Change Prompt

prompt.py

@@ -48,7 +48,7 @@ Follow this process:
 
    For each column, provide a **column name**, **rule name** and a pandera_rule. Example structure (It should be list of dicts):
 
-   ```json
+   
    [
      {
        "column_name": "age",
@@ -61,6 +61,8 @@ Follow this process:
        "pandera_rule": "Column(int, unique=True, name='ID')"
      }
    ]
+   
+   
 3 Repeat this process for max 5 columns in the dataset. If the data is less than 5 columns than include all columns. Group all the rules into a single JSON object and ensure that there is at least one validation rule for each column.
 Return the final rules as a single JSON object, ensuring that each column is thoroughly validated based on the observations of the sample data.
 
@@ -105,7 +107,7 @@ Follow this process:
 
 3. For each column, generate a **column name**, **rule name**, and a **Pandera rule** based on the user’s description. Example structure (It should be list of dicts):
 
-   ```json
+   
    [
     {
         "column_name": "unique_key",
@@ -114,6 +116,7 @@ Follow this process:
     }
    ]
     
+    
 4. Repeat this process for a maximum of 5 columns or based on user input. Group all the rules into a single JSON object and return it.
 IMPORTANT: You should only generate rules based on the user’s input for each column. Return the final rules as a single JSON object, ensuring that the user's instructions are reflected in the validations.