The model is able to execute multiple parallel function calls to gather information about the weather and population of New York and San Francisco. It first calls the get_current_weather function for both cities, retrieving the current weather conditions. It then calls the get_population function for each city, obtaining the population data.

With the gathered information, the model is able to provide a comparative analysis, stating that "New York is currently warmer than San Francisco and it has a significantly larger population." This demonstrates the model's capability to integrate data from different sources, perform comparative analysis, and generate a concise, informative response.

The parallel function calls allow the model to efficiently collect the necessary information to address the user's query, showcasing its ability to handle complex, multi-faceted requests involving various data sources.

Okay, so the next one is going to be building a SQL Assistant. This is basically an example of the LLM or agent interacting with a database. Again, a very simple example in the current form, but we're going to expand on this later in the video.

The idea would be the user asks natural language queries and then the LLM will convert those natural language queries into SQL queries that it can execute over a SQL database. We're going to be using SQLite for this.

Here is the sample database description. Here's the data that we want to put into the database. And the data has four different columns: employee number, name, their department, and their corresponding salaries.

Now we expect the model to get a natural language query and generate a SQL query. We have this function called execute_sql_query that is going to receive the SQL query from the LLM, execute it, return the results, and those results are going to be used by the LLM to generate the final response.

So, very similar to what we saw for that weather example, we pass this execute_sql_query function as a tool. Now, in this case, we have a system prompt which tells it to always check the database schema first. The database has an employees table with these columns.

Our query is "What are the average salaries by department?" and it's able to execute that query. It returns us the average salaries: for finance, it's 88,000; for HR, it's 65,000; for engineering, there are two entries, so it takes the average of those and returns us the results.

If you look at what exactly happened here, here is the intermediate SQL query that it generated based on the user input. Now, as we go along in this video, we're going to be building on these simple examples into more complex situations.

In this section, we explore how to leverage the capabilities of Gemini 2.5 Pro to build a powerful business intelligence system that combines unstructured data sources and parallel function calls.

The key components of this system include:

1. SQL Query Execution: The agent can execute SQL queries against a database to retrieve structured data, such as sales figures, product information, and employee details.

2. Schema Extraction: The agent can automatically extract the schema of the database, allowing it to understand the structure and relationships of the data.

3. Market Data Integration: The agent can access external market data sources, such as industry reports and competitor analysis, to gather relevant unstructured information.

4. Sales Trend Analysis: The agent can analyze sales data and identify trends, providing valuable insights to the business.

5. Parallel Function Calls: The agent can make multiple function calls in parallel, allowing it to efficiently gather and synthesize information from various sources.

By integrating these capabilities, the agent can provide a comprehensive business intelligence solution that answers complex queries from the user. For example, when the user asks for an analysis of the electronics product category, the agent can:

1. Query the database to retrieve sales data for the electronics category, comparing it to other product categories.
2. Fetch external market data to understand the industry outlook and competitive landscape.
3. Analyze sales trends and identify key insights.
4. Synthesize the information from the various sources and present a well-structured analysis with data-backed insights.

The step-by-step approach, as outlined in the system prompt, ensures that the agent follows a well-defined workflow and does not rely solely on its probabilistic decision-making. This helps to create a more reliable and trustworthy business intelligence system.

By leveraging the powerful capabilities of Gemini 2.5 Pro, businesses can build sophisticated decision-support tools that provide valuable insights and drive informed decision-making.

Gemini 2.5 Pro is indeed a powerful model that showcases impressive capabilities, particularly in its reasoning abilities and coding skills. The independent benchmarks confirm the model's superior performance, making it a strong contender for complex workflows and reasoning-based tasks.

The examples presented in the video demonstrate the model's versatility in handling various use cases, from simple function calling to building a comprehensive business intelligence dashboard. The model's ability to execute sequential and parallel function calls, as well as its text-to-SQL conversion capabilities, highlight its potential for practical applications.

The video emphasizes the importance of providing the model with clear, step-by-step instructions and system prompts to guide its behavior, rather than relying solely on its probabilistic decision-making. This approach is crucial, especially when dealing with complex enterprise data and workflows.

Overall, Gemini 2.5 Pro appears to be a highly capable model that can be leveraged for a wide range of applications, from coding to business intelligence. The video provides a valuable introduction to the model's features and showcases its potential for solving real-world problems.