This code will allow you to create a local api for your sql database. We will then connect the API to chatGPT using NGROK, allowing it to interact with the database
1: connect to tailscale (make sure account has access to ai_playground database or whatever database for usage)
2: clone the repository
3: install requirements.txt in your environment
4: (In the gpt_sql.py file) enter table_catalog variable with the database_name
5: (In the gpt_sql.py file) create a connection link with the given format
6: run "python {file_name}.py" in terminal to start the local host
7: In the output, you will see a link after "running on", it will look like this "Running on 'Link'". That link will be used for the NGROK <LOCALHOST_HTTP_LINK_HERE>
Using NGROK to Connect Local Host to Internet (https://ngrok.com/download)
BEFORE INSTALLATION
1: run command prompt with administrator access
2: run "choco install ngrok" for Windows or "brew install ngrok/ngrok/ngrok" on Mac
3: run "ngrok config add-authtoken <AUTHCODE_HERE>" (authcode will be given when making ngrok account)
AFTER INSTALLTION
1: ngrok http <LOCALHOST_HTTP_LINK_HERE>
2: "Forwarding" link will be the link used for the external api endpoint
3: (optional) run python code below to test
import requests
base_url = #TODO REPLACE WITH GIVEN FORWARDING LINK
table_name = #REPLACE WITH TABLE NAME
column_name = #REPLACE WITH COLUMN NAME (MUST BE VALUE SINCE WE'RE TESTING AVERAGE"
url = f'{base_url}/get_avg/{table_name}/{column_name}'
response = requests.get(url)
if response.status_code == 200:
try:
data = response.json()
print(data)
except requests.exceptions.JSONDecodeError:
print("Not JSON Format Error")
else:
print(f"Error Code: {response.status_code}")
print(f"Response Content: {response.content}")
1: Open chat-GPT
2: On the custom gpts, find the "Weather+DB GPT"
3: Go to edit GPT on the top left
4: scroll down and edit the ngrok action (Not the api.weather one)
5: Replace the find the "servers" section under the schema (should be near the top) and replace the url with the "forwarding" Link given by NGROK
6: (MAKE SURE NOT TO CLOESE NGROK, THIS LINK WILL HAVE TO BE REPLACED EVERY TIME YOU CLOSE THE LOCAL HOST/NGROK)
7: Update the custom GPT and test!
import requests
import json
import datetime
from datetime import date
from datetime import timedelta
import psycopg2
def run_query(input_query):
# Connect to oats1.server.oats server
# Requires tailscale to be active
CONNECTION = "postgresql://lool:[email protected]:5432/ai_playground"
conn = None
try:
print("Connecting to purdue database...")
# Create a connection with the database
conn = psycopg2.connect(CONNECTION)
# Create a cursor to interact with the database
cursor = conn.cursor()
# Run the input query on the database
# Must be in the format of an SQL query string ending in a ';'
cursor.execute(input_query)
info = None
if input_query.strip().lower().startswith("select"):
info = cursor.fetchall() # Fetch all rows for SELECT queries
else:
conn.commit() # Commit changes for non-SELECT queries
# Close the cursor
cursor.close()
# Return info
return info
except(Exception, psycopg2.Error) as error:
# Error handling
print("error: " + error.pgerror)
finally:
# Close the database connection
if conn is not None:
conn.close()
def get_table_schema():
# SQL query to get a table schema including column name and data type
query_get_table_schema = """SELECT table_name, column_name, data_type
FROM information_schema.columns
WHERE table_catalog = 'ai_playground'
AND table_name = 'newa_weather';
"""
# Query the table
schema = run_query(query_get_table_schema)
# Print all rows
for row in schema:
print(row)
print('\n')
def get_first_row(table_name, col_to_order):
query_get_first_row_ascending = f"""
SELECT *
FROM {table_name}
ORDER BY {col_to_order}
LIMIT 1;
"""
# Run the query on the given table name
schema = run_query(query_get_first_row_ascending)
# Print the results
for row in schema:
print(schema)
print('\n')
def print_all_records(table_name):
query_get_all_records = f"""
SELECT *
FROM {table_name};
"""
# Run the query
records = run_query(query_get_all_records)
# Print all records
for row in records:
print(row)
print('\n')
# Call the function to print all records in the 'newa_weather' table
def add_to_newa(json_ob):
json_ob = json.loads(json_ob)
prcp = json_ob['dlyData'][0][1]
maxt = json_ob['dlyData'][0][2]
mint = json_ob['dlyData'][0][3]
time_string = get_yesterday_timestamptz()
query_insert_data = f"""
INSERT INTO newa_weather(time, prcp, max_temp, min_temp)
VALUES ({time_string},{prcp},{maxt},{mint});
"""
# Run the query
run_query(query_insert_data)
def add_to_table():
time_string = get_yesterday_timestamptz()
prcp = 0.88
maxt = 88.5
mint = 56.7
query_insert_data = f"""
INSERT INTO newa_weather(time, prcp, max_temp, min_temp)
VALUES ('{time_string}',{prcp},{maxt},{mint});
"""
# Run the query
run_query(query_insert_data)
def newa_date_times():
curr_date = date.today() - timedelta(days=1)
edate = curr_date.strftime('%Y%m%d') + "00"
curr_date = date.today() - timedelta(days=1)
sdate = curr_date.strftime('%Y%m%d') + "00"
return sdate, edate
def format_json(start, end):
payload = {"sid": "in_con newa", "sdate": start, "edate": end, "extraelems": ""}
# headers = {
# "Content-Type": "application/json"
# }
r = requests.post("https://hrly.nrcc.cornell.edu/stnHrly", json=payload, headers={'User-Agent': 'Mozilla/5.0'})
print(r.status_code)
print(r.text)
res = r.json()
fields = res["dlyFields"]
values = res["dlyData"]
dly_data = {
"dlyFields": fields,
"dlyData": values
}
json_obj = json.dumps(dly_data, indent=4)
return json_obj
def get_yesterday_timestamptz():
# Get the current time in UTC
now = datetime.datetime.now(datetime.timezone.utc)
# Subtract one day to get yesterday's date and time
yesterday = now - datetime.timedelta(days=1)
# Format the datetime to fit into a TIMESTAMPTZ column
timestamptz_value = yesterday.isoformat()
return f'{timestamptz_value}'
get_table_schema()