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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
This commit is contained in:
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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@ -2,8 +2,16 @@ import random
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from fastapi import FastAPI, Response, HTTPException
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from pydantic import BaseModel
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from load_models import load_ner_models, load_transformers, load_zero_shot_models
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from datetime import date, timedelta
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from datetime import datetime, date, timedelta, timezone
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import string
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import pandas as pd
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from load_models import load_sql
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import logging
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from dateparser import parse
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from network_data_generator import convert_to_ago_format, load_params
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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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transformers = load_transformers()
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ner_models = load_ner_models()
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@ -144,6 +152,163 @@ async def weather(req: WeatherRequest, res: Response):
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return weather_forecast
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'''
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*****
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Adding new functions to test the usecases - Sampreeth
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*****
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'''
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conn = load_sql()
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name_col = "name"
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class TopEmployees(BaseModel):
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grouping: str
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ranking_criteria: str
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top_n: int
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@app.post("/top_employees")
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async def top_employees(req: TopEmployees, res: Response):
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name_col = "name"
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# Check if `req.ranking_criteria` is a Text object and extract its value accordingly
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logger.info(f"{'* ' * 50}\n\nCaptured Ranking Criteria: {req.ranking_criteria}\n\n{'* ' * 50}")
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if req.ranking_criteria == "yoe":
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req.ranking_criteria = "years_of_experience"
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elif req.ranking_criteria == "rating":
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req.ranking_criteria = "performance_score"
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logger.info(f"{'* ' * 50}\n\nFinal Ranking Criteria: {req.ranking_criteria}\n\n{'* ' * 50}")
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query = f"""
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SELECT {req.grouping}, {name_col}, {req.ranking_criteria}
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FROM (
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SELECT {req.grouping}, {name_col}, {req.ranking_criteria},
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DENSE_RANK() OVER (PARTITION BY {req.grouping} ORDER BY {req.ranking_criteria} DESC) as emp_rank
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FROM employees
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) ranked_employees
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WHERE emp_rank <= {req.top_n};
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"""
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result_df = pd.read_sql_query(query, conn)
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result = result_df.to_dict(orient='records')
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return result
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class AggregateStats(BaseModel):
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grouping: str
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aggregate_criteria: str
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aggregate_type: str
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@app.post("/aggregate_stats")
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async def aggregate_stats(req: AggregateStats, res: Response):
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logger.info(f"{'* ' * 50}\n\nCaptured Aggregate Criteria: {req.aggregate_criteria}\n\n{'* ' * 50}")
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if req.aggregate_criteria == "yoe":
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req.aggregate_criteria = "years_of_experience"
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logger.info(f"{'* ' * 50}\n\nFinal Aggregate Criteria: {req.aggregate_criteria}\n\n{'* ' * 50}")
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logger.info(f"{'* ' * 50}\n\nCaptured Aggregate Type: {req.aggregate_type}\n\n{'* ' * 50}")
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if req.aggregate_type.lower() not in ["sum", "avg", "min", "max"]:
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if req.aggregate_type.lower() == "count":
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req.aggregate_type = "COUNT"
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elif req.aggregate_type.lower() == "total":
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req.aggregate_type = "SUM"
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elif req.aggregate_type.lower() == "average":
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req.aggregate_type = "AVG"
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elif req.aggregate_type.lower() == "minimum":
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req.aggregate_type = "MIN"
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elif req.aggregate_type.lower() == "maximum":
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req.aggregate_type = "MAX"
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else:
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raise HTTPException(status_code=400, detail="Invalid aggregate type")
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logger.info(f"{'* ' * 50}\n\nFinal Aggregate Type: {req.aggregate_type}\n\n{'* ' * 50}")
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query = f"""
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SELECT {req.grouping}, {req.aggregate_type}({req.aggregate_criteria}) as {req.aggregate_type}_{req.aggregate_criteria}
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FROM employees
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GROUP BY {req.grouping};
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"""
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result_df = pd.read_sql_query(query, conn)
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result = result_df.to_dict(orient='records')
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return result
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class PacketDropCorrelationRequest(BaseModel):
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from_time: str = None # Optional natural language timeframe
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ifname: str = None # Optional interface name filter
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region: str = None # Optional region filter
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min_in_errors: int = None
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max_in_errors: int = None
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min_out_errors: int = None
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max_out_errors: int = None
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min_in_discards: int = None
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max_in_discards: int = None
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min_out_discards: int = None
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max_out_discards: int = None
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@app.post("/interface_down_pkt_drop")
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async def interface_down_packet_drop(req: PacketDropCorrelationRequest, res: Response):
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params, filters = load_params(req)
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# Join the filters using AND
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where_clause = " AND ".join(filters)
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if where_clause:
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where_clause = "AND " + where_clause
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# Step 3: Query packet errors and flows from interfacestats and ts_flow
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query = f"""
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SELECT
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d.switchip AS device_ip_address,
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i.in_errors,
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i.in_discards,
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i.out_errors,
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i.out_discards,
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i.ifname,
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t.src_addr,
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t.dst_addr,
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t.time AS flow_time,
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i.time AS interface_time
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FROM
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device d
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INNER JOIN
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interfacestats i
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ON d.device_mac_address = i.device_mac_address
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INNER JOIN
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ts_flow t
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ON d.switchip = t.sampler_address
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WHERE
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i.time >= :from_time -- Using the converted timestamp
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{where_clause}
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ORDER BY
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i.time;
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"""
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correlated_data = pd.read_sql_query(query, conn, params=params)
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if correlated_data.empty:
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default_response = {
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"device_ip_address": "0.0.0.0", # Placeholder IP
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"in_errors": 0,
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"in_discards": 0,
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"out_errors": 0,
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"out_discards": 0,
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"ifname": req.ifname or "unknown", # Placeholder or interface provided in the request
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"src_addr": "0.0.0.0", # Placeholder source IP
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"dst_addr": "0.0.0.0", # Placeholder destination IP
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"flow_time": str(datetime.now(timezone.utc)), # Current timestamp or placeholder
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"interface_time": str(datetime.now(timezone.utc)) # Current timestamp or placeholder
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}
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return [default_response]
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logger.info(f"Correlated Packet Drop Data: {correlated_data}")
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return correlated_data.to_dict(orient='records')
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class InsuranceClaimDetailsRequest(BaseModel):
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policy_number: str
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@ -159,3 +324,82 @@ async def insurance_claim_details(req: InsuranceClaimDetailsRequest, res: Respon
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}
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return claim_details
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class FlowPacketErrorCorrelationRequest(BaseModel):
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from_time: str = None # Optional natural language timeframe
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ifname: str = None # Optional interface name filter
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region: str = None # Optional region filter
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min_in_errors: int = None
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max_in_errors: int = None
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min_out_errors: int = None
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max_out_errors: int = None
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min_in_discards: int = None
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max_in_discards: int = None
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min_out_discards: int = None
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max_out_discards: int = None
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@app.post("/packet_errors_impact_flow")
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async def packet_errors_impact_flow(req: FlowPacketErrorCorrelationRequest, res: Response):
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params, filters = load_params(req)
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# Join the filters using AND
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where_clause = " AND ".join(filters)
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if where_clause:
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where_clause = "AND " + where_clause
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# Step 3: Query the packet errors and flows, correlating by timestamps
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query = f"""
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SELECT
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d.switchip AS device_ip_address,
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i.in_errors,
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i.in_discards,
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i.out_errors,
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i.out_discards,
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i.ifname,
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t.src_addr,
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t.dst_addr,
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t.src_port,
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t.dst_port,
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t.packets,
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t.time AS flow_time,
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i.time AS error_time
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FROM
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device d
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INNER JOIN
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interfacestats i
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ON d.device_mac_address = i.device_mac_address
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INNER JOIN
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ts_flow t
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ON d.switchip = t.sampler_address
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WHERE
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i.time >= :from_time
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AND ABS(strftime('%s', t.time) - strftime('%s', i.time)) <= 300 -- Correlate within 5 minutes
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{where_clause}
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ORDER BY
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i.time;
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"""
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correlated_data = pd.read_sql_query(query, conn, params=params)
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if correlated_data.empty:
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default_response = {
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"device_ip_address": "0.0.0.0", # Placeholder IP
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"in_errors": 0,
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"in_discards": 0,
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"out_errors": 0,
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"out_discards": 0,
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"ifname": req.ifname or "unknown", # Placeholder or interface provided in the request
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"src_addr": "0.0.0.0", # Placeholder source IP
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"dst_addr": "0.0.0.0", # Placeholder destination IP
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"src_port": 0,
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"dst_port": 0,
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"packets": 0,
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"flow_time": str(datetime.now(timezone.utc)), # Current timestamp or placeholder
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"error_time": str(datetime.now(timezone.utc)) # Current timestamp or placeholder
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}
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return [default_response]
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# Return the correlated data if found
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return correlated_data.to_dict(orient='records')
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