Code
file_input_for_tool.py
"""
Example showing how tools can access media (images, videos, audio, files) passed to the agent.
This demonstrates:
1. Uploading a PDF file to an agent
2. A tool that can access and process the uploaded file (OCR simulation)
3. The LLM responding based on the tool's processing result
"""
from typing import Optional, Sequence
from agno.agent import Agent
from agno.media import File
from agno.models.google import Gemini
from agno.tools import Toolkit
class DocumentProcessingTools(Toolkit):
def __init__(self):
tools = [
self.extract_text_from_pdf,
]
super().__init__(name="document_processing_tools", tools=tools)
def extract_text_from_pdf(self, files: Optional[Sequence[File]] = None) -> str:
"""
Extract text from uploaded PDF files using OCR.
This tool can access any files that were passed to the agent.
In a real implementation, you would use a proper OCR service.
Args:
files: Files passed to the agent (automatically injected)
Returns:
Extracted text from the PDF files
"""
if not files:
return "No files were uploaded to process."
print(f"--> Files: {files}")
extracted_texts = []
for i, file in enumerate(files):
if file.content:
# Simulate OCR processing
# In reality, you'd use a service like Tesseract, AWS Textract, etc.
file_size = len(file.content)
extracted_text = f"""
[SIMULATED OCR RESULT FOR FILE {i + 1}]
Document processed successfully!
File size: {file_size} bytes
Sample extracted content:
"This is a sample document with important information about quarterly sales figures.
Q1 Revenue: $125,000
Q2 Revenue: $150,000
Q3 Revenue: $175,000
The growth trend shows a 20% increase quarter over quarter."
"""
extracted_texts.append(extracted_text)
else:
extracted_texts.append(
f"File {i + 1}: Content is empty or inaccessible."
)
return "\n\n".join(extracted_texts)
def create_sample_pdf_content() -> bytes:
"""Create a sample PDF-like content for demonstration."""
# This is just sample binary content - in reality you'd have actual PDF bytes
sample_content = """
%PDF-1.4
Sample PDF content for demonstration
This would be actual PDF binary data in a real scenario
""".encode("utf-8")
return sample_content
def main():
# Create an agent with document processing tools
agent = Agent(
model=Gemini(id="gemini-2.5-pro"),
tools=[DocumentProcessingTools()],
name="Document Processing Agent",
description="An agent that can process uploaded documents. Use the tool to extract text from the PDF.",
debug_mode=True,
send_media_to_model=False,
store_media=True,
)
print("=== Tool Media Access Example ===\n")
# Example 1: PDF Processing
print("1. Testing PDF processing...")
# Create sample file content
pdf_content = create_sample_pdf_content()
sample_file = File(content=pdf_content)
response = agent.run(
input="I've uploaded a PDF document. Please extract the text from it and summarize the key financial information.",
files=[sample_file],
session_id="test_files",
)
print(f"Agent Response: {response.content}")
print("\n" + "=" * 50 + "\n")
if __name__ == "__main__":
main()
Usage
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Set your API key
export GOOGLE_API_KEY=xxx
# or for OpenAI
# export OPENAI_API_KEY=xxx
3
Install dependencies
uv pip install -U agno google-generativeai
# or for OpenAI
# uv pip install -U agno openai
4
Run Agent
python file_input_for_tool.py