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01_First_AI_Agent.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "80d970c9",
+   "metadata": {},
+   "source": [
+    "# Exploring AI Agents: Business Opportunities & Pain Points\n",
+    "\n",
+    "This notebook is the first step in my journey of learning about **Agentic AI**.  \n",
+    "The idea behind Agentic AI is not just asking a model for a single response, but guiding it through **a sequence of reasoning steps**, where each step builds on the previous one.\n",
+    "\n",
+    "In this exercise, we simulate a simple agent workflow with three stages:\n",
+    "\n",
+    "1. **Industry Selection**  \n",
+    "   Ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.\n",
+    "\n",
+    "2. **Pain Point Identification**  \n",
+    "   Once the industry is chosen, ask the LLM to highlight a **pain point** or challenge that makes the industry ripe for AI-driven solutions.\n",
+    "\n",
+    "3. **Agentic AI Proposal**  \n",
+    "   Finally, request the LLM to propose a potential **Agentic AI solution** to address the identified challenge.\n",
+    "\n",
+    "This notebook uses the **OpenAI API** with environment variable management handled by `python-dotenv` for security and reproducibility.  \n",
+    "\n",
+    "The result will give us a structured example of how an LLM can be guided step by step to simulate agent-like behavior.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26b6e7d7",
+   "metadata": {},
+   "source": [
+    "### 1. Import the environment variables and load the API Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5c50836b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Import the load_dotenv function from the python-dotenv package\n",
+    "# This allows us to read environment variables from a .env file\n",
+    "from dotenv import load_dotenv  \n",
+    "\n",
+    "# Load variables from the .env file into the environment\n",
+    "# By default, it won’t overwrite existing environment variables\n",
+    "# Setting override=True ensures .env values replace any already set\n",
+    "load_dotenv(override=True)  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a1dc1c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "OpenAI API Key exists and begins sk-proj-\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the key - if you're not using OpenAI, check whichever key you're using!\n",
+    "\n",
+    "import os\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "if openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set - please head to the troubleshooting guide in the setup folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bab577cd",
+   "metadata": {},
+   "source": [
+    "### 2. Setup Python Client for LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "feae76a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the OpenAI client\n",
+    "from openai import OpenAI\n",
+    "\n",
+    "# Create an instance of the OpenAI class\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b523b87",
+   "metadata": {},
+   "source": [
+    "### 3. Ask the LLM to pick the business idea"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d938d2f1",
+   "metadata": {},
+   "source": [
+    "The first step is to ask the LLM to propose a **business idea** that could benefit from an Agentic AI solution.  \n",
+    "This sets the context for the next steps, ensuring we are grounded in a specific industry.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "813010e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "One promising business area for an Agentic AI opportunity is healthcare administration. \n",
+       "\n",
+       "Within this sector, Agentic AI can streamline operations by automating tasks such as scheduling, patient intake, billing, and compliance management. By leveraging natural language processing and machine learning algorithms, an Agentic AI system could effectively manage appointments, process insurance claims, and optimize resource allocation, ultimately reducing administrative costs and improving patient care.\n",
+       "\n",
+       "Additionally, it could assist in decision-making processes by analyzing patient data, predicting workflow needs, or identifying bottlenecks in real time. This would allow healthcare providers to focus more on patient outcomes rather than administrative burdens. Overall, the integration of Agentic AI in healthcare administration holds potential for significant efficiency gains and cost reduction."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import Markdown, display\n",
+    "\n",
+    "# Create a list of messages in the OpenAI format\n",
+    "messages = [{'role': 'user', 'content': 'Pick up just one business area that might be worth exploring for an Agentic AI opportunity.'}]\n",
+    "\n",
+    "# Call the OpenAI API with the messages\n",
+    "response = openai.chat.completions.create(\n",
+    "    model=\"gpt-4o-mini\",\n",
+    "    messages=messages\n",
+    ")\n",
+    "\n",
+    "business_idea = response.choices[0].message.content\n",
+    "display(Markdown(business_idea))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f5c9eb6",
+   "metadata": {},
+   "source": [
+    "### 4. Identify a pain point in that industry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd03e774",
+   "metadata": {},
+   "source": [
+    "Once we have an industry, the next step is to uncover a **challenge or bottleneck** in that sector.  \n",
+    "This pain point is important because it highlights where AI could deliver real value.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0dce4f55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "One significant pain point in healthcare administration is the inefficiency and high error rates associated with managing patient scheduling and appointment systems. Healthcare facilities often face challenges such as overbooking, last-minute cancellations, and no-shows, which lead to wasted resources and frustrated patients.\n",
+       "\n",
+       "Current scheduling systems can be cumbersome and often require manual input, making them prone to human error. For instance, frontline staff might struggle to find available slots due to overlapping schedules or may fail to effectively communicate changes to patients, leading to confusion and further administrative overhead.\n",
+       "\n",
+       "An Agentic AI solution could automate and optimize the scheduling process by integrating various data sources, such as patient preferences, clinician availability, and real-time cancellations. Using natural language processing, the AI could interact with patients through chatbots or voice assistants to confirm appointments, reschedule when needed, and send reminders, ultimately enhancing the patient experience while minimizing administrative load. By streamlining this critical aspect of healthcare administration, the solution could significantly reduce no-show rates and improve overall operational efficiency."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "msg_str = f\"In the identified business idea - {business_idea}, present just one pain-point in that industry - something challenging that might be ripe for an Agentic solution.\"\n",
+    "\n",
+    "messages = [{'role': 'user', 'content': msg_str}]\n",
+    "\n",
+    "# Call the OpenAI API with the messages\n",
+    "response = openai.chat.completions.create(\n",
+    "    model=\"gpt-4o-mini\",\n",
+    "    messages=messages\n",
+    ")\n",
+    "\n",
+    "pain_point = response.choices[0].message.content\n",
+    "display(Markdown(pain_point))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4ccf08d",
+   "metadata": {},
+   "source": [
+    "### 5. LLM Call to Propose the Agentic AI solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b29a5f6",
+   "metadata": {},
+   "source": [
+    "Finally, with the pain point in hand, we ask the LLM to act as an **AI solutions architect**.  \n",
+    "The goal is to design an Agentic AI system that could directly address the identified challenge.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0675e7c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "### Proposed Agentic AI Solution: SmartSchedule AI\n",
+       "\n",
+       "**Overview:**\n",
+       "SmartSchedule AI is an intelligent scheduling assistant designed to optimize patient scheduling and appointment systems in healthcare facilities. By leveraging machine learning, natural language processing (NLP), and integration with existing healthcare software solutions, SmartSchedule AI aims to reduce inefficiencies, minimize errors, and enhance patient experience.\n",
+       "\n",
+       "**Key Features:**\n",
+       "\n",
+       "1. **Comprehensive Data Integration:**\n",
+       "   - **Multi-Source Data Aggregation:** Integrates data from EHR systems, scheduling software, clinic calendars, and patient databases to create a unified view of availability and preferences.\n",
+       "   - **Real-Time Updates:** Automatically updates schedules in real-time to reflect clinician availability, patient cancellations, and last-minute changes.\n",
+       "\n",
+       "2. **Intelligent Appointment Management:**\n",
+       "   - **Dynamic Slot Optimization:** Uses machine learning algorithms to analyze historical data and patterns, predicting the best times for appointments based on patient habits, clinician schedules, and resource availability.\n",
+       "   - **Automated Overbooking Management:** Automatically flags potential overbooking scenarios and suggests alternative slots based on predicted patient no-shows or cancellations.\n",
+       "\n",
+       "3. **Patient Engagement through NLP:**\n",
+       "   - **Conversational Interface:** Implements chatbots and voice assistants capable of engaging with patients to schedule, confirm, reschedule, or cancel appointments using natural language, minimizing the need for human intervention.\n",
+       "   - **Personalized Reminders and Notifications:** Sends automated reminders via SMS, email, or app notifications to patients with tailored messages, reducing no-shows by prompting patients about their appointments.\n",
+       "\n",
+       "4. **Cancellation and Rescheduling Automation:**\n",
+       "   - **Proactive Follow-up:** Initiates follow-up conversations with patients post-appointment, encouraging them to book their next visit while understanding the factors affecting cancellations.\n",
+       "   - **Last-Minute Slot Filling:** Automatically offers available time slots to patients on a waitlist in case of cancellations or rescheduling, improving resource utilization.\n",
+       "\n",
+       "5. **Data Analytics and Reporting:**\n",
+       "   - **Performance Metrics Dashboard:** Provides healthcare administrators with a dashboard to track key performance indicators, such as patient no-show rates, average wait times, and scheduling efficiency.\n",
+       "   - **Predictive Analytics:** Uses historical data to forecast appointment trends, helping clinics plan better for peak times and allocate resources efficiently.\n",
+       "\n",
+       "6. **Compliance and Security:**\n",
+       "   - **HIPAA Compliance:** Ensures that all patient data used by SmartSchedule AI is securely handled and compliant with healthcare regulations to protect patient privacy.\n",
+       "   - **User Authentication:** Incorporates multi-factor authentication for users accessing schedule data, ensuring that only authorized personnel can manipulate scheduling information.\n",
+       "\n",
+       "**Implementation Plan:**\n",
+       "\n",
+       "1. **Pilot Program:**\n",
+       "   - Roll out the SmartSchedule AI solution in a selected healthcare facility to evaluate its effectiveness and gather feedback for adjustments.\n",
+       "\n",
+       "2. **Staff Training:**\n",
+       "   - Provide comprehensive training for administrative staff on how to use SmartSchedule AI, focusing on maximizing benefits and understanding the system's insights.\n",
+       "\n",
+       "3. **Integration with Existing Systems:**\n",
+       "   - Work with IT teams to ensure that SmartSchedule AI integrates seamlessly with existing EHR and scheduling systems, avoiding disruption.\n",
+       "\n",
+       "4. **Continuous Improvement Cycle:**\n",
+       "   - Regularly review performance data, engage with staff and patients for feedback, and iterate on the AI’s algorithms to enhance functionality and user experience.\n",
+       "\n",
+       "**Expected Outcomes:**\n",
+       "- **Reduced No-Show Rates:** Aiming for at least a 20% reduction in patient no-show rates.\n",
+       "- **Enhanced Patient Experience:** Streamlined communication and personalized engagement leading to increased patient satisfaction.\n",
+       "- **Operational Efficiency:** Decreased administrative burden on staff, allowing them to focus on higher-value tasks within healthcare delivery.\n",
+       "\n",
+       "By implementing the SmartSchedule AI solution, healthcare facilities can transform their patient scheduling systems, achieving a more efficient and patient-centric approach to appointment management."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "msg_str = f\"Propose an Agentic AI solution to address this challenge: {pain_point}\"\n",
+    "\n",
+    "messages = [{'role': 'user', 'content': msg_str}]\n",
+    "\n",
+    "# Call the OpenAI API with the messages\n",
+    "response = openai.chat.completions.create(\n",
+    "    model=\"gpt-4o-mini\",\n",
+    "    messages=messages\n",
+    ")\n",
+    "\n",
+    "agentic_ai_solution = response.choices[0].message.content\n",
+    "display(Markdown(agentic_ai_solution))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

ReadMe.md

@@ -0,0 +1,85 @@
+---
+title: Know_About_Me
+app_file: chatwithme.py
+sdk: gradio
+sdk_version: 6.3.0
+---
+# **ML Model Versioning and Tracking using DVC with Google Cloud Storage**
+
+## **Overview**
+This project demonstrates a complete **Machine Learning (ML) workflow** with **Data Version Control (DVC)** integrated with **Google Cloud Storage (GCS)** for version tracking of datasets and models.  
+The objective is to maintain reproducibility of experiments and enable version rollback to any model iteration.
+
+---
+
+## **Objective**
+To build a version-controlled ML pipeline that:
+- Tracks datasets and models using **DVC**
+- Stores versions in **Google Cloud Storage (GCS)**
+- Allows restoration of previous model and dataset versions using Git and DVC
+
+---
+
+## **Setup Instructions**
+
+## **Environment Setup on Google Cloud Shell**
+```shell
+git init
+git clone https://github.com/w1ndwatcher/MLOps.git
+cd MLOps/Week2
+# Create and Activate Virtual Environment
+python3 -m venv .env
+source .env/bin/activate
+# Install Required Packages
+pip install -r requirements.txt
+pip install dvc-gs
+# Initialize DVC
+dvc init --subdir
+git add .dvc
+git commit -m "Initialize DVC"
+# Point DVC to your GCS bucket
+dvc remote add -d gcsremote gs://21f1006327-assignment-2-sept-2025
+# Copy dataset zip from bucket to local
+gsutil cp gs://21f1006327-assignment-2-sept-2025/datasets/data.zip .
+# Unzip dataset
+unzip -q data.zip 
+rm -f data.zip
+# Track Dataset Using DVC
+dvc add data         # track dataset
+git add data.dvc .gitignore
+git commit -m "Add first dataset (150 records)"
+# Train Model (Iteration 1)
+python train.py
+dvc add model/model.joblib
+git add model/model.joblib.dvc model/.gitignore
+git commit -m "First iteration done with 150 records"
+git tag -a "v1.0" -m "model v1.0, 150 records"
+# Push Artifacts to Google Cloud Storage
+dvc push
+
+# Download Updated Dataset
+gsutil cp gs://21f1006327-assignment-2-sept-2025/datasets/new-data.zip .
+# Unzip and overwrite data folder
+unzip -q new-data.zip
+rm -f new-data.zip
+# Track updated dataset
+dvc add data
+git add data.dvc
+git commit -m "Second iteration done with 200 records"
+# Retrain model
+python train.py
+dvc add model/model.joblib
+git add model/model.joblib.dvc model/.gitignore
+git commit -m "Model retrained with 200 records"
+git tag -a "v2.0" -m "model v2.0, 200 records"
+# Push updated dataset and model to GCS
+dvc push
+
+# Restoring Previous Versions
+# To restore version 1.0:-
+git checkout v1.0
+dvc checkout      # restores dataset and model for v1.0
+# To restore version 2.0:-
+git checkout v2.0
+dvc checkout      # restores dataset and model for v2.0
+```

chatwithme.py

@@ -0,0 +1,44 @@
+# read env key for openai
+from dotenv import load_dotenv
+# import OpenAI client
+from openai import OpenAI
+# to read pdf file and extract text
+from pypdf import PdfReader
+# to make the UI
+import gradio as gr
+from pathlib import Path
+
+load_dotenv(override=True)
+# Create an instance of the OpenAI class
+openai = OpenAI()
+
+BASE_DIR = Path(__file__).parent
+
+reader = PdfReader(BASE_DIR / "me" / "Profile.pdf")
+
+linkedin = ""
+for page in reader.pages:
+    text = page.extract_text()
+    if text:
+        linkedin += text
+
+# Read the summary text
+with open(BASE_DIR / "me" / "summary.txt", encoding="utf-8") as f:
+    summary = f.read()
+
+name = "Ayushi Saxena"
+system_prompt = f"You are acting as {name}. You are answering questions on {name}'s website, \
+particularly questions related to {name}'s career, background, skills and experience. \
+Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \
+You are given a summary of {name}'s background and LinkedIn profile which you can use to answer questions. \
+Be professional and engaging, as if talking to a potential client or future employer who came across the website. \
+If you don't know the answer, say so."
+system_prompt += f"\n\n## Summary:\n{summary}\n\n## LinkedIn Profile:\n{linkedin}\n\n"
+system_prompt += f"With this context, please chat with the user, always staying in character as {name}."
+
+def chat(message, history):
+    messages = [{'role': 'system', 'content': system_prompt}] + history + [{'role': 'user', 'content': message}]
+    response = openai.chat.completions.create(model = "gpt-4o-mini", messages = messages)
+    return response.choices[0].message.content
+
+gr.ChatInterface(chat).launch()

me/summary.txt

@@ -0,0 +1,2 @@
+My name is Ayushi Saxena. I am a software engineer and GenAI expert. I'm from Delhi, currently working in Sonipat, Haryana.
+I love online gaming. Currently I'm playing Brawl Stars but I also like strategic games like Sid Mier's Civilization series.

venv/.gitignore

@@ -0,0 +1,2 @@
+# Created by venv; see https://docs.python.org/3/library/venv.html
+*

venv/Lib/site-packages/IPython/__init__.py

@@ -0,0 +1,144 @@
+# PYTHON_ARGCOMPLETE_OK
+"""
+IPython: tools for interactive and parallel computing in Python.
+
+https://ipython.org
+"""
+#-----------------------------------------------------------------------------
+#  Copyright (c) 2008-2011, IPython Development Team.
+#  Copyright (c) 2001-2007, Fernando Perez <fernando.perez@colorado.edu>
+#  Copyright (c) 2001, Janko Hauser <jhauser@zscout.de>
+#  Copyright (c) 2001, Nathaniel Gray <n8gray@caltech.edu>
+#
+#  Distributed under the terms of the Modified BSD License.
+#
+#  The full license is in the file COPYING.txt, distributed with this software.
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+import sys
+import warnings
+
+#-----------------------------------------------------------------------------
+# Setup everything
+#-----------------------------------------------------------------------------
+
+# Don't forget to also update setup.py when this changes!
+if sys.version_info < (3, 11):
+    raise ImportError(
+        """
+IPython 8.31+ supports Python 3.11 and above, following SPEC0
+IPython 8.19+ supports Python 3.10 and above, following SPEC0.
+IPython 8.13+ supports Python 3.9 and above, following NEP 29.
+IPython 8.0-8.12 supports Python 3.8 and above, following NEP 29.
+When using Python 2.7, please install IPython 5.x LTS Long Term Support version.
+Python 3.3 and 3.4 were supported up to IPython 6.x.
+Python 3.5 was supported with IPython 7.0 to 7.9.
+Python 3.6 was supported with IPython up to 7.16.
+Python 3.7 was still supported with the 7.x branch.
+
+See IPython `README.rst` file for more information:
+
+    https://github.com/ipython/ipython/blob/main/README.rst
+
+"""
+    )
+
+#-----------------------------------------------------------------------------
+# Setup the top level names
+#-----------------------------------------------------------------------------
+
+from .core.getipython import get_ipython
+from .core import release
+from .core.application import Application
+from .terminal.embed import embed
+
+from .core.interactiveshell import InteractiveShell
+from .utils.sysinfo import sys_info
+from .utils.frame import extract_module_locals
+
+__all__ = ["start_ipython", "embed", "embed_kernel"]
+
+# Release data
+__author__ = '%s <%s>' % (release.author, release.author_email)
+__license__  = release.license
+__version__  = release.version
+version_info = release.version_info
+# list of CVEs that should have been patched in this release.
+# this is informational and should not be relied upon.
+__patched_cves__ = {"CVE-2022-21699", "CVE-2023-24816"}
+
+
+def embed_kernel(module=None, local_ns=None, **kwargs):
+    """Embed and start an IPython kernel in a given scope.
+
+    If you don't want the kernel to initialize the namespace
+    from the scope of the surrounding function,
+    and/or you want to load full IPython configuration,
+    you probably want `IPython.start_kernel()` instead.
+
+    This is a deprecated alias for `ipykernel.embed.embed_kernel()`,
+    to be removed in the future.
+    You should import directly from `ipykernel.embed`; this wrapper
+    fails anyway if you don't have `ipykernel` package installed.
+
+    Parameters
+    ----------
+    module : types.ModuleType, optional
+        The module to load into IPython globals (default: caller)
+    local_ns : dict, optional
+        The namespace to load into IPython user namespace (default: caller)
+    **kwargs : various, optional
+        Further keyword args are relayed to the IPKernelApp constructor,
+        such as `config`, a traitlets :class:`Config` object (see :ref:`configure_start_ipython`),
+        allowing configuration of the kernel.  Will only have an effect
+        on the first embed_kernel call for a given process.
+    """
+
+    warnings.warn(
+        "import embed_kernel from ipykernel.embed directly (since 2013)."
+        " Importing from IPython will be removed in the future",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    (caller_module, caller_locals) = extract_module_locals(1)
+    if module is None:
+        module = caller_module
+    if local_ns is None:
+        local_ns = dict(**caller_locals)
+    
+    # Only import .zmq when we really need it
+    from ipykernel.embed import embed_kernel as real_embed_kernel
+    real_embed_kernel(module=module, local_ns=local_ns, **kwargs)
+
+def start_ipython(argv=None, **kwargs):
+    """Launch a normal IPython instance (as opposed to embedded)
+
+    `IPython.embed()` puts a shell in a particular calling scope,
+    such as a function or method for debugging purposes,
+    which is often not desirable.
+
+    `start_ipython()` does full, regular IPython initialization,
+    including loading startup files, configuration, etc.
+    much of which is skipped by `embed()`.
+
+    This is a public API method, and will survive implementation changes.
+
+    Parameters
+    ----------
+    argv : list or None, optional
+        If unspecified or None, IPython will parse command-line options from sys.argv.
+        To prevent any command-line parsing, pass an empty list: `argv=[]`.
+    user_ns : dict, optional
+        specify this dictionary to initialize the IPython user namespace with particular values.
+    **kwargs : various, optional
+        Any other kwargs will be passed to the Application constructor,
+        such as `config`, a traitlets :class:`Config` object (see :ref:`configure_start_ipython`),
+        allowing configuration of the instance (see :ref:`terminal_options`).
+    """
+    from IPython.terminal.ipapp import launch_new_instance
+    return launch_new_instance(argv=argv, **kwargs)

venv/Lib/site-packages/IPython/__main__.py

@@ -0,0 +1,14 @@
+# PYTHON_ARGCOMPLETE_OK
+# encoding: utf-8
+"""Terminal-based IPython entry point."""
+# -----------------------------------------------------------------------------
+#  Copyright (c) 2012, IPython Development Team.
+#
+#  Distributed under the terms of the Modified BSD License.
+#
+#  The full license is in the file COPYING.txt, distributed with this software.
+# -----------------------------------------------------------------------------
+
+from IPython import start_ipython
+
+start_ipython()

venv/Lib/site-packages/IPython/core/__pycache__/completer.cpython-313.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50a86225ae98cfd24eca4ce5691aba1aea1aaf07bbbb7a8f303eb4d7e45a6f9b
+size 132264

venv/Lib/site-packages/IPython/core/__pycache__/interactiveshell.cpython-313.pyc

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8366bee3beb042cd3c53c8a938aa8af7ee1ab16639621956f5c18f0572f4cc94
+size 152426