ, Redis List, or Laravel Queue). The Worker process in the background will take out tasks from the queue, call AI services asynchronously for classification, and call back the results to the main system. In this way, the front-end page can immediately display "processing" and the user experience will be much better.
• Error handling and retry mechanism:
  • External AI services may return errors due to network problems, service overload, API current limit, etc. Your system must be able to handle these situations gracefully.
  • Strategy: Implement the retry mechanism (with index backoff), that is, wait for a period of time after failure and try again, and the waiting time gradually increases each time. Meanwhile, for some specific errors (such as invalid API keys), they should be marked as failed immediately, rather than infinite retry.
  • Manual intervention: For documents that cannot be classified or have low confidence in classification, there should be mechanisms to mark them out and wait for manual review and intervention to ensure that no documents "follow the internet".
• Data storage and indexing:
  • Document storage: The document itself can be stored in a file system, cloud storage (such as AWS S3, Alibaba Cloud OSS) or a distributed file system. Which one you choose depends on your size and budget.
  • Metadata storage: Classification results, extracted key information, document paths and other metadata need to be stored in relational databases (such as MySQL, PostgreSQL).
  • Full-text search: In order to achieve efficient intelligent search, you need a powerful full-text search engine, such as Elasticsearch or Solr. When a document is classified and critical information is extracted, this information should be indexed into the search engine to support fast, multi-dimensional searches.
• Security and permission management:
  • Access control: Who can upload, view, edit and delete which documents? Permission management is the core.
  • Data Encryption: Both data in transit (HTTPS) and data in storage (encryption at rest) should be encrypted, especially when sensitive information is involved.
  • API key management: Your AI service API key must never be hard-coded, and should be loaded through environment variables, key management services, or secure configuration files.
• Scalability and monitoring:
  • Service decoupling: Design modules such as document analysis, AI classification, data storage, etc. into relatively independent services, which facilitates future replacement or expansion.
  • Performance monitoring: Real-time monitoring of system indicators, such as CPU, memory, disk I/O, queue length, API call success rate and latency. This can help you identify and resolve potential bottlenecks in a timely manner.

To be honest, these technical details may sound a bit scary, but they are the cornerstone of building a robust, efficient, and maintainable intelligent document management system. Ignoring any link may cause huge trouble in the later stage. My experience is that considering these from the beginning is much more worry-free than later repairs.

The above is the detailed content of How to use PHP combined with AI to classify text PHP intelligent document management system. For more information, please follow other related articles on the PHP Chinese website!