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 先放参考的原文链接大语言模型实战——搭建纯本地迷你版RAG_本地rag-CSDN博客

一、大模型选择

在我之前的文章中有讲到，我用的是ollama中的llama3.1

Ollama在Windows安装，使用，简单调用API_ollama如何对外提供api-CSDN博客

二、嵌入模型

与原文的嵌入模型不同，我选取的是这个，笔者也可以尽可能地多尝试，给出下载链接

git clone https://www.modelscope.cn/maidalun/bce-embedding-base_v1.git

三、业务模块

业务模块完全根据参考文章，我只是记录我实现的过程。

1、整体结构

解释：

data文件夹我是随便放的一个文档

LRAG文件夹放的是主要py文件

maidalun1020文件夹是拉取的嵌入模型

下面我将逐一给出各个代码

2、文档读取模块

import os
from docx import Documentclass ReadFiles:def __init__(self, path: str):self.path = pathdef list_files(self):file_list = []for filepath, _, filenames in os.walk(self.path):for filename in filenames:file_list.append(os.path.join(filepath, filename))return file_listdef read_file_content(self, file_path: str):# 根据文件扩展名选择读取方法if file_path.endswith('.txt') or file_path.endswith('.stt'):return self.read_text(file_path)elif file_path.endswith('.docx'):return self.read_docx(file_path)else:print(f"Unsupported file type: {file_path}")return Nonedef read_text(self, file_path: str):with open(file_path, 'r', encoding='utf-8') as file:return file.read()def read_docx(self, file_path: str):doc = Document(file_path)contents = [para.text for para in doc.paragraphs]return "\n\n".join(contents)def split_chunks(self, text: str):return text.split("\n\n")def load_content(self):docs = []file_list = self.list_files()for file_path in file_list:# 读取文件内容content = self.read_file_content(file_path)if content is None:continuedocs.extend(self.split_chunks(content))return docs# 使用示例
if __name__ == "__main__":path_to_files = 'ollama-python-main/zdf/data/深度学习.txt'reader = ReadFiles(path_to_files)content = reader.load_content()for doc in content:print(doc)

3、嵌入模块

from typing import Listimport numpy as npclass LocalEmbedding:def __init__(self, path: str) -> None:self.path = pathself._model = self.load_model()def load_model(self):import torchfrom sentence_transformers import SentenceTransformerif torch.cuda.is_available():device = torch.device("cuda")else:device = torch.device("cpu")model = SentenceTransformer(self.path, device=device, trust_remote_code=True)return modeldef get_embedding(self, text: str) -> List[float]:return self._model.encode([text])[0].tolist()def cosine_similarity(cls, vector1: List[float], vector2: List[float]) -> float:"""calculate cosine similarity between two vectors"""dot_product = np.dot(vector1, vector2)magnitude = np.linalg.norm(vector1) * np.linalg.norm(vector2)if not magnitude:return 0return dot_product / magnitude

4、向量库模块 

from typing import List
from Embedding import LocalEmbedding
import numpy as np
from tqdm import tqdmclass VectorStore:def __init__(self, embedding_model: LocalEmbedding) -> None:self._embedding_model = embedding_modeldef embedding(self, documents: List[str] = ['']) -> List[List[float]]:self._documents = documentsself._vectors = []for doc in tqdm(self._documents, desc="Calculating embeddings"):self._vectors.append(self._embedding_model.get_embedding(doc))return self._vectorsdef query(self, query: str, k: int = 1) -> List[str]:query_vector = self._embedding_model.get_embedding(query)result = np.array([self._embedding_model.cosine_similarity(query_vector, vector)for vector in self._vectors])return np.array(self._documents)[result.argsort()[-k:][::-1]].tolist()

5、大模型导入模块

from typing import List, DictRAG_PROMPT_TEMPLATE = """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 OllamaChat:def __init__(self, model: str = "llama3.1") -> None:self.model = modeldef _build_messages(self, prompt: str, content: str):prompt_message = RAG_PROMPT_TEMPLATE.format(question=prompt, context=content)messages = [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": prompt_message}]return messagesdef chat(self, prompt: str, history: List[Dict], content: str) -> str:import ollama# 给语言模型发送请求response = ollama.chat(model=self.model,messages=self._build_messages(prompt, content),stream=True)# 解析并组装响应结果final_response = ''for chunk in response:if isinstance(chunk, str):final_response += chunkelif 'content' in chunk.get('message', {}):final_response += chunk['message']['content']return final_response

6、主程序调用模块

from utils import ReadFiles
from Embedding import LocalEmbedding
from VertorStore import VectorStore
from LLM import OllamaChat# 获得data目录下的所有文件内容并分割
docs = ReadFiles('ollama-python-main/zdf/data').load_content()
print(f"docs count:{len(docs)} \n first doc: {docs[0]}")embedding = LocalEmbedding(path='ollama-python-main/zdf/maidalun1020/bce-embedding-base_v1')
print(f"model: {embedding}")vector = VectorStore(embedding_model=embedding)
embeddings = vector.embedding(docs)
print(f"embeddings count: {len(embeddings)} \n dimentions: {len(embeddings[0])} \n embedding content: {embeddings[0][:10]}")question = '深度学习的应用场景有哪些？'
content = vector.query(question, k=1)[0]
print(f"这是：\n{content}")model = OllamaChat("llama3.1")
print(f"这是大模型回答的：\n{model.chat(question, [], content)}")

7、实现结果

实现结果可谓是一沓糊涂，哈哈哈哈哈，不过没关系，刚尝试已经很开心了

 

 可以细心看下三个图，因为是照着写的，所以输出比较多。为了区分，我特意将大模型回答的和查找到的做了换行区分，如上图。

做的很简陋。。。。。