Added default env path as mentioned in the docs

	modified:   .gitignore
Added file to extract on column at a time
	new file:   evadb/functions/extract_column.py
Removed the previous implementation
	deleted:    evadb/functions/extract_columns.py
Updated the notebook
	modified:   tutorials/20-structured-data.ipynb

.gitignore

@@ -101,6 +101,7 @@ env.bak/
 venv.bak/
 env38/
 env_eva/
+evadb-venv/
 
 # Spyder project settings
 .spyderproject

evadb/functions/extract_column.py

@@ -0,0 +1,116 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from io import BytesIO
+
+import numpy as np
+import pandas as pd
+import json
+from retry import retry
+
+from evadb.catalog.catalog_type import NdArrayType
+from evadb.functions.abstract.abstract_function import AbstractFunction
+from evadb.functions.decorators.decorators import forward
+from evadb.functions.decorators.io_descriptors.data_types import PandasDataframe
+from evadb.functions.chatgpt import ChatGPT
+from evadb.utils.generic_utils import try_to_import_openai
+from evadb.utils.logging_manager import logger
+
+
+class ExtractColumnFunction(ChatGPT):
+    @property
+    def name(self) -> str:
+        return "EXTRACT_COLUMN"
+
+    def setup(
+        self, 
+        model="gpt-3.5-turbo",
+        temperature: float = 0,
+        openai_api_key=""
+    ) -> None:
+        super(ExtractColumnFunction, self).setup(model, temperature, openai_api_key)
+
+    @forward(
+        input_signatures=[
+            PandasDataframe(
+                columns=[
+                    "field_name"
+                    "description",
+                    "data_type",
+                    "input_rows"
+                ],
+                column_types=[
+                    NdArrayType.STR,
+                    NdArrayType.STR,
+                    NdArrayType.STR,
+                    NdArrayType.STR,
+                ],
+                column_shapes=[
+                    (1,),
+                    (1,),
+                    (1,),
+                    (1,),
+                ],
+            )
+        ],
+        output_signatures=[
+            PandasDataframe(
+                columns=["response"],
+                column_types=[
+                    NdArrayType.STR,
+                ],
+                column_shapes=[(1,)],
+            )
+        ],
+    )
+    def forward(self, unstructured_df):
+        """
+        NOTE (QUESTION) : Can we structure the inputs and outputs better
+        The circumvent issues surrounding the input being only one pandas dataframe and output columns being predefined 
+        Will add all column types as a JSON and parse in the forward function
+        Provide only the file name from which the input will be read
+        Output in JSON which can be serialized and stored in the results column of the DF
+        """
+        field_name = unstructured_df.iloc[0, 0]
+        description = unstructured_df.iloc[0, 1]
+        data_type = unstructured_df.iloc[0, 2]
+        input_rows = unstructured_df[unstructured_df.columns[3]]
+        prompt = """
+            You are given a user query. Your task is to extract the following fields from the query and return the result in string format.
+            IMPORTANT: RETURN ONLY THE EXTRACTED VALUE (one word or phrase). DO NOT RETURN THE FIELD NAME OR ANY OTHER INFORMATION.
+        """
+        content = """
+            Extract the following fields from the unstructured text below:
+            
+            Format of the field is given in the format
+            Field Name: Field Description: Field Type
+            {}: {}: {}
+
+            The unstructured text is as follows:
+        """.format(field_name, description, data_type)
+
+        print(prompt)
+        print(content)
+
+        output_df = pd.DataFrame({"response": []})
+
+        for row in input_rows:
+            query = row
+            input_df = pd.DataFrame({"query": [query],"content": content, "prompt": prompt})
+            print(query)
+            df = super(ExtractColumnFunction, self).forward(input_df)
+            output_df = pd.concat([output_df, df], ignore_index=True)
+        return output_df