Agent-Based Task Execution

Setup the LLM and Agent

First, we’ll set up our LLM instance that will power our agent. For agent-based tasks, models with strong reasoning abilities work best. We’ll use GPT-4o-mini for these examples, but you could also use other local or cloud models like Llama-3.1-8B, Mixtral, or Claude.

from onprem import LLM
from onprem.pipelines import Agent
llm = LLM('openai/gpt-4o-mini', mute_stream=True) 
agent = Agent(llm)

Giving the Agent Access to Tools

Next, we will give the agent access tools when executing a given task. Examples of tool types include the ability to do the following: 1. perform a web search 2. visit a web page 3. search your documents stored within a vector store (e.g., agentic RAG) 4. accessing Python interpreter 5. accessing an MCP server 6. executing a custom function that you provide (i.e., implementing your own custom tools)

Example: Web Information Extraction

In the next example, we will use the Web View tool to extract information from a Web page.

agent = Agent(llm)
agent.add_webview_tool()
answer = agent.run("What is the highest level of education of the person listed on this page: https://arun.maiya.net?")
╭──────────────────────────────────────────────────── New run ────────────────────────────────────────────────────╮
│                                                                                                                 │
│ What is the highest level of education of the person listed on this page: https://arun.maiya.net?               │
│                                                                                                                 │
╰─ AgentModel - None ─────────────────────────────────────────────────────────────────────────────────────────────╯

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Calling tool: 'visit_webpage' with arguments: {'url': 'https://arun.maiya.net'}                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Observations: Arun S. Maiya

Arun S. Maiya   
    
arun |at] maiya |dot] net  
|CV](asmcv.pdf) | |LinkedIn](https://www.linkedin.com/in/arun-maiya/) | |GitHub](https://github.com/amaiya)

!|](asmpic.jpg)

Basics
======

I am a computer scientist at the |Institute for Defense Analyses (IDA)](http://www.ida.org), a federally-funded 
|think tank](http://en.wikipedia.org/wiki/List_of_federally_funded_research_and_development_centers) in the 
Washington D.C. metro area. My research broadly focuses on the study of computational methods to *extract meaning 
from raw data* and includes the areas of natural language processing, machine learning, data mining, computer 
vision, and network science (e.g., social network analysis). I like building tools to make machine learning easier 
to apply in new ways and new areas. Through my work, I have contributed to national-level strategic-planning 
activities and R&D roadmaps. I completed a Ph.D. in Computer Science at the |Laboratory for Computational 
Population Biology](http://compbio.cs.uic.edu/), which is within the |Department of Computer 
Science](http://www.cs.uic.edu/) at the
|University of Illinois at Chicago (UIC)](http://www.uic.edu/). My CV is |here](http://arun.maiya.net/asmcv.pdf).

Software
========

* **|ktrain](https://github.com/amaiya/ktrain)** is a Python library that makes deep learning and AI more 
accessible and easier to apply. With support for many different data types including text, images, and graphs, 
ktrain has been used for a wide range of use cases in industry, government, and academia. Examples include analyses
for the U.S. Economic Census, financial crime analytics at Big 4 accounting firms, intelligence analyses, and 
|CoronaCentral.ai](https://coronacentral.ai), a machine-learning-enhanced search engine for coronavirus 
publications at Stanford University.
* **|CausalNLP](https://github.com/amaiya/causalnlp)** is a practical toolkit for causal inference with text as 
treatment, outcome, or "controlled-for" variable.
* **|IDATA](https://arxiv.org/abs/1308.2359)** is a suite of software capabilities designed to facilitate search, 
exploration, and analyses of very large document sets using state-of-the-art machine learning, NLP, and information
retrieval. It has been used for a variety of different application in the |DoD](https://www.defense.gov) including 
cyber damage assessments, biosurveillance, and policy analyses.
* **|OnPrem.LLM](https://github.com/amaiya/onprem)** is a simple Python package for generative AI that makes it 
easier to run large language models (LLMs) on your own machine using non-public data.

Publications
============

|**ktrain: A Low-Code Library for Augmented Machine 
Learning**](https://www.jmlr.org/papers/volume23/21-1124/21-1124.pdf)  
A.S. Maiya  
 *Journal of Machine Learning Research (JMLR).*  May 2022.

|**CausalNLP: A Practical Toolkit for Causal Inference with Text**](https://arxiv.org/abs/2106.08043)   
A.S. Maiya  
 *arXiv preprint arXiv:2106.08043 .*  Jun 2021. |arXiv only]

|**A Framework for Comparing Groups of Documents**](https://arxiv.org/abs/1508.05902)  
A.S. Maiya  
 *Proc. 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP '15).*  Lisbon, Portugal. Sep 
2015.

|**Mining Measured Information from Text**](https://arxiv.org/abs/1505.01072)  
A.S. Maiya, D. Visser, and A. Wan  
 *Proc. 38th Annual ACM SIGIR Conference (SIGIR '15).* Santiago, Chile. Aug 2015.

|**Topic Similarity Networks: Visual Analytics for Large Document Sets**](https://arxiv.org/abs/1409.7591)  
A.S. Maiya and R.M. Rolfe  
 *Proc. 2014 IEEE International Conference on Big Data (IEEE BigData '14).* Washington, D.C., Oct 2014.

|**Exploratory Analysis of Highly Heterogeneous Document Collections**](https://arxiv.org/abs/1308.2359)  
A.S. Maiya, J.P. Thompson, F. Loaiza-Lemos, and R.M. Rolfe  
 *Proc. 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '13).* Chicago, IL, Aug 2013.

|**Expansion and Decentralized Search in Complex 
Networks**](http://link.springer.com/article/10.1007/s10115-012-0596-4)  
A.S. Maiya and T.Y. Berger-Wolf  
 *Journal of Knowledge and Information Systems.* First published online January 2013.

|**Supervised Learning in the Wild: Text Classification for Critical 
Technologies**](https://ieeexplore.ieee.org/document/6415660)  
A.S. Maiya, F. Loaiza-Lemos, and R.M. Rolfe  
 *Proc. IEEE Military Communications Conference (MILCOM '12).* Orlando, FL, Oct 2012.

|**Benefits of Bias: Towards Better Characterization of Network Sampling**](https://arxiv.org/abs/1109.3911)  
A.S. Maiya and T.Y. Berger-Wolf  
 *Proc. 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '11).* San Diego, CA, Aug 2011.

|**Aggression, Grooming, and Group-level Cooperation in White-faced Capuchins: Insights from Social 
Networks**](http://onlinelibrary.wiley.com/doi/10.1002/ajp.20959/abstract)  
M.C. Crofoot, D.I. Rubenstein, A.S. Maiya, and T.Y. Berger-Wolf  
 *American Journal of Primatology.* First published online May 2011.

|**Sampling and Inference in Complex Networks**](https://dl.acm.org/doi/abs/10.5555/2395432)  
A.S. Maiya  
 *Ph.D. Dissertation, University of Illinois at Chicago (UIC).* Chicago, IL, Apr 2011.

|**Expansion and Search in Networks**](https://arxiv.org/abs/1009.4383)  
A.S. Maiya and T.Y. Berger-Wolf  
 *Proc. 19th ACM Intl. Conference on Information and Knowledge Management (CIKM '10).* Toronto, Canada, Oct 2010.

|**Online Sampling of High Centrality Individuals in Social 
Networks**](https://link.springer.com/chapter/10.1007/978-3-642-13657-3_12)  
A.S. Maiya and T.Y. Berger-Wolf  
 *Proc. 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '10).* Hyderabad, India, Jun 
2010.

|**Sampling Community Structure**](https://dl.acm.org/doi/10.1145/1772690.1772762)  
A.S. Maiya and T.Y. Berger-Wolf  
 *Proc. 19th ACM Intl. Conference on the World Wide Web (WWW '10).* Raleigh, NC, Apr 2010.

|**Inferring the Maximum Likelihood Hierarchy in Social Networks**](https://ieeexplore.ieee.org/document/5284124)  
A.S. Maiya and T.Y. Berger-Wolf  
 *Proc. 12th IEEE Intl. Conference on Computational Science and Engineering (CSE '09).* Vancouver, Canada, Aug 
2009.

|**The Impact of Structural Changes on Predictions of Diffusion in 
Networks**](https://www.computer.org/csdl/proceedings-article/icdmw/2008/3503a939/12OmNBU1jIK)  
M. Lahiri, A.S. Maiya, R. Sulo, Habiba and T.Y. Berger-Wolf  
*ICDM '08 Workshop on Analysis of Dynamic Networks*. Pisa, Italy, Dec 2008.

Honors and Awards
=================

**Goodpaster Award for Excellence in Research**, Institute for Defense Analyses, 2021 ||News 
Release](https://www.ida.org/research-and-publications/publications/all/a/ar/arun-maiya-receives-2021-goodpaster-aw
ard-for-excellence-in-research)]

> *This prize is named for Gen. Andrew J. Goodpaster (USA, retired) and is awarded to an individual demonstrating 
"research excellence, exceptional analytic achievement and intellectual leadership."*

**Welch Award for Best External Research**, Institute for Defense Analyses, 2016

> *Named in honor of General Larry D. Welch (USAF, ret.), this award "honors individuals whose external research 
publications exemplify General Welch's high standards of analytic excellence and relevance."*

**AFEI Award for Excellence in Enterprise Information**, NDIA (formerly Association for Enterprise Information), 
2015

> *This award is to "recognize and reward the contributions and achievements of project teams that exemplify 
excellence in achieving integrated enterprises. Winning teams are models of the best applications of technology and
leadership to improve enterprise performance."*

<script

[Step 1: Duration 1.50 seconds]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ Calling tool: 'final_answer' with arguments: {'answer': 'Ph.D. in Computer Science'}                            │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Final answer: Ph.D. in Computer Science

[Step 2: Duration 1.43 seconds]

print(answer)
Ph.D. in Computer Science

Example: Agentic RAG

You can also give the agent access to a vector store containing our documents. In this example, we will ingest a document about Generative AI into a vector store and provide the store to the agent as a tool.

from onprem.ingest.stores import DenseStore

store = DenseStore('/tmp/myvectordb')
store.ingest('tests/sample_data/docx_example/')
Creating new vectorstore at /tmp/myvectordb
Loading documents from tests/sample_data/docx_example/
Loading new documents: 100%|██████████████████████| 1/1 [00:04<00:00,  4.05s/it]
Processing and chunking 1 new documents: 100%|█████████████████████████████████████| 1/1 [00:00<00:00, 2184.53it/s]
Split into 17 chunks of text (max. 500 chars each for text; max. 2000 chars for tables)
Creating embeddings. May take some minutes...
100%|████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.67it/s]
Ingestion complete! You can now query your documents using the LLM.ask or LLM.chat methods

So far, we have been using the default tool-calling agent, which relies on the LLM to generate precise JSON structures that specify tool names and arguments required to complete a task.

By constrast, the code-agent generates and runs code to solve a task. We will use the code-agent in this final example.

agent = Agent(llm, agent_type='code')
agent.add_vectorstore_tool(name='genai_search', 
                           description='Searches a database of information on generative AI.',
                           store=store)

answer = agent.run("Summarize some generative AI use cases in Markdown format. You may need to run at least three queries of the database.")
╭──────────────────────────────────────────────────── New run ────────────────────────────────────────────────────╮
│                                                                                                                 │
│ Summarize some generative AI use cases in Markdown format. You may need to run at least three queries of the    │
│ database.                                                                                                       │
│                                                                                                                 │
╰─ AgentModel - None ─────────────────────────────────────────────────────────────────────────────────────────────╯

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── 
  use_cases_general = genai_search(query="generative AI use cases")                                                
  print(use_cases_general)                                                                                         
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 

Execution logs:
[{'id': '901846fb-fc97-42ef-810a-cedc8abfbcb8', 'score': 0.6182159185409546, 'text': '3. What is generative 
AI?\n\nEven though AI is not new, you have probably been hearing a lot about easy-to-use, publicly available 
Generative AI tools like ChatGPT and Google Gemini.\u202f \n\nBut what is generative AI?\u202f\u202f \n\nIt is a 
type of AI technology that automatically generates (or creates) content in response to prompts given to it by 
users.\u202f \n\nThese tools can generate text, images, music, video, code and other formats.'}, {'id': 
'0b660b75-3bf3-47ab-a4bb-0f52039ec442', 'score': 0.5850328207015991, 'text': 'An introduction to generative 
AI\n\n1. Introduction\n\nThis activity will give a brief overview of what Generative AI is and how it is being 
used.\n\nUsing this tutorial\n\nUse the back and next buttons at the bottom right of the screen to navigate through
the tutorial. Alternatively, use the contents button to jump to a specific page.\n\nYou will need to allow 
approximately\xa05\xa0minutes\xa0to complete the tutorial.\n\nLearning outcomes\n\nAfter completing this tutorial 
you will be able to:'}, {'id': '57831af5-b767-4565-ba01-8845d8e6b261', 'score': 0.568540632724762, 'text': 
'Question 5 Feedback for Option 3\n\nTrue. Generative AI tools rely on pattern recognition and do not truly 
understand the content, which can lead to plausible but misleading or incorrect outputs.\n\nQuestion 5 Feedback for
Option 4\n\nFalse. Each response is unique; generative AI tools do not provide the same response every 
time.\n\nBeing digital Copyright © 2024 The Open University'}, {'id': '3ab09f45-f5d2-468e-ae8e-710c116f51b7', 
'score': 0.5618582367897034, 'text': 'Recognise generative AI in daily life: Identify common generative AI 
applications like chatbots, facial recognition, streaming services, and digital assistants.\n\nUnderstand 
generative AI: Explain generative AI and its ability to create diverse content such as text, images, music, video, 
and code.\n\nDescribe LLMs: Understand how\xa0Large Language Models (LLMs) work.\n\n2. Examples of AI in everyday 
life'}, {'id': 'ec272b15-9ff9-448b-a39b-3b89f0d6f524', 'score': 0.5566920042037964, 'text': '5. Quiz\n\nQuestion 1 
of 5\n\nWhat is generative AI?\n\nAI that only processes data\n\nAI that creates content based on user 
prompts\n\nAI that cannot update its data\n\nAI that performs physical tasks\n\nQuestion 1 Feedback for Option 
1\n\nFalse. Typing on a keyboard is a manual activity that does not involve AI.\n\nQuestion 1 Feedback for Option 
2\n\nFalse. Watching a sunset is a natural experience, not related to generative AI.\n\nQuestion 1 Feedback for 
Option 3'}]

Out: None

[Step 1: Duration 2.20 seconds]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── 
  use_cases_content_creation = genai_search(query="generative AI content creation use cases")                      
  print(use_cases_content_creation)                                                                                
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 

Execution logs:
[{'id': '901846fb-fc97-42ef-810a-cedc8abfbcb8', 'score': 0.6875638961791992, 'text': '3. What is generative 
AI?\n\nEven though AI is not new, you have probably been hearing a lot about easy-to-use, publicly available 
Generative AI tools like ChatGPT and Google Gemini.\u202f \n\nBut what is generative AI?\u202f\u202f \n\nIt is a 
type of AI technology that automatically generates (or creates) content in response to prompts given to it by 
users.\u202f \n\nThese tools can generate text, images, music, video, code and other formats.'}, {'id': 
'3d776586-b95b-4cad-8915-4d65926a5fe4', 'score': 0.6328049302101135, 'text': 'Question 2 Feedback for Option 
1\n\nFalse. This describes data processing AI, not generative AI.\n\nQuestion 2 Feedback for Option 2 \n\nTrue. 
Generative AI creates content like text, images, music, etc., in response to user prompts.\n\nQuestion 2 Feedback 
for Option 3\n\nFalse. Generative AI can update its data with new information.\n\nQuestion 2 Feedback for Option 4 
\n\nFalse. Generative AI focuses on content creation, not performing physical tasks.\n\nQuestion 3 of 5'}, {'id': 
'cd369b7e-383b-42e2-bc5e-f514d260d7dd', 'score': 0.6124569177627563, 'text': 'Question 3 of 5\n\nWhich of the 
following is a text generative AI tool?\n\nSpotify\n\nGoogle Maps\n\nDall-E2\n\nChatGPT\n\nQuestion 3 Feedback for 
Option 1\n\nFalse. Spotify uses AI for music recommendations, not text generation.\n\nQuestion 3 Feedback for 
Option 2\n\nFalse. Google Maps uses AI for navigation and travel information, not text generation.\n\n\n\nQuestion 
3 Feedback for Option 3\n\nFalse. DALL-E2 generates images and art, not text.\n\nQuestion 3 Feedback for Option 
4'}, {'id': '0b660b75-3bf3-47ab-a4bb-0f52039ec442', 'score': 0.582166314125061, 'text': 'An introduction to 
generative AI\n\n1. Introduction\n\nThis activity will give a brief overview of what Generative AI is and how it is
being used.\n\nUsing this tutorial\n\nUse the back and next buttons at the bottom right of the screen to navigate 
through the tutorial. Alternatively, use the contents button to jump to a specific page.\n\nYou will need to allow 
approximately\xa05\xa0minutes\xa0to complete the tutorial.\n\nLearning outcomes\n\nAfter completing this tutorial 
you will be able to:'}, {'id': 'ec272b15-9ff9-448b-a39b-3b89f0d6f524', 'score': 0.5623699426651001, 'text': '5. 
Quiz\n\nQuestion 1 of 5\n\nWhat is generative AI?\n\nAI that only processes data\n\nAI that creates content based 
on user prompts\n\nAI that cannot update its data\n\nAI that performs physical tasks\n\nQuestion 1 Feedback for 
Option 1\n\nFalse. Typing on a keyboard is a manual activity that does not involve AI.\n\nQuestion 1 Feedback for 
Option 2\n\nFalse. Watching a sunset is a natural experience, not related to generative AI.\n\nQuestion 1 Feedback 
for Option 3'}]

Out: None

[Step 2: Duration 2.12 seconds]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── 
  use_cases_applications = genai_search(query="applications of generative AI in various domains")                  
  print(use_cases_applications)                                                                                    
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 

Execution logs:
[{'id': '0b660b75-3bf3-47ab-a4bb-0f52039ec442', 'score': 0.5789191722869873, 'text': 'An introduction to generative
AI\n\n1. Introduction\n\nThis activity will give a brief overview of what Generative AI is and how it is being 
used.\n\nUsing this tutorial\n\nUse the back and next buttons at the bottom right of the screen to navigate through
the tutorial. Alternatively, use the contents button to jump to a specific page.\n\nYou will need to allow 
approximately\xa05\xa0minutes\xa0to complete the tutorial.\n\nLearning outcomes\n\nAfter completing this tutorial 
you will be able to:'}, {'id': '901846fb-fc97-42ef-810a-cedc8abfbcb8', 'score': 0.5656728744506836, 'text': '3. 
What is generative AI?\n\nEven though AI is not new, you have probably been hearing a lot about easy-to-use, 
publicly available Generative AI tools like ChatGPT and Google Gemini.\u202f \n\nBut what is generative 
AI?\u202f\u202f \n\nIt is a type of AI technology that automatically generates (or creates) content in response to 
prompts given to it by users.\u202f \n\nThese tools can generate text, images, music, video, code and other 
formats.'}, {'id': '3ab09f45-f5d2-468e-ae8e-710c116f51b7', 'score': 0.55094313621521, 'text': 'Recognise generative
AI in daily life: Identify common generative AI applications like chatbots, facial recognition, streaming services,
and digital assistants.\n\nUnderstand generative AI: Explain generative AI and its ability to create diverse 
content such as text, images, music, video, and code.\n\nDescribe LLMs: Understand how\xa0Large Language Models 
(LLMs) work.\n\n2. Examples of AI in everyday life'}, {'id': '14de7734-74a7-43c7-aa72-75a42655e545', 'score': 
0.5244572162628174, 'text': 'surprisingly knowledgeable, or with an image / computing code. Although outputs 
produced by Generative AI tools look plausible, they are often misleading, made up, or may be entirely 
wrong.\n\nEach response is unique, you will probably never get the exact same reply twice.\n\nResponses are 
conversational. Once you have asked a question you can just carry on the conversation by adding new questions or 
prompts, rather than having to redo your search, as you would in a search engine like Google.'}, {'id': 
'2751a020-1728-46d2-ae5c-0cfac3ee3d88', 'score': 0.5166183710098267, 'text': 'Online shopping – personalised 
recommendations and adverts.\n\nDigital assistants, e.g.\u202fAmazon Alexa, Google Assistant, Siri.\n\nSending an 
email – spell check and spam filters.\n\n\n\nTravel and navigation, e.g. Google Maps.\n\nWriting assistants, e.g.  
Grammarly.\n\nHealth and fitness apps, e.g. Fitbit.\n\nSocial media feeds – AI controls the feeds that you get to 
see while browsing through social media platforms (e.g., Facebook, X, Instagram) or the notifications you 
receive.\n\n3. What is generative AI?'}]

Out: None

[Step 3: Duration 2.56 seconds]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 4 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

 ─ Executing parsed code: ──────────────────────────────────────────────────────────────────────────────────────── 
  markdown_summary = """                                                                                           
  # Generative AI Use Cases                                                                                        
                                                                                                                   
  Generative AI refers to a category of artificial intelligence technologies that can create content in response   
  to user prompts. Here are some key applications of generative AI:                                                
                                                                                                                   
  ## 1. Content Creation                                                                                           
  - **Text Generation**: Tools like ChatGPT generate coherent and relevant text based on prompts.                  
  - **Image Generation**: Models such as DALL-E create unique images from text descriptions.                       
  - **Music and Video Creation**: Generative models can compose music or create video clips based on user inputs.  
                                                                                                                   
  ## 2. Business Applications                                                                                      
  - **Personalized Recommendations**: In online shopping, generative AI analyzes user behavior to recommend        
  products tailored to individual preferences.                                                                     
  - **Chatbots and Digital Assistants**: Applications like Amazon Alexa and Google Assistant help users by         
  providing information and performing tasks based on voice commands.                                              
                                                                                                                   
  ## 3. Health and Fitness                                                                                         
  - **Health Monitoring**: Apps like Fitbit use generative AI to analyze data and provide health recommendations.  
  - **Telemedicine**: AI systems assist healthcare professionals by generating patient reports and insights based  
  on input data.                                                                                                   
                                                                                                                   
  ## 4. Social Media                                                                                               
  - **Content Moderation**: Generative AI helps filter out harmful or inappropriate content from social media      
  feeds.                                                                                                           
  - **Algorithm-Driven Feeds**: AI systems personalize content delivery on platforms like Facebook and Instagram   
  based on user interaction patterns.                                                                              
                                                                                                                   
  These use cases illustrate the versatility of generative AI across various sectors, enhancing both productivity  
  and user experience.                                                                                             
  """                                                                                                              
                                                                                                                   
  print(markdown_summary)                                                                                          
  final_answer(markdown_summary)                                                                                   
 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── 

Execution logs:

# Generative AI Use Cases

Generative AI refers to a category of artificial intelligence technologies that can create content in response to 
user prompts. Here are some key applications of generative AI:

## 1. Content Creation
- **Text Generation**: Tools like ChatGPT generate coherent and relevant text based on prompts.
- **Image Generation**: Models such as DALL-E create unique images from text descriptions.
- **Music and Video Creation**: Generative models can compose music or create video clips based on user inputs.

## 2. Business Applications
- **Personalized Recommendations**: In online shopping, generative AI analyzes user behavior to recommend products 
tailored to individual preferences.
- **Chatbots and Digital Assistants**: Applications like Amazon Alexa and Google Assistant help users by providing 
information and performing tasks based on voice commands.

## 3. Health and Fitness
- **Health Monitoring**: Apps like Fitbit use generative AI to analyze data and provide health recommendations.
- **Telemedicine**: AI systems assist healthcare professionals by generating patient reports and insights based on 
input data.

## 4. Social Media
- **Content Moderation**: Generative AI helps filter out harmful or inappropriate content from social media feeds.
- **Algorithm-Driven Feeds**: AI systems personalize content delivery on platforms like Facebook and Instagram 
based on user interaction patterns.

These use cases illustrate the versatility of generative AI across various sectors, enhancing both productivity and
user experience.


Out - Final answer: 
# Generative AI Use Cases

Generative AI refers to a category of artificial intelligence technologies that can create content in response to 
user prompts. Here are some key applications of generative AI:

## 1. Content Creation
- **Text Generation**: Tools like ChatGPT generate coherent and relevant text based on prompts.
- **Image Generation**: Models such as DALL-E create unique images from text descriptions.
- **Music and Video Creation**: Generative models can compose music or create video clips based on user inputs.

## 2. Business Applications
- **Personalized Recommendations**: In online shopping, generative AI analyzes user behavior to recommend products 
tailored to individual preferences.
- **Chatbots and Digital Assistants**: Applications like Amazon Alexa and Google Assistant help users by providing 
information and performing tasks based on voice commands.

## 3. Health and Fitness
- **Health Monitoring**: Apps like Fitbit use generative AI to analyze data and provide health recommendations.
- **Telemedicine**: AI systems assist healthcare professionals by generating patient reports and insights based on 
input data.

## 4. Social Media
- **Content Moderation**: Generative AI helps filter out harmful or inappropriate content from social media feeds.
- **Algorithm-Driven Feeds**: AI systems personalize content delivery on platforms like Facebook and Instagram 
based on user interaction patterns.

These use cases illustrate the versatility of generative AI across various sectors, enhancing both productivity and
user experience.


[Step 4: Duration 7.26 seconds]

from IPython.display import display, Markdown
display(Markdown(answer))

Generative AI Use Cases

Generative AI refers to a category of artificial intelligence technologies that can create content in response to user prompts. Here are some key applications of generative AI:

1. Content Creation

  • Text Generation: Tools like ChatGPT generate coherent and relevant text based on prompts.
  • Image Generation: Models such as DALL-E create unique images from text descriptions.
  • Music and Video Creation: Generative models can compose music or create video clips based on user inputs.

2. Business Applications

  • Personalized Recommendations: In online shopping, generative AI analyzes user behavior to recommend products tailored to individual preferences.
  • Chatbots and Digital Assistants: Applications like Amazon Alexa and Google Assistant help users by providing information and performing tasks based on voice commands.

3. Health and Fitness

  • Health Monitoring: Apps like Fitbit use generative AI to analyze data and provide health recommendations.
  • Telemedicine: AI systems assist healthcare professionals by generating patient reports and insights based on input data.

4. Social Media

  • Content Moderation: Generative AI helps filter out harmful or inappropriate content from social media feeds.
  • Algorithm-Driven Feeds: AI systems personalize content delivery on platforms like Facebook and Instagram based on user interaction patterns.

These use cases illustrate the versatility of generative AI across various sectors, enhancing both productivity and user experience.