from application.llm.base import BaseLLM from application.core.settings import settings import threading class LlamaSingleton: _instances = {} _lock = threading.Lock() # Add a lock for thread synchronization @classmethod def get_instance(cls, llm_name): if llm_name not in cls._instances: try: from llama_cpp import Llama except ImportError: raise ImportError( "Please install llama_cpp using pip install llama-cpp-python" ) cls._instances[llm_name] = Llama(model_path=llm_name, n_ctx=2048) return cls._instances[llm_name] @classmethod def query_model(cls, llm, prompt, **kwargs): with cls._lock: return llm(prompt, **kwargs) class LlamaCpp(BaseLLM): def __init__( self, api_key=None, user_api_key=None, llm_name=settings.MODEL_PATH, *args, **kwargs, ): super().__init__(*args, **kwargs) self.api_key = api_key self.user_api_key = user_api_key self.llama = LlamaSingleton.get_instance(llm_name) def _raw_gen(self, baseself, model, messages, stream=False, **kwargs): context = messages[0]["content"] user_question = messages[-1]["content"] prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n" result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False) return result["choices"][0]["text"].split("### Answer \n")[-1] def _raw_gen_stream(self, baseself, model, messages, stream=True, **kwargs): context = messages[0]["content"] user_question = messages[-1]["content"] prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n" result = LlamaSingleton.query_model(self.llama, prompt, max_tokens=150, echo=False, stream=stream) for item in result: for choice in item["choices"]: yield choice["text"]