修改项目结构+7，4 一部分

docs/chapter7/RAG/LLM.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+@File    :   LLM.py
+@Time    :   2024/02/12 13:50:47
+@Author  :   不要葱姜蒜
+@Version :   1.0
+@Desc    :   None
+'''
+import os
+from typing import Dict, List, Optional, Tuple, Union
+
+PROMPT_TEMPLATE = dict(
+    RAG_PROMPT_TEMPALTE="""使用以上下文来回答用户的问题。如果你不知道答案，就说你不知道。总是使用中文回答。
+        问题: {question}
+        可参考的上下文：
+        ···
+        {context}
+        ···
+        如果给定的上下文无法让你做出回答，请回答数据库中没有这个内容，你不知道。
+        有用的回答:""",
+    InternLM_PROMPT_TEMPALTE="""先对上下文进行内容总结,再使用上下文来回答用户的问题。如果你不知道答案，就说你不知道。总是使用中文回答。
+        问题: {question}
+        可参考的上下文：
+        ···
+        {context}
+        ···
+        如果给定的上下文无法让你做出回答，请回答数据库中没有这个内容，你不知道。
+        有用的回答:"""
+)
+
+
+class BaseModel:
+    def __init__(self, path: str = '') -> None:
+        self.path = path
+
+    def chat(self, prompt: str, history: List[dict], content: str) -> str:
+        pass
+
+    def load_model(self):
+        pass
+
+class OpenAIChat(BaseModel):
+    def __init__(self, path: str = '', model: str = "gpt-3.5-turbo-1106") -> None:
+        super().__init__(path)
+        self.model = model
+
+    def chat(self, prompt: str, history: List[dict], content: str) -> str:
+        from openai import OpenAI
+        client = OpenAI()
+        client.api_key = os.getenv("OPENAI_API_KEY")   
+        client.base_url = os.getenv("OPENAI_BASE_URL")
+        history.append({'role': 'user', 'content': PROMPT_TEMPLATE['RAG_PROMPT_TEMPALTE'].format(question=prompt, context=content)})
+        response = client.chat.completions.create(
+            model=self.model,
+            messages=history,
+            max_tokens=150,
+            temperature=0.1
+        )
+        return response.choices[0].message.content
+
+class InternLMChat(BaseModel):
+    def __init__(self, path: str = '') -> None:
+        super().__init__(path)
+        self.load_model()
+
+    def chat(self, prompt: str, history: List = [], content: str='') -> str:
+        prompt = PROMPT_TEMPLATE['InternLM_PROMPT_TEMPALTE'].format(question=prompt, context=content)
+        response, history = self.model.chat(self.tokenizer, prompt, history)
+        return response
+
+
+    def load_model(self):
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM
+        self.tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(self.path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
+
+class DashscopeChat(BaseModel):
+    def __init__(self, path: str = '', model: str = "qwen-turbo") -> None:
+        super().__init__(path)
+        self.model = model
+
+    def chat(self, prompt: str, history: List[Dict], content: str) -> str:
+        import dashscope
+        dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")
+        history.append({'role': 'user', 'content': PROMPT_TEMPLATE['RAG_PROMPT_TEMPALTE'].format(question=prompt, context=content)})
+        response = dashscope.Generation.call(
+            model=self.model,
+            messages=history,
+            result_format='message',
+            max_tokens=150,
+            temperature=0.1
+        )
+        return response.output.choices[0].message.content
+    
+
+class ZhipuChat(BaseModel):
+    def __init__(self, path: str = '', model: str = "glm-4") -> None:
+        super().__init__(path)
+        from zhipuai import ZhipuAI
+        self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
+        self.model = model
+
+    def chat(self, prompt: str, history: List[Dict], content: str) -> str:
+        history.append({'role': 'user', 'content': PROMPT_TEMPLATE['RAG_PROMPT_TEMPALTE'].format(question=prompt, context=content)})
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=history,
+            max_tokens=150,
+            temperature=0.1
+        )
+        return response.choices[0].message