🔑 利用google-generativeai，PyMuPDF和python-dotenv，安装构建AI驱动的PDF问答系统所需的依赖项。google-generativeai提供对Gemini Flash 1.5的访问，PyMuPDF（也称为Fitz）可以从PDF高效提取文本，python-dotenv有助于在notebook中安全地管理API密钥等环境变量。

📤 通过google.colab上传本地文件到Google Colab。执行后，会打开一个文件选择对话框，允许选择上传文件（例如PDF）。上传的文件存储在类似字典的对象（uploaded）中，其中键表示文件名，值包含文件的二进制数据。

📄 使用PyMuPDF（fitz）从Google Colab中的PDF文件中提取文本。函数extract_pdf_text(pdf_path)读取PDF，遍历其页面并检索文本内容。提取的文本存储在document_text中，并打印前1000个字符以预览内容。

🤖 配置并查询Gemini Flash 1.5，利用PDF文档进行AI驱动的文本生成。使用API密钥初始化genai库，并加载Gemini Flash 1.5模型（gemini-1.5-flash-001）。query_gemini_flash()函数接收问题和提取的PDF文本作为输入，形成结构化提示，并检索AI生成的响应。

In this tutorial, we demonstrate how to build an AI-powered PDF interaction system in Google Colab using Gemini Flash 1.5, PyMuPDF, and the Google Generative AI API. By leveraging these tools, we can seamlessly upload a PDF, extract its text, and interactively ask questions, receiving intelligent responses from Google’s latest Gemini Flash 1.5 model.

!pip install -q -U google-generativeai PyMuPDF python-dotenv

First we install the necessary dependencies for building an AI-powered PDF Q&A system in Google Colab. google-generativeai provides access to Gemini Flash 1.5, enabling natural language interactions, while PyMuPDF (also known as Fitz) allows efficient text extraction from PDFs. Also, python-dotenv helps manage environment variables, such as API keys, securely within the notebook.

from google.colab import filesuploaded = files.upload()

We upload files from your local device to Google Colab. When executed, it opens a file selection dialog, allowing you to choose a file (e.g., a PDF) to upload. The uploaded file is stored in a dictionary-like object (uploaded), where keys represent file names and values contain the file’s binary data. This step is essential for directly processing documents, datasets, or model weights in a Colab environment.

import fitzdef extract_pdf_text(pdf_path):    doc = fitz.open(pdf_path)    full_text = ""    for page in doc:        full_text += page.get_text()    return full_textpdf_file_path = '/content/Paper.pdf'document_text = extract_pdf_text(pdf_path=pdf_file_path)print("Document text extracted!")print(document_text[:1000]) 

We use PyMuPDF (fitz) to extract text from a PDF file in Google Colab. The function extract_pdf_text(pdf_path) reads the PDF, iterates through its pages, and retrieves the text content. The extracted text is then stored in document_text, with the first 1000 characters printed to preview the content. This step is crucial for enabling text-based analysis and AI-driven question answering from PDFs.

import osos.environ["GOOGLE_API_KEY"] = 'Use your own API key here'

We set the Google API key as an environment variable in Google Colab. The API key is required to authenticate requests to Google Generative AI, allowing access to Gemini Flash 1.5 for AI-powered text processing. Replacing ‘Use your own API key here’ with a valid key ensures that the model can generate responses securely within the notebook.

import google.generativeai as genaigenai.configure(api_key=os.environ["GOOGLE_API_KEY"])model_name = "models/gemini-1.5-flash-001"def query_gemini_flash(question, context):    model = genai.GenerativeModel(model_name=model_name)    prompt = f"""Context: {context[:20000]}Question: {question}Answer:"""    response = model.generate_content(prompt)    return response.textpdf_text = extract_pdf_text("/content/Paper.pdf")question = "Summarize the key findings of this document."answer = query_gemini_flash(question, pdf_text)print("Gemini Flash Answer:")print(answer)

Finally, we configure and query Gemini Flash 1.5 using a PDF document for AI-powered text generation. It initializes the genai library with the API key and loads the Gemini Flash 1.5 model (gemini-1.5-flash-001). The query_gemini_flash() function takes a question and extracted PDF text as input, formulates a structured prompt, and retrieves an AI-generated response. This setup enables automated document summarization and intelligent Q&A from PDFs.

In conclusion, following this tutorial, we have successfully built an interactive PDF-based interaction system in Google Colab using Gemini Flash 1.5, PyMuPDF, and the Google Generative AI API. This solution enables users to extract information from PDFs and interactively query them easily. The combination of Google’s cutting-edge AI models and Colab’s cloud-based environment provides a powerful and accessible way to process large documents without requiring heavy computational resources.

Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 80k+ ML SubReddit.

The post A Code Implementation to Build an AI-Powered PDF Interaction System in Google Colab Using Gemini Flash 1.5, PyMuPDF, and Google Generative AI API appeared first on MarkTechPost.