如何在windows电脑上加载pdf文档到本地大模型上从无到有实现_ollama pdf

1.环境配置

1.1 ollama配置

        在ollama官网下载ollama软件并安装，安装时需将安装路径添加到环境变量path里面。

1.2 ollama加载模型

        ollama官网上有对应大模型，通过ollama run 模型名称，即可下载。例如 ollama run qwen:7b；下载完成后即可进行聊天对话功能。模型大小根据自己的电脑性能进行选择，当前事例16G内存可运行。

1.3 python环境搭建，langchain 安装，vscode软件下载

2.pdf文档处理

2.1扫描版pdf文字识别并保存到world

当前大模型在处理扫描版pdf时，对扫描版的处理会报错，需先将其转换成可进行识别的pdf文件

代码如下，运行前需先进行相应包的安装，通过pip install 对应安装包即可：

 
 
from docx import Document
 
from paddleocr import PaddleOCR  
 
import docx
 
import os
 
from docx import Document
 
from docx.enum.text import WD_PARAGRAPH_ALIGNMENT
 
from docx.shared import  Cm
 
import fitz
 
from PIL import Image, ImageEnhance, ImageFilter
 
# 初始化 OCR 模型  
 
ocr = PaddleOCR(use_gpu=True, lang='ch' or 'en')
 
def enhance_and_process_image(image_path, lang="chi_sim+eng"):
 
    """图像增强后执行OCR处理，返回纯文本内容，忽略布局信息"""
 
    try:
 
        with Image.open(image_path) as im:
 
     
 
            # 图像增强（根据需要调整增强参数）
 
            enhancer = ImageEnhance.Contrast(im)
 
            im = enhancer.enhance(1.5)  # 增强对比度
 
            #enhancer = ImageEnhance.Brightness(im)
 
            #im = enhancer.enhance(1.2)  # 增亮
 
            im = im.filter(ImageFilter.MedianFilter(size=3))  # 减少噪声
 
            im.save(image_path)
 
    except FileNotFoundError:
 
        print(f"文件未找到: {image_path}")
 
    except Exception as e:
 
        print(f"处理{image_path}时发生未知错误：{e}")
 
     
 
def img_deal(image_path):
 
    try:
 
        result = ocr.ocr(image_path)
 
        if result:  # Check if result is not None or empty before iterating
 
            all_data = []
 
            for page_items in result:
 
                for region in page_items:
 
                    raw_bbox, (text, _) = region
 
                    all_data.append((raw_bbox, text))
 
            return all_data
 
        else:
 
            print(f"No text detected in image: {image_path}")
 
            return []  # Return an empty list to avoid iteration over None
 
    except Exception as e:
 
        print(f"An error occurred processing image {image_path}: {e}")
 
        return []  # In case of an exception, also return an empty list
 
   
 
def coord_to_indent(coord, scale_factor=1000):
 
    """将坐标转换为Word的缩进量，这里假设1单位坐标等于scale_factor的厘米缩进"""
 
    x, _ = coord
 
    return Cm(x / scale_factor)  # 仅使用x坐标调整左缩进，您可以根据需要加入y坐标处理上下边距
 
 
 
 
def process_and_save_images_to_word(image_paths, output_word_path):
 
    doc = Document()
 
    all_recognized_data = []
 
    for image_path in image_paths:
 
        print(image_path)
 
        #enhance_and_process_image(image_path)  # 先增强并保存图片
 
        recognized_data = img_deal(image_path)
 
        if recognized_data:  # 确保有数据才保存
 
            all_recognized_data.append(recognized_data)
 
            # 实时保存当前图片的识别结果到Word
 
            for bbox, text in recognized_data:
 
                left_indent = coord_to_indent(bbox[0])
 
                paragraph = doc.add_paragraph(style='Normal')
 
                paragraph.paragraph_format.left_indent = left_indent
 
                paragraph.alignment = WD_PARAGRAPH_ALIGNMENT.LEFT
 
                run = paragraph.add_run(text)
 
                font = run.font
 
                font.size = docx.shared.Pt(12)
 
            doc.add_page_break()  # 每处理完一个图片后添加分页符
 
        # 实时保存文档，以确保每处理完一张图片就更新一次
 
        temp_output_path = f"{output_word_path}_temp.docx"
 
        doc.save(temp_output_path)
 
       
 
    # 最终保存文档并删除临时文件（如果需要的话）
 
    os.rename(temp_output_path, output_word_path)
 
    print(f"Text with position saved to Word document at: {output_word_path}")
 
if __name__ == "__main__":
 
    pdf_file_path = 'F:/ai/output'  
 
    image_folder_path = "F:/ai/output/images/"
 
    image_paths =sorted([os.path.join(image_folder_path, img) for img in os.listdir(image_folder_path) if img.endswith(('.png', '.jpg', '.jpeg'))])
 
   
 
    output_word_path = os.path.splitext(pdf_file_path)[0] + '.docx'
 
    process_and_save_images_to_word(image_paths, output_word_path)
 
​

2.2  world文档转pdf

可将doc文件打开另存为pdf，亦可通过代码直接转换

2.3 pdf分割，实现多线程共同训练加载

通过将大文件分解成小文件，再通过多线程的方式进而提升训练速度，代码如下：

import os
import fitz  # PyMuPDF
 
def split_pdf(input_path, output_dir, max_pages=20):
    """
    将一个PDF文件分割成多个不超过max_pages的PDF文件，
    且在分割后的文件名中保留原文档的名称。
    
    :param input_path: 输入PDF文件的路径。
    :param output_dir: 输出PDF文件的目录。
    :param max_pages: 单个输出PDF文件的最大页数，默认为20。
    """
    for input_path_file in os.listdir(input_path):
        file = os.path.join(input_path, input_path_file)
        base_name = os.path.splitext(input_path_file)[0]  # 提取不含扩展名的文件名
        # 打开PDF文件
        doc = fitz.open(file)
        total_pages = doc.page_count
        
        # 分割PDF
        for i in range(0, total_pages, max_pages):
            # 计算当前批次结束的页码，确保不会超过总页数
            end_page = min(i + max_pages, total_pages)
            
            # 创建一个新的PDF文档用于存储当前批次的页面
            new_doc = fitz.Document()
            
            # 将页面复制到新文档
            for page_num in range(i, end_page):
                page = doc.load_page(page_num)
                new_doc.insert_pdf(doc, from_page=page_num, to_page=page_num)
            
            # 构造包含原文档名称的输出文件名
            output_filename = f"{base_name}_split_{i+1}-{end_page}.pdf"
            output_path = os.path.join(output_dir, output_filename)
            new_doc.save(output_path)
            new_doc.close()
        
        doc.close()
 
# 示例使用
input_pdf_path = r'F:\ai\DB\文件\原文件2'
output_directory =r'F:\ai\DB\文件\文件2'
 
# 确保输出目录存在
os.makedirs(output_directory, exist_ok=True)
 
split_pdf(input_pdf_path, output_directory)

3.开始pdf文件训练，并将其保存，代码如下，该代码的优势是不仅可以对新文档进行训练保存，亦可在原有已训练模型的基础上进行加载，进而将两个模型保存：

import os
from langchain_community.document_loaders import PDFPlumberLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import FAISS
from concurrent.futures import ProcessPoolExecutor
import time
import threading
import concurrent.futures
 
import pickle
from typing import List, Tuple
 
 
def process_pdf(file_path):
    """处理单个PDF文件并返回切分后的文档"""
    loader = PDFPlumberLoader(file_path)
    data = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=256, chunk_overlap=128)
    return text_splitter.split_documents(data)
 
 
def batch_process_pdfs(paths,embeddings):
    """批量处理PDF文件并构建FAISS数据库，每次最多并行处理2个文件"""
    all_docs = []
    failed_files = []  # 用于记录处理失败的文件路径
 
    # 定义每次处理的文件数
    batch_size = 2
    print(len(paths))
    # 分批次处理文件
    for i in range(0, len(paths), batch_size):
        # 获取当前批次的文件路径
        current_batch_paths = paths[i:i+batch_size]
        
        try:
            with ProcessPoolExecutor(max_workers=batch_size) as executor:
                # 并行处理每个PDF文件并捕获异常
                futures = {executor.submit(process_pdf, path): path for path in current_batch_paths}
                
                for future in futures:
                    path = futures[future]
                    try:
                        docs = future.result()
                        all_docs.extend(docs)
                    except Exception as e:
                        print(f"处理文件 {path} 时发生错误: {e}")
                        failed_files.append(path)
                        continue
            
            # 确保当前批次所有任务完成后再继续
            concurrent.futures.wait(futures)
 
        except Exception as e:
            print(f"处理过程中发生未知错误: {e}")
            return None
 
    # 确保所有文件处理完毕后才进行后续操作
    if all_docs:
        print(f"处理完成，共处理 {len(all_docs)} 个文档。")
        db = FAISS.from_documents(all_docs, embeddings)
        print(f"成功构建数据库")
        db.save_local("faiss_index.pkl")
        
 
    else:
        print("没有成功处理的文档，无法继续构建数据库。")
        return None
    print(f"处理失败的文件有: {failed_files}")
    return db 
 
def save_db_async(db, path):
    
    try:
        if db is None:
            print("db is not initialized properly.")
        else:
            db.save_local(path)
        
    except Exception as e:
        print(f"异步保存数据库时发生错误: {e}")
 
def load_existing_db(file_path,embeddings) :
    
    try:
        
        db = FAISS.load_local(file_path, embeddings=embeddings, allow_dangerous_deserialization=True)
        return db
    
    except FileNotFoundError:
        print("未找到现有数据库，将创建新的数据库。")
        return None
    except Exception as e:
        print(f"加载现有数据库时发生其他错误: {e}")
        return None
 
 
def merge_dbs(old_db_data, new_db):
    """合并旧数据库和新文档"""
    if old_db_data:
        # 合并
        old_db_data.merge_from(new_db)
        db=old_db_data
        print(db.docstore._dict)
    else:
        db=new_db
    return db
        
 
def main():
    start_time = time.time()
    path = r"F:\ai\DB\文件\文件2"
    pdf_files = [os.path.join(path, filename) for filename in os.listdir(path) if filename.endswith(".pdf")]
    
    embeddings =  OllamaEmbeddings(model="shaw/dmeta-embedding-zh:latest")
    
    # 加载可能存在的旧数据库
    path1=r"F:\ai\DB\IMAGE"
    existing_db = load_existing_db(path1,embeddings)
    
 
    # 批量处理所有PDF文件
    vector_db = batch_process_pdfs(pdf_files,embeddings)
    if vector_db is None:
        print("警告：处理文档时出现问题，无法继续。")
        return
 
    # 合并新旧数据
    final_db = merge_dbs(existing_db, vector_db) 
    save_db_async(final_db, r"F:\ai\DB\IMAGE")
 
    end_time = time.time()
    elapsed_time = end_time - start_time
    print(f"处理完成，总耗时: {elapsed_time:.2f} 秒")
    os._exit(0)
 
if __name__ == '__main__':
    main()

4.加载训练模型并对话

4.1 单次多伦对话，并添加有记忆功能，代码如下：
 

# -*- coding: utf-8 -*-
import logging
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms import Ollama
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.callbacks.base import BaseCallbackManager
from typing import List
 
 
# 配置日志
logging.basicConfig(level=logging.INFO)
 
 
 
def load_file(path):
    embeddings = OllamaEmbeddings(model="shaw/dmeta-embedding-zh:latest")
    db = FAISS.load_local(path, embeddings=embeddings, 
                          allow_dangerous_deserialization=True)
    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 24})
    
    callback_manager = BaseCallbackManager([StreamingStdOutCallbackHandler()])
    ollama_llm = Ollama(model="qwen:7b", temperature=0.2, top_p=0.9, 
                         callback_manager=callback_manager)
    
    memory = ConversationBufferWindowMemory(memory_key="history", k=5, 
                                            return_messages=True)
    
    
    # 直接将memory作为参数传递
    qa_chain = RetrievalQA.from_chain_type(
        llm=ollama_llm,
        chain_type="stuff",
        retriever=retriever,
        memory=memory,
        verbose=True,
    )
    
    return qa_chain, retriever
 
 
def handle_user_query(qa, retriever, user_query: str):
    try:
        docs = retriever.invoke(user_query)
        answer = qa.invoke(user_query, documents=docs)
            
                # 检查answer类型并打印
        if isinstance(answer, dict) and 'result' in answer:
            #print(f"\n问题: {user_query}\n回答: {answer['result']}")
            
            # 判断回答是否明显引用了文档
            if answer['result'] not in docs:
                # 检查回答内容是否提及了检索到的文档内容
                referenced = any(doc.page_content in answer['result'] for doc in docs)
                if not referenced:
                    print("回答未直接基于现有文件。")
 
                else:
                    print("没有找到直接的参考文献")
                    return
            else:
                print("\n参考文献或相关文档:")
        
            # 显示检索到的文档信息
            unique_sources = set()
            for doc in docs:
                source = doc.metadata.get('source', '未知')
                unique_sources.add(source)
            i=0
            for source in unique_sources:
                if i==4:
                    break
                i+=1
                print(f"来源: {source}")
        else:
            print("未预期的响应类型，请检查qa.invoke的返回值。")
        return answer
        
    except Exception as e:
        logging.error(f"Error handling user query: {e}")
 
if __name__ == "__main__":
    path = r"F:\ai\DB\IMAGE"
    qa_chain, retriever = load_file(path)
    if qa_chain and retriever:
        while True:
            user_query = input("请输入您的问题（输入'退出'以结束）: ")
            if user_query.lower() == '退出':
                break
            handle_user_query(qa_chain, retriever, user_query)
    else:
        logging.warning("Initialization failed. QA Chain or Retriever not properly loaded.")

4.2 通过读取word文档，添加辅助内容，进行多轮问答，并将问和答写入在word文档中，代码如下：

# -*- coding: utf-8 -*-
import logging
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import RetrievalQA
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.llms import Ollama
from langchain_community.vectorstores import FAISS
from typing import Dict, List
 
from docx import Document
 
# 配置日志
logging.basicConfig(level=logging.INFO)
 
def read_docx_lines(file_path):
    """从.docx文件中读取每一行文本"""
    doc = Document(file_path)
    lines = []
    for para in doc.paragraphs:
        lines.extend(para.text.split('\n'))
    return [line.strip() for line in lines if line.strip()]
 
def save_answers_to_word(answers, output_path):
    """将问题与回答保存至新的Word文档"""
    
    doc = Document()
    for question, answer in answers.items():
        print(answer)
        doc.add_paragraph(question)
        doc.add_paragraph(answer)
 
    doc.save(output_path)
    print(f"问答已保存至: {output_path}")
 
def process_doc_and_query_ai(file_path, qa_chain, retriever):
    """从.docx文件读取内容，逐行查询AI并附加附加信息进行回答"""
    lines = read_docx_lines(file_path)
    answers = {}
    # 将附加信息定义为一个常量或直接在询问时添加，而不是在循环内修改列表项
    additional_info = (
        "请结合以下信息进行回答：结合医疗器械可用性工程注册审查指导原则;”
    )
    
    for line in lines:
        if line:  # 确保行不为空
            # 正确的方式是构造一个包含原始问题和附加信息的新问题字符串
            full_query = f"{line} {additional_info}"
            answer = handle_user_query(qa_chain, retriever, full_query)
            answers[line] = answer['result']
            #print(f"问题: {line}\n回答: {answer['result']}\n") 
    return answers
def load_file(path):
    embeddings = OllamaEmbeddings(model="shaw/dmeta-embedding-zh:latest")
    db = FAISS.load_local(path, embeddings=embeddings, 
                          allow_dangerous_deserialization=True)
    retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 24})
    
    callback_manager = BaseCallbackManager([StreamingStdOutCallbackHandler()])
    ollama_llm = Ollama(model="qwen:7b", temperature=0.2, top_p=0.9, 
                         callback_manager=callback_manager)
    
    memory = ConversationBufferWindowMemory(memory_key="history", k=5, 
                                            return_messages=True)
   
    
    # 直接将memory作为参数传递
    qa_chain = RetrievalQA.from_chain_type(
        llm=ollama_llm,
        chain_type="stuff",
        retriever=retriever,
        memory=memory,
        verbose=True,
    )
    
    return qa_chain, retriever
def handle_user_query(qa, retriever, user_query):
    """处理用户查询并返回答案及出处"""
    # 使用retriever检索最相关的文档
    docs = retriever.invoke(user_query)
    
    # 构建答案
    answer = qa.invoke(user_query, documents=docs)
    
    
    # 检查answer类型并打印
    if isinstance(answer, dict) and 'result' in answer:
        #print(f"\n问题: {user_query}\n回答: {answer['result']}")
        
        # 判断回答是否明显引用了文档
        if answer['result'] not in docs:
            # 检查回答内容是否提及了检索到的文档内容
            referenced = any(doc.page_content in answer['result'] for doc in docs)
            if not referenced:
                print("回答未直接基于现有文件。")
            else:
                print("没有找到直接的参考文献")
                return
        else:
            print("\n参考文献或相关文档:")
    
        # 显示检索到的文档信息
        unique_sources = set()
        for doc in docs:
            source = doc.metadata.get('source', '未知')
            unique_sources.add(source)
        i=0
        for source in unique_sources:
            if i==4:
                break
            i+=1
            print(f"来源: {source}")
    else:
        print("未预期的响应类型，请检查qa.invoke的返回值。")
    return answer
if __name__ == "__main__":
    path=r"F:\ai\DB\inventor\db_faiss"
    # 初始化资源
    qa_chain ,retriever= load_file(path)
    # .docx文件路径
    docx_file_path = r"F:\ai\path\可用性模板文件.docx"
    processed_answers = process_doc_and_query_ai(docx_file_path, qa_chain,retriever)
    
    # 保存结果到新的Word文档
    output_docx_path = r"F:\ai\path\output.docx"
    save_answers_to_word(processed_answers, output_docx_path)