{"id":3645,"date":"2024-05-08T11:11:48","date_gmt":"2024-05-08T03:11:48","guid":{"rendered":"https:\/\/www.aqwu.net\/wp\/?p=3645"},"modified":"2024-05-08T17:49:25","modified_gmt":"2024-05-08T09:49:25","slug":"rag-%e5%85%a5%e9%97%a8%e6%95%99%e7%a8%8bpdf","status":"publish","type":"post","link":"https:\/\/www.aqwu.net\/wp\/?p=3645","title":{"rendered":"RAG \u5165\u95e8\u6559\u7a0b(PDF-WSL2)"},"content":{"rendered":"\n<p>\u672c\u6559\u7a0b\u4f7f\u7528\u4e86<strong><a href=\"https:\/\/github.com\/VikParuchuri\/surya\">surya-ocr<\/a><\/strong>\u5e93\uff0c\u5b9e\u73b0\u672c\u5730RAG\uff0c<\/p>\n\n\n\n<p>\u4f7f\u7528\u4e86\u5d4c\u5165\u6a21\u578b bert-base-multilingual-cased\uff08\u652f\u6301\u591a\u8bed\u8a00\uff09<\/p>\n\n\n\n<p>\u548c\u63a8\u7406\u6a21\u578b Qwen1.5-1.8B-Chat<\/p>\n\n\n\n<p>Surya \u662f\u4e00\u4e2a\u6587\u6863 OCR \u5de5\u5177\u5305\uff0c\u53ef\u4ee5\u5904\u7406pdf\u6587\u4ef6\u548c\u56fe\u7247\u7b49<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>90+ \u79cd\u8bed\u8a00\u7684 OCR\uff0c\u4e0e\u4e91\u670d\u52a1\u76f8\u6bd4\u5177\u6709\u4f18\u52bf<\/li>\n\n\n\n<li>\u4efb\u4f55\u8bed\u8a00\u7684\u884c\u7ea7\u6587\u672c\u68c0\u6d4b<\/li>\n\n\n\n<li>\u5e03\u5c40\u5206\u6790\uff08\u8868\u683c\u3001\u56fe\u50cf\u3001\u9875\u7709\u7b49\u68c0\u6d4b\uff09<\/li>\n\n\n\n<li>\u8bfb\u53d6\u987a\u5e8f\u68c0\u6d4b<\/li>\n<\/ul>\n\n\n\n<p>\u6d4b\u8bd5\u73af\u5883\uff1aWSL2\uff0c \u9700\u8981\u663e\u5361<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>1. \u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:sh decode:true \">!pip install surya-ocr\n\n!pip install python-magic\n!pip install -U transformers\n!pip install -U sentence_transformers\n!pip install -U numpy\n!pip install faiss-cpu<\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>2. \u5f15\u5165\u6240\u6709\u7684\u5e93<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \">import torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nfrom sentence_transformers import SentenceTransformer\nimport faiss\nimport numpy as np\n\nimport io\nimport magic\nimport pypdfium2\nfrom typing import List\n\nfrom surya.detection import batch_text_detection\nfrom surya.layout import batch_layout_detection\nfrom surya.model.detection.segformer import load_model, load_processor\nfrom surya.model.recognition.model import load_model as load_rec_model\nfrom surya.model.recognition.processor import load_processor as load_rec_processor\nfrom surya.model.ordering.processor import load_processor as load_order_processor\nfrom surya.model.ordering.model import load_model as load_order_model\nfrom surya.ordering import batch_ordering\nfrom surya.postprocessing.heatmap import draw_polys_on_image\nfrom surya.ocr import run_ocr\nfrom surya.postprocessing.text import draw_text_on_image\nfrom PIL import Image\nfrom surya.languages import CODE_TO_LANGUAGE\nfrom surya.input.langs import replace_lang_with_code\nfrom surya.schema import OCRResult, TextDetectionResult, LayoutResult, OrderResult\nfrom surya.settings import settings<\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>3. \u5904\u7406 pdf \u7684\u51fd\u6570\u5b9a\u4e49<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \">def open_pdf(pdf_file):\n    # \u6253\u5f00\u6587\u4ef6\u5e76\u8bfb\u53d6\u5185\u5bb9\u5230\u5185\u5b58\n    with open(pdf_file, 'rb') as file:\n        pdf_data = file.read()\n    stream = io.BytesIO(pdf_data)\n    return pypdfium2.PdfDocument(stream)\n\ndef page_count(pdf_file):\n    doc = open_pdf(pdf_file)\n    return len(doc)\n\ndef get_page_image(pdf_file, page_num, dpi=96):\n    doc = open_pdf(pdf_file)\n    renderer = doc.render(\n        pypdfium2.PdfBitmap.to_pil,\n        page_indices=[page_num - 1],\n        scale=dpi \/ 72,\n    )\n    png = list(renderer)[0]\n    png_image = png.convert(\"RGB\")\n    return png_image\n\ndef ocr(img, langs: List[str]) -&gt; (Image.Image, OCRResult):\n    replace_lang_with_code(langs)\n    img_pred = run_ocr([img], [langs], det_model, det_processor, rec_model, rec_processor)[0]\n\n    bboxes = [l.bbox for l in img_pred.text_lines]\n    text = [l.text for l in img_pred.text_lines]\n    rec_img = draw_text_on_image(bboxes, text, img.size, langs, has_math=\"_math\" in langs)\n    return rec_img, img_pred\n\ndef load_det_cached():\n    checkpoint = settings.DETECTOR_MODEL_CHECKPOINT\n    return load_model(checkpoint=checkpoint), load_processor(checkpoint=checkpoint)\n\ndef load_rec_cached():\n    return load_rec_model(), load_rec_processor()<\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>4. \u5d4c\u5165\u6a21\u578b\u548c\u63a8\u7406\u6a21\u578b<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \"># \u5c0f\u6a21\u578b\u7528\u4e8e\u521b\u5efa\u5d4c\u5165\n#embedder = SentenceTransformer('Qwen\/Qwen1.5-0.5B-Chat')\nembedder = SentenceTransformer('bert-base-multilingual-cased')\n\n# \u5927\u6a21\u578b\u7528\u4e8e\u751f\u6210\ntokenizer = AutoTokenizer.from_pretrained('Qwen\/Qwen1.5-1.8B-Chat')\n\ndevice = \"cuda\" # the device to load the model onto\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    \"Qwen\/Qwen1.5-1.8B-Chat\",\n    torch_dtype=\"auto\",\n    device_map=\"auto\"\n)<\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>5. \u53c2\u6570\u521d\u59cb\u5316<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \">languages=[\"English\"]\n\n# Initialize an empty list to store the embeddings\nembeddings_list = []\ndocuments = []\n\ndet_model, det_processor = load_det_cached()\nrec_model, rec_processor = load_rec_cached()<\/pre><\/div>\n\n\n\n<p>languages=[&#8220;English&#8221;]\uff0c\u652f\u6301\u591a\u8bed\u8a00\uff0c\u53ef\u4ee5\u81ea\u884c\u52a0\u5165\u5176\u4ed6\u8bed\u8a00,\u6bd4\u5982\u52a0\u5165\u4e2d\u6587<\/p>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \">languages=[\"English\", \"Chinese\"]<\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>6. \u5904\u7406 pdf \u6587\u4ef6<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \">in_file = \"data\/Learning to Model the World with Language.pdf\"\nfile_type = magic.from_file(in_file, mime=True)\n#print(file_type)  # \u8f93\u51fa\u53ef\u80fd\u662f 'application\/pdf'\n\nif \"pdf\" in file_type:\n    page_count = page_count(in_file)\n    #print(f\"page_count=\", page_count)\n\n# \u5faa\u73af\u904d\u5386\u6bcf\u4e00\u9875\nfor page_number in range(page_count):\n    pil_image = get_page_image(in_file, page_number + 1)\n    rec_img, pred = ocr(pil_image, languages)\n    document = \"\\n\".join([p.text for p in pred.text_lines])\n\n    embeddings = embedder.encode(document)   \n    embeddings_list.append(embeddings)\n    print(f\"page {page_number + 1},{len(document)}:\", document)\n    # print(f\"embeddings:{len(embeddings)},\", embeddings)\n    documents.append(document)\n\n\n<\/pre><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong> 7. \u521b\u5efa FAISS \u7d22\u5f15\u548c\u63a8\u7406<\/strong><\/h2>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:python decode:true \"># \u521b\u5efaFAISS\u7d22\u5f15\nif embeddings_list:\n    embeddings_array = np.vstack(embeddings_list)\n    index = faiss.IndexFlatL2(embeddings_array.shape[1])\n    index.add(embeddings_array.astype('float32'))\n    \n    # \u7528\u6237\u95ee\u9898\u5904\u7406\u4e0e\u63a8\u7406\n    #question = \"What is the theme of the document? \"\n    question = \"\u8fd9\u4efd\u6587\u6863\u7684\u4e3b\u9898\u662f\u4ec0\u4e48\uff1f\"\n    query_embedding = embedder.encode([question])[0].astype('float32')\n    \n    # \u68c0\u7d22\u6700\u76f8\u5173\u7684\u51e0\u4e2a\u6587\u6863\u6bb5\u843d\n    combined_segments = \"\"\n    k = 3  # \u4f60\u5e0c\u671b\u68c0\u7d22\u7684\u76f8\u5173\u6587\u6863\u6570\u91cf\n    D, I = index.search(np.array([query_embedding]), k=k)\n    print(\"D:\", D)\n    print(\"I:\", I)\n    print(\"Top\", k, \"most relevant document segments:\")\n    for idx, segment_index in enumerate(I[0]):\n        most_relevant_segment = documents[segment_index]\n        print(f\"{idx+1}: {most_relevant_segment}\\n\")\n        combined_segments += \" \" + most_relevant_segment\n    \n    prompt = combined_segments + \"\\n\\n###\\n\\n\" + question + \"\\n\\n\u7528\u4e2d\u6587\u56de\u7b54\"\n    messages = [\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": prompt}\n    ]\n    text = tokenizer.apply_chat_template(\n        messages,\n        tokenize=False,\n        add_generation_prompt=True\n    )\n    model_inputs = tokenizer([text], return_tensors=\"pt\").to(device)\n    \n    generated_ids = model.generate(\n        model_inputs.input_ids,\n        max_new_tokens=512\n    )\n    generated_ids = [\n        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)\n    ]\n    \n    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]    \n    print(\"Answer to the question:\", response)\nelse:\n    print(\"No embeddings found. Please check your data.\")<\/pre><\/div>\n\n\n\n<p>\u663e\u793a\u90e8\u5206\u7ed3\u679c\u5185\u5bb9\uff1a<\/p>\n\n\n\n<div class=\"wp-block-urvanov-syntax-highlighter-code-block\"><pre class=\"lang:sh decode:true \">Answer to the question: \u672c\u6587\u63a2\u8ba8\u4e86\u5229\u7528\u8bed\u8a00\u6765\u4e0e\u4eba\u7c7b\u5728\u89c6\u89c9\u73af\u5883\u4e2d\u7684\u4ea4\u4e92\uff0c\u5e76\u4e14\u63d0\u51fa\u4e86\u4e00\u79cd\u540d\u4e3a\"Dynalang\"\u7684Agent\uff0c\u5b83\u901a\u8fc7\u9884\u6d4b\u672a\u6765\u6587\u672c\u548c\u56fe\u50cf\u8f93\u5165\u7684\u65b9\u5f0f\u6765\u5b66\u4e60\u5982\u4f55\u4f7f\u7528\u8fd9\u79cd\u8bed\u8a00\u3002\u5177\u4f53\u6b65\u9aa4\u5982\u4e0b\uff1a\n1. **\u63d0\u51faDynalang**\uff1a\u8be5Agent\u5229\u7528\u591a\u6a21\u6001\u4e16\u754c\u6a21\u578b\uff0c\u5373\u7f16\u7801\u5305\u542b\u6240\u6709\u611f\u5b98\u8f93\u5165\uff08\u5982\u89c6\u9891\u548c\u6587\u672c\uff09\u7684\u538b\u7f29\u8868\u793a\uff0c\u5e76\u5c06\u8fd9\u4e9b\u4fe1\u606f\u4e0e\u5176\u884c\u52a8\u76f8\u7ed3\u5408\u6765\u5b9e\u73b0\u89c6\u89c9\u73af\u5883\u4e0b\u7684\u884c\u4e3a\u7406\u89e3\u3002\n2. **\u73af\u5883\u6a21\u578b\u5b66\u4e60**\uff1a\u4e16\u754c\u6a21\u578b\u4ee5\u6bcf\u5e27\u89c6\u9891\u548c\u6bcf\u4e2a\u65f6\u95f4\u6b65\u8bed\u8a00\u8f93\u5165\u4e3a\u8f93\u5165\uff0c\u540c\u65f6\u5bf9\u52a8\u4f5c\u5e8f\u5217\u8fdb\u884c\u538b\u7f29\u8868\u793a\u5e76\u5c06\u5176\u9988\u9001\u7ed9\u5e8f\u5217\u6a21\u578b\u9884\u6d4b\u4e0b\u4e00\u4e2a\u4ee3\u8868\u53d8\u91cf \u02c6 z t + 1\u3002\u8fd9\u4e2a\u4e16\u754c\u6a21\u578b\u7531\u4e00\u4e2a\u5faa\u73af\u72b6\u6001\u7a7a\u95f4\u6a21\u578b\uff08RSTM\uff09\u7ec4\u6210\uff0c\u5176\u4e2d\u5e8f\u5217\u6a21\u578b\u7531GRU\uff08\u683c\u96f7\u7801\u7f16\u7801\u5668\uff09\u5b9e\u73b0\uff0c\u80fd\u591f\u4ece\u5f53\u524d\u72b6\u6001\uff08h t\uff09\u4e2d\u8fde\u7eed\u5b66\u4e60\u548c\u63a8\u65ad\u8f93\u51fa\u7684\u7f16\u7801\u8868\u793a\u3002\n3. **\u8bed\u8a00\u751f\u6210\u80fd\u529b**\uff1aDynalang\u80fd\u591f\u6839\u636e\u5176\u611f\u77e5\u5411\u91cf\u548c\u4e4b\u524d\u7684\u7ecf\u9a8c\u751f\u6210\u8bed\u8a00\uff0c\u4f8b\u5982\uff0c\u5728\"\u6211\u653e\u4e0b\u4e86\u7897\"\u8fd9\u6837\u7684\u63cf\u8ff0\u6027\u4efb\u52a1\u4e2d\uff0c\u5f53\u8bed\u8a00\u4e0d\u8c08\u8bba\u4efb\u52a1\u65f6\uff0c\u4ec5\u4e0e\u5176\u5173\u8054\u7684\u90e8\u5206\u8bed\u8a00\u4fe1\u53f7\u53ef\u4ee5\u4f5c\u4e3a\u4f18\u5316\u884c\u52a8\u7684\u6f5c\u5728\u7ebf\u7d22\u3002\n4. **\u6a21\u578b\u878d\u5408\u4e0e\u66f4\u65b0**\uff1a\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cDynalang\u4e0d\u4ec5\u4f1a\u5229\u7528\u8fc7\u53bb\u8bed\u8a00\u5bf9\u5f53\u524d\u72b6\u6001\u8fdb\u884c\u9884\u6d4b\uff0c\u8fd8\u4f1a\u4e0d\u65ad\u66f4\u65b0\u4e16\u754c\u6a21\u578b\u4ee5\u6700\u5c0f\u5316\u57fa\u4e8e\u672a\u6765\u5956\u52b1\u7684\u671f\u671b\u6298\u6263\u603b\u548c\uff0c\u5373Lpred + Lrepr\uff0c\u5176\u4e2dL\u662f\u672a\u6765\u5956\u52b1\u51fd\u6570\uff0c\u03b3&lt;1 \u662f\u6298\u6263\u56e0\u5b50\uff0cT\u662fepisode\u957f\u5ea6\uff0cCT=0 \u8868\u793aEpisode\u7ed3\u675f\u3002\n5. **\u5e94\u7528\u8303\u56f4\u4e0e\u4efb\u52a1\u591a\u6837\u6027**\uff1aDynalang\u53ef\u4ee5\u5e94\u7528\u4e8e\u591a\u79cd\u4e0d\u540c\u7c7b\u578b\u7684\u73af\u5883\u4e2d\uff0c\u5305\u62ec\u590d\u6742\u3001\u89c6\u89c9\u5bc6\u96c6\u578b\u7684\u5bb6\u5ead\u6e05\u6d01\u73af\u5883\uff0c\u4ee5\u53ca\u5177\u6709\u7b26\u53f7\u8f93\u5165\u7684\u4efb\u52a1\uff0c\u5982\u6e38\u620f\u624b\u518c\uff0c\u9700\u8981\u8fdb\u884c\u590d\u6742\u7684\u591a\u7ea7\u63a8\u7406\u548c\u4e0a\u4e0b\u6587\u7406\u89e3\u3002\u6b64\u5916\uff0c\u5b83\u8fd8\u53ef\u4ee5\u901a\u8fc7\u9884\u8bad\u7ec3\u6570\u636e\u96c6\uff0c\u5373\u6587\u672c\u6216\u89c6\u9891\u6570\u636e\u96c6\uff0c\u5728\u6ca1\u6709\u5b9e\u9645\u6267\u884c\u6216\u4efb\u52a1\u5956\u52b1\u7684\u60c5\u51b5\u4e0b\uff0c\u8fdb\u884c\u5728\u7ebf\u8bad\u7ec3\uff0c\u4f7fAgent\u6301\u7eed\u5b66\u4e60\u8bed\u8a00\u53ca\u5176\u5982\u4f55\u4e0e\u73b0\u5b9e\u4e16\u754c\u7684\u76f8\u4e92\u5173\u7cfb\u3002\n6. **\u603b\u7ed3\u4e0e\u8ba8\u8bba**\uff1a\u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u4e2a\u5229\u7528\u8bed\u8a00\u8fdb\u884c\u4ea4\u4e92\u7684\u65b0\u6a21\u578b-Dynalang\uff0c\u901a\u8fc7\u6784\u5efa\u8bed\u8a00\u6761\u4ef6\u5316\u7684\u4e16\u754c\u6a21\u578b\u5e76\u4e0e\u672a\u6765\u9884\u8a00\u76f8\u7ed3\u5408\uff0c\u5b9e\u73b0\u4e86\u89c6\u89c9\u73af\u5883\u4e2d\u4e0d\u540c\u79cd\u7c7b\u8bed\u8a00\u7684\u7406\u89e3\u548c\u6709\u6548\u5e94\u7528\uff0c\u5305\u62ec\u73af\u5883\u63cf\u8ff0\u3001\u6e38\u620f\u89c4\u5219\u548c\u6307\u793a\u3002\u5c3d\u7ba1\u4e3b\u8981\u7814\u7a76\u96c6\u4e2d\u5728\u4f7f\u7528\u4efb\u52a1\u5bfc\u5411\u7684\u8bed\u8a00\u547d\u4ee4\u76f4\u63a5\u5411\u7b56\u7565\u4f20\u9012\u4fe1\u606f\u4e0a\uff0c\u4f46\u6587\u4e2d\u8fd8\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u5e7f\u6cdb\u6db5\u76d6\u8bed\u8a00\u7684\u591a\u6837\u6027\u4e0e\u5355\u4e00\u6a21\u578b\u4e2dnext-token\u9884\u6d4b\n<\/pre><\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u672c\u6559\u7a0b\u4f7f\u7528\u4e86surya-ocr\u5e93\uff0c\u5b9e\u73b0\u672c\u5730RAG\uff0c 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