fix demos code (#76)

demos/employee_details_copilot/Bolt-FC-1B-Q3_K_L.model_file

@@ -0,0 +1,25 @@
+FROM Bolt-Function-Calling-1B-Q3_K_L.gguf
+
+# Set the size of the context window used to generate the next token
+# PARAMETER num_ctx 16384
+PARAMETER num_ctx 4096
+
+# Set parameters for response generation
+PARAMETER num_predict 1024
+PARAMETER temperature 0.1
+PARAMETER top_p 0.5
+PARAMETER top_k 32022
+PARAMETER repeat_penalty 1.0
+PARAMETER stop "<|EOT|>"
+
+# Set the random number seed to use for generation
+PARAMETER seed 42
+
+# Set the prompt template to be passed into the model
+TEMPLATE """{{ if .System }}<｜begin▁of▁sentence｜>
+{{ .System }}
+{{ end }}{{ if .Prompt }}### Instruction:
+{{ .Prompt }}
+{{ end }}### Response:
+{{ .Response }}
+<|EOT|>"""

demos/employee_details_copilot/Bolt-FC-1B-Q4_K_M.model_file

@@ -0,0 +1,24 @@
+FROM Bolt-Function-Calling-1B-Q4_K_M.gguf
+
+# Set the size of the context window used to generate the next token
+PARAMETER num_ctx 4096
+
+# Set parameters for response generation
+PARAMETER num_predict 1024
+PARAMETER temperature 0.1
+PARAMETER top_p 0.5
+PARAMETER top_k 32022
+PARAMETER repeat_penalty 1.0
+PARAMETER stop "<|EOT|>"
+
+# Set the random number seed to use for generation
+PARAMETER seed 42
+
+# Set the prompt template to be passed into the model
+TEMPLATE """{{ if .System }}<｜begin▁of▁sentence｜>
+{{ .System }}
+{{ end }}{{ if .Prompt }}### Instruction:
+{{ .Prompt }}
+{{ end }}### Response:
+{{ .Response }}
+<|EOT|>"""

demos/employee_details_copilot/README.md

@@ -1,7 +1,7 @@
 # Function calling
 This demo shows how you can use intelligent prompt gateway to act a copilot for calling the correct proc by capturing the required and optional parametrs from the prompt. This demo assumes you are using ollama running natively. If you want to run ollama running inside docker then please update ollama endpoint in docker-compose file.
 
-# Startig the demo
+# Starting the demo
 1. Ensure that submodule is up to date
    ```sh
    git submodule sync --recursive

demos/employee_details_copilot/api_server/.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "function-calling api server",
+      "cwd": "${workspaceFolder}/app",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "uvicorn",
+      "args": ["main:app","--reload", "--port", "8001"],
+    }
+  ]
+}

demos/employee_details_copilot/api_server/Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3 AS base
+
+FROM base AS builder
+
+WORKDIR /src
+
+COPY requirements.txt /src/
+RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
+
+COPY . /src
+
+FROM python:3-slim AS output
+
+COPY --from=builder /runtime /usr/local
+
+COPY /app /app
+WORKDIR /app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]

demos/employee_details_copilot/api_server/app/main.py

@@ -0,0 +1,289 @@
+import random
+from typing import List
+from fastapi import FastAPI, HTTPException, Response
+from datetime import datetime, date, timedelta, timezone
+import logging
+from pydantic import BaseModel
+from utils import load_sql
+import pandas as pd
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+@app.get("/healthz")
+async def healthz():
+    return {
+        "status": "ok"
+    }
+
+conn = load_sql()
+name_col = "name"
+
+
+class TopEmployees(BaseModel):
+    grouping: str
+    ranking_criteria: str
+    top_n: int
+
+
+@app.post("/top_employees")
+async def top_employees(req: TopEmployees, res: Response):
+    name_col = "name"
+    # Check if `req.ranking_criteria` is a Text object and extract its value accordingly
+    logger.info(
+        f"{'* ' * 50}\n\nCaptured Ranking Criteria: {req.ranking_criteria}\n\n{'* ' * 50}"
+    )
+
+    if req.ranking_criteria == "yoe":
+        req.ranking_criteria = "years_of_experience"
+    elif req.ranking_criteria == "rating":
+        req.ranking_criteria = "performance_score"
+
+    logger.info(
+        f"{'* ' * 50}\n\nFinal Ranking Criteria: {req.ranking_criteria}\n\n{'* ' * 50}"
+    )
+
+    query = f"""
+    SELECT {req.grouping}, {name_col}, {req.ranking_criteria}
+    FROM (
+        SELECT {req.grouping}, {name_col}, {req.ranking_criteria},
+               DENSE_RANK() OVER (PARTITION BY {req.grouping} ORDER BY {req.ranking_criteria} DESC) as emp_rank
+        FROM employees
+    ) ranked_employees
+    WHERE emp_rank <= {req.top_n};
+    """
+    result_df = pd.read_sql_query(query, conn)
+    result = result_df.to_dict(orient="records")
+    return result
+
+
+class AggregateStats(BaseModel):
+    grouping: str
+    aggregate_criteria: str
+    aggregate_type: str
+
+
+@app.post("/aggregate_stats")
+async def aggregate_stats(req: AggregateStats, res: Response):
+    logger.info(
+        f"{'* ' * 50}\n\nCaptured Aggregate Criteria: {req.aggregate_criteria}\n\n{'* ' * 50}"
+    )
+
+    if req.aggregate_criteria == "yoe":
+        req.aggregate_criteria = "years_of_experience"
+
+    logger.info(
+        f"{'* ' * 50}\n\nFinal Aggregate Criteria: {req.aggregate_criteria}\n\n{'* ' * 50}"
+    )
+
+    logger.info(
+        f"{'* ' * 50}\n\nCaptured Aggregate Type: {req.aggregate_type}\n\n{'* ' * 50}"
+    )
+    if req.aggregate_type.lower() not in ["sum", "avg", "min", "max"]:
+        if req.aggregate_type.lower() == "count":
+            req.aggregate_type = "COUNT"
+        elif req.aggregate_type.lower() == "total":
+            req.aggregate_type = "SUM"
+        elif req.aggregate_type.lower() == "average":
+            req.aggregate_type = "AVG"
+        elif req.aggregate_type.lower() == "minimum":
+            req.aggregate_type = "MIN"
+        elif req.aggregate_type.lower() == "maximum":
+            req.aggregate_type = "MAX"
+        else:
+            raise HTTPException(status_code=400, detail="Invalid aggregate type")
+
+    logger.info(
+        f"{'* ' * 50}\n\nFinal Aggregate Type: {req.aggregate_type}\n\n{'* ' * 50}"
+    )
+
+    query = f"""
+    SELECT {req.grouping}, {req.aggregate_type}({req.aggregate_criteria}) as {req.aggregate_type}_{req.aggregate_criteria}
+    FROM employees
+    GROUP BY {req.grouping};
+    """
+    result_df = pd.read_sql_query(query, conn)
+    result = result_df.to_dict(orient="records")
+    return result
+
+# 1. Top Employees by Performance, Projects, and Timeframe
+class TopEmployeesProjects(BaseModel):
+    min_performance_score: float
+    min_years_experience: int
+    department: str
+    min_project_count: int = None  # Optional
+    months_range: int = None  # Optional (for filtering recent projects)
+
+
+@app.post("/top_employees_projects")
+async def employees_projects(req: TopEmployeesProjects, res: Response):
+    params, filters = {}, []
+
+    # Add optional months_range filter
+    if req.months_range:
+        params['months_range'] = req.months_range
+        filters.append(f"p.start_date >= DATE('now', '-{req.months_range} months')")
+
+    # Add project count filter if provided
+    if req.min_project_count:
+        filters.append(f"COUNT(p.project_id) >= {req.min_project_count}")
+
+    where_clause = " AND ".join(filters)
+    if where_clause:
+        where_clause = "AND " + where_clause
+
+    query = f"""
+    SELECT e.name, e.department, e.years_of_experience, e.performance_score, COUNT(p.project_id) as project_count
+    FROM employees e
+    LEFT JOIN projects p ON e.eid = p.eid
+    WHERE e.performance_score >= {req.min_performance_score}
+      AND e.years_of_experience >= {req.min_years_experience}
+      AND e.department = '{req.department}'
+      {where_clause}
+    GROUP BY e.eid, e.name, e.department, e.years_of_experience, e.performance_score
+    ORDER BY e.performance_score DESC;
+    """
+
+    result_df = pd.read_sql_query(query, conn, params=params)
+    return result_df.to_dict(orient='records')
+
+
+# 2. Employees with Salary Growth Since Last Promotion
+class SalaryGrowthRequest(BaseModel):
+    min_salary_increase_percentage: float
+    department: str = None  # Optional
+
+
+@app.post("/salary_growth")
+async def salary_growth(req: SalaryGrowthRequest, res: Response):
+    params, filters = {}, []
+
+    if req.department:
+        filters.append("e.department = :department")
+        params['department'] = req.department
+
+    where_clause = " AND ".join(filters)
+    if where_clause:
+        where_clause = "AND " + where_clause
+
+    query = f"""
+    SELECT e.name, e.department, s.salary_increase_percentage
+    FROM employees e
+    JOIN salary_history s ON e.eid = s.eid
+    WHERE s.salary_increase_percentage >= {req.min_salary_increase_percentage}
+      AND s.promotion_date IS NOT NULL
+      {where_clause}
+    ORDER BY s.salary_increase_percentage DESC;
+    """
+
+    result_df = pd.read_sql_query(query, conn, params=params)
+    return result_df.to_dict(orient='records')
+
+
+# 4. Employees with Promotions and Salary Increases
+class PromotionsIncreasesRequest(BaseModel):
+    year: int
+    min_salary_increase_percentage: float = None  # Optional
+    department: str = None  # Optional
+
+
+@app.post("/promotions_increases")
+async def promotions_increases(req: PromotionsIncreasesRequest, res: Response):
+    params, filters = {}, []
+
+    if req.min_salary_increase_percentage:
+        filters.append(f"s.salary_increase_percentage >= {req.min_salary_increase_percentage}")
+
+    if req.department:
+        filters.append("e.department = :department")
+        params['department'] = req.department
+
+    where_clause = " AND ".join(filters)
+    if where_clause:
+        where_clause = "AND " + where_clause
+
+    query = f"""
+    SELECT e.name, e.department, s.salary_increase_percentage, s.promotion_date
+    FROM employees e
+    JOIN salary_history s ON e.eid = s.eid
+    WHERE strftime('%Y', s.promotion_date) = '{req.year}'
+      {where_clause}
+    ORDER BY s.salary_increase_percentage DESC;
+    """
+
+    result_df = pd.read_sql_query(query, conn, params=params)
+    return result_df.to_dict(orient='records')
+
+
+# 5. Employees with Highest Average Project Performance
+class AvgProjPerformanceRequest(BaseModel):
+    min_project_count: int
+    min_performance_score: float
+    department: str = None  # Optional
+
+
+@app.post("/avg_project_performance")
+async def avg_project_performance(req: AvgProjPerformanceRequest, res: Response):
+    params, filters = {}, []
+
+    if req.department:
+        filters.append("e.department = :department")
+        params['department'] = req.department
+
+    filters.append(f"p.performance_score >= {req.min_performance_score}")
+
+    where_clause = " AND ".join(filters)
+
+    query = f"""
+    SELECT e.name, e.department, AVG(p.performance_score) as avg_performance_score, COUNT(p.project_id) as project_count
+    FROM employees e
+    JOIN projects p ON e.eid = p.eid
+    WHERE {where_clause}
+    GROUP BY e.eid, e.name, e.department
+    HAVING COUNT(p.project_id) >= {req.min_project_count}
+    ORDER BY avg_performance_score DESC;
+    """
+
+    result_df = pd.read_sql_query(query, conn, params=params)
+    return result_df.to_dict(orient='records')
+
+
+# 6. Employees by Certification and Years of Experience
+class CertificationsExperienceRequest(BaseModel):
+    certifications: List[str]
+    min_years_experience: int
+    department: str = None  # Optional
+
+@app.post("/employees_certifications_experience")
+async def certifications_experience(req: CertificationsExperienceRequest, res: Response):
+    # Convert the list of certifications into a format for SQL query
+    certs_filter = ', '.join([f"'{cert}'" for cert in req.certifications])
+
+    params, filters = {}, []
+
+    # Add department filter if provided
+    if req.department:
+        filters.append("e.department = :department")
+        params['department'] = req.department
+
+    filters.append("e.years_of_experience >= :min_years_experience")
+    params['min_years_experience'] = req.min_years_experience
+
+    where_clause = " AND ".join(filters)
+
+    query = f"""
+    SELECT e.name, e.department, e.years_of_experience, COUNT(c.certification_name) as cert_count
+    FROM employees e
+    JOIN certifications c ON e.eid = c.eid
+    WHERE c.certification_name IN ({certs_filter})
+      AND {where_clause}
+    GROUP BY e.eid, e.name, e.department, e.years_of_experience
+    HAVING COUNT(c.certification_name) = {len(req.certifications)}
+    ORDER BY e.years_of_experience DESC;
+    """
+
+    result_df = pd.read_sql_query(query, conn, params=params)
+    return result_df.to_dict(orient='records')

demos/employee_details_copilot/api_server/app/utils.py

@@ -0,0 +1,157 @@
+import pandas as pd
+import random
+import datetime
+import sqlite3
+
+def load_sql():
+    # Example Usage
+    conn = sqlite3.connect(":memory:")
+
+    # create and load the employees table
+    generate_employee_data(conn)
+
+    # create and load the projects table
+    generate_project_data(conn)
+
+    # create and load the salary_history table
+    generate_salary_history(conn)
+
+    # create and load the certifications table
+    generate_certifications(conn)
+
+    return conn
+
+# Function to generate random employee data with `eid` as the primary key
+def generate_employee_data(conn):
+    # List of possible names, positions, departments, and locations
+    names = [
+        "Alice",
+        "Bob",
+        "Charlie",
+        "David",
+        "Eve",
+        "Frank",
+        "Grace",
+        "Hank",
+        "Ivy",
+        "Jack",
+    ]
+    positions = [
+        "Manager",
+        "Engineer",
+        "Salesperson",
+        "HR Specialist",
+        "Marketing Analyst",
+    ]
+    # List of possible names, positions, departments, locations, and certifications
+    names = ["Alice", "Bob", "Charlie", "David", "Eve", "Frank", "Grace", "Hank", "Ivy", "Jack"]
+    positions = ["Manager", "Engineer", "Salesperson", "HR Specialist", "Marketing Analyst"]
+    departments = ["Engineering", "Marketing", "HR", "Sales", "Finance"]
+    locations = ["New York", "San Francisco", "Austin", "Boston", "Chicago"]
+    certifications = ["AWS Certified", "Google Cloud Certified", "PMP", "Scrum Master", "Cisco Certified"]
+
+    # Generate random hire dates
+    def random_hire_date():
+        start_date = datetime.date(2000, 1, 1)
+        end_date = datetime.date(2023, 12, 31)
+        time_between_dates = end_date - start_date
+        days_between_dates = time_between_dates.days
+        random_number_of_days = random.randrange(days_between_dates)
+        return start_date + datetime.timedelta(days=random_number_of_days)
+
+    # Generate random employee records with an employee ID (eid)
+    employees = []
+    for eid in range(1, 101):  # 100 employees with `eid` starting from 1
+        name = random.choice(names)
+        position = random.choice(positions)
+        salary = round(random.uniform(50000, 150000), 2)  # Salary between 50,000 and 150,000
+        department = random.choice(departments)
+        location = random.choice(locations)
+        hire_date = random_hire_date()
+        performance_score = round(random.uniform(1, 5), 2)  # Performance score between 1.0 and 5.0
+        years_of_experience = random.randint(1, 30)  # Years of experience between 1 and 30
+
+        employee = {
+            "eid": eid,  # Employee ID
+            "name": name,
+            "position": position,
+            "salary": salary,
+            "department": department,
+            "location": location,
+            "hire_date": hire_date,
+            "performance_score": performance_score,
+            "years_of_experience": years_of_experience
+        }
+
+        employees.append(employee)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_employees = pd.DataFrame(employees)
+    df_employees.to_sql('employees', conn, index=False, if_exists='replace')
+
+# Function to generate random project data with `eid`
+def generate_project_data(conn):
+    employees = pd.read_sql_query("SELECT eid FROM employees", conn)
+    projects = []
+
+    for _ in range(500):  # 500 projects
+        eid = random.choice(employees['eid'])
+        project_name = f"Project_{random.randint(1, 100)}"
+        start_date = datetime.date(2020, 1, 1) + datetime.timedelta(days=random.randint(0, 365 * 3))  # Within the last 3 years
+        performance_score = round(random.uniform(1, 5), 2)  # Performance score for the project between 1.0 and 5.0
+
+        project = {
+            "eid": eid,  # Foreign key from employees table
+            "project_name": project_name,
+            "start_date": start_date,
+            "performance_score": performance_score
+        }
+
+        projects.append(project)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_projects = pd.DataFrame(projects)
+    df_projects.to_sql('projects', conn, index=False, if_exists='replace')
+
+# Function to generate random salary history data with `eid`
+def generate_salary_history(conn):
+    employees = pd.read_sql_query("SELECT eid FROM employees", conn)
+    salary_history = []
+
+    for _ in range(300):  # 300 salary records
+        eid = random.choice(employees['eid'])
+        salary_increase_percentage = round(random.uniform(5, 30), 2)  # Salary increase between 5% and 30%
+        promotion_date = datetime.date(2018, 1, 1) + datetime.timedelta(days=random.randint(0, 365 * 5))  # Promotions in the last 5 years
+
+        salary_record = {
+            "eid": eid,  # Foreign key from employees table
+            "salary_increase_percentage": salary_increase_percentage,
+            "promotion_date": promotion_date
+        }
+
+        salary_history.append(salary_record)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_salary_history = pd.DataFrame(salary_history)
+    df_salary_history.to_sql('salary_history', conn, index=False, if_exists='replace')
+
+# Function to generate random certifications data with `eid`
+def generate_certifications(conn):
+    employees = pd.read_sql_query("SELECT eid FROM employees", conn)
+    certifications_list = ["AWS Certified", "Google Cloud Certified", "PMP", "Scrum Master", "Cisco Certified"]
+    employee_certifications = []
+
+    for _ in range(300):  # 300 certification records
+        eid = random.choice(employees['eid'])
+        certification = random.choice(certifications_list)
+
+        cert_record = {
+            "eid": eid,  # Foreign key from employees table
+            "certification_name": certification
+        }
+
+        employee_certifications.append(cert_record)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_certifications = pd.DataFrame(employee_certifications)
+    df_certifications.to_sql('certifications', conn, index=False, if_exists='replace')

demos/employee_details_copilot/api_server/requirements.txt

@@ -0,0 +1,4 @@
+fastapi
+uvicorn
+pandas
+dateparser

demos/employee_details_copilot/bolt_config.yaml

@@ -4,7 +4,7 @@ timeout_ms: 5000
 
 overrides:
   # confidence threshold for prompt target intent matching
-  prompt_target_intent_matching_threshold: 0.8
+  prompt_target_intent_matching_threshold: 0.7
 
 llm_providers:
 
@@ -30,15 +30,15 @@ prompt_targets:
         required: true
         type: string
         description: |
-          Choose how you'd like to rank the employees. You can rank them by their salary, their yoe, or their rating. The tool will sort the employees based on this ranking and return the best ones from each group.
+          Choose how you'd like to rank the employees. You can rank them by their salary, their years of experience, or their rating. The tool will sort the employees based on this ranking and return the best ones from each group.
-        enum: [salary, yoe, rating]
+        enum: [salary, years_of_experience, performance_score]
       - name: top_n
         required: true
         type: integer
         description: |
           Enter how many of the top employees you want to see in each group. For example, if you enter 3, the tool will show you the top 3 employees for each group you selected.
     endpoint:
-      cluster: databasehost
+      cluster: api_server
       path: /top_employees
     system_prompt: |
       You are responsible for retrieving the top N employees per group ranked by a constraint.
@@ -46,7 +46,7 @@ prompt_targets:
   - type: function_resolver
     name: aggregate_stats
     description: |
-       Calculate summary statistics for groups of employees. You can group employees by categories like department or location and then compute totals, averages, or other statistics for specific attributes such as salary or yoe.
+       Calculate summary statistics for groups of employees. You can group employees by categories like department or location and then compute totals, averages, or other statistics for specific attributes such as salary or years of experience.
     parameters:
       - name: grouping
         description: |
@@ -55,20 +55,143 @@ prompt_targets:
         enum: [department, location, position]
       - name: aggregate_criteria
         description: |
-          Select the specific attribute you'd like to analyze. This could be something like salary, yoe, or rating. The tool will calculate the statistic you request for this attribute.
+          Select the specific attribute you'd like to analyze. This could be something like salary, years of experience, or rating. The tool will calculate the statistic you request for this attribute.
         required: true
-        enum: [salary, yoe, rating]
+        enum: [salary, years_of_experience, performance_score]
       - name: aggregate_type
         description: |
           Choose the type of statistic you'd like to calculate for the selected attribute. For example, you can calculate the sum, average, minimum, or maximum value for each group.
         required: true
         enum: [SUM, AVG, MIN, MAX]
     endpoint:
-      cluster: databasehost
+      cluster: api_server
       path: /aggregate_stats
     system_prompt: |
       You help calculate summary statistics for groups of employees. First, organize the employees by the specified grouping (e.g., department, location, or position). Then, compute the requested statistic (e.g., total, average, minimum, or maximum) for a specific attribute like salary, experience, or rating.
 
-clusters:
+  # 1. Top Employees by Performance, Projects, and Timeframe
-  databasehost:
+  - type: function_resolver
-    address: model_server
+    name: employees_projects
+    description: |
+      Fetch employees with the highest performance scores, considering their project participation and years of experience. You can filter by minimum performance score, years of experience, and department. Optionally, you can also filter by recent project participation within the last Y months.
+    parameters:
+      - name: min_performance_score
+        description: Minimum performance score to filter employees.
+        required: true
+        type: float
+      - name: min_years_experience
+        description: Minimum years of experience to filter employees.
+        required: true
+        type: integer
+      - name: department
+        description: Department to filter employees by.
+        required: true
+        type: string
+      - name: min_project_count
+        description: Minimum number of projects employees participated in (optional).
+        required: false
+        type: integer
+      - name: months_range
+        description: Timeframe (in months) for filtering recent projects (optional).
+        required: false
+        type: integer
+    endpoint:
+      cluster: api_server
+      path: /employees_projects
+    system_prompt: |
+      You are responsible for retrieving the top N employees ranked by performance and project participation. Use filters for experience and optional project criteria.
+
+
+  # 2. Employees with Salary Growth Since Last Promotion
+  - type: function_resolver
+    name: salary_growth
+    description: |
+      Fetch employees with the highest salary growth since their last promotion, grouped by department. You can filter by a minimum salary increase percentage and department.
+    parameters:
+      - name: min_salary_increase_percentage
+        description: Minimum percentage increase in salary since the last promotion.
+        required: true
+        type: float
+      - name: department
+        description: Department to filter employees by (optional).
+        required: false
+        type: string
+    endpoint:
+      cluster: api_server
+      path: /salary_growth
+    system_prompt: |
+      You are responsible for retrieving employees with the highest salary growth since their last promotion. Filter by minimum salary increase percentage and department.
+
+  # 4. Employees with Promotions and Salary Increases by Year
+  - type: function_resolver
+    name: promotions_increases
+    description: |
+      Fetch employees who were promoted and received a salary increase in a specific year, grouped by department. You can optionally filter by minimum percentage salary increase and department.
+    parameters:
+      - name: year
+        description: The year in which the promotion and salary increase occurred.
+        required: true
+        type: integer
+      - name: min_salary_increase_percentage
+        description: Minimum percentage salary increase to filter employees.
+        required: false
+        type: float
+      - name: department
+        description: Department to filter by (optional).
+        required: false
+        type: string
+    endpoint:
+      cluster: api_server
+      path: /promotions_increases
+    system_prompt: |
+      You are responsible for fetching employees who were promoted and received a salary increase in a specific year. Apply filters for salary increase percentage and department.
+
+
+  # 5. Employees with Highest Average Project Performance
+  - type: function_resolver
+    name: avg_project_performance
+    description: |
+      Fetch employees with the highest average performance across all projects they have worked on over time. You can filter by minimum project count, department, and minimum performance score.
+    parameters:
+      - name: min_project_count
+        description: Minimum number of projects an employee must have participated in.
+        required: true
+        type: integer
+      - name: min_performance_score
+        description: Minimum performance score to filter employees.
+        required: true
+        type: float
+      - name: department
+        description: Department to filter by (optional).
+        required: false
+        type: string
+    endpoint:
+      cluster: api_server
+      path: /avg_project_performance
+    system_prompt: |
+      You are responsible for fetching employees with the highest average performance across all projects they’ve worked on. Apply filters for minimum project count, performance score, and department.
+
+
+  # 6. Employees by Certification and Years of Experience
+  - type: function_resolver
+    name: certifications_experience
+    description: |
+      Fetch employees who have all the required certifications and meet the minimum years of experience. You can filter by department and provide a list of certifications to match.
+    parameters:
+      - name: certifications
+        description: List of required certifications.
+        required: true
+        type: list
+      - name: min_years_experience
+        description: Minimum years of experience.
+        required: true
+        type: integer
+      - name: department
+        description: Department to filter employees by (optional).
+        required: false
+        type: string
+    endpoint:
+      cluster: api_server
+      path: /certifications_experience
+    system_prompt: |
+      You are responsible for fetching employees who have the required certifications and meet the minimum years of experience. Optionally, filter by department.

demos/employee_details_copilot/docker-compose.yaml

@@ -40,6 +40,18 @@ services:
         retries: 20
     volumes:
       - ~/.cache/huggingface:/root/.cache/huggingface
+      - ./bolt_config.yaml:/root/bolt_config.yaml
+
+  api_server:
+    build:
+      context: api_server
+      dockerfile: Dockerfile
+    ports:
+      - "18083:80"
+    healthcheck:
+        test: ["CMD", "curl" ,"http://localhost:80/healthz"]
+        interval: 5s
+        retries: 20
 
   function_resolver:
     build:
@@ -58,6 +70,7 @@ services:
       - OLLAMA_ENDPOINT=${OLLAMA_ENDPOINT:-host.docker.internal}
       # uncomment following line to use ollama endpoint that is hosted by docker
       # - OLLAMA_ENDPOINT=ollama
+      - OLLAMA_MODEL=Bolt-Function-Calling-1B:Q4_K_M
 
   ollama:
     image: ollama/ollama
@@ -85,6 +98,8 @@ services:
     extra_hosts:
       - host.docker.internal:host-gateway
     restart: unless-stopped
+    profiles:
+      - monitoring
 
   chatbot_ui:
     build:
@@ -94,7 +109,7 @@ services:
       - "18080:8080"
     environment:
       - OPENAI_API_KEY=${OPENAI_API_KEY:?error}
-      - CHAT_COMPLETION_ENDPOINT=http://bolt:10000/v1/chat/completions
+      - CHAT_COMPLETION_ENDPOINT=http://bolt:10000/v1
 
   prometheus:
     image: prom/prometheus

demos/employee_details_copilot/grafana/dashboard.yaml

@@ -0,0 +1,12 @@
+apiVersion: 1
+
+providers:
+  - name: "Dashboard provider"
+    orgId: 1
+    type: file
+    disableDeletion: false
+    updateIntervalSeconds: 10
+    allowUiUpdates: false
+    options:
+      path: /var/lib/grafana/dashboards
+      foldersFromFilesStructure: true

demos/employee_details_copilot/grafana/dashboards/envoy_overview.json

@@ -0,0 +1,355 @@
+{
+  "annotations": {
+    "list": [
+      {
+        "builtIn": 1,
+        "datasource": {
+          "type": "grafana",
+          "uid": "-- Grafana --"
+        },
+        "enable": true,
+        "hide": true,
+        "iconColor": "rgba(0, 211, 255, 1)",
+        "name": "Annotations & Alerts",
+        "type": "dashboard"
+      }
+    ]
+  },
+  "editable": true,
+  "fiscalYearStartMonth": 0,
+  "graphTooltip": 1,
+  "links": [],
+  "panels": [
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "PBFA97CFB590B2093"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "linear",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 0
+      },
+      "id": 2,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": true
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_internal_upstream_rq_time_sum[1m]) / rate(envoy_cluster_internal_upstream_rq_time_count[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "hide": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        }
+      ],
+      "title": "request latency - internal (ms)",
+      "type": "timeseries"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "PBFA97CFB590B2093"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "linear",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 12,
+        "y": 0
+      },
+      "id": 1,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": true
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_external_upstream_rq_time_sum[1m]) / rate(envoy_cluster_external_upstream_rq_time_count[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "hide": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        }
+      ],
+      "title": "request latency - external (ms)",
+      "type": "timeseries"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "PBFA97CFB590B2093"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "linear",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 8
+      },
+      "id": 3,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": true
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_internal_upstream_rq_completed[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        },
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_external_upstream_rq_completed[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "hide": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "B",
+          "useBackend": false
+        }
+      ],
+      "title": "Upstream request count",
+      "type": "timeseries"
+    }
+  ],
+  "schemaVersion": 39,
+  "tags": [],
+  "templating": {
+    "list": []
+  },
+  "time": {
+    "from": "now-15m",
+    "to": "now"
+  },
+  "timepicker": {},
+  "timezone": "browser",
+  "title": "Intelligent Gateway Overview",
+  "uid": "adt6uhx5lk8aob",
+  "version": 3,
+  "weekStart": ""
+}

demos/employee_details_copilot/grafana/datasource.yaml

@@ -0,0 +1,9 @@
+apiVersion: 1
+
+datasources:
+- name: Prometheus
+  type: prometheus
+  url: http://prometheus:9090
+  isDefault: true
+  access: proxy
+  editable: true

demos/employee_details_copilot/prometheus/prometheus.yaml

@@ -0,0 +1,23 @@
+global:
+  scrape_interval: 15s
+  scrape_timeout: 10s
+  evaluation_interval: 15s
+alerting:
+  alertmanagers:
+  - static_configs:
+    - targets: []
+    scheme: http
+    timeout: 10s
+    api_version: v1
+scrape_configs:
+- job_name: envoy
+  honor_timestamps: true
+  scrape_interval: 15s
+  scrape_timeout: 10s
+  metrics_path: /stats
+  scheme: http
+  static_configs:
+  - targets:
+    - bolt:9901
+  params:
+    format: ['prometheus']

demos/function_calling/README.md

@@ -1,7 +1,7 @@
 # Function calling
 This demo shows how you can use intelligent prompt gateway to do function calling. This demo assumes you are using ollama running natively. If you want to run ollama running inside docker then please update ollama endpoint in docker-compose file.
 
-# Startig the demo
+# Starting the demo
 1. Ensure that submodule is up to date
    ```sh
    git submodule sync --recursive

demos/network_copilot/Bolt-FC-1B-Q3_K_L.model_file

@@ -0,0 +1,25 @@
+FROM Bolt-Function-Calling-1B-Q3_K_L.gguf
+
+# Set the size of the context window used to generate the next token
+# PARAMETER num_ctx 16384
+PARAMETER num_ctx 4096
+
+# Set parameters for response generation
+PARAMETER num_predict 1024
+PARAMETER temperature 0.1
+PARAMETER top_p 0.5
+PARAMETER top_k 32022
+PARAMETER repeat_penalty 1.0
+PARAMETER stop "<|EOT|>"
+
+# Set the random number seed to use for generation
+PARAMETER seed 42
+
+# Set the prompt template to be passed into the model
+TEMPLATE """{{ if .System }}<｜begin▁of▁sentence｜>
+{{ .System }}
+{{ end }}{{ if .Prompt }}### Instruction:
+{{ .Prompt }}
+{{ end }}### Response:
+{{ .Response }}
+<|EOT|>"""

demos/network_copilot/Bolt-FC-1B-Q4_K_M.model_file

@@ -0,0 +1,24 @@
+FROM Bolt-Function-Calling-1B-Q4_K_M.gguf
+
+# Set the size of the context window used to generate the next token
+PARAMETER num_ctx 4096
+
+# Set parameters for response generation
+PARAMETER num_predict 1024
+PARAMETER temperature 0.1
+PARAMETER top_p 0.5
+PARAMETER top_k 32022
+PARAMETER repeat_penalty 1.0
+PARAMETER stop "<|EOT|>"
+
+# Set the random number seed to use for generation
+PARAMETER seed 42
+
+# Set the prompt template to be passed into the model
+TEMPLATE """{{ if .System }}<｜begin▁of▁sentence｜>
+{{ .System }}
+{{ end }}{{ if .Prompt }}### Instruction:
+{{ .Prompt }}
+{{ end }}### Response:
+{{ .Response }}
+<|EOT|>"""

demos/network_copilot/README.md

@@ -1,7 +1,7 @@
 # Function calling
 This demo shows how you can use intelligent prompt gateway as a network copilot that could give information about correlation between packet loss with device reboots, downs, or maintainence. This demo assumes you are using ollama running natively. If you want to run ollama running inside docker then please update ollama endpoint in docker-compose file.
 
-# Startig the demo
+# Starting the demo
 1. Ensure that submodule is up to date
    ```sh
    git submodule sync --recursive

demos/network_copilot/api_server/.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "function-calling api server",
+      "cwd": "${workspaceFolder}/app",
+      "type": "debugpy",
+      "request": "launch",
+      "module": "uvicorn",
+      "args": ["main:app","--reload", "--port", "8001"],
+    }
+  ]
+}

demos/network_copilot/api_server/Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3 AS base
+
+FROM base AS builder
+
+WORKDIR /src
+
+COPY requirements.txt /src/
+RUN pip install --prefix=/runtime --force-reinstall -r requirements.txt
+
+COPY . /src
+
+FROM python:3-slim AS output
+
+COPY --from=builder /runtime /usr/local
+
+COPY /app /app
+WORKDIR /app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "80"]

demos/network_copilot/api_server/app/main.py

@@ -0,0 +1,184 @@
+from fastapi import FastAPI, Response
+from datetime import datetime, timezone
+import logging
+from pydantic import BaseModel
+from utils import load_sql, load_params
+import pandas as pd
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+app = FastAPI()
+
+@app.get("/healthz")
+async def healthz():
+    return {
+        "status": "ok"
+    }
+
+conn = load_sql()
+name_col = "name"
+
+
+class PacketDropCorrelationRequest(BaseModel):
+    from_time: str = None  # Optional natural language timeframe
+    ifname: str = None  # Optional interface name filter
+    region: str = None  # Optional region filter
+    min_in_errors: int = None
+    max_in_errors: int = None
+    min_out_errors: int = None
+    max_out_errors: int = None
+    min_in_discards: int = None
+    max_in_discards: int = None
+    min_out_discards: int = None
+    max_out_discards: int = None
+
+
+@app.post("/interface_down_pkt_drop")
+async def interface_down_packet_drop(req: PacketDropCorrelationRequest, res: Response):
+    params, filters = load_params(req)
+
+    # Join the filters using AND
+    where_clause = " AND ".join(filters)
+    if where_clause:
+        where_clause = "AND " + where_clause
+
+    # Step 3: Query packet errors and flows from interfacestats and ts_flow
+    query = f"""
+    SELECT
+      d.switchip AS device_ip_address,
+      i.in_errors,
+      i.in_discards,
+      i.out_errors,
+      i.out_discards,
+      i.ifname,
+      t.src_addr,
+      t.dst_addr,
+      t.time AS flow_time,
+      i.time AS interface_time
+    FROM
+      device d
+    INNER JOIN
+      interfacestats i
+      ON d.device_mac_address = i.device_mac_address
+    INNER JOIN
+      ts_flow t
+      ON d.switchip = t.sampler_address
+    WHERE
+      i.time >= :from_time  -- Using the converted timestamp
+      {where_clause}
+    ORDER BY
+      i.time;
+    """
+
+    correlated_data = pd.read_sql_query(query, conn, params=params)
+
+    if correlated_data.empty:
+        default_response = {
+            "device_ip_address": "0.0.0.0",  # Placeholder IP
+            "in_errors": 0,
+            "in_discards": 0,
+            "out_errors": 0,
+            "out_discards": 0,
+            "ifname": req.ifname
+            or "unknown",  # Placeholder or interface provided in the request
+            "src_addr": "0.0.0.0",  # Placeholder source IP
+            "dst_addr": "0.0.0.0",  # Placeholder destination IP
+            "flow_time": str(
+                datetime.now(timezone.utc)
+            ),  # Current timestamp or placeholder
+            "interface_time": str(
+                datetime.now(timezone.utc)
+            ),  # Current timestamp or placeholder
+        }
+        return [default_response]
+
+    logger.info(f"Correlated Packet Drop Data: {correlated_data}")
+
+    return correlated_data.to_dict(orient='records')
+
+
+class FlowPacketErrorCorrelationRequest(BaseModel):
+    from_time: str = None  # Optional natural language timeframe
+    ifname: str = None  # Optional interface name filter
+    region: str = None  # Optional region filter
+    min_in_errors: int = None
+    max_in_errors: int = None
+    min_out_errors: int = None
+    max_out_errors: int = None
+    min_in_discards: int = None
+    max_in_discards: int = None
+    min_out_discards: int = None
+    max_out_discards: int = None
+
+
+@app.post("/packet_errors_impact_flow")
+async def packet_errors_impact_flow(
+    req: FlowPacketErrorCorrelationRequest, res: Response
+):
+    params, filters = load_params(req)
+
+    # Join the filters using AND
+    where_clause = " AND ".join(filters)
+    if where_clause:
+        where_clause = "AND " + where_clause
+
+    # Step 3: Query the packet errors and flows, correlating by timestamps
+    query = f"""
+    SELECT
+      d.switchip AS device_ip_address,
+      i.in_errors,
+      i.in_discards,
+      i.out_errors,
+      i.out_discards,
+      i.ifname,
+      t.src_addr,
+      t.dst_addr,
+      t.src_port,
+      t.dst_port,
+      t.packets,
+      t.time AS flow_time,
+      i.time AS error_time
+    FROM
+      device d
+    INNER JOIN
+      interfacestats i
+      ON d.device_mac_address = i.device_mac_address
+    INNER JOIN
+      ts_flow t
+      ON d.switchip = t.sampler_address
+    WHERE
+      i.time >= :from_time
+      AND ABS(strftime('%s', t.time) - strftime('%s', i.time)) <= 300  -- Correlate within 5 minutes
+      {where_clause}
+    ORDER BY
+      i.time;
+    """
+
+    correlated_data = pd.read_sql_query(query, conn, params=params)
+
+    if correlated_data.empty:
+        default_response = {
+            "device_ip_address": "0.0.0.0",  # Placeholder IP
+            "in_errors": 0,
+            "in_discards": 0,
+            "out_errors": 0,
+            "out_discards": 0,
+            "ifname": req.ifname
+            or "unknown",  # Placeholder or interface provided in the request
+            "src_addr": "0.0.0.0",  # Placeholder source IP
+            "dst_addr": "0.0.0.0",  # Placeholder destination IP
+            "src_port": 0,
+            "dst_port": 0,
+            "packets": 0,
+            "flow_time": str(
+                datetime.now(timezone.utc)
+            ),  # Current timestamp or placeholder
+            "error_time": str(
+                datetime.now(timezone.utc)
+            ),  # Current timestamp or placeholder
+        }
+        return [default_response]
+
+    # Return the correlated data if found
+    return correlated_data.to_dict(orient='records')

demos/network_copilot/api_server/app/utils.py

@@ -0,0 +1,247 @@
+import pandas as pd
+import random
+from datetime import datetime, timedelta, timezone
+import re
+import logging
+from dateparser import parse
+import sqlite3
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+def load_sql():
+    # Example Usage
+    conn = sqlite3.connect(":memory:")
+
+    # create and load the devices table
+    device_data = generate_device_data(conn)
+
+    # create and load the interface_stats table
+    generate_interface_stats_data(conn, device_data)
+
+    # create and load the flow table
+    generate_flow_data(conn, device_data)
+
+    return conn
+
+# Function to convert natural language time expressions to "X {time} ago" format
+def convert_to_ago_format(expression):
+    # Define patterns for different time units
+    time_units = {
+        r"seconds": "seconds",
+        r"minutes": "minutes",
+        r"mins": "mins",
+        r"hrs": "hrs",
+        r"hours": "hours",
+        r"hour": "hour",
+        r"hr": "hour",
+        r"days": "days",
+        r"day": "day",
+        r"weeks": "weeks",
+        r"week": "week",
+        r"months": "months",
+        r"month": "month",
+        r"years": "years",
+        r"yrs": "years",
+        r"year": "year",
+        r"yr": "year",
+    }
+
+    # Iterate over each time unit and create regex for each phrase format
+    for pattern, unit in time_units.items():
+        # Handle "for the past X {unit}"
+        match = re.search(rf"(\d+) {pattern}", expression)
+        if match:
+            quantity = match.group(1)
+            return f"{quantity} {unit} ago"
+
+    # If the format is not recognized, return None or raise an error
+    return None
+
+
+# Function to generate random MAC addresses
+def random_mac():
+    return "AA:BB:CC:DD:EE:" + ":".join(
+        [f"{random.randint(0, 255):02X}" for _ in range(2)]
+    )
+
+
+# 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)}"
+
+
+# Generate synthetic data for the device table
+def generate_device_data(
+    conn,
+    n=1000,
+):
+    device_data = {
+        "switchip": [random_ip() for _ in range(n)],
+        "hwsku": [f"HW{i+1}" for i in range(n)],
+        "hostname": [f"switch{i+1}" for i in range(n)],
+        "osversion": [f"v{i+1}" for i in range(n)],
+        "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)}"
+            for _ in range(n)
+        ],
+        "device_mac_address": [random_mac() for _ in range(n)],
+    }
+    df = pd.DataFrame(device_data)
+    df.to_sql("device", conn, index=False)
+    return df
+
+
+# Generate synthetic data for the interfacestats table
+def generate_interface_stats_data(conn, device_df, n=1000):
+    interface_stats_data = []
+    for _ in range(n):
+        device_mac = random.choice(device_df["device_mac_address"])
+        ifname = random.choice(["eth0", "eth1", "eth2", "eth3"])
+        time = datetime.now(timezone.utc) - timedelta(
+            minutes=random.randint(0, 1440 * 5)
+        )  # random timestamps in the past 5 day
+        in_discards = random.randint(0, 1000)
+        in_errors = random.randint(0, 500)
+        out_discards = random.randint(0, 800)
+        out_errors = random.randint(0, 400)
+        in_octets = random.randint(1000, 100000)
+        out_octets = random.randint(1000, 100000)
+
+        interface_stats_data.append(
+            {
+                "device_mac_address": device_mac,
+                "ifname": ifname,
+                "time": time,
+                "in_discards": in_discards,
+                "in_errors": in_errors,
+                "out_discards": out_discards,
+                "out_errors": out_errors,
+                "in_octets": in_octets,
+                "out_octets": out_octets,
+            }
+        )
+    df = pd.DataFrame(interface_stats_data)
+    df.to_sql("interfacestats", conn, index=False)
+    return
+
+
+# Generate synthetic data for the ts_flow table
+def generate_flow_data(conn, device_df, n=1000):
+    flow_data = []
+    for _ in range(n):
+        sampler_address = random.choice(device_df["switchip"])
+        proto = random.choice(["TCP", "UDP"])
+        src_addr = random_ip()
+        dst_addr = random_ip()
+        src_port = random.randint(1024, 65535)
+        dst_port = random.randint(1024, 65535)
+        in_if = random.randint(1, 10)
+        out_if = random.randint(1, 10)
+        flow_start = int(
+            (datetime.now() - timedelta(days=random.randint(1, 30))).timestamp()
+        )
+        flow_end = int(
+            (datetime.now() - timedelta(days=random.randint(1, 30))).timestamp()
+        )
+        bytes_transferred = random.randint(1000, 100000)
+        packets = random.randint(1, 1000)
+        flow_time = datetime.now(timezone.utc) - timedelta(
+            minutes=random.randint(0, 1440 * 5)
+        )  # random flow time
+
+        flow_data.append(
+            {
+                "sampler_address": sampler_address,
+                "proto": proto,
+                "src_addr": src_addr,
+                "dst_addr": dst_addr,
+                "src_port": src_port,
+                "dst_port": dst_port,
+                "in_if": in_if,
+                "out_if": out_if,
+                "flow_start": flow_start,
+                "flow_end": flow_end,
+                "bytes": bytes_transferred,
+                "packets": packets,
+                "time": flow_time,
+            }
+        )
+    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")
+        parsed_time = parse(req.from_time, settings={"RELATIVE_BASE": datetime.now()})
+        if not parsed_time:
+            conv_time = convert_to_ago_format(req.from_time)
+            if conv_time:
+                parsed_time = parse(
+                    conv_time, settings={"RELATIVE_BASE": datetime.now()}
+                )
+            else:
+                return {
+                    "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")
+        from_time = parsed_time
+        logger.info(f"Using parsed from_time: {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}")
+
+    # Step 2: Build the dynamic SQL query based on the optional filters
+    filters = []
+    params = {"from_time": from_time}
+
+    if req.ifname:
+        filters.append("i.ifname = :ifname")
+        params["ifname"] = req.ifname
+
+    if req.region:
+        filters.append("d.region = :region")
+        params["region"] = req.region
+
+    if req.min_in_errors is not None:
+        filters.append("i.in_errors >= :min_in_errors")
+        params["min_in_errors"] = req.min_in_errors
+
+    if req.max_in_errors is not None:
+        filters.append("i.in_errors <= :max_in_errors")
+        params["max_in_errors"] = req.max_in_errors
+
+    if req.min_out_errors is not None:
+        filters.append("i.out_errors >= :min_out_errors")
+        params["min_out_errors"] = req.min_out_errors
+
+    if req.max_out_errors is not None:
+        filters.append("i.out_errors <= :max_out_errors")
+        params["max_out_errors"] = req.max_out_errors
+
+    if req.min_in_discards is not None:
+        filters.append("i.in_discards >= :min_in_discards")
+        params["min_in_discards"] = req.min_in_discards
+
+    if req.max_in_discards is not None:
+        filters.append("i.in_discards <= :max_in_discards")
+        params["max_in_discards"] = req.max_in_discards
+
+    if req.min_out_discards is not None:
+        filters.append("i.out_discards >= :min_out_discards")
+        params["min_out_discards"] = req.min_out_discards
+
+    if req.max_out_discards is not None:
+        filters.append("i.out_discards <= :max_out_discards")
+        params["max_out_discards"] = req.max_out_discards
+
+    return params, filters

demos/network_copilot/api_server/requirements.txt

@@ -0,0 +1,4 @@
+fastapi
+uvicorn
+pandas
+dateparser

demos/network_copilot/bolt_config.yaml

@@ -65,7 +65,7 @@ prompt_targets:
         required: false
         type: integer
     endpoint:
-      cluster: databasehost
+      cluster: api_server
       path: /interface_down_packet_drop
     system_prompt: |
       You are responsible for correlating packet drops with interface down events by analyzing packet errors from the given data.
@@ -120,11 +120,7 @@ prompt_targets:
         required: false
         type: integer
     endpoint:
-      cluster: databasehost
+      cluster: api_server
       path: /packet_errors_impact_flow
     system_prompt: |
       You are responsible for finding and correlating packet errors with the packet flows based on timestamps given in the data. This correlation helps identify if packet flows are impacted by packet errors.
-
-clusters:
-  databasehost:
-    address: model_server

demos/network_copilot/docker-compose.yaml

@@ -40,6 +40,18 @@ services:
         retries: 20
     volumes:
       - ~/.cache/huggingface:/root/.cache/huggingface
+      - ./bolt_config.yaml:/root/bolt_config.yaml
+
+  api_server:
+    build:
+      context: api_server
+      dockerfile: Dockerfile
+    ports:
+      - "18083:80"
+    healthcheck:
+        test: ["CMD", "curl" ,"http://localhost:80/healthz"]
+        interval: 5s
+        retries: 20
 
   function_resolver:
     build:
@@ -85,6 +97,8 @@ services:
     extra_hosts:
       - host.docker.internal:host-gateway
     restart: unless-stopped
+    profiles:
+      - monitoring
 
   chatbot_ui:
     build:

demos/network_copilot/grafana/dashboard.yaml

@@ -0,0 +1,12 @@
+apiVersion: 1
+
+providers:
+  - name: "Dashboard provider"
+    orgId: 1
+    type: file
+    disableDeletion: false
+    updateIntervalSeconds: 10
+    allowUiUpdates: false
+    options:
+      path: /var/lib/grafana/dashboards
+      foldersFromFilesStructure: true

demos/network_copilot/grafana/dashboards/envoy_overview.json

@@ -0,0 +1,355 @@
+{
+  "annotations": {
+    "list": [
+      {
+        "builtIn": 1,
+        "datasource": {
+          "type": "grafana",
+          "uid": "-- Grafana --"
+        },
+        "enable": true,
+        "hide": true,
+        "iconColor": "rgba(0, 211, 255, 1)",
+        "name": "Annotations & Alerts",
+        "type": "dashboard"
+      }
+    ]
+  },
+  "editable": true,
+  "fiscalYearStartMonth": 0,
+  "graphTooltip": 1,
+  "links": [],
+  "panels": [
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "PBFA97CFB590B2093"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "linear",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 0
+      },
+      "id": 2,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": true
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_internal_upstream_rq_time_sum[1m]) / rate(envoy_cluster_internal_upstream_rq_time_count[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "hide": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        }
+      ],
+      "title": "request latency - internal (ms)",
+      "type": "timeseries"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "PBFA97CFB590B2093"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "linear",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 12,
+        "y": 0
+      },
+      "id": 1,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": true
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_external_upstream_rq_time_sum[1m]) / rate(envoy_cluster_external_upstream_rq_time_count[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "hide": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        }
+      ],
+      "title": "request latency - external (ms)",
+      "type": "timeseries"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "PBFA97CFB590B2093"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "linear",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 8
+      },
+      "id": 3,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": true
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_internal_upstream_rq_completed[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        },
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "PBFA97CFB590B2093"
+          },
+          "disableTextWrap": false,
+          "editorMode": "code",
+          "expr": "avg(rate(envoy_cluster_external_upstream_rq_completed[1m])) by (envoy_cluster_name)",
+          "fullMetaSearch": false,
+          "hide": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "B",
+          "useBackend": false
+        }
+      ],
+      "title": "Upstream request count",
+      "type": "timeseries"
+    }
+  ],
+  "schemaVersion": 39,
+  "tags": [],
+  "templating": {
+    "list": []
+  },
+  "time": {
+    "from": "now-15m",
+    "to": "now"
+  },
+  "timepicker": {},
+  "timezone": "browser",
+  "title": "Intelligent Gateway Overview",
+  "uid": "adt6uhx5lk8aob",
+  "version": 3,
+  "weekStart": ""
+}

demos/network_copilot/grafana/datasource.yaml

@@ -0,0 +1,9 @@
+apiVersion: 1
+
+datasources:
+- name: Prometheus
+  type: prometheus
+  url: http://prometheus:9090
+  isDefault: true
+  access: proxy
+  editable: true

demos/network_copilot/prometheus/prometheus.yaml

@@ -0,0 +1,23 @@
+global:
+  scrape_interval: 15s
+  scrape_timeout: 10s
+  evaluation_interval: 15s
+alerting:
+  alertmanagers:
+  - static_configs:
+    - targets: []
+    scheme: http
+    timeout: 10s
+    api_version: v1
+scrape_configs:
+- job_name: envoy
+  honor_timestamps: true
+  scrape_interval: 15s
+  scrape_timeout: 10s
+  metrics_path: /stats
+  scheme: http
+  static_configs:
+  - targets:
+    - bolt:9901
+  params:
+    format: ['prometheus']