The ingest attachment plugin lets Elasticsearch extract file attachments in common formats (such as PPT, XLS, and PDF) by using the Apache text extraction library Tika.

You can use the ingest attachment plugin as a replacement for the mapper attachment plugin.

The source field must be a base64 encoded binary. If you do not want to incur the overhead of converting back and forth between base64, you can use the CBOR format instead of JSON and specify the field as a bytes array instead of a string representation. The processor will skip the base64 decoding then.

Installation

This plugin can be installed using the plugin manager:

sudo bin/elasticsearch-plugin install ingest-attachment

The plugin must be installed on every node in the cluster, and each node must be restarted after installation.

This plugin can be downloaded for offline install from https://artifacts.elastic.co/downloads/elasticsearch-plugins/ingest-attachment/ingest-attachment-7.5.0.zip.

Removal

The plugin can be removed with the following command:

sudo bin/elasticsearch-plugin remove ingest-attachment

Example

The below code here Pdf to elastic search, the code extracts pdf and put into elastic search

import PyPDF2
import re
import requests
import json
import os
from datetime import date

class ElasticModel:

    name = ""
    msg = ""

    def toJSON(self):
        return json.dumps(self, default=lambda o: o.__dict__, 
            sort_keys=True, indent=4)

def __readPDF__(path):
    # pdf file object
    # you can find find the pdf file with complete code in below
    pdfFileObj = open(path, 'rb')
    # pdf reader object
    pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
    # number of pages in pdf
    print(pdfReader.numPages)
    # a page object
    pageObj = pdfReader.getPage(0)
    # extracting text from page.
    # this will print the text you can also save that into String
    line = pageObj.extractText() 
    line = line.replace("\n","")
    print(line)
    return line


#line = pageObj.extractText()

def __prepareElasticModel__(line, name):
    eModel = ElasticModel();

    eModel.name = name
    eModel.msg = line
    return eModel


def __sendToElasticSearch__(elasticModel):
    print("Name : " + str(eModel))

############################################
####  #CHANGE INDEX NAME IF NEEDED
#############################################
    index = "samplepdf"

    url = "http://localhost:9200/" + index +"/_doc?pretty"
    data = elasticModel.toJSON()
    #data = serialize(eModel)
    response = requests.post(url, data=data,headers={
                    'Content-Type':'application/json',
                    'Accept-Language':'en'

                })
    print("Url : " + url)
    print("Data : " + str(data))

    print("Request : " + str(requests))
    print("Response : " + str(response))


#################################
#Change pdf dir path
###################################
pdfdir = "C:/Users/muthali/Desktop/TemplatesPDF/SamplePdf"

listFiles = os.listdir(pdfdir)
for file in listFiles :
    path = pdfdir + "/" + file
    print(path)

    line = __readPDF__(path)
    eModel = __prepareElasticModel__(line, file)
    __sendToElasticSearch__(eModel)

No code PDF search engine using expertrec

If you want to skip all the coding, you can just create a PDF search engine using expertrec.

How to create a PDF Search engine