code: def detect_intent_knowledge(project_id, session_id, language_code,
                              knowledge_base_id, texts):
      
      import dialogflow_v2beta1 as dialogflow
      session_client = dialogflow.SessionsClient()
  
      session_path = session_client.session_path(project_id, session_id)
      print('Session path: {}\n'.format(session_path))
  
      for text in texts:
          text_input = dialogflow.types.TextInput(
              text=text, language_code=language_code)
  
          query_input = dialogflow.types.QueryInput(text=text_input)
  
          knowledge_base_path = dialogflow.knowledge_bases_client \
              .KnowledgeBasesClient \
              .knowledge_base_path(project_id, knowledge_base_id)
  
          query_params = dialogflow.types.QueryParameters(
              knowledge_base_names=[knowledge_base_path])
  
          response = session_client.detect_intent(
              session=session_path, query_input=query_input,
              query_params=query_params)
  
          print('=' * 20)
          print('Query text: {}'.format(response.query_result.query_text))
          print('Detected intent: {} (confidence: {})\n'.format(
              response.query_result.intent.display_name,
              response.query_result.intent_detection_confidence))
          print('Fulfillment text: {}\n'.format(
              response.query_result.fulfillment_text))
          print('Knowledge results:')
          knowledge_answers = response.query_result.knowledge_answers
          for answers in knowledge_answers.answers:
              print(' - Answer: {}'.format(answers.answer))
              print(' - Confidence: {}'.format(
                  answers.match_confidence))
docs: Returns the result of detect intent with querying Knowledge Connector.
  
      Args:
      project_id: The GCP project linked with the agent you are going to query.
      session_id: Id of the session, using the same `session_id` between requests
                allows continuation of the conversation.
      language_code: Language of the queries.
      knowledge_base_id: The Knowledge base's id to query against.
      texts: A list of text queries to send.