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"name": "python3",
"display_name": "Python 3"
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"language_info": {
"name": "python"
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"cells": [
{
"cell_type": "code",
"source": [
"pip install faiss-cpu numpy pypdf sentence-transformers\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LqTTG2cy0L1A",
"outputId": "c8be3a59-e763-47a7-f1de-4a010dae06f4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: faiss-cpu in /usr/local/lib/python3.11/dist-packages (1.10.0)\n",
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"Requirement already satisfied: pypdf in /usr/local/lib/python3.11/dist-packages (5.3.0)\n",
"Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.11/dist-packages (3.4.1)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from faiss-cpu) (24.2)\n",
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]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "V2T0CkLD0Cnh",
"outputId": "176443e5-f99f-4d65-c6c5-e1ca43699006"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Extracting text from PDF...\n",
"Extracted text (first 500 chars): Machine Learning For Absolute\n",
"Beginners\n",
" \n",
" \n",
" \n",
" \n",
"Oliver Theobald\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"Second Edition\n",
"Copyright © 2017 by Oliver Theobald\n",
"All rights reserved. No part of this publication may be reproduced,\n",
"distributed, or transmitted in any form or by any means, including\n",
"photocopying, recording, or other electronic or mechanical\n",
"methods, without the prior written permission of the publisher,\n",
"except in the case of brief quotations embodied in critical reviews\n",
"and certain other non-commercial uses permitted b\n",
"Chunking text...\n",
"Total chunks created: 53\n",
"Generating embeddings...\n",
"Embedding 1/53 generated, Shape: (1, 384)\n",
"Embedding 2/53 generated, Shape: (1, 384)\n",
"Embedding 3/53 generated, Shape: (1, 384)\n",
"Embedding 4/53 generated, Shape: (1, 384)\n",
"Embedding 5/53 generated, Shape: (1, 384)\n",
"Embedding 6/53 generated, Shape: (1, 384)\n",
"Embedding 7/53 generated, Shape: (1, 384)\n",
"Embedding 8/53 generated, Shape: (1, 384)\n",
"Embedding 9/53 generated, Shape: (1, 384)\n",
"Embedding 10/53 generated, Shape: (1, 384)\n",
"Embedding 11/53 generated, Shape: (1, 384)\n",
"Embedding 12/53 generated, Shape: (1, 384)\n",
"Embedding 13/53 generated, Shape: (1, 384)\n",
"Embedding 14/53 generated, Shape: (1, 384)\n",
"Embedding 15/53 generated, Shape: (1, 384)\n",
"Embedding 16/53 generated, Shape: (1, 384)\n",
"Embedding 17/53 generated, Shape: (1, 384)\n",
"Embedding 18/53 generated, Shape: (1, 384)\n",
"Embedding 19/53 generated, Shape: (1, 384)\n",
"Embedding 20/53 generated, Shape: (1, 384)\n",
"Embedding 21/53 generated, Shape: (1, 384)\n",
"Embedding 22/53 generated, Shape: (1, 384)\n",
"Embedding 23/53 generated, Shape: (1, 384)\n",
"Embedding 24/53 generated, Shape: (1, 384)\n",
"Embedding 25/53 generated, Shape: (1, 384)\n",
"Embedding 26/53 generated, Shape: (1, 384)\n",
"Embedding 27/53 generated, Shape: (1, 384)\n",
"Embedding 28/53 generated, Shape: (1, 384)\n",
"Embedding 29/53 generated, Shape: (1, 384)\n",
"Embedding 30/53 generated, Shape: (1, 384)\n",
"Embedding 31/53 generated, Shape: (1, 384)\n",
"Embedding 32/53 generated, Shape: (1, 384)\n",
"Embedding 33/53 generated, Shape: (1, 384)\n",
"Embedding 34/53 generated, Shape: (1, 384)\n",
"Embedding 35/53 generated, Shape: (1, 384)\n",
"Embedding 36/53 generated, Shape: (1, 384)\n",
"Embedding 37/53 generated, Shape: (1, 384)\n",
"Embedding 38/53 generated, Shape: (1, 384)\n",
"Embedding 39/53 generated, Shape: (1, 384)\n",
"Embedding 40/53 generated, Shape: (1, 384)\n",
"Embedding 41/53 generated, Shape: (1, 384)\n",
"Embedding 42/53 generated, Shape: (1, 384)\n",
"Embedding 43/53 generated, Shape: (1, 384)\n",
"Embedding 44/53 generated, Shape: (1, 384)\n",
"Embedding 45/53 generated, Shape: (1, 384)\n",
"Embedding 46/53 generated, Shape: (1, 384)\n",
"Embedding 47/53 generated, Shape: (1, 384)\n",
"Embedding 48/53 generated, Shape: (1, 384)\n",
"Embedding 49/53 generated, Shape: (1, 384)\n",
"Embedding 50/53 generated, Shape: (1, 384)\n",
"Embedding 51/53 generated, Shape: (1, 384)\n",
"Embedding 52/53 generated, Shape: (1, 384)\n",
"Embedding 53/53 generated, Shape: (1, 384)\n",
"Storing in FAISS...\n",
"FAISS database saved as 'vector_database.faiss'\n"
]
}
],
"source": [
"import os\n",
"import faiss\n",
"import numpy as np\n",
"import pypdf # Using pypdf for text extraction\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"# Load an open-source embedding model from Hugging Face\n",
"model = SentenceTransformer(\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
"# Load text from PDF using pypdf\n",
"def load_pdf(pdf_path):\n",
" text = \"\"\n",
" with open(pdf_path, \"rb\") as file:\n",
" reader = pypdf.PdfReader(file)\n",
" for page in reader.pages:\n",
" text += page.extract_text() + \"\\n\" if page.extract_text() else \"\" # Handle empty pages\n",
" return text.strip() if text.strip() else None # Ensure non-empty text\n",
"\n",
"# Split text into chunks\n",
"def chunk_text(text, chunk_size=500):\n",
" words = text.split()\n",
" chunks = [\" \".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]\n",
" return [c for c in chunks if c.strip()] # Remove empty chunks\n",
"\n",
"# Generate embeddings using Hugging Face model\n",
"def get_embedding(text):\n",
" return model.encode(text, convert_to_numpy=True).reshape(1, -1) # Ensure 2D shape\n",
"\n",
"# Store embeddings in FAISS\n",
"def store_in_faiss(embeddings):\n",
" if len(embeddings) == 0:\n",
" raise ValueError(\"No embeddings found! Check your text extraction and chunking.\")\n",
"\n",
" embeddings = np.vstack(embeddings) # Stack into 2D array\n",
" dim = embeddings.shape[1]\n",
" index = faiss.IndexFlatL2(dim)\n",
" index.add(embeddings)\n",
" faiss.write_index(index, \"vector_database.faiss\")\n",
"\n",
"def main():\n",
" pdf_path = \"/content/[Oliver_Theobald]_Machine_Learning_for_Absolute_Be.pdf\"\n",
"\n",
" print(\"Extracting text from PDF...\")\n",
" text = load_pdf(pdf_path)\n",
" if text is None:\n",
" raise ValueError(\"No text extracted from PDF. Check if it's a scanned document!\")\n",
"\n",
" print(\"Extracted text (first 500 chars):\", text[:500])\n",
"\n",
" print(\"Chunking text...\")\n",
" chunks = chunk_text(text)\n",
" print(f\"Total chunks created: {len(chunks)}\")\n",
" if not chunks:\n",
" raise ValueError(\"No valid text chunks found!\")\n",
"\n",
" print(\"Generating embeddings...\")\n",
" embeddings = []\n",
" for i, chunk in enumerate(chunks):\n",
" emb = get_embedding(chunk)\n",
" print(f\"Embedding {i+1}/{len(chunks)} generated, Shape: {emb.shape}\")\n",
" embeddings.append(emb)\n",
"\n",
" if not embeddings:\n",
" raise ValueError(\"No embeddings were generated! Check the text chunks.\")\n",
"\n",
" embeddings = np.vstack(embeddings)\n",
"\n",
" print(\"Storing in FAISS...\")\n",
" store_in_faiss(embeddings)\n",
"\n",
" print(\"FAISS database saved as 'vector_database.faiss'\")\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "4FyvMg221DIg"
},
"execution_count": null,
"outputs": []
}
]
} |