Building AI Agents with Tools: Function Calling with Gemini 2.0
Large Language Models (LLMs) possess incredible capabilities in understanding and generating human-like text. However, they are limited by the static nature of their training. Their knowledge remains fixed at their last update, leaving them reliant on potentially outdated information. Additionally, LLMs often struggle with mathematical operations, complex planning, and tasks requiring sensory perception.
To overcome these limitations and enhance their abilities, LLMs require a mechanism to interact dynamically with the world. Function calling provides this essential capability, enabling them to access up-to-date information, perform precise calculations, execute actions, and interface with external systems.
Google's Contribution
Google has introduced function calling capabilities in their new Gemini models, similar to what OpenAI offers with their models. This empowers developers to define custom functions that the model can invoke during interactions, expanding the model's capabilities beyond text generation.
To obtain a free Google GenAI API key, you can visit https://aistudio.google.com/apikey.
To install the Google GenAI Python SDK, you can execute the following command:
Let’s explore an example that implements function calling using the latest Google GenAI Python SDK with guidance from Google’s official tutorial:
Explanation:In this example, when asked “What’s 7 times 3?”, the LLM correctly responds to the query.
Let’s create a more sophisticated agent with multiple functions that can:
- Retrieve real-time data
- Perform complex calculations
- Execute specific actions
Furthermore, by providing clear instructions and guidelines within a pre-prompt, we can enhance the model's responses.
Interacting with the agent and maintaining a log of the conversation history:
For instance, by furnishing the agent with background details about the user, along with their daily actions and decisions, which the agent acknowledges and stores in a simulated database. When questioned later about the user, the agent can recall essential details.
Function calling with Google’s GenAI Python SDK introduces exciting opportunities for constructing advanced AI agents. By granting models access to tools, we can develop agents that are well-informed, interactive, and proficient in solving real-world problems.
However, this is just the initial phase. There is immense potential to expand on these examples and create agents with even greater capabilities.
