403 resolved

embedding.py

@@ -1,255 +1,254 @@
-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
-
-
-gemini = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBmZtXjJgp7yIAo9joNCZGSxK9PbGMcVaA',temperature = 0.1)
-gemini1 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyABsaDjPujPCBlz4LLxcXDX_bDA9uEL7Xc',temperature = 0.1)
-gemini2 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBCIQgt1uK7-sJH5Afg5vUZ99EWkx5gSU0',temperature = 0.1)
-gemini3 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBot9W5Q-BKQ66NAYRUmVeloXWEbXOXTmM',temperature = 0.1)
-
-genai.configure(api_key="AIzaSyBmZtXjJgp7yIAo9joNCZGSxK9PbGMcVaA")
-
-
-def pdf_extractor(link):
-    text = ''
-
-    try:
-        loader = PyPDFLoader(link)
-        pages = loader.load_and_split()
-
-        for page in pages:
-            text+=page.page_content
-    except:
-        pass
-    
-    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 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])
-    result = model.invoke(prompt)
-
-    return result.content
-
-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): 
-
-        print(f"\nCreating Embeddings ----- {link}")
-        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")
-        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}")
-            
-            # history = detailed_history(history)
-        print("Creating Vectors")
-        print(history)
-        genai_embeddings=[]
-
-        for tag in history:
-            try:    
-                result = genai.embed_content(
-                        model="models/embedding-001",
-                        content=history[tag],
-                        task_type="retrieval_document")
-                genai_embeddings.append(result['embedding'])
-            except:
-                genai_embeddings.append([0]*768)
-
-
-        return history,genai_embeddings
-            
-global text_splitter
-global data
-global history
-
-
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size = 10000,
-    chunk_overlap  = 100,
-    separators = ["",''," "]
-)
-
-
-if __name__ == '__main__':
-    pass
+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 io import BytesIO
+
+
+
+gemini = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBmZtXjJgp7yIAo9joNCZGSxK9PbGMcVaA',temperature = 0.1)
+gemini1 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyABsaDjPujPCBlz4LLxcXDX_bDA9uEL7Xc',temperature = 0.1)
+gemini2 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBCIQgt1uK7-sJH5Afg5vUZ99EWkx5gSU0',temperature = 0.1)
+gemini3 = ChatGoogleGenerativeAI(model="gemini-1.0-pro-001",google_api_key='AIzaSyBot9W5Q-BKQ66NAYRUmVeloXWEbXOXTmM',temperature = 0.1)
+
+genai.configure(api_key="AIzaSyBmZtXjJgp7yIAo9joNCZGSxK9PbGMcVaA")
+
+
+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 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 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): 
+
+        print(f"\nCreating Embeddings ----- {link}")
+        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")
+        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}")
+            
+            # history = detailed_history(history)
+        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
+            
+global text_splitter
+global data
+global history
+
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size = 10000,
+    chunk_overlap  = 100,
+    separators = ["",''," "]
+)
+
+
+if __name__ == '__main__':
+    pass