code: import os
  import logging
  import pickle
  import ssl
  
  import dill
  import langchain
  from langchain.prompts import PromptTemplate
  from langchain.llms import OpenAI, GooglePalm
  from langchain.chains import LLMChain, RetrievalQAWithSourcesChain, AnalyzeDocumentChain
  from langchain.chains.qa_with_sources import load_qa_with_sources_chain
  from langchain.document_loaders import TextLoader, UnstructuredURLLoader
  from langchain.vectorstores import FAISS
  from langchain.embeddings import OpenAIEmbeddings
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain.schema import StrOutputParser
  from dotenv import load_dotenv
  
  class Vectorizer():
      llm = OpenAI(temperature=0.7, max_tokens=1024)
      embeddings = OpenAIEmbeddings()
      logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)
      logging.getLogger("httpx").setLevel(logging.WARNING)
      logger = logging.getLogger(__name__)
      
      def __init__(self, file_path: str):
          self.file_path = os.path.join(os.getcwd(), 'vectors', f'{file_path[:-4]}.pkl')
          
      def vector(self, split_docs: list, ) -> bool:
          self.logger.info('docs: %s', len(split_docs))
          # Using OpenAIEmbeddings models to provide further correlational data for our resulting vector for better semantic relationship identification
          vector_index = FAISS.from_documents(split_docs, self.embeddings)
          self.logger.info('Vector embedding created')
          
          # Exclude SSLContext from pickling
          dill._dill._reverse_typemap[type(ssl.create_default_context())] = None
          
          with open(self.file_path, 'wb') as f:
              dill.dump(vector_index, f)
              self.logger.info('Vector index saved')
          
          return True
      
      def load_index(self):
          if os.path.exists(self.file_path):
              with open(self.file_path, 'rb') as f:
                  vector_index = dill.load(f)
                  self.logger.info('Vector index loaded')
              
              return vector_index
          else:
              self.logger.info('Vector index not found at the provided file path')
              return False
  
apis: langchain.vectorstores.FAISS.from_documents langchain.llms.OpenAI langchain.embeddings.OpenAIEmbeddings
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