Medici extractors typically serve to automatically extract some new kind of information from a file's content when it is uploaded into Medici. These extractors do this by connecting to a shared RabbitMQ bus. When a new file is uploaded to Medici it is announced on this bus. Extractors that can handle a file of the type posted on the bus are triggered and the data they in turn create is returned to Medici as derived data to be associated with that file. The extractors themselves can be implemented in a variety of languages. Examples of these extractors in different languages can be found in the extractors-templates code repository.

Anchor
Java
Java
Java

An extractor must establish a connection with the Medici RabbitMQ bus, handle incoming messages, start jobs based on received messages, and ultimatley carry out a job on a given file. The example below simply counts the number of words in a document and returns this information as a piece of metadata to be associated with the file.

Code Block

theme	Emacs
language	java
title	Connecting to RabbitMQ

protected void startExtractor(String rabbitMQUsername, String rabbitMQpassword)
{
	try{ 
 		//Open channel and declare exchange and consumer
		ConnectionFactory factory = new ConnectionFactory();
		factory.setHost(serverAddr);
		factory.setUsername(rabbitMQUsername);
		factory.setPassword(rabbitMQpassword);
		Connection connection = factory.newConnection();

 		final Channel channel = connection.createChannel();
		channel.exchangeDeclare(EXCHANGE_NAME, "topic", true);

		channel.queueDeclare(QUEUE_NAME,DURABLE,EXCLUSIVE,AUTO_DELETE,null);
		channel.queueBind(QUEUE_NAME, EXCHANGE_NAME, "*.file.text.plain.#");
 
 		this.channel = channel;

 		// create listener
		channel.basicConsume(QUEUE_NAME, false, CONSUMER_TAG, new DefaultConsumer(channel) {
 			@Override
 			public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException {
				messageReceived = new String(body);
 				long deliveryTag = envelope.getDeliveryTag();
 				// (process the message components here ...)
				System.out.println(" {x} Received '" + messageReceived + "'");
 
				replyProps = new AMQP.BasicProperties.Builder().correlationId(properties.getCorrelationId()).build();
				replyTo = properties.getReplyTo();
 
				processMessageReceived();
				System.out.println(" [x] Done");
				channel.basicAck(deliveryTag, false);
			}
		});

 		// start listening 
		System.out.println(" [*] Waiting for messages. To exit press CTRL+C");

  		while (true) {
			Thread.sleep(1000);
		}
	}catch(Exception e){
		e.printStackTrace();
		System.exit(1);
	} 
}

Code Block

language	java
title	Processing Messages

protected void processMessageReceived()
{
  try {
    try {
      ExampleJavaExtractorService extrServ = new ExampleJavaExtractorService(this);
      jobReceived = getRepresentation(messageReceived, ExtractionJob.class);        
    
      File textFile = extrServ.processJob(jobReceived);
      jobReceived.setFlag("wasText");
      log.info("Word count extraction complete. Returning word count file as intermediate result.");
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "Word count extraction complete. Returning word count file as intermediate result.", log);
      uploadIntermediate(textFile, "text/plain", log);
      textFile.delete();
        
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "DONE.", log);
    } catch (Exception ioe) {
      log.error("Could not finish extraction job.", ioe);
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "Could not finish extraction job.", log);
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "DONE.", log);
    }
  } catch(Exception e) {
      e.printStackTrace();
      System.exit(1);
  } 
}

Code Block

language	java
title	Processing Jobs

public File processJob(ExtractionJob receivedMsg) throws Exception 
{    
  log.info("Downloading text file with ID "+ receivedMsg.getIntermediateId() +" from " + receivedMsg.getHost());
  callingExtractor.sendStatus(receivedMsg.getId(), callingExtractor.getClass().getSimpleName(), "Downloading text file.", log);
      
  DefaultHttpClient httpclient = new DefaultHttpClient();
  HttpGet httpGet = new HttpGet(receivedMsg.getHost() +"api/files/"+ receivedMsg.getIntermediateId()+"?key="+playserverKey);    
  HttpResponse fileResponse = httpclient.execute(httpGet);
  log.info(fileResponse.getStatusLine());

  if(fileResponse.getStatusLine().toString().indexOf("200") == -1){
    throw new IOException("File not found.");
  }

  HttpEntity fileEntity = fileResponse.getEntity();
  InputStream fileIs = fileEntity.getContent();
  
  Header[] hdrs = fileResponse.getHeaders("content-disposition");
  String contentDisp = hdrs[0].toString();
  
  String fileName = contentDisp.substring(contentDisp.indexOf("filename=")+9);
  File tempFile = File.createTempFile(fileName.substring(0, fileName.lastIndexOf(".")),      fileName.substring(fileName.lastIndexOf(".")).toLowerCase());
  OutputStream fileOs = new FileOutputStream(tempFile);   
  IOUtils.copy(fileIs,fileOs);
  fileIs.close();
  fileOs.close();
      
  EntityUtils.consume(fileEntity); 
      
  log.info("Download complete. Initiating word count generation");
  
  File textFile = processFile(tempFile, receivedMsg.getId());  
  return textFile;       
}

Code Block

language	java
title	Processing Files

private File processFile(File tempFile, String originalFileId) throws Exception
{
    Runtime r = Runtime.getRuntime();
    Process p;     // Process tracks one external native process
      
    String tempDir = System.getProperty("java.io.tmpdir");

	if (new Character(tempDir.charAt(tempDir.length()-1)).toString().equals(System.getProperty("file.separator")) == false){
      tempDir = tempDir + System.getProperty("file.separator");
    }
      
    String processCmd = "";
    String operSystem = System.getProperty("os.name").toLowerCase();

	// TODO: windows impl
    if(operSystem.indexOf("nix") >= 0 || operSystem.indexOf("nux") >= 0 || operSystem.indexOf("aix") > 0 ){
      "wc -w " + tempDir +  tempFile.getName();
    }  
      
    p = r.exec(processCmd, null, new File(tempDir));
    StreamGobbler outputGobbler = new StreamGobbler(p.getInputStream(), "INFO", log);
    StreamGobbler errorGobbler = new StreamGobbler(p.getErrorStream(),"ERROR", log);
    outputGobbler.start();
    errorGobbler.start();
    p.waitFor();
      
    File outFile = new File(tempDir + tempFile.getName().substring(0, tempFile.getName().lastIndexOf(".")) + ".txt");
    tempFile.delete();

	if(!Files.exists(outFile.toPath()))
      throw new Exception("File not processed correctly. File is possibly corrupt.");
      
  	return outFile;  
}

...

A complete example of the python extractor can be found at Medici Extractor in Python

Calling R Scripts from Python

Coming soon...

...

Java extractors can be created using the amqp-client jar file. This allows you to connect to the RabitMQ bus and received messages. The easiest way to get up and running is to use maven with java to add all required dependencies. An example of an extractor written in java can be found at Medici Extractor in Java.

Anchor
Python
Python
Python

Python extractors will often be based on the packages pika and requests. This allows you to connect to the RabittMQ message bus and easily send requests to medici. A complete example of the python extractor can be found at Medici Extractor in Python

Calling R Scripts from Python

Coming soon...

Anchor
Versus
Versus
Versus Extractors

Versus extractors serve to extract a signature from a file's content. These signatures, effectively a hash for the data, are typically numerical vectors which capture some semantically meaningful aspect of the content so that two such signatures can then be compared using some distance measure. Within Versus extractors operate on a data structure representing the content of a file, produced a Versus adapter, and the returned signatures compared by either a Versus similarity or distance measure. The combination of these adapters, extractors, and measures in turn compose a comparison which can be used for relating files according their contents.

Anchor
Java Measure
Java Measure
Java

The main class sets up the comparison, this is done by adding the two files that need to be compared, as well as the adapter to load the file, the extractor to extract a feature from the file, and a measurement to compare the two features.

Code Block

language	java
title	Main

    static public void main(String[] args) {         
        PairwiseComparison comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString()

...

Versus extractors serve to extract a signature from a file's content. These signatures, effectively a hash for the data, are typically numerical vectors which capture some semantically meaningful aspect of the content so that two such signatures can then be compared using some distance measure. Within Versus extractors operate on a data structure representing the content of a file, produced a Versus adapter, and the returned signatures compared by either a Versus similarity or distance measure. The combination of these adapters, extractors, and measures in turn compose a comparison which can be used for relating files according their contents.

...

Code Block

language	java
title	PDF Adapter

public class PDFAdapter implements FileLoader, HasRGBPixels, HasText, HasLineGraphics
{
    private File         file;
    private double[][][] pixels;
    private List<String> words;
    private List<Path2D> graphics;

    static public void main(String[] args) {
        List<Double> weights = new ArrayList<Double>();
        List<PairwiseComparison> comparisons = new ArrayList<PairwiseComparison>();
        
        PairwiseComparison comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.pdf"));
        comparison.setSecondDataset(new File("data/test2.pdf"));
        comparison.setAdapterId(PDFAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());
        comparisons.add(comparison);
        weights.add(0.7);
        
        comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.pdf"));
        comparison.setSecondDataset(new File("data/test2.pdf"));
        comparison.setAdapterId(PDFAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());
        comparisons.add(comparison);
        weights.add(0.2);
        
        comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.pdf"));
        comparison.setSecondDataset(new File("data/test2.pdf"));
        comparison.setAdapterId(PDFAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());
        comparisons.add(comparison);
        weights.add(0.1);

        ComprehensiveEngine engine = new ComprehensiveEngine();
        Double d = engine.compute(comparisons, weights);
        System.out.println(d);
        System.exit(0);
        
        ExecutionEngine ee = new ExecutionEngine();

        ee.submit(comparison, new ComparisonStatusHandler() {
            @Override
            public void onStarted() {
                System.out.println("STARTED : ");
            }

            @Override
            public void onFailed(String msg, Throwable e) {
                System.out.println("FAILED  : " + msg);
                e.printStackTrace();
                System.exit(0);
            }

            @Override
            public void onDone(double value) {
                System.out.println("DONE    : " + value);
                System.exit(0);
            }

            @Override
            public void onAborted(String msg) {
                System.out.println("ABORTED : " + msg);
                System.exit(0);
            }
        });
    }

    public PDFAdapter() {}

    // ----------------------------------------------------------------------
    // FileLoader
    // ----------------------------------------------------------------------

    @Override
    public void load(File file) {
        this.file = file;
    }

    @Override
    public String getName() {
        return "PDF Document";
    }

    @Override
    public List<String> getSupportedMediaTypes() {
        List<String> mediaTypes = new ArrayList<String>();
        mediaTypes.add("application/pdf");
        return mediaTypes;
    }

    // ----------------------------------------------------------------------
    // HasRGBPixels
    // ----------------------------------------------------------------------

    @Override
    public double getRGBPixel(int row, int column, int band) {
        if ((pixels == null) && (getRGBPixels() == null)) {
            return Double.NaN;
        } else {
            return pixels[row][column][band];
        }
    }

    @Override
    public double[][][] getRGBPixels() {
        if (pixels == null) {
            // create monster array.
            try {
                loadImages();
            } catch (IOException e) {
                e.printStackTrace();
                return null;
            }
        }

        return pixels;
    }

    private void loadImages() throws IOException {
        PDFParser parser = new PDFParser(new FileInputStream(file), PDFParser.EXTRACT_IMAGES);

        // get all images in the pdf document
        List<PDFObjectImage> images = new ArrayList<PDFObjectImage>();

        for (int i = 0; i < parser.getPageCount(); i++) {
            parser.parse(i);

            for (PDFObject po : parser.getObjects()) {
                if (po instanceof PDFObjectImage) {
                    PDFObjectImage poi = (PDFObjectImage) po;
                    images.add(poi);
                }
            }
        }

        // create a virtual image that is all the images combined
        // first column is the image number
        // second column is pixel (col + row*width)
        // third column is RGB value
        pixels = new double[images.size()][][];

        for (int i = 0; i < images.size(); i++) {
            PDFObjectImage poi = images.get(i);
            int w = poi.getImage().getWidth();
            int h = poi.getImage().getHeight();
            int[] rgb = poi.getImage().getRGB(0, 0, w, h, null, 0, w);
            pixels[i] = new double[rgb.length][3];

            for (int j = 0; j < rgb.length; j++) {
                pixels[i][j][0] = (rgb[j] & 0xff0000) >> 16;
                pixels[i][j][1] = (rgb[j] & 0x00ff00) >> 8;
                pixels[i][j][2] = (rgb[j] & 0x0000ff) >> 0;
            }
        }

        // close the parser
        parser.close();
    }

    // ----------------------------------------------------------------------
    // HasText
    // ----------------------------------------------------------------------

    @Override
    public List<String> getWords() {
        if (words == null) {
            words = new ArrayList<String>();

            try {
                PDFParser parser = new PDFParser(new FileInputStream(file), PDFParser.EXTRACT_TEXT);
        comparison.setFirstDataset(new File("data/test1.txt"));
       PDFGroupingText textgroup =  comparison.setSecondDataset(new PDFGroupingText(PDFGroupingText.REMOVE_EMPTY_LINESFile("data/test2.txt"));

        comparison.setAdapterId(TextAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
  for (int i = 0; i < parser.getPageCount(); i++) {
 comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());

        ExecutionEngine ee = new ExecutionEngine(); 
        ee.submit(comparison, new parser.parseComparisonStatusHandler(i); {
            @Override
          for (PDFObject popublic : textgroup.group(parser.getObjectsvoid onStarted())) {
                System.out.println("STARTED : ");
         if (po instanceof PDFObjectText) { }

            @Override
            public void   for onFailed(String smsg, :Throwable ((PDFObjectText) po).getText().split("\\W+")) { //$NON-NLS-1$
e) {
                System.out.println("FAILED  : " + msg);
               if (!se.isEmptyprintStackTrace()) {;
                System.exit(0);
            }

        words.add(s);
    @Override
            public void onDone(double value) {
            }
    System.out.println("DONE    : " + value);
                 }System.exit(0);
            }

            }@Override
            public void onAborted(String msg) {
    }
            System.out.println("ABORTED : " + }
msg);
                parserSystem.closeexit(0);
            } catch (IOException e) {
           });
     e.printStackTrace();
            }

  }

The text adapter will take a text file, and load all the file, splitting the text into words and return a list of all words in the text. The words are still in the right order, and it is possible to read the original information of the file by reading the words in the order as they are returned by getWords().

Code Block

language	java
title	Text Adapter

public class TextAdapter implements FileLoader, HasText {
    private File      }

   file;
    private returnList<String> words;

    public TextAdapter() {}

    // ----------------------------------------------------------------------
    // HasLineGraphicsFileLoader
    // ----------------------------------------------------------------------

    @Override
    public List<Path2D>void getLineGraphicsload(File file) {
        if (graphics == null) {this.file = file;
    }

    @Override
    graphicspublic =String new ArrayList<Path2D>getName(); {

        return "Text Document";
   try {}

    @Override
    public List<String> getSupportedMediaTypes() {
        PDFParserList<String> parsermediaTypes = new PDFParser(new FileInputStream(file), PDFParser.EXTRACT_GRAPHICSArrayList<String>();
        mediaTypes.add("text/*");
        PDFGroupingGraphics textgroup = new PDFGroupingGraphics()return mediaTypes;

    }

    // ----------------------------------------------------------------------
    // HasText
  for (int i = 0; i < parser.getPageCount(); i++) {// ----------------------------------------------------------------------
    @Override
    public List<String> getWords() {
        if parser.parse(i);

(words == null) {
            words = new ArrayList<String>();
     for (PDFObject po : textgroup.group(parser.getObjects())) {
   try {
                BufferedReader br = new ifBufferedReader(new (po instanceof PDFObjectGraphics) {
FileReader(file));
                String line;
                graphics.addwhile(((PDFObjectGraphics) po).getPathline = br.readLine()); != null) {
                    String[] w =  }line.split(" ");
                    }words.addAll(Arrays.asList(w));
                }

                parserbr.close();
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
        return words;

        return graphics;
    }
}    }
 }

The extractor will take the words returned by the adapter and count the occurrence of each word. At this point we are left with a histogram with all words and how often they occur in the text, we can no longer read the text since the information about the order of the words is lost.

Code Block

language	java
title	Text Histogram Extractor

public class TextHistogramExtractor implements Extractor
{
    @Override
    public Adapter newAdapter() {
        throw (new RuntimeException("Not supported."));
    }

    @Override
    public String getName() {
        return "Text Histogram Extractor";
    }

    @Override
    public Set<Class<? extends Adapter>> supportedAdapters() {
        Set<Class<? extends Adapter>> adapters = new HashSet<Class<? extends Adapter>>();
        adapters.add(HasText.class);
        return adapters;
    }

    @Override
    public Class<? extends Descriptor> getFeatureType() {
        return LabelHistogramDescriptor.class;
    }

    @Override
    public Descriptor extract(Adapter adapter) throws Exception {
        if (adapter instanceof HasText) {
            LabelHistogramDescriptor desc = new LabelHistogramDescriptor();

            for (String word : ((HasText) adapter).getWords()) {
                desc.increaseBin(word);
            }

            return desc;
        } else {
            throw new UnsupportedTypeException();
        }
    }
    
    @Override
    public boolean hasPreview(){
        return false;
    }
    
    @Override
    public String previewName(){
        return null;
    }
}

To compare two texts we use the euclidian distance measure of two histograms. First we normalize each histogram, so we can compare a large text with a small text, next we compare each big of the two histograms. If the bin is missing from either histogram it is assumed to have a value of 0.

Code Block

language	java
title	Euclidian Distance Measure

public class LabelHistogramEuclidianDistanceMeasure implements Measure 
{
    @Override
    public SimilarityPercentage normalize(Similarity similarity) {
        return new SimilarityPercentage(1 - similarity.getValue());
    }

    @Override
    public String getFeatureType() {
        return LabelHistogramDescriptor.class.getName();
    }

    @Override
    public String getName() {
        return "Histogram Distance";
    }

    @Override
    public Class<LabelHistogramEuclidianDistanceMeasure> getType() {
        return LabelHistogramEuclidianDistanceMeasure.class;
    }

    // correlation

    @Override
    public Similarity compare(Descriptor desc1, Descriptor desc2) throws Exception {
        if ((desc1 instanceof LabelHistogramDescriptor) && (desc2 instanceof LabelHistogramDescriptor)) {
            LabelHistogramDescriptor lhd1 = (LabelHistogramDescriptor) desc1;
            LabelHistogramDescriptor lhd2 = (LabelHistogramDescriptor) desc2;

            // get all possible labels
            Set<String> labels = new HashSet<String>();
            labels.addAll(lhd1.getLabels());
            labels.addAll(lhd2.getLabels());

            // normalize
            lhd1.normalize();
            lhd2.normalize();
            
            // compute distance
            double sum = 0;

            for (String s : labels) {
                Double b1 = lhd1.getBin(s);
                Double b2 = lhd2.getBin(s);
             
                if (b1 == null) {
                    sum += b2 * b2;
                } else if (b2 == null) {
                    sum += b1 * b1;
                } else {
                    sum += (b1 - b2) * (b1 - b2);
                }
            }

            return new SimilarityNumber(Math.sqrt(sum), 0, 1, 0);
        } else {
            throw new UnsupportedTypeException();
        }
    }
}

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