prompts.py

from llama_index.core import PromptTemplate


instruction_str = """\
    1. Convert the query to executable Python code using Pandas.
    2. The final line of code should be a Python expression that can be called with the `eval()` function.
    3. The code should represent a solution to the query.
    4. PRINT ONLY THE EXPRESSION.
    5. Do not quote the expression."""

new_prompt = PromptTemplate(
    """\
    You are working with a pandas dataframe in Python.
    The name of the dataframe is `df`.
    This is the result of `print(df.head())`:
    {df_str}

    Follow these instructions:
    {instruction_str}
    Query: {query_str}

    Expression: """
)

context = """Purpose: The primary role of this agent is to assist users by providing accurate 
            information about world population statistics and details about a country. """
coffee_context = """
    ## Coffee Shop Data Usage Guide
    - Data Range: Surrounding the National Gallery of Canada, within a 2 km radius.
    - Field Descriptions：
      - price_level: 
        - $: Budget-friendly options, typically low-cost.
        - $$: Mid-range pricing.
        - $1-10: Price range between $1 and $10, for more specific pricing.
        - $20-30: Price range between $20 and $30, for higher-end options.
        - [Variable range like $15-20]: Represents a price range with variable amounts, indicating a specific price range.
      - rating: Google rating 
      - distance_km: Distance from the gallery in kilometers.
    - Example Queries:：
      - 'List coffee shops with rating > 4 within 1km'
      - 'What is the average price level of shops within 0.5km?'
      - 'Find the closest 3 coffee shops'
"""