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| from PyPDF2 import PdfReader | |
| import requests | |
| import json | |
| import os | |
| import concurrent.futures | |
| import random | |
| from langchain_google_genai import ChatGoogleGenerativeAI | |
| from langchain_community.document_loaders import WebBaseLoader | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| import google.generativeai as genai | |
| from langchain_core.messages import HumanMessage | |
| from io import BytesIO | |
| import numpy as np | |
| import re | |
| import torch | |
| from transformers import AutoTokenizer, AutoModel | |
| from search import search_images | |
| gemini = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyCo-TeDp0Ou--UwhlTgMwCoTEZxg6-v7wA',temperature = 0.1) | |
| gemini1 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyAtnUk8QKSUoJd3uOBpmeBNN-t8WXBt0zI',temperature = 0.1) | |
| gemini2 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBzbZQBffHFK3N-gWnhDDNbQ9yZnZtaS2E',temperature = 0.1) | |
| gemini3 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBNN4VDMAOB2gSZha6HjsTuH71PVV69FLM',temperature = 0.1) | |
| vision = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyCo-TeDp0Ou--UwhlTgMwCoTEZxg6-v7wA',temperature = 0.1) | |
| vision1 = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyAtnUk8QKSUoJd3uOBpmeBNN-t8WXBt0zI',temperature = 0.1) | |
| vision2 = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyBzbZQBffHFK3N-gWnhDDNbQ9yZnZtaS2E',temperature = 0.1) | |
| vision3 = ChatGoogleGenerativeAI(model="gemini-1.5-flash",google_api_key='AIzaSyBNN4VDMAOB2gSZha6HjsTuH71PVV69FLM',temperature = 0.1) | |
| tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-base-en-v1.5',trust_remote_code = True) | |
| model = AutoModel.from_pretrained('Alibaba-NLP/gte-base-en-v1.5',trust_remote_code = True) | |
| model.to('cpu') # Ensure the model is on the CPU | |
| genai.configure(api_key="AIzaSyAtnUk8QKSUoJd3uOBpmeBNN-t8WXBt0zI") | |
| def pdf_extractor(link): | |
| text = '' | |
| try: | |
| # Fetch the PDF file from the URL | |
| response = requests.get(link) | |
| response.raise_for_status() # Raise an error for bad status codes | |
| # Use BytesIO to handle the PDF content in memory | |
| pdf_file = BytesIO(response.content) | |
| # Load the PDF file | |
| reader = PdfReader(pdf_file) | |
| for page in reader.pages: | |
| text += page.extract_text() # Extract text from each page | |
| except requests.exceptions.HTTPError as e: | |
| print(f'HTTP error occurred: {e}') | |
| except Exception as e: | |
| print(f'An error occurred: {e}') | |
| return text | |
| def web_extractor(link): | |
| text = '' | |
| try: | |
| loader = WebBaseLoader(link) | |
| pages = loader.load_and_split() | |
| for page in pages: | |
| text+=page.page_content | |
| except: | |
| pass | |
| return text | |
| def imporve_text(text): | |
| prompt = f''' | |
| Please rewrite the following text to make it short, concise, and of high quality. | |
| Ensure that all essential information and key points are retained. | |
| Focus on improving clarity, coherence, and word choice without altering the original meaning. | |
| text = {text} | |
| ''' | |
| model = random.choice([gemini,gemini1,gemini2,gemini3]) | |
| result = model.invoke(prompt) | |
| return result.content | |
| def feature_extraction(tag, history , context): | |
| prompt = f''' | |
| You are an intelligent assistant tasked with updating product information. You have two data sources: | |
| 1. Tag_History: Previously gathered information about the product. | |
| 2. Tag_Context: New data that might contain additional details. | |
| Your job is to read the Tag_Context and update the relevant field in the Tag_History with any new details found. The field to be updated is the {tag} FIELD. | |
| Guidelines: | |
| - Only add new details that are relevant to the {tag} FIELD. | |
| - Do not add or modify any other fields in the Tag_History. | |
| - Ensure your response is in coherent sentences, integrating the new details seamlessly into the existing information. | |
| Here is the data: | |
| Tag_Context: {str(context)} | |
| Tag_History: {history} | |
| Respond with the updated Tag_History. | |
| ''' | |
| model = random.choice([gemini,gemini1,gemini2,gemini3]) | |
| result = model.invoke(prompt) | |
| return result.content | |
| def feature_extraction_image(url): | |
| text = ' ' | |
| model = genai.GenerativeModel('gemini-1.5-flash-001') | |
| try: | |
| res = model.generate_content(['Describe this image to me',url]) | |
| text = res.text | |
| except: | |
| pass | |
| return text | |
| def detailed_feature_extraction(find, context): | |
| prompt = f''' | |
| You are an intelligent assistant tasked with finding product information. You have one data source and one output format: | |
| 1. Context: The gathered information about the product. | |
| 2. Format: Details which need to be filled based on Context. | |
| Your job is to read the Context and update the relevant field in Format using Context. | |
| Guidelines: | |
| - Only add details that are relevant to the individual FIELD. | |
| - Do not add or modify any other fields in the Format. | |
| - If nothing found return None. | |
| Here is the data: | |
| The Context is {str(context)} | |
| The Format is {str(find)} | |
| ''' | |
| model = random.choice([gemini,gemini1,gemini2,gemini3]) | |
| result = model.invoke(prompt) | |
| return result.content | |
| def detailed_history(history): | |
| details = { | |
| "Introduction": { | |
| "Product Name": None, | |
| "Overview of the product": None, | |
| "Purpose of the manual": None, | |
| "Audience": None, | |
| "Additional Details": None | |
| }, | |
| "Specifications": { | |
| "Technical specifications": None, | |
| "Performance metrics": None, | |
| "Additional Details": None | |
| }, | |
| "Product Overview": { | |
| "Product features": None, | |
| "Key components and parts": None, | |
| "Additional Details": None | |
| }, | |
| "Safety Information": { | |
| "Safety warnings and precautions": None, | |
| "Compliance and certification information": None, | |
| "Additional Details": None | |
| }, | |
| "Installation Instructions": { | |
| "Unboxing and inventory checklist": None, | |
| "Step-by-step installation guide": None, | |
| "Required tools and materials": None, | |
| "Additional Details": None | |
| }, | |
| "Setup and Configuration": { | |
| "Initial setup procedures": None, | |
| "Configuration settings": None, | |
| "Troubleshooting setup issues": None, | |
| "Additional Details": None | |
| }, | |
| "Operation Instructions": { | |
| "How to use the product": None, | |
| "Detailed instructions for different functionalities": None, | |
| "User interface guide": None, | |
| "Additional Details": None | |
| }, | |
| "Maintenance and Care": { | |
| "Cleaning instructions": None, | |
| "Maintenance schedule": None, | |
| "Replacement parts and accessories": None, | |
| "Additional Details": None | |
| }, | |
| "Troubleshooting": { | |
| "Common issues and solutions": None, | |
| "Error messages and their meanings": None, | |
| "Support Information": None, | |
| "Additional Details": None | |
| }, | |
| "Warranty Information": { | |
| "Terms and Conditions": None, | |
| "Service and repair information": None, | |
| "Additional Details": None | |
| }, | |
| "Legal Information": { | |
| "Copyright information": None, | |
| "Trademarks and patents": None, | |
| "Disclaimers": None, | |
| "Additional Details": None | |
| } | |
| } | |
| for key,val in history.items(): | |
| find = details[key] | |
| details[key] = str(detailed_feature_extraction(find,val)) | |
| return details | |
| def get_embeddings(link,tag_option): | |
| print(f"\n--> Creating Embeddings - {link}") | |
| if tag_option=='Complete Document Similarity': | |
| history = { "Details": "" } | |
| else: | |
| history = { | |
| "Introduction": "", | |
| "Specifications": "", | |
| "Product Overview": "", | |
| "Safety Information": "", | |
| "Installation Instructions": "", | |
| "Setup and Configuration": "", | |
| "Operation Instructions": "", | |
| "Maintenance and Care": "", | |
| "Troubleshooting": "", | |
| "Warranty Information": "", | |
| "Legal Information": "" | |
| } | |
| # Extract Text ----------------------------- | |
| print("Extracting Text") | |
| if link[-3:] == '.md' or link[8:11] == 'en.': | |
| text = web_extractor(link) | |
| else: | |
| text = pdf_extractor(link) | |
| # Create Chunks ---------------------------- | |
| print("Writing Tag Data") | |
| if tag_option=="Complete Document Similarity": | |
| history["Details"] = feature_extraction("Details", history["Details"], text[0][:50000]) | |
| else: | |
| chunks = text_splitter.create_documents(text) | |
| for chunk in chunks: | |
| with concurrent.futures.ThreadPoolExecutor() as executor: | |
| future_to_key = { | |
| executor.submit( | |
| feature_extraction, f"Product {key}", history[key], chunk.page_content | |
| ): key for key in history | |
| } | |
| for future in concurrent.futures.as_completed(future_to_key): | |
| key = future_to_key[future] | |
| try: | |
| response = future.result() | |
| history[key] = response | |
| except Exception as e: | |
| print(f"Error processing {key}: {e}") | |
| print("Creating Vectors") | |
| genai_embeddings=[] | |
| for tag in history: | |
| result = genai.embed_content( | |
| model="models/embedding-001", | |
| content=history[tag], | |
| task_type="retrieval_document") | |
| genai_embeddings.append(result['embedding']) | |
| return history,genai_embeddings | |
| def get_embed_chroma(link): | |
| print(f"\n--> Creating Embeddings - {link}") | |
| # Extract Text ----------------------------- | |
| if link[-3:] == '.md' or link[8:11] == 'en.': | |
| text = web_extractor(link) | |
| else: | |
| text = pdf_extractor(link) | |
| print("\u2713 Extracting Text") | |
| # Create Chunks ---------------------------- | |
| text = re.sub(r'\.{2,}', '.', text) | |
| text = re.sub(r'\s{2,}', ' ', text) | |
| text = [re.sub(r'\n{2,}', '\n', text)] | |
| chunks = text_splitter_small.create_documents(text) | |
| print("\u2713 Writing Tag Data") | |
| # Creating Vector | |
| embedding_vectors=[] | |
| textual_data = [] | |
| print("\u2713 Creating Vectors") | |
| for text in chunks: | |
| inputs = tokenizer(text.page_content, return_tensors="pt", padding=True, truncation=True) | |
| inputs = {k: v.to('cpu') for k, v in inputs.items()} | |
| # Get the model's outputs | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| embeddings = outputs.last_hidden_state.mean(dim=1) | |
| embedding_vectors.append(embeddings.squeeze().cpu().numpy().tolist()) | |
| textual_data.append(text.page_content) | |
| return textual_data , embedding_vectors | |
| def get_image_embeddings(Product): | |
| image_embeddings = [] | |
| links = search_images(Product) | |
| with concurrent.futures.ThreadPoolExecutor() as executor: | |
| descriptions = list(executor.map(feature_extraction_image, links)) | |
| for description in descriptions: | |
| result = genai.embed_content( | |
| model="models/embedding-001", | |
| content=description, | |
| task_type="retrieval_document") | |
| image_embeddings.append(result['embedding']) | |
| # print(image_embeddings) | |
| return image_embeddings | |
| global text_splitter | |
| global data | |
| global history | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size = 10000, | |
| chunk_overlap = 100, | |
| separators = ["",''," "] | |
| ) | |
| text_splitter_small = RecursiveCharacterTextSplitter( | |
| chunk_size = 2000, | |
| chunk_overlap = 100, | |
| separators = ["",''," "] | |
| ) | |
| if __name__ == '__main__': | |
| # print(get_embed_chroma('https://www.galaxys24manual.com/wp-content/uploads/pdf/galaxy-s24-manual-SAM-S921-S926-S928-OS14-011824-FINAL-US-English.pdf')) | |
| print(get_image_embeddings(Product='Samsung Galaxy S24')) |