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demos for network copilot and sql analyzer (#57)
* pulled from main branch after adding enums and made changes * added sql_analyzer folder and built a demo for Employee stats function calling. "top_employees" and "aggregate_stats". * sql_anayzer * After addressing PR comments * PR comments * PR comments * Addeed Network Analyzer FC Code * Added network Analyzer code for diff timeframes * Network Copilot and Employee Details demos are updated with their descriptions and resolved the PR comments * Added 2nd function in network copilot * Added 2nd function in network copilot * Added 2nd function in network copilot * Added 2nd function in network copilot * Added 2nd function in network copilot
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Sampreeth Sarma
GitHub
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commit
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11 changed files with 1052 additions and 1 deletions
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model_server/app/network_data_generator.py
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model_server/app/network_data_generator.py
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import pandas as pd
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import random
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from datetime import datetime, timedelta, timezone
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import re
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import logging
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from dateparser import parse
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Function to convert natural language time expressions to "X {time} ago" format
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def convert_to_ago_format(expression):
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# Define patterns for different time units
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time_units = {
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r'seconds': 'seconds',
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r'minutes': 'minutes',
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r'mins': 'mins',
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r'hrs': 'hrs',
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r'hours': 'hours',
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r'hour': 'hour',
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r'hr': 'hour',
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r'days': 'days',
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r'day': 'day',
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r'weeks': 'weeks',
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r'week': 'week',
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r'months': 'months',
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r'month': 'month',
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r'years': 'years',
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r'yrs': 'years',
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r'year': 'year',
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r'yr': 'year',
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}
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# Iterate over each time unit and create regex for each phrase format
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for pattern, unit in time_units.items():
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# Handle "for the past X {unit}"
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match = re.search(fr'(\d+) {pattern}', expression)
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if match:
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quantity = match.group(1)
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return f"{quantity} {unit} ago"
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# If the format is not recognized, return None or raise an error
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return None
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# Function to generate random MAC addresses
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def random_mac():
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return "AA:BB:CC:DD:EE:" + ':'.join([f"{random.randint(0, 255):02X}" for _ in range(2)])
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# Function to generate random IP addresses
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def random_ip():
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return f"{random.randint(1, 255)}.{random.randint(1, 255)}.{random.randint(1, 255)}.{random.randint(1, 255)}"
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# Generate synthetic data for the device table
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def generate_device_data(conn, n=1000,):
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device_data = {
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'switchip': [random_ip() for _ in range(n)],
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'hwsku': [f'HW{i+1}' for i in range(n)],
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'hostname': [f'switch{i+1}' for i in range(n)],
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'osversion': [f'v{i+1}' for i in range(n)],
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'layer': ['L2' if i % 2 == 0 else 'L3' for i in range(n)],
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'region': [random.choice(['US', 'EU', 'ASIA']) for _ in range(n)],
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'uptime': [f'{random.randint(0, 10)} days {random.randint(0, 23)}:{random.randint(0, 59)}:{random.randint(0, 59)}' for _ in range(n)],
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'device_mac_address': [random_mac() for _ in range(n)]
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}
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df = pd.DataFrame(device_data)
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df.to_sql('device', conn, index=False)
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return df
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# Generate synthetic data for the interfacestats table
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def generate_interface_stats_data(conn, device_df, n=1000):
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interface_stats_data = []
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for _ in range(n):
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device_mac = random.choice(device_df['device_mac_address'])
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ifname = random.choice(['eth0', 'eth1', 'eth2', 'eth3'])
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time = datetime.now(timezone.utc) - timedelta(minutes=random.randint(0, 1440 * 5)) # random timestamps in the past 5 day
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in_discards = random.randint(0, 1000)
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in_errors = random.randint(0, 500)
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out_discards = random.randint(0, 800)
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out_errors = random.randint(0, 400)
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in_octets = random.randint(1000, 100000)
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out_octets = random.randint(1000, 100000)
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interface_stats_data.append({
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'device_mac_address': device_mac,
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'ifname': ifname,
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'time': time,
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'in_discards': in_discards,
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'in_errors': in_errors,
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'out_discards': out_discards,
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'out_errors': out_errors,
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'in_octets': in_octets,
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'out_octets': out_octets
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})
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df = pd.DataFrame(interface_stats_data)
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df.to_sql('interfacestats', conn, index=False)
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return
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# Generate synthetic data for the ts_flow table
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def generate_flow_data(conn, device_df, n=1000):
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flow_data = []
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for _ in range(n):
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sampler_address = random.choice(device_df['switchip'])
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proto = random.choice(['TCP', 'UDP'])
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src_addr = random_ip()
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dst_addr = random_ip()
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src_port = random.randint(1024, 65535)
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dst_port = random.randint(1024, 65535)
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in_if = random.randint(1, 10)
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out_if = random.randint(1, 10)
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flow_start = int((datetime.now() - timedelta(days=random.randint(1, 30))).timestamp())
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flow_end = int((datetime.now() - timedelta(days=random.randint(1, 30))).timestamp())
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bytes_transferred = random.randint(1000, 100000)
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packets = random.randint(1, 1000)
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flow_time = datetime.now(timezone.utc) - timedelta(minutes=random.randint(0, 1440 * 5)) # random flow time
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flow_data.append({
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'sampler_address': sampler_address,
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'proto': proto,
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'src_addr': src_addr,
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'dst_addr': dst_addr,
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'src_port': src_port,
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'dst_port': dst_port,
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'in_if': in_if,
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'out_if': out_if,
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'flow_start': flow_start,
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'flow_end': flow_end,
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'bytes': bytes_transferred,
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'packets': packets,
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'time': flow_time
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})
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df = pd.DataFrame(flow_data)
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df.to_sql('ts_flow', conn, index=False)
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return
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def load_params(req):
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# Step 1: Convert the from_time natural language string to a timestamp if provided
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if req.from_time:
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# Use `dateparser` to parse natural language timeframes
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logger.info(f"{'* ' * 50}\n\nCaptured from time: {req.from_time}\n\n")
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parsed_time = parse(req.from_time, settings={'RELATIVE_BASE': datetime.now()})
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if not parsed_time:
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conv_time = convert_to_ago_format(req.from_time)
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if conv_time:
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parsed_time = parse(conv_time, settings={'RELATIVE_BASE': datetime.now()})
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else:
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return {"error": "Invalid from_time format. Please provide a valid time description such as 'past 7 days' or 'since last month'."}
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logger.info(f"\n\nConverted from time: {parsed_time}\n\n{'* ' * 50}\n\n")
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from_time = parsed_time
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logger.info(f"Using parsed from_time: {from_time}")
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else:
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# If no from_time is provided, use a default value (e.g., the past 7 days)
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from_time = datetime.now() - timedelta(days=7)
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logger.info(f"Using default from_time: {from_time}")
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# Step 2: Build the dynamic SQL query based on the optional filters
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filters = []
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params = {"from_time": from_time}
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if req.ifname:
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filters.append("i.ifname = :ifname")
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params["ifname"] = req.ifname
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if req.region:
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filters.append("d.region = :region")
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params["region"] = req.region
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if req.min_in_errors is not None:
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filters.append("i.in_errors >= :min_in_errors")
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params["min_in_errors"] = req.min_in_errors
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if req.max_in_errors is not None:
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filters.append("i.in_errors <= :max_in_errors")
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params["max_in_errors"] = req.max_in_errors
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if req.min_out_errors is not None:
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filters.append("i.out_errors >= :min_out_errors")
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params["min_out_errors"] = req.min_out_errors
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if req.max_out_errors is not None:
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filters.append("i.out_errors <= :max_out_errors")
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params["max_out_errors"] = req.max_out_errors
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if req.min_in_discards is not None:
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filters.append("i.in_discards >= :min_in_discards")
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params["min_in_discards"] = req.min_in_discards
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if req.max_in_discards is not None:
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filters.append("i.in_discards <= :max_in_discards")
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params["max_in_discards"] = req.max_in_discards
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if req.min_out_discards is not None:
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filters.append("i.out_discards >= :min_out_discards")
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params["min_out_discards"] = req.min_out_discards
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if req.max_out_discards is not None:
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filters.append("i.out_discards <= :max_out_discards")
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params["max_out_discards"] = req.max_out_discards
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return params, filters
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