1380 lines
108 KiB
Plaintext
1380 lines
108 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "6a2d4624-4d76-4c52-a0f8-b353b6848549",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"این یک مشکل رایج است که اغلب به دلایل مختلف ایجاد می شود. برخی از عللن های متداول قطع اینترنت عبارتند از:\n",
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"\n",
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"1. اتصال نادرست: ممکن است اتصال شما با ارائه دهنده خدمات اینترنت (ISP) نادرست باشد، در نتیجه قطع دسترسی به اینترنت.\n",
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"2. خطای شبکه: مشکلاتی مانند خرابی تجهیزات شبکه یا خطاهای مربوطه ممکن است باعث قطع اینترنت شوند.\n",
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"3. مسدود بودن آدرس IP: اگر آدرس آی پی شما توسط ارائه دهنده خدمات اینترنت مسدود شود، نمی توانید به اینترنت دسترسی داشته باشید.\n",
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"4. مصرف زیاد باند باس: اگر مصرف زیادی از باند باس در طول یک زمان خاص یا مداوم باشد، ممکن است موجب قطع اینترنت شود.\n",
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"5. مشکلات تهدید کننده ایمنی: حملات سایبری و ویروس های رایانه ای نیز می توانند باعث قطع اینترنت شوند.\n",
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"\n",
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"از سوی دیگر، ارائه دهندگان خدمات اینترنت اغلب در نظر می گیرند که به دلیل مسائل فنی یا نگهداری شبکه، اینترنت را برای چند ساعت یا یک روز قطع کنند. در چنین مواردی معمولاً اطلاع رسانی قبلی داده نمی شود و زمانی که مشکل حل شد، اینترنت مجدداً قابل دسترسی است.\n",
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"\n",
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"در صورت تجربه قطع اینترنت، توصیه می شود که با ارائه دهنده خدمات اینترنت تماس بگیرید و از وضعیت اتصال خود آگاه شوید. در بسیاری از موارد، مشکلات به سرعت حل می شود و شما می توانید دوباره به اینترنت دسترسی داشته باشید.\n",
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"این یک مشکل رایج است که اغلب به دلایل مختلف ایجاد می شود. برخی از عللن های متداول قطع اینترنت عبارتند از:\n",
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"\n",
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"1. اتصال نادرست: ممکن است اتصال شما با ارائه دهنده خدمات اینترنت (ISP) نادرست باشد، در نتیجه قطع دسترسی به اینترنت.\n",
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"2. خطای شبکه: مشکلاتی مانند خرابی تجهیزات شبکه یا خطاهای مربوطه ممکن است باعث قطع اینترنت شوند.\n",
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"3. مسدود بودن آدرس IP: اگر آدرس آی پی شما توسط ارائه دهنده خدمات اینترنت مسدود شود، نمی توانید به اینترنت دسترسی داشته باشید.\n",
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"4. مصرف زیاد باند باس: اگر مصرف زیادی از باند باس در طول یک زمان خاص یا مداوم باشد، ممکن است موجب قطع اینترنت شود.\n",
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"5. مشکلات تهدید کننده ایمنی: حملات سایبری و ویروس های رایانه ای نیز می توانند باعث قطع اینترنت شوند.\n",
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"\n",
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"از سوی دیگر، ارائه دهندگان خدمات اینترنت اغلب در نظر می گیرند که به دلیل مسائل فنی یا نگهداری شبکه، اینترنت را برای چند ساعت یا یک روز قطع کنند. در چنین مواردی معمولاً اطلاع رسانی قبلی داده نمی شود و زمانی که مشکل حل شد، اینترنت مجدداً قابل دسترسی است.\n",
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"\n",
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"در صورت تجربه قطع اینترنت، توصیه می شود که با ارائه دهنده خدمات اینترنت تماس بگیرید و از وضعیت اتصال خود آگاه شوید. در بسیاری از موارد، مشکلات به سرعت حل می شود و شما می توانید دوباره به اینترنت دسترسی داشته باشید.\n"
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]
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}
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],
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"source": [
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"#Test Dorna\n",
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"response: ChatResponse = chat(model='partai/dorna-llama3', messages=[\n",
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" {\n",
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" 'role': 'user',\n",
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" 'content': 'چرا اینترنت قطع میشه؟',\n",
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" },\n",
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"])\n",
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"print(response['message']['content'])\n",
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"print(response.message.content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "a312f740-400a-49a5-a79e-41195aa49746",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting faiss-cpu\n",
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"Installing collected packages: safetensors, faiss-cpu, huggingface-hub, tokenizers, transformers, sentence-transformers\n",
|
||
"Successfully installed faiss-cpu-1.9.0.post1 huggingface-hub-0.27.1 safetensors-0.5.2 sentence-transformers-3.4.0 tokenizers-0.21.0 transformers-4.48.1\n"
|
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]
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}
|
||
],
|
||
"source": [
|
||
"!pip install faiss-cpu sentence-transformers ollama numpy"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "9100712e-4b06-4168-b8ac-36f0c2865e42",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Building optimized index...\n",
|
||
"پردازش سوال 1/6 تکمیل شد\n",
|
||
"پردازش سوال 2/6 تکمیل شد\n",
|
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"پردازش سوال 3/6 تکمیل شد\n",
|
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"پردازش سوال 4/6 تکمیل شد\n",
|
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"پردازش سوال 5/6 تکمیل شد\n",
|
||
"پردازش سوال 6/6 تکمیل شد\n",
|
||
"تمامی سوالات با موفقیت پردازش شدند!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# MANUALLY RAG\n",
|
||
"from ollama import chat\n",
|
||
"import numpy as np\n",
|
||
"import faiss\n",
|
||
"from sentence_transformers import SentenceTransformer\n",
|
||
"import os\n",
|
||
"import re\n",
|
||
"\n",
|
||
"DOCUMENT_PATHS = [\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\Hamrah.txt', #replace path\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\vape.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\Shah.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\Khalife.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\carbon.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\takapoo.txt'\n",
|
||
"]\n",
|
||
"\n",
|
||
"EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
|
||
"LLM_MODEL = 'partai/dorna-llama3'\n",
|
||
"CHUNK_SIZE = 1000\n",
|
||
"OVERLAP = 200\n",
|
||
"INDEX_PATH = r'C:\\Users\\ASUS\\Downloads\\doc_index.faiss'\n",
|
||
"CHUNK_MAP_PATH = r'C:\\Users\\ASUS\\Downloads\\chunk_map.npy'\n",
|
||
"\n",
|
||
"class AdvancedRAG:\n",
|
||
" def __init__(self):\n",
|
||
" self.encoder = SentenceTransformer(EMBEDDING_MODEL)\n",
|
||
" self.index = None\n",
|
||
" self.chunk_map = []\n",
|
||
" \n",
|
||
" def create_index(self):\n",
|
||
" \"\"\"Create FAISS index with cosine similarity and document mapping\"\"\"\n",
|
||
" all_chunks = []\n",
|
||
" doc_mapping = []\n",
|
||
" \n",
|
||
" # Process via CHUNKING (REQ 4 RAG)\n",
|
||
" for doc_idx, path in enumerate(DOCUMENT_PATHS):\n",
|
||
" with open(path, 'r', encoding='utf-8') as f:\n",
|
||
" text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
|
||
" chunks = [text[i:i+CHUNK_SIZE] for i in range(0, len(text), CHUNK_SIZE - OVERLAP)]\n",
|
||
" all_chunks.extend(chunks)\n",
|
||
" doc_mapping.extend([doc_idx] * len(chunks))\n",
|
||
" \n",
|
||
" # Normalized embeddings (REQ 4 cosine similarity)\n",
|
||
" embeddings = self.encoder.encode(all_chunks)\n",
|
||
" faiss.normalize_L2(embeddings) \n",
|
||
" \n",
|
||
" # FAISS index & Mapping\n",
|
||
" self.index = faiss.IndexFlatIP(embeddings.shape[1])\n",
|
||
" self.index.add(embeddings.astype(np.float32))\n",
|
||
" self.chunk_map = np.array(doc_mapping)\n",
|
||
" \n",
|
||
" # Index \n",
|
||
" faiss.write_index(self.index, INDEX_PATH)\n",
|
||
" # Mapping \n",
|
||
" np.save(CHUNK_MAP_PATH, self.chunk_map)\n",
|
||
" \n",
|
||
" def load_index(self):\n",
|
||
" \"\"\"LOAD EXISTING DATA\"\"\"\n",
|
||
" self.index = faiss.read_index(INDEX_PATH)\n",
|
||
" self.chunk_map = np.load(CHUNK_MAP_PATH, allow_pickle=True)\n",
|
||
" \n",
|
||
" def query(self, question, doc_index, top_k=5):\n",
|
||
" \"\"\"DOCUMENT-SPECIFIC QUERY WITH COSINE SIMILARITY \"\"\"\n",
|
||
" # Encode \n",
|
||
" query_embed = self.encoder.encode([question])\n",
|
||
" # Normalize \n",
|
||
" faiss.normalize_L2(query_embed)\n",
|
||
" \n",
|
||
" distances, indices = self.index.search(query_embed.astype(np.float32), top_k*3)\n",
|
||
" \n",
|
||
" relevant_chunks = []\n",
|
||
" for idx in indices[0]:\n",
|
||
" if self.chunk_map[idx] == doc_index:\n",
|
||
" relevant_chunks.append(idx)\n",
|
||
" if len(relevant_chunks) >= top_k:\n",
|
||
" break\n",
|
||
" \n",
|
||
" return relevant_chunks\n",
|
||
"\n",
|
||
"class AnswerGenerator:\n",
|
||
" def __init__(self, rag_system):\n",
|
||
" self.rag = rag_system\n",
|
||
" self.chunks = [] \n",
|
||
" \n",
|
||
" def get_answer(self, question, doc_index):\n",
|
||
" \"\"\"GENERATING CONTEXT-AWARE ANSWER\"\"\"\n",
|
||
" if not self.chunks:\n",
|
||
" self._load_chunks()\n",
|
||
" \n",
|
||
" chunk_indices = self.rag.query(question, doc_index)\n",
|
||
" context = \"\\n\".join([self.chunks[idx] for idx in chunk_indices])\n",
|
||
" \n",
|
||
" prompt = f\"\"\"با استفاده از متن زیر به سوال پاسخ دهید:\n",
|
||
"{context}\n",
|
||
"\n",
|
||
"اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید\n",
|
||
"\n",
|
||
"سوال: {question}\n",
|
||
"پاسخ:\"\"\"\n",
|
||
" \n",
|
||
" response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])\n",
|
||
" return response['message']['content']\n",
|
||
" \n",
|
||
" def _load_chunks(self):\n",
|
||
" \"\"\"LOAD ALL CHUNKS(LAZY)\"\"\"\n",
|
||
" self.chunks = []\n",
|
||
" for path in DOCUMENT_PATHS:\n",
|
||
" with open(path, 'r', encoding='utf-8') as f:\n",
|
||
" text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
|
||
" self.chunks.extend([text[i:i+CHUNK_SIZE] for i in range(0, len(text), CHUNK_SIZE - OVERLAP)])\n",
|
||
"\n",
|
||
"# MAIN EXE of RAG\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" # RAG init\n",
|
||
" rag = AdvancedRAG()\n",
|
||
" \n",
|
||
" if not os.path.exists(INDEX_PATH):\n",
|
||
" print(\"Building optimized index...\")\n",
|
||
" rag.create_index()\n",
|
||
" else:\n",
|
||
" print(\"Loading existing index...\")\n",
|
||
" rag.load_index()\n",
|
||
" # Answer Generator init\n",
|
||
" generator = AnswerGenerator(rag)\n",
|
||
" \n",
|
||
" queries = [\n",
|
||
" (\"چرا اینترنت همراه اول گوشی وصل نمیشود؟\", 0),\n",
|
||
" (\"چطوری ویپ مورد نظرمو پیدا کنم؟\", 1),\n",
|
||
" (\"شاه عباس که بود؟\", 2),\n",
|
||
" (\"خلیفه سلطان که بود و چه کرد؟\", 3),\n",
|
||
" (\"کربن اکتیو و کربن بلک چه هستند و چه تفاوتی دارند و برای چه استفاده میشن؟\", 4),\n",
|
||
" (\"شرکت تکاپو صنعت نامی چه محصولاتی ارایه میدهد؟ چه چیزی این شرکت را منحصر به فرد میسازد؟ سهام این شرکت صعودی است یا نزولی؟\", 5)\n",
|
||
" ]\n",
|
||
" \n",
|
||
" with open(r'C:\\Users\\ASUS\\Downloads\\representation.txt', 'w', encoding='utf-8') as f: #replace path\n",
|
||
" for q_idx, (query, doc_idx) in enumerate(queries):\n",
|
||
" answer = generator.get_answer(query, doc_idx)\n",
|
||
" f.write(f\"سوال {q_idx+1} ({doc_idx+1}):\\n{query}\\n\\nپاسخ:\\n{answer}\\n\\n{'='*50}\\n\\n\")\n",
|
||
" print(f\"پردازش سوال {q_idx+1}/{len(queries)} تکمیل شد\")\n",
|
||
"\n",
|
||
"print(\"تمامی سوالات با موفقیت پردازش شدند!\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
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"Downloading googleapis_common_protos-1.66.0-py2.py3-none-any.whl (221 kB)\n",
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"Downloading websockets-14.2-cp311-cp311-win_amd64.whl (164 kB)\n",
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"Downloading zstandard-0.23.0-cp311-cp311-win_amd64.whl (495 kB)\n",
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"Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n",
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"Downloading flatbuffers-25.1.24-py2.py3-none-any.whl (30 kB)\n",
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"Downloading pyproject_hooks-1.2.0-py3-none-any.whl (10 kB)\n",
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"Downloading requests_oauthlib-2.0.0-py2.py3-none-any.whl (24 kB)\n",
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"Downloading asgiref-3.8.1-py3-none-any.whl (23 kB)\n",
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"Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
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"Downloading rsa-4.9-py3-none-any.whl (34 kB)\n",
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"Downloading pyreadline3-3.5.4-py3-none-any.whl (83 kB)\n",
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"Building wheels for collected packages: pypika\n",
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" Building wheel for pypika (pyproject.toml): started\n",
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" Building wheel for pypika (pyproject.toml): finished with status 'done'\n",
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" Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53885 sha256=22504c977d53f1de4940cd5d9ebc31b49e5f50777a9953ee83f8b7c2b43cfca0\n",
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" Stored in directory: c:\\users\\asus\\appdata\\local\\pip\\cache\\wheels\\a3\\01\\bd\\4c40ceb9d5354160cb186dcc153360f4ab7eb23e2b24daf96d\n",
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||
"Successfully built pypika\n",
|
||
"Installing collected packages: pypika, monotonic, flatbuffers, durationpy, zstandard, websockets, tenacity, shellingham, rsa, pyreadline3, pyproject_hooks, protobuf, packaging, orjson, opentelemetry-util-http, oauthlib, mmh3, jsonpatch, importlib-resources, httptools, grpcio, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, starlette, requests-oauthlib, posthog, opentelemetry-proto, opentelemetry-api, humanfriendly, googleapis-common-protos, google-auth, build, typer, opentelemetry-semantic-conventions, opentelemetry-exporter-otlp-proto-common, langsmith, kubernetes, fastapi, coloredlogs, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, langchain-core, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, langchain-text-splitters, opentelemetry-instrumentation-fastapi, langchain, chromadb\n",
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||
" Attempting uninstall: zstandard\n",
|
||
" Found existing installation: zstandard 0.19.0\n",
|
||
" Uninstalling zstandard-0.19.0:\n",
|
||
" Successfully uninstalled zstandard-0.19.0\n",
|
||
" Attempting uninstall: tenacity\n",
|
||
" Found existing installation: tenacity 8.2.2\n",
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||
" Uninstalling tenacity-8.2.2:\n",
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||
" Successfully uninstalled tenacity-8.2.2\n",
|
||
" Attempting uninstall: protobuf\n",
|
||
" Found existing installation: protobuf 3.20.3\n",
|
||
" Uninstalling protobuf-3.20.3:\n",
|
||
" Successfully uninstalled protobuf-3.20.3\n",
|
||
" Attempting uninstall: packaging\n",
|
||
" Found existing installation: packaging 23.1\n",
|
||
" Uninstalling packaging-23.1:\n",
|
||
" Successfully uninstalled packaging-23.1\n",
|
||
" Attempting uninstall: jsonpatch\n",
|
||
" Found existing installation: jsonpatch 1.32\n",
|
||
" Uninstalling jsonpatch-1.32:\n",
|
||
" Successfully uninstalled jsonpatch-1.32\n",
|
||
" Attempting uninstall: bcrypt\n",
|
||
" Found existing installation: bcrypt 3.2.0\n",
|
||
" Uninstalling bcrypt-3.2.0:\n",
|
||
" Successfully uninstalled bcrypt-3.2.0\n",
|
||
"Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.2.1 build-1.2.2.post1 chroma-hnswlib-0.7.6 chromadb-0.6.3 coloredlogs-15.0.1 deprecated-1.2.18 durationpy-0.9 fastapi-0.115.7 flatbuffers-25.1.24 google-auth-2.38.0 googleapis-common-protos-1.66.0 grpcio-1.70.0 httptools-0.6.4 humanfriendly-10.0 importlib-resources-6.5.2 jsonpatch-1.33 kubernetes-32.0.0 langchain-0.3.16 langchain-core-0.3.32 langchain-text-splitters-0.3.5 langsmith-0.3.2 mmh3-5.1.0 monotonic-1.6 oauthlib-3.2.2 onnxruntime-1.20.1 opentelemetry-api-1.29.0 opentelemetry-exporter-otlp-proto-common-1.29.0 opentelemetry-exporter-otlp-proto-grpc-1.29.0 opentelemetry-instrumentation-0.50b0 opentelemetry-instrumentation-asgi-0.50b0 opentelemetry-instrumentation-fastapi-0.50b0 opentelemetry-proto-1.29.0 opentelemetry-sdk-1.29.0 opentelemetry-semantic-conventions-0.50b0 opentelemetry-util-http-0.50b0 orjson-3.10.15 packaging-24.2 posthog-3.11.0 protobuf-5.29.3 pypika-0.48.9 pyproject_hooks-1.2.0 pyreadline3-3.5.4 requests-oauthlib-2.0.0 rsa-4.9 shellingham-1.5.4 starlette-0.45.3 tenacity-9.0.0 typer-0.15.1 uvicorn-0.34.0 watchfiles-1.0.4 websockets-14.2 zstandard-0.23.0\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" WARNING: Failed to remove contents in a temporary directory 'C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\~standard'.\n",
|
||
" You can safely remove it manually.\n",
|
||
"ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
||
"streamlit 1.30.0 requires packaging<24,>=16.8, but you have packaging 24.2 which is incompatible.\n",
|
||
"streamlit 1.30.0 requires protobuf<5,>=3.20, but you have protobuf 5.29.3 which is incompatible.\n",
|
||
"streamlit 1.30.0 requires tenacity<9,>=8.1.0, but you have tenacity 9.0.0 which is incompatible.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!pip install langchain chromadb sentence-transformers ollama"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "b5fd21a3-d820-4e6e-aef4-9ce4955ce2ff",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Collecting langchain-community\n",
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||
" Downloading langchain_community-0.3.16-py3-none-any.whl.metadata (2.9 kB)\n",
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"Requirement already satisfied: PyYAML>=5.3 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (6.0.1)\n",
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"Requirement already satisfied: SQLAlchemy<3,>=1.4 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (2.0.25)\n",
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"Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (3.9.3)\n",
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||
"Collecting dataclasses-json<0.7,>=0.5.7 (from langchain-community)\n",
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" Downloading dataclasses_json-0.6.7-py3-none-any.whl.metadata (25 kB)\n",
|
||
"Collecting httpx-sse<0.5.0,>=0.4.0 (from langchain-community)\n",
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" Downloading httpx_sse-0.4.0-py3-none-any.whl.metadata (9.0 kB)\n",
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||
"Requirement already satisfied: langchain<0.4.0,>=0.3.16 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (0.3.16)\n",
|
||
"Requirement already satisfied: langchain-core<0.4.0,>=0.3.32 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (0.3.32)\n",
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||
"Requirement already satisfied: langsmith<0.4,>=0.1.125 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (0.3.2)\n",
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||
"Requirement already satisfied: numpy<2,>=1.22.4 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (1.26.4)\n",
|
||
"Collecting pydantic-settings<3.0.0,>=2.4.0 (from langchain-community)\n",
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||
" Downloading pydantic_settings-2.7.1-py3-none-any.whl.metadata (3.5 kB)\n",
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||
"Requirement already satisfied: requests<3,>=2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (2.31.0)\n",
|
||
"Requirement already satisfied: tenacity!=8.4.0,<10,>=8.1.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from langchain-community) (9.0.0)\n",
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||
"Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.2.0)\n",
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||
"Requirement already satisfied: attrs>=17.3.0 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (23.1.0)\n",
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||
"Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from aiohttp<4.0.0,>=3.8.3->langchain-community) (1.4.0)\n",
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"Installing collected packages: typing-inspect, marshmallow, httpx-sse, dataclasses-json, pydantic-settings, langchain-community\n",
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"Successfully installed dataclasses-json-0.6.7 httpx-sse-0.4.0 langchain-community-0.3.16 marshmallow-3.26.0 pydantic-settings-2.7.1 typing-inspect-0.9.0\n"
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}
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],
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"source": [
|
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"!pip install -U langchain-community"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "960bc8ae-5d3d-401c-93ae-85b45fe9adee",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_1004\\3712172232.py:26: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``.\n",
|
||
" self.embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL)\n"
|
||
]
|
||
},
|
||
{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
||
"Loading existing vector store...\n"
|
||
]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_1004\\3712172232.py:60: LangChainDeprecationWarning: The class `Chroma` was deprecated in LangChain 0.2.9 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-chroma package and should be used instead. To use it run `pip install -U :class:`~langchain-chroma` and import as `from :class:`~langchain_chroma import Chroma``.\n",
|
||
" self.vector_db = Chroma(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"پردازش سوال 1/6 تکمیل شد\n",
|
||
"پردازش سوال 2/6 تکمیل شد\n",
|
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"پردازش سوال 3/6 تکمیل شد\n",
|
||
"پردازش سوال 4/6 تکمیل شد\n",
|
||
"پردازش سوال 5/6 تکمیل شد\n",
|
||
"پردازش سوال 6/6 تکمیل شد\n",
|
||
"تمامی سوالات با موفقیت پردازش شدند!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# CHROMOA\n",
|
||
"from langchain.vectorstores import Chroma\n",
|
||
"from langchain.embeddings import SentenceTransformerEmbeddings\n",
|
||
"from langchain.schema import Document\n",
|
||
"from ollama import chat\n",
|
||
"import os\n",
|
||
"import re\n",
|
||
"\n",
|
||
"DOCUMENT_PATHS = [\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\Hamrah.txt', #replace path\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\vape.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\Shah.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\Khalife.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\carbon.txt',\n",
|
||
" r'C:\\Users\\ASUS\\Downloads\\takapoo.txt'\n",
|
||
"]\n",
|
||
"\n",
|
||
"EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
|
||
"LLM_MODEL = 'partai/dorna-llama3'\n",
|
||
"CHUNK_SIZE = 1000\n",
|
||
"OVERLAP = 200\n",
|
||
"CHROMA_PERSIST_DIR = r'C:\\Users\\ASUS\\Downloads\\chroma_db' \n",
|
||
"\n",
|
||
"class ChromaRAGSystem:\n",
|
||
" def __init__(self):\n",
|
||
" # Init embedding model\n",
|
||
" self.embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL)\n",
|
||
" # Vector store instance\n",
|
||
" self.vector_db = None\n",
|
||
" \n",
|
||
" def build_vector_store(self):\n",
|
||
" \"\"\"Process documents and create Chroma vector store\"\"\"\n",
|
||
" all_docs = []\n",
|
||
" \n",
|
||
"\n",
|
||
" for doc_idx, path in enumerate(DOCUMENT_PATHS):\n",
|
||
" with open(path, 'r', encoding='utf-8') as f:\n",
|
||
" text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
|
||
" # sliding window chunking\n",
|
||
" chunks = [\n",
|
||
" text[i:i+CHUNK_SIZE] \n",
|
||
" for i in range(0, len(text), CHUNK_SIZE - OVERLAP)\n",
|
||
" ]\n",
|
||
" # LangChain documents with metadata\n",
|
||
" for chunk in chunks:\n",
|
||
" all_docs.append(Document(\n",
|
||
" page_content=chunk,\n",
|
||
" metadata={\"source_doc\": doc_idx}\n",
|
||
" ))\n",
|
||
" \n",
|
||
" # Chroma vector store\n",
|
||
" self.vector_db = Chroma.from_documents(\n",
|
||
" documents=all_docs,\n",
|
||
" embedding=self.embeddings,\n",
|
||
" persist_directory=CHROMA_PERSIST_DIR\n",
|
||
" )\n",
|
||
" self.vector_db.persist()\n",
|
||
" \n",
|
||
" def load_vector_store(self):\n",
|
||
" \"\"\"Load existing Chroma vector store\"\"\"\n",
|
||
" self.vector_db = Chroma(\n",
|
||
" persist_directory=CHROMA_PERSIST_DIR,\n",
|
||
" embedding_function=self.embeddings\n",
|
||
" )\n",
|
||
" \n",
|
||
" def document_query(self, query, doc_index, top_k=5):\n",
|
||
" \"\"\"Retrieve context from specific document\"\"\"\n",
|
||
" # Chroma metadata filtering\n",
|
||
" results = self.vector_db.similarity_search(\n",
|
||
" query=query,\n",
|
||
" k=top_k,\n",
|
||
" filter={\"source_doc\": doc_index}\n",
|
||
" )\n",
|
||
" return [doc.page_content for doc in results]\n",
|
||
"\n",
|
||
"class AnswerGenerator:\n",
|
||
" def __init__(self, rag_system):\n",
|
||
" self.rag = rag_system\n",
|
||
" \n",
|
||
" def generate_response(self, question, doc_index):\n",
|
||
" \"\"\"Generate context-aware answer using LLM\"\"\"\n",
|
||
" # Retrieve relevant context\n",
|
||
" context_chunks = self.rag.document_query(question, doc_index)\n",
|
||
" context = \"\\n\".join(context_chunks)\n",
|
||
" \n",
|
||
" prompt = f\"\"\"با استفاده از متن زیر به سوال پاسخ دهید:\n",
|
||
"{context}\n",
|
||
"\n",
|
||
"اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید\n",
|
||
"\n",
|
||
"سوال: {question}\n",
|
||
"پاسخ:\"\"\"\n",
|
||
" \n",
|
||
" response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])\n",
|
||
" return response['message']['content']\n",
|
||
"\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" rag_system = ChromaRAGSystem()\n",
|
||
" \n",
|
||
" # Init vector store\n",
|
||
" if not os.path.exists(CHROMA_PERSIST_DIR):\n",
|
||
" print(\"Creating new vector store...\")\n",
|
||
" rag_system.build_vector_store()\n",
|
||
" else:\n",
|
||
" print(\"Loading existing vector store...\")\n",
|
||
" rag_system.load_vector_store()\n",
|
||
" \n",
|
||
" # Init answer generator\n",
|
||
" answer_engine = AnswerGenerator(rag_system)\n",
|
||
"\n",
|
||
" queries = [\n",
|
||
" (\"چرا اینترنت همراه اول گوشی وصل نمیشود؟\", 0),\n",
|
||
" (\"چطوری ویپ مورد نظرمو پیدا کنم؟\", 1),\n",
|
||
" (\"شاه عباس که بود؟\", 2),\n",
|
||
" (\"خلیفه سلطان که بود و چه کرد؟\", 3),\n",
|
||
" (\"کربن اکتیو و کربن بلک چه هستند و چه تفاوتی دارند و برای چه استفاده میشن؟\", 4),\n",
|
||
" (\"شرکت تکاپو صنعت نامی چه محصولاتی ارایه میدهد؟ چه چیزی این شرکت را منحصر به فرد میسازد؟ سهام این شرکت صعودی است یا نزولی؟\", 5)\n",
|
||
" ]\n",
|
||
" \n",
|
||
" with open(r'C:\\Users\\ASUS\\Downloads\\representation2.txt', 'w', encoding='utf-8') as output_file: #repalce path\n",
|
||
" for q_num, (query, doc_idx) in enumerate(queries):\n",
|
||
" answer = answer_engine.generate_response(query, doc_idx)\n",
|
||
" output_file.write(f\"سوال {q_num+1} ({doc_idx+1}):\\n{query}\\n\\nپاسخ:\\n{answer}\\n\\n{'='*50}\\n\\n\")\n",
|
||
" print(f\"پردازش سوال {q_num+1}/{len(queries)} تکمیل شد\")\n",
|
||
"\n",
|
||
"print(\"تمامی سوالات با موفقیت پردازش شدند!\")"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.11.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|