#3887 Google Gemini Python API Support for Passing a PDF with a Prompt

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#3887 Google Gemini Python API Support for Passing a PDF with a Prompt

#3887 Does the Google Gemini python API support passing a PDF with a prompt?

Yes, the Google Gemini Python API natively supports passing a PDF along with a prompt.

You do not need to use an external library (like PyPDF2) to extract text first. The model can "see" and analyze the PDF document directly, including charts, graphs, and formatting.

There are two ways to do this depending on the file size.

Method 1: The Files API (Recommended)

This is the standard approach. You upload the file to Google's servers first, which returns a file handle you can pass to the model. This is required for larger files and efficient if you plan to ask multiple questions about the same PDF.

Prerequisites: pip install -q -U google-generativeai

Code:

import google.generativeai as genai
import os

# 1. Setup
genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-1.5-flash")

# 2. Upload the PDF
# This uploads the file to Google's temporary storage (files persist for 48 hours)
pdf_file = genai.upload_file(path="my_document.pdf", display_name="Project Report")

# 3. Verify upload (optional but good practice)
# The file needs to be processed before the model can use it. 
# Usually instant for small files, but good to check state.
import time
while pdf_file.state.name == "PROCESSING":
    time.sleep(2)
    pdf_file = genai.get_file(pdf_file.name)

# 4. Prompt with the PDF
response = model.generate_content([
    "Summarize the key findings in this document.", 
    pdf_file
])

print(response.text)

Method 2: Inline Data (For small files)

If your PDF is small, you can pass the raw byte data directly in the API call without uploading it first. This is faster for "one-off" tasks on small documents.

Code:

import google.generativeai as genai
import pathlib

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-1.5-flash")

# Read file as bytes
pdf_path = pathlib.Path("my_document.pdf")
pdf_data = pdf_path.read_bytes()

# Pass bytes directly
response = model.generate_content([
    "What is the main conclusion of this paper?",
    {
        "mime_type": "application/pdf",
        "data": pdf_data
    }
])

print(response.text)

Important Limitations

  • Models: You must use Gemini 1.5 (Flash or Pro) or newer. Older models (Gemini 1.0) do not support PDF input.

  • Size Limit: 3,000 pages or 50MB per file.

  • Files API Storage: Files uploaded via upload_file are automatically deleted after 48 hours. They are not private to your Google Drive; they are stored in a temporary bucket accessible only via your API key.


Is Gemini's recent file search API preferred?

Not necessarily. It depends entirely on whether you are doing Deep Analysis (reading the whole document) or Information Retrieval (finding a specific fact in a large library).

Since you asked about passing "a PDF" (singular), the Standard Files API is likely the better choice for you.

Here is the breakdown of when to use which:

1. Standard Files API (Long Context)

Use this for: Single documents, deep reasoning, or visual analysis. This is the method described in the previous answer. It loads the entire PDF into the model's short-term memory (context window).

  • Why it's preferred for single files: The model "sees" every page, including charts, graphs, and layout. It can summarize the whole document or compare Section A to Section Z.

  • The "Multimodal" Advantage: Because it processes the raw PDF visually, it can understand a pie chart on page 5. The Search API (below) often only extracts text, losing that visual data.

  • Cost: You pay per token for the entire document every time you ask a question (unless you use Context Caching to keep it active).

2. File Search API (RAG)

Use this for: 100+ documents, "Needle in a Haystack" queries. This is the "new" API you are likely referring to. It is a managed RAG (Retrieval-Augmented Generation) system. It breaks your PDF into small text chunks, indexes them, and only retrieves the few paragraphs it thinks are relevant to your question.

  • Why it's preferred for large sets: If you have 500 manuals and want to know "how to reset the device," you don't want to pay to feed 500 manuals into the model. You use this API to find the one relevant page.

  • The Downside: It loses the "big picture." It can't effectively "summarize the whole document" because it only retrieves small chunks. It also struggles with images/charts inside PDFs since it relies heavily on text extraction.

Summary Recommendation

  • If you have one PDF (e.g., a 50-page report): Use the Standard Files API. The model is smart enough to hold the whole thing in memory (up to ~2 million tokens), giving you the highest quality analysis.

  • If you have a library of PDFs (e.g., 1,000 invoices): Use the File Search API. It is cheaper and faster for finding specific data points across many files.


What if I want to search for specific information within 30 documents, and summarize the results for each of the 30 documents?

This is a classic "coverage" problem.

If you use the File Search API (RAG), it will likely fail your requirement. RAG is designed to find the "top 10 most relevant chunks" from your entire dataset. It might find 5 chunks from Document A and 5 from Document B, completely ignoring the other 28 documents because they were slightly less relevant.

To ensure you get a result for each of the 30 documents, you should use the Files API with Gemini's Long Context window. You have two valid architectural approaches:

Approach 1: The "All-In-One" Context (Gemini's Superpower)

Since Gemini 1.5 Pro has a 2-million-token window, you can likely fit all 30 PDF documents into a single prompt context simultaneously (assuming they average <200 pages each).

  • How it works: You upload all 30 files and pass them all to the model in one request.

  • The Prompt: "Review these 30 documents. For each document, search for mentions of [Topic X] and provide a brief summary of those findings. Format the output as a list."

  • Pros: The model can "reason across files" (e.g., "Document A contradicts Document B"). It is also just one API call to manage.

  • Cons: Higher cost per call (you process all tokens at once).

Code Pattern:

import google.generativeai as genai
import time

# 1. Upload all files (or get handles if already uploaded)
pdf_files = []
for filename in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]: # Imagine 30 files here
    print(f"Uploading {filename}...")
    f = genai.upload_file(filename)
    pdf_files.append(f)

# Wait for processing
# (In production, use a loop to check f.state.name == "ACTIVE")
time.sleep(10) 

# 2. Single Prompt with ALL files
model = genai.GenerativeModel("gemini-1.5-pro")

response = model.generate_content([
    "Search these files for 'safety protocols'. For each file, output a summary of the safety protocols found. If none are found, state 'None'.",
    *pdf_files # Unpack the list of 30 file handles
])

print(response.text)

Approach 2: The Iterative Loop (The "Thrifty" Method)

If you want to save money or strictly isolate each document so the contents don't bleed into each other, you should loop through them in your Python code.

  • How it works: You upload File 1, ask the question, get the answer, and repeat for File 2.

  • Pros: Absolute certainty that Document A's summary only contains info from Document A. You don't pay to "re-read" other files.

  • Cons: Slower (30 sequential network calls) unless you use Python asyncio to run them in parallel.

Code Pattern:

results = {}

for filename in all_my_files:
    # Upload one
    pdf_file = genai.upload_file(filename)
    
    # Wait for processing...
    
    # Prompt one
    response = model.generate_content([
        "Find specific information regarding X and summarize it.", 
        pdf_file
    ])
    
    results[filename] = response.text
    
    # Optional: Delete file from server to keep it clean
    pdf_file.delete()

Summary: Which should you choose?

  • Choose Approach 1 (All-in-One) if you need to compare the documents (e.g., "How does the invoice in Doc 1 compare to the quote in Doc 30?") or if you want the simplest code.

  • Choose Approach 2 (Iterative) if your priority is precision per document (ensuring the model doesn't get confused between files) or if the total size of all 30 files exceeds 2 million tokens.

Google Gemini File API Tutorial This video is relevant because it demonstrates the foundational step of passing a PDF to Gemini for summarization, which is the core mechanic you will either loop or batch for your 30 documents.


From <https://gemini.google.com/app/>  Google Gemini 3.0 Pro (Thinking)

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