Fix feedback logging

.github/workflows/main.yml

@@ -1,6 +1,6 @@
-name: black
+name: tests
 
-on: [push, pull_request]
+on: [push]
 
 jobs:
   lint:

README.md

@@ -1,6 +1,6 @@
 # Defog Python
 
-[![black](https://github.com/defog-ai/defog-python/actions/workflows/main.yml/badge.svg)](https://github.com/defog-ai/defog-python/actions/workflows/main.yml)
+[![tests](https://github.com/defog-ai/defog-python/actions/workflows/main.yml/badge.svg)](https://github.com/defog-ai/defog-python/actions/workflows/main.yml)
 
 Defog converts your natural language text queries into SQL and other machine readable code. This library allows you to easily integrate Defog into your python application, and has a CLI to help you get started.
 

defog/__init__.py

@@ -1005,18 +1005,6 @@ def get_predefined_queries(self):
         else:
             return []
 
-    def execute_predefined_query(self, query):
-        """
-        Executes a predefined query
-        """
-        resp = execute_query(
-            query["query"],
-            self.api_key,
-            self.db_type,
-            self.db_creds,
-        )
-        return resp
-
     def update_db_schema_csv(self, path_to_csv):
         """
         Update the DB schema via a CSV, rather than by via a Google Sheet

defog/cli.py

@@ -10,8 +10,7 @@
 import requests
 
 import defog
-from defog import Defog
-from defog.util import parse_update
+from defog.util import get_feedback, parse_update
 from prompt_toolkit import prompt
 
 USAGE_STRING = """
@@ -327,8 +326,6 @@ def query():
     else:
         query = sys.argv[2]
 
-    feedback_mode = False
-    feedback = ""
     user_question = ""
     sql_generated = ""
     while True:
@@ -338,100 +335,44 @@ def query():
         elif query == "":
             print("Your query cannot be empty.")
             query = prompt("Enter a query, or type 'e' to exit: ")
-        if feedback_mode:
-            if feedback not in ["y", "n"]:
-                pass
-            elif feedback == "y":
-                # send data to /feedback endpoint
-                try:
-                    requests.post(
-                        "https://api.defog.ai/feedback",
-                        json={
-                            "api_key": df.api_key,
-                            "feedback": "good",
-                            "db_type": df.db_type,
-                            "question": user_question,
-                            "query": sql_generated,
-                        },
-                        timeout=1,
-                    )
-                    print("Thank you for the feedback!")
-                    feedback_mode = False
-                except:
-                    pass
-
-            elif feedback == "n":
-                # send data to /feedback endpoint
-                feedback_text = prompt(
-                    "Could you tell us why this was a bad query? This will help us improve the model for you. Just hit enter if you want to leave this blank.\n"
-                )
-                try:
-                    requests.post(
-                        "https://api.defog.ai/feedback",
-                        json={
-                            "api_key": df.api_key,
-                            "feedback": "bad",
-                            "text": feedback_text,
-                            "db_type": df.db_type,
-                            "question": user_question,
-                            "query": sql_generated,
-                        },
-                        timeout=1,
-                    )
-                except:
-                    pass
+        user_question = query
+        resp = df.run_query(query, retries=3)
+        if not resp["ran_successfully"]:
+            if "query_generated" in resp:
+                print("Defog generated the following query to answer your question:\n")
+                print(f"\033[1m{resp['query_generated']}\033[0m\n")
+
                 print(
-                    "Thank you for the feedback! We retrain our models every week, and you should see much better performance on these kinds of queries in another week.\n"
+                    f"However, your query did not run successfully. The error message generated while running the query on your database was\n\n\033[1m{resp['error_message']}\033[0m\n."
                 )
+
                 print(
                     f"If you continue to get these errors, please consider updating the metadata in your schema by editing the google sheet generated and running `defog update <url>`, or by updating your glossary.\n"
                 )
-            feedback_mode = False
-            query = prompt("Enter another query, or type 'e' to exit: ")
-        else:
-            user_question = query
-            resp = df.run_query(query, retries=3)
-            if not resp["ran_successfully"]:
-                if "query_generated" in resp:
-                    print(
-                        "Defog generated the following query to answer your question:\n"
-                    )
-                    print(f"\033[1m{resp['query_generated']}\033[0m\n")
-
-                    print(
-                        f"However, your query did not run successfully. The error message generated while running the query on your database was\n\n\033[1m{resp['error_message']}\033[0m\n."
-                    )
-
-                    print(
-                        f"If you continue to get these errors, please consider updating the metadata in your schema by editing the google sheet generated and running `defog update <url>`, or by updating your glossary.\n"
-                    )
-                else:
-                    print(
-                        f"Defog was unable to generate a query for your question. The error message generated while running the query on your database was\n\n\033[1m{resp.get('error_message')}\033[0m\n."
-                    )
-                query = prompt("Enter another query, or type 'e' to exit: ")
             else:
-                sql_generated = resp.get("query_generated")
-                print("Defog generated the following query to answer your question:\n")
-                print(f"\033[1m{resp['query_generated']}\033[0m\n")
-                reason_for_query = resp.get("reason_for_query", "")
-                reason_for_query = reason_for_query.replace(". ", "\n\n")
-
-                print("This was its reasoning for generating this query:\n")
-                print(f"\033[1m{reason_for_query}\033[0m\n")
-
-                print("Results:\n")
-                # print results in tabular format using 'columns' and 'data' keys
-                try:
-                    print_table(resp["columns"], resp["data"])
-                except:
-                    print(resp)
-
-                print()
-                feedback_mode = True
-                feedback = prompt(
-                    "Did Defog answer your question well? Just hit enter to skip (y/n):\n"
+                print(
+                    f"Defog was unable to generate a query for your question. The error message generated while running the query on your database was\n\n\033[1m{resp.get('error_message')}\033[0m\n."
                 )
+        else:
+            sql_generated = resp.get("query_generated")
+            print("Defog generated the following query to answer your question:\n")
+            print(f"\033[1m{resp['query_generated']}\033[0m\n")
+            reason_for_query = resp.get("reason_for_query", "")
+            reason_for_query = reason_for_query.replace(". ", "\n\n")
+
+            print("This was its reasoning for generating this query:\n")
+            print(f"\033[1m{reason_for_query}\033[0m\n")
+
+            print("Results:\n")
+            # print results in tabular format using 'columns' and 'data' keys
+            try:
+                print_table(resp["columns"], resp["data"])
+            except:
+                print(resp)
+
+            print()
+            get_feedback(df.api_key, df.db_type, user_question, sql_generated)
+        query = prompt("Please enter another query, or type 'e' to exit: ")
 
 
 def deploy():

defog/query.py

@@ -1,4 +1,5 @@
 import json
+import re
 import requests
 from defog.util import write_logs
 
@@ -139,18 +140,22 @@ def execute_query(
     retries: int = 3,
     schema: dict = None,
 ):
+    """
+    Execute the query and retry with adaptive learning if there is an error.
+    Raises an Exception if there are no retries left, or if the error is a connection error.
+    """
     err_msg = None
     try:
         return execute_query_once(db_type, db_creds, query) + (query,)
     except Exception as e:
         err_msg = str(e)
-        print(
-            "There was an error when running the previous query. Retrying with adaptive learning..."
-        )
-
-        # log this error to our feedback system
+        if is_connection_error(err_msg):
+            raise Exception(
+                f"There was a connection issue to your database:\n{err_msg}\n\nPlease check your database credentials and try again."
+            )
+        # log this error to our feedback system first (this is a 1-way side-effect)
         try:
-            r = requests.post(
+            requests.post(
                 "https://api.defog.ai/feedback",
                 json={
                     "api_key": api_key,
@@ -164,8 +169,9 @@ def execute_query(
             )
         except:
             pass
-
+        # log locally
         write_logs(str(e))
+        # retry with adaptive learning
         while retries > 0:
             write_logs(f"Retries left: {retries}")
             try:
@@ -196,3 +202,10 @@ def execute_query(
                 write_logs(str(e))
                 retries -= 1
         raise Exception(err_msg)
+
+
+def is_connection_error(err_msg: str) -> bool:
+    return (
+        isinstance(err_msg, str)
+        and re.search(r"connection.*failed", err_msg) is not None
+    )

defog/util.py

@@ -1,6 +1,9 @@
 import os
 from typing import List
 
+from prompt_toolkit import prompt
+import requests
+
 
 def parse_update(
     args_list: List[str], attributes_list: List[str], config_dict: dict
@@ -94,3 +97,51 @@ def identify_categorical_columns(
                 top_values = [i[0] for i in top_values if i[0] is not None]
                 rows[idx]["top_values"] = top_values
     return rows
+
+
+def get_feedback(api_key: str, db_type: str, user_question: str, sql_generated: str):
+    """
+    Get feedback from the user on whether the query was good or bad, and why.
+    """
+    feedback = prompt(
+        "Did Defog answer your question well? Just hit enter to skip (y/n):\n"
+    )
+    while feedback not in ["y", "n", ""]:
+        feedback = prompt("Please enter y or n:\n")
+    if feedback == "":
+        # user skipped feedback
+        return
+    # get explanation for negative feedback
+    if feedback == "n":
+        feedback_text = prompt(
+            "Could you tell us why this was a bad query? This will help us improve the model for you. Just hit enter if you want to leave this blank.\n"
+        )
+    else:
+        feedback_text = ""
+    try:
+        data = {
+            "api_key": api_key,
+            "feedback": "good" if feedback == "y" else "bad",
+            "db_type": db_type,
+            "question": user_question,
+            "query": sql_generated,
+        }
+        if feedback_text != "":
+            data["feedback_text"] = feedback_text
+        requests.post(
+            "https://api.defog.ai/feedback",
+            json=data,
+            timeout=1,
+        )
+        if feedback == "y":
+            print("Thank you for the feedback!")
+        else:
+            print(
+                "Thank you for the feedback! We retrain our models every week, and you should see much better performance on these kinds of queries in another week.\n"
+            )
+            print(
+                f"If you continue to get these errors, please consider updating the metadata in your schema by editing the google sheet generated and running `defog update <url>`, or by updating your glossary.\n"
+            )
+    except Exception as e:
+        write_logs(f"Error in get_feedback:\n{e}")
+        pass

requirements.txt

@@ -1,5 +1,6 @@
-requests>=2.28.2
-psycopg2-binary>=2.9.5
 pandas
-tabulate
-pwinput
+prompt_toolkit
+psycopg2-binary>=2.9.5
+pwinput
+requests>=2.28.2
+tabulate

tests/test_query.py

@@ -4,7 +4,7 @@
 import unittest
 from unittest import mock
 
-from defog.query import execute_query_once, execute_query
+from defog.query import is_connection_error, execute_query_once, execute_query
 
 
 class ExecuteQueryOnceTestCase(unittest.TestCase):
@@ -166,5 +166,36 @@ def side_effect(db_type, db_creds, query):
             self.assertIn(json.dumps(json_req), lines[2])
 
 
+class TestConnectionError(unittest.TestCase):
+    def test_connection_failed(self):
+        self.assertTrue(
+            is_connection_error(
+                """connection to server on socket "/tmp/.s.PGSQL.5432" failed: No such file or directory
+    Is the server running locally and accepting connections on that socket?"""
+            )
+        )
+
+    def test_not_connection_failed(self):
+        self.assertFalse(
+            is_connection_error(
+                'psycopg2.errors.UndefinedTable: relation "nonexistent_table" does not exist'
+            )
+        )
+        self.assertFalse(
+            is_connection_error(
+                'psycopg2.errors.SyntaxError: syntax error at or near "nonexistent_table"'
+            )
+        )
+        self.assertFalse(
+            is_connection_error(
+                'psycopg2.errors.UndefinedColumn: column "nonexistent_column" does not exist'
+            )
+        )
+
+    def test_empty_string(self):
+        self.assertFalse(is_connection_error(""))
+        self.assertFalse(is_connection_error(None))
+
+
 if __name__ == "__main__":
     unittest.main()