The recent update to the Gemini models, including both the Pro and Flash versions, has brought several improvements. One key enhancement is the improved rate limits, allowing users to make more requests within a given time frame. This update provides increased access and flexibility for users.

Additionally, the Gemini Flash version will soon offer the ability to fine-tune the model on your own data set. This feature enables users to customize the model's performance and tailor it to their specific needs, further enhancing the model's capabilities.

The update has also improved the JSON mode and function calling capabilities of the Gemini models. These improvements in the core functionality of the models are expected to result in better overall performance.

The Gemini models, including the Pro and Flash versions, have recently received an update. This update has brought several improvements, including enhanced rate limits and the ability to fine-tune the Flash version on your own data set. Additionally, the JSON mode and function calling capabilities have been improved.

The performance of the Gemini models has also been enhanced, as evidenced by their recent ranking in the ChatBot Arena leaderboard. Both the Pro and Advanced versions of Gemini are currently sitting at number two, while the smaller Gemini Flash is at number nine, just behind GPT-4 and CLA Opus. This is an impressive feat, showcasing the capabilities of the Gemini models.

The Gemini Flash, in particular, is of great interest as it sits in a sweet spot when it comes to the quality of outputs, price, and throughput. Compared to Cloud Hau, Gemini Flash offers increased throughput, and it is better than Hau and GPT-3.5 in terms of the compromise between quality and price.

For use cases involving LLMs, such as Retrieval Augmented Generation (RAG) and agent or tool usage, the Gemini models' function calling capabilities are particularly noteworthy. The tutorial will explore a practical use case of a customer support agent, demonstrating the model's ability to make both sequential and parallel function calls.

The ability to make function calls is a powerful feature of large language models (LLMs) like Gemini. It allows the model to access external data and functionality that may not be present in its training data, enabling it to provide more comprehensive and up-to-date responses to user queries.

Function calling works as follows:

1. The user provides a query to the LLM.
2. The LLM determines whether it needs to use an external function to respond to the query.
3. If a function is required, the LLM selects the appropriate function from the available tools.
4. The LLM provides the necessary inputs to the function and requests the user to execute it.
5. The user executes the function and returns the result to the LLM.
6. The LLM incorporates the function output into its final response to the user.

This process allows the LLM to leverage external data sources and capabilities, such as real-time stock prices, weather information, or customer support tools. By combining its own knowledge with the ability to make function calls, the LLM can provide more comprehensive and useful responses to a wide range of queries.

The Gemini models, in particular, have recently been updated to improve their function calling capabilities, including better rate limits and the ability to fine-tune the Flash version on custom data sets. This makes Gemini an attractive option for use cases that require access to external data or functionality, such as customer support agents or task-oriented chatbots.

To set up the customer support agent with Gemini Flash, we'll follow these steps:

1. Install the Google Generative AI Python Package: We'll start by installing the necessary package to interact with Gemini Flash.

2. Import Required Packages: We'll import the packages we'll need throughout the tutorial.

3. Set Up the API Key: We'll set up the API key to interact with Gemini Flash, either by setting it as a secret in Colab or as an environment variable if using a local setup.

4. Define the Available Functions: We'll define the functions the customer support agent can use, such as get_order_status and initiate_return.

5. Set Up the Gemini Flash Client: We'll set up the Gemini Flash client, specifying the model name and the list of available tools.

6. Start a Chat Session: We'll start a chat session with Gemini Flash, enabling automatic function calling to allow the model to execute the necessary functions.

7. Demonstrate Simple Function Calls: We'll demonstrate how to make simple function calls, such as checking the status of an order and initiating a return.

8. Explore the Chat History: We'll examine the chat history to understand the internal communication between the model and the user, and how the function calls are executed.

9. Implement Sequential Function Calls: We'll demonstrate how the agent can make sequential function calls, where the output of one function call is dependent on the previous one.

10. Implement Parallel Function Calls: We'll show an example of making parallel function calls, where the agent needs to execute multiple independent functions to generate the final response.

11. Expand the Available Functions: We'll increase the number of available functions to the agent, demonstrating its ability to handle a more complex set of operations.

12. Manually Execute Function Calls: We'll show an alternative approach where the agent provides the list of functions to be executed, and the user is responsible for making the actual function calls.

By following these steps, you'll have a solid understanding of how to set up a customer support agent using Gemini Flash, and how to leverage its capabilities for sequential and parallel function calls.

To execute sequential and parallel function calls with the Gemini models, we can follow these steps:

1. Install the required packages: Start by installing the Google Generative AI Python package.

2. Import the necessary packages: Import the required packages, such as the Generative AI package and any other utilities you might need.

3. Set up the API key: Obtain your API key from the Google AI Studio and set it up either as a secret in your Colab notebook or as an environment variable if you're using a local setup.

4. Define the available functions: Create a set of functions that the Gemini model can use to interact with external data sources or perform specific tasks. Make sure to provide detailed docstrings for each function to help the model understand their purpose.

5. Set up the Gemini client: Initialize the Generative AI client and specify the Gemini 1.5 Flash model as the model to use. Provide the list of available tools (functions) to the model.

6. Start a chat session: Begin a chat session with the Gemini model, enabling automatic function calling if desired.

7. Handle sequential function calls: When the user query requires sequential function calls, the model will determine the appropriate functions to use and provide the necessary inputs. You can then execute these functions and pass the results back to the model to generate the final response.

8. Handle parallel function calls: For queries that require parallel function calls, the model will provide a list of the necessary functions and their corresponding inputs. You can then execute these functions concurrently and pass the results back to the model to generate the final response.

9. Provide the function call results to the model: Whether executing sequential or parallel function calls, you need to pass the results of the function calls back to the Gemini model to generate the final response.

By following these steps, you can effectively leverage the Gemini models' capabilities to handle complex queries that require external data or functionality. The model's ability to determine the appropriate functions and manage the flow of information makes it a powerful tool for building conversational agents and other applications that require integration with external data sources.

To handle complex prompts that require multiple function calls, the Gemini model demonstrates impressive capabilities. It can execute sequential and parallel function calls, seamlessly integrating the results to generate accurate responses.

The key steps involved are:

1. Determine Function Calls: The model analyzes the user's prompt and identifies the necessary functions to execute, whether sequential or parallel.
2. Execute Functions: The model provides the required inputs to the identified functions, which are then executed by the user/interpreter.
3. Integrate Results: The model takes the results of the function calls and combines them to generate the final response.

This process allows the model to handle complex scenarios, such as checking the status of an order, initiating a return, and canceling an order, all within a single prompt. The model's ability to manage nested function calls and provide accurate responses is particularly noteworthy.

Furthermore, the model can handle an increasing number of functions, up to 10 in the example provided, without confusion or difficulty. This flexibility and scalability make the Gemini model a powerful tool for building sophisticated customer support agents and other applications that require real-time data integration and decision-making.

The example showcases the model's ability to execute both automatic and manual function calls, providing users with the flexibility to customize the integration based on their specific requirements. This level of control and transparency is a valuable feature, allowing developers to understand and fine-tune the model's behavior as needed.

Overall, the Gemini model's handling of complex prompts with multiple function calls demonstrates its advanced capabilities and suitability for building robust, intelligent applications that require seamless integration with external data sources and services.

The recent update to the Gemini models, including the Pro and Flash versions, has brought several improvements. The models now have access to better rate limits, and users will soon be able to fine-tune the Flash version on their own datasets. The JSON mode and function calling capabilities have also been enhanced, leading to improved performance.

The ranking of the Gemini models in the ChatbotArena leaderboard is impressive, with the Pro and Advanced versions sitting at number two, while the Gemini Flash is at number nine, just behind GPT-4 and CLA Opus. The Gemini Flash model is particularly interesting, as it offers a good balance between output quality, price, and throughput, making it a viable option for those seeking a high-quality model with increased throughput.

The tutorial focused on using the Gemini models for customer support agent applications, demonstrating the ability to perform sequential and parallel function calls. The step-by-step explanations and examples provided a comprehensive understanding of how the Gemini models handle function calling, which is different from other proprietary LLM frameworks.

Overall, the recent updates to the Gemini models have made them more capable and versatile, with the Gemini Flash model standing out as a compelling option for users seeking a balance between quality, price, and performance.