code: import os
  from langchain.llms.bedrock import Bedrock
  from langchain import PromptTemplate
  
  
  def get_llm():
      
      model_kwargs =  { 
          "maxTokenCount": 1024, 
          "stopSequences": [], 
          "temperature": 0, 
          "topP": 0.9 
      }
      
      llm = Bedrock(
          # credentials_profile_name=os.environ.get("BWB_PROFILE_NAME"), #sets the profile name to use for AWS credentials (if not the default)
          region_name=os.environ.get("BWB_REGION_NAME"), #sets the region name (if not the default)
          endpoint_url=os.environ.get("BWB_ENDPOINT_URL"), #sets the endpoint URL (if necessary)
          model_id="amazon.titan-tg1-large", #use the Anthropic Claude model
          model_kwargs=model_kwargs) #configure the properties for Claude
      
      return llm
  
  
  def get_prompt(user_input, template):
      
      prompt_template = PromptTemplate.from_template(template) #this will automatically identify the input variables for the template
  
      prompt = prompt_template.format(user_input=user_input)
      
      return prompt
  
  
  def get_text_response(user_input, template): #text-to-text client function
      llm = get_llm()
      
      prompt = get_prompt(user_input, template)
      
      return llm.predict(prompt) #return a response to the prompt
  
  
apis: langchain.PromptTemplate.from_template
extract_api: [((844, 882), 'langchain.PromptTemplate.from_template', 'PromptTemplate.from_template', (['template'], {}), '(template)\n', (872, 882), False, 'from langchain import PromptTemplate\n'), ((437, 470), 'os.environ.get', 'os.environ.get', (['"""BWB_REGION_NAME"""'], {}), "('BWB_REGION_NAME')\n", (451, 470), False, 'import os\n'), ((536, 570), 'os.environ.get', 'os.environ.get', (['"""BWB_ENDPOINT_URL"""'], {}), "('BWB_ENDPOINT_URL')\n", (550, 570), False, 'import os\n')]