Code Execution with Google Gemini Flash

Google's Gemini: Code Execution Capabilities of Large Language Models

Large Language Models (LLMs), successors to Transformers, have revolutionized Natural Language Processing (NLP) and Natural Language Understanding (NLU). Initially replacing rule-based chatbots due to their superior text comprehension and natural conversation generation, LLMs now perform far more complex tasks. These include natural language to SQL query conversion, internet browsing for information retrieval, and, most recently, code execution. This article explores Gemini's newly released code execution feature.

Learning Objectives:

• Understand LLM code execution.
• Explore Gemini Flash 1.5.
• Obtain a Gemini API key.
• Examine LLMs' limitations in mathematical tasks.
• Utilize LLMs with code execution for precise answers.

Gemini: Google's Multimodal LLM

Gemini, Google's family of LLMs, rivals closed-source models like OpenAI's GPT-4 and Anthropic's Claude. A multimodal model, Gemini handles text, images, audio, and video. While GPT-4 possesses similar capabilities, Gemini's key differentiator is its ability to execute the code it generates. This code execution, enabled by Gemini's function calling capabilities, runs in a secure, sandboxed environment. Currently, only NumPy and SymPy libraries are supported; the generated code cannot download or install additional Python libraries.

Getting Started with Code Execution

To access Gemini's code execution capabilities, obtain a free API key via [link here]. Install the necessary library:

!pip install -q -U google-generativeai

The -U flag ensures you have the latest version, crucial for accessing the new code execution feature. Authenticate using your API key:

import google.generativeai as genai

GOOGLE_API_KEY = "YOUR API KEY"

genai.configure(api_key=GOOGLE_API_KEY)

Now, instantiate the Gemini model:

model = genai.GenerativeModel(model_name='gemini-1.5-flash')

response = model.generate_content("How are you?")

print(response.text)

LLMs often struggle with tasks requiring precise counting or mathematical operations. For example:

response = model.generate_content("Trim this word to first 5 letters, Mississippi")

print(response.text)

This demonstrates a common LLM limitation: lack of backward counting ability. Similarly, mathematical problems pose challenges:

response = model.generate_content("What is the sum of the first 100 Fibonacci numbers?")

print(response.text)

The model fails to calculate the sum, instead providing procedural steps. This highlights the inherent limitations of LLMs as text-generation models.

Gemini Code Execution in Action

Enabling code execution enhances Gemini's capabilities. Let's re-ask the previous questions:

model2 = genai.GenerativeModel(model_name='gemini-1.5-flash', tools='code_execution')

response = model2.generate_content("Trim this word to first 5 letters, Mississippi. Use code execution tool")

print(response.text)

Gemini now generates and executes Python code to correctly slice the string. For the Fibonacci sum:

response = model2.generate_content("What is the sum of the first 100 Fibonacci numbers?")

print(response.text)

Gemini generates a function, executes it, and provides the correct answer.

Conclusion

Gemini's code execution significantly expands LLM capabilities. It overcomes limitations in precise calculations and procedural tasks, enhancing its utility across various applications.

Key Takeaways:

• Gemini's multimodal nature allows processing of diverse data types.
• LLMs often struggle with mathematical precision due to their lack of computational ability.
• Code execution enables LLMs to run code in a sandboxed environment.
• Google's free API provides access to Gemini Flash's code execution.

Frequently Asked Questions:

• Q1. What is Gemini? A. Google's family of multimodal LLMs.
• Q2. Does Gemini execute code? A. Yes, via its recently released code execution feature.
• Q3. What libraries are supported? A. Currently, NumPy and SymPy.
• Q4. How does code execution improve Gemini? A. It enables accurate calculations and procedural task completion.
• Q5. How to enable code execution? A. Instantiate GenerativeModel with tools='code_execution'.

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