修改项目结构+7，4 一部分
This commit is contained in:
KMnO4-zx
117
docs/chapter7/RAG/Embeddings.py
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117
docs/chapter7/RAG/Embeddings.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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@File : Embeddings.py
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@Time : 2024/02/10 21:55:39
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@Author : 不要葱姜蒜
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@Version : 1.0
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@Desc : None
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'''
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import os
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from copy import copy
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from typing import Dict, List, Optional, Tuple, Union
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import numpy as np
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os.environ['CURL_CA_BUNDLE'] = ''
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from dotenv import load_dotenv, find_dotenv
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_ = load_dotenv(find_dotenv())
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class BaseEmbeddings:
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"""
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Base class for embeddings
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"""
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def __init__(self, path: str, is_api: bool) -> None:
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self.path = path
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self.is_api = is_api
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def get_embedding(self, text: str, model: str) -> List[float]:
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raise NotImplementedError
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@classmethod
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def cosine_similarity(cls, vector1: List[float], vector2: List[float]) -> float:
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"""
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calculate cosine similarity between two vectors
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"""
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dot_product = np.dot(vector1, vector2)
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magnitude = np.linalg.norm(vector1) * np.linalg.norm(vector2)
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if not magnitude:
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return 0
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return dot_product / magnitude
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class OpenAIEmbedding(BaseEmbeddings):
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"""
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class for OpenAI embeddings
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"""
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def __init__(self, path: str = '', is_api: bool = True) -> None:
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super().__init__(path, is_api)
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if self.is_api:
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from openai import OpenAI
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self.client = OpenAI()
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self.client.api_key = os.getenv("OPENAI_API_KEY")
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self.client.base_url = os.getenv("OPENAI_BASE_URL")
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def get_embedding(self, text: str, model: str = "text-embedding-3-large") -> List[float]:
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if self.is_api:
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text = text.replace("\n", " ")
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return self.client.embeddings.create(input=[text], model=model).data[0].embedding
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else:
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raise NotImplementedError
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class JinaEmbedding(BaseEmbeddings):
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"""
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class for Jina embeddings
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"""
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def __init__(self, path: str = 'jinaai/jina-embeddings-v2-base-zh', is_api: bool = False) -> None:
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super().__init__(path, is_api)
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self._model = self.load_model()
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def get_embedding(self, text: str) -> List[float]:
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return self._model.encode([text])[0].tolist()
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def load_model(self):
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import torch
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from transformers import AutoModel
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if torch.cuda.is_available():
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device = torch.device("cuda")
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else:
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device = torch.device("cpu")
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model = AutoModel.from_pretrained(self.path, trust_remote_code=True).to(device)
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return model
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class ZhipuEmbedding(BaseEmbeddings):
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"""
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class for Zhipu embeddings
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"""
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def __init__(self, path: str = '', is_api: bool = True) -> None:
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super().__init__(path, is_api)
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if self.is_api:
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from zhipuai import ZhipuAI
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self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
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def get_embedding(self, text: str) -> List[float]:
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response = self.client.embeddings.create(
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model="embedding-2",
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input=text,
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)
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return response.data[0].embedding
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class DashscopeEmbedding(BaseEmbeddings):
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"""
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class for Dashscope embeddings
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"""
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def __init__(self, path: str = '', is_api: bool = True) -> None:
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super().__init__(path, is_api)
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if self.is_api:
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import dashscope
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dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")
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self.client = dashscope.TextEmbedding
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def get_embedding(self, text: str, model: str='text-embedding-v1') -> List[float]:
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response = self.client.call(
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model=model,
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input=text
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)
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return response.output['embeddings'][0]['embedding']
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113
docs/chapter7/RAG/LLM.py
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113
docs/chapter7/RAG/LLM.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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@File : LLM.py
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@Time : 2024/02/12 13:50:47
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@Author : 不要葱姜蒜
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@Version : 1.0
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@Desc : None
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'''
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import os
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from typing import Dict, List, Optional, Tuple, Union
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PROMPT_TEMPLATE = dict(
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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。
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问题: {question}
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可参考的上下文：
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···
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{context}
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···
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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。
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有用的回答:""",
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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。
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问题: {question}
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可参考的上下文：
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···
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{context}
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···
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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。
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有用的回答:"""
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)
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class BaseModel:
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def __init__(self, path: str = '') -> None:
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self.path = path
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def chat(self, prompt: str, history: List[dict], content: str) -> str:
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pass
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def load_model(self):
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pass
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class OpenAIChat(BaseModel):
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def __init__(self, path: str = '', model: str = "gpt-3.5-turbo-1106") -> None:
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super().__init__(path)
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self.model = model
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def chat(self, prompt: str, history: List[dict], content: str) -> str:
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from openai import OpenAI
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client = OpenAI()
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client.api_key = os.getenv("OPENAI_API_KEY")
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client.base_url = os.getenv("OPENAI_BASE_URL")
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history.append({'role': 'user', 'content': PROMPT_TEMPLATE['RAG_PROMPT_TEMPALTE'].format(question=prompt, context=content)})
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response = client.chat.completions.create(
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model=self.model,
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messages=history,
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max_tokens=150,
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temperature=0.1
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)
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return response.choices[0].message.content
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class InternLMChat(BaseModel):
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def __init__(self, path: str = '') -> None:
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super().__init__(path)
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self.load_model()
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def chat(self, prompt: str, history: List = [], content: str='') -> str:
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prompt = PROMPT_TEMPLATE['InternLM_PROMPT_TEMPALTE'].format(question=prompt, context=content)
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response, history = self.model.chat(self.tokenizer, prompt, history)
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return response
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def load_model(self):
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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self.tokenizer = AutoTokenizer.from_pretrained(self.path, trust_remote_code=True)
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self.model = AutoModelForCausalLM.from_pretrained(self.path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
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class DashscopeChat(BaseModel):
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def __init__(self, path: str = '', model: str = "qwen-turbo") -> None:
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super().__init__(path)
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self.model = model
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def chat(self, prompt: str, history: List[Dict], content: str) -> str:
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import dashscope
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dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")
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history.append({'role': 'user', 'content': PROMPT_TEMPLATE['RAG_PROMPT_TEMPALTE'].format(question=prompt, context=content)})
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response = dashscope.Generation.call(
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model=self.model,
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messages=history,
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result_format='message',
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max_tokens=150,
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temperature=0.1
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)
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return response.output.choices[0].message.content
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class ZhipuChat(BaseModel):
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def __init__(self, path: str = '', model: str = "glm-4") -> None:
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super().__init__(path)
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from zhipuai import ZhipuAI
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self.client = ZhipuAI(api_key=os.getenv("ZHIPUAI_API_KEY"))
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self.model = model
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def chat(self, prompt: str, history: List[Dict], content: str) -> str:
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history.append({'role': 'user', 'content': PROMPT_TEMPLATE['RAG_PROMPT_TEMPALTE'].format(question=prompt, context=content)})
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response = self.client.chat.completions.create(
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model=self.model,
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messages=history,
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max_tokens=150,
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temperature=0.1
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)
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return response.choices[0].message
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52
docs/chapter7/RAG/VectorBase.py
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52
docs/chapter7/RAG/VectorBase.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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@File : VectorBase.py
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@Time : 2024/02/12 10:11:13
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@Author : 不要葱姜蒜
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@Version : 1.0
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@Desc : None
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'''
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import os
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from typing import Dict, List, Optional, Tuple, Union
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import json
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from RAG.Embeddings import BaseEmbeddings, OpenAIEmbedding, JinaEmbedding, ZhipuEmbedding
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import numpy as np
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from tqdm import tqdm
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class VectorStore:
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def __init__(self, document: List[str] = ['']) -> None:
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self.document = document
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def get_vector(self, EmbeddingModel: BaseEmbeddings) -> List[List[float]]:
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self.vectors = []
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for doc in tqdm(self.document, desc="Calculating embeddings"):
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self.vectors.append(EmbeddingModel.get_embedding(doc))
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return self.vectors
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def persist(self, path: str = 'storage'):
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if not os.path.exists(path):
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os.makedirs(path)
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with open(f"{path}/doecment.json", 'w', encoding='utf-8') as f:
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json.dump(self.document, f, ensure_ascii=False)
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if self.vectors:
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with open(f"{path}/vectors.json", 'w', encoding='utf-8') as f:
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json.dump(self.vectors, f)
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def load_vector(self, path: str = 'storage'):
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with open(f"{path}/vectors.json", 'r', encoding='utf-8') as f:
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self.vectors = json.load(f)
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with open(f"{path}/doecment.json", 'r', encoding='utf-8') as f:
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self.document = json.load(f)
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def get_similarity(self, vector1: List[float], vector2: List[float]) -> float:
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return BaseEmbeddings.cosine_similarity(vector1, vector2)
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def query(self, query: str, EmbeddingModel: BaseEmbeddings, k: int = 1) -> List[str]:
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query_vector = EmbeddingModel.get_embedding(query)
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result = np.array([self.get_similarity(query_vector, vector)
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for vector in self.vectors])
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return np.array(self.document)[result.argsort()[-k:][::-1]].tolist()
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160
docs/chapter7/RAG/utils.py
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160
docs/chapter7/RAG/utils.py
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@@ -0,0 +1,160 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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'''
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@File : utils.py
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@Time : 2024/02/11 09:52:26
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@Author : 不要葱姜蒜
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@Version : 1.0
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@Desc : None
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'''
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import os
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from typing import Dict, List, Optional, Tuple, Union
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import PyPDF2
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import markdown
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import html2text
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import json
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from tqdm import tqdm
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import tiktoken
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from bs4 import BeautifulSoup
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import re
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enc = tiktoken.get_encoding("cl100k_base")
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class ReadFiles:
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"""
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class to read files
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"""
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def __init__(self, path: str) -> None:
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self._path = path
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self.file_list = self.get_files()
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def get_files(self):
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# args：dir_path，目标文件夹路径
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file_list = []
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for filepath, dirnames, filenames in os.walk(self._path):
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# os.walk 函数将递归遍历指定文件夹
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for filename in filenames:
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# 通过后缀名判断文件类型是否满足要求
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if filename.endswith(".md"):
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# 如果满足要求，将其绝对路径加入到结果列表
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file_list.append(os.path.join(filepath, filename))
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elif filename.endswith(".txt"):
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file_list.append(os.path.join(filepath, filename))
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elif filename.endswith(".pdf"):
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file_list.append(os.path.join(filepath, filename))
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return file_list
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def get_content(self, max_token_len: int = 600, cover_content: int = 150):
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docs = []
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# 读取文件内容
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for file in self.file_list:
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content = self.read_file_content(file)
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chunk_content = self.get_chunk(
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content, max_token_len=max_token_len, cover_content=cover_content)
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docs.extend(chunk_content)
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return docs
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@classmethod
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def get_chunk(cls, text: str, max_token_len: int = 600, cover_content: int = 150):
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chunk_text = []
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curr_len = 0
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curr_chunk = ''
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token_len = max_token_len - cover_content
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lines = text.splitlines() # 假设以换行符分割文本为行
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for line in lines:
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line = line.replace(' ', '')
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line_len = len(enc.encode(line))
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if line_len > max_token_len:
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# 如果单行长度就超过限制，则将其分割成多个块
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num_chunks = (line_len + token_len - 1) // token_len
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for i in range(num_chunks):
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start = i * token_len
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end = start + token_len
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# 避免跨单词分割
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while not line[start:end].rstrip().isspace():
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start += 1
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end += 1
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if start >= line_len:
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break
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curr_chunk = curr_chunk[-cover_content:] + line[start:end]
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chunk_text.append(curr_chunk)
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# 处理最后一个块
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start = (num_chunks - 1) * token_len
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curr_chunk = curr_chunk[-cover_content:] + line[start:end]
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chunk_text.append(curr_chunk)
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if curr_len + line_len <= token_len:
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curr_chunk += line
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curr_chunk += '\n'
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curr_len += line_len
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curr_len += 1
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else:
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chunk_text.append(curr_chunk)
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curr_chunk = curr_chunk[-cover_content:]+line
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curr_len = line_len + cover_content
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if curr_chunk:
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chunk_text.append(curr_chunk)
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return chunk_text
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@classmethod
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def read_file_content(cls, file_path: str):
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# 根据文件扩展名选择读取方法
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if file_path.endswith('.pdf'):
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return cls.read_pdf(file_path)
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elif file_path.endswith('.md'):
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return cls.read_markdown(file_path)
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elif file_path.endswith('.txt'):
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return cls.read_text(file_path)
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else:
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raise ValueError("Unsupported file type")
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@classmethod
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def read_pdf(cls, file_path: str):
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# 读取PDF文件
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with open(file_path, 'rb') as file:
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reader = PyPDF2.PdfReader(file)
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text = ""
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for page_num in range(len(reader.pages)):
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text += reader.pages[page_num].extract_text()
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return text
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@classmethod
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def read_markdown(cls, file_path: str):
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# 读取Markdown文件
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with open(file_path, 'r', encoding='utf-8') as file:
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md_text = file.read()
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html_text = markdown.markdown(md_text)
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# 使用BeautifulSoup从HTML中提取纯文本
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soup = BeautifulSoup(html_text, 'html.parser')
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plain_text = soup.get_text()
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# 使用正则表达式移除网址链接
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text = re.sub(r'http\S+', '', plain_text)
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return text
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@classmethod
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def read_text(cls, file_path: str):
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# 读取文本文件
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with open(file_path, 'r', encoding='utf-8') as file:
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return file.read()
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class Documents:
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"""
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获取已分好类的json格式文档
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"""
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def __init__(self, path: str = '') -> None:
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self.path = path
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def get_content(self):
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with open(self.path, mode='r', encoding='utf-8') as f:
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content = json.load(f)
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return content
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||||
Reference in New Issue
Block a user