on the model type.# Set our LLMllm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=1,    n_batch=512,    n_ctx=2048,    f16_kv=True,      callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),    verbose=True,)Set the associated prompt based upon the model version.from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.chains.prompt_selector import ConditionalPromptSelectorDEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(    input_variables=["question"],    template="""<<SYS>> \n You are an assistant tasked with improving Google search \results. \n <</SYS>> \n\n [INST] Generate THREE Google search queries that \are similar to this question. The output should be a numbered list of questions \and each should have a question mark at the end: \n\n {question} [/INST]""",)DEFAULT_SEARCH_PROMPT = PromptTemplate(    input_variables=["question"],    template="""You are an assistant tasked with improving Google search \results. Generate THREE Google search queries that are similar to \this question. The output should be a numbered list of questions and each \should have a question mark at the end: {question}""",)QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(                default_prompt=DEFAULT_SEARCH_PROMPT,                conditionals=[                    (lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)                ],            )prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)prompt    PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='<<SYS>> \n You are an assistant tasked with improving Google search results. \n <</SYS>> \n\n [INST] Generate THREE Google search queries that are similar to this question. The output should be a numbered list of questions and each should have a question mark at the end: \n\n {question} [/INST]', template_format='f-string',
Use case
Use case ->: on the model type.# Set our LLMllm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=1,    n_batch=512,    n_ctx=2048,    f16_kv=True,      callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),    verbose=True,)Set the associated prompt based upon the model version.from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.chains.prompt_selector import ConditionalPromptSelectorDEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(    input_variables=["question"],    template="""<<SYS>> \n You are an assistant tasked with improving Google search \results. \n <</SYS>> \n\n [INST] Generate THREE Google search queries that \are similar to this question. The output should be a numbered list of questions \and each should have a question mark at the end: \n\n {question} [/INST]""",)DEFAULT_SEARCH_PROMPT = PromptTemplate(    input_variables=["question"],    template="""You are an assistant tasked with improving Google search \results. Generate THREE Google search queries that are similar to \this question. The output should be a numbered list of questions and each \should have a question mark at the end: {question}""",)QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(                default_prompt=DEFAULT_SEARCH_PROMPT,                conditionals=[                    (lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)                ],            )prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)prompt    PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='<<SYS>> \n You are an assistant tasked with improving Google search results. \n <</SYS>> \n\n [INST] Generate THREE Google search queries that are similar to this question. The output should be a numbered list of questions and each should have a question mark at the end: \n\n {question} [/INST]', template_format='f-string',