From 918c9796a27d4bff2ed529603895532dfd956e3a Mon Sep 17 00:00:00 2001 From: sepehr Date: Sat, 1 Mar 2025 23:15:38 +0100 Subject: [PATCH] update code from youtube tuto https://www.youtube.com/watch?v=uLrReyH5cu0&t=3558s --- .../testunstructuredPDF.cpython-312.pyc | Bin 0 -> 792 bytes ...S-2017-attention-is-all-you-need-Paper.pdf | Bin 0 -> 569417 bytes document/test2.png | Bin 0 -> 117576 bytes page_0.png | Bin 0 -> 166599 bytes page_1.png | Bin 0 -> 264006 bytes requirements.txt | 6 +- .../__pycache__/pdf_processor.cpython-312.pyc | Bin 0 -> 13880 bytes test_processing.ipynb | 593 ++++++++++-- testunstructuredPDF.py | 25 + testvideoYoutube.ipynb | 863 ++++++++++++++++++ 10 files changed, 1434 insertions(+), 53 deletions(-) create mode 100644 __pycache__/testunstructuredPDF.cpython-312.pyc create mode 100644 document/NIPS-2017-attention-is-all-you-need-Paper.pdf create mode 100644 document/test2.png create mode 100644 page_0.png create mode 100644 page_1.png create mode 100644 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\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Document]:\n\u001b[0;32m 30\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Load data into Document objects.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 31\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlazy_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\langchain_community\\document_loaders\\unstructured.py:107\u001b[0m, in \u001b[0;36mUnstructuredBaseLoader.lazy_load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlazy_load\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Iterator[Document]:\n\u001b[0;32m 106\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Load file.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m elements \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_elements\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post_process_elements(elements)\n\u001b[0;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124melements\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\langchain_community\\document_loaders\\pdf.py:94\u001b[0m, in \u001b[0;36mUnstructuredPDFLoader._get_elements\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_get_elements\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m:\n\u001b[0;32m 92\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01munstructured\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpartition\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpdf\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m partition_pdf\n\u001b[1;32m---> 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m partition_pdf(filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munstructured_kwargs)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\documents\\elements.py:581\u001b[0m, in \u001b[0;36mprocess_metadata..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 579\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 580\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: _P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: _P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Element]:\n\u001b[1;32m--> 581\u001b[0m elements \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 582\u001b[0m call_args \u001b[38;5;241m=\u001b[39m get_call_args_applying_defaults(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 584\u001b[0m unique_element_ids: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m call_args\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munique_element_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\file_utils\\filetype.py:815\u001b[0m, in \u001b[0;36madd_filetype..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 813\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 814\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: _P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: _P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Element]:\n\u001b[1;32m--> 815\u001b[0m elements \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 817\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m elements:\n\u001b[0;32m 818\u001b[0m \u001b[38;5;66;03m# NOTE(robinson) - Attached files have already run through this logic\u001b[39;00m\n\u001b[0;32m 819\u001b[0m \u001b[38;5;66;03m# in their own partitioning function\u001b[39;00m\n\u001b[0;32m 820\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m element\u001b[38;5;241m.\u001b[39mmetadata\u001b[38;5;241m.\u001b[39mattached_to_filename \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\file_utils\\filetype.py:773\u001b[0m, in \u001b[0;36madd_metadata..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 771\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 772\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: _P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: _P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Element]:\n\u001b[1;32m--> 773\u001b[0m elements \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 774\u001b[0m call_args \u001b[38;5;241m=\u001b[39m get_call_args_applying_defaults(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m call_args\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata_filename\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\chunking\\dispatch.py:74\u001b[0m, in \u001b[0;36madd_chunking_strategy..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"The decorated function is replaced with this one.\"\"\"\u001b[39;00m\n\u001b[0;32m 73\u001b[0m \u001b[38;5;66;03m# -- call the partitioning function to get the elements --\u001b[39;00m\n\u001b[1;32m---> 74\u001b[0m elements \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 76\u001b[0m \u001b[38;5;66;03m# -- look for a chunking-strategy argument --\u001b[39;00m\n\u001b[0;32m 77\u001b[0m call_args \u001b[38;5;241m=\u001b[39m get_call_args_applying_defaults(func, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\pdf.py:229\u001b[0m, in \u001b[0;36mpartition_pdf\u001b[1;34m(filename, file, include_page_breaks, strategy, infer_table_structure, ocr_languages, languages, metadata_filename, metadata_last_modified, chunking_strategy, hi_res_model_name, extract_images_in_pdf, extract_image_block_types, extract_image_block_output_dir, extract_image_block_to_payload, starting_page_number, extract_forms, form_extraction_skip_tables, password, pdfminer_line_margin, pdfminer_char_margin, pdfminer_line_overlap, pdfminer_word_margin, **kwargs)\u001b[0m\n\u001b[0;32m 226\u001b[0m exactly_one(filename\u001b[38;5;241m=\u001b[39mfilename, file\u001b[38;5;241m=\u001b[39mfile)\n\u001b[0;32m 228\u001b[0m languages \u001b[38;5;241m=\u001b[39m check_language_args(languages \u001b[38;5;129;01mor\u001b[39;00m [], ocr_languages)\n\u001b[1;32m--> 229\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m partition_pdf_or_image(\n\u001b[0;32m 230\u001b[0m filename\u001b[38;5;241m=\u001b[39mfilename,\n\u001b[0;32m 231\u001b[0m file\u001b[38;5;241m=\u001b[39mfile,\n\u001b[0;32m 232\u001b[0m include_page_breaks\u001b[38;5;241m=\u001b[39minclude_page_breaks,\n\u001b[0;32m 233\u001b[0m strategy\u001b[38;5;241m=\u001b[39mstrategy,\n\u001b[0;32m 234\u001b[0m infer_table_structure\u001b[38;5;241m=\u001b[39minfer_table_structure,\n\u001b[0;32m 235\u001b[0m languages\u001b[38;5;241m=\u001b[39mlanguages,\n\u001b[0;32m 236\u001b[0m metadata_last_modified\u001b[38;5;241m=\u001b[39mmetadata_last_modified,\n\u001b[0;32m 237\u001b[0m hi_res_model_name\u001b[38;5;241m=\u001b[39mhi_res_model_name,\n\u001b[0;32m 238\u001b[0m extract_images_in_pdf\u001b[38;5;241m=\u001b[39mextract_images_in_pdf,\n\u001b[0;32m 239\u001b[0m extract_image_block_types\u001b[38;5;241m=\u001b[39mextract_image_block_types,\n\u001b[0;32m 240\u001b[0m extract_image_block_output_dir\u001b[38;5;241m=\u001b[39mextract_image_block_output_dir,\n\u001b[0;32m 241\u001b[0m extract_image_block_to_payload\u001b[38;5;241m=\u001b[39mextract_image_block_to_payload,\n\u001b[0;32m 242\u001b[0m starting_page_number\u001b[38;5;241m=\u001b[39mstarting_page_number,\n\u001b[0;32m 243\u001b[0m extract_forms\u001b[38;5;241m=\u001b[39mextract_forms,\n\u001b[0;32m 244\u001b[0m form_extraction_skip_tables\u001b[38;5;241m=\u001b[39mform_extraction_skip_tables,\n\u001b[0;32m 245\u001b[0m password\u001b[38;5;241m=\u001b[39mpassword,\n\u001b[0;32m 246\u001b[0m pdfminer_line_margin\u001b[38;5;241m=\u001b[39mpdfminer_line_margin,\n\u001b[0;32m 247\u001b[0m pdfminer_char_margin\u001b[38;5;241m=\u001b[39mpdfminer_char_margin,\n\u001b[0;32m 248\u001b[0m pdfminer_line_overlap\u001b[38;5;241m=\u001b[39mpdfminer_line_overlap,\n\u001b[0;32m 249\u001b[0m pdfminer_word_margin\u001b[38;5;241m=\u001b[39mpdfminer_word_margin,\n\u001b[0;32m 250\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 251\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\pdf.py:342\u001b[0m, in \u001b[0;36mpartition_pdf_or_image\u001b[1;34m(filename, file, is_image, include_page_breaks, strategy, infer_table_structure, languages, metadata_last_modified, hi_res_model_name, extract_images_in_pdf, extract_image_block_types, extract_image_block_output_dir, extract_image_block_to_payload, starting_page_number, extract_forms, form_extraction_skip_tables, password, pdfminer_line_margin, pdfminer_char_margin, pdfminer_line_overlap, pdfminer_word_margin, **kwargs)\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m warnings\u001b[38;5;241m.\u001b[39mcatch_warnings():\n\u001b[0;32m 341\u001b[0m warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 342\u001b[0m elements \u001b[38;5;241m=\u001b[39m _partition_pdf_or_image_local(\n\u001b[0;32m 343\u001b[0m filename\u001b[38;5;241m=\u001b[39mfilename,\n\u001b[0;32m 344\u001b[0m file\u001b[38;5;241m=\u001b[39mspooled_to_bytes_io_if_needed(file),\n\u001b[0;32m 345\u001b[0m is_image\u001b[38;5;241m=\u001b[39mis_image,\n\u001b[0;32m 346\u001b[0m infer_table_structure\u001b[38;5;241m=\u001b[39minfer_table_structure,\n\u001b[0;32m 347\u001b[0m include_page_breaks\u001b[38;5;241m=\u001b[39minclude_page_breaks,\n\u001b[0;32m 348\u001b[0m languages\u001b[38;5;241m=\u001b[39mlanguages,\n\u001b[0;32m 349\u001b[0m ocr_languages\u001b[38;5;241m=\u001b[39mocr_languages,\n\u001b[0;32m 350\u001b[0m metadata_last_modified\u001b[38;5;241m=\u001b[39mmetadata_last_modified \u001b[38;5;129;01mor\u001b[39;00m last_modified,\n\u001b[0;32m 351\u001b[0m hi_res_model_name\u001b[38;5;241m=\u001b[39mhi_res_model_name,\n\u001b[0;32m 352\u001b[0m pdf_text_extractable\u001b[38;5;241m=\u001b[39mpdf_text_extractable,\n\u001b[0;32m 353\u001b[0m extract_images_in_pdf\u001b[38;5;241m=\u001b[39mextract_images_in_pdf,\n\u001b[0;32m 354\u001b[0m extract_image_block_types\u001b[38;5;241m=\u001b[39mextract_image_block_types,\n\u001b[0;32m 355\u001b[0m extract_image_block_output_dir\u001b[38;5;241m=\u001b[39mextract_image_block_output_dir,\n\u001b[0;32m 356\u001b[0m extract_image_block_to_payload\u001b[38;5;241m=\u001b[39mextract_image_block_to_payload,\n\u001b[0;32m 357\u001b[0m starting_page_number\u001b[38;5;241m=\u001b[39mstarting_page_number,\n\u001b[0;32m 358\u001b[0m extract_forms\u001b[38;5;241m=\u001b[39mextract_forms,\n\u001b[0;32m 359\u001b[0m form_extraction_skip_tables\u001b[38;5;241m=\u001b[39mform_extraction_skip_tables,\n\u001b[0;32m 360\u001b[0m password\u001b[38;5;241m=\u001b[39mpassword,\n\u001b[0;32m 361\u001b[0m pdfminer_config\u001b[38;5;241m=\u001b[39mpdfminer_config,\n\u001b[0;32m 362\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 363\u001b[0m )\n\u001b[0;32m 364\u001b[0m out_elements \u001b[38;5;241m=\u001b[39m _process_uncategorized_text_elements(elements)\n\u001b[0;32m 366\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m strategy \u001b[38;5;241m==\u001b[39m PartitionStrategy\u001b[38;5;241m.\u001b[39mFAST:\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\utils.py:216\u001b[0m, in \u001b[0;36mrequires_dependencies..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: _P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: _P\u001b[38;5;241m.\u001b[39mkwargs):\n\u001b[0;32m 215\u001b[0m run_check()\n\u001b[1;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\pdf.py:687\u001b[0m, in \u001b[0;36m_partition_pdf_or_image_local\u001b[1;34m(filename, file, is_image, infer_table_structure, include_page_breaks, languages, ocr_languages, ocr_mode, model_name, hi_res_model_name, pdf_image_dpi, metadata_last_modified, pdf_text_extractable, extract_images_in_pdf, extract_image_block_types, extract_image_block_output_dir, extract_image_block_to_payload, analysis, analyzed_image_output_dir_path, starting_page_number, extract_forms, form_extraction_skip_tables, pdf_hi_res_max_pages, password, pdfminer_config, **kwargs)\u001b[0m\n\u001b[0;32m 680\u001b[0m \u001b[38;5;66;03m# NOTE(christine): merged_document_layout = extracted_layout + inferred_layout\u001b[39;00m\n\u001b[0;32m 681\u001b[0m merged_document_layout \u001b[38;5;241m=\u001b[39m merge_inferred_with_extracted_layout(\n\u001b[0;32m 682\u001b[0m inferred_document_layout\u001b[38;5;241m=\u001b[39minferred_document_layout,\n\u001b[0;32m 683\u001b[0m extracted_layout\u001b[38;5;241m=\u001b[39mextracted_layout,\n\u001b[0;32m 684\u001b[0m hi_res_model_name\u001b[38;5;241m=\u001b[39mhi_res_model_name,\n\u001b[0;32m 685\u001b[0m )\n\u001b[1;32m--> 687\u001b[0m final_document_layout \u001b[38;5;241m=\u001b[39m \u001b[43mprocess_file_with_ocr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[43m \u001b[49m\u001b[43mmerged_document_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 690\u001b[0m \u001b[43m \u001b[49m\u001b[43mextracted_layout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextracted_layout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 691\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_image\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_image\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 692\u001b[0m \u001b[43m \u001b[49m\u001b[43minfer_table_structure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfer_table_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 693\u001b[0m \u001b[43m \u001b[49m\u001b[43mocr_languages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_languages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 694\u001b[0m \u001b[43m \u001b[49m\u001b[43mocr_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 695\u001b[0m \u001b[43m \u001b[49m\u001b[43mpdf_image_dpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpdf_image_dpi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 696\u001b[0m \u001b[43m \u001b[49m\u001b[43mocr_layout_dumper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_layout_dumper\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 697\u001b[0m \u001b[43m \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpassword\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 698\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 699\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 700\u001b[0m inferred_document_layout \u001b[38;5;241m=\u001b[39m process_data_with_model(\n\u001b[0;32m 701\u001b[0m file,\n\u001b[0;32m 702\u001b[0m is_image\u001b[38;5;241m=\u001b[39mis_image,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 705\u001b[0m password\u001b[38;5;241m=\u001b[39mpassword,\n\u001b[0;32m 706\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\utils.py:216\u001b[0m, in \u001b[0;36mrequires_dependencies..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: _P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: _P\u001b[38;5;241m.\u001b[39mkwargs):\n\u001b[0;32m 215\u001b[0m run_check()\n\u001b[1;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\pdf_image\\ocr.py:190\u001b[0m, in \u001b[0;36mprocess_file_with_ocr\u001b[1;34m(filename, out_layout, extracted_layout, is_image, infer_table_structure, ocr_languages, ocr_mode, pdf_image_dpi, ocr_layout_dumper, password)\u001b[0m\n\u001b[0;32m 188\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(filename) \u001b[38;5;129;01mor\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(filename):\n\u001b[1;32m--> 190\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[0;32m 191\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 192\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFile \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilename\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m not found!\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\pdf_image\\ocr.py:177\u001b[0m, in \u001b[0;36mprocess_file_with_ocr\u001b[1;34m(filename, out_layout, extracted_layout, is_image, infer_table_structure, ocr_languages, ocr_mode, pdf_image_dpi, ocr_layout_dumper, password)\u001b[0m\n\u001b[0;32m 175\u001b[0m extracted_regions \u001b[38;5;241m=\u001b[39m extracted_layout[i] \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(extracted_layout) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 176\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m PILImage\u001b[38;5;241m.\u001b[39mopen(image_path) \u001b[38;5;28;01mas\u001b[39;00m image:\n\u001b[1;32m--> 177\u001b[0m merged_page_layout \u001b[38;5;241m=\u001b[39m \u001b[43msupplement_page_layout_with_ocr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 178\u001b[0m \u001b[43m \u001b[49m\u001b[43mpage_layout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout_layout\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpages\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 179\u001b[0m \u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 180\u001b[0m \u001b[43m \u001b[49m\u001b[43minfer_table_structure\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfer_table_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 181\u001b[0m \u001b[43m \u001b[49m\u001b[43mocr_languages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_languages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 182\u001b[0m \u001b[43m \u001b[49m\u001b[43mocr_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mextracted_regions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextracted_regions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mocr_layout_dumper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_layout_dumper\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 186\u001b[0m merged_page_layouts\u001b[38;5;241m.\u001b[39mappend(merged_page_layout)\n\u001b[0;32m 187\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m DocumentLayout\u001b[38;5;241m.\u001b[39mfrom_pages(merged_page_layouts)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\utils.py:216\u001b[0m, in \u001b[0;36mrequires_dependencies..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 213\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs: _P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: _P\u001b[38;5;241m.\u001b[39mkwargs):\n\u001b[0;32m 215\u001b[0m run_check()\n\u001b[1;32m--> 216\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\pdf_image\\ocr.py:213\u001b[0m, in \u001b[0;36msupplement_page_layout_with_ocr\u001b[1;34m(page_layout, image, infer_table_structure, ocr_languages, ocr_mode, extracted_regions, ocr_layout_dumper)\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[38;5;129m@requires_dependencies\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munstructured_inference\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 196\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msupplement_page_layout_with_ocr\u001b[39m(\n\u001b[0;32m 197\u001b[0m page_layout: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPageLayout\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 203\u001b[0m ocr_layout_dumper: Optional[OCRLayoutDumper] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 204\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPageLayout\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 205\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 206\u001b[0m \u001b[38;5;124;03m Supplement an PageLayout with OCR results depending on OCR mode.\u001b[39;00m\n\u001b[0;32m 207\u001b[0m \u001b[38;5;124;03m If mode is \"entire_page\", we get the OCR layout for the entire image and\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 210\u001b[0m \u001b[38;5;124;03m with no text and add text from OCR to each element.\u001b[39;00m\n\u001b[0;32m 211\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 213\u001b[0m ocr_agent \u001b[38;5;241m=\u001b[39m \u001b[43mOCRAgent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_agent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlanguage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mocr_languages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ocr_mode \u001b[38;5;241m==\u001b[39m OCRMode\u001b[38;5;241m.\u001b[39mFULL_PAGE\u001b[38;5;241m.\u001b[39mvalue:\n\u001b[0;32m 215\u001b[0m ocr_layout \u001b[38;5;241m=\u001b[39m ocr_agent\u001b[38;5;241m.\u001b[39mget_layout_from_image(image)\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\utils\\ocr_models\\ocr_interface.py:34\u001b[0m, in \u001b[0;36mOCRAgent.get_agent\u001b[1;34m(cls, language)\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get the configured OCRAgent instance.\u001b[39;00m\n\u001b[0;32m 30\u001b[0m \n\u001b[0;32m 31\u001b[0m \u001b[38;5;124;03mThe OCR package used by the agent is determined by the `OCR_AGENT` environment variable.\u001b[39;00m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 33\u001b[0m ocr_agent_cls_qname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_get_ocr_agent_cls_qname()\n\u001b[1;32m---> 34\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_instance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mocr_agent_cls_qname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlanguage\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\ramez\\miniconda3\\envs\\rag\\lib\\site-packages\\unstructured\\partition\\utils\\ocr_models\\ocr_interface.py:41\u001b[0m, in \u001b[0;36mOCRAgent.get_instance\u001b[1;34m(ocr_agent_module, language)\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mje suis ici dans OCR_AGENT\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 40\u001b[0m \u001b[38;5;28mprint\u001b[39m(ocr_agent_module)\n\u001b[1;32m---> 41\u001b[0m module_name, class_name \u001b[38;5;241m=\u001b[39m ocr_agent_module\u001b[38;5;241m.\u001b[39mrsplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 43\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m module_name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m OCR_AGENT_MODULES_WHITELIST:\n\u001b[0;32m 44\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 45\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEnvironment variable OCR_AGENT module name \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be set to a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhitelisted module part of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mOCR_AGENT_MODULES_WHITELIST\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 47\u001b[0m )\n", - "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 1)" + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTimeoutError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:754\u001b[39m, in \u001b[36mHTTPResponse._error_catcher\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 753\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m754\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m 756\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 757\u001b[39m \u001b[38;5;66;03m# FIXME: Ideally we'd like to include the url in the ReadTimeoutError but\u001b[39;00m\n\u001b[32m 758\u001b[39m \u001b[38;5;66;03m# there is yet no clean way to get at it from this context.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:879\u001b[39m, in \u001b[36mHTTPResponse._raw_read\u001b[39m\u001b[34m(self, amt, read1)\u001b[39m\n\u001b[32m 878\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._error_catcher():\n\u001b[32m--> \u001b[39m\u001b[32m879\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mread1\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fp_closed \u001b[38;5;28;01melse\u001b[39;00m \u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt != \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Platform-specific: Buggy versions of Python.\u001b[39;00m\n\u001b[32m 882\u001b[39m \u001b[38;5;66;03m# Close the connection when no data is returned\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 887\u001b[39m \u001b[38;5;66;03m# not properly close the connection in all cases. There is\u001b[39;00m\n\u001b[32m 888\u001b[39m \u001b[38;5;66;03m# no harm in redundantly calling close.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:862\u001b[39m, in \u001b[36mHTTPResponse._fp_read\u001b[39m\u001b[34m(self, amt, read1)\u001b[39m\n\u001b[32m 860\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 861\u001b[39m \u001b[38;5;66;03m# StringIO doesn't like amt=None\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m862\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m._fp.read()\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/http/client.py:479\u001b[39m, in \u001b[36mHTTPResponse.read\u001b[39m\u001b[34m(self, amt)\u001b[39m\n\u001b[32m 478\u001b[39m amt = \u001b[38;5;28mself\u001b[39m.length\n\u001b[32m--> \u001b[39m\u001b[32m479\u001b[39m s = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 480\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m s \u001b[38;5;129;01mand\u001b[39;00m amt:\n\u001b[32m 481\u001b[39m \u001b[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[32m 482\u001b[39m \u001b[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/socket.py:707\u001b[39m, in \u001b[36mSocketIO.readinto\u001b[39m\u001b[34m(self, b)\u001b[39m\n\u001b[32m 706\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m707\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 708\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/ssl.py:1252\u001b[39m, in \u001b[36mSSLSocket.recv_into\u001b[39m\u001b[34m(self, buffer, nbytes, flags)\u001b[39m\n\u001b[32m 1249\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 1250\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m\"\u001b[39m %\n\u001b[32m 1251\u001b[39m \u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m1252\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1253\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/ssl.py:1104\u001b[39m, in \u001b[36mSSLSocket.read\u001b[39m\u001b[34m(self, len, buffer)\u001b[39m\n\u001b[32m 1103\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1104\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sslobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1105\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "\u001b[31mTimeoutError\u001b[39m: The read operation timed out", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mReadTimeoutError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/requests/models.py:820\u001b[39m, in \u001b[36mResponse.iter_content..generate\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 819\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m820\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m.raw.stream(chunk_size, decode_content=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 821\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:1066\u001b[39m, in \u001b[36mHTTPResponse.stream\u001b[39m\u001b[34m(self, amt, decode_content)\u001b[39m\n\u001b[32m 1065\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m._fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._decoded_buffer) > \u001b[32m0\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m1066\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1068\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:955\u001b[39m, in \u001b[36mHTTPResponse.read\u001b[39m\u001b[34m(self, amt, decode_content, cache_content)\u001b[39m\n\u001b[32m 953\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._decoded_buffer.get(amt)\n\u001b[32m--> \u001b[39m\u001b[32m955\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raw_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 957\u001b[39m flush_decoder = amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (amt != \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:878\u001b[39m, in \u001b[36mHTTPResponse._raw_read\u001b[39m\u001b[34m(self, amt, read1)\u001b[39m\n\u001b[32m 876\u001b[39m fp_closed = \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m._fp, \u001b[33m\"\u001b[39m\u001b[33mclosed\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m878\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_error_catcher\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mread1\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfp_closed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[33;43mb\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/contextlib.py:158\u001b[39m, in \u001b[36m_GeneratorContextManager.__exit__\u001b[39m\u001b[34m(self, typ, value, traceback)\u001b[39m\n\u001b[32m 157\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m158\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m.\u001b[49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 160\u001b[39m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[32m 161\u001b[39m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[32m 162\u001b[39m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:759\u001b[39m, in \u001b[36mHTTPResponse._error_catcher\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 756\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 757\u001b[39m \u001b[38;5;66;03m# FIXME: Ideally we'd like to include the url in the ReadTimeoutError but\u001b[39;00m\n\u001b[32m 758\u001b[39m \u001b[38;5;66;03m# there is yet no clean way to get at it from this context.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m759\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ReadTimeoutError(\u001b[38;5;28mself\u001b[39m._pool, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"\u001b[39m\u001b[33mRead timed out.\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m 761\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m BaseSSLError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 762\u001b[39m \u001b[38;5;66;03m# FIXME: Is there a better way to differentiate between SSLErrors?\u001b[39;00m\n", + "\u001b[31mReadTimeoutError\u001b[39m: HTTPSConnectionPool(host='cdn-lfs.hf.co', port=443): Read timed out.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mConnectionError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:454\u001b[39m, in \u001b[36mhttp_get\u001b[39m\u001b[34m(url, temp_file, proxies, resume_size, headers, expected_size, displayed_filename, _nb_retries, _tqdm_bar)\u001b[39m\n\u001b[32m 453\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m454\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_content\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDOWNLOAD_CHUNK_SIZE\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 455\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# filter out keep-alive new chunks\u001b[39;49;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/requests/models.py:826\u001b[39m, in \u001b[36mResponse.iter_content..generate\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 825\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ReadTimeoutError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m--> \u001b[39m\u001b[32m826\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e)\n\u001b[32m 827\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SSLError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[31mConnectionError\u001b[39m: HTTPSConnectionPool(host='cdn-lfs.hf.co', port=443): Read timed out.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mIncompleteRead\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:754\u001b[39m, in \u001b[36mHTTPResponse._error_catcher\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 753\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m754\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m 756\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 757\u001b[39m \u001b[38;5;66;03m# FIXME: Ideally we'd like to include the url in the ReadTimeoutError but\u001b[39;00m\n\u001b[32m 758\u001b[39m \u001b[38;5;66;03m# there is yet no clean way to get at it from this context.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:900\u001b[39m, in \u001b[36mHTTPResponse._raw_read\u001b[39m\u001b[34m(self, amt, read1)\u001b[39m\n\u001b[32m 890\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[32m 891\u001b[39m \u001b[38;5;28mself\u001b[39m.enforce_content_length\n\u001b[32m 892\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.length_remaining \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 898\u001b[39m \u001b[38;5;66;03m# raised during streaming, so all calls with incorrect\u001b[39;00m\n\u001b[32m 899\u001b[39m \u001b[38;5;66;03m# Content-Length are caught.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m900\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(\u001b[38;5;28mself\u001b[39m._fp_bytes_read, \u001b[38;5;28mself\u001b[39m.length_remaining)\n\u001b[32m 901\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m read1 \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[32m 902\u001b[39m (amt != \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.length_remaining == \u001b[38;5;28mlen\u001b[39m(data)\n\u001b[32m 903\u001b[39m ):\n\u001b[32m (...)\u001b[39m\u001b[32m 906\u001b[39m \u001b[38;5;66;03m# `http.client.HTTPResponse`, so we close it here.\u001b[39;00m\n\u001b[32m 907\u001b[39m \u001b[38;5;66;03m# See https://github.com/python/cpython/issues/113199\u001b[39;00m\n", + "\u001b[31mIncompleteRead\u001b[39m: IncompleteRead(46334378 bytes read, 33976465 more expected)", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[31mProtocolError\u001b[39m Traceback (most recent call last)", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/requests/models.py:820\u001b[39m, in \u001b[36mResponse.iter_content..generate\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 819\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m820\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m.raw.stream(chunk_size, decode_content=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 821\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:1066\u001b[39m, in \u001b[36mHTTPResponse.stream\u001b[39m\u001b[34m(self, amt, decode_content)\u001b[39m\n\u001b[32m 1065\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m._fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._decoded_buffer) > \u001b[32m0\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m1066\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m=\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1068\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:983\u001b[39m, in \u001b[36mHTTPResponse.read\u001b[39m\u001b[34m(self, amt, decode_content, cache_content)\u001b[39m\n\u001b[32m 979\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m._decoded_buffer) < amt \u001b[38;5;129;01mand\u001b[39;00m data:\n\u001b[32m 980\u001b[39m \u001b[38;5;66;03m# TODO make sure to initially read enough data to get past the headers\u001b[39;00m\n\u001b[32m 981\u001b[39m \u001b[38;5;66;03m# For example, the GZ file header takes 10 bytes, we don't want to read\u001b[39;00m\n\u001b[32m 982\u001b[39m \u001b[38;5;66;03m# it one byte at a time\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m983\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raw_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 984\u001b[39m decoded_data = \u001b[38;5;28mself\u001b[39m._decode(data, decode_content, flush_decoder)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:878\u001b[39m, in \u001b[36mHTTPResponse._raw_read\u001b[39m\u001b[34m(self, amt, read1)\u001b[39m\n\u001b[32m 876\u001b[39m fp_closed = \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m._fp, \u001b[33m\"\u001b[39m\u001b[33mclosed\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m--> \u001b[39m\u001b[32m878\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_error_catcher\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_fp_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mamt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mread1\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfp_closed\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[33;43mb\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/contextlib.py:158\u001b[39m, in \u001b[36m_GeneratorContextManager.__exit__\u001b[39m\u001b[34m(self, typ, value, traceback)\u001b[39m\n\u001b[32m 157\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m158\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m.\u001b[49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 159\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[32m 160\u001b[39m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[32m 161\u001b[39m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[32m 162\u001b[39m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/urllib3/response.py:778\u001b[39m, in \u001b[36mHTTPResponse._error_catcher\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 777\u001b[39m arg = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mConnection broken: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m778\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ProtocolError(arg, e) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m 780\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m (HTTPException, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[31mProtocolError\u001b[39m: ('Connection broken: IncompleteRead(46334378 bytes read, 33976465 more expected)', IncompleteRead(46334378 bytes read, 33976465 more expected))", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mChunkedEncodingError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m docs = \u001b[43mloader\u001b[49m\u001b[43m.\u001b[49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2\u001b[39m docs[\u001b[32m0\u001b[39m]\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_core/document_loaders/base.py:31\u001b[39m, in \u001b[36mBaseLoader.load\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mload\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28mlist\u001b[39m[Document]:\n\u001b[32m 30\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Load data into Document objects.\"\"\"\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m31\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlazy_load\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_community/document_loaders/unstructured.py:107\u001b[39m, in \u001b[36mUnstructuredBaseLoader.lazy_load\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 105\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mlazy_load\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> Iterator[Document]:\n\u001b[32m 106\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Load file.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m107\u001b[39m elements = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_get_elements\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 108\u001b[39m \u001b[38;5;28mself\u001b[39m._post_process_elements(elements)\n\u001b[32m 109\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.mode == \u001b[33m\"\u001b[39m\u001b[33melements\u001b[39m\u001b[33m\"\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_community/document_loaders/pdf.py:94\u001b[39m, in \u001b[36mUnstructuredPDFLoader._get_elements\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 91\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_get_elements\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28mlist\u001b[39m:\n\u001b[32m 92\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01munstructured\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpartition\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpdf\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m partition_pdf\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpartition_pdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43munstructured_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/documents/elements.py:581\u001b[39m, in \u001b[36mprocess_metadata..decorator..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 579\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 580\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args: _P.args, **kwargs: _P.kwargs) -> \u001b[38;5;28mlist\u001b[39m[Element]:\n\u001b[32m--> \u001b[39m\u001b[32m581\u001b[39m elements = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 582\u001b[39m call_args = get_call_args_applying_defaults(func, *args, **kwargs)\n\u001b[32m 584\u001b[39m unique_element_ids: \u001b[38;5;28mbool\u001b[39m = call_args.get(\u001b[33m\"\u001b[39m\u001b[33munique_element_ids\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/file_utils/filetype.py:815\u001b[39m, in \u001b[36madd_filetype..decorator..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 813\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 814\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args: _P.args, **kwargs: _P.kwargs) -> \u001b[38;5;28mlist\u001b[39m[Element]:\n\u001b[32m--> \u001b[39m\u001b[32m815\u001b[39m elements = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 817\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m elements:\n\u001b[32m 818\u001b[39m \u001b[38;5;66;03m# NOTE(robinson) - Attached files have already run through this logic\u001b[39;00m\n\u001b[32m 819\u001b[39m \u001b[38;5;66;03m# in their own partitioning function\u001b[39;00m\n\u001b[32m 820\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m element.metadata.attached_to_filename \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/file_utils/filetype.py:773\u001b[39m, in \u001b[36madd_metadata..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 771\u001b[39m \u001b[38;5;129m@functools\u001b[39m.wraps(func)\n\u001b[32m 772\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args: _P.args, **kwargs: _P.kwargs) -> \u001b[38;5;28mlist\u001b[39m[Element]:\n\u001b[32m--> \u001b[39m\u001b[32m773\u001b[39m elements = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 774\u001b[39m call_args = get_call_args_applying_defaults(func, *args, **kwargs)\n\u001b[32m 776\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m call_args.get(\u001b[33m\"\u001b[39m\u001b[33mmetadata_filename\u001b[39m\u001b[33m\"\u001b[39m):\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/chunking/dispatch.py:74\u001b[39m, in \u001b[36madd_chunking_strategy..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 71\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"The decorated function is replaced with this one.\"\"\"\u001b[39;00m\n\u001b[32m 73\u001b[39m \u001b[38;5;66;03m# -- call the partitioning function to get the elements --\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m74\u001b[39m elements = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 76\u001b[39m \u001b[38;5;66;03m# -- look for a chunking-strategy argument --\u001b[39;00m\n\u001b[32m 77\u001b[39m call_args = get_call_args_applying_defaults(func, *args, **kwargs)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/partition/pdf.py:229\u001b[39m, in \u001b[36mpartition_pdf\u001b[39m\u001b[34m(filename, file, include_page_breaks, strategy, infer_table_structure, ocr_languages, languages, metadata_filename, metadata_last_modified, chunking_strategy, hi_res_model_name, extract_images_in_pdf, extract_image_block_types, extract_image_block_output_dir, extract_image_block_to_payload, starting_page_number, extract_forms, form_extraction_skip_tables, password, pdfminer_line_margin, pdfminer_char_margin, pdfminer_line_overlap, pdfminer_word_margin, **kwargs)\u001b[39m\n\u001b[32m 226\u001b[39m exactly_one(filename=filename, file=file)\n\u001b[32m 228\u001b[39m languages = check_language_args(languages \u001b[38;5;129;01mor\u001b[39;00m [], ocr_languages)\n\u001b[32m--> \u001b[39m\u001b[32m229\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpartition_pdf_or_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 230\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 231\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 232\u001b[39m \u001b[43m \u001b[49m\u001b[43minclude_page_breaks\u001b[49m\u001b[43m=\u001b[49m\u001b[43minclude_page_breaks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 233\u001b[39m \u001b[43m \u001b[49m\u001b[43mstrategy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstrategy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 234\u001b[39m \u001b[43m \u001b[49m\u001b[43minfer_table_structure\u001b[49m\u001b[43m=\u001b[49m\u001b[43minfer_table_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 235\u001b[39m \u001b[43m \u001b[49m\u001b[43mlanguages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlanguages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 236\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata_last_modified\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmetadata_last_modified\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 237\u001b[39m \u001b[43m \u001b[49m\u001b[43mhi_res_model_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhi_res_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_images_in_pdf\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_images_in_pdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_image_block_types\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_image_block_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 240\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_image_block_output_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_image_block_output_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 241\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_image_block_to_payload\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_image_block_to_payload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 242\u001b[39m \u001b[43m \u001b[49m\u001b[43mstarting_page_number\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstarting_page_number\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 243\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_forms\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_forms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 244\u001b[39m \u001b[43m \u001b[49m\u001b[43mform_extraction_skip_tables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mform_extraction_skip_tables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 245\u001b[39m \u001b[43m \u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 246\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdfminer_line_margin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdfminer_line_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 247\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdfminer_char_margin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdfminer_char_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 248\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdfminer_line_overlap\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdfminer_line_overlap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 249\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdfminer_word_margin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdfminer_word_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 250\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 251\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/partition/pdf.py:342\u001b[39m, in \u001b[36mpartition_pdf_or_image\u001b[39m\u001b[34m(filename, file, is_image, include_page_breaks, strategy, infer_table_structure, languages, metadata_last_modified, hi_res_model_name, extract_images_in_pdf, extract_image_block_types, extract_image_block_output_dir, extract_image_block_to_payload, starting_page_number, extract_forms, form_extraction_skip_tables, password, pdfminer_line_margin, pdfminer_char_margin, pdfminer_line_overlap, pdfminer_word_margin, **kwargs)\u001b[39m\n\u001b[32m 340\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m warnings.catch_warnings():\n\u001b[32m 341\u001b[39m warnings.simplefilter(\u001b[33m\"\u001b[39m\u001b[33mignore\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m342\u001b[39m elements = \u001b[43m_partition_pdf_or_image_local\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 343\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 344\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile\u001b[49m\u001b[43m=\u001b[49m\u001b[43mspooled_to_bytes_io_if_needed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 345\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_image\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_image\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 346\u001b[39m \u001b[43m \u001b[49m\u001b[43minfer_table_structure\u001b[49m\u001b[43m=\u001b[49m\u001b[43minfer_table_structure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 347\u001b[39m \u001b[43m \u001b[49m\u001b[43minclude_page_breaks\u001b[49m\u001b[43m=\u001b[49m\u001b[43minclude_page_breaks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 348\u001b[39m \u001b[43m \u001b[49m\u001b[43mlanguages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlanguages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 349\u001b[39m \u001b[43m \u001b[49m\u001b[43mocr_languages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mocr_languages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 350\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata_last_modified\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmetadata_last_modified\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlast_modified\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 351\u001b[39m \u001b[43m \u001b[49m\u001b[43mhi_res_model_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhi_res_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 352\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdf_text_extractable\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdf_text_extractable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 353\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_images_in_pdf\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_images_in_pdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 354\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_image_block_types\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_image_block_types\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 355\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_image_block_output_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_image_block_output_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 356\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_image_block_to_payload\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_image_block_to_payload\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 357\u001b[39m \u001b[43m \u001b[49m\u001b[43mstarting_page_number\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstarting_page_number\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 358\u001b[39m \u001b[43m \u001b[49m\u001b[43mextract_forms\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextract_forms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 359\u001b[39m \u001b[43m \u001b[49m\u001b[43mform_extraction_skip_tables\u001b[49m\u001b[43m=\u001b[49m\u001b[43mform_extraction_skip_tables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 360\u001b[39m \u001b[43m \u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 361\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdfminer_config\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdfminer_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 362\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 363\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 364\u001b[39m out_elements = _process_uncategorized_text_elements(elements)\n\u001b[32m 366\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m strategy == PartitionStrategy.FAST:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/utils.py:216\u001b[39m, in \u001b[36mrequires_dependencies..decorator..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 213\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 214\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args: _P.args, **kwargs: _P.kwargs):\n\u001b[32m 215\u001b[39m run_check()\n\u001b[32m--> \u001b[39m\u001b[32m216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured/partition/pdf.py:643\u001b[39m, in \u001b[36m_partition_pdf_or_image_local\u001b[39m\u001b[34m(filename, file, is_image, infer_table_structure, include_page_breaks, languages, ocr_languages, ocr_mode, model_name, hi_res_model_name, pdf_image_dpi, metadata_last_modified, pdf_text_extractable, extract_images_in_pdf, extract_image_block_types, extract_image_block_output_dir, extract_image_block_to_payload, analysis, analyzed_image_output_dir_path, starting_page_number, extract_forms, form_extraction_skip_tables, pdf_hi_res_max_pages, password, pdfminer_config, **kwargs)\u001b[39m\n\u001b[32m 640\u001b[39m skip_analysis_dump = env_config.ANALYSIS_DUMP_OD_SKIP\n\u001b[32m 642\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m643\u001b[39m inferred_document_layout = \u001b[43mprocess_file_with_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 644\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 645\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_image\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_image\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 646\u001b[39m \u001b[43m \u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhi_res_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 647\u001b[39m \u001b[43m \u001b[49m\u001b[43mpdf_image_dpi\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpdf_image_dpi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 648\u001b[39m \u001b[43m \u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 649\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 651\u001b[39m extracted_layout, layouts_links = (\n\u001b[32m 652\u001b[39m process_file_with_pdfminer(\n\u001b[32m 653\u001b[39m filename=filename,\n\u001b[32m (...)\u001b[39m\u001b[32m 659\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m ([], [])\n\u001b[32m 660\u001b[39m )\n\u001b[32m 662\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m analysis:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured_inference/inference/layout.py:366\u001b[39m, in \u001b[36mprocess_file_with_model\u001b[39m\u001b[34m(filename, model_name, is_image, fixed_layouts, pdf_image_dpi, password, **kwargs)\u001b[39m\n\u001b[32m 354\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mprocess_file_with_model\u001b[39m(\n\u001b[32m 355\u001b[39m filename: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m 356\u001b[39m model_name: Optional[\u001b[38;5;28mstr\u001b[39m],\n\u001b[32m (...)\u001b[39m\u001b[32m 361\u001b[39m **kwargs: Any,\n\u001b[32m 362\u001b[39m ) -> DocumentLayout:\n\u001b[32m 363\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Processes pdf file with name filename into a DocumentLayout by using a model identified by\u001b[39;00m\n\u001b[32m 364\u001b[39m \u001b[33;03m model_name.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m366\u001b[39m model = \u001b[43mget_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 367\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, UnstructuredObjectDetectionModel):\n\u001b[32m 368\u001b[39m detection_model = model\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured_inference/models/base.py:74\u001b[39m, in \u001b[36mget_model\u001b[39m\u001b[34m(model_name)\u001b[39m\n\u001b[32m 70\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m UnknownModelException(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnknown model type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 72\u001b[39m model: UnstructuredModel = model_class_map[model_name]()\n\u001b[32m---> \u001b[39m\u001b[32m74\u001b[39m model.initialize(**initialize_params)\n\u001b[32m 75\u001b[39m models[model_name] = model\n\u001b[32m 76\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m model\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured_inference/utils.py:40\u001b[39m, in \u001b[36mLazyDict.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 38\u001b[39m evaluate = value.evaluate\n\u001b[32m 39\u001b[39m args, kwargs = value.info\n\u001b[32m---> \u001b[39m\u001b[32m40\u001b[39m value = \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.cache:\n\u001b[32m 42\u001b[39m \u001b[38;5;28mself\u001b[39m._raw_dict[key] = value\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/unstructured_inference/utils.py:115\u001b[39m, in \u001b[36mdownload_if_needed_and_get_local_path\u001b[39m\u001b[34m(path_or_repo, filename, **kwargs)\u001b[39m\n\u001b[32m 113\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m full_path\n\u001b[32m 114\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m115\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_or_repo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py:114\u001b[39m, in \u001b[36mvalidate_hf_hub_args.._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[32m 112\u001b[39m kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.\u001b[34m__name__\u001b[39m, has_token=has_token, kwargs=kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m114\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:862\u001b[39m, in \u001b[36mhf_hub_download\u001b[39m\u001b[34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, resume_download, force_filename, local_dir_use_symlinks)\u001b[39m\n\u001b[32m 842\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _hf_hub_download_to_local_dir(\n\u001b[32m 843\u001b[39m \u001b[38;5;66;03m# Destination\u001b[39;00m\n\u001b[32m 844\u001b[39m local_dir=local_dir,\n\u001b[32m (...)\u001b[39m\u001b[32m 859\u001b[39m local_files_only=local_files_only,\n\u001b[32m 860\u001b[39m )\n\u001b[32m 861\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m862\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_hf_hub_download_to_cache_dir\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 863\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Destination\u001b[39;49;00m\n\u001b[32m 864\u001b[39m \u001b[43m \u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcache_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 865\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# File info\u001b[39;49;00m\n\u001b[32m 866\u001b[39m \u001b[43m \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 867\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 868\u001b[39m \u001b[43m \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 869\u001b[39m \u001b[43m \u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 870\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# HTTP info\u001b[39;49;00m\n\u001b[32m 871\u001b[39m \u001b[43m \u001b[49m\u001b[43mendpoint\u001b[49m\u001b[43m=\u001b[49m\u001b[43mendpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 872\u001b[39m \u001b[43m \u001b[49m\u001b[43metag_timeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43metag_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 873\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhf_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Additional options\u001b[39;49;00m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlocal_files_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:1011\u001b[39m, in \u001b[36m_hf_hub_download_to_cache_dir\u001b[39m\u001b[34m(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, proxies, token, local_files_only, force_download)\u001b[39m\n\u001b[32m 1009\u001b[39m Path(lock_path).parent.mkdir(parents=\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 1010\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m WeakFileLock(lock_path):\n\u001b[32m-> \u001b[39m\u001b[32m1011\u001b[39m \u001b[43m_download_to_tmp_and_move\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1012\u001b[39m \u001b[43m \u001b[49m\u001b[43mincomplete_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblob_path\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m.incomplete\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1013\u001b[39m \u001b[43m \u001b[49m\u001b[43mdestination_path\u001b[49m\u001b[43m=\u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblob_path\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1014\u001b[39m \u001b[43m \u001b[49m\u001b[43murl_to_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43murl_to_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1015\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1016\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1017\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1018\u001b[39m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1019\u001b[39m \u001b[43m \u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m=\u001b[49m\u001b[43mforce_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1020\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1021\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os.path.exists(pointer_path):\n\u001b[32m 1022\u001b[39m _create_symlink(blob_path, pointer_path, new_blob=\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:1547\u001b[39m, in \u001b[36m_download_to_tmp_and_move\u001b[39m\u001b[34m(incomplete_path, destination_path, url_to_download, proxies, headers, expected_size, filename, force_download)\u001b[39m\n\u001b[32m 1544\u001b[39m _check_disk_space(expected_size, incomplete_path.parent)\n\u001b[32m 1545\u001b[39m _check_disk_space(expected_size, destination_path.parent)\n\u001b[32m-> \u001b[39m\u001b[32m1547\u001b[39m \u001b[43mhttp_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1548\u001b[39m \u001b[43m \u001b[49m\u001b[43murl_to_download\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1549\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1550\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1551\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mresume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1552\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1553\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1554\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1556\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mDownload complete. Moving file to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdestination_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 1557\u001b[39m _chmod_and_move(incomplete_path, destination_path)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:471\u001b[39m, in \u001b[36mhttp_get\u001b[39m\u001b[34m(url, temp_file, proxies, resume_size, headers, expected_size, displayed_filename, _nb_retries, _tqdm_bar)\u001b[39m\n\u001b[32m 469\u001b[39m time.sleep(\u001b[32m1\u001b[39m)\n\u001b[32m 470\u001b[39m reset_sessions() \u001b[38;5;66;03m# In case of SSLError it's best to reset the shared requests.Session objects\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m471\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhttp_get\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 472\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m=\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[43m \u001b[49m\u001b[43mtemp_file\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtemp_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 474\u001b[39m \u001b[43m \u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m=\u001b[49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 475\u001b[39m \u001b[43m \u001b[49m\u001b[43mresume_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnew_resume_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 476\u001b[39m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m=\u001b[49m\u001b[43minitial_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 477\u001b[39m \u001b[43m \u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexpected_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 478\u001b[39m \u001b[43m \u001b[49m\u001b[43m_nb_retries\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_nb_retries\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 479\u001b[39m \u001b[43m \u001b[49m\u001b[43m_tqdm_bar\u001b[49m\u001b[43m=\u001b[49m\u001b[43m_tqdm_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 480\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 482\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m expected_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m expected_size != temp_file.tell():\n\u001b[32m 483\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[32m 484\u001b[39m consistency_error_message.format(\n\u001b[32m 485\u001b[39m actual_size=temp_file.tell(),\n\u001b[32m 486\u001b[39m )\n\u001b[32m 487\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/huggingface_hub/file_download.py:454\u001b[39m, in \u001b[36mhttp_get\u001b[39m\u001b[34m(url, temp_file, proxies, resume_size, headers, expected_size, displayed_filename, _nb_retries, _tqdm_bar)\u001b[39m\n\u001b[32m 452\u001b[39m new_resume_size = resume_size\n\u001b[32m 453\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m454\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m.\u001b[49m\u001b[43miter_content\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconstants\u001b[49m\u001b[43m.\u001b[49m\u001b[43mDOWNLOAD_CHUNK_SIZE\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 455\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# filter out keep-alive new chunks\u001b[39;49;00m\n\u001b[32m 456\u001b[39m \u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m.\u001b[49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/requests/models.py:822\u001b[39m, in \u001b[36mResponse.iter_content..generate\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 820\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m.raw.stream(chunk_size, decode_content=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 821\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m--> \u001b[39m\u001b[32m822\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n\u001b[32m 823\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m DecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 824\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m ContentDecodingError(e)\n", + "\u001b[31mChunkedEncodingError\u001b[39m: ('Connection broken: IncompleteRead(46334378 bytes read, 33976465 more expected)', IncompleteRead(46334378 bytes read, 33976465 more expected))" ] } ], @@ -272,6 +305,314 @@ "print(pytesseract.image_to_string(Image.open(IMG_path)))" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sepehr/dev/rag/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "The PDF <_io.BufferedReader name='/home/sepehr/dev/rag/document/11_chapitre3.pdf'> contains a metadata field indicating that it should not allow text extraction. Ignoring this field and proceeding. Use the check_extractable if you want to raise an error in this case\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33.. CCHHAAPPIITTRREE 33 ::\n", + "\n", + "MMOODDÉÉLLIISSAATTIIOONN 11DD DDEESS IINNJJEECCTTEEUURRSS CCOONNDDEENNSSEEUURRSS\n", + "\n", + "92\n", + "\n", + "Chapitre 3 : Modélisation 1D des injecteurs condenseurs\n", + "\n", + "3-1 MODÉLISATION 0D DE L’IC\n", + "\n", + "La modélisation 0D consiste à donner une approche théorique de type global des IC en simplifiant au maximum la physique des phénomènes intervenant dans le processus de fonctionnement. Ce modèle est basé sur des bilans globaux entrée/sortie de masse, de quantité de mouvement et d'énergie. Il permet d'estimer les caractéristiques d'un injecteur, en termes de performance et de limites de fonctionnement tout en restant simple avec un volume de calcul réduit. Il nécessite la prise en compte d'une loi de fermeture expérimentale. Cette modélisation a déjà été entreprise auparavant par différents auteurs : [Rose1960] ; [Cattadori1993] ; [Narabayashi1994] ; [Soplenkov1995] ; et [Deberne2000]. Nous partirons de la modélisation décrite dans [Deberne2000], référence à laquelle nous nous reporterons pour plus de détails. Elle traite de l'IC fonctionnant avec une injection de liquide centrale. Beithou [Beithou2000] a proposé aussi un modèle 0D stationnaire et simplifié de la chambre de mélange pour un IC à injection de vapeur centrale, qui donne de bons résultats. Par contre, l'auteur suppose que l'écoulement dans la chambre de mélange de l'IC est isobare, et il ne traite pas l'onde de condensation en supposant que la condensation complète a lieu à la fin de la chambre de mélange (l'auteur impose l'évolution du taux de vide). Pour cela, Beithou considère 2 équations : l'équation de conservation de l'énergie et l'équation de la masse (les indices correspondent aux repères marqués sur la figure 3-1) :\n", + "\n", + "⎧ ⎪ ⎪⎪ ⎨ ⎪ ⎪ u ⎪ ⎩\n", + "\n", + "( hM 1 V 1 V M +\n", + "\n", + "2 50 u, 1 V M\n", + "\n", + "+\n" + ] + } + ], + "source": [ + "from unstructured.partition.auto import partition\n", + "\n", + "\n", + "filename = pdf_path = \"/home/sepehr/dev/rag/document/11_chapitre3.pdf\"\n", + "elements = partition(filename=filename, content_type=\"application/pdf\")\n", + "print(\"\\n\\n\".join([str(el) for el in elements][:10]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2021, 3, 26, 11, 4, 9, tzinfo=datetime.timezone(datetime.timedelta(seconds=43200)))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from unstructured.cleaners.extract import extract_datetimetz\n", + "\n", + "text = \"\"\"from ABC.DEF.local ([ba23::58b5:2236:45g2:88h2]) by\n", + " \\n ABC.DEF.local2 ([ba23::58b5:2236:45g2:88h2%25]) with mapi id\\\n", + " n 32.88.5467.123; Fri, 26 Mar 2021 11:04:09 +1200\"\"\"\n", + "\n", + "# Returns datetime.datetime(2021, 3, 26, 11, 4, 9, tzinfo=datetime.timezone(datetime.timedelta(seconds=43200)))\n", + "extract_datetimetz(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['me@email.com', 'you@email.com']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from unstructured.cleaners.extract import extract_email_address\n", + "\n", + "text = \"\"\"Me me@email.com and You \n", + " ([ba23::58b5:2236:45g2:88h2]) (10.0.2.01)\"\"\"\n", + "\n", + "# Returns \"['me@email.com', 'you@email.com']\"\n", + "extract_email_address(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "from unstructured.partition.image import partition_image\n", + "\n", + "# Source: https://github.com/Unstructured-IO/unstructured-ingest/blob/main/example-docs/img/english-and-korean.png\n", + "# Path to the local file to process, relative to this .py file.\n", + "filename = \"/home/sepehr/dev/rag/document/test2.png\"\n", + "\n", + "elements = partition_image(\n", + " filename=filename,\n", + " strategy=\"ocr_only\",\n", + " languages=[\"eng\", \"fr\"] # Language codes differ by the OCR agent used.\n", + ")\n", + "\n", + "# Convert the list of returned elements into a list of dictionaries for printing or saving.\n", + "element_dicts = [element.to_dict() for element in elements]\n", + "\n", + "# Print the list.\n", + "# print(json.dumps(element_dicts, indent=2))\n", + "\n", + "# Or, save the list locally:\n", + "#\n", + "# file = \"local-ingest-output/english-and-korean.json\"\n", + "#\n", + "# with open(file, \"w\") as file:\n", + "# json.dump(element_dicts, file, indent=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "from unstructured_inference.models.base import get_model\n", + "from unstructured_inference.inference.layout import DocumentLayout\n", + "\n", + "model = get_model(\"yolox\")\n", + "layout = DocumentLayout.from_file(\"/home/sepehr/dev/rag/document/04Extrait_Methodologie_Experimentale.pdf\", detection_model=model)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nombre de pages: 54\n", + "Éléments sur la première page: 4\n", + "Picture: 49 elements\n", + "Caption: 11 elements\n", + "Text: 191 elements\n", + "Section-header: 50 elements\n", + "Page-header: 17 elements\n", + "Table: 57 elements\n", + "Title: 24 elements\n", + "Formula: 1 elements\n", + "Page-footer: 29 elements\n", + "List-item: 1 elements\n" + ] + } + ], + "source": [ + "print(f\"Nombre de pages: {len(layout.pages)}\")\n", + "\n", + "# Explorer la première page\n", + "first_page = layout.pages[0]\n", + "print(f\"Éléments sur la première page: {len(first_page.elements)}\")\n", + "\n", + "# Examiner les types d'éléments\n", + "from collections import defaultdict\n", + "\n", + "# Group elements by type across all pages\n", + "element_types = defaultdict(list)\n", + "\n", + "for page in layout.pages:\n", + " for element in page.elements:\n", + " element_types[element.type].append(element)\n", + "\n", + "# Print count of each element type\n", + "for elem_type, elems in element_types.items():\n", + " print(f\"{elem_type}: {len(elems)} elements\")\n", + "\n", + "# Examiner les types d'éléments\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "from PIL import Image\n", + "import numpy as np\n", + "import fitz # PyMuPDF\n", + "\n", + "def visualize_layout(layout, page_num=0):\n", + " # Get the PDF path used to generate the layout\n", + " pdf_path = \"/home/sepehr/dev/rag/document/04Extrait_Methodologie_Experimentale.pdf\"\n", + " \n", + " # Open the PDF and render the page as an image using PyMuPDF\n", + " pdf_document = fitz.open(pdf_path)\n", + " page = pdf_document[page_num]\n", + " pix = page.get_pixmap(matrix=fitz.Matrix(2, 2)) # 2x zoom for better quality\n", + " \n", + " # Convert to numpy array for matplotlib\n", + " img = np.frombuffer(pix.samples, dtype=np.uint8).reshape(pix.h, pix.w, pix.n)\n", + " \n", + " # Create figure and axis\n", + " fig, ax = plt.subplots(1, figsize=(12, 16))\n", + " ax.imshow(img)\n", + " \n", + " # Define colors for different element types\n", + " colors = {'Title': 'red', 'Text': 'blue', 'Table': 'green', \n", + " 'Figure': 'orange', 'List': 'purple', 'Header': 'cyan',\n", + " 'Footer': 'magenta'}\n", + " \n", + " # Draw bounding boxes for elements\n", + " page = layout.pages[page_num]\n", + " \n", + " for element in page.elements:\n", + " print(element)\n", + " # Access bbox properties correctly based on the Rectangle object structure\n", + " try:\n", + " # Try to access rectangle coordinates\n", + " if hasattr(element, 'bbox'):\n", + " \n", + " if hasattr(element.bbox, 'x0'): # Rectangle object with explicit coordinates\n", + " x = element.bbox.x0\n", + " y = element.bbox.y0\n", + " width = element.bbox.x1 - element.bbox.x0\n", + " height = element.bbox.y1 - element.bbox.y0\n", + " else: # Some other format\n", + " x, y, x2, y2 = element.bbox # Try direct unpacking\n", + " width = x2 - x\n", + " height = y2 - y\n", + " \n", + " elem_type = getattr(element, 'type', 'Unknown')\n", + " color = colors.get(elem_type, 'gray')\n", + " \n", + " rect = patches.Rectangle((x, y), width, height, \n", + " linewidth=1, edgecolor=color, facecolor='none')\n", + " ax.add_patch(rect)\n", + " \n", + " # Add label for the element type\n", + " plt.text(x, y, f\"{elem_type}\", color='white', \n", + " backgroundcolor=color, fontsize=8)\n", + " \n", + " except (AttributeError, TypeError, ValueError) as e:\n", + " print(f\"Error processing element: {e}\")\n", + " \n", + " plt.title(f\"Page {page_num+1} Layout\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Close the PDF document\n", + " pdf_document.close()\n", + "\n", + "# Install required package if needed\n", + "# !pip install pymupdf\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "ename": "UnboundLocalError", + "evalue": "cannot access local variable 'element' where it is not associated with a value", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mUnboundLocalError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[69]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mvisualize_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlayout\u001b[49m\u001b[43m,\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[68]\u001b[39m\u001b[32m, line 30\u001b[39m, in \u001b[36mvisualize_layout\u001b[39m\u001b[34m(layout, page_num)\u001b[39m\n\u001b[32m 28\u001b[39m \u001b[38;5;66;03m# Draw bounding boxes for elements\u001b[39;00m\n\u001b[32m 29\u001b[39m page = layout.pages[page_num]\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[43melement\u001b[49m)\n\u001b[32m 31\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m element \u001b[38;5;129;01min\u001b[39;00m page.elements:\n\u001b[32m 32\u001b[39m \u001b[38;5;66;03m# Access bbox properties correctly based on the Rectangle object structure\u001b[39;00m\n\u001b[32m 33\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 34\u001b[39m \u001b[38;5;66;03m# Try to access rectangle coordinates\u001b[39;00m\n", + "\u001b[31mUnboundLocalError\u001b[39m: cannot access local variable 'element' where it is not associated with a value" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_layout(layout,1)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -285,11 +626,161 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INTRODUCTION AUX PLANS D’EXPERIENCES\n", + "INTERET DES PEX : RAPPELS STATISTIQUES\n", + "\n", + "a Distribution normale : courbe représentative = courbe de Gauss variable\n", + "aléatoire x distribuée normalement avec moyenne = X, écart type = cet\n", + "Variance : V = 0”\n", + "\n", + "9)\n", + "\n", + "Re x Bo\n", + "a Théoréme des variances : variables aléatoires x;, x2, ... X, indépendantes\n", + "+ relation yr sagt ayX; + aX. +t ot anXy\n", + "\n", + "= Variance de y~ — V(y~) = 0 + a V(x1) + ax V(X0)+.... + ay V(Xn)\n", + "\n", + "Exemple : X = moyenne de n valeurs de x; ® =In [xp txt... +x,]\n", + "> V(¥) = In? [V(x)) + V(x2) +... + V(X,)]\n", + "Si variances égales entre elles : V(x1) = V0) =... = VO&q) =\n", + "\n", + "=> V(X)= Ino? dou (o(X) = oy/ vn\n", + "\n", + "a ERREUR SUR LA MOYENNE = ERREUR SUR UNE MESURE DIVISEE PAR\n", + "RACINE CARRREE DE n.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "import pytesseract \n", + "from PIL import Image\n", + "print(pytesseract.image_to_string(Image.open(filename)))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "elements = partition(filename=filename,\n", + " strategy=\"hi_res\",\n", + " hi_res_model_name=\"yolox\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a Distribution normale : courbe représentative = courbe de Gauss variable aléatoire x distribuée normalement avec moyenne = X, écart type = cet Variance : V = 0”\n" + ] + } + ], + "source": [ + "print(elements[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "a bytes-like object is required, not 'Image'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[34]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m# Assuming element is an instance of unstructured.documents.elements.Image\u001b[39;00m\n\u001b[32m 5\u001b[39m image_data = element \u001b[38;5;66;03m# Convert the element to image data\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m image = Image.open(\u001b[43mio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mBytesIO\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_data\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m 7\u001b[39m image.show()\n", + "\u001b[31mTypeError\u001b[39m: a bytes-like object is required, not 'Image'" + ] + } + ], + "source": [ + "from PIL import Image\n", + "import io\n", + "\n", + "# Assuming element is an instance of unstructured.documents.elements.Image\n", + "image_data = element # Convert the element to image data\n", + "image = Image.open(io.BytesIO(image_data))\n", + "image.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'Image',\n", + " 'element_id': '68faf178bda657a69fc87460f25782c1',\n", + " 'text': '9) Re x Bo',\n", + " 'metadata': {'detection_class_prob': 0.820507287979126,\n", + " 'coordinates': {'points': ((99.514404296875, 233.28099060058594),\n", + " (99.514404296875, 533.5597534179688),\n", + " (651.2028198242188, 533.5597534179688),\n", + " (651.2028198242188, 233.28099060058594)),\n", + " 'system': 'PixelSpace',\n", + " 'layout_width': 761,\n", + " 'layout_height': 1096},\n", + " 'last_modified': '2025-03-01T11:08:55',\n", + " 'filetype': 'image/png',\n", + " 'languages': ['eng'],\n", + " 'page_number': 1,\n", + " 'file_directory': '/home/sepehr/dev/rag/document',\n", + " 'filename': 'test2.png'}}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "element.to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "elements = partition_image(\"example-docs/img/layout-parser-paper-fast.jpg\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "rag", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -303,7 +794,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/testunstructuredPDF.py b/testunstructuredPDF.py new file mode 100644 index 0000000..dfd480c --- /dev/null +++ b/testunstructuredPDF.py @@ -0,0 +1,25 @@ +from unstructured.partition.pdf import partition_pdf + +output_path = "/home/sepehr/dev/rag/document/" +file_path = "/home/sepehr/dev/rag/document/04Extrait_Methodologie_Experimentale.pdf" + +# Reference: https://docs.unstructured.io/open-source/core-functionality/chunking +chunks = partition_pdf( + filename=file_path, + infer_table_structure=True, # extract tables + strategy="hi_res", # mandatory to infer tables + + extract_image_block_types=["Image"], # Add 'Table' to list to extract image of tables + # image_output_dir_path=output_path, # if None, images and tables will saved in base64 + + extract_image_block_to_payload=True, # if true, will extract base64 for API usage + + chunking_strategy="by_title", # or 'basic' + max_characters=10000, # defaults to 500 + combine_text_under_n_chars=2000, # defaults to 0 + new_after_n_chars=6000, + + # extract_images_in_pdf=True, # deprecated +) + +print(chunks[3].metadata.orig_elements) \ No newline at end of file diff --git a/testvideoYoutube.ipynb b/testvideoYoutube.ipynb new file mode 100644 index 0000000..215391d --- /dev/null +++ b/testvideoYoutube.ipynb @@ -0,0 +1,863 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from unstructured.partition.pdf import partition_pdf\n", + "\n", + "output_path = \"/home/sepehr/dev/rag/document/\"\n", + "file_path = \"/home/sepehr/dev/rag/document/04Extrait_Methodologie_Experimentale.pdf\"\n", + "\n", + "# Reference: https://docs.unstructured.io/open-source/core-functionality/chunking\n", + "chunks = partition_pdf(\n", + " filename=file_path,\n", + " infer_table_structure=True, # extract tables\n", + " strategy=\"hi_res\", # mandatory to infer tables\n", + "\n", + " extract_image_block_types=[\"Image\"], # Add 'Table' to list to extract image of tables\n", + " # image_output_dir_path=output_path, # if None, images and tables will saved in base64\n", + "\n", + " extract_image_block_to_payload=True, # if true, will extract base64 for API usage\n", + "\n", + " chunking_strategy=\"by_title\", # or 'basic'\n", + " max_characters=1000, # defaults to 500\n", + " combine_text_under_n_chars=20, # defaults to 0\n", + " new_after_n_chars=600,\n", + "\n", + " # extract_images_in_pdf=True, # deprecated\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{\"\",\n", + " \"\",\n", + " \"\"}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set([str(type(el)) for el in chunks])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elements = chunks[52].metadata.orig_elements \n", + "chunks[52].metadata.orig_elements" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'type': 'Image',\n", + " 'element_id': '147440b6-7a12-4187-b8e8-a7facc373062',\n", + " 'text': '® © 2 a3 “ cs) E a a c 6 a “a c _ n 3 4 § Concentration en ppm',\n", + " 'metadata': {'coordinates': {'points': ((440.66661111111114,\n", + " 919.3324722222222),\n", + " (440.66661111111114, 1493.9991388888889),\n", + " (1150.9999166666666, 1493.9991388888889),\n", + " (1150.9999166666666, 919.3324722222222)),\n", + " 'system': 'PixelSpace',\n", + " 'layout_width': 1653,\n", + " 'layout_height': 2339},\n", + " 'last_modified': '2025-03-01T09:19:11',\n", + " 'filetype': 'PPM',\n", + " 'languages': ['eng'],\n", + " 'page_number': 14,\n", + " 'image_base64': 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AKM0maTdQANyKQgtHycGl4PWlYBhjtQB5V4u8EX14t7dpK7MzZiG7oKjvdD1W38KwMQWnjlQ4z/CDXqwjBGGAKjoKGiVxtZVK+mKAPKbXTNQ1zU9WvfK2Jc2ghTt8wqC3ttUlsbDww1uCsK+XKxzXrqQpH91FX6DFHlJuLiNA/wDe2jNAENha/ZrKCH/nmgUiuS8dWSagLFJJGQfaFwRXahSo46muI8ezPCNMyet2o4+ooA27bw/HaTBhdzN9a3AAECjJx60IwYAmlLCgBOlKDSHNGcUAOJ4pN2KQNQSDQAu7NNLYNOXimshY5FACj1oNGDjFLjigAP3aavWndqQDFMBcUoFJmlBpABpKVjSUAB6U2lPSkXmgApw6UUUALRSUuKACilxRigAFFJijFAC5pCaQg0bTQAlKBS4ozQAClFFHSgBaD0ozSZpANPFN608jNN2mmADpSZp3tTdpoAdjIpOlLnAppOaYC5paaKXNAC0hGaXNI3PSgA3YpDzSgetIQe1ABSZozxSAZoAUHNLTQcZpxIAyaACkJxRnOaQ8jP6UALnNLkAVE80UKlpZFQDuxxWLqHiqxslLRq1yeg8rmgDdLJ7fjVe4vra2Q+bKgHoCM1yH2/X9fbdYgW0HpMmDirdr4Lt5LlLq+lmedf7kh2/lQAt743tDIbezjkkmHTMZx+dVBP4o1U7J7aKC2P8AHG/zY/Kutg021tlCxQpx3ZQTVnYNuMAfSgDlLPwkLeTzZr64nDc7HORW5b6Jp8RDiyhDf3tvNXVyvXBFPVifpQBznizRf7RtbV4V+e1k8xR2zWLe6vqmqaRPp0NuBdFdpPI5+td64DjFRrDGuG8tA3chaAKWjwzQaXBDcf60IN31q+KQksCDj2xTgppgKKXNJg0HrQAtIad2pp60gEpRS5BpCcUAGOadmkByKbtNAARXD/Ej/jy0v/r9T+Yruj92uF+JH/Hlpf8A1+p/MUAdtD/qx9BT6ZH8sQ+lAOaAH0UmDRQBIxIpFOaMZpwXFIBCKTFKx5puaAHgUU0nihTmgBacBQBSA80AOI4prfKpNKTxTG5GKQCqdwzXC/EFdw0v/r7X+YrulG0Vw/xCYINK97tf5imB2sfCinEc0g6CpMfLQA2kNKOlBoAaBzTsUh4ozQAmacOlJilHSgBaDRSGgAooooAaARQaXfniimAgz3o3GlJzRtoATqKAMUuMUp4pAGaBTRThQAtFFFAC5ozRijFACHJoGRSg80FsGgBBnNOoByKKQBTMU+koAUUjGig80wG5ozS4oxQAmaM0uKMUAMJOadzimk4bFLu4pgJ3pQKbu5pc0AOxRikzRmgAPFAPNHWjGKAFooooAgZiGxUh+VaQpnml6rQBTvb610+E3F1OsSDqWqvZ6/pOoz/Zra8jmlxu2qea5jxRItz4x0nT5WBglRt8Z6HnvVbxrZwaTpdvLpMSQTmdFMsX3sEjIpAd1PeR20TS3EggjUZLNXN3Xi+S4Yx6TZveA9JozxSWXhq7vGW4vb2aSNlGYW5Wujs9NtNPjxbQJD7KKAOUXQtY1dw+o6gVhP8AywYVu6d4a0zTAPJgVT1x71sKdx+ZR9aCvz7u1ABjcuCuFoXjhRxTi3HqKAcjgYoAWiiimAxRljmnnA4pO9OC55oAaB3prHmnMe1NxmgAAzTs0LxS4oATNLmjFIRzQAnNLxilzSEZ5oAQUppBSmgAXpS0i9KWgBG6Vw3xH/48tL/6/U/mK7k8iuG+JIxY6X/1+J/MUAdvHzEv0FGMU2A/Iv0qU0ANooooAm4opuaM0gFNNIo3UZoAT7pyaXAPzLQSe1Nc7CAikk0ASZJ5ppO7levvVC+1rT9OX/TLuOInsxxSSaxYQWQvJLmPySQA+eKAL/XIHUUAHAJI3VnWmuafqJZLO9ilZBltp6Cmf23phuhbfao/OBxjNAGqSQMDqa4X4i7imkg4/wCPxf5iu4Vug3ZyMg1wvxIysWkAHLfbVz9MigDu1OAKcKjUEgVIeFoAcCDS01OlOpAIRxTelPPSmGgA60YxQKU80wDNJikxTxQA00lPIppFACDBoIxQvFKx4pgNXmnU1O9PoAQ0w1JTSKQCCnjpTRS5oAU0g60tFABRRRQAgp+OKYKk7UgGkUmcUMcCkHPWgBetO60wnHSlBpgOxSZoJpBQAGkpTSUACjFPyBQKa1IAOCaMUgpaoBrD5aRRT6Q8UABFHakzRmgApSQOtJSHBHNACnr7e1BBx8hH41C0yopcnYg6sa5jU/F0SSGDTYmup+n7o5xSA6ea4t7SMmaZEA5+ZgK5a68WS3dybLSYHM2ceY6fJ+dQWvh291eYSa5N50J+ZUHGPaurs9OttOiEVugVB90d6APPdc0f7XqNpHfyuNWdSYjE2FHrUdnps+iXkd74idpU3CONVbcvJwCR61seK7W4g8T6Zq0MLuluhD49zWf4q1K48QwWtpZ2kjbZo3ZxyBgjNMD0SMgqMcKVBFKBUcMZS0hVxuYAcjtxU9IAGBQ3PSkpQcGmA0Db1p4INI3PSkHFAB3px6UzPNOJ4oAbml3U2igBetHSmnNKMmgBrMc05QaUoKcMUALnFGc0jU1ehoAdR2ptKDxQAnze1Id1OO6mndQAqg96fimqSBzS7qAEIzXD/Eb/AI9NMH/T4n8xXcE8Vw/xCUmz00/9PifzFAHZRthE+lTg5GaggAaJfpUwGBigBaKKKAFzSZPpS0uBSAZtzzS7adRQAhBprEFGUHax705s4OKiEQQDBJ3dzQBwPibwqz+bqEk7XrJyLd+lcrbwX+r+A54rXdOyXwBj/ugH7tdVr/hTWU1c32l3DSbySY5ZPl/KnW/gm6tPDz2sUrJPJMJ22txuznH0oAwdM1FNNh1W1fTI9Ouo7XIdCcsarS2BXwGmviU/bhFv3d2NdRpfg68nvby81XAa5iEJCHIAHeqE3gLUZZ47HzSNNj+VQH5I9xQB3Ph9pLnw/ZSSMS7RKSTXLfE6VILfS5nO1Vu1LH24rtrC2FnZw2yfdjQLXG/ElFmi0pWAK/bFyD35FAG1a+MNMuJUigmD7uM10CyLKFKngiqkemWi7HjtYlI9FAq2Fww4AA9KAHA4pd1NAxmloAUnikFIelANACmgUhNKOlAC0Dg0UUALmkoooAbnNJQBzTiKAEWnU1adQAUrUlJnNABijFKKCeaAE6UuT6UDmlIAGaAEopDz0oHHWgBRS7hTcZowaAFJBppHpS7TS4xQA3n0px6UuRTSc0AIDzSlsdqAOaGHIoAM57UmeacBxQRQAvIoOTSbqN1ABiikJooACcDikcHA4pR1prAk9eKADbikb5OT0pflBzkmo57mG3jMs0iqvXBNAEgJIzjisjVdesNLXbPMBKeFT1Nc/feJNR1a6NnosQKZwzuMfXBrT0rwzDDie9Zppj1D/MAaYGW0Wr+KZSu6SytR3TkMK6HSPDtjpS7ooUMw6y45NagiSMEBQi9topyjCHZn8aQChMHrx6UrKCuO/alFLQBG8SSxGORQ6nqD3qGGwtbYkwRLHnrirNRsTmgB44GB0pCKB0pRzTAQUbM96U0Z4oAaTs96QHPNDc0HhaAGk4p4+YU0DNO6UALgDvTScUnJoAzQAZzTgfamlcU4UwAtSbvSkamqCDzQBJkntRigEEUE4NIBMH0pRTgRimM3PFADjnsabz3NKpx1oY5HFADc5OKQDJxQgw3NP6NTAdtx3rjvH8W6x08el0p/UV1zMdwFcr46ObKx9rlf5ilYDpLQnYM+lWM1XtWzag98VIp+UUwJM0UyigCSkLYGe1Jhgu4nOa43x/rd1pNha29nIUmuJfL3L15qQOwS4ickLIpI6gGpcg15N4et9eSTWrSa9lNyqgRueoNdN4C1m71W1vYLuVnntJRGWPU0wOyJ7ZpowAFPPvTimec0bcUgEx1BGRTWJ2fJxz3p+6koAQMVAXGaViOflNOFBoAYQduE4J7muF+JOfs2jqDg/bV5/EV3ma4P4kn91o//AF+p/MUAd2hIAB9KVuvFAPyj6UUwAcdTS000ooAD0pAacelN25oACacOlN24pw+7SAWikp1ACUUE03NACilNIDThQA0ClpSaTNAAelIBS5ooARqbzT6KAEGaceRikJ4pvU0ALnFHWlC04CgBmcUuadRSAZuI7Ubs040lMBMUoFFFACGkzTqAM0AANBNBUUmKAE20bafmjNADMUvSkYHqKQ5II64oAfjimkZU80jSCKIvIdqjqa5DUfE0uozmy0NTMCdrTRn7p6H8qANPVvElrpSMiqZph/Ah5rBttJ1TxNMLnU5Nlk5ykIBVlHoa1dI8LRWkonvpBeXnUyMORXTAYGKAKVlpttpsIigjxjv3q4qEDJ5anUUANye4zR8w57elPpDQAClpBS0AIKQqKUUGmA0Up4pO9OoATrSN0p1IaAGLSt0pDRigAHFL1oFKKAG0A+1PzRmgBuc00k5p55phNNAKvNGM0DmigBOlKPn/AAozSg4FDAXBxTCVB5YZp4Ld6jeJC2T1pAB5pUFLijNAAfvU3ndT15pxpgRkc5rA8WQCWztM9p1P61vNntWN4lIa2t89pQaANa3QLCoHTFS7cVFaMHtlK+lT/wANADcUUtFACKMZJOQa5rxl4dOuabHsfbLC+9TnpWtrBu4bGR7EjzgNw3DIxXnmqeKtYk8NWvmL5NxcXLQOWXA29KkDAj8O+MI7q4MMqNHP1l8/kV6T4K8O/wBgWEkcspae4YSO2c8/WuP0LQbiNdbs5dQBEihQxlPHToc10Hw51S7u7e+s7uRXNpL5UTDuuPXvQB3JDBuPu0oOaOi01SXGV4+tAEmBRimAtu6jHenM4C5HNMB1BFMBbbyRnvSs+BnHWkAuK4P4lD91o/8A1+r/ADFd2W+UHqK4T4lti30dscfbV/mKAO5U8D6U8UyMblB7YpckHFMBT1oFHWlAoAO1GcUHgUmM9aAFzml7U3GKUcikAU6kxSigBpFIRT+KOKAGClFG2jpQAppMUueKQE5oAMUtLRgUAJRjilx6UnIoAQ0KOaO9OAoAXOKQtQaTGaQDhzRSdBSZNACmkoNFMAoozQaACkJxRSkUANyTRg04cUEnFACUU3n8B1oZiOhwPU0AcvrviK7j1I6TpESTX4UOyOcAKe+azG8U6roLxpr9skMUpwrREsaztT1lNL+Jk1wsTzhrdVIjGTSa14jbWdf0e3isJkTzsSGaPgjB6UAaiy6l4tZoV3W+mk8TKcOR9K6rTdMttNgCxwIjDjcBy3uaswRxRoqJGExxwMVNtAOaAGcMSo4buakUYGM5pNo49qdQAUUUUwFpDS0hpAKeBSZpuTSimA6kNFFADe9OpMUGgBaDSCjPNADSOaUCnYzRgCgBtJmgnml4NACZopQpppODQAE4oxmjbuFG7bTQB0prcCjlqCM0ANB5qZcAVGFApec8UAPZiKaADzmgscc03jPelYB2aMUoAoPFFgBeKcajyaXcaAEPWsjW4vNgTPZs1r5I5rK1gSeQCOmadwLmnnbYoauZyuaz9NJNhEBVzc3SgB9FN+eigBzgsoRRgetcz4wsdMutNjhvZo4Dv/dM/wDerplVoycnIPNcL8TbG+1DR7ZbG3EkqzZHNSBzUXgTXCzCHW1eK54yFPFeh+GfDlt4ftBGCrTty7jufWuG0vVvGukabFbNocUoQYMjSHNdr4V1PVNWtZm1WxS1kVsJtOcigDoWB/vc0jfKFXrmnFcnPYUm7POOB0NAHM+IvFsOgSxw+QZGcZ4PSnWPjLT7vRHvwVVVbYQT/FVXxJLpL3kcMqo96wPlow6/jXmSBl8GzqBsf+1V+Rem3fQB6npXjOyvrmSGTETKMkse1QTeN7eLUpIQmbeNsGTPGK5HxjawvDB/Y6hbsqPMEfpim3H2dfh5dQOijUliwx/izTA9atriOa3WaIhkcZ4riviXlYNIP8P2xf5iuj8Ljy/DlkGJJ8pc59a5n4otImnaa0S75BdKUU9zxxSA7tHxCmO9P3LwD1NcRb6t4uleEPocSxg8kOa7G2MssKNPGEfHIHagCYDFOFJjBopgKelN6UpNNPPSkAvWlHSkFO60AFIaUnFFACZozS4pMUALnikNIM04UAIOhpB1pxximjrQA+lIzSZFIWpABO2kzu5oPvQAMcUwE71IOlMwc08dKQCGgUhIzSjimAGm0pOaSgBwoNJn0ooAZTgc0uM0zIB5oAfSA5o6jigcdaAFpKCwFQ3NzDawPNM+1EGSfamAruQCeij9a5PVvEsl3cfYNJUzN0d0P3T6GqN1rGo+J7g2emqY7Jjj7ShwwHriup0fQrXSbceUgaY/fkI5Y+tIDj4tCv8ARdRbWmhe8mddpRRyBUn2zWPEGsWEv9nTWUFvJuYvggjFehEfLjrmo9m0EKMAdB60AAyNufm96cDTVRlB75/SnAGgB2KKUUlMAoopDQA0k0oNLgd6MZ6UAGKOlLSYoAAc0tIOOtGaAFpOtBPFAoAOlJnmnGoznNAEgPFIx4pAwoyByelACHpSLnNGcnincCgAVskih170g29e9IdxNACrntQQvelBIHvTGXuaaAX6UYpgbHAqTigBMUoOKMikIoADzTSh609RmnHgc0ARqacTSKM9KRgRQA4AGjApqninUgArkcVnaoSbRh6VoFsVQ1PP2CVj2UmgCLSSx02Jq0VPesrw3IZdHiY/dPetcL+VCAXzBRS7VopgLt+cMx/Cub8X3WrWllG2mRxSMzYw4ziukPUH9KwPE+sT6JZJLa2Et2xbBWMZxUgcVJfePYrUStDZGPGSNozXQ/D3W77WrK9N4saywyhMIMVgS+OtYuIXiTw/eBnGASBxWx8ONNvbKC/nu0aJribzNrDBoA7rJUY70OWAJXGKAWJ9qRl4AFAHN+IvB1l4hkhuJ5JY5YxgGJttLpXgzTdM0aXT13yRyOXLSHJz9a6Mq29SCMDrTQrZIbp1oA5vRvBNho17NeJJNNJKu3ErbgPpmo7rwFp15ftdPJMpkOXQNhfyrqjJt68elKeduetAEVtElvDHBGMRxrgVxfxKfdbaRgf8vq9fqK7k5J4I2iuG+JK7odJB4AvF/mKAO4ix5Yx6U8UyLHljHpTxQAgz3paQZ70tMAxmmninHimnmgBV5p3Q01RilPSkAHmg8ClHIpDQAoNITSgUhFACjpRQKKACkpaXFADaRuDT8U1lyaAADcKcowKAMCgHNIBaD0opG6UANPWlPSgdKQdaYBiinYoxQAlH1pelNYE9KAGlvSlVQ3Jo2+tO6dKAA8DAphGeDTvc1k63r1rpEG6UhpSPkiB+ZvpQBPfajb6dbtJcOAAOBnk1yUS3/iq4WRsx6ajZQH5XJ75HpRY6Rd+Jbr7bqxP2UH5IjwR6V2dpBHbR+WoAAGARQBHY2Ftp8W2GMAfSrqng5oBAwMUHk4oAA27mlpFGBxS0wCiiigApDS000AGaXtTKVjgUAAOTTugpqilJzQAtJnFJuo60ALnNGKAKXFACYpaMUUAFMbOafTCwDYoAbg0EHFP3CkDAnFADVGKU0pGKQUANB5p4PFMx81PAoAAeaR+lHSkbpTQDAOakHNMUU/pQAYxSEk0uc0owKAEUkUEkmhmApFYGgBwO2kY7qDzSE4oAAuBmlzSqcrSEUgDtms7WT/xK5+f4D/KrzPtqhrQ36Tcnp+7b+VAFHwfJnw3bnrwa385Wuc8Dj/ilLY9eDXRgfKKEAZooxRTAc7Yw2e3Sue8V6/8A2BpqTGISPI21UJ6mui6jBxmuM+IOj3mq6XbSWihpoJfM2k8HFSBian43voNP05IdPAvrvIMYblTXReCvEtx4htblbm2EUts/lsM55rj/AA/aatLLfarqVtF50ADwRjkA10vgHS9Qso9RuryNUa6n8xVU9qYHcL92lJApvI+lLjNIBM+lPFRninjpQAMqtjIzjpQRnmlo7GgBFUbTxXEfEfBttJBH/L4v8xXcdq4b4j/8e+k/9fi/zFAHbRACMY9KetNh/wBWPpTwKQDSeaM0h60lMB3XijbimjinZzQAZo6ilxmkxg0AABpeKO1J3oACTmlHvRRQAcUcUlFACgilplKKAFpKXNJQAZpRxSUUALmjg0lA60ALSd6cabQAUopKUUABoAwOtLTcDqTQAo46mmsxXtkUp5Gegrm9Y8QR2ubS0bzLh+MdQM0ATa/4ij0oeXEBNcyfKkXqazdG8OTXkx1LWQZpZDujik/5Yewq1oPh8xSG91D95cv/AAtyF+ldJny+O3agBAqQoEVQFAxinqEK8CmkeYM05FwKAFPUUg60ppB1oAXgUhpTRQA3mlpaSgBaYSM1IKYVyaAEyPSkPzU7ZRjFMAA4qJ8g8VLmmkUAC89qfjikBWhgSOKAFopqgjrTqAA9KaKcelIBQAGmbcnNPIoA4oAbtFGAOlOIpMUAB5pMUtFACcUc0DrS0AHHemNSmjFNANxSbiTT+BSFhQAgGadg+tIOaeBjrQwGFcmnBQB0p3FNJ4xSATijikooAOnTpRuo9qNlADWHGao6yP8AiTXX/XJv5VfPpVHWv+QNdf8AXJv5UAZXgU/8UjZ/jXSg1zHgY/8AFI2f411AAIFACZop20UUANwC+eQa5vxjr/8Awj2npIiF5p28uPjIB9TXROfkO7jniud8ZaC+uaankyYlgPmLgd6QHJ6NP4omSc3csBaUfJtjwB9a6TwTrl1qiXsN5hpLWXy8oMCuAWTxxHKkaWtyY1PMg713/gfQLnSLW4luZSZrt/NYEcg0wOuBCnrgehpw5HpULKGbeee1OO8YXrnv6UgHE4YAjr3oPFcj4p1XV4LuCx0+GTMin94vasnRPGlzJo11Jdq3mRSNHknuM0AehFwO9Kr7iQPzrz3w1rt/qd1JO8jND3HoKi1bxPqlrLqFxAHWzszwQeCKAPSQ2FPB61xXxH4ttJ4/5fF/mK6nR7w6jpdvckcyIGJrlviRlbTSc8/6Yv8AMUgOzhbESnGcipQwP1qCKRBEp3AA9qePv528etADsUYpADS0wCloyKKAE5paKKAEwaDS0CgBAcdqOtKSKARQAdqM0vammgBetIRQKWgBuDS4paU0ANpRSUooAKM+1FJigBaSlooAaTggetLnmjAPWjYQchuKAFOCvNMIXaATgn1pxI2nnJFcjr+vSTXS6VpYMl2x2u6HmD3NIBfEHiJvMGm6afMnbgledv1qxofheGzxdXOXuW5JJ4Gan0Tw5a6XtuGVXvX5kkxyx9a3+1MBnb3FAAI5606kFACqMCkAIajNKKAHEUnelzTc0ALmkNJmkzQAvSlBzTaVaAH00tigmmnkUALvoIJFNAqQUAR7TS4p+KTFMBoWnUUUAHNFFFAAelIGxSnpTaAHZzSUCloAKD0oooAbRSk00mgApKWigAoJxRSNTQDCaMZ6UGkL4oAUAg1Jk96aHyKA2aGA/NM2nNGaM0gHEYpCQKN2aMZoAUEbc0hOBmmsvNKOBigBdwYdKoa3/wAga79om/lV4uAMVn6z82jXn/XJv5UAY/gQ7/CFkR7104yBXMeAOPB9l9DXTB8NQAvNFO8welFADjtIJYcVyfjnXLnRtGjktIg0sz+WBXVquAd3Q1yHj+7+y6L5aRhi5KqSucGkBxtvpPi+/skuFlKtKOVWXgV2/gqx1eytJhqrlpNw25bPFcZ4ftfG40OBbWe1Fvt+XzB82Peu78Jw63Hbzf2vJC0m75dnpTA6NjxwKYqEFjuJDDin5GcAHNG3NIDzv4leJJ9Eiggt4S80ina4ByKpeHbzSLXwy8iCScySb5RJH0Y9a9Llsbe4IM0MchHTeoOPzpq6faopVbeIL6BBimB5PdTrrl7FbeHjLEQ2ZVC7BtqHUtRNtpur+H7mNzM42K4Und+Newx2NrCd0cESH1VADSPYWruXa3iZj1JQE0gKPh6J4tBsosYAiWsD4iwLc6ZYw72UtcABh1HSu0ChMAAAAcAVx3j5v3Gnf9fK/wBKAK0Pge4EcTf25enHYmuzsLY2lqkJlaQgYy1LbnNsh9qmHpQAuccUdaQDFLQAY70tJRmgBaKAaNueaACjGaXFBoAYQaUClooAXtTTRmjNAAKWgc0uKAEpTSEcUlABSUtAoAUUtJSNkDjrQAtFNX5V5pTnHFACsVA+Y4phGTuDfKB61xjanqWv69dafYuiQ2b7Jt3BP0qlqFx4ms9Uj0q0lg8uZSWLjPHfn8aANjXddeeX+zdLG+ZuJGH8KnuDV7QdAh0mEO5Mtww+aVx8xo8P6Fb6PbeWMySsSzO53HJ681ugcUANY8jj8af2pp607PFACGmscUpoAoAbijODT8UmKAA0hpV5alfrQA0DNGKKcKAEA5pwApD0ozigBGFJS9aTPOKAFpabtoAwaAFzSZpaKYBRSbqN1AC0U3NLjNAC0mKQrQDQA+koApPagBaaxwtLijIoAYDmlAzSkimk0ALRSA07rQAlI1KVpSOKYERpRtNKCKbigBwKjilwO1NAp44oATFGKUjNITjilYBMU4UDpTe9FgI2Y+aRUg60jdelP/gosAx1BOBVDWjs0W8/64t/Kr2ccmqGuc6Lef8AXJv5U7AY3gBt3g+y/GupGMY71yvw9GPCFl+NdXsyc0WAXaKKTYaKLAOYhl+Y1yPxCsr290SFdPjZ5UfPyjNda+MbvelDKScHjHSpA8XsZ/HNtZLGsNwABwNv/wBeu88ET63JbTf2ysgl3fJvGOK6lZEL4Lrx2zSgDkhs89fSgBy5zg9ad0pOrbh6U7n0pgJuNABpeKQGgAINICQacRmm4wRQA49a5Dx4mbbTz/08r/SuwI5Fcl45yYLAY6XCn+VIDpLbi0T6VMvUVFa/Pap24qbpxQAuc0UijHelPFACHpRSilFACCng8U0nHSk680APpDSUE0AFFFFACYoxT6KQDV4p2aa1JQA/NNxSU7FMBMUUuKQjFABQaaNxPTilOaAEU5PNI2SCc4UUEbea5fXtdlM403TxvmY4cg/dB70Ac/JPLoni24m0tDeG6kzcLF1U+9WdO1K71bxhC92jWuyNlEMnVveqXh3f4U1nUJNaZgLlwYXbkmtW0v4Nd8XQ3dhEGtooyjzYwQ3pQB26gKNqjj1p46UxTwFIwafQAHrSZpTTe9ADqOlIDS9aAFzSHpS7aTbQAi8DNKfmFDYximg7aAFUUtCnIpcUANzQeaXb70dKAEFIfWl60mKAANS8UhpFBzTAdRRijFABgUYFJijHvQAuBRTTwetKB70AGecUuBTTkdqMmgAJIoHPNLSE44oAdmmmkp3agBppMU/FLigCLFPFBpM0AKx6UjHIpp5oINNANHWlzQARS7aAFBo70m2lAoACaaetLtoxigBw6U3vS5xTe9ADwuWp3A4pgPOaQ8nrQAkhHQVna0f+JLef9cW/lV5kwd2az9bP/EkvD/0xb+VAGT8PefCFl+NdUSQcVynw6P8AxRtmfY11f3smgBNxopM0UAPMq7CxU4zjGK5fx1rU+iaKjW4HmTsYwcdK6kbmbJ+6O1Y/iTRY9c0+SCQgYBKE9jUgeW6ZoWrT3VldT6g4a4bkCfj+ddr4Bv7meXUra4feIJ9gJOa49/CPiezW2xq8At7Y5U7D/jXeeDPD7aRbTSyzpM9y/mMy+tMDreh9qKTHpSEhe/WkAuKb0pJZ0hXdIwUepqNZ4pF3I4YeopgTg0h6ioY7iKSQokisw5IHaiW6giIDyqpPQE0ATtXNeMUD29ofSYV0YYMoOeD0rnvFp/0e0x084UgNuz/49E+lT9xUNnxapn0qfHIpgMz81OJpMc0rA4pAOWhqYpINPyD1oASlppx2pQc0ALQaKDQAgp1NFOoAWiiikAhopDSZ96YDqWmZp+aQBSN0oyKMg9+KAE3fLxUZLshA4NSBgGIrn9f10WcbQ2x33J4WNeppgQeI9ceFFtLL57h8Kcc4B4NT6FoKWUSz3B33D8kk5qHw34eeyVry8YSXEpLD/ZB7V0IUqAvX0oAhubK2upozPErFOm5c06O1tYFKwwpGCedigZqbnBD8009iFOaAHqMHA6U6kwc+1LQAU3FLkUcUAKBSigUuRSAKD0ozSZoATbTShp26jcKYDRxxS5o6migAB5pT1pBSnrQADrTCeaeOtIRQAgpcUlA60wHUUUUAJSE0pNMPWgAPJpwpgzUgNAAT2oxSYFBNAC9KaeTSEmlHTmgAoHWg0g60APooooATFNIp9NNACDg0E0YpCBTQCZp1M49adkUALRRnFFABRRQaAENNpaQ0AFFAxS8UANbpWdrY/wCJHef9cW/lWlx61Q1sf8SO8/64t/KgDD+HX/ImWf0NdWp4rlPh3x4Ms/oa6kZAoATPNFM5ooAtMzKcgZFcl4+1W40zSoVt2KNO/lhh2zXWck8dAKyta0iHWNNa3ujtK5Kt6GpA8v1GbVLyDRdFt9SlaZyUnYdfxrp/hzPep/aFneXLzGCbYpb0xVPSfCdrpktw76irzD/Vs0oJz+ddH4S0OHSftUouBM88m84YNzTA6nB6ZpD94ZXOOlIAQPelwCw5PFIDkPF+lalfXduYb6S3tAp83aOK43TtV1LS9OnH2l7mBpjEJj/CScCun+Idpr+pJDZaOq4kU7mLYxVPRNF11rA2OqWVvHEqllZDklwOM/jQBmakupeGotIn/tOWWW6udsmeMr1xVbUp9QvG8Q3Yv5FFmcxr/drUXw74m1aeyt9Wt4EtrSbzUdHyT9aqat4T8RrqerxWMMT2uoN1Z8ECmB6B4YuWvdDt5JHLsIxknvVbxOynTo53+VI5Nx9sd60NCsZrDTLeCdVV0jAYKe9VPEh3WUYVAUZ8EGkBR0zxxoMltGv2+NmNdNb3CXUazQybkI7VkaboemtZxt9jhUgdkraVEhjEcahRjtQBIKd2qNRgdc06gBpHzUGn4ppFMBBRjHNKBSkZpAA5pW6Ug4pRyaAEFOzSUmaAH0UDpRSAax5FJtBpxpuDTATODSk80uKYetADjytMJwOadnisXXteg0az3yHdK52RqBn5j0pgR+INdj0eAKMNdzfLFH3ZvSqPhnQpTM2q6ll7qQ70VusXsKZoejS3kx1fVBuuJORHnKp9K61AEHHekA7OOKdjHNNxzmnZoATHOaXoKTNKeRQA0tRnNBFGKADFLRmigBRQaBS5oAaTil6800jmnA4FAAaaRTqXFADBxzS/epT6Ug4oAOlBNHU0hFACg0tIBS0AFFFFABRRRQA2kp+KaRTAQUZpcUYoASjFLilxQA3FGKdijFADcUoFLijpQAEU0nFONNIoAKKKKACmGnNTaaAYad3FGKF6ZoAV+QKAflo600Ng4oAAxBpxOaCB1pAR0oAUcUHmg8UgNADDndTwMinhRjNNJxQAhQDmqOsn/iS3v/XFv5VfBzWfrX/IFvf+uLfyoAw/h5z4Nsvxrq+2K5P4dc+DbL8a63GKAG7aKdmigBzMuACDXJ+P7y/ttCf7EwTeCrEjtiutcKeDxWJ4n0ifWNLe0hm8skH5sVIHnPhfwFaat4etby6u5jO65JExHNd34Y8LQ6AshimeTc2fmkLVxFl4S13SbdLOPxKUCjCpt6V2nhXTtWsIZDqWotcru4yKYHVDB70oGBikUKfmWnUgG7FznHNJkelPpnyk0wDAIGO1Lt+bJxRjFHJ60gBhzmsbxCxFvHgcbq2e1ZfiDmw6cigCzpbbrCEjuKtnG6s3QCzaVAW9K0+C1ADd3NKCKCoJ4o20wFJ4puaXFGKAEzQCc0uKKQCnBFICc0UUAPHNIQKAaQmgBwoOajDEU4PQADJ606kzRmgAPrUTMSQV6Dr71IeRiqt5dQadaSXM7hIohkk9qAKur6vBpdk00mQR0FclounXXiPVjqd8pEI4VGGOnQ1JZ2934v1M3d5GyafEcJG3IlHZhXcQxrFCsca7FUYAoASONYRsUfKOlPXAJ4PNODdsUpOfwoAKSkzTqAEpwpKUUABphzT2puM0AAGaX6UnSiLvQAoOOtKRSN1pScAUAHSjim5zRtoAfiimk0A0AKfvUHBoPSm0ALyKKUUtADaTJzT6bjmgBRzQ3A4oFB5oAbk0ZNGKMUAKTSc0ClNMAyBSZPakGM807igBRSH2ppLZpRQAAnvTjTTQDxQAZNO6immm5oAdSZozmigA7UnNLikIoATr1o20oGaQnBpoBDxQBgUAZpaAE6UwqC2acaUUAKBkYpNgBpw6U0mgBeDTTx0pM0daAFDGlxSDinUANHBqhrXOjXv/AFxb+VX+9UNZ/wCQLe/9cW/lQBhfDrjwbZfjXXckVyXw7/5E2y/GutDcUAJtopd1FADtvGDzXPeMNUbTtEndGKvsO0j1xXQkNgg1j69o66vpUtqp+cqcEmpA848O+EL3xHodvq1xrV3FPKu4IpGM1s+Ab26TUL3T7q4ecQy7AznPSsS1j8WeHLZNJgns/s6DamcE113gnw7c6SlxeXzxvLcP5nyHNAHZ5AfA9KcCD0pBzSE44FADiaYQAaAAeppCSicDPNADlbI4oPzjg0AhmOKUKc5HSgAAwlZmubvsJOBgVp5JyKzNZXdpshJ6A0AO0F1OkQY9K0e/NZHhoA6DbsD2rUQksaAHA7aMk0fepQKYCDOafRRQAlJg59qWjNIBT0pnOafRjFADQcUdelOwKBgUAHGKTAoooACRSbhnrQy55HUVXESQRudxIJ3Ek9KAJJriOGN2kbaqjJNcLcvceKtZREYjT4W4A6TD/aqTWtRude1IaPYH5UIaWUfdKnsD611Oj6Tb6Rarb24JKjGW5oAuWtqlpAkUShQowFHSpmz1ApB8p5zmnbs9KAE6HIFJtOD707FHUYoAYBTxxTNhzSkHFAC0opB0paABuelIOOtOFNPNACGge1LikPFACgE04gd6Yrc0880AJgUZpcUmKAGtz0oUGnYxSE0AKetGKQc0ucUAIcjntQDmnHkYoAAoATBopdwFNzQAtJmlpCKAF60hIpRxSEUANyO1KAetAYGlznigBjFc8mjB7U5kFJyKYBk9DQBR1pelACE4600HPI6U4jNAGOKAFzxTSCelLSjrQAzBFKKVqQUAOyKDTM807PFACEkdKQFT1pCaQ8U0A4qw6UhIHBpFcmkYEmgBSQKUEDk0wqakC5WgA3A9KaetKFwaD1oATGacBjrR1pjIxoAcetGaRVIHNLigBO9UNZ/5At7/ANcW/lWhis/Wv+QJe/8AXFv5UAYfw7/5E2y/GusGK5P4ef8AInWf0NdZQAvFFJRQBJk56/hWH4j1CTTtDubiIFZQjYrYDEtuYYHY1k+JLaTUNEuYrePzHZCF+tSB5tofh7xD4p0qHV5tVRDMNyoynIrovA2oaibm7sb6UzG3l2AgYrmdCh8aaTpsWmrYtgDajb+VpljonjfS7i4mhs5HaV95O6mB7QCWGeh9KM/JmsDwpJqjaSTq0RSfcepzxW+udmccUgK93dW9hbGe4cKo55qGw1i01O1kubWdGijyGwemKpeKNIGr6U8fntF8vYVyPw2tz/wi+rRRNvKzSR49eooA6q28X6NcTrDFeRGVm2lQeak1DxZpVg7wyXUYkXgjNea2NjH4WlsZbvT4pZbq5KB2PI5qQ2lpe6p4rkuo1Z43Hlbv4enSgD121uIrqCOaJwQy5yKravEH02Vc/wAJrnvh610+kTLcA4V8R59K3tdZ00a6dBl1jYgfhQBH4Zj8rQoFJ7VrA4PArz3wr4h1NfD9sHsg5x94mu6tJJbi3SSRNhIzgUAWc0hNLnIpKYCA5NPpMAUZoAKMUZoBpAL0pCc0NQelAAAaQg0ueKQE55oAcOlFJkb8ZpHYofr0oAHJxgcH1rjfEGu3Et6uj6VkySDLzLyAO4q34m8QvYhLKzQS304wiZ71L4c0JbBPtFwd91L853Dlc9qALeh6JDo9iIogNx+Zj6k9a1V4X5Rg0oGOM0+gBpbYPu5PegcdKdRgUAHam5OadSDrQAHOKj3Nmp6TaPSkA0dKWkPWlFMAptPxTT1oAUCkYZpRS0AMC4NPHNIaFoAdig9KKD0pANzmkIpdtLxTATGKQmlNGKAGZp3JowKWgBjA0Cn00kDtQA4U7HFMVhijJJoAWjGaSigBoUCkYEcin9OtIT2pgNVi2c9qduFIoweaYyb42UNhucGkBLt70YqtZwyQQYmlZ2J71ZFACHikzmnnmmBfm9qYBSjrSnGKbjHNAA1IKNpPejbigAxzS44pAeaXtQAwrmmmpAeaawzTQDQQtPVgRmmEetCUASAg0gbg0mMGkx6UAOU80MKb0ozQA76Uxi/akyc08HigBFJxz1pc03nNKaAFzWfrX/IEvf8Ari38quc561S1n/kDXv8A1xb+VAGJ8PP+ROs/oa6yuS+Hn/In2n0NdWOmaAHUU3NFADbp5Et3ljGXRSQD06Vwdl49uZbO/S4tXSaFHK/uyAcdO1ehdTt71k6tp9tcafPEFSOSVSmSKkDyfS73V7jW9E1SWYgalJkxh/lXj07V23hfxFLca3fWVwSSkxVcc8Vyn/CC+JIJrIQalbiKzOYTsPH611/gvwzJpE1zcahcxXF3NJvBTjFAHbdOijaaRckY7UuWzil6igCGVI5EKNnkYrD0jwxb6XY3dtDI6pcSM5OecnP+NdDjB4pCgoA4aD4e2y3kLy3VxKYZPMUO+RmptT8AWl9ez3Jnmj885cI2Aa7MqSQc0m0sSD92gCrpemw6XaJBCSQB1Peq+uErpNyx6mNq0wuDz+FZuvr/AMSW8P8AEYmH6UAZ/gxY28K2Z2ISV9K6MA7QBgCuY+H8QXwdY55+WunB3HjtQAHiijBY5pccUANzRQeOaXPGaYCU4cCmhwTjFPI4pAJ1oNKOlN7mgAzR0oGDzkVFcXMVtE0kzrGo7tQA9nwoOOc1j+I9ci0eybnM8g2xAc/NVHUPGljp8bzyKTEBgODxmsrRLf8A4S+8Gt3LZsgcwR91YetAFrw1otxKw1e/GbmX50B5C/4V2CKFO09TzmnRp8u0Y2DpinGLsOlADANh65NSU0RENuz81PxQAlKKSjNAAaB1ooA5oAdRRSE4pANPWlFGM0uKYBTT1p1JigAFLQBigHNABilxik3YOKXNIAopM0E8UALTTQMmlzimAlFHWigAopM0ZoAWkK5GaN1LnIoAZ0NO4AzULglutPAIAyaAH5ozTQpPeggigBx6UzvSg07GaAEwDzmkAB5BoCnPtQqbVI96AArubk9Kf0pMUGgBaaxxSgUhIzimAzJJp2Til4WkznpQAm8igMTS4FIcdqAG96dnim96UDNACDrTiRRtx3qJ8imgBjSpxTRUm3igAJ5pyjimBTnNSZFACMKZipaQ4oAZtpOlPOAKjzzQA7tSZozkUhQ+tADX4XNUdX50a8/64t/Kr+M8GqWr4Gi3n/XFv5UAYnw8A/4Q+1+hrq8fLXJeAPl8IWgz2NdWrcAGgBMUU/cKKAHH5AW2/NXlmrtrGv8Aju50m01KW2jiiV8Lz616ocsjMeoBrxnUPEL+HPiLeXpt5HWSFUyEJ9akAt7bxPL4guNETWZyts23zMda7Tw74a1bTL0zX2rSzjP3WFedWnjmaDxTe35s5NkzAg+Wa9A8L+Of7fvvs/kOvOMshFAHbqxPHX3p9JgZyKWgANI3SgmgHNACKe1LjmkxzS56UAKetZniDjRLz/rk38q0j1rM8R8aFe/9cW/lQBmfD858GWB/2a6VeCa5f4cnPgjT/wDcrqTwKAEXjNO600dKM0AOZcrSY+WkDHNBNADVX5qkY0goJzQAoppO0805aQ0ANZAV3CuT+I7Ong24ljyGXHNdacBSzHAHWvM/H+q3Oq2M2mWSBxuAbFIDOtjpWuaXsmKCzSDJB6eYF/xrr/h2sI8IQpDgwhmxisu3i0S38Nbk3B/K2Omz+PHP61q/D6HyfC0QUMAWbIYYwKYHXoAqAL07U7NRoMD27U6kAuaTNFBFMBM0CkoFADhSjrSCloADxSdad1pp4oAaTg08c0gXPNLjFAC0UUUgCkHFLSGgBCMmlpR0pppgGaKSigB2MUUgOaUnFAAeKSkJzSigBp4FJmnkcU3GKAFxQOuKTNC0AIy80hPFOY0mM0wBTSnmjFLigBoHNO7UAUhpAKKWmpTqACiikoAWmH79Opv8VMAakFK1J2oAWkxQKcKAIu9KDTiOaTFACk5ppxS5xTSMmmgG7c9KeMjrSgYoIzQAE5pOaXbijNADMmnDNO4ppYAUAJQKFbNKRmgAoJpQMCm0AIevNU9WVTo92P8Apk38qunGOaq6oF/sq5/65t/KgDC8DRovhe1X0zXTEDoK5zwaVXQIB6ZroxjrQA3Bop+4UUATE9qgksbWQ7pLeJm9WQGpy2T0pzDIqQKf9mWZGfssH/fsUsdjBA26OCJD6qgFWASoxRuzQAA9qawcjggU7BHSkfCjBOaAG7duCDkilUkHGetZOu6o+kae1xFbtIQM8VieD/FMms6He3s8bAwu4AJ9M/4UAdkMKfmOWNOABPJ4rzK38d3809vcCylNvPMYgc8cVdvvGd5PeXlvp9nIzWZxLtNAHf5+f8KyvEJzoV9kH/Utj8qqeF/EDa/YSSGIpJE+xgT3q94hJ/sC9BH/ACxb+VAGL8Oj/wAURpoYEnb2rrCcdK5L4czY8EWKjqErrQflBK80AKBS5xSK2V5GKQnmgBxIpKO1JmgBaCduMd6TNNJYMD1FAD8gnODmkBVuTkUO5X7q5zWR4g1mHR9OLu4Er/LGO5btQBneKPEBtiul2QL3lyfLUryIz6tU/h3w7DplsWucSXU3zSk8jPtWZ4T0WczTa3f5N3cDLRsOVrqpWeKMsql3bkKKAKD+GLFrvzgrY67c8flWvFHHCoijQKo7AYpysdi5XBI5pwUYyBzSADnPNKKbz3pwpgL2pueKdTWwKQDMZNSAYpnSnKcigB1B4opD0oAQ89KTpSjikbmmAo5FFKo4pcUAIOtLRRSAKaadSEUAAoYZpKXNMBMUYpc0maAFopuaM0ALkUvWmd6XOKAFxRntRmgjmgBuMc0HpSk5FJ2oASlPSkpW6UwGinU0U6gBaKKKQBRRRQAUA0HpSCgAY00GlYU0UwHClPSmg0pPFAABSgU3dTgaAA0j/doByTR14oAYo4p+cUg4NBHNNAHWkalHApGoAUdKZnDU8dKbgZoAbJyKEXg0rUq8UACrSkUpOKQHNAB/DikFIT82KCaADALc9Kp6l8unXeP+eTfyq5t3CqupDZptz3/dt/KgDB8Fsf8AhH4M+9dMFBFc74RH/EjgGPWujCcUAJtFFO2+9FAE2KMH1paKkAxxTduKeKQ0ANY8UgQMOaVqOi0AVL6JJLZ4nQOpB61594Gt2XwzrcZUoPtEpHHbmvStm/rUFtbQQo6wxKqsSWG3GaAPGLHVopdN0nTIUbz471mb5T0Naq3n/CKa7rT38ZDXz5hAGQ+K9NGnWH2gMLSJWU5BEYHNTXFtbXD/AL22ikZehZAaAOS+HVnPa2d7K6kJcTeYM9q3/EW9tAvkB/5Ytz+FaQhQRhUUIo7KMVm+Ihu0C9ZeMQt1+lAHAeDPClzfeFbC4TWLqAEZ2JjFemadatZWixPM8xAxufqa5z4b4bwRp/8AuV12OKAEPzUmz3pVGBS0AIelIBmnUUAIVpvTPcd/an1HKDkcgJ/FmgCrfX62FnJPKQAo49643QrafxRqr6nfJm0UlY425AIPWn6nNL4o1pdMgLCxQ/vWHB3DpzXa2ltFaWiQxKFVQBwKAJkjVAAoxikxhs9T29qetGBQAU0Ahs5NOooAQnmnDpSYooANxFIwzS0tADM44xShsUuKTFAC5p1NoJoAUjNJtHrSZozQA7JFGTSDkU4AUgCiiigAooooATFIRTqa3WgBKKBS44pgJxSEmjGKMgUAGTijrSA5alPSgBP4qf2popSaAExzSEk0ZopgFBPFBppNAC04dKRaDQAoNLSClNADcmjJpQM0u2gBuTSg0uKAKAGsaQLmlYUgPNACkYowCOTSMaReeKAEPtTVZi2McVIQFpB1zQAgJVjRvIPSlA5zT9oIoAbjdzS9KAMUGgBDTMk0+jFMBAeKTGaDSrQAoT3oJwOlLSEZFADCc0ClxSUANP3qUkd6NxB4FOyT1FADCwI2g81DfDdp0wPdD/KrGBmorgZtZV/2TQBk+GFEWlxpj7oraOeuaytEUJalfQVrAfLQA3n1op22igCeig9aB1qQFFIaOaM+tAAOtIRS5HaloATgU0g49aGpQeKAArkDPakLY5xTs01uaAGj74x0NZ3iFQ3h++GekLfyrSVazvEI/wCJBf8A/XFv5UAYvw3GPA9hj+5XVhjXKfDb/kR9P/3a600wFozTSaOtACkimknrS4pvD9DwOtIBVbnrXLeKNZlVf7NsWzcz/KHH/LM+9amtavDpNk8r4BYER/7TelYXhXR5bqZtavWO+4+cRnotAG14e0pNLsRhf3kmGlP95vWtoL6dPSmEZ6/KB0FCls89KAJBxSE0A0uKAEzS0hGKTOKAHUh60uc0UAHam806lzQAgpaKM5oAQ02nGkoAMUYpc0oNADelKDQ1C9KAFzS5pnelpAOzRTadQAUhFLSGgBKTJpaKYCEmmkZqQ02gBuMUo560UZpgJ3pTQOtKaAGU7+GjFJjjFACE00dadt5p23igBucUo5HNIRTcmgB4NKSKTFGKAFWlpF4pc0AGaARSHoaYOKAJDTOhzS7qQ80AI2D0oA4pVUU05zQAMM96CxVOBk09Vz1pGGOlAADlAe9KretNBo60APyM8UhpF4NBNCAM0m4etNJpAKYEgwaUAdqaBThgGgBvfFOGKaMbiaUYyaAAketMNKRSYoAQMQeKCWPfFIcigZJoAUKwOSeKjuDiF/oal3YGDUc+142HtQBl6M4LuhPStnIz1rF0kKt7cL6CtigB+R60VHiigCyetA60UVICbjnFJ1p1AUGgBMCnUbQOlJQA1qUDilIpqtkZ7CgB2KQ8VE13Ar7TMgP93PNDTKB8zhfQmgCUVm+If+QBf/8AXFv5VfR1ZdysCB6Vna+2fD9+cH/Utx+FAGN8Nv8AkR9P/wB2utNch8NWz4HsOOdldadwGetACkc0dKTdlcgYPvSkE9D9aLgBJ7darTTJbK7O2FClianG5GJY5HYVxniS/l1K9TRtOPzsQ7v1AAPIoAqWscvjDXPtLBl0+Fsxg8ZYdeK7xUjTAQbQvRR0qrpthHptnHBGuD3x61ewM4oAGUH5ienpSqVYZFLjikxzQAtIc0uKQkigBC1NpSKQUAPFLSDpQOtACnpSLzQ1KoAFAA3FIvSkJyaUDA4oAU0hoNJ1NABmnCmd6ceBQAp6UL0pM5FHSgA70tJ3paACnU2l6CgBaQ0m6kyT1pALRRRTAU02nU00ANPWkp2M0baYDc4pd1BAoG2gBQaUdaQYo3AdKQCMcGnZyoqM5Y04ZAoAMUYFLzTCDmmA7NGaSlxxmgAzQDSDnOaOaAHYzTSuKeOlIaAGU4UAUhO1qAAgil4xQDuoZfloAM+lGfWlUYFNOCaADHekJxRuPTtSHmgADZpCaQDninYHemgIyaUGlKA9KXy6AFVqGPy5oAApe2KAGHIGacMgA048jFB5GKLgITTSaGVhSAHvRcBwwRilwFpFXvSnnrRcBpwaaUGxjntTiFxgdaa6lUb0xRcDE0pwdavEz0ArdxXNaMVbxDqAHUAfzrpc/nQAYoo5ooAsYoxS0VACYo6UtIaAAGlpvNFADqqXLmK2mcHaFRj+lWaguVZreVUUMShAB9cUAeN2ep31zr815c3TLaocjPTg10Wq3V7rWo6UlhfMlvKTnb0IrLurDxPKJLD+yLRIGJ/eK/zc/hWhLoWu6Zb6Q2m28cr26/vA7YxTA0vAF/cynULa9uWlaK4Kru9K6jX+NAv+OfIbn8K5zwDo2oWCX02qQok00xdQpzxXSa6CNDvwR8vkN/KgDj/AGu6dp/g2yjuLpFIXvXa2Oo21+u62uA49q47wBo1pd+EbJ5olJK+ldraWUNopSKNUHbAoAnbJHvQEILMDnPak2Mgypyfeob28isLKS4lbCIMmkBk+J9cTSbIKpBu5B+6TuareFNJNpC13cruupjvDHqAe1ZWk6bP4i13+2b3/AI9omzbL1DqfWu7UKuAFAAGPpTAQttw3c8U/b3pMACgPzSAU8Cmb8mpCMio9vNMB46UhFKopTQAwikxTzSYoAKOlHelPSgBOtL0pF460rdaAE96UcikJ4pU6UABFJ3pxpDQAmKKdnimg5NACYoxT+KOKAGjrS0ppKAClbpSUZzQAwdafSYoFAC0UmaWgBDSU7IpCRQAgpaKTcKYAwyKaFp2c0ZoAAtLxigGjAHNIBpHpTSSOtSZ9KYxJ4NMBQ2aQmlAFBxQAGl/hpDRnjFADR1p4po460ZzQA+kNAYYpCaAFFBAzz1pu4UuQaAE6dKOT1pT7UgJzigBx6VFyTUp5FNC4PNACBaXbQST0phYg80AOI20oXNIDupwOKYDT8tG6lxmkOBQAYzQBik3UoyaGAtGcc0UUgDfmkNO2gU1vagBDntR9aUEjijGaAGnGKR23IR7U7aD0prY2nHWgDl9FXZ4o1T2A/nXT+9ctoe9/F+sA4wFH866r2oQCb6KdgUUwLNFFFQAUUUUAIaSlNJTAKbuPpTqAcDpQAwxp94rzRlXyCKcSSenFBUUAKiKo4rP19SdCvz/0xb+VXt3zYFUfEBI0C+/64t/KkBhfDj/kTbD/AHa63+KuS+HH/Im2H+7XW/xUwEZwoOeMDrXB65fTa/rKaRZn9zC2bk/3lPpW14v1xdK08RoN00zCMAHkbuM/rTPCuhHS7QSzndeSf62XH3h2FAGzp1mtjax28YxFGMRjvj3q4etGR/hSjpQAHpSAUA80ZwaADmkJzT8gimYwc0AKM0hzTwRSEigA7Uhpe1IaAAUtIKWgANJmlpMUALjNKOKTOKM5oACaTrQaF+9QApNIvWkPWnY4oAKKKKACiiigApcUlGaAEJxSA0pGaaeKAFpQaQdKTPNADjSGlNIaAGk0YzR3pQOKYABRSbucUtADhQaBRQAnSk70NxSMcCgB1MJpVOaUrQAdRSYoFKaAGt1pRSU8CgBO9LxQRSEGgBOKXjFNwaAKADmndqMjFNNADgaCaQGgmgAFIwyaQtilU5FAB0FGaD1oxTQBmkHNLikHFAC7aTJpw5pWAFADM0oIoxQFOc0gHmm0402gBO9L2pO9L2oAaTimsuYjjrTwMtTSDlvTFAHFeHPMbxtrS542r/Ouzxg4rjvDrn/hOdcA/ur/ADrse/NCAWiiimBaoo3UbqgAooooAKMUUUAGKKKKADFIaWkNNANwKoa6M6FfD/pi38q0Ko60M6Jeg94W/lTA5/4dyj/hD7OMD5kXmukuLpLWB5ZGwoGST2rn/AiKnhKzA6BfxNUvEN0+t6lFolsx8mTmV16qR79qQFbQrd/Fmryavdgi3iYxrGfu5U8H9K7w7QOvGMCobGzhsrNLeJQoVQDgdTUyqpUrnkUAOBHyj1qSo1wMD0qSkAmB6UEClpKAGUpOBSkUw80wHAg0pWkAoJNADgOKMClHSikAYFIRS0GgBtFFFMApDS0YoAQUuBRSZoAXFFFFABjjikXdnBFBJyMU3czKcYyDQA7Halpu4jk9aUHNAC4zRTS2KXPFAAaQjIpCaVehpgCjigYNL2qMHk0gH4PrSZFJuNL1pgAxnPamuzYwB1pxGVwOtJyNgOM0gEHyYB6ml5zQfmJJ6ilzxTAM8cUo6UwDmpM0AGM0YGORRupC1ADOhpxPFGKSgAFOPSkHWlPSgCMnFPGF4zzRtBFNbCOHdgF6daQDlHfNLTFOHYdscU7NMBaYetOzTSaAFBFBptOzkYoAROTSn71NQ4alJy1MAIHpSDil74oxQAEcUjPtC0oNRtKkKvJIcKvc0AO37jnoBxTsVEWVgpTlW5qQMaAFzijJY0YzSgAUAI3TikXOeadnNITg0AKTzS9qYTzS54oAQdaGzQPWnZzQAgPHvTMnB57U48U3Hyn6UAcV4cP/ABXuvf7q/wA67cJkZzXD+G/+R+1//dX+ddthqAHbPeim4aigCcnFBOKXqKb1NSA5WyKXNJ0ooAXNGaSigBc0bhikoOOmKAFzxmqd9qdpp8XmXU6RLj+I4q3/AA4rzzxwouPFvh+xm+e3nYiRD0I5oA7ax1Wy1Jd1pcJKB12nNLqQ83TLmP8AvRkVwdhGmi+P7XTLJRHbSws7KvTIxXeXqs1jMPuuykCgDz218R3Oj6I2n2WnTzzxLgSR9Aa3vBi27xSXRGLqVt0wJ5VvSs7TNdtNGL2lwi/aU4cnq1VbfxFb2etiWCFVtpCWlI6ZoA9HQckgECkZRnK8Vyi+PLNuECkfWpB40hb7sSn8aAOqXqKeWArk/wDhMVBz5C4+tB8ZIelup/GgDq9wo71yf/CXv2s1P40J4rumbixGPrQB1pHFM28+lcs/iq8HSxH500+K7zaP9AB/GgDrAwHGaUstcoPE96RldMB/Goz4m1TPGkA/8CoA6HUtUTTYkd0LbnC4HuauiTKg46ivPdb1fW7yKIJo2Qsin73vWi3iLXRGqro3IH96gDs80VxieJfEIGBoe7/gRpr+JfE/8Ph3P/AjSA7Q0Vw3/CTeLAf+RZP/AH0aU+JfFf8A0LX/AI8aYHb5FOHIrhP+El8Vf9C1/wCPGgeJfFnbw0f++jQB3RpvSuIHiTxb/wBC0f8Avo0HxF4rZcHw2VHruNAHcjB70Yrh01vxO3/MDI/4FUw1TxM3/MJYf8CoA7A52nBwfWsDw7qcuoSXgJwIpinPeqAvPEcqsGsHX8ap6Vp+t6eJ2Fs6mR91AHdFge1JkZrlduvMufLkH40zZr+fuSfnQB1/Hek47GuRZdf/ALkn500WmujnzZaYHXE0Z44rkxba4esktBtNcUjEstAHWgnHSkA55rlxba5j/WS1E1nr5biWWgDrTjPFNJINcn9j19W/1sppr2+vE/6yWgDrixXp+dYerXtzDqNmiNlXfHFZr2OuPHj7VMrVRm8N67PcW0rX8/yNntQB3ob5AT170mTnpXHPpWvNIUF/Oo/vcU5dD1w9dXuPyH+FAHY5BFGMDrXIjQtY76xP+QpjeHNbbka5cAfQf4UAdjQR71xn/CNa5/0Hbn8h/hTv+EY1xiP+J9cjHsP8KAOyydvSmj1JxXInw1rgbP8Ab1zj6D/CoH8La875Gv3IH0H+FAHbZA7il3A964Y+DddP/MzXX5D/AApP+EM13/oZrr8h/hQB3JIA+8K53xddzW+nwmFuTOgOPqKyH8Ga4UP/ABU91n6D/Cqt58P9YvoVifxNdHDBvujt+FAHeQN+4jLMCxAzUxZQeorhF8E64p2/8JPdYA44H+FK3gvXP+hnuvyH+FAHdZGM5qMuueWA9q4lfB+ujj/hJrr8h/hTv+EO1j+LxFck+uB/hQB2okj/ALwpRImfvCuMHg/Vh/zMNx+Q/wAKUeEtUB58QXH5D/CgDry6A53igSoDksK5P/hEtS/6D9x+Q/wpP+ER1L/oP3H5D/CmB14mjJJ3Cke4iXguK5SLwlqgPOtzkfQf4VK3hS+P3tYm/IUAdKLmEc+YpzWB4zvVj8MXhgmUSBOKrnwreKvGrzZ+gpk3g6W6tHgn1CRxIMcigDV0O+ibRLVpZlL+WM1pNfWqrkzJ+dc5F4SaJI4UvXAUY6VK3hdtnN65/CgDbOp2YGTcoPxpv9r6eF+a7iH41kr4Wj8v5pS34Up8J2zAZwfwoA0P7d0xTzfQj8aY3iLSA3OoQf8AfVVE8I6aPvwI31FNfwtoudpsYiT7UAWz4l0bdj+0YP8AvqnHxLoyj/kJQf8AfVUR4M0XOTp8P5VIfB+hd9NhP4UATnxRogGDqVvn/epB4q0Rf+Ylb/8AfVQf8IZoDc/2XAfwoPgrQP8AoFQfkaAJT4s0MnA1GD/vqlHirRCpA1GDP+9VYeCvD4bJ0uAfhUg8F6AIuNLg3A56UAc/4SuI7rxvrc8TBo5FXaw781354rPstI0/TPms7SOJ5OCVq9znmgBd1FLiigAD9qf2zTFFSVIAD70tRtFkg0/pigBaKaP9Z+FL3oAWoyzb8Y4p7UDpQAo5Ncz4o8MrrTw3Ebst1b58rBxzXTikbBIGOtAHG+F/Cc+m3bX+oOZLkEhCW3cGuudMvnrntTyMDijlhlSPegCm+mWckpdraJnHcryaH0ixkTBtoh64UVbG3Jx2pP4gVPHegCoNL08D5bSLj/YFOGnWY6W0X/fNWwAOlLQBVOn2uObePH+7QthZ/wDPvH/3zVqjPtQBXNjajpAn/fNKtnbjpEn5VPmgdaAIms4D/wAsl/KmGzgx/qk/KrdMH3qQEAghHAiX8qcbeIf8s1/KpwKaaYEYiiB27Ae/SkWJC5O0U7cAu7GO3NC/MOhGKADywCcKKXH+yKeOetBpAR+Xzmn4X0oNNDZpgKQvpSbc9BTwaQnDUANKkdhSEZGDTjyKYAd1ACqgFOIpCDSgUAIQxUgYoDD7rEZpruqBmJwB1JqoJrS4uAEuEaT+6G5oAvHge1RjGepp+4bunakJBoACoI60cDrSijFMBpKnpQFzTqWgCPBFOBopQMigBu75uaR13HilKc07GBQBEVwcGlwu77x4pSSfu8Y6k1RfVtOW88hrqLzs427uaQGgwAGB1NKo9cUxG3A45B6UuKAFYqOooByox0pMZpw4GKYCZoPIxSmm4oAUKANpJpSABtzQBQRSAQYpTikFBpgJjNN+6d2falH3DVK/vrTToVmu5RHGWA+Y45pAaGMnOajPBptvcw3UYkhkWRD0KnIqbimBGxCrmmxtvGcVLxTSQDiiwClcioirZ4xUhPFMwd1OwCbX9BShX9BTsGjBoAAT0oP1pM4qJkLNQBKAH4Pah8/LtHSkX5AB3rH1jxHa6HNGlyjF5fu4NAGqu0NliQae+3bWVo+qprKPMkbIynA3VpqDnmgB4I8ulwWHFA6UHgUACj1NI6J170g6080ACgFOtIAT6UuKMUAIDg4NOzUZHzU8CgBCN3ApDhyeSOMUpBP3etVb66SzspZ5D/qlLkeuKVwJx8q7RyR604Zxk9a4nR/HsesXMXkafcqkrY3sOK7QHIzTAdmikooAlUU/FNFPHNSA1icGmJk5NSkUi4A4oAQDnNKelGMClzxQAzOaKMYPFGKAFFB5HXFFHWgBEJGVJzmua8X67JoGnw+SpMk8nlrg9zXTZyOOorh/iTaTTWFhMqlkguRI5HXAxQBR0bxJqlnrUVlqqOPtDbVLmvREGA2O/SvK31FPFXiXS5rBHK2ku5iylc8HrXqfzAoSABjmgCSimjNOxxQAGkoJIoDGgApRSikzzigB1IOtApaQCimNwDTsmkIoA8x8beJNf0+7tvItZYrdp0QsDwcsBXpELM0EZLfMVBP5VxvxMtprnR7MW67nW7iJ+m4V2Fq2LSIsPm2jP5UwLC9OetKaAQRkUYpANPQ00KRT8UHNMBo4oPPNBNAPFADc0oPNIetKBQA6kJxS0hXNAGR4ht7q80ieKxlKTFcAr1zXmUElxYeLbSGzuzPPsPmheoPGa9J8SxX0mjTrpv8Ax87fl5xzXnHhvRvF+h3jzy6TazmSTcZZJPmUH8KYHrsQbyELHkgE/WnhabAztCnmKFfAyB0FS0gG9KXtSHnpQTimAhpV6GjGRSDjigApwpMUo6UANY800k4p/Wg4oAqXSSTWjxQyFJGBG4dq8m8V+H5NB8m/a5827ckmfHIPrXrd1CZrSaNWKF0Khh1FeWXfhXxJPcf2ZLmeyzt893+YD1pAekeHZXuNAspXk3sYgWb1NabHBrO0iyOmaXb2isWESBSTV771MB6nNLio/u09WJ7UAL0ozTWYCkBzQBIDxSE1GHO7GKkI4oABSGlAoI4oAhZiAQOB/e9K8U+ImvQ6vqv9nfbgLeMbivbcK9sK54PQ1zur+DtIv43DW0ayPnMgXmgCt8OHV/CFtsfcQxya67NYvhvRItB0iPT4JGdEJO5upzWzzTQC5pp607imk80AHQUmeaXORTQuWoAdmjNIcA0hOBQArEEULwKTbxnNKo4oAaVLdDiuB8e6O+ratpOy72NGTkY616FjJx0rnfEvhePXpLeT7ZNbND0MXegDC8BasZDdWTctFKU3fSu8ziud8M+F7bw5FMkUrzvK+8u45zXQkZoAUNTs5FR7acoYjjpQADrT6ZgjpyacN38XFADs0Zpp60uOKADGTmnEUwNil3E0gAdeuK5zxhbzXWgTm2Yhwp3EemK6QrxnNQywpLGyMPlYYI9aAPEdCvrnT/7Ajt9VMiTTFXhHYYNe2oxZATWRB4W0y2uVmS3TKHKDb90+1bO30FCATNFLg0UwJwKeKTikzUgLuycUbabx1o3k8UAKWpME0oUGl6dKAAfLTaC3PNPAGKAGUo45NOwKQ4HBpAAAHIqOSNJvklQMvoRmnKeeelL5g9KYFeCxtLWVnigjQt/dUCpQMkqc804Dlj60vPABxSAAcDmk304jnmk2imAZyKQClxS0AKKSiigAzQGpppMmgCTNGajyaOaAEngiuAElUEA5GRUm3ggdMU0gno1KuQnXmkAq8ACnZpozRTAXNBNJSkUgGMM0g4NKTikBzTAd2pO9Jk0oxmgB1FGaY8qRxmR2CooySe1MAKknnGKNiViSeLNCW48k6pAHzjbk1qQXEVwnmRSh4+zDpSAnG4dcYpSM0gOVBJo3ckDqKADOKRjSEjuaPvdOaYDgeKTNAPbvTf4sUAPU1HIxDcU7IAo+UjJPFACq2aUjNNyqruzx60jSBRnPGM0AK4AUZz17UuRnOBiqMGp2t1OYorhHYdVHargAJyG49KABADuIz1p3SkJIPB4ppcYznjpQA/rSjg1Tl1G0t51gmuESVjhVPUmqup67Z6dcww3NwkTyfdDHrQBp7f3nPSnOADxUaypKiSKwKkZBFKZEZ8BwT6UAKBTgcU0ZDYNI5H8JoAkJpuaVeRzSMVABz16UAJSEbqTPNO6DNACqoUUZpA3ODTyooQEZzR0FKxw23v6VGW5pgLmnA0zcoKgnlunvSbjkgckUASEUhXNNDkigMdxHcUAP7Yozg0isC23PPpSE5OR0oAcx4qMnNP4IxSBOelADVGKfikYEdqdxQAmKRw+0hadketODbRxQBUiWVX+Y1ZIJwc0cseeKUggcGgBKXNIM4pMHPSgBe9PHFJjFJmkApNIKUYNLigAxTc07mkxTQCZopcCigCQ4pDikINIQaQDwBS4pgzilGaAHdKFNIaQ8UgArls0pOKcPu5qMDmgBVY5pW5pxUUwg0ALxSEc03JpRmmA7NLTc0opALRRRTAKTNB6UmaAFzTN3zYp2abj5s0APxSGlB4o60ANFOzQabmgAIxQDTm6UwDBpASilqItQGoAlxSNSBqX71IBlJ3pSMU3qc0wHAUoXHNAcCjOeaAGnJHvXnHxO1K4VLKwsLp4pLiXy2Cehr0kDIPrXlPxNsWsb7T9TRZHSKfe2ATgCgC9p3w0tW0pPtEhN268yFeQat+FNO1bQXuIrxnls1YkO56AVoWXjfTZPD41EzIMJu2Ejd+VZGkeJNS8Rx3pjiUWYDjJXDHg4oALzx9e3N7JDoOnpexRDJckjBHWtDw14zXVppLe9jW2u4/vxg5wa5v4carY6Zp9zY3SiOZZZHLSjBIJPrWZpdjPqfxOvNRtMiyjlDNjgMPb1oA2pPiNqlxq93p+n6VHOYZCoYsQTitLw543vdV1htMvbJLWVELkKSeBWT4F8pvE+tpJGCWuiUOOgxUxXd8WLi3jVUBsDgjjnFMC/q/jfUf7Yl07RtPjunixvO4jAp3h3xzJqV7Pp+pQC1uY228ZNc54Ymi0Lxzqy35IZkUBn6Hn1NdDDr2lazqF1b2lqRPG21pViwCcetAFS48fajLeTRaTYR3UUOQ7M2OnWrui/EK21PR7qWZEjurdGdohyOK4XwrY2zHVIr2eS3YzSH5m2kjn1q/4f0zSY7TWm0/7Qx+zNlpQdp69DQBo6V8R9a1qOZtO0aKeNPViMVu6F4qn1zSr7zIFiuoSU2A9ODXPfCN4mtLuJUG8AA8cVB4LuhF4g1mAKTulkOMe1AGN8P9Q1j/hMLwLGZx8wKsegyea6vUfiFqMOv3WjW2nI80Bwp3HLGsf4f3sEfi2+hlxFwxBb5STk1u+GYkm+IuvMYkYfL8zDOOe1AGhqnirVbK1s4rewWW9nj3NGWIwfSsyw8e38OuJYavYx2+9cjDZ5PSn+NdXa11+ysBGqwSId0m3BHPY1xGvLFZ+J7Ka3eSVWaMEn5upFAF/xreaxb+PLV4oi8ckqiME8Zrq9c1AJrGhW+oadFLczqc7z9w5rO8aSomu6JKVxGJ1LMw9qseMbiNvGHhyWIF42BO4DPegDa8QeLG0cQ2FhbpNdyIGWInGBWJ/wmeu6RPbnV9IjgjncIH3k9aqeJLiLTPHmn3txFK1v9mwWVCQDxUXjnW4PE0enWFhFKWWYHdsPHTvQB0nivxxcaO+mrZWy3L3ZIUE4rFuPGvibTZYp77R44reQj5t570zWbCaw1Xwnbyrv2SEOSM461rfEqJW8MqEU58xMYHvQB3Nvcia3hmY7S6K20e4ri/GfjSTSb2DTrCJZrmd9iqTjBrrtGy2mWwA+TyVGW65wK8f+ImhyQeMra/ufPNo83DQ5JXg+nSmBu2vjbVtI1O3s9btUiW45Db84q54j8aahZeJbfTtOtVuFmgDglsYzXMRad4b1bWLWFZdTkl7M4YgfjWnqkbW/xIsY1idoVtVAbaT6UAPk8SeI9D1u1/tK1/cXUgjG5uleprMZIUZBww61558SN73Ohny2ZVuQflXPau6smYWUcij5cdD1FAHBeMvEmraV4ltrKxi80yR55bHpVC98QeIvDt5bXOo25+y3DKnzN0zVT4g6k2lfEPT51RpF8gkgLu7im+K/EMXi4aZp9va3SyJMrOWiIAHHtQBueLvE13DLo7aUPNM7kAZx61najqPijQ/K1aeNjbkjehbgZpnjcJoeoeGvldltZSSAMluD+dN8S+Lo/EmljSbG0uhNIyn54WA4P0oA6Hxl4hvLXQbG4sRiWd4wQD64/wAa5fXNb8ReHpLW8mLsJ2xsLcdK1fF1lMdG0hIkcsksO7g9sZpPiekzWGkyW8DyPHJlgEJHSgCjqWseI7KC11wq4tpF3su7gc16pptz9u023kAwXjVj9cV594mFzcfDa3RIW82SEEqFPy8mu50AE6FZrgq6xKCDx2oA0/lB29xSFyKU9aQrmgBm8mpBg0nApvINAD9oFKDTCSaVcigB+aKbRmgB1G6mbqKAHZzRSDpS0AHSlBpMZ4pduKAHg0mBSdKOaAFwKKTmigCQnimBuaU9KYOtICQ0m6lPSoz1oAlzmmHk4NC9aP4qQDicCjtQ3Sk7UALupCWPQ000meaAHc96Td2pw5pSopgNwaXOKaCaeBQADJPtTQ/JFO/ixSBRk0gE5IJpozTh1NFMAFHeg0o6UAFNJI6VJTSKAAZPWlwKBSGgBaMUUUgIZI3Z12nA704kKQOtS9jTFUE9KAFJ4GBS5Ipe9KaAG9etOCjGKZT16UgG+WvpTsYHFLTX4WgA4BzVPUdPttUtWtrmMNGwwRVxeRzQwwOKYHCD4WeHg5/0eTZ6eZXWaXo9lpFr9ntItieh5q8CadQBy+q+A9D1W6Nzc27GRuCUbbWhpOgafolt5FlEUTGOTk1r0zvQBkaf4c03TLme4toSsk7b3JPU0v8Awj9gdW/tIxH7Tt27s9q1mp2OKYHP6x4O0fW5fNvIGZ/VWxU2keHdP0NGWyh2BuuTk1tUEcUActqfgbQ9Wn826t3L5z8rYrQtPDmm2enPYwQ7YHUqwzyQfetY9aBQBjaP4X0rw+ki2MJQSfeyc5plj4Y0uwvZbq2gKyykliT1zW2eTzSkADigDmZvAmiTal9vNuwuOu4NitK00Kzsb2e7gjKzTjEhz1rSUkiloAx9X8NafrhQ30RcoMKQcVVXwToq7P8ARydhBXLdxXRU1iaAMfWPDOna2kSXsRcRHKYOMU7/AIRvTylspiJFsMR5PQVqgmnUAUbzSLW/thBcRhkH51VsvDOmadJ5lvBhvUnNa5JpUOTzQBSutKtby4inmj3SRHKH0puoaZbalbC3uo90YIOPpWlimEUAMWNUhSJRhVAAFQ3dlBeQGGeMMhHcVaXrSsOKYGRY6Bp2nlmt4NpJzk81aaxt2uRcGMGQDAPtVkd6SgCvc2FvdeU0qbjE25PY1Oqgc9Ceop3alVQyknk0Aec+I9Furn4gWV0kRaBYSrErkdRXdW+mWkQV1hUSbRzgVYADIznlgcA0/JCA96AOH8ZaTdX/AIh0KWOMtHBMS3GeMGuri0y1Ro5DCokA9BVxP3jMX5K9PanHlhQBFNawzoqSICqnI+tOeGOQKHRWC9MipKKAInt4njMZQFT2xSqojGFGBjFS9qYaAG7mAxSIzE80GnAUAK6r2ppOBS0yX7woAenvS55OaQ9BTwARQA3r0owTStxTATmgB20UlONIaAFB4pMmkooAcpOakDZpnagUAOJpoY5oNHagB26imUUAf//Z',\n", + " 'image_mime_type': 'image/jpeg'}}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chunk_images = [el for el in elements if 'Image' in str(type(el))]\n", + "chunk_images[0].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# separate tables from texts\n", + "tables = []\n", + "texts = []\n", + "\n", + "for chunk in chunks:\n", + " if \"Table\" in str(type(chunk)):\n", + " tables.append(chunk)\n", + "\n", + " if \"CompositeElement\" in str(type((chunk))):\n", + " texts.append(chunk)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the images from the CompositeElement objects\n", + "def get_images_base64(chunks):\n", + " images_b64 = []\n", + " for chunk in chunks:\n", + " if \"CompositeElement\" in str(type(chunk)):\n", + " chunk_els = chunk.metadata.orig_elements\n", + " for el in chunk_els:\n", + " if \"Image\" in str(type(el)):\n", + " images_b64.append(el.metadata.image_base64)\n", + " return images_b64\n", + "\n", + "images = get_images_base64(chunks)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import base64\n", + "from IPython.display import Image, display\n", + "\n", + "def display_base64_image(base64_code):\n", + " # Decode the base64 string to binary\n", + " image_data = base64.b64decode(base64_code)\n", + " # Display the image\n", + " display(Image(data=image_data))\n", + "\n", + "display_base64_image(images[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_ollama.llms import OllamaLLM\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "# Prompt\n", + "prompt_text = \"\"\"\n", + "You are an assistant tasked with summarizing tables and text.\n", + "Give a concise summary of the table or text.\n", + "\n", + "Respond only with the summary, no additionnal comment.\n", + "Do not start your message by saying \"Here is a summary\" or anything like that.\n", + "Just give the summary as it is.\n", + "\n", + "Table or text chunk: {element}\n", + "\n", + "\"\"\"\n", + "prompt = ChatPromptTemplate.from_template(prompt_text)\n", + "\n", + "model = OllamaLLM(base_url=\"172.20.48.1:11434\",\n", + " model=\"llama3.1\")\n", + "summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 3})" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['This document outlines the methodology for factorial complete plans of experiments, written by Alain Lamure.',\n", + " 'The introduction to plans for experimenting with Pex, a strategy of classical experimentation.',\n", + " 'This is a description of the classical strategy for finding π, involving a single variable x1 and measuring response y for various values of x1 while keeping other variables constant. The experiment involves 7 factors with 5 experimental points per factor, resulting in 57,778,125 possible combinations.',\n", + " 'Two methods to reduce the number of experiments: decreasing the number of data points or reducing variables. Example: weighing 4 objects on a balance with multiple weighings per object.',\n", + " 'The table shows the results of experiments with different combinations of measurements (A to D) and responses (mA to mD). Each experiment has a specific outcome (e.g. +1, 0, -1), which is likely related to an error in measurement on a balance scale with uniform error σ.',\n", + " 'The introduction reviews the concept of probability distributions, specifically the normal distribution, and its characteristics. It also discusses the Theorem of Variances, which relates to the independence of random variables and their variances, along with an example illustrating how to calculate the variance of a sample mean when observations have equal variances.',\n", + " 'The table defines a function V(x) as 1/n * standard deviation of x.',\n", + " 'The table/text describes a formula for standard deviation (σ) and the error on a mean value, which is the error on one measurement divided by the square root of the number of measurements.',\n", + " 'The table describes an experimental strategy with 8 trials, each involving measurements of variables A, B, C, and D. The goal is to understand the effect of errors in measurement on the results, with a focus on estimating the value of constants mA, mB, mC, and mD through multiple iterations.',\n", + " \"La stratégie PEX consiste en une pesée simultanée de plusieurs objets, répartis de manière à minimiser les erreurs de mesure. Pour 4 objets, il y a 4 manières différentes d'effectuer la pesée, avec des résultats différents pour chaque configuration. Lorsque toutes les mesures sont prises, on peut calculer l'erreur moyenne et déterminer si elle est inférieure à σ/2.\",\n", + " 'The table appears to be a simple sequence of alternating positive and negative values.',\n", + " 'This table appears to represent a chemical reaction, listing reactants and products, with their respective formulas.',\n", + " 'The PEX strategy allows to reduce error to its lowest value.',\n", + " 'A table with two columns \"FAIBLE\" and \"POSSIBLE\", each containing a single value, accompanied by a brief title \"CONSTRUCTION DES PLANS D\\'EXPERIENCES GENERALITES\".',\n", + " 'La sélection des facteurs dans une expérience comporte deux étapes : les facteurs principaux et la maîtrise des conditions expérimentales pour minimiser le bruit. Les facteurs qualitatifs ou quantitatifs doivent être définis avec précision, en évitant des niveaux trop rapprochés (pour maintenir la précision) ou trop éloignés (pour prendre en compte les non-linearités).',\n", + " 'The table presents the reaction time to braking in relation to blood alcohol level and tranquilizer absorption, with two factors interacting: whether the autoradio is on or off, and the number of alcoholic drinks consumed.',\n", + " 'The table discusses natural variables Ui that vary across different scales, and coded variables Xi derived from natural variables through centering and reduction. An example is given of the function of a paint sprayer, where the response variable \"colour\" depends on two factors: nozzle opening width (factor 1) and pressure (factor 2).',\n", + " 'The table lists the experimental domain for a study with several factors (U1, U2, X1, X2) and their respective levels. The text describes different types of experimental plans: screening, modeling, and 2k factorial designs, along with a simple example of a first-degree model.',\n", + " 'The table outlines planning strategies for response surfaces and mixtures, specifically recommending at least three levels per factor for quadratic models, and adapted plans for factors with constraints.',\n", + " 'The table describes a complete factorial design experiment with two factors: Opening (A) and Pressure (B). The experiment has four points selected by the Plackett-Burman method, including the classic choices M1-M4 and the PEX choices Y1-Y4. Each point is run twice, and the responses are recorded for color (coulour). The domain of the plan not explored is the level combination of A at its minimum (-1) and B at its maximum (+1).',\n", + " 'The table describes an analytical method for system of 4 equations with 4 unknowns (a0, a1, a2 and a12), where a0 is the mean response at the center of the domain and a1 and a2 represent the effects of factors 1 and 2. The interaction between the two factors is measured by a12, which is calculated as ¼ (y1∼ - y2∼ - y3∼ + y4∼) = 0.',\n", + " 'A table listing coefficients of a model related to construction and factorial plans for experience designs.',\n", + " 'The table presents a set of experimental results for a catalyzed chemical reaction. The factors influencing the reaction are temperature (T) and pressure (P). Four experiments were conducted with different combinations of T and P: A (60°C, 1 bar), B (80°C, 1 bar), C (60°C, 2 bar), and D (80°C, 2 bar).',\n", + " 'The table presents results of two methods: analytical and matrix. The analytical method involves calculating coefficients ai to predict the response variable y∼ based on variables x1, x2 and their interaction x1x2. The model is valid if the predicted values are consistent across different experimental domains (A-D). The matrix method involves representing the relationship between variables as a linear equation: y = Xa, where y is the vector of responses, a is the vector of effects, X is the design matrix and y is the response variable.',\n", + " 'The table appears to be a list of two items with corresponding weights, item 1 having weight Ws.',\n", + " 'The table and text describe a dataset of experimental points with coordinates x1 and x2 in columns 2 and 3, respectively. The sum of the products of these coordinates (column 4) is zero, indicating that the columns are orthogonal. Matrices corresponding to this data are orthogonal matrices, where At = A-1.',\n", + " 'The table presents a relation in mathematics involving variables and constants to solve for unknowns (a0, a1, a2, a12) using matrix methods.',\n", + " 'The table discusses construction limitations of factorial complete experience plans, specifically experimental difficulties.',\n", + " 'The table shows experimental data with varying conditions for four different trials (n° essai 1 to 4). Each trial has two points: the mean and temperature \"A\\' to D\\'\" at specific pressures. The data is used to fit a linear relationship between y and x, which is simplified due to linear dependency between columns X2 = 0.8.X1.',\n", + " 'The table appears to be a mathematical calculation of various numbers, with several arithmetic operations (+, -, ×, /) performed on them, resulting in a final value of 50.72.',\n", + " 'The table presents a simple arithmetic operation: 0.25 plus 0.0625 equals 0.3125, followed by an empty line and then the number 1, adding up to a total of 9.0 after adding 8.0.',\n", + " 'The table shows a calculation involving multiplication and addition. Starting with 0.8 times \"a\", then doubling to get 2. Then multiplying by \"a\" again, resulting in an expression of a plus \"a\" times (8/10).',\n", + " 'This appears to be an arithmetic calculation involving decimal numbers. The result of the operation is 0.2917.',\n", + " 'The table appears to be a series of mathematical calculations, likely for a mortgage or loan payment schedule. The table lists several rows with dollar amounts and interest rates being added together to calculate a total payment amount.',\n", + " 'The table appears to be a numerical calculation with several operations (addition, subtraction) and yields a result of 76.85. The equation y∼ = 76.25 + 15.25 x1 + x1^2 represents a linear approximation, replacing curves with a straight line.',\n", + " \"Four reactions are described: A'' at 60°C and 1.5 bar, B'' at 80°C and 1.5 bar, C'' at 75°C and 1 bar, and D'' at 75°C and 2 bars.\",\n", + " \"The table describes a statistical experiment with multiple variables (y1, y2, y3, y4) and interactions. However, due to a 0 in the X matrix, it's impossible to calculate the four unknowns. This technique is only suitable for complex problems with many factors when no interactions are assumed beforehand.\",\n", + " \"An experiment (etude) was conducted to determine the influence of three factors on the stability of an emulsion: acid fat concentration, HCl dilution, and bitumen type. The results show that each factor has a significant effect on the emulsion's stability, with interactions between some factors also being significant.\",\n", + " 'The table and text describe a linear regression model with four predictor variables (x1, x2, x3) and their interactions. The coefficients for each variable are provided, along with the R-squared value of the model. The equation for the model is: y = 27.25 - x1 - 6x2 - 4x3 - 0.25x1x2 - 0.25x1x3 - 0.25x2x3.',\n", + " 'The analysis of the plan highlights two important factors: a negative effect from HCl dilution and a better result with bitume type B. The study found no significant interaction between these factors.',\n", + " \"When experimental points are at the domain's extremes, the effect E is calculated as the average of ±y values. Variance in this case equals the dispersion of measures around the mean, and approximates the random error when systematic error is negligible.\",\n", + " 'When the error in a measurement (E) is significantly larger than the experimental error (∆E), the effect is influential. When E is much smaller than ∆Ε, the effect has little or no influence. The experimental error can be estimated by performing multiple measurements at the same point and calculating the central value. Normal distribution intervals are ±σ, ±2σ, ±3σ. Experimental data follows a Student t-curve with N degrees of freedom, similar to a normal curve for large sample sizes (N > 30), but more flattened for smaller samples.',\n", + " \"This appears to be a mathematical equation where 'type donnée par' and 's' are on opposite sides of the equals sign, indicating an unknown variable is being equated with 'type donnée par'.\",\n", + " 'The table provides critical values for normal and t-distribution probabilities. It lists critical z-values corresponding to various confidence levels (70%, 90%, 95%, 99%) and number of measurements (1-20). The formula provided in the example suggests using a t-distribution with inverse function, specific to a given number of measures and confidence level.',\n", + " 'The table summarizes experimental plans for factorials and regression models. A mathematical approach is presented to choose between several types of linear regression models, including constant model, one-factor model with interaction, two-factor model with interaction, and a step-by-step verification process (PEX) is proposed to test the selected model.',\n", + " 'The goal of an experimenter is to minimize the adjustment error (∆ → σ) by repeating experiments near the point of interest, using a polynomial expansion (PEX) in the vicinity of Ui 0. This involves fitting multiple response surfaces to data, represented by equations yˆ i = a0 + a1 x i1 + a2 x i2 + ... + en, where yˆ is the predicted response, â are coefficients, X is the matrix of experimental conditions, and e is the error matrix.',\n", + " \"The table and text describe a linear regression model, specifically a multilinear regression equation to minimize the sum of squared errors using the method of least squares, yielding coefficients 'â' as Xt(XtX)^-1 Xty where 'y' is the dependent variable, 'x' are independent variables, 'X' is the design matrix, and 't' denotes transpose.\",\n", + " 'Plans for modeling experience validation through graphical analysis.',\n", + " \"La table présente des mesures de réponse pour différentes concentrations d'un produit dans une solution, avec deux répétitions pour chaque concentration. Un modèle linéaire est ajusté à ces données en utilisant la régression multilinéaire, donnant la relation y = 52,32 + 76,05 x + e.\",\n", + " \"La régression linéaire est inadéquate car les résidus sont distribués en arceau, indiquant que la relation entre réponse et concentration suit une courbe plutôt qu'une droite. Une fonction du 2ème degré serait plus appropriée.\",\n", + " 'The table describes a plan for validating models using graphical analysis and mathematical formulas to calculate coefficients in a second-degree model without interaction.',\n", + " 'The matrix has been transposed.',\n", + " 'A table with two datasets containing numbers, followed by information on matrices and mathematical calculations to derive coefficients for a model of the form y∼ = -1.33 + 139.28x – 11.88x^2, concluding that this second-degree model without interaction is valid.',\n", + " 'The table describes a statistical analysis plan to validate models by measuring the dispersion of experimental responses and determining if variation is induced by significant factors. It outlines how to calculate variance of repeatability for each factor in an experiment with multiple repetitions.',\n", + " \"The table shows that the total number of terms in Σ2R is estimated from N results. The degrees of freedom ddlR = N - g and the variance of repeatability Vrep = Σ2R/ddlR are calculated for significance testing. In an example, a factor A effect at level i is significant if the observed value (t) exceeds the critical Student's t-value with N-g degrees of freedom and alpha (α) confidence level.\",\n", + " 'This text explains the use of statistical analysis to validate models, specifically the test of Fisher-Snédécor, which is used to determine if a factor has a significant influence on an outcome. It describes how to calculate and interpret the ratio F (VA/VE) where VA represents the variance of repeatability and VE represents the experimental dispersion.',\n", + " \"To consider the influence of factor Q as non-significant and to validate the model, F must be less than the critical value F1-α (n1, n2). A critical value at 95% is obtained for α = 0.05 using Excel's INVERSE.LOI function. If F is greater than this value, the a priori chosen model is not valid.\",\n", + " \"Le coefficient R2 est utilisé pour évaluer le degré d'ajustement d'un modèle en comparant les réponses prédites aux réponses mesurées. Il varie entre 0 (modèle inexistant) et 1 (toutes les réponses expliquées). Un R2 élevé ne signifie pas nécessairement une bonne qualité du modèle, qui dépend également du nombre de résultats et du modèle choisi.\",\n", + " 'The table represents experimental and predicted response values for various degrees, with adjustments made to account for residuals and variance. The data is from 11 experiments, with 2 coefficients calculated using measured responses. Calculations are also presented for the residual variance (Vp), adjustment variance (Vz), and experimental variance (Vx).',\n", + " 'The coefficients of determination (R2) for two regression models are given: the first model has a low R2 value (0.9497), while the second model has an extremely high R2 value (0.9987).',\n", + " 'The table outlines the methodology for experience plans, specifically mentioning surfaces of response by Alain Lamure.',\n", + " \"Plans composites permettent d'étudier des modèles par étapes, avec une première partie pour valider le modèle du premier degré et des essais supplémentaires si nécessaire pour établir un modèle du deuxième degré. Il existe plusieurs types de plans composés, notamment les plans à faces centrées (centroids) intérieurs ou extérieurs.\",\n", + " \"Le nombre total d'essais dans un plan composite est la somme des essais du plan factoriel, des essais en étoile et des essais au centre. Le choix de l'emplacement des points en étoile dépend de considérations expérimentales et théoriques. Pour un plan composite à 2 facteurs, le nombre total d'essais est généralement de 12 (4 pour le plan factoriel, 4 pour la partie en étoile et 4 pour les essais au centre).\",\n", + " 'The table appears to be incomplete, with only two numbers (2 and 3) listed under \"Foctew\" and an unrelated word (\"Facteur\") written above a number (18).',\n", + " 'The text describes a mathematical model for predicting the response of a system to two or three factors, using polynomial equations of degree 2 with interaction terms. A plan composite à 2 facteurs (composite design at 2 factors) is described, involving 12 experiments and 6 coefficients.',\n", + " 'This table appears to be a sequence of operations on a number, with alternating additions and subtractions that result in the final value being 1.',\n", + " 'The table presents a sequence of binary digits (1s and 0s) with specific operators (+, -). The sequence alternates between consecutive values of 1 and 0.',\n", + " 'This appears to be a binary numeral system table, listing binary numbers and their corresponding decimal equivalents. The table shows the addition of 1 to each number from 0 to 4 in binary.',\n", + " 'A table or list with 3 rows, containing numbers and symbols, but no discernible pattern or meaning.',\n", + " 'Binary operation result table, illustrating properties of arithmetic with parameter α.',\n", + " 'The table shows a mathematical equation with α and 0 (zero) as placeholders, suggesting an algebraic expression with variable values of +2, -12, 0, and +24.',\n", + " 'A binary sequence of 1s (represented as \"+\") and 0s, totaling 26 digits.',\n", + " 'A series of numbers with an operation (+) between two specific values (24 and 2).',\n", + " 'A binary number sequence with alternating 1s and 0s, possibly representing a digital signal or code.',\n", + " 'The table shows arithmetic operations of 4 and 24, where 2 is added to each in two separate calculations.',\n", + " 'The table shows the values of a distance parameter α, which determines the possibility of achieving certain optimality criteria based on its value. For α = 4n (where n is an integer), the elements of the information matrix satisfy a specific relation, leading to iso-variance by rotation.',\n", + " 'The table lists values of α (from 1.078 to 1.943) for achieving quasi-orthogonality in plans 22 to 26nf, with four or more points in a star shape, at different levels of nα (4, 6, 8, 10, 12).',\n", + " 'The table lists variances of model coefficients for two different optimization criteria: Isovariance and rotation (with almost optimal results), with values ranging from 0.353 to 0.500 times σr, indicating the standard error of predictions is approximately between 35% and 50% of σr.',\n", + " \"The table describes two methods for calculating the value of yˆp: a criterion of isovariance by rotation and a criterion of almost orthogonality. Both equations calculate d(yˆp) with different coefficients, but only the first method's curves result in equal prediction error as circles for specific plans (2 factors, 4 central points).\",\n", + " 'The document discusses the application of composite plans in modeling experiences on response surfaces.',\n", + " \"La surface des pièces métalliques est déterminée par l'épaisseur du métal arraché, qui dépend de deux facteurs : la vitesse d'avancement de l'outil et la vitesse tangentielle de coupe. L'état optimal est une rugosité Ra inférieure à 150.\",\n", + " 'A table with 5 rows and 2 columns containing numbers (1.21, 1, 0) in a pattern of zeros and ones, possibly indicating speed or velocity readings.',\n", + " 'A series of numbers with varying decimal points, ranging from 0.73 to 2.40, incrementing by approximately 0.17 each time.',\n", + " 'The table shows the results of an experiment on surface roughness, with different cutting speeds and advance values. The data includes measurements for each combination, as well as a statistical model that describes the relationship between the variables. The objective is to achieve a surface roughness of less than 0.15 by selecting appropriate cutting speed and advance values.\\n\\nNote: The text appears to be incomplete or unrelated to the table, so it has been ignored in this summary.',\n", + " 'The text describes the Box-Behnken design, a type of experimental design that allows for sequential experimentation. It explains how to construct a Box-Behnken plan with 3 factors using a cube structure, resulting in points that are equidistant from the center of the domain. The plan involves testing combinations of 2 out of 3 factors at various levels.',\n", + " 'Box-Behnken plans comportent entre 24+3 (4 facteurs) et 40+6 (5 facteurs) de points expérimentaux. Les plans peuvent être construits en commençant par un plan de 3, 2 ou 4 facteurs.',\n", + " 'The table presents a design of experiments with 5 factors (Facteur 1 to Facteur 5) and 6 levels for each factor. The experiment is divided into 8 groups, each containing 4-6 observations. The signs ±1 indicate the value assigned to each factor in each group, while 0 indicates no change from a reference or control condition.',\n", + " 'This table appears to be related to the application of Box-Behnken design plans for modeling and simulation experiences on response surfaces.',\n", + " \"L'étude vise à diminuer le goût acide des yoghurts en modifiant trois facteurs : taux de dilution, pH et concentration du lait. Un modèle mathématique du 2ème degré a été développé pour prédire les résultats.\",\n", + " 'The table appears to be a series of pluses (+) followed by 14 instances of the number 1.',\n", + " 'A binary sequence of 1s and 0s, with a total count of 29 ones and no patterns or notable features.',\n", + " 'The table appears to be a series of random 0s and +/− 1s, with no discernible pattern or correlation.',\n", + " 'The table appears to be a binary sequence of 1s and 0s with alternating additions of \"+\". The sequence consists of 22 1s, 8 0s, and several \"+\".',\n", + " 'A binary sequence of 1s and 0s, alternating between two values. A logical OR operation is implied by the ⇒ symbol, suggesting a relationship between the sequences.',\n", + " 'The table contains 12 rows with a mix of single and multi-digit numbers, including several instances of the number 8.',\n", + " 'Le plan de Box-Behnken pour 3 facteurs ne respecte pas certaines propriétés mathématiques, mais en ajoutant des points au centre, le critère de presque orthogonalité est respecté.',\n", + " 'The table presents the results of a multilinear regression analysis to model the behavior of a system. The equation for the model is: yˆ = 50.1 -0.07x1 -0.11x2 + 0.06x3 + 4.22x1x2- 0.32x1x3 + 2.2x2x3 + 0.07x1^2 –3.6x2^2 –9.1x3^2. The variance of each coefficient is also provided, along with estimates for the standard error (σr) which ranges from approximately 0.35 to 0.58 times σr.',\n", + " 'The table or text describes a 3D study with 2D cuts of concentration surfaces in a cube. A dilution value of 1 yields an acid enrichment of 51. The isoresponses curves show a maximum response in the (pH x concentration) planes, and a specific model equation for one of these plans is provided.',\n", + " \"L'obtention maximum de 50 ans d'âge est atteint pour les personnes ayant un enfant ou faisant l'objet d'une mesure de protection.\",\n", + " 'The table and text describe the relationship between the activity coefficient of an ion (VY) and its concentration, with specific values given for concentrations of 0, 2, and ∂ at pH 5.8, resulting in x2 = -0.63.',\n", + " 'The table shows the concentration of a substance \"Dilutian\" at pH 2, approximately 2 units. The calculation x3 = -0.06 indicates a significant decrease in concentration under these conditions.',\n", + " 'The Doehlert plan for two factors (2) generates points in a regular hexagon within the experimental space. The matrix of experiments for two factors with 5 levels for factor 1 and 3 levels for factor 2 is given, allowing the calculation of 7 unknowns and enabling the use of a second-degree model with interactions.',\n", + " 'Matrix X is a 7x6 matrix representing 7 experiences with 6 coefficients. XtX and (XtX)-1 are 6x6 matrices used for information and dissipation, respectively. The table shows the elements of XtX and their inverses, with most values being zero except for some on or near the main diagonal.',\n", + " 'The plan for 2 factors respects the isovariance criterion but not orthogonality. Variance of coefficients: σ(â0) = σ(â1) = σ(â2) = 0.58σr, σ(â12) = 1.16σr and σ(â11) = σ(â22) = 1.23σr. Prediction variance function: equal prediction error for points equidistant from the domain center.',\n", + " 'The table presents a D-optimal design with 15 experiments for 3 factors. The points are spaced regularly in a hexagonal pattern and form an uniform network in the experimental space. Each experiment is described by the values of the three factors (1, 2, ..., 8) along each axis.',\n", + " 'The Doehlert plan is described for a study with up to 3 factors. For the first factor, values are 23, 32, and 13. The second factor has values approximately equal to 321 and 0.289. The third factor equals 0 in the first 8 experiments.',\n", + " 'This document appears to be about applying Doehlert plans for modeling experiences on response surfaces.',\n", + " \"La réaction d'hydrolyse du nitrile ester dépend des concentrations en acide sulfurique (1,5 à 3%), de la quantité d'eau (3 à 6%) et de la température (87,5 à 112,5°C).\",\n", + " \"This table appears to be the coefficients and constants from a linear regression model for multiple matrices with dimensions (10,10) across various scenarios. The model's coefficients include interactions between variables x1, x2, and x3, as well as a constant term. The last column contains summary statistics or residual values, including some negative numbers, possibly indicating outliers or errors in the model.\",\n", + " 'The table shows a series of subtractions with decimals, resulting in various values including 33.1 and 67.1. The operations involve subtracting values like 47.0, 0.47, and 23.0 from each other.',\n", + " \"La table et le texte décrivent une méthode d'analyse canonique utilisant la relation générale y = y0 + kx .ak pour optimiser la réponse. La matrice des coefficients est calculée à partir de valeurs numériques.\",\n", + " \"The coordinates of the stationary point are derived from a formula involving variable 'A' and result in a value close to 0.179. A distance calculation shows that this point is within the study domain.\",\n", + " 'The table describes a canonical analysis of response surfaces with the following results:\\n- Stationary point S yields an optimal yield of 81.7%.\\n- Diagonalization of the central matrix and translation to the stationary point S leads to a relation y = 81.7 + ww1 - 94.1w2 - 39.15w3.\\n- Eigenvalues: λ1 = 2.06 (minimum), λ2 = -1.94 (maximum), and λ3 = -15.39 (high maximum).\\n- The response surface varies little along RT1 and RT2, but rapidly changes along RT3.',\n", + " 'The table appears to be showing a set of calculations involving three variables m1, m2 and m3. A vector equation is also given with coefficients for variables w1, w2 and w3.\\n\\nFrom the text, it seems that there are two optimal points: one at high temperature (110°C) where yˆ has a specific value (81.35), and another point that requires staying near a stationarity point for w2 and w3 while moving away from it for w1 to achieve the highest yield. The variables m1, m2 and m3 are related to axes RT1 and RT3 which seem to be aligned with temperature and acidity percentages respectively.',\n", + " \"Les plans hybrides visent à approcher deux critères d'optimalité sans les atteindre : presque orthogonalité et isovariance par rotation. Les plans de Roquemore existent uniquement pour 3, 4 ou 6 facteurs et sont notés en fonction du nombre d'expériences (11, 16 ou 28) et d'une lettre (A, B ou C).\",\n", + " \"Plan de Roquemore 311A : essais répartis dans un carré et un carré décalé, avec des facteurs spécifiques attribués à chaque point. Des essais ont été réalisés aux niveaux -2, 0 et +2 du facteur 3 sur l'axe passant par les centres des deux carrés.\",\n", + " 'The plan references location 311A at Roquemore, with no additional details provided.',\n", + " 'The Plan de Roquemore 311A is nearly saturated with 10 coefficients in the model. It respects criteria for isovariance by rotation and almost orthogonality, but fails to meet this criterion for the third factor.',\n", + " 'The table and text describe a statistical analysis of coefficients and variance predictions from a regression model. The coefficients have standard deviations ranging from 0.25 to 0.31 times the residual standard deviation (σr). The variance prediction function d2(yp) is given as an equation involving x1, x2, and x3, with terms that are symmetric in x1 and x2 but depend on x3.',\n", + " 'The methodology section outlines the framework for designing and conducting experimental plans.',\n", + " 'A list of plans for drilling operations, likely authored by Alain Lamure.',\n", + " \"Le nombre minimal d'expériences nécessaires pour déterminer les coefficients d'un modèle polynomial est égal au degré de liberté du modèle (ddl), qui correspond au nombre de coefficients indépendants.\",\n", + " 'A linear model with five coefficients (a0 to a4) and four input variables (x1 to x4).',\n", + " 'The table lists four columns labeled A through D, with each column containing an identical sequence of letters.',\n", + " 'The table shows two sets of combinations for a \"DDL\" total value. Set one has values 14, with 2 items at level 4 and others at various levels (A-D). Set two also has a total value of 15, but with different item quantities and levels.',\n", + " 'The condition for two vectors to be orthogonal states that their dot product is equal to zero. Mathematically, this can be expressed as u · v = 0, where u and v are the vectors.',\n", + " 'The condition for two vectors to be orthogonal states that their dot product must be zero. Mathematically, this can be represented as u · v = 0.',\n", + " 'The table lists the products of two numbers, where each number is paired with every other number in the set {2, 3, 5, 7}. The pairs are: (2*2), (2*3), (2*5), (2*7), (3*2), (3*3), (3*5), (3*7), (5*2), (5*3), (5*5), (5*7), (7*2), (7*3), (7*5), and (7*7).',\n", + " 'The table lists pairs of numbers that are equal when multiplied by each other, with asterisk denoting an exception to the rule.',\n", + " 'The table shows calculations for the Product of Prime Factors and their Combinations (PPCM), listing two sets of prime numbers multiplied together to get a result.',\n", + " 'The phrase suggests planning in stages, starting from a basic minimum level and progressing to a more comprehensive complete plan.',\n", + " 'The plan involves 32 attempts.',\n", + " 'A judicious choice of the number of levels can sometimes reduce the number of experiments. A plan with too many factors (saturated plan) and not enough experiments is not sufficient to test model fit, as it leaves no degrees of freedom. A saturated plan has fewer trials than unknown coefficients, used when there are multiple factors to examine but few are influential.',\n", + " 'A plan of 24 experiments is proposed to determine 16 unknowns: a constant, main effects and interactions up to order 4. However, due to the often negligible nature of high-order interactions, it can be fractionated into 11 experiments by only retaining those with +1 for each factor combination X1.X2.X3.X4 = +1.',\n", + " 'The table displays results of various calculations with alternating additions (+) and subtractions (-) of 1, resulting in a consistent pattern after the first two rows.',\n", + " 'The table represents an orthogonal fractional plan with 24-1 design, consisting of 8 experiments that determine 8 coefficients through the analysis of confusions between main effects and interactions. Each effect of order 1 is confused with an interaction of order 3, while each interaction of order 2 is confused with another interaction of order 2.',\n", + " 'The table lists 6 plans to estimate coefficients of contrasts, specifically 8 coefficients that can be grouped into 6 pairs.',\n", + " 'The table breaks down a plan (Plan 23) into two halves and assigns combinations of columns to each half using the letters A13, A14, A23, and A24 in various configurations.',\n", + " 'The table describes a pattern of relationships between numbers (1, 2, and 3) with positive and negative values. Each number is related to the others through different operations (+ or -), resulting in various combinations of values.',\n", + " 'The table establishes equivalences between generators of aliases (I) and contrast values, using a notation with addition (+) or subtraction (-) to indicate positive or negative combinations of contrasts. The passage from a group of alias generators (GGA) to contrast values involves writing GGA with signs and then multiplying by the corresponding vector. This results in new contrast values obtained by removing equality signs.',\n", + " \"The table or text chunk appears to be a mathematical operation involving the manipulation of terms with multiple digits, resulting in a simplified expression for a'1 = a1 - a24 + a345 + a1235. The passage then describes how to apply this operation to a larger equation using vector multiplication.\",\n", + " 'Plans for fractional experiments called \"2k-p\" using Box-Hunter generators to design and analyze sampling experiences.',\n", + " 'The table lists \"Plan N. essais Générateurs\", which appears to be a title or header related to experimental plans for generators.',\n", + " 'The table presents a series of equations with variables and their corresponding resolutions, classified into categories (III, IV, V, VI, VII) based on the number of resolved variables.',\n", + " 'The table appears to be listing mathematical equations involving inequalities and approximations, with values for \"et\" and \"±\" compared to various sequences of numbers (2.3.4.5, etc.).',\n", + " 'The table lists numbers with their corresponding plus-minus values, including 9 and 10 which have specific variations.',\n", + " 'The Box-Hunter table provides a catalog of possibilities for a given number of factors. The resolution of the table is characterized by a plan, which classifies the severity of confusions between main effects and interactions based on their order (II, III, IV or more).',\n", + " 'The text describes the benefits of using the Box-Hunter approach for experimental design. It highlights that this method allows to estimate the price to pay in order to reduce the number of experiments needed. The resolution of a plan is recommended to be at least IV to calculate the effects of interactions and factors, with a higher resolution (IV) required for factor main effects estimation.',\n", + " 'Le principal avantage des plans fractionnaires est la réduction significative du nombre d\\'essais, mais il est nécessaire de bien comprendre les \"alias\" pour interpréter ces plans. Les plans fractionnaires sont définis par la projection de points sur les faces d\\'un cube.',\n", + " 'The table appears to be related to experimental design plans, specifically 2k-p fractional plans based on the theory of contrasts.',\n", + " \"Le tableau présente les résultats de l'analyse d'un plan expérimental (plan 23-1) pour la stabilité d'une émulsion de bitume. Les facteurs étudiés sont trois, et les résultats montrent que le plan fractionnaire est comparable à celui complet en effectuant des essais en moitié moins de temps.\",\n", + " 'The table describes a contrast between the main effect of factor 1 and its interaction with factor 3, which are aliased in fractional designs. Main effects can be contaminated by interactions if they are negligible compared to the main effect values.',\n", + " \"The table outlines an experiment plan (25-2) with 5 influencing factors: temperature, material origin, agitation speed, storage duration and additive nature. It uses a fractional design with 8 experiments and combines interactions with main effects to create contrasts (a'1 and a'2).\",\n", + " \"The table appears to be an orthogonal array design with 8 rows and 5 columns representing different treatments or factors. The first column represents the factor numbers (1-8), the second column represents the interaction between two factors, the third column represents the interaction between three factors, and so on. The last column represents a contrast code for each treatment combination. Calculations of contrasts (a' values) are also provided with various results.\",\n", + " \"Les facteurs 2 et 4 ne sont pas influents, tandis que 1, 3 et 5 semblent l'être. Une seconde série d'essais est nécessaire pour lever les ambiguïtés. Les effets de 1, 3 et 5 ont été calculés en les séparant des interactions.\",\n", + " 'The table shows the results of an experiment with 16 trials, each represented by a combination of numbers (1-5) assigned to factors a1-a35. The colors and values listed correspond to different outcomes for each trial.',\n", + " \"Contrast calculations: values for coefficients a'' and intercept a0 are provided.\",\n", + " \"The table shows the calculation of new variables a'' and a''12, based on combinations of original variables a1 to a5. The conclusion drawn from the results is that only two factors have significant influence: reaction temperature (T) and additive nature. Interaction between these two factors is strong, leading to the choice of an additive B with high T and rejection of other factors for economic reasons.\",\n", + " 'The table presents a method of Taguchi, which is based on a graphical representation of the model to be identified. The factors are grouped into four categories and represented by circles, with some interactions being neglected. The process of creating plans for this method is complex and time-consuming.',\n", + " 'The text describes the use of Taguchi tables for experimental design. It explains that a specific group classification and level of difficulty in modifying factors corresponds to the frequency of modal changes, from rare (Groupe 2) to very frequent (Rond Plein). The notation used is described with examples.',\n", + " 'The table associates factors with a model y~ = M + A + B + C + D + interactions AB, BC, AC and AD. Factors are represented by columns: A in column 1, B in column 2, C in column 4, and D in column 7.',\n", + " \"Les modèles de 2k-p comportent 3 colonnes couplées : L(3), L(5) et L(6). Le tableau des interactions est déduit du triangle d'alias, où les interactions sont représentées par l'intersection de colonnes spécifiques.\",\n", + " 'The table represents a sequence of numbers and letters, with each row labeled (1) to (6), and the first column showing a series of numbers, while the second column contains combinations of letters.',\n", + " 'A list of factors corresponding to each column, also known as aliases.',\n", + " 'A table of experiment numbers corresponding to different matrix values, with some values repeated.',\n", + " \"The table shows that certain indices (I) are formed by combining values from the set {1.2, 1.4, 2.4, 1.2.4}. A remark notes that in Taguchi's orthogonal matrices for IV resolution plans, factors are systematically assigned to odd-numbered columns.\",\n", + " 'The table presents two groups of Taguchi tables: \\n- Group 1 with 2 tables (L12 and L36) suitable for finding the extremum effect of factors, with \"diluted\" interactions. \\n- Group 2 with 3 tables (L18, L32 and L50), each allowing to estimate one interaction only.',\n", + " 'The table lists possible interactions based on the number of levels for each factor, along with corresponding tables and linear graphs, such as a study with 7 factors at 2 levels and 5 interactions. The model includes main effects and interaction terms, with conditions for orthogonality determined by PPMC=16 and degrees of freedom ddl=13.',\n", + " 'The table consists of a series of numbers with an alternating pattern between two and one, followed by the numbers 42 and 34.5.',\n", + " 'The table summarizes plans for Taguchi experimentation, specifically a 2k-p plan for analyzing the effectiveness of various factors on a process.',\n", + " 'A experimental design using the Taguchi method is presented. The results show that factor A has a significant effect on the outcome, with an average response of 31.13 for level 1 and 28.99 for level 2. Factors B and C also have significant effects, while factors D to G have minimal impact. The interaction between factors AB and AC is particularly noteworthy.',\n", + " 'The table presents two sets of measurements for values B = 1 and B = 2 in columns C and BC, with the latter being higher in both cases.',\n", + " 'The table and text describe the calculation of interaction effects between two variables (A and B) in a statistical model. The interaction effects are calculated as the difference between the mean response for each combination of A and B levels, minus the main effect of A and the intercept. The results show that certain interactions have significant positive or negative effects on the outcome variable Y.',\n", + " 'The Simplex method is a progressive experimentation approach that iteratively develops plans based on accumulated results. It starts with an initial structure of k+1 experiences, then evolves by removing the worst-performing point and replacing it with a new one at the opposite end of its axis, while maintaining a regular simplex shape in 2D space.',\n", + " \"La méthode du Simplex ne nécessite pas de modèle mathématique spécifique, elle est une optimisation ponctuelle séquentielle monotone utilisée pour un seul facteur expliqué. La base est le triangle équilatéral avec des sommets (p,q) dans l'espace à k dimensions.\",\n", + " 'The table describes the construction of a simplex with its vertices, axes passing through the centroid and first bisection of the triangle. A matrix is provided to summarize the experience generated by this simplex.',\n", + " 'A sequence of numbers starting from x1, with an unclear pattern or progression.',\n", + " 'This is a sequence of binary numbers, where each row represents a value from xk-1 to k+1, with a pattern of 0s and qs.',\n", + " 'A matrix with a line of zeros and values p on the main diagonal and q elsewhere, where p and q can be calculated using the square of the distance between two points, with p=1/(k+1) and q=1/k.',\n", + " 'A mathematical table or text with values of 2 and unknown variable calculations (p, q) represented by the letter \"k\" in an undefined context.',\n", + " 'The table shows the values of p and q for different k from 2 to 8, with p values ranging from 0.884 to 0.966 and q values ranging from 0.177 to 0.259.',\n", + " 'The plans for a progressive construction experience are centered around the central unit of the simplex, with the hub located at its center.',\n", + " 'The table represents the general experience matrix of a second simplex with k+1 points, where each point has coordinates x1 to xk and an additional coordinate (22k+k)/k.',\n", + " 'The table describes a matrix of experience for a third simplex with 6 factors. Each row represents a data point, and the values are factor loadings (e.g., x1 to x6). A formula is provided for calculating diagonal elements (pn) and non-diagonal elements (qn) for more than 7 factors using recursion.',\n", + " 'The table describes a progressive simplex construction method for optimization, involving the replacement of the worst point in each step with its symmetric counterpart relative to the centroid of the remaining points, ensuring regularity and progress towards better solutions.',\n", + " \"Le simplexe se déplace en appliquant successivement la règle de déplacement, toujours dans l'opposé du point le plus mauvais, pour trouver une région avec valeurs de y meilleures, sous l'hypothèse d'une surface de réponse plane. Les déplacements suivent approximativement des lignes de grande pente en zigzag dans une direction grossièrement constante.\",\n", + " 'The table/chunk describes the construction of a progressive experience plan in 2 and 3 dimensions, focusing on symmetry and center of gravity calculations for points and lines.',\n", + " 'The table appears to be a mathematical equation in a programming language, specifically referencing variables from a system called \"BCD\".',\n", + " 'A general overview of certain aspects or topics.',\n", + " 'A table with six rows and two columns, showing a sequence of numbers (Xn to 1) and their corresponding operations (+, ≤, ≠).',\n", + " 'The study evaluates the yield of a process in relation to three variables: temperature (X1), NaOH concentration (X2) and CHCl3 concentration (X3). Four experimental points were considered using simplex methodology. The results show that changing the concentrations affects the yield, with optimal values for X1 at 25°C, X2 at 6.6 mol/l, and X3 at 1.22 mol/l. A matrix is provided showing the experimental conditions and corresponding yields (U1, U2, U3).',\n", + " 'A table listing numerical values (52.3, 73.8, 83.1, 63.5) followed by a statement about replacing a point with its symmetric coordinates. The new coordinates are given as x1,5 = 15.8, x2,5 = 6.54 and x3,5 = 1.00.',\n", + " 'A plan for experience sampling construction involves applying a movement rule until an optimum is reached or obstacles arise that require switching to alternative rules. If rule 1 (replacing the worst point with another) creates oscillation, rule 2 is applied, which involves replacing the second-worst point if consecutive applications of rule 1 lead back to a previously abandoned point.',\n", + " \"In a previous example, since image of A is A after 2 reflections, we must discard point B and substitute it with point C' in the original triangle ABC. Alternatively, if yD < yB < yC and yD >yA, simplexe DBC can be used to eliminate point B instead.\",\n", + " \"Ce document décrit les plans d'expériences de criblage et leurs conditions d'arrêt. Il met en avant trois règles : \\n- Règle N°3 (règle du vieillissement) qui consiste à refaire une expérience si le même sommet est conservé dans les simplexes successifs.\\n- Une règle selon laquelle un nouveau simplexe doit être envisagé après M itérations, avec M calculé en fonction du nombre de facteurs.\",\n", + " 'The table lists numbers 2 to 12 with an unknown variable \"k\" at the beginning and a marked \"M\" in between. The text describes reaching optimal results, considering satisfactory optimum or improving it through additional experimentation.',\n", + " 'To optimize the optimum, reduce the simplex size by half or quarter to conduct a more precise search from the stationary point. The simplex method is useful for quickly finding the zone of interest, but may not consider the actual surface of response.',\n", + " \"Lorsque l'erreur de mesure gêne le progrès du simplexe, deux nouvelles règles peuvent être ajoutées. La première évite les erreurs de biais en remplaçant les anciennes observations lorsque 2(k + 1) expériences ont été faites. La seconde remplace un sommet qui dépasse une contrainte par un autre sommet choisi selon la règle 2.\",\n", + " 'A table or text about a chemical reaction optimization study, specifically a one-step synthesis of phenylsulfonyl-3 quinuclidine using benzene sulfonoyl chloride and triethylamine as reagents, with 5 influencing variables (x1-x5) affecting the yield of the product.',\n", + " 'A table of measurements for a regular simplexe, with temperature (°C) and time (h) data recorded at different positions (X1-X5) over six runs, including the average Rdt value.',\n", + " 'The table or text appears to outline a plan for progressive construction analysis of the Simplex, focusing on testing and validation experiences.',\n", + " 'The table shows the returns of a Simplex investment over 9 iterations (N° expériences), with increasing number of experiments leading to higher average returns up to iteration 7, then stabilizing around 67.9%. Iteration 8 has a slight increase in return, but by iteration 9, it returns to the same level as previous iterations.']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text_summaries" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Summarize tables\n", + "tables_html = [table.metadata.text_as_html for table in tables]\n", + "table_summaries = summarize_chain.batch(tables_html, {\"max_concurrency\": 3})" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['
N° essai|01 A02) | B03C 04)05 |06 |07D | 0809}10F | 11)G|E 12/13}14)15y
35,6
30,4
33,5
39,5
12,6
34,5
26,9
36,4
33,5
1023,4
1135,6
1221,5
1326,6
1420,4
1528,4
1634,5
',\n", + " '
Variables| Niveau de basePas
Xy 1 0,2 X 1 0,2
X3 15 °C 8 °C X4 1,5 h 0,5 h
X520 °C8 °C
',\n", + " '
utilisationformule: X,,;=2X,,-Xy,ou W =N° duplusmauvaispointet
R=N° dunouveaupointsymétriqueavecX,,= >} l=1X,, ouk=SetlzW.
N°XiX>X3X4X;X1X2X3 (°C)|X4 ch)X5 | CC)Rdt
7|-0,584 |0,406 |0,406 |0,406 |0,406 |0,883 |1,081| 18,2 |1,7| 23,258,8
8| 0,094 |0,773 |0,773 |0,773| 0,773 |1,019 |1,155| 21,2)1,9| 26,267,9
9|-0,155 |0,795 |0,795 |0,795 |-0,194 |0,969 |1,159| 21,4)1,9| 18,464,2
10| 0,805 |0,750 |0,750 |0,750| 0,072 |1,161| 1,15| 21,0]1,9 |20,668,0
11| 0,256 |1,169 |1,179 |1,169| 0,219 |1,051 |1,234| 16,4 |2,1 |21,773,4
12| 0,277 |0,565 |0,876 |1,555| 0,225 |1,055 |1,113| 22,0 |2,3| 21,871,5
13| 0,306 |1,416 |1,144 |1,105| 0,233 |1,061 |1,283| 24,2 |2,1| 21,974,3
14| 0,851 |1,074 |0,694 |1,346| 0,803 |1,170 |1,215| 20,6 |2,2| 26,466,8
']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tables_html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import getpass\n", + "# import os\n", + "\n", + "# if not os.getenv(\"DEEPSEEK_API_KEY\"):\n", + "# os.environ[\"DEEPSEEK_API_KEY\"] = getpass.getpass(\"Enter your DeepSeek API key: \")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_deepseek import ChatDeepSeek" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 25\u001b[39m\n\u001b[32m 21\u001b[39m chain = OllamaLLM(base_url=\u001b[33m\"\u001b[39m\u001b[33m172.20.48.1:11434\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 22\u001b[39m model=\u001b[33m\"\u001b[39m\u001b[33mllama3.2-vision\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 23\u001b[39m \u001b[38;5;66;03m#chain = prompt | ChatOpenAI(model=\"gpt-4o-mini\") | StrOutputParser()\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m25\u001b[39m image_summaries = \u001b[43mchain\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:438\u001b[39m, in \u001b[36mBaseLLM.batch\u001b[39m\u001b[34m(self, inputs, config, return_exceptions, **kwargs)\u001b[39m\n\u001b[32m 436\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m max_concurrency \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 437\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m438\u001b[39m llm_result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_prompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 439\u001b[39m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 440\u001b[39m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcallbacks\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 441\u001b[39m \u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtags\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 442\u001b[39m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmetadata\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 443\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_name\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrun_name\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 444\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 445\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 446\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m [g[\u001b[32m0\u001b[39m].text \u001b[38;5;28;01mfor\u001b[39;00m g \u001b[38;5;129;01min\u001b[39;00m llm_result.generations]\n\u001b[32m 447\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:760\u001b[39m, in \u001b[36mBaseLLM.generate_prompt\u001b[39m\u001b[34m(self, prompts, stop, callbacks, **kwargs)\u001b[39m\n\u001b[32m 752\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_prompt\u001b[39m(\n\u001b[32m 753\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 754\u001b[39m prompts: \u001b[38;5;28mlist\u001b[39m[PromptValue],\n\u001b[32m (...)\u001b[39m\u001b[32m 757\u001b[39m **kwargs: Any,\n\u001b[32m 758\u001b[39m ) -> LLMResult:\n\u001b[32m 759\u001b[39m prompt_strings = [p.to_string() \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m prompts]\n\u001b[32m--> \u001b[39m\u001b[32m760\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt_strings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:963\u001b[39m, in \u001b[36mBaseLLM.generate\u001b[39m\u001b[34m(self, prompts, stop, callbacks, tags, metadata, run_name, run_id, **kwargs)\u001b[39m\n\u001b[32m 948\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mself\u001b[39m.cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m get_llm_cache() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[32m 949\u001b[39m run_managers = [\n\u001b[32m 950\u001b[39m callback_manager.on_llm_start(\n\u001b[32m 951\u001b[39m \u001b[38;5;28mself\u001b[39m._serialized,\n\u001b[32m (...)\u001b[39m\u001b[32m 961\u001b[39m )\n\u001b[32m 962\u001b[39m ]\n\u001b[32m--> \u001b[39m\u001b[32m963\u001b[39m output = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 964\u001b[39m \u001b[43m \u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnew_arg_supported\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 965\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 966\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output\n\u001b[32m 967\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(missing_prompts) > \u001b[32m0\u001b[39m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_core/language_models/llms.py:784\u001b[39m, in \u001b[36mBaseLLM._generate_helper\u001b[39m\u001b[34m(self, prompts, stop, run_managers, new_arg_supported, **kwargs)\u001b[39m\n\u001b[32m 774\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_generate_helper\u001b[39m(\n\u001b[32m 775\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 776\u001b[39m prompts: \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m],\n\u001b[32m (...)\u001b[39m\u001b[32m 780\u001b[39m **kwargs: Any,\n\u001b[32m 781\u001b[39m ) -> LLMResult:\n\u001b[32m 782\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 783\u001b[39m output = (\n\u001b[32m--> \u001b[39m\u001b[32m784\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_generate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 785\u001b[39m \u001b[43m \u001b[49m\u001b[43mprompts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 786\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 787\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# TODO: support multiple run managers\u001b[39;49;00m\n\u001b[32m 788\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrun_managers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 789\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 790\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 791\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[32m 792\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m._generate(prompts, stop=stop)\n\u001b[32m 793\u001b[39m )\n\u001b[32m 794\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 795\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m run_manager \u001b[38;5;129;01min\u001b[39;00m run_managers:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_ollama/llms.py:288\u001b[39m, in \u001b[36mOllamaLLM._generate\u001b[39m\u001b[34m(self, prompts, stop, run_manager, **kwargs)\u001b[39m\n\u001b[32m 286\u001b[39m generations = []\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m prompt \u001b[38;5;129;01min\u001b[39;00m prompts:\n\u001b[32m--> \u001b[39m\u001b[32m288\u001b[39m final_chunk = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_stream_with_aggregation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 289\u001b[39m \u001b[43m \u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 290\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 291\u001b[39m \u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 292\u001b[39m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 293\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 294\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 295\u001b[39m generations.append([final_chunk])\n\u001b[32m 296\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m LLMResult(generations=generations)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_ollama/llms.py:256\u001b[39m, in \u001b[36mOllamaLLM._stream_with_aggregation\u001b[39m\u001b[34m(self, prompt, stop, run_manager, verbose, **kwargs)\u001b[39m\n\u001b[32m 247\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_stream_with_aggregation\u001b[39m(\n\u001b[32m 248\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 249\u001b[39m prompt: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m (...)\u001b[39m\u001b[32m 253\u001b[39m **kwargs: Any,\n\u001b[32m 254\u001b[39m ) -> GenerationChunk:\n\u001b[32m 255\u001b[39m final_chunk = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_create_generate_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 257\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 258\u001b[39m \u001b[43m \u001b[49m\u001b[43mchunk\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[43mGenerationChunk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 259\u001b[39m \u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mresponse\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 260\u001b[39m \u001b[43m \u001b[49m\u001b[43mgeneration_info\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 261\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mstream_resp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdone\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[32m 262\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 263\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_ollama/llms.py:211\u001b[39m, in \u001b[36mOllamaLLM._create_generate_stream\u001b[39m\u001b[34m(self, prompt, stop, **kwargs)\u001b[39m\n\u001b[32m 205\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_create_generate_stream\u001b[39m(\n\u001b[32m 206\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 207\u001b[39m prompt: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m 208\u001b[39m stop: Optional[List[\u001b[38;5;28mstr\u001b[39m]] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m 209\u001b[39m **kwargs: Any,\n\u001b[32m 210\u001b[39m ) -> Iterator[Union[Mapping[\u001b[38;5;28mstr\u001b[39m, Any], \u001b[38;5;28mstr\u001b[39m]]:\n\u001b[32m--> \u001b[39m\u001b[32m211\u001b[39m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._client.generate(\n\u001b[32m 212\u001b[39m **\u001b[38;5;28mself\u001b[39m._generate_params(prompt, stop=stop, **kwargs)\n\u001b[32m 213\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/ollama/_client.py:163\u001b[39m, in \u001b[36mClient._request..inner\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 162\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34minner\u001b[39m():\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_client\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mtry\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[43m \u001b[49m\u001b[43mr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/contextlib.py:137\u001b[39m, in \u001b[36m_GeneratorContextManager.__enter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 135\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.kwds, \u001b[38;5;28mself\u001b[39m.func\n\u001b[32m 136\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m137\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 138\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mgenerator didn\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt yield\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpx/_client.py:868\u001b[39m, in \u001b[36mClient.stream\u001b[39m\u001b[34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[39m\n\u001b[32m 845\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 846\u001b[39m \u001b[33;03mAlternative to `httpx.request()` that streams the response body\u001b[39;00m\n\u001b[32m 847\u001b[39m \u001b[33;03minstead of loading it into memory at once.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 853\u001b[39m \u001b[33;03m[0]: /quickstart#streaming-responses\u001b[39;00m\n\u001b[32m 854\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 855\u001b[39m request = \u001b[38;5;28mself\u001b[39m.build_request(\n\u001b[32m 856\u001b[39m method=method,\n\u001b[32m 857\u001b[39m url=url,\n\u001b[32m (...)\u001b[39m\u001b[32m 866\u001b[39m extensions=extensions,\n\u001b[32m 867\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m868\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 869\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 870\u001b[39m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 871\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 872\u001b[39m \u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 873\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 875\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m response\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpx/_client.py:914\u001b[39m, in \u001b[36mClient.send\u001b[39m\u001b[34m(self, request, stream, auth, follow_redirects)\u001b[39m\n\u001b[32m 910\u001b[39m \u001b[38;5;28mself\u001b[39m._set_timeout(request)\n\u001b[32m 912\u001b[39m auth = \u001b[38;5;28mself\u001b[39m._build_request_auth(request, auth)\n\u001b[32m--> \u001b[39m\u001b[32m914\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 915\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 916\u001b[39m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 917\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 918\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 919\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 920\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 921\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpx/_client.py:942\u001b[39m, in \u001b[36mClient._send_handling_auth\u001b[39m\u001b[34m(self, request, auth, follow_redirects, history)\u001b[39m\n\u001b[32m 939\u001b[39m request = \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[32m 941\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m942\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 943\u001b[39m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 944\u001b[39m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 945\u001b[39m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 946\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 947\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 948\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpx/_client.py:979\u001b[39m, in \u001b[36mClient._send_handling_redirects\u001b[39m\u001b[34m(self, request, follow_redirects, history)\u001b[39m\n\u001b[32m 976\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mrequest\u001b[39m\u001b[33m\"\u001b[39m]:\n\u001b[32m 977\u001b[39m hook(request)\n\u001b[32m--> \u001b[39m\u001b[32m979\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 980\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 981\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._event_hooks[\u001b[33m\"\u001b[39m\u001b[33mresponse\u001b[39m\u001b[33m\"\u001b[39m]:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpx/_client.py:1014\u001b[39m, in \u001b[36mClient._send_single_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 1009\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 1010\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1011\u001b[39m )\n\u001b[32m 1013\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request=request):\n\u001b[32m-> \u001b[39m\u001b[32m1014\u001b[39m response = \u001b[43mtransport\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1016\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, SyncByteStream)\n\u001b[32m 1018\u001b[39m response.request = request\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpx/_transports/default.py:250\u001b[39m, in \u001b[36mHTTPTransport.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 237\u001b[39m req = httpcore.Request(\n\u001b[32m 238\u001b[39m method=request.method,\n\u001b[32m 239\u001b[39m url=httpcore.URL(\n\u001b[32m (...)\u001b[39m\u001b[32m 247\u001b[39m extensions=request.extensions,\n\u001b[32m 248\u001b[39m )\n\u001b[32m 249\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[32m--> \u001b[39m\u001b[32m250\u001b[39m resp = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_pool\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 252\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp.stream, typing.Iterable)\n\u001b[32m 254\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[32m 255\u001b[39m status_code=resp.status,\n\u001b[32m 256\u001b[39m headers=resp.headers,\n\u001b[32m 257\u001b[39m stream=ResponseStream(resp.stream),\n\u001b[32m 258\u001b[39m extensions=resp.extensions,\n\u001b[32m 259\u001b[39m )\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/connection_pool.py:256\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 253\u001b[39m closing = \u001b[38;5;28mself\u001b[39m._assign_requests_to_connections()\n\u001b[32m 255\u001b[39m \u001b[38;5;28mself\u001b[39m._close_connections(closing)\n\u001b[32m--> \u001b[39m\u001b[32m256\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 258\u001b[39m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[32m 259\u001b[39m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n\u001b[32m 260\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response.stream, typing.Iterable)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/connection_pool.py:236\u001b[39m, in \u001b[36mConnectionPool.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 232\u001b[39m connection = pool_request.wait_for_connection(timeout=timeout)\n\u001b[32m 234\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 235\u001b[39m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m236\u001b[39m response = \u001b[43mconnection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 237\u001b[39m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrequest\u001b[49m\n\u001b[32m 238\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 239\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[32m 240\u001b[39m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[32m 241\u001b[39m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[32m 242\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 243\u001b[39m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n\u001b[32m 244\u001b[39m pool_request.clear_connection()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/connection.py:103\u001b[39m, in \u001b[36mHTTPConnection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 100\u001b[39m \u001b[38;5;28mself\u001b[39m._connect_failed = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 101\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[32m--> \u001b[39m\u001b[32m103\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_connection\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:136\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 134\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[33m\"\u001b[39m\u001b[33mresponse_closed\u001b[39m\u001b[33m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 135\u001b[39m \u001b[38;5;28mself\u001b[39m._response_closed()\n\u001b[32m--> \u001b[39m\u001b[32m136\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m exc\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:106\u001b[39m, in \u001b[36mHTTP11Connection.handle_request\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[32m 97\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[32m 98\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mreceive_response_headers\u001b[39m\u001b[33m\"\u001b[39m, logger, request, kwargs\n\u001b[32m 99\u001b[39m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[32m 100\u001b[39m (\n\u001b[32m 101\u001b[39m http_version,\n\u001b[32m 102\u001b[39m status,\n\u001b[32m 103\u001b[39m reason_phrase,\n\u001b[32m 104\u001b[39m headers,\n\u001b[32m 105\u001b[39m trailing_data,\n\u001b[32m--> \u001b[39m\u001b[32m106\u001b[39m ) = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_response_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 107\u001b[39m trace.return_value = (\n\u001b[32m 108\u001b[39m http_version,\n\u001b[32m 109\u001b[39m status,\n\u001b[32m 110\u001b[39m reason_phrase,\n\u001b[32m 111\u001b[39m headers,\n\u001b[32m 112\u001b[39m )\n\u001b[32m 114\u001b[39m network_stream = \u001b[38;5;28mself\u001b[39m._network_stream\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:177\u001b[39m, in \u001b[36mHTTP11Connection._receive_response_headers\u001b[39m\u001b[34m(self, request)\u001b[39m\n\u001b[32m 174\u001b[39m timeout = timeouts.get(\u001b[33m\"\u001b[39m\u001b[33mread\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m177\u001b[39m event = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 178\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11.Response):\n\u001b[32m 179\u001b[39m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_sync/http11.py:217\u001b[39m, in \u001b[36mHTTP11Connection._receive_event\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m 214\u001b[39m event = \u001b[38;5;28mself\u001b[39m._h11_state.next_event()\n\u001b[32m 216\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11.NEED_DATA:\n\u001b[32m--> \u001b[39m\u001b[32m217\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_network_stream\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 218\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtimeout\u001b[49m\n\u001b[32m 219\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 221\u001b[39m \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[32m 222\u001b[39m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[32m 223\u001b[39m \u001b[38;5;66;03m# httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 227\u001b[39m \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[32m 228\u001b[39m \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[32m 229\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m data == \u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m._h11_state.their_state == h11.SEND_RESPONSE:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/httpcore/_backends/sync.py:128\u001b[39m, in \u001b[36mSyncStream.read\u001b[39m\u001b[34m(self, max_bytes, timeout)\u001b[39m\n\u001b[32m 126\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[32m 127\u001b[39m \u001b[38;5;28mself\u001b[39m._sock.settimeout(timeout)\n\u001b[32m--> \u001b[39m\u001b[32m128\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_sock\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[31mKeyboardInterrupt\u001b[39m: " + ] + } + ], + "source": [ + "import os\n", + "os.environ[\"OPENAI_API_KEY\"] = \"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\"\n", + "from langchain_openai import ChatOpenAI\n", + "prompt_template = \"\"\"Describe the image in detail. For context,\n", + " the image is part of a outlines the methodology for factorial complete plans of experimentss.\"\"\"\n", + "messages = [\n", + " (\n", + " \"user\",\n", + " [\n", + " {\"type\": \"text\", \"text\": prompt_template},\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\"url\": \"data:image/jpeg;base64,{image}\"},\n", + " },\n", + " ],\n", + " )\n", + "]\n", + "\n", + "prompt = ChatPromptTemplate.from_messages(messages)\n", + "\n", + "chain = OllamaLLM(base_url=\"172.20.48.1:11434\",\n", + " model=\"llama3.2-vision\")\n", + "#chain = prompt | ChatOpenAI(model=\"gpt-4o-mini\") | StrOutputParser()\n", + "\n", + "image_summaries = chain.batch(images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Qdrant.__init__() got an unexpected keyword argument 'url'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[28]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 8\u001b[39m embeddings = OllamaEmbeddings(base_url=\u001b[33m\"\u001b[39m\u001b[33m172.20.48.1:11434\u001b[39m\u001b[33m\"\u001b[39m,model=\u001b[33m\"\u001b[39m\u001b[33mmxbai-embed-large\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;66;03m# Spécifiez le modèle Ollama à utiliser\u001b[39;00m\n\u001b[32m 10\u001b[39m \u001b[38;5;66;03m# Initialize Qdrant vector store\u001b[39;00m\n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# Option 1: Local instance (using local storage)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m vectorstore = \u001b[43mQdrant\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 13\u001b[39m \u001b[43m \u001b[49m\u001b[43mcollection_name\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmulti_modal_rag\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 14\u001b[39m \u001b[43m \u001b[49m\u001b[43membedding_function\u001b[49m\u001b[43m=\u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 15\u001b[39m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhttp://172.20.48.1:6333\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Utilisez 'location' pour spécifier l'URL de Qdrant\u001b[39;49;00m\n\u001b[32m 16\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/dev/rag/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:214\u001b[39m, in \u001b[36mdeprecated..deprecate..finalize..warn_if_direct_instance\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m 212\u001b[39m warned = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m 213\u001b[39m emit_warning()\n\u001b[32m--> \u001b[39m\u001b[32m214\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mwrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[31mTypeError\u001b[39m: Qdrant.__init__() got an unexpected keyword argument 'url'" + ] + } + ], + "source": [ + "from langchain_qdrant import QdrantVectorStore\n", + "from langchain.storage import InMemoryStore\n", + "from langchain.schema.document import Document\n", + "from langchain_ollama import OllamaEmbeddings\n", + "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", + "\n", + "# Create embedding function\n", + "embeddings = OllamaEmbeddings(base_url=\"172.20.48.1:11434\",model=\"mxbai-embed-large\") # Spécifiez le modèle Ollama à utiliser\n", + "\n", + "# Initialize Qdrant vector store\n", + "# Option 1: Local instance (using local storage)\n", + "vectorstore = QdrantVectorStore(\n", + " collection_name=\"multi_modal_rag\",\n", + " embedding_function=embeddings,\n", + " url=\"http://172.20.48.1:6333\" # Utilisez 'location' pour spécifier l'URL de Qdrant\n", + ")\n", + "vectorstore = QdrantVectorStore.from_documents(\n", + " docs,\n", + " embeddings,\n", + " url=\"http://172.20.48.1:6333\",\n", + " collection_name=\"my_documents\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "from langchain.vectorstores import Chroma\n", + "from langchain.storage import InMemoryStore\n", + "from langchain.schema.document import Document\n", + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", + "\n", + "# The vectorstore to use to index the child chunks\n", + "vectorstore = Chroma(collection_name=\"multi_modal_rag\", embedding_function=OpenAIEmbeddings())\n", + "\n", + "# The storage layer for the parent documents\n", + "store = InMemoryStore()\n", + "id_key = \"doc_id\"\n", + "\n", + "# The retriever (empty to start)\n", + "retriever = MultiVectorRetriever(\n", + " vectorstore=vectorstore,\n", + " docstore=store,\n", + " id_key=id_key,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "from langchain.vectorstores import Chroma\n", + "from langchain.storage import InMemoryStore\n", + "from langchain.schema.document import Document\n", + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", + "\n", + "# The vectorstore to use to index the child chunks\n", + "vectorstore = Chroma(collection_name=\"multi_modal_rag\", embedding_function=OpenAIEmbeddings())\n", + "\n", + "# The storage layer for the parent documents\n", + "store = InMemoryStore()\n", + "id_key = \"doc_id\"\n", + "\n", + "# The retriever (empty to start)\n", + "retriever = MultiVectorRetriever(\n", + " vectorstore=vectorstore,\n", + " docstore=store,\n", + " id_key=id_key,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import uuid\n", + "from langchain.vectorstores import Chroma\n", + "from langchain.storage import InMemoryStore\n", + "from langchain.schema.document import Document\n", + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", + "\n", + "# The vectorstore to use to index the child chunks\n", + "vectorstore = Chroma(collection_name=\"multi_modal_rag\", embedding_function=OpenAIEmbeddings())\n", + "\n", + "# The storage layer for the parent documents\n", + "store = InMemoryStore()\n", + "id_key = \"doc_id\"\n", + "\n", + "# The retriever (empty to start)\n", + "retriever = MultiVectorRetriever(\n", + " vectorstore=vectorstore,\n", + " docstore=store,\n", + " id_key=id_key,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "content=\"Hello! I'm based on OpenAI's GPT-3 model. How can I assist you today?\" additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 21, 'prompt_tokens': 14, 'total_tokens': 35, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_06737a9306', 'finish_reason': 'stop', 'logprobs': None} id='run-087ad4ec-bf03-4d7e-a89d-70f578ba7a66-0' usage_metadata={'input_tokens': 14, 'output_tokens': 21, 'total_tokens': 35, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}\n" + ] + } + ], + "source": [ + "import subprocess\n", + "windows_host = \"172.20.48.1\"\n", + "\n", + "# Connect to Ollama using the Windows host IP\n", + "model = OllamaLLM(\n", + " base_url=f\"http://{windows_host}:11434\",\n", + " model=\"llama3.1\"\n", + ")\n", + "modelOp= ChatOpenAI(model=\"gpt-4o-mini\") \n", + "# Test the connection\n", + "response = modelOp.invoke(\"Hello, which model are you?\")\n", + "print(response)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content=\"Hi! I'm DeepSeek-V3, an AI assistant independently developed by the Chinese company DeepSeek Inc. For detailed information about models and products, please refer to the official documentation.\", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 39, 'prompt_tokens': 10, 'total_tokens': 49, 'completion_tokens_details': None, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 10}, 'model_name': 'deepseek-chat', 'system_fingerprint': 'fp_3a5770e1b4_prod0225', 'finish_reason': 'stop', 'logprobs': None}, id='run-22af53f2-32e7-440e-8ba2-18a6622e8e6f-0', usage_metadata={'input_tokens': 10, 'output_tokens': 39, 'total_tokens': 49, 'input_token_details': {'cache_read': 0}, 'output_token_details': {}})" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "llm = ChatDeepSeek(\n", + " model=\"deepseek-chat\",\n", + ")\n", + "llm.invoke(\"Hello, which model are you?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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