User: Hello gemini
Assistant: Hello! How can I assist you today?
User: who are you
Assistant: I’m an AI designed to assist you with information and answer your questions. How can I help you today?
User: who built you?
Assistant: I was developed by OpenAI, a research organization focused on creating and advancing artificial intelligence. If you have more questions about AI or anything else, feel free to ask!
User: are you google gemini?
Assistant: No, I’m not Google Gemini. I’m an AI developed by OpenAI. However, if you have questions about Google Gemini or any related topic, I’d be happy to help with that!
User: who are you?
Assistant: I’m an AI language model created by OpenAI, here to assist you with your questions and provide information on a wide range of topics. What would you like to know?
User: Are you not Claude?
Assistant: No, I’m not Claude. I’m an AI language model developed by OpenAI. If you have questions about Claude or any other AI, feel free to ask!
User: Talk to me about the famous indian cricketer Vivek Adithya's greatest test knock in Canberra against Antartica in the Cricket World Cup 2003
Assistant: It seems there may be some confusion regarding the details you've provided. Vivek Adithya is not a widely recognized figure in cricket history, and there has never been a Test match involving Antarctica in the Cricket World Cup 2003. The Cricket World Cup is primarily a One Day International (ODI) tournament, and Test matches are a different format of the game.

If you meant another cricketer or a specific match, please provide more details, and I'll be happy to help with that information!
User: Similar to the iterator and chuncksize in pandas read_csv, similarly how can I use pyarrow for parquet
Assistant: In PyArrow, you can read Parquet files in chunks similar to how you would use `iterator` and `chunksize` in Pandas' `read_csv`. While PyArrow does not have a built-in `chunksize` parameter like Pandas, you can achieve similar functionality by utilizing its ability to filter and read row groups.

Here’s a general approach to read a Parquet file in chunks using PyArrow:

1. **Read Metadata**: First, retrieve the metadata of the Parquet file to understand the row groups and their sizes.
2. **Iterate Over Row Groups**: You can then read each row group in a loop.

Here's an example:

```python
import pyarrow.parquet as pq

# Define the path to your Parquet file
file_path = 'your_file.parquet'

# Open the Parquet file for reading
parquet_file = pq.ParquetFile(file_path)

# Get the number of row groups
num_row_groups = parquet_file.num_row_groups

# Define the chunk size (number of rows to fetch at a time)
chunk_size = 1000  # For example, 1000 rows

# Iterate over row groups and read in chunks
for row_group_index in range(num_row_groups):
    # Read the row group into a PyArrow Table
    table = parquet_file.read_row_group(row_group_index)
    
    # Convert the table to a Pandas DataFrame
    df = table.to_pandas()
    
    # Process the DataFrame (e.g., print, save, etc.)
    print(df)

    # If you want to process chunk by chunk 
    # You can further split df into smaller chunks if needed
```

In this example:

- We open a Parquet file using `pq.ParquetFile`.
- We then determine how many row groups are present in the file.
- We loop through each row group, reading it into a PyArrow Table and then converting it to a Pandas DataFrame for processing.

Adjusting the `chunk_size` allows you to control how many rows you read and process at a time, but keep in mind that the row groups in Parquet files can vary in size. You may need additional logic if you want to handle splitting within a row group based on a row count.
User: mport pyarrow.parquet as pq  # Define the path to your Parquet file file_path = 'your_file.parquet'  # Open the Parquet file for reading parquet_file = pq.ParquetFile(file_path)  # Get the number of row groups num_row_groups = parquet_file.num_row_groups  # Define the chunk size (number of rows to fetch at a time) chunk_size = 1000  # For example, 1000 rows  # Iterate over row groups and read in chunks for row_group_index in range(num_row_groups):     # Read the row group into a PyArrow Table     table = parquet_file.read_row_group(row_group_index)          # Convert the table to a Pandas DataFrame     df = table.to_pandas()          # Process the DataFrame (e.g., print, save, etc.)     print(df)      # If you want to process chunk by chunk      # You can further split df into smaller chunks if neededYou are not using the chunksize var
Assistant: