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
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.
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.
uploaded = 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.
def extract_pdf_text(pdf_path):
doc = fitz.open(pdf_path)
full_text = “”
for page in doc:
full_text += page.get_text()
return full_text
pdf_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.
os.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.
genai.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.text
pdf_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.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
