The big pixtral is now available at Amazon Bedrock

$ aws bedrock-runtime invoke-model  
--model-id mistral.pixtral-large-2502-v1:0 
--body "{"prompt":"[INST] this is where you place your input text [/INST]", "max_tokens":200, "temperature":0.5, "top_p":0.9, "top_k":50}"  
--cli-binary-format raw-in-base64-out 
--region eu-north-1  
invoke-model-output.txt

prompt = """
You are a database designer. You have created an ER diagram. You need to generate PostgreSQL compatible SQL statements to create the tables"
"""

Sure! Below are the SQL statements to create the tables based on the provided 
ER diagram for a PostgreSQL database.

```sql
-- Create the Airport table
CREATE TABLE Airport (
    Airport_ID SERIAL PRIMARY KEY,
    AirportCity VARCHAR(255),
    AirportCountry VARCHAR(255)
);

-- Create the Calendar table
CREATE TABLE Calendar (
    Day_Date DATE PRIMARY KEY,
    Business_Day_YN BOOLEAN
);

-- Create the Flight_Details table
CREATE TABLE Flight_Details (
    Flight_ID SERIAL PRIMARY KEY,
    Source_Airport_ID INT REFERENCES Airport(Airport_ID),
    Destination_Airport_ID INT REFERENCES Airport(Airport_ID),
    Departure_Date_Time TIMESTAMP,
    Arrival_Date_Time TIMESTAMP,
    Airplane_Type VARCHAR(255)
);

-- Create the Travel_Class table
CREATE TABLE Travel_Class (
    Travel_Class_ID SERIAL PRIMARY KEY,
    Travel_Class_Name VARCHAR(255),
    Travel_Class_Capacity INT
);

-- Create the Seat_Details table
CREATE TABLE Seat_Details (
    Seat_ID SERIAL PRIMARY KEY,
    Travel_Class_ID INT REFERENCES Travel_Class(Travel_Class_ID),
    Flight_ID INT REFERENCES Flight_Details(Flight_ID)
);

-- Create the Flight_Cost table
CREATE TABLE Flight_Cost (
    Seat_ID INT REFERENCES Seat_Details(Seat_ID),
    Valid_From_Date DATE,
    Cost DECIMAL(10, 2),
    Valid_To_Date DATE,
    PRIMARY KEY (Seat_ID, Valid_From_Date)
);

-- Create the Flight_Service table
CREATE TABLE Flight_Service (
    Service_ID SERIAL PRIMARY KEY,
    Service_Name VARCHAR(255)
);

-- Create the Service_Offering table
CREATE TABLE Service_Offering (
    Travel_Class_ID INT REFERENCES Travel_Class(Travel_Class_ID),
    Service_ID INT REFERENCES Flight_Service(Service_ID),
    Offered_YN BOOLEAN,
    From_Date DATE,
    To_Date DATE,
    PRIMARY KEY (Travel_Class_ID, Service_ID)
);

-- Create the Passenger table
CREATE TABLE Passenger (
    Passenger_ID SERIAL PRIMARY KEY,
    P_FirstName VARCHAR(255),
    P_LastName VARCHAR(255),
    P_Email VARCHAR(255),
    P_PhoneNumber VARCHAR(255),
    P_Address VARCHAR(255),
    P_City VARCHAR(255),
    P_State VARCHAR(255),
    P_Zipcode VARCHAR(255),
    P_Country VARCHAR(255)
);

-- Create the Reservation table
CREATE TABLE Reservation (
    Reservation_ID SERIAL PRIMARY KEY,
    Passenger_ID INT REFERENCES Passenger(Passenger_ID),
    Seat_ID INT REFERENCES Seat_Details(Seat_ID),
    Date_Of_Reservation DATE
);

-- Create the Payment_Status table
CREATE TABLE Payment_Status (
    Payment_ID SERIAL PRIMARY KEY,
    Payment_Status_YN BOOLEAN,
    Payment_Due_Date DATE,
    Payment_Amount DECIMAL(10, 2),
    Reservation_ID INT REFERENCES Reservation(Reservation_ID)
);
```

These SQL statements will create the tables as per the ER diagram you provided. Make sure to run these statements in the order they are listed to ensure that foreign key constraints are satisfied.

prompt = """
Extract organization hierarchy from the given org structure. provide response in a structured json format with below:
- role
- name
- reporting_manager
"""

Here is the organization hierarchy extracted from the given org structure in a 
structured JSON format:

```json
[
    {
        "role": "President",
        "name": "John Smith",
        "reporting_manager": null
    },
    {
        "role": "VP Marketing",
        "name": "Susan Jones",
        "reporting_manager": "John Smith"
    },
    {
        "role": "VP Sales",
        "name": "Rachel Parker",
        "reporting_manager": "John Smith"
    },
    {
        "role": "VP Production",
        "name": "Tom Allen",
        "reporting_manager": "John Smith"
    },
    {
        "role": "Manager",
        "name": "Alice Johnson",
        "reporting_manager": "Susan Jones"
    },
    {
        "role": "Manager",
        "name": "Tim Moore",
        "reporting_manager": "Susan Jones"
    },
    {
        "role": "Manager",
        "name": "Michael Gross",
        "reporting_manager": "Rachel Parker"
    },
    {
        "role": "Manager",
        "name": "Kim Dole",
        "reporting_manager": "Rachel Parker"
    },
    {
        "role": "Manager",
        "name": "Kathy Roberts",
        "reporting_manager": "Tom Allen"
    },
    {
        "role": "Manager",
        "name": "Betsy Foster",
        "reporting_manager": "Tom Allen"
    }
]
```

prompt = """Examine this visualization:
1. First, describe what this visualization represents:
   - What information is being shown?
   - How is the data displayed?
   - What do the different components represent?
   - What does the size variation indicate?
2. Analyze specific patterns:
   - Which region shows the highest proportion of the first category?
   - Which region shows the highest proportion of the second category?
   - Where do you observe the largest total values?
   - Which areas show the most balanced distribution?
3. Compare regional trends:
   - How do the proportions differ between continents?
   - What patterns emerge between different hemispheres?
   - Are there clear differences between different economic zones?
4. Identify interesting outliers:
   - Which regions stand out from their neighbors?
   - Can you identify any unexpected patterns?
   - Where do you notice significant data variations?
5. Consider geographical and demographic factors:
   - How might local conditions influence these patterns?
   - What socioeconomic factors might explain the variations?
   - Can you identify any correlation between size and proportions?
6. Make comparisons between:
   - Different geographical zones
   - Various population densities
   - Different economic development levels"""

Description of the Visualization:
Information Shown: The visualization presents data on the population (in millions) and the proportion of vehicles (cars and motorcycles) across various regions worldwide.
Display Method: The data is displayed using pie charts overlaid on a world map. The size of each pie chart corresponds to the population of the region, with larger pies indicating larger populations.
Components:
Pie Charts: Each pie chart is divided into two segments representing the proportion of cars (light blue) and motorcycles (dark blue).
Size Variation: The size of the pie charts indicates the total population of the region, with larger pies representing higher populations.
Specific Patterns:
Highest Proportion of Cars: Regions like the United States and parts of Europe show a higher proportion of cars.
Highest Proportion of Motorcycles: Southeast Asian countries like Indonesia and Vietnam show a higher proportion of motorcycles.
Largest Total Values: Large pie charts are observed in highly populated regions like India, China, and parts of Southeast Asia.
Most Balanced Distribution: Some regions in Europe and South America show a more balanced distribution of cars and motorcycles.
Regional Trends:
Continental Differences:
North America: Predominantly cars.
Europe: A mix with a slight preference for cars.
Africa: Generally lower total values with varied proportions.
Asia: High population with a significant proportion of motorcycles in Southeast Asia.
Hemispheric Patterns: The Northern Hemisphere has more densely populated regions with varied vehicle proportions, while the Southern Hemisphere shows lower population densities and more balanced distributions.
Economic Zones: Developed regions tend to have higher car proportions, while developing regions, especially in Asia, show higher motorcycle usage.
Interesting Outliers:
Indonesia: Stands out with a very high proportion of motorcycles compared to neighboring regions.
United States: Shows a predominantly high proportion of cars, distinct from other regions.
Unexpected Patterns: Some smaller African countries show unexpectedly high proportions of motorcycles despite lower populations.
Geographical and Demographic Factors:
Local Conditions: Urbanization and infrastructure development influence vehicle preferences.
Socioeconomic Factors: Economic status and affordability play significant roles in vehicle choices.
Correlation: Larger populations in developing regions often correlate with higher motorcycle usage due to affordability and convenience.
Comparisons:
Geographical Zones: Urban areas in developed countries show higher car usage, while rural and developing regions favor motorcycles.
Population Densities: Highly populated areas in Asia show a preference for motorcycles due to traffic congestion and affordability.
Economic Development: Developed economies have higher car proportions, while developing economies rely more on motorcycles.