PointOfContactAPI

• PointOfContactAPI
  • Setup
    • API
    • Example
  • Documentation
    • Contact from Repository
    • Contact from Product
    • Entire Team Contact
    • Optimization

Setup

API

Clone the repository

git clone https://github.com/gannont14/PointOfContactAPI.git

Create a virtual environment

python3 -m venv .venv

Activate the virtual environment

source ./.venv/bin/activate

Install the requirements

pip install -r requirements.txt

Set the Flask environment variable

export FLASK_APP=flaskr

Run the flask app

flask run

Example Usage

• Example proof of concept web app, using the API to allow the user to search for results based on product name or repository name, as well as autocompletion with fuzzy finding with Levenshtein distance algorithm

How To Use It Yourself

While having the flask application running, create a new terminal instance,

Activate the virtual environment in the repository again,

source ./.venv/bin/activate

Run the example web app

streamlit run streamlit/main.py

If you want to add more data on top of the sample data, you can run the database population script

python3 flaskr/demo/populate_db.py

Documentation

Get Point of Contact From Repository Name

GET /api/repos
Parameters:
    search_query=repo_name

[
  {
    "repo name": "string",
    "repo url": "string",
    "product name": "string",
    "first name": "string",
    "last name": "string",
    "email": "string",
    "chat username": "string",
    "location": "string",
    "role": "string"
  }
]

• Example using a search query of "Enterprise-wide-empowering-circuit-repo"

GET /api/repos
Parameters:
    search_query=enterprise-wide-empowering-circuit-repo

[
  {
    "repo name": "Enterprise-wide-empowering-circuit-repo",
    "repo url": "https://github.com/sc1701d/enterprise-wide-empowering-circuit-repo",
    "product name": "Enterprise-wide empowering circuit",
    "first name": "Erika",
    "last name": "McKnight",
    "email": "erica.mcknight@sc1701d.com",
    "chat username": "@erikamcknight",
    "location": "Chicago, IL",
    "role": "Scrum Master"
  }
]

Get Point of Contact From Product Name

GET /api/products
Parameters:
    search_query=repo_name

[
  {
    "product name": "string",
    "first name": "string",
    "last name": "string",
    "email": "string",
    "chat username": "string",
    "location": "string",
    "role": "string"
  }
]

• Example using a search query of "Managed systematic Intranet"

GET /api/products
Parameters:
    search_query=managed systematic intranet

[
    {
        "product name": "Managed systematic intranet",
        "first name": "Chad",
        "last name": "Hunt",
        "email": "chad.hunt@sc1701d.com",
        "chat username": "@chadhunt",
        "location": "Bloomington, IN",
        "role": "Scrum Master"
    }
]

Get Entire Team Contact Information From Product Name

GET /api/products/all_contacts
Parameters:
    product_name=product_name

• Returns a list of all contacts associated with a project, and their roles

[
  {
    "product name": "string",
    "first name": "string",
    "last name": "string",
    "email": "string",
    "chat username": "string",
    "location": "string",
    "role": "string"
  },
  "..."
]

• Example using a search query of "fundamental optimizing paradigm"

GET /api/products/all_contacts
Parameters:
    product_name=fundamental optimizing paradigm

[
    {
        "product name": "Fundamental optimizing paradigm",
        "first name": "Gregory",
        "last name": "Barton",
        "email": "gregory.barton@sc1701d.com",
        "chat username": "@gregorybarton",
        "location": "Berlin, DE",
        "role": "Scrum Master"
    },
    {
        "chat username": "@michaelbaker",
        "email": "michael.baker@sc1701d.com",
        "first name": "Michael",
        "last name": "Baker",
        "location": "Berlin, DE",
        "product name": "Fundamental optimizing paradigm",
        "role": "Product Owner"
    },
    "..."

Optimization

The application was optimized and tested with a substantial dataset:

• 5,000 unique products
• 6 team members per product
• 30,000 total contacts in the database

Performance optimizations include:

• Custom SQL indexes for efficient querying
• Levenshtein✨ distance algorithm for fuzzy string matching
• SQL query optimization
• Composite indexes for frequently joined columns

These optimizations ensure responsive search performance even with large datasets.