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Use intent model from archfc to pick prompt gateway (#328)

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Shuguang Chen 2024-12-20 13:25:01 -08:00 • committed by GitHub
parent 67b8fd635e
commit ba7279becb
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151 changed files with 8642 additions and 10932 deletions
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34
demos/network_agent/utils.py
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@ -1,10 +1,11 @@
import pandas as pd
import random
from datetime import datetime, timedelta, timezone
import re
import logging
from dateparser import parse
import random
import re
import sqlite3
from datetime import datetime, timedelta, timezone
import pandas as pd
from dateparser import parse
logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
@ -12,7 +13,7 @@ logging.basicConfig(
logger = logging.getLogger(__name__)
def load_sql():
def loadsql():
# Example Usage
conn = sqlite3.connect(":memory:")
@ -72,7 +73,10 @@ def random_mac():
# Function to generate random IP addresses
def random_ip():
return f"{random.randint(1, 255)}.{random.randint(1, 255)}.{random.randint(1, 255)}.{random.randint(1, 255)}"
return f"""{random.randint(1, 255)}
.{random.randint(1, 255)}
.{random.randint(1, 255)}
.{random.randint(1, 255)}"""
# Generate synthetic data for the device table
@ -88,7 +92,8 @@ def generate_device_data(
"layer": ["L2" if i % 2 == 0 else "L3" for i in range(n)],
"region": [random.choice(["US", "EU", "ASIA"]) for _ in range(n)],
"uptime": [
f"{random.randint(0, 10)} days {random.randint(0, 23)}:{random.randint(0, 59)}:{random.randint(0, 59)}"
f"""{random.randint(0, 10)} days {random.randint(0, 23)}
:{random.randint(0, 59)}:{random.randint(0, 59)}"""
for _ in range(n)
],
"device_mac_address": [random_mac() for _ in range(n)],
@ -129,7 +134,6 @@ def generate_interface_stats_data(conn, device_df, n=1000):
)
df = pd.DataFrame(interface_stats_data)
df.to_sql("interfacestats", conn, index=False)
return
# Generate synthetic data for the ts_flow table
@ -175,14 +179,13 @@ def generate_flow_data(conn, device_df, n=1000):
)
df = pd.DataFrame(flow_data)
df.to_sql("ts_flow", conn, index=False)
return
def load_params(req):
# Step 1: Convert the from_time natural language string to a timestamp if provided
if req.from_time:
# Use `dateparser` to parse natural language timeframes
logger.info(f"{'* ' * 50}\n\nCaptured from time: {req.from_time}\n\n")
logger.info("%s\n\nCaptured from time: %s\n\n", "* " * 50, req.from_time)
parsed_time = parse(req.from_time, settings={"RELATIVE_BASE": datetime.now()})
if not parsed_time:
conv_time = convert_to_ago_format(req.from_time)
@ -192,15 +195,16 @@ def load_params(req):
)
else:
return {
"error": "Invalid from_time format. Please provide a valid time description such as 'past 7 days' or 'since last month'."
"error": """Invalid from_time format. Please provide a valid time description
such as 'past 7 days' or 'since last month'."""
}
logger.info(f"\n\nConverted from time: {parsed_time}\n\n{'* ' * 50}\n\n")
logger.info("\n\nConverted from time: %s\n\n%s\n\n", parsed_time, "* " * 50)
from_time = parsed_time
logger.info(f"Using parsed from_time: {from_time}")
logger.info("Using parsed from_time: %f", from_time)
else:
# If no from_time is provided, use a default value (e.g., the past 7 days)
from_time = datetime.now() - timedelta(days=7)
logger.info(f"Using default from_time: {from_time}")
logger.info("Using default from_time: %f", from_time)
# Step 2: Build the dynamic SQL query based on the optional filters
filters = []