6088 أسطر
220 KiB
Plaintext
6088 أسطر
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"Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.12/dist-packages (5.3.0)\n",
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"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.12/dist-packages (from sentence-transformers) (1.6.1)\n",
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"Requirement already satisfied: scipy in /usr/local/lib/python3.12/dist-packages (from sentence-transformers) (1.16.3)\n",
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"Requirement already satisfied: typing_extensions>=4.5.0 in /usr/local/lib/python3.12/dist-packages (from sentence-transformers) (4.15.0)\n",
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"Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from faiss-cpu) (26.0)\n",
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"Requirement already satisfied: filelock>=3.10.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (3.25.2)\n",
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"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (2025.3.0)\n",
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"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (6.0.3)\n",
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"Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.20.0->sentence-transformers) (0.24.1)\n",
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"Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers) (75.2.0)\n",
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"Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers) (1.14.0)\n",
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"Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers) (3.6.1)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.6)\n",
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||
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers<6.0.0,>=4.41.0->sentence-transformers) (2025.11.3)\n",
|
||
"Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers<6.0.0,>=4.41.0->sentence-transformers) (0.22.2)\n",
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||
"Requirement already satisfied: typer-slim in /usr/local/lib/python3.12/dist-packages (from transformers<6.0.0,>=4.41.0->sentence-transformers) (0.24.0)\n",
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"Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers<6.0.0,>=4.41.0->sentence-transformers) (0.7.0)\n",
|
||
"Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn->sentence-transformers) (1.5.3)\n",
|
||
"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn->sentence-transformers) (3.6.0)\n",
|
||
"Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.20.0->sentence-transformers) (4.12.1)\n",
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||
"Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.20.0->sentence-transformers) (2026.2.25)\n",
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||
"Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.20.0->sentence-transformers) (1.0.9)\n",
|
||
"Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.20.0->sentence-transformers) (3.11)\n",
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||
"Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub>=0.20.0->sentence-transformers) (0.16.0)\n",
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||
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
|
||
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (3.0.3)\n",
|
||
"Requirement already satisfied: click>=8.2.1 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub>=0.20.0->sentence-transformers) (8.3.1)\n",
|
||
"Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub>=0.20.0->sentence-transformers) (1.5.4)\n",
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||
"Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub>=0.20.0->sentence-transformers) (13.9.4)\n",
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"Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub>=0.20.0->sentence-transformers) (0.0.4)\n",
|
||
"Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub>=0.20.0->sentence-transformers) (4.0.0)\n",
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||
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub>=0.20.0->sentence-transformers) (2.19.2)\n",
|
||
"Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub>=0.20.0->sentence-transformers) (0.1.2)\n",
|
||
"Downloading faiss_cpu-1.13.2-cp310-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (23.8 MB)\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.8/23.8 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25hInstalling collected packages: faiss-cpu\n",
|
||
"Successfully installed faiss-cpu-1.13.2\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"pip install sentence-transformers faiss-cpu numpy"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"pip install nltk"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "P1q0tTkZWYL6",
|
||
"outputId": "9f7fe29b-e951-4703-9226-9ef092fff4a4"
|
||
},
|
||
"execution_count": 2,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Requirement already satisfied: nltk in /usr/local/lib/python3.12/dist-packages (3.9.1)\n",
|
||
"Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk) (8.3.1)\n",
|
||
"Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk) (1.5.3)\n",
|
||
"Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk) (2025.11.3)\n",
|
||
"Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk) (4.67.3)\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import nltk\n",
|
||
"\n",
|
||
"nltk.download('punkt')\n",
|
||
"nltk.download('punkt_tab')"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "JZLP7kwlWbNQ",
|
||
"outputId": "1affcdd9-962a-465e-dd34-dcd970ccd83e"
|
||
},
|
||
"execution_count": 3,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
|
||
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
|
||
"[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
|
||
"[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"True"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 3
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import json\n",
|
||
"\n",
|
||
"with open(\"moad_cleaned_spacy.json\", \"r\", encoding=\"utf-8\") as f:\n",
|
||
" data = json.load(f)\n",
|
||
"\n",
|
||
"texts = [item[\"Text\"] for item in data]"
|
||
],
|
||
"metadata": {
|
||
"id": "ajo-Gpv-UOmZ"
|
||
},
|
||
"execution_count": 5,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def chunk_text(text, chunk_size=300, overlap=100):\n",
|
||
" chunks = []\n",
|
||
" start = 0\n",
|
||
"\n",
|
||
" while start < len(text):\n",
|
||
" end = start + chunk_size\n",
|
||
" chunk = text[start:end]\n",
|
||
" chunks.append(chunk)\n",
|
||
" start += chunk_size - overlap\n",
|
||
"\n",
|
||
" return chunks\n",
|
||
"\n",
|
||
"\n",
|
||
"all_chunks = []\n",
|
||
"\n",
|
||
"for text in texts:\n",
|
||
" chunks = chunk_text(text)\n",
|
||
" all_chunks.extend(chunks)\n",
|
||
"\n",
|
||
"print(\"Total chunks:\", len(all_chunks))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "GdgGVv9AUOo0",
|
||
"outputId": "2d6da2a0-0a49-4bf6-f96a-9aed79da862d"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Total chunks: 283\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# from nltk.tokenize import sent_tokenize\n",
|
||
"\n",
|
||
"# def chunk_by_sentences(text, max_words=120):\n",
|
||
"\n",
|
||
"# sentences = sent_tokenize(text)\n",
|
||
"\n",
|
||
"# chunks = []\n",
|
||
"# current_chunk = []\n",
|
||
"# word_count = 0\n",
|
||
"\n",
|
||
"# for sentence in sentences:\n",
|
||
"# words = sentence.split()\n",
|
||
"\n",
|
||
"# if word_count + len(words) <= max_words:\n",
|
||
"# current_chunk.append(sentence)\n",
|
||
"# word_count += len(words)\n",
|
||
"\n",
|
||
"# else:\n",
|
||
"# chunks.append(\" \".join(current_chunk))\n",
|
||
"# current_chunk = [sentence]\n",
|
||
"# word_count = len(words)\n",
|
||
"\n",
|
||
"# if current_chunk:\n",
|
||
"# chunks.append(\" \".join(current_chunk))\n",
|
||
"\n",
|
||
"# return chunks\n",
|
||
"\n",
|
||
"\n",
|
||
"# all_chunks = []\n",
|
||
"\n",
|
||
"# for text in texts:\n",
|
||
"# all_chunks.extend(chunk_by_sentences(text))\n",
|
||
"\n",
|
||
"# print(\"Total chunks:\", len(all_chunks))"
|
||
],
|
||
"metadata": {
|
||
"id": "nfwBQvMaWQ9j"
|
||
},
|
||
"execution_count": 7,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sentence_transformers import SentenceTransformer\n",
|
||
"\n",
|
||
"model = SentenceTransformer(\"BAAI/bge-m3\")\n",
|
||
"\n",
|
||
"embeddings = model.encode(\n",
|
||
" all_chunks,\n",
|
||
" show_progress_bar=True\n",
|
||
")"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 624,
|
||
"referenced_widgets": [
|
||
"46b81dee680a4c9ba935efbed57ef26d",
|
||
"30f275e87cc64233968b3727d31c639b",
|
||
"935a63f7b2af4bf6834ad79e13958aa6",
|
||
"b1dd8f45a1554ee6ae5ecb04fa2d1570",
|
||
"64b91e1b52014274ab17ef1f14c41091",
|
||
"30669d5d399c4489a5e6b4300dad9530",
|
||
"8334a5b409be4ecfb3e84565ac0fd67f",
|
||
"45b12d7b26c34047a7eac4ad613df6cb",
|
||
"843420251fd344d283da76125358ce3c",
|
||
"2b645e69accc42e5a4ac03ad4f23ce25",
|
||
"57fa46b58d004025a97731830277e18b",
|
||
"e99ff50da41243c69587b6ccf11b6165",
|
||
"e42d35f22a3b4ce7bf778c2bb96dd663",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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|
||
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||
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|
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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||
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|
||
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|
||
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|
||
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"343ffd46ca764bfcb4a8a4f27919c14f",
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"4e200ad3fdf94c81b15b4f7b99e2b457",
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"57f332a817604f1db720602ec2ca3f1e",
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"b81a0144480b43bf9b49c0b79b9532eb",
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"bc25d4033d0b4a6b9ea0ac03770364ff",
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"b10338d3570749aea9ea8733edfae644",
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"fcbdb6e8205f4dcf80e30cb6e4b910af",
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"20a51dd3dbb84c1e9438b5149ecf2428",
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"8fdcad23231348e7a62bc50c1803381f",
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"bbd114a964714864871e3c45460ed94b",
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"58776c0ca9974f85a32d155c7b93ac12",
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"f87fa73ded9b4021a89e39b6825b015a",
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"b1039ca388314a9281297f39718c196b"
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|
||
"id": "W1jqFKkVUOrT",
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||
"outputId": "25d30457-b775-4fdb-da5a-0fc562124a8b"
|
||
},
|
||
"execution_count": 8,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
|
||
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
|
||
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
|
||
"You will be able to reuse this secret in all of your notebooks.\n",
|
||
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
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"output_type": "display_data",
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|
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},
|
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{
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"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
|
||
"WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
|
||
]
|
||
},
|
||
{
|
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"output_type": "display_data",
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"data": {
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},
|
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{
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|
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"metadata": {}
|
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},
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"version_minor": 0,
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"model_id": "0674d62aa54a405691d8cd3dcdd07bb7"
|
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}
|
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},
|
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"metadata": {}
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},
|
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{
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|
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}
|
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},
|
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"metadata": {}
|
||
},
|
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"model_id": "c1c39f89457c492bb002913a2a5e5ecc"
|
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|
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"metadata": {}
|
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},
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}
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},
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"metadata": {}
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|
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|
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"metadata": {}
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"metadata": {}
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},
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}
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},
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"metadata": {}
|
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},
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"version_minor": 0,
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"model_id": "b81a0144480b43bf9b49c0b79b9532eb"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import faiss\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"dimension = embeddings.shape[1]\n",
|
||
"\n",
|
||
"index = faiss.IndexFlatL2(dimension)\n",
|
||
"\n",
|
||
"index.add(np.array(embeddings))\n",
|
||
"\n",
|
||
"print(\"Total vectors in DB:\", index.ntotal)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "UtXewDTLUOts",
|
||
"outputId": "e7e9cc72-a30b-4af1-d439-b5c74a1e389a"
|
||
},
|
||
"execution_count": 9,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Total vectors in DB: 283\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"dimension"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "Jt1v4xz_Xh7w",
|
||
"outputId": "506d090c-333d-44ec-e334-3a36c371903e"
|
||
},
|
||
"execution_count": 10,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"1024"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 10
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"faiss.write_index(index, \"rag_index.faiss\")\n",
|
||
"\n",
|
||
"with open(\"chunks.json\", \"w\", encoding=\"utf-8\") as f:\n",
|
||
" json.dump(all_chunks, f, ensure_ascii=False)"
|
||
],
|
||
"metadata": {
|
||
"id": "ZAvxj_EoUe9K"
|
||
},
|
||
"execution_count": 11,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import faiss\n",
|
||
"import json\n",
|
||
"import numpy as np\n",
|
||
"from sentence_transformers import SentenceTransformer\n",
|
||
"\n",
|
||
"model = SentenceTransformer(\"BAAI/bge-m3\")\n",
|
||
"\n",
|
||
"index = faiss.read_index(\"rag_index.faiss\")\n",
|
||
"\n",
|
||
"with open(\"chunks.json\", \"r\", encoding=\"utf-8\") as f:\n",
|
||
" chunks = json.load(f)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 49,
|
||
"referenced_widgets": [
|
||
"9d7d5c1d2d1b49c9b6ba0507fde12845",
|
||
"fb36a2c5cb2944719a41894e6d95c45c",
|
||
"1b0426c23e55427998baecfa66386eb2",
|
||
"96c8ac5038414fef8018542d5099d4ba",
|
||
"9489c5ea228b4f58b5ea8cb9da377600",
|
||
"226a7a23b1ab4d8f85085e673ee3fa41",
|
||
"62ef24ec70054ab096fa08b0a065a8ac",
|
||
"7e5221aebe3b4f7aaffb902731140fe3",
|
||
"bd7424ae08b7411b817c6dcbf5a00bfc",
|
||
"1d6d6b99b78b47958758fba4598da98e",
|
||
"1a1a84cbba4e4709b07d6b429f51d3c1"
|
||
]
|
||
},
|
||
"id": "hNz15I83UfAz",
|
||
"outputId": "bfc402fd-b564-46b4-c249-ebe9ab059466"
|
||
},
|
||
"execution_count": 12,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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"Loading weights: 0%| | 0/391 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "9d7d5c1d2d1b49c9b6ba0507fde12845"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def search(query, top_k=5):\n",
|
||
"\n",
|
||
" query = \"Represent this sentence for searching relevant passages: \" + query\n",
|
||
"\n",
|
||
" query_embedding = model.encode([query])\n",
|
||
"\n",
|
||
" distances, indices = index.search(\n",
|
||
" np.array(query_embedding),\n",
|
||
" top_k\n",
|
||
" )\n",
|
||
"\n",
|
||
" results = [chunks[i] for i in indices[0]]\n",
|
||
"\n",
|
||
" return results\n",
|
||
"\n",
|
||
"query = \"ما هي وزارة التنمية الادارية؟\"\n",
|
||
"\n",
|
||
"results = search(query)\n",
|
||
"\n",
|
||
"for r in results:\n",
|
||
" print(\"-----\")\n",
|
||
" print(r)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "4Av1EANMYmQy",
|
||
"outputId": "f7a4014f-b0c6-4f6b-b657-34c0ad7d343f"
|
||
},
|
||
"execution_count": 13,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"-----\n",
|
||
"يةالوزارةعن الوزارةعن الوزارةلمحة عن وزارة التنمية الاداريةتاسست وزارة التنمية الادارية بموجب المرسوم التشريعي رقم /39/ لعام 2014، وحددت مهامها بموجب المرسوم رقم /281/. وفي عام 2018 صدر القانون رقم /28/ الذي حدد مهامها واختصاصاتها وهيكلها التنظيميهدف الوزارةتنفيذ السياسة العامة للدولة وتوجيهاتها في \n",
|
||
"-----\n",
|
||
"يةالوزارةعن الوزارةعن الوزارةلمحة عن وزارة التنمية الاداريةتاسست وزارة التنمية الادارية بموجب المرسوم التشريعي رقم /39/ لعام 2014، وحددت مهامها بموجب المرسوم رقم /281/. وفي عام 2018 صدر القانون رقم /28/ الذي حدد مهامها واختصاصاتها وهيكلها التنظيميهدف الوزارةتنفيذ السياسة العامة للدولة وتوجيهاتها في \n",
|
||
"-----\n",
|
||
"الخدمات الحكومية بشفافية وكفاءةالاخبار والفعالياتتواصل معناالاسئلة الشائعةاكثر الاسئلة تكراراروابط مهمةجهات مرتبطة بعمل الوزارةEnglishEnglishالوزارةالتشريعات و الادلةالاخبار والفعالياتتواصل معناالرئيسيةالوزارةعن الوزارةعن الوزارةلمحة عن وزارة التنمية الاداريةتاسست وزارة التنمية الادارية بموجب المرسو\n",
|
||
"-----\n",
|
||
"الخدمات الحكومية بشفافية وكفاءةالاخبار والفعالياتتواصل معناالاسئلة الشائعةاكثر الاسئلة تكراراروابط مهمةجهات مرتبطة بعمل الوزارةEnglishEnglishالوزارةالتشريعات و الادلةالاخبار والفعالياتتواصل معناالرئيسيةالوزارةعن الوزارةعن الوزارةلمحة عن وزارة التنمية الاداريةتاسست وزارة التنمية الادارية بموجب المرسو\n",
|
||
"-----\n",
|
||
"يةتواصل معناالاسئلة الشائعةالاسئلة الشائعةما هي وزارة التنمية الادارية؟تاسست وزارة التنمية الادارية بموجب المرسوم التشريعي رقم 39 لعام 2014، وحددت مهامها بموجب المرسوم رقم 281، وفي عام 2018 صدر القانون رقم 28 الذي حدد مهامها واختصاصاتها وهيكلها التنظيميما هي الاهداف الرئيسية للوزارة؟تهدف الوزارة الى\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"print(\"Query embedding dimension:\", model.encode([\"test\"]).shape)\n",
|
||
"print(\"FAISS index dimension:\", index.d)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "DjdUut0yUxqd",
|
||
"outputId": "fe3ba400-9e1f-4865-90df-ec5dba4b16f9"
|
||
},
|
||
"execution_count": 14,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Query embedding dimension: (1, 1024)\n",
|
||
"FAISS index dimension: 1024\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import pickle\n",
|
||
"\n",
|
||
"data_to_save = {\n",
|
||
" \"chunks\": chunks, # قائمة النصوص\n",
|
||
" \"embeddings\": embeddings # numpy array\n",
|
||
"}\n",
|
||
"\n",
|
||
"with open(\"chunks_data.pkl\", \"wb\") as f:\n",
|
||
" pickle.dump(data_to_save, f)"
|
||
],
|
||
"metadata": {
|
||
"id": "5awyaZuzYauV"
|
||
},
|
||
"execution_count": 15,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from google.colab import drive\n",
|
||
"drive.mount('/content/drive')"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "cgPaNv6emgde",
|
||
"outputId": "54b7dc66-bab8-4654-9726-488377cdd748"
|
||
},
|
||
"execution_count": 16,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Mounted at /content/drive\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"save_path = \"/content/drive/MyDrive/my_project/\"\n",
|
||
"\n",
|
||
"import os\n",
|
||
"os.makedirs(save_path, exist_ok=True)\n",
|
||
"\n",
|
||
"# حفظ chunks\n",
|
||
"import pickle\n",
|
||
"with open(save_path + \"chunks.pkl\", \"wb\") as f:\n",
|
||
" pickle.dump(chunks, f)\n",
|
||
"\n",
|
||
"# حفظ FAISS index\n",
|
||
"import faiss\n",
|
||
"faiss.write_index(index, save_path + \"faiss_index.bin\")"
|
||
],
|
||
"metadata": {
|
||
"id": "zIWvr7z8m6Ew"
|
||
},
|
||
"execution_count": 17,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"to re use it\n"
|
||
],
|
||
"metadata": {
|
||
"id": "0zBv1oHOnNVt"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# chunks\n",
|
||
"with open(save_path + \"chunks.pkl\", \"rb\") as f:\n",
|
||
" chunks = pickle.load(f)\n",
|
||
"\n",
|
||
"# index\n",
|
||
"index = faiss.read_index(save_path + \"faiss_index.bin\")"
|
||
],
|
||
"metadata": {
|
||
"id": "9m4WTgC4nJu6"
|
||
},
|
||
"execution_count": 18,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [],
|
||
"metadata": {
|
||
"id": "UNibylm_nQNf"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |