RAG_database_preparation.ipynb

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   "id": "d380598d-3090-4b27-a467-946e659e3eb8",
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   "source": [
    "import PyPDF2\n",
    "import os\n",
    "import json"
   ]
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  {
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   "execution_count": 55,
   "id": "e0350297-d3e2-4dfb-95ea-f0817cbd5e9a",
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    "def extract_text_from_pdf(script,file_name,chunk_list):\n",
    "    pdf = PyPDF2.PdfReader(script)\n",
    "    text = ''\n",
    "    for n,page in enumerate(range(len(pdf.pages))):#len(pdf.pages)):\n",
    "        page_obj = pdf.pages[page]\n",
    "        chunk_list.append(f'{page_obj.extract_text()}, from \"{file_name}\",page {n}\\n')\n",
    "    return chunk_list\n"
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      "1\n",
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      " \n",
      " \n",
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      "Acknowledgments\n",
      "This work was partially supported by USDA –National Robotics\n",
      "Initiative grant # 2015-67021-23857 and China Schol\n",
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      " 14 \n",
      "()\n",
      "Oil Water WaterOil Oil Fiber\n",
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      "Chandra Sekhar Nandi received the M.Tech.\n",
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      "mobile application with live camera viewfinder. The results \n",
      "will show instantly in a real time. We did the test three times \n",
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      "towards laboratory studies ( Albdullah et al., 2002; Choong et al\n",
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      "                                                                                \n",
      "International Journal of E ngineering  and Adva\n",
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      "progressive 3d point set upsamplin\n",
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      "Brown, G.K., 2002. Mechanical harvesting systems for the Florida citrus juice\n",
      "industry. In: ASABE. St. Joseph, MI. pp. ASAE (pap\n",
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      "Text extracting from Near Infrared Spectroscopy A Rapid and Non-Destructive Technique to Assess the Ripeness of Oil Palm.pdf:\n",
      "View publication stats, from \"Near Infrared Spectroscopy A Rapid and Non-Destructive Technique to Assess the Ripeness of Oil Pal\n",
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      "modal physical simulation. arXiv 2020. arXiv:2007.04954\n",
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      " SN Computer Science (2023) 4:302\n",
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      "Fig. 9. Test images from the T-LESS dataset showing the estimated object\n",
      "pose of the three objects, by combining the bounding bo\n",
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      "is highly sensitive to oil palm FFB maturity, because of the opposite\n",
      "behavior of BGF and FRF. This explains the large variation\n",
      "-----------------------\n",
      "119\n",
      "Text extracting from Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis.pdf:\n",
      "International Journal of\n",
      "AerospaceEngineering\n",
      "Hindawi\n",
      "www.hindawi.com Volume 2018\n",
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      "OIL PALM ECONOMIC PERFORMANCE IN MALAYSIA AND R&D PROGRESS IN 2017\n",
      "195removal of imazapic and imazapyr herbicides. J. \n",
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      " \n",
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      "adju\n",
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      "Plants 2022 ,11, 1936 16 of 16\n",
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      " \n",
      "  \n",
      " \n",
      "  \n",
      "4. Conclusions \n",
      "In conclusion, the solutions to overcome the factors affecting the sterilization process and the fruit\n",
      "-----------------------\n",
      "126\n",
      "Text extracting from Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment a review.pdf:\n",
      "Precision Agriculture \n",
      "1 3\n",
      "Guangzhou 510225, China\n",
      "2 College of Mechanical and Electrical Engineering, Zhongkai University of Ag\n",
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      "Computers and Electronics in Agriculture 209 (2023) 107834\n",
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      "I-12 Index\n",
      "Tip angle, 515\n",
      "TMS (tetramethylsilane), 223TOF. SeeTime-of-flight mass \n",
      "analyzer\n",
      "Toluene\n",
      "carbon-13 NMR spectrum of, 3\n",
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      " \n",
      " \n",
      " \n",
      " \n",
      "  2022  2021  DIFFERENCE  \n",
      "VOLUME / \n",
      "VALUE  (%) \n",
      "PRICE (RM/TONNE)  \n",
      "FFB (MILL GATE)  1,087.00  955.00  132.00  13.8  \n",
      "CP\n",
      "-----------------------\n",
      "130\n",
      "Text extracting from Palm oil FFB ripeness grading recognition using convolutional neural network.pdf:\n",
      "Palm Oil Fresh Fruit Bunch Ripeness Grading Recognition Using Convolutional Neural Network  \n",
      " e-ISSN: 2289 -8131   Vol. 10 No. 3\n",
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      "Rossell, J. B. (2001). Factors affecting the quality of frying oils and\n",
      "fats. In J. B. Rossell (Ed.), Frying –Improving quality \n",
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      "Fig. 5. 3D-plot of ripe and unripe FFBs at speciﬁc wavelength, 940 nm, in\n",
      "HSV color space.\n",
      "wavelength, the overlap values betwee\n",
      "-----------------------\n",
      "133\n",
      "Text extracting from PIDFusion Fusing Dense LiDAR Points and Camera Images at Pixel-Instance Level for 3D Object Detection.pdf:\n",
      "Mathematics 2023 ,11, 4277 15 of 15\n",
      "46. Yin, T.; Zhou, X.; Krahenbuhl, P . Center-based 3d object detection and tracking. In Pro\n",
      "-----------------------\n",
      "134\n",
      "Text extracting from Pineapple (Ananas comosus) fruit detection and localization in natural environment based on binocular stereo vision and improved YOLOv3 model.pdf:\n",
      "160 Precision Agriculture (2023) 24:139–160\n",
      "1 3\n",
      "Liu, F., Liu, Y. K., Lin, S., Guo, W. Z., Xu, F., & Zhang, B. (2020a). Fast reco\n",
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      "135\n",
      "Text extracting from Plant detection and mapping for agricultural robots using a 3D LIDAR sensor.pdf:\n",
      "U.Weiss,P.Biber/RoboticsandAutonomousSystems59(2011)265–273 273\n",
      "5. Conclusions and future works\n",
      "Inthisworkweevaluatedthestate-of\n",
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      "136\n",
      "Text extracting from Potential Application of Color And Hyperspectral Images for Estimation of Weight and Ripeness of Oil Palm.pdf:\n",
      "Agricultural Information Research  18(2),2009\n",
      "81ence and Technology, 109: 296–306.\n",
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      " TABLE 18 \n",
      "The table below presents a record of the May projection and the final Estimate. Using world wheat production as an \n",
      "e\n",
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      "140\n",
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      "potatoes stored at an ambient room temperature.\n",
      "4. Conclusions\n",
      "The present study reveals the capability of the LLBI technique to\n",
      "-----------------------\n",
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      "Sensors 2017 ,17, 52 10 of 10\n",
      "The unripe sample is stated to be selected before the 12th WAA with the initial 80.1% water\n",
      "conten\n",
      "-----------------------\n",
      "150\n",
      "Text extracting from remotesensing-13-04999.pdf:\n",
      "Remote Sens. 2021 ,13, 4999 21 of 21\n",
      "49. Shermeyer, J.; Hossler, T.; Van Etten, A.; Hogan, D.; Lewis, R.; Kim, D. RarePlanes: Sy\n",
      "-----------------------\n",
      "151\n",
      "Text extracting from Ripeness Classification of Bananas Using an Artificial Neural Network.pdf:\n",
      "Arabian Journal for Science and Engineering\n",
      "nate between the four banana fruits classes. The system has\n",
      "optimal performance as c\n",
      "-----------------------\n",
      "152\n",
      "Text extracting from Ripeness Classification of Oil Palm Fresh Fruit Bunch Using an optical probe.pdf:\n",
      "J. Aceh Phys . Soc., Vol. 8, No. 3 pp.72-77, 2019                                                                   e-ISSN : 235\n",
      "-----------------------\n",
      "153\n",
      "Text extracting from Ripeness detection simulation of Oil Palm FFB using laser-based imaging system.pdf:\n",
      "12. H.K. Noh and R. Lu. Postharvest Biology and Technology  43, 193-201 (2007).  \n",
      "13. A. Jiménez,  R.  Ceres, J.  Pons, Mach. Vi\n",
      "-----------------------\n",
      "154\n",
      "Text extracting from Ripeness Inspection of Oil Palm Fruits by Applying Hardness Test Technique.pdf:\n",
      " \n",
      "ACKNOWLEDGEMENTS \n",
      "We would like to express our gratitude to Prince of \n",
      "Songkla University, Surat Thani Campus, The Center \n",
      "of \n",
      "-----------------------\n",
      "155\n",
      "Text extracting from Row Detection Based Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields Methods and Application.pdf:\n",
      "Agronomy 2023 ,13, 1780 30 of 30\n",
      "233. Gong, J.; Fan, W.; Peng, J. Application analysis of hydraulic nozzle and rotary atomizatio\n",
      "-----------------------\n",
      "156\n",
      "Text extracting from s41598-020-62275-6.pdf:\n",
      "16 Scientific  RepoRtS  |         (2020) 10:6464  | https://doi.org/10.1038/s41598-020-62275-6\n",
      "www.nature.com/scientificreports \n",
      "-----------------------\n",
      "157\n",
      "Text extracting from s41598-021-97857-5.pdf:\n",
      "11\n",
      "Vol.:(0123456789) Scientific Reports  |        (2021) 11:18315  | https://doi.org/10.1038/s41598-021-97857-5\n",
      "www.nature.com/s\n",
      "-----------------------\n",
      "158\n",
      "Text extracting from s42256-022-00463-x.pdf:\n",
      "Review AR ticle NaTURe MachINe INTellIGeNceand VR3-172639, and generous donations for COVID-19 research from TELUS, Teck \n",
      "Resour\n",
      "-----------------------\n",
      "159\n",
      "Text extracting from Segmenting_Unknown_3D_Objects_from_Real_Depth_Images_using_Mask_R-CNN_Trained_on_Synthetic_Data.pdf:\n",
      "environments,” in Experimental Robotics . Springer, 2014, pp. 241–\n",
      "252.\n",
      "[45] C. Eppner, S. H ¨ofer, R. Jonschkowski, R. M. Marti\n",
      "-----------------------\n",
      "160\n",
      "Text extracting from Selective Harvesting Robotics Current Research, Trends and Future Directions.pdf:\n",
      "Comput Electron Agric. 2008;63(1):81 –8.https://doi.org/10.1016/\n",
      "j.compag.2008.01.015 .\n",
      "52. van Henten EJ, Enzing C. CPS for agr\n",
      "-----------------------\n",
      "161\n",
      "Text extracting from Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards.pdf:\n",
      "fuse the features from both objects’ texture and geometrical\n",
      "appearances. Meanwhile, the proposed method utilized an\n",
      "under-sampl\n",
      "-----------------------\n",
      "162\n",
      "Text extracting from Sensors and systems for fruit detection and localization A review.pdf:\n",
      "Hunter, R.S., 1948. Photoelectric Color-Difference Meter. In: Proceedings of the\n",
      "Winter Meeting of the Optical Society of Americ\n",
      "-----------------------\n",
      "163\n",
      "Text extracting from Silverstein - Spectrometric Identification of Organic Compounds 7th ed.pdf:\n",
      ", from \"Silverstein - Spectrometric Identification of Organic Compounds 7th ed.pdf\",page 549\n",
      "\n",
      "-----------------------\n",
      "164\n",
      "Text extracting from Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles Recent Advances and Challenges.pdf:\n",
      "Drones 2022 ,6, 85 35 of 35\n",
      "158. Chauchat, P .; Barrau, A.; Bonnabel, S. Factor Graph-Based Smoothing Without Matrix Inversion f\n",
      "-----------------------\n",
      "165\n",
      "Text extracting from Structured Domain Randomization Bridging the Reality Gap by Context-Aware Synthetic Data.pdf:\n",
      "[3] A. Dosovitskiy, P. Fischer, E. Ilg, P. H ¨ausser, C. Hazırbas ¸, V . Golkov,\n",
      "P. Smagt, D. Cremers, and T. Brox, “FlowNet: Le\n",
      "-----------------------\n",
      "166\n",
      "Text extracting from Surface color based prediction of oil content in oil palm.pdf:\n",
      " \n",
      " \n",
      " \n",
      " \n",
      "REFERENCES  \n",
      " \n",
      "Abdullah ZM, Guan CL, Abdul MDM, Mohid AMN (2002).Color vision \n",
      "system for ripeness Inspection of Oil pal\n",
      "-----------------------\n",
      "167\n",
      "Text extracting from Synthetic Data accelerates the development of generalizable learning-based algorithms for X-Ray image analysis.pdf:\n",
      ", from \"Synthetic Data accelerates the development of generalizable learning-based algorithms for X-Ray image analysis.pdf\",page\n",
      "-----------------------\n",
      "168\n",
      "Text extracting from SyntheticdatasetsforDeepLearningincomputer-visionassistedtasksinmanufacturing.pdf:\n",
      "242 Christos Manettas  et al. / Procedia CIRP 103 (2021) 237–242\n",
      "6 Author name / Procedia CIRP 00 (2019) 000 –000 \n",
      "differed from\n",
      "-----------------------\n",
      "169\n",
      "Text extracting from Tang_A_New_Benchmark_On_the_Utility_of_Synthetic_Data_With_CVPR_2023_paper.pdf:\n",
      "[52] VSR Veeravasarapu, Constantin Rothkopf, and Ramesh Vis-\n",
      "vanathan. Adversarially tuned scene generation. In Proceed-\n",
      "ings of\n",
      "-----------------------\n",
      "170\n",
      "Text extracting from Team Profile - WG Technologies.pdf:\n",
      "A b o u t  t h e  t e a m\n",
      "Goh Jin Yu\n",
      "E&E ENGINEERRight mix of Passion & Experience\n",
      "Lim Say Loong\n",
      "MECHANICAL DESIGN\n",
      "ENGINEERFarza\n",
      "-----------------------\n",
      "171\n",
      "Text extracting from Thermal Vision of Oil Palm FFB under difference ripeness quality.pdf:\n",
      "ICATES 2020 \n",
      "IOP Conf. Series: Earth and Environmental Science 644 (2021) 012044 IOP Publishing \n",
      "doi:10.1088/1755-1315/644/1/012\n",
      "-----------------------\n",
      "172\n",
      "Text extracting from Tomato recognition and location algorithm based on improved YOLOv5.pdf:\n",
      "Computers and Electronics in Agriculture 208 (2023) 107759\n",
      "12Zhang, Q., Chen, J.M., Li, B., Xu, C., 2021a. Method for recognizin\n",
      "-----------------------\n",
      "173\n",
      "Text extracting from Tomato_Crop_Identification_and_Recognition_for_an_Autonomous_Agricultural_Robotic_System.pdf:\n",
      "[5]  “Compare Differences between Images - MATLA B Imshowpair.” \n",
      "MathWorks , \n",
      "https://www.mathworks.com/help/images/ref/imshowpa\n",
      "-----------------------\n",
      "174\n",
      "Text extracting from Towards a Real-Time Oil Palm Fruit Maturity System using Supervised Classifiers Based on Feature Analysis.pdf:\n",
      "Agriculture 2022 ,12, 1461 28 of 28\n",
      "96. Sudheer, K.P .; Gowda, P .; Chaubey, I.; Howell, T. Artiﬁcial Neural Network Approach fo\n",
      "-----------------------\n",
      "175\n",
      "Text extracting from Towards Autonomous Robotics Precision Harvesting Mapping, Localization, Planning and Control for a Legged Tree Harvester.pdf:\n",
      "Yue, Y., Yang, C., Senarathne, P., Zhang, J., Wen, M., and Wang, D. (2018). Online collaborative 3d\n",
      "mapping in forest environmen\n",
      "-----------------------\n",
      "176\n",
      "Text extracting from Training Deep Networks With Synthetic Data Bridging the Reality Gap by Domain Randomization.pdf:\n",
      "tude. COURSERA: Neural networks for machine learning ,\n",
      "4(2):26–31, 2012. 4\n",
      "[32] J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zare\n",
      "-----------------------\n",
      "177\n",
      "Text extracting from Unity Perception Generate Synthetic Data for Computer Vision.pdf:\n",
      "Training Data (number of training examples) mAP(error) \u0001mAP(p-value) mAPIoU50(error) \u0001mAPIoU50(p-\n",
      "value)mARmax=100(error) \u0001mARma\n",
      "-----------------------\n",
      "178\n",
      "Text extracting from USDA report.pdf:\n",
      "Year Cottonseed Palm Canola Coconut Corn\n",
      "Begin U.S. Brz Arg Rott U.S. U.S. Rott U.S. Rott Malay Rott Rott U.S.\n",
      "Oct 1 1/ 2/ 3/ 4/\n",
      "-----------------------\n",
      "179\n",
      "Text extracting from Using Genetic Algorithm Neural Network on Near Infrared Spectral Data for Ripeness Grading of Oil Palm Fresh Fruit.pdf:\n",
      "[18] Chipperﬁeld A, Fleming P, Pohlheim H, Fonseca C. Genetic\n",
      "algorithm toolbox user’s guide. Department of AutomaticControl and\n",
      "-----------------------\n",
      "180\n",
      "Text extracting from Vision systems for harvesting robots Produce detection and localization.pdf:\n",
      "Computers and Electronics in Agriculture 192 (2022) 106562\n",
      "27Zeng, L., Feng, J., He, L., 2020. Semantic segmentation of sparse 3\n",
      "-----------------------\n",
      "181\n",
      "Text extracting from Window_ZoomingBased_Localization_Algorithm_of_Fruit_and_Vegetable_for_Harvesting_Robot.pdf:\n",
      "C. Wang et al.: Window Zooming\u0015Based Localization Algorithm of Fruit and Vegetable for Harvesting Robot\n",
      "CHENGLIN WANG received t\n",
      "-----------------------\n"
     ]
    }
   ],
   "source": [
    "folder_path = \".\"\n",
    "chunk_list = []\n",
    "pdf_files = [file for file in os.listdir(folder_path) if file.endswith('.pdf')]\n",
    "n = 1\n",
    "for file_name in pdf_files:\n",
    "    print(n)\n",
    "    n+=1\n",
    "    file_path = os.path.join(folder_path, file_name)\n",
    "    print(f\"Text extracting from {file_name}:\")\n",
    "    chunk_list = extract_text_from_pdf(file_path,file_name,chunk_list)\n",
    "    print(chunk_list[-1][:128])\n",
    "    print(\"-----------------------\")\n",
    "with open('RAG_dict.json', 'w') as file:\n",
    "    json.dump(chunk_list, file, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3b276cad-1df9-4ee0-b42d-eafafd1d71aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RAG_dict.json', 'w') as file:\n",
    "    json.dump(chunk_list, file, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "69bc17aa-0cbb-466a-bc5f-343630a8f5c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C. Wang et al.: Window Zooming\\x15Based Localization Algorithm of Fruit and Vegetable for Harvesting Robot\\nCHENGLIN WANG received the B.E. degree\\nin mathematics and the M.E. degree in CIS\\nfrom Qiqihar University, Qiqihar, China, in 2009\\nand 2013, respectively, and the Ph.D. degree in\\nAME from South China Agricultural University,\\nGuangzhou, China, in 2018. He has a total of\\nten-year experience in academic profession. His\\nresearch interests include the computer vision,\\nimage processing, and development of harvesting\\nrobot.\\nTIANHONG LUO received the B.E. and M.E.\\ndegrees in mechanical design theory and the Ph.D.\\ndegree in mechanical design theory and automa-\\ntion from Chongqing University, Chongqing,\\nChina, in 1998 and 2002, and 2005, respectively.\\nHis research interests include kinematics and sys-\\ntem dynamics of robotic mechanisms, and new\\nfunctional components of robots.\\nLIJUN ZHAO received the B.E. degree in mechan-\\nical design theory and automation and the M.E.\\nand Ph.D. degrees in agricultural mechanization\\nengineering from Northeast Agricultural Univer-\\nsity, Harbin, China, in 2005, 2013, and 2016,\\nrespectively. His research interests include the\\ncomputer vision, image processing, and agricul-\\ntural machinery.\\nYUNCHAO TANG, photograph and biography not available at the time of\\npublication.\\nXIANGJUN ZOU, photograph and biography not available at the time of\\npublication.\\nVOLUME 7, 2019 103649, from \"Window_ZoomingBased_Localization_Algorithm_of_Fruit_and_Vegetable_for_Harvesting_Robot.pdf\",page 10\\n'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chunk_list[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0f52f2ae-8782-4c34-a569-128d3c4de142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current working directory: C:\\Users\\yoeya\\Desktop\\Palm Oil\n"
     ]
    }
   ],
   "source": [
    "cwd = os.getcwd()\n",
    "print(\"Current working directory:\", cwd)\n",
    "if os.path.exists('RAG_dict.json'):\n",
    "    # Load the dictionary from the file\n",
    "    with open('RAG_dict.json', 'r') as file:\n",
    "        chunk_list = json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "44eebf5e-94c8-4793-8f5c-9bf4dfe061da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1-s2.0-S2214317322000105-main.pdf'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf_files[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ebe92b5-44bf-41e2-9b52-349a48b37102",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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