523 lines
20 KiB
Plaintext
523 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6a2d4624-4d76-4c52-a0f8-b353b6848549",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"#Test Dorna\n",
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"response: ChatResponse = chat(model='llama3.2', 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": null,
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"id": "a312f740-400a-49a5-a79e-41195aa49746",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"!pip install faiss-cpu sentence-transformers ollama numpy"
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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": null,
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"id": "9100712e-4b06-4168-b8ac-36f0c2865e42",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# MANUALLY RAG\n",
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"from ollama import chat\n",
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"import numpy as np\n",
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"import faiss\n",
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"from sentence_transformers import SentenceTransformer\n",
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"import torch\n",
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"import os\n",
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"import re\n",
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"\n",
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"DOCUMENT_PATHS = [\n",
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" r'/home/masih/rag_data/Hamrah.txt', #replace path\n",
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" r'/home/masih/rag_data/vape.txt',\n",
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" r'/home/masih/rag_data/Shah.txt',\n",
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" r'/home/masih/rag_data/Khalife.txt',\n",
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" r'/home/masih/rag_data/carbon.txt',\n",
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" r'/home/masih/rag_data/takapoo.txt'\n",
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"]\n",
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"\n",
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"EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
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"LLM_MODEL = 'llama3.2'\n",
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"CHUNK_SIZE = 1000\n",
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"OVERLAP = 200\n",
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"INDEX_PATH = r'C:\\Users\\ASUS\\Downloads\\doc_index.faiss'\n",
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"CHUNK_MAP_PATH = r'C:\\Users\\ASUS\\Downloads\\chunk_map.npy'\n",
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"\n",
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"class AdvancedRAG:\n",
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" def __init__(self):\n",
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" self.encoder = SentenceTransformer(EMBEDDING_MODEL, device='cuda' if torch.cuda.is_available() else 'cpu')\n",
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" self.index = None\n",
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" self.chunk_map = []\n",
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" \n",
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" def create_index(self):\n",
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" \"\"\"Create FAISS index with cosine similarity and document mapping\"\"\"\n",
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" all_chunks = []\n",
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" doc_mapping = []\n",
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" \n",
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" # Process via CHUNKING (REQ 4 RAG)\n",
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" for doc_idx, path in enumerate(DOCUMENT_PATHS):\n",
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" with open(path, 'r', encoding='utf-8') as f:\n",
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" text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
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" chunks = [text[i:i+CHUNK_SIZE] for i in range(0, len(text), CHUNK_SIZE - OVERLAP)]\n",
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" all_chunks.extend(chunks)\n",
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" doc_mapping.extend([doc_idx] * len(chunks))\n",
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" \n",
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" # Normalized embeddings (REQ 4 cosine similarity)\n",
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" embeddings = self.encoder.encode(all_chunks, convert_to_tensor=True, device='cuda' if torch.cuda.is_available() else 'cpu')\n",
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" embeddings = embeddings.cpu().numpy() # Move back to CPU for FAISS\n",
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" \n",
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" faiss.normalize_L2(embeddings) \n",
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" \n",
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" # FAISS index & Mapping\n",
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" self.index = faiss.IndexFlatIP(embeddings.shape[1])\n",
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" self.index.add(embeddings.astype(np.float32))\n",
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" self.chunk_map = np.array(doc_mapping)\n",
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" \n",
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" # Index \n",
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" faiss.write_index(self.index, INDEX_PATH)\n",
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" # Mapping \n",
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" np.save(CHUNK_MAP_PATH, self.chunk_map)\n",
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" \n",
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" def load_index(self):\n",
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" \"\"\"LOAD EXISTING DATA\"\"\"\n",
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" self.index = faiss.read_index(INDEX_PATH)\n",
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" self.chunk_map = np.load(CHUNK_MAP_PATH, allow_pickle=True)\n",
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" \n",
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" def query(self, question, doc_index, top_k=5):\n",
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" \"\"\"DOCUMENT-SPECIFIC QUERY WITH COSINE SIMILARITY \"\"\"\n",
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" # Encode \n",
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" query_embed = self.encoder.encode([question], convert_to_tensor=True, device='cuda' if torch.cuda.is_available() else 'cpu')\n",
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" query_embed = query_embed.cpu().numpy() # Move back to CPU for FAISS\n",
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" \n",
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" # Normalize \n",
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" faiss.normalize_L2(query_embed)\n",
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" \n",
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" distances, indices = self.index.search(query_embed.astype(np.float32), top_k*3)\n",
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" \n",
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" relevant_chunks = []\n",
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" for idx in indices[0]:\n",
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" if self.chunk_map[idx] == doc_index:\n",
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" relevant_chunks.append(idx)\n",
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" if len(relevant_chunks) >= top_k:\n",
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" break\n",
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" \n",
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" return relevant_chunks\n",
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"\n",
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"class AnswerGenerator:\n",
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" def __init__(self, rag_system):\n",
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" self.rag = rag_system\n",
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" self.chunks = [] \n",
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" \n",
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" def get_answer(self, question, doc_index):\n",
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" \"\"\"GENERATING CONTEXT-AWARE ANSWER\"\"\"\n",
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" if not self.chunks:\n",
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" self._load_chunks()\n",
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" \n",
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" chunk_indices = self.rag.query(question, doc_index)\n",
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" context = \"\\n\".join([self.chunks[idx] for idx in chunk_indices])\n",
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" \n",
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" prompt = f\"\"\"با استفاده از متن زیر به سوال پاسخ دهید:\n",
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"{context}\n",
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"\n",
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"اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید\n",
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"\n",
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"سوال: {question}\n",
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"پاسخ:\"\"\"\n",
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" \n",
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" response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])\n",
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" return response['message']['content']\n",
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" \n",
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" def _load_chunks(self):\n",
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" \"\"\"LOAD ALL CHUNKS(LAZY)\"\"\"\n",
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" self.chunks = []\n",
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" for path in DOCUMENT_PATHS:\n",
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" with open(path, 'r', encoding='utf-8') as f:\n",
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" text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
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" self.chunks.extend([text[i:i+CHUNK_SIZE] for i in range(0, len(text), CHUNK_SIZE - OVERLAP)])\n",
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"\n",
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"# MAIN EXE of RAG\n",
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"if __name__ == \"__main__\":\n",
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" # RAG init\n",
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" rag = AdvancedRAG()\n",
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" \n",
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" if not os.path.exists(INDEX_PATH):\n",
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" print(\"Building optimized index...\")\n",
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" rag.create_index()\n",
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" else:\n",
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" print(\"Loading existing index...\")\n",
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" rag.load_index()\n",
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" # Answer Generator init\n",
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" generator = AnswerGenerator(rag)\n",
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" \n",
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" queries = [\n",
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" (\"چرا اینترنت همراه اول گوشی وصل نمیشود؟\", 0),\n",
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" (\"چطوری ویپ مورد نظرمو پیدا کنم؟\", 1),\n",
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" (\"شاه عباس که بود؟\", 2),\n",
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" (\"خلیفه سلطان که بود و چه کرد؟\", 3),\n",
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" (\"کربن اکتیو و کربن بلک چه هستند و چه تفاوتی دارند و برای چه استفاده میشن؟\", 4),\n",
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" (\"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",
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" ]\n",
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" \n",
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" with open(r'C:\\Users\\ASUS\\Downloads\\representation.txt', 'w', encoding='utf-8') as f: #replace path\n",
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" for q_idx, (query, doc_idx) in enumerate(queries):\n",
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" answer = generator.get_answer(query, doc_idx)\n",
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" f.write(f\"سوال {q_idx+1} ({doc_idx+1}):\\n{query}\\n\\nپاسخ:\\n{answer}\\n\\n{'='*50}\\n\\n\")\n",
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" print(f\"پردازش سوال {q_idx+1}/{len(queries)} تکمیل شد\")\n",
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"\n",
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"print(\"تمامی سوالات با موفقیت پردازش شدند!\")\n"
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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": null,
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"id": "1acad95b-6ae6-480a-98cc-29b0e38d2646",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install langchain chromadb sentence-transformers ollama"
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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": null,
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"id": "b5fd21a3-d820-4e6e-aef4-9ce4955ce2ff",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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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": null,
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"id": "960bc8ae-5d3d-401c-93ae-85b45fe9adee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# CHROMOA\n",
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"from langchain.vectorstores import Chroma\n",
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"from langchain.embeddings import SentenceTransformerEmbeddings\n",
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"from langchain.schema import Document\n",
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"from ollama import chat\n",
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"import os\n",
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"import re\n",
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"\n",
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"DOCUMENT_PATHS = [\n",
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" r'/home/masih/rag_data/Hamrah.txt', #replace path\n",
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" r'/home/masih/rag_data/vape.txt',\n",
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" r'/home/masih/rag_data/Shah.txt',\n",
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" r'/home/masih/rag_data/Khalife.txt',\n",
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" r'/home/masih/rag_data/carbon.txt',\n",
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" r'/home/masih/rag_data/takapoo.txt'\n",
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"]\n",
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"\n",
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"EMBEDDING_MODEL = 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2'\n",
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"LLM_MODEL = 'llama3.2'\n",
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"CHUNK_SIZE = 1000\n",
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"OVERLAP = 200\n",
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"CHROMA_PERSIST_DIR = r'\\home\\Masih\\chroma_db\\chroma_db' \n",
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"\n",
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"class ChromaRAGSystem:\n",
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" def __init__(self):\n",
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" # Init embedding model\n",
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" self.embeddings = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL)\n",
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" # Vector store instance\n",
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" self.vector_db = None\n",
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" \n",
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" def build_vector_store(self):\n",
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" \"\"\"Process documents and create Chroma vector store\"\"\"\n",
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" all_docs = []\n",
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" \n",
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"\n",
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" for doc_idx, path in enumerate(DOCUMENT_PATHS):\n",
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" with open(path, 'r', encoding='utf-8') as f:\n",
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" text = re.sub(r'\\s+', ' ', f.read()).strip()\n",
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" # sliding window chunking\n",
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" chunks = [\n",
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" text[i:i+CHUNK_SIZE] \n",
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" for i in range(0, len(text), CHUNK_SIZE - OVERLAP)\n",
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" ]\n",
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" # LangChain documents with metadata\n",
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" for chunk in chunks:\n",
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" all_docs.append(Document(\n",
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" page_content=chunk,\n",
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" metadata={\"source_doc\": doc_idx}\n",
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" ))\n",
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" \n",
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" # Chroma vector store\n",
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" self.vector_db = Chroma.from_documents(\n",
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" documents=all_docs,\n",
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" embedding=self.embeddings,\n",
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" persist_directory=CHROMA_PERSIST_DIR\n",
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" )\n",
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" self.vector_db.persist()\n",
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" \n",
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" def load_vector_store(self):\n",
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" \"\"\"Load existing Chroma vector store\"\"\"\n",
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" self.vector_db = Chroma(\n",
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" persist_directory=CHROMA_PERSIST_DIR,\n",
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" embedding_function=self.embeddings\n",
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" )\n",
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" \n",
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" def document_query(self, query, doc_index, top_k=5):\n",
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" \"\"\"Retrieve context from specific document\"\"\"\n",
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" # Chroma metadata filtering\n",
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" results = self.vector_db.similarity_search(\n",
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" query=query,\n",
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" k=top_k,\n",
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" filter={\"source_doc\": doc_index}\n",
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" )\n",
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" return [doc.page_content for doc in results]\n",
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"\n",
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"class AnswerGenerator:\n",
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" def __init__(self, rag_system):\n",
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" self.rag = rag_system\n",
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" \n",
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" def generate_response(self, question, doc_index):\n",
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" \"\"\"Generate context-aware answer using LLM\"\"\"\n",
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" # Retrieve relevant context\n",
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" context_chunks = self.rag.document_query(question, doc_index)\n",
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" context = \"\\n\".join(context_chunks)\n",
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" \n",
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" prompt = f\"\"\"با استفاده از متن زیر به سوال پاسخ دهید:\n",
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"{context}\n",
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"\n",
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"اگر پاسخ در متن وجود ندارد عبارت 'پاسخی یافت نشد' را برگردانید\n",
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"\n",
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"سوال: {question}\n",
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"پاسخ:\"\"\"\n",
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" \n",
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" response = chat(model=LLM_MODEL, messages=[{'role': 'user', 'content': prompt}])\n",
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" return response['message']['content']\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" rag_system = ChromaRAGSystem()\n",
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" \n",
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" # Init vector store\n",
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" if not os.path.exists(CHROMA_PERSIST_DIR):\n",
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" print(\"Creating new vector store...\")\n",
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" rag_system.build_vector_store()\n",
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" else:\n",
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" print(\"Loading existing vector store...\")\n",
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" rag_system.load_vector_store()\n",
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" \n",
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" # Init answer generator\n",
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" answer_engine = AnswerGenerator(rag_system)\n",
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"\n",
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" queries = [\n",
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" (\"چرا اینترنت همراه اول گوشی وصل نمیشود؟\", 0),\n",
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" (\"چطوری ویپ مورد نظرمو پیدا کنم؟\", 1),\n",
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" (\"شاه عباس که بود؟\", 2),\n",
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" (\"خلیفه سلطان که بود و چه کرد؟\", 3),\n",
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" (\"کربن اکتیو و کربن بلک چه هستند و چه تفاوتی دارند و برای چه استفاده میشن؟\", 4),\n",
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" (\"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",
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" ]\n",
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" \n",
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" with open( r'/home/masih/rag_data/response.txt', 'w', encoding='utf-8') as output_file: #repalce path\n",
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" for q_num, (query, doc_idx) in enumerate(queries):\n",
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" answer = answer_engine.generate_response(query, doc_idx)\n",
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" output_file.write(f\"سوال {q_num+1} ({doc_idx+1}):\\n{query}\\n\\nپاسخ:\\n{answer}\\n\\n{'='*50}\\n\\n\")\n",
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" print(f\"پردازش سوال {q_num+1}/{len(queries)} تکمیل شد\")\n",
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"\n",
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"print(\"تمامی سوالات با موفقیت پردازش شدند!\")"
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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": null,
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"id": "a8858c90-1d8d-4b7b-9c25-d94370930f04",
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"metadata": {},
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"outputs": [],
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"source": [
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"# AGENT\n",
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"from langchain.agents import Tool, initialize_agent, AgentType\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain_community.llms import Ollama\n",
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"import requests\n",
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"from bs4 import BeautifulSoup\n",
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"\n",
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"# 1. Add web browsing tool\n",
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"def web_browser_tool(url: str) -> str:\n",
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" \"\"\"Fetch webpage content (5000 character limit)\"\"\"\n",
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" try:\n",
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" response = requests.get(url, timeout=10)\n",
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" soup = BeautifulSoup(response.text, 'html.parser')\n",
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" \n",
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" # Clean HTML\n",
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" for element in soup(['script', 'style', 'header', 'footer', 'nav']):\n",
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" element.decompose()\n",
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" \n",
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" text = soup.get_text(separator='\\n', strip=True)\n",
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" return text[:5000] # Prevent token overflow\n",
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" except Exception as e:\n",
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" return f\"Error accessing website: {str(e)}\"\n",
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"\n",
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"# 2. Agent \n",
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"class AgentEnhancedGenerator:\n",
|
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" def __init__(self, rag_system):\n",
|
||
" self.rag = rag_system # Use RAG \n",
|
||
" self.llm = Ollama(model=LLM_MODEL)\n",
|
||
" self.memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
|
||
" \n",
|
||
" # Define tools\n",
|
||
" self.tools = [\n",
|
||
" Tool(\n",
|
||
" name=\"Document_Search\",\n",
|
||
" func=self._document_search, # Directly use RAG\n",
|
||
" description=\"For questions about mobile networks, historical figures, materials, or companies\"\n",
|
||
" ),\n",
|
||
" Tool(\n",
|
||
" name=\"Web_Browser\",\n",
|
||
" func=web_browser_tool,\n",
|
||
" description=\"For live web data or current information\"\n",
|
||
" )\n",
|
||
" ]\n",
|
||
" \n",
|
||
" # Init agent\n",
|
||
" self.agent = initialize_agent(\n",
|
||
" tools=self.tools,\n",
|
||
" llm=self.llm,\n",
|
||
" agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,\n",
|
||
" memory=self.memory,\n",
|
||
" verbose=True,\n",
|
||
" handle_parsing_errors=True,\n",
|
||
" max_iterations=3 # Prevent infinite loops\n",
|
||
" )\n",
|
||
" \n",
|
||
" def _document_search(self, query: str) -> str:\n",
|
||
" \"\"\"Search across ALL documents in your existing RAG\"\"\"\n",
|
||
" results = self.rag.vector_db.similarity_search(query, k=5)\n",
|
||
" return \"\\n\".join([doc.page_content for doc in results])\n",
|
||
" \n",
|
||
" def generate_response(self, question):\n",
|
||
" \"\"\"Generate answer using agent system\"\"\"\n",
|
||
" try:\n",
|
||
" response = self.agent.run(question)\n",
|
||
" return response\n",
|
||
" except Exception as e:\n",
|
||
" return f\"خطا در پردازش: {str(e)}\"\n",
|
||
"\n",
|
||
"# 3. Example\n",
|
||
"if __name__ == \"__main__\":\n",
|
||
" # Init RAG\n",
|
||
" rag_system = ChromaRAGSystem()\n",
|
||
" \n",
|
||
" if not os.path.exists(CHROMA_PERSIST_DIR):\n",
|
||
" rag_system.build_vector_store()\n",
|
||
" else:\n",
|
||
" rag_system.load_vector_store()\n",
|
||
" \n",
|
||
" # Create agent-enhanced generator\n",
|
||
" enhanced_agent = AgentEnhancedGenerator(rag_system)\n",
|
||
" \n",
|
||
" # Test questions (mix of document and web queries)\n",
|
||
" test_questions = [\n",
|
||
" \"آخرین اخبار درباره شرکت تکاپو صنعت چیست؟\", # Will use web browser\n",
|
||
" \"تفاوت کربن اکتیو و کربن بلک چیست؟\", # Uses document search\n",
|
||
" \"آیا شاه عباس با خلیفه سلطان همکاری داشت؟\" # Uses conversation memory\n",
|
||
" ]\n",
|
||
" # Run queries\n",
|
||
" with open(r'~\\Desktop\\agent_results.txt', 'w', encoding='utf-8') as output_file:\n",
|
||
" for idx, question in enumerate(test_questions):\n",
|
||
" answer = enhanced_agent.generate_response(question)\n",
|
||
" output_file.write(f\"سوال {idx+1}:\\n{question}\\n\\nپاسخ:\\n{answer}\\n\\n{'='*50}\\n\\n\")\n",
|
||
" print(f\"پردازش سوال {idx+1}/{len(test_questions)} تکمیل شد\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "bbb77f3d-6aec-4414-ae74-4c849eda1ffc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ef439935-9a7a-43fb-a314-8adde514551a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "c37998a3-c371-4dcd-a8b8-c7ee2b574266",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "fe3a2891-f668-4105-901e-808d28ba7657",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "9ecdd22e-d9eb-4d37-8222-d90a4aa8ea3c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a05aea00-3ad5-4b67-8924-87adbb935e25",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python [conda env:base] *",
|
||
"language": "python",
|
||
"name": "conda-base-py"
|
||
},
|
||
"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": 5
|
||
}
|