class_docstr: Integration with the Anthropic LLM Python Library
class_docstr_tok: ["Integration","with","the","Anthropic","LLM","Python","Library"]
docstr: connects with anthropic api to predict the answer for the particular question
docstr_tok: ["connects","with","anthropic","api","to","predict","the","answer","for","the","particular","question"]
code: def predict_answer(self, text):
      """
      connects with anthropic api to predict the answer for the particular question
  
      """
  
      args = self.model_storage.json_get("args")
  
      completion = self.connection.completions.create(
          model=args["using"]["model"],
          max_tokens_to_sample=args["using"]["max_tokens"],
          prompt=f"{HUMAN_PROMPT} {text} {AI_PROMPT}",
      )
  
      return completion.completion
  
file_path: mindsdb/mindsdb/integrations/handlers/anthropic_handler/anthropic_handler.py
imports: import os
  from typing import Dict, Optional
  import pandas
  from anthropic import AI_PROMPT, HUMAN_PROMPT, Anthropic
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
  from mindsdb.utilities.config import Config
label_desc: Use image node_id 5 for calling the AnthropicHandler obj's underlying member method code with example usage: obj.predict_answer(text) and returns: completion