Gemini Flash is SURPRISINGLY Good for Agents and Function Calling

The Gemini models' recent update has enhanced performance, as evidenced by their high rankings on the chatbot Arena leaderboard. The update includes improved rate limits, user data tuning for Flash version, and enhanced Json mode and function calling. The tutorial focuses on building a customer support agent capable of sequential and parallel function calls, integrating real-time data via external APIs. The process involves setting up the Google generative AI python package, configuring API keys, and creating chat sessions with function execution enabled. The tutorial demonstrates Gemini's unique function call execution compared to other platforms and its ability to handle complex prompts and multiple functions, making it a cost-effective option for quality outputs and increased throughput.

• Gemini models have undergone an update affecting both Pro and Flash versions.
• The update includes improved rate limits and upcoming customization for The Flash version using personal datasets.
• Improvements to the Json mode and function calling are a significant part of the update.
• Performance enhancements are evidenced by the Gemini models' rankings on the chatbot Arena leaderboard.
• Gemini Pro and Advanced versions are ranked second, while Gemini Flash is ranked ninth.

"The Gemini models recently had an update to both Pro and the Flash version and with this update now you have access to improved rate limits and soon you will be able to fine-tune The Flash version on your own data set."

• This quote highlights the recent updates to the Gemini models, focusing on improved rate limits and upcoming features for personal data set customization.

"With the recently released ranking of say chatbot Arena leaderboard both the pro and advanced version of Gemini are sitting at number two while the smaller Gemini flash is at number nine just behind gp4 and CLA Opus which is pretty impressive."

• This quote provides information about the improved performance of the Gemini models as reflected in their ranking on a chatbot leaderboard.

• Gemini Flash is noted for its balance of quality, price, and throughput.
• Considered superior to Cloud Hau, Gemini Flash offers increased throughput at a better cost.
• Gemini Flash is positioned as a good quality model with increased tokens per second.
• It is a preferred option for those seeking a compromise between quality and price.

"I'm personally interested in Gemini flash because it is sitting at a sweet spot when it comes to quality of outputs, price, and throughput."

• The quote expresses a personal interest in Gemini Flash due to its optimal balance of output quality, cost, and processing speed.

"So here if you're interested in a good quality model at increased throughput or tokens per second, Gemini flash I think is a really good candidate for that."

• This quote suggests that Gemini Flash is a suitable model for those looking for high-quality outputs with faster processing capabilities.

• The main use cases for LMMs are Rag and agent/tool usage.
• A practical use case examined is a customer support agent capable of sequential and parallel function calls.
• Function calling is essential when real-time information is needed, which LMMs cannot provide from their training data.

"Now when it comes to LMMs, my main two use cases are Rag and agent or tool usage."

• This quote identifies the primary applications for language models being discussed, focusing on retrieval-augmented generation (Rag) and agent or tool usage.

"So I wanted to look at a practical use case of customer support agent which is going to be making both sequential as well as parallel function calls."

• The quote introduces the practical application of a customer support agent using language models to perform function calls.

• Function calling allows LMMs to interact with real-world data via external APIs.
• The LMM determines whether to use an external function and selects the appropriate one.
• The LMM provides inputs and expected outputs but does not execute functions; execution is done externally.
• The response from the function call is combined with the original query to generate the final LMM response.

"Let's say you want to have access to the real-time information, so for example stock prices or weather information, and the LMM is not able to provide you that information because it's not in their training data."

• This quote explains why function calling is necessary, providing the example of accessing real-time information that is not part of the LMM's training data.

"The LMM will look at all the available tools or functions, and it will determine in the first step whether it wants to use an external function or not."

• The quote describes the initial decision-making process of an LMM when faced with a user query, deciding whether to use an external function.

• The tutorial will guide through creating a customer support agent with sequential and parallel function call capabilities.
• The process starts with a simple setup and gradually adds complexity.
• Gemini's approach to function calling is distinct from other proprietary LMM frameworks.

"For this video tutorial, we're going to be building a customer support agent that is going to have the ability to do sequential as well as parallel function calls."

• This quote outlines the objective of the tutorial, which is to build a customer support agent that can perform both types of function calls.

• To interact with Gemini Flash, one must install the Google generative AI python package.
• Various packages required for the tutorial need to be imported.
• An API key from Google AI studio is necessary to use Gemini Flash.
• The API key must be set as a secret in a collaborative environment or an environment variable locally.

"We will first need to install the Google generative AI python package then we need to import different packages that we'll need throughout this tutorial."

• This quote details the initial steps in setting up the environment for the tutorial, including the installation of necessary packages.

"You can get your API key from Google AI studio so go to your account then go to API Keys click on create API key and copy that API key."

• The quote provides instructions on how to obtain an API key necessary for interacting with Gemini Flash.

(Note: The transcript ends abruptly, and therefore the notes end here as well. If there were more content in the transcript, the notes would continue to capture all key ideas and topics discussed.)

• The Gemini Flash agent can perform two operations: "get order status" and "initiate returns."
• The operations will be expanded in the future.
• "Get order status" requires an order number.
• A demo order dataset acts as a placeholder for real API calls to an external database.
• "Initiate return" takes an order number and a return reason, returning a string.
• Functions have docstrings to describe their purpose and usage.

"One of the most important aspects is this doc string which actually is going to tell Gemini Flash what a specific function does."

• Docstrings are essential for documenting the purpose and behavior of functions within the Gemini Flash agent.

• The client setup involves using the generative AI package and model.
• The model name and version must be specified, e.g., Gemini 1.5 Flash.
• Initially, only two tools are used: "get order status" and "initiate return."
• The list of available tools and their descriptions can be accessed via the tools to Proto.
• Function calling in Gemini requires starting a chat session with the chat start _. chat function.
• Automatic function execution can be enabled with a single line of code, which differs from other platforms like Mistal OpenAI or Cloud.

"Then you will need to provide a set of tools in the beginning we're going to only use two tools and those are the get order status and initiate return."

• At the start, only two tools are provided to the Gemini Flash model for performing operations.

• The chat object is created to interact with the Gemini Flash model.
• Users can send messages to the chat object to perform tasks like checking an order status.
• The response from the agent indicates the execution of the function call, such as confirming an order has shipped.
• To understand the process, one can review the chat history, which shows the internal communications between the model and the user.
• The model determines the need for a function call based on the user's query and the function's description.
• The Python interpreter executes the function as if the user did, and the result is passed to the model for the final response.

"So for example, this message is what is the status of order 1 2 3 4 5 and the response that we get is the order is shipped."

• An example of a function call where a user asks for the status of an order and the Gemini Flash agent provides the shipping status as a response.

• Gemini Flash internally manages function execution by expecting the user to execute the function.
• The user input is processed to determine which function to use and what input is required.
• The agent passes the order ID to the function and awaits a response.
• The internal process is visible in the chat history, which shows the conversation steps and the Python interpreter's role in executing the function.

"The model basically determines that I actually need to use a function for this and based on the description of the function plus the actual query it decides to use get order status function and the input is going to be order ID that is coming from the user input."

• The model decides to use the "get order status" function based on the user's query and the function's description, taking the order ID as input.

• AI model can initiate returns for defective orders.
• Sequential or parallel function calls enable complex customer service interactions.
• Chat history is used to maintain context in conversations.
• The AI model uses internal functions to generate appropriate responses.
• The AI can handle nested function calls for tasks like checking order status and initiating returns.

"I have initiated a return for that order."

• This quote indicates the AI model has executed a customer's request to initiate a return.

"We can again look at the internal working by looking at the chart history."

• This quote explains that the AI model uses chat history to understand the context of the conversation.

"The model determines that I need to use the initiate return function then again it will use the user role to actually make that function call."

• Here, the AI model is described as determining the necessary action (initiating a return) and then executing it based on the user's role.

• The AI model can make multiple function calls where the output of one affects another.
• The model checks order status before deciding to initiate a return.
• The AI's ability to handle complex prompts is demonstrated through its sequential actions.

"Can you check the status of order this if it's delivered please initiate return as it was at the wrong order right now it says the order has been delivered I have initiated a return because it was wrong order."

• This quote shows a customer asking the AI to check an order's status and initiate a return if necessary, which the AI confirms it has done.

"This order is currently being processed I can't initiate return until it's delivered."

• The AI model explains that it cannot initiate a return for an order that is still being processed.

• The AI model's limitations are highlighted when asked to cancel an order without an associated function.
• Extending the AI's capabilities requires adding new functions, such as order cancellation.
• After updating its functions, the AI can handle more complex tasks like canceling orders.

"The order is currently being processed I can't initiate a return until it's delivered I will try to cancel it for you."

• The AI communicates its current limitations and attempts to fulfill the customer's alternate request.

"I have canceled the order."

• This quote indicates that after updating its capabilities, the AI successfully cancels the order as requested by the customer.

• The AI follows a logical sequence of steps to handle customer requests.
• It assesses the status of an order before taking action.
• The AI uses user roles to execute function calls.
• The model's responses are accurate and reflect its ability to process multi-stage nested function calls.

"Based on the initial prompt it actually looks for the status of the order and it's able to figure out that we are still processing it so it doesn't have to initiate the return."

• The AI assesses the order's status to determine the appropriate action, in this case, not initiating a return.

"Then the next step it's going to cancel the order so for that it makes a call to or it picks the cancel order function then it will use the user rule to actually make a call to that function."

• The AI model decides to cancel the order and uses the appropriate function and user role to execute this action.

• The AI can manage complex customer service scenarios involving multiple orders and actions.
• It accurately reports the status of orders and executes customer requests.
• The model demonstrates flexibility and accuracy in responding to varied and complex prompts.

"For example here we are trying to ask it for status of one order then order need to cancel another order and initiate return for another order because that is defective."

• This quote illustrates a complex scenario where the AI is asked to handle multiple tasks involving different orders.

"Based on the model response it says the status of for this is shipped because that's the order that the status that we wanted and it's actually shipped then it's able to cancel the order for us and initiate return for another order."

• The AI model successfully reports the status of an order, cancels another, and initiates a return, showcasing its comprehensive response capabilities.

• Models can handle multiple function calls sequentially or in parallel.
• The number of functions increased to a total of 10 for the example.
• Functions include: order inquiries, address updates, shipment tracking, and applying discounts.
• It's recommended to keep function calls under 200 for their models.
• The demonstration shows executing parallel function calls manually.
• The model object created has a large list of tools but won't automatically call functions.
• The model suggests functions to use, but the user must initiate the calls.
• Function calls are more complex, requiring parallel execution for independent tasks.
• The example query involves checking order status and updating the shipping address simultaneously.
• The model's response is split into two parts: one for each function (order status and address update).
• The user extracts the functions and their inputs from the model's response.
• A dictionary is used to map function calls to their execution.
• The model is tested for accuracy and efficiency in distinguishing between functions.

"So I actually increased the number of different functions to a total of 10 open I think recommend to keep them under 200 their models but for this example we just created 10 different functions which looks at different aspects of customer agent so you have the first three orders functions similar to what we had then you can update address you can track shipments apply discounts and so on and so forth."

This quote explains the expansion of functions within the model to 10 and a recommendation to keep them under 200 for practicality. It also lists the types of functions added.

"Now for this example, I'm going to show you how to execute the function calls yourself, and it's going to be making parallel function calls."

This quote introduces the instructional part of the example, indicating that the user will learn to manually execute parallel function calls.

"You will see that I'm not passing on that automatic function calling flag so the model itself will not be able to make function calls anymore."

The quote clarifies that the model will not automatically call functions in this example, shifting the responsibility to the user.

"It will return a list of function that it thinks needs to be used, and we will have to make those function calls ourselves."

This quote indicates that the model will suggest which functions to use, but the user must manually initiate the function calls.

"The response candidate... has actually two parts now the first part is get order status and the second part is going to be using the update shipping address."

Here, the quote describes the two-part response from the model corresponding to the two functions that need to be called based on the user's query.

"We basically run through this Loop we get the actual functions then what are going to be the input to that function."

The quote explains the process of extracting the functions and their inputs from the model's response.

"So here's how the function call for the first one is going to look like if I had to do it if you provide the customer or the order ID all you get is the status."

This quote provides an example of how the first function call is executed to get the order status using the order ID.

"And in the second case, we're going to get the updated address."

This quote explains the execution of the second function call, which updates the shipping address.

"So basically we're doing this step the model picked the appropriate function we executed the function calls then we give the output of those function calls to the model again to generate a final response."

The quote summarizes the entire process from model function suggestion to execution and then providing the results back to the model for a final response.

"I hope uh you found this video useful thanks for watching and as always see you in the next one."

The quote concludes the example, expressing hope that the video was informative and useful for the viewers.