本文介绍了Llama2模型集成LangChain框架的具体实现，这样可更方便地基于Llama2开发文档检索、问答机器人和智能体应用等。

1.调用Llama2类
  针对LangChain[1]框架封装的Llama2 LLM类见examples/llama2_for_langchain.py，调用代码如下所示：

from llama2_for_langchain import Llama2# 这里以调用4bit量化压缩的Llama2-Chinese参数FlagAlpha/Llama2-Chinese-13b-Chat-4bit为例llm = Llama2(model_name_or_path='FlagAlpha/Llama2-Chinese-13b-Chat-4bit', bit4=True)while True:    human_input = input("Human: ")    response = llm(human_input)    print(f"Llama2: {response}")
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2.Llama2 LLM类具体实现
  主要是def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str函数实现。LangChain八股文也不难实现，如下所示：

from langchain.llms.base import LLMfrom typing import Dict, List, Any, Optionalimport torch,sys,osfrom transformers import AutoTokenizerclass Llama2(LLM): # LLM是一个抽象类，需要实现_call方法    max_token: int = 2048     # 最大token数    temperature: float = 0.1  # 生成温度    top_p: float = 0.95       # 生成概率    tokenizer: Any            # 分词器    model: Any                # 模型        def __init__(self, model_name_or_path, bit4=True):        super().__init__()        self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,use_fast=False)        self.tokenizer.pad_token = self.tokenizer.eos_token        if bit4==False: # 32bit            from transformers import AutoModelForCausalLM            self.model = AutoModelForCausalLM.from_pretrained(model_name_or_path,device_map='auto',torch_dtype=torch.float16,load_in_8bit=True)            self.model.eval()        else: # 4bit            from auto_gptq import AutoGPTQForCausalLM            self.model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,low_cpu_mem_usage=True, device="cuda:0", use_triton=False,inject_fused_attention=False,inject_fused_mlp=False)                    if torch.__version__ >= "2" and sys.platform != "win32":            self.model = torch.compile(self.model)                @property # @property装饰器将方法转换为属性    def _llm_type(self) -> str:        return "Llama2"    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:        print('prompt:',prompt)        input_ids = self.tokenizer(prompt, return_tensors="pt",add_special_tokens=False).input_ids.to('cuda')        generate_input = {            "input_ids":input_ids,            "max_new_tokens":1024,            "do_sample":True,            "top_k":50,            "top_p":self.top_p,            "temperature":self.temperature,            "repetition_penalty":1.2,            "eos_token_id":self.tokenizer.eos_token_id,            "bos_token_id":self.tokenizer.bos_token_id,            "pad_token_id":self.tokenizer.pad_token_id        }        generate_ids = self.model.generate(**generate_input)        generate_ids = [item[len(input_ids[0]):-1] for  item in generate_ids]        result_message = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]        return result_message # 返回生成的文本
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参考文献：
[1]https://github.com/FlagAlpha/Llama2-Chinese/blob/main/examples/llama2_for_langchain.py
[2]https://github.com/langchain-ai/langchain