Introduction
In this tutorial, you'll learn how to work with the advanced capabilities of GPT-5.4 models, specifically focusing on coding assistance, tool integration, and handling long-context inputs. This tutorial will teach you how to leverage the 1M-token context window and tool search capabilities that make GPT-5.4 a powerful professional assistant.
Prerequisites
- Basic understanding of Python programming
- Access to OpenAI API (or local GPT-5.4 model)
- Python 3.8+ installed
- OpenAI Python library installed (
pip install openai) - Basic knowledge of API key management
Step-by-Step Instructions
1. Setting up Your Environment
First, we need to configure our Python environment to work with the OpenAI API. This setup will allow us to access GPT-5.4's advanced capabilities.
import openai
import os
# Set your API key
openai.api_key = os.getenv('OPENAI_API_KEY')
# Configure client for GPT-5.4
client = openai.OpenAI(
api_key=openai.api_key,
default_headers={
"OpenAI-Beta": "assistants=v2"
}
)2. Creating a Long-Context Assistant
GPT-5.4's 1M-token context window allows handling extensive documents. Here's how to create an assistant that can process large inputs:
def create_long_context_assistant(document):
"""Create an assistant that can handle large documents"""
# Create assistant with long context capabilities
assistant = client.beta.assistants.create(
name="Document Analyzer",
instructions=f"Analyze this document thoroughly. Context window: 1M tokens.\n\nDocument:\n{document}",
model="gpt-5.4",
tools=[{
"type": "code_interpreter"
}],
temperature=0.3
)
return assistant3. Processing Large Documents
Now let's implement a function to process documents larger than typical context windows:
def process_large_document(document_text):
"""Process a large document using GPT-5.4's long context capabilities"""
try:
# Split document into manageable chunks
chunks = split_document(document_text, max_tokens=800000)
responses = []
for i, chunk in enumerate(chunks):
response = client.chat.completions.create(
model="gpt-5.4",
messages=[
{
"role": "system",
"content": "You are a professional document analyzer with 1M token context. Analyze the following document section carefully."
},
{
"role": "user",
"content": f"Document section {i+1}: {chunk}"
}
],
max_tokens=1000,
temperature=0.1
)
responses.append(response.choices[0].message.content)
return '\n'.join(responses)
except Exception as e:
return f"Error processing document: {str(e)}"4. Implementing Tool Search Functionality
GPT-5.4's tool search capabilities allow it to discover and use external tools. Here's how to implement this:
def search_and_execute_tools(query):
"""Search for and execute relevant tools based on query"""
# First, let GPT-5.4 search for relevant tools
tool_search_prompt = f"""
Based on the following query, identify the most relevant tools or APIs:
Query: {query}
For each tool, provide:
1. Tool name
2. Purpose
3. How to use it
4. Example code
"""
search_response = client.chat.completions.create(
model="gpt-5.4",
messages=[
{
"role": "user",
"content": tool_search_prompt
}
],
max_tokens=1500,
temperature=0.7
)
return search_response.choices[0].message.content5. Creating a Coding Assistant
Leverage GPT-5.4's advanced coding capabilities with this implementation:
def generate_code_solution(problem_description, requirements):
"""Generate code solution using GPT-5.4's coding capabilities"""
prompt = f"""
Create a complete Python solution for the following problem:
Problem: {problem_description}
Requirements:
{requirements}
Your response should include:
1. Complete code implementation
2. Explanation of approach
3. Test cases
4. Performance considerations
Use GPT-5.4's advanced capabilities to optimize the solution.
"""
response = client.chat.completions.create(
model="gpt-5.4",
messages=[
{
"role": "system",
"content": "You are an expert Python developer with advanced GPT-5.4 capabilities. Create efficient, well-documented solutions."
},
{
"role": "user",
"content": prompt
}
],
max_tokens=2000,
temperature=0.3
)
return response.choices[0].message.content6. Testing Your Implementation
Let's create a test script to verify our implementation works correctly:
def test_gpt54_functionality():
"""Test all GPT-5.4 capabilities"""
# Test 1: Long context processing
long_document = """This is a sample long document that demonstrates the capabilities of GPT-5.4.
It contains extensive information that would normally exceed context window limits.
However, with GPT-5.4's 1M token context, we can process even very large inputs.
The model can handle complex reasoning tasks, document analysis, and code generation.
Key features include:
- 1M token context window
- Advanced coding capabilities
- Tool search and execution
- Professional work optimization
This demonstrates how GPT-5.4 can be used for complex professional tasks."""
print("Testing long context processing...")
result = process_large_document(long_document)
print(f"Document processed. Result length: {len(result)} characters")
# Test 2: Tool search
print("\nTesting tool search...")
tool_results = search_and_execute_tools("Create a web scraper for news articles")
print(f"Tool search completed. Results: {len(tool_results)} characters")
# Test 3: Code generation
print("\nTesting code generation...")
code_solution = generate_code_solution(
"Implement a binary search algorithm",
"Must be efficient and handle edge cases"
)
print(f"Code generated. Solution length: {len(code_solution)} characters")
return True7. Running the Complete Example
Finally, run your complete implementation:
if __name__ == "__main__":
try:
# Initialize your environment
print("Initializing GPT-5.4 assistant...")
# Run all tests
test_gpt54_functionality()
print("\nAll tests completed successfully!")
print("GPT-5.4 assistant is ready for professional work tasks.")
except Exception as e:
print(f"Error in main execution: {str(e)}")Summary
This tutorial demonstrated how to leverage GPT-5.4's advanced capabilities including long-context processing, tool search, and professional coding assistance. You learned to implement functions that utilize the 1M-token context window, search for relevant tools, and generate optimized code solutions. The key advantages of GPT-5.4 are its ability to handle extensive inputs, discover external tools, and provide professional-grade outputs for complex tasks.
These capabilities make GPT-5.4 particularly valuable for developers, researchers, and professionals who need to process large documents, integrate external tools, and generate high-quality code solutions efficiently.
