Merge pull request #79 from Madhuvod/ai-personal-learning-phidata
Added new demo
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Shubham Saboo
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ai_agent_tutorials/ai_personal_learning_agent/README.md
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ai_agent_tutorials/ai_personal_learning_agent/README.md
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# AI Personal Learning Agent
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A Personal learning Roadmap Architect assistant built on Phidata Framework that explains a particular topic, creates learning plans and roadmaps using multiple specialized AI agents which are hierarchical. The system uses OpenAI's GPT-4o to generate comprehensive learning materials, roadmaps, and practice exercises. This uses streamlit for UI.
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## Demo
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https://github.com/user-attachments/assets/67e81377-d80e-4221-b1f2-e25cffb71c93
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## Features
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- 🧠 Knowledge Building: Researches and creates comprehensive knowledge bases
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- 🗺️ Learning Roadmaps: Generates structured learning paths with time estimates
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- 📚 Resource Curation: Finds and validates high-quality learning materials
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- ✍️ Practice Materials: Creates progressive exercises and projects
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- 🔍 Internet Search Integration: Used a DuckDuckGo tool for real-time research
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- 📊 Live Terminal Output: Shows real-time agent interactions in terminal - also in streamlit UI
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## Agents
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1. **KnowledgeBuilder**: Research specialist that gathers and organizes information
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2. **RoadmapArchitect**: Curriculum designer that creates structured learning paths
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3. **ResourceCurator**: Resource specialist that finds and validates learning materials
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4. **PracticeDesigner**: Exercise creator that develops practice materials
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## How to Run
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1. Clone the repository
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```bash
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# Clone the repository
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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cd ai_agent_tutorials/ai_personal_learning_agent
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# Install dependencies
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pip install -r requirements.txt
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```
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## Configuration - IMPORTANT STEP
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1. Get your OpenAI API Key
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- Create an account on [OpenAI Platform](https://platform.openai.com/)
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- Navigate to API Keys section
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- Create a new API key
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2. Get your Composio API Key
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- Create an account on [Composio Platform](https://composio.ai/)
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- [IMPORTANT] - For you to use the app, you need to make new connection ID with google docs and composio.Follow the below two steps to do so:
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- composio add googledocs (IN THE TERMINAL) -> Create a new connection -> Select OAUTH2 -> Select Google Account and Done.
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- In the composio account website, go to apps, select google docs tool, and click create integration (violet button) and click Try connecting default’s googldocs button and we are done. (https://app.composio.dev/app/googledocs )
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## Usage
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1. Start the Streamlit app
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```bash
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streamlit run ai_personal_learning_agent.py
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```
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2. Use the application
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- Enter your OpenAI API key in the sidebar (if not set in environment)
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- Enter your Composio API key in the sidebar
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- Type a topic you want to learn about (e.g., "Python Programming", "Machine Learning")
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- Click "Generate Learning Plan"
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- Wait for the agents to generate your personalized learning plan
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- View the results and terminal output in the interface
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import streamlit as st
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from phi.agent import Agent, RunResponse
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from phi.model.openai import OpenAIChat
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from composio_phidata import Action, ComposioToolSet
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import os
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from phi.tools.arxiv_toolkit import ArxivToolkit
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from phi.utils.pprint import pprint_run_response
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from phi.tools.duckduckgo import DuckDuckGo
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# Set page configuration
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st.set_page_config(page_title="Learning Path Generator", layout="centered")
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# Initialize session state for API keys and topic
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if 'openai_api_key' not in st.session_state:
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st.session_state['openai_api_key'] = ''
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if 'composio_api_key' not in st.session_state:
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st.session_state['composio_api_key'] = ''
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if 'topic' not in st.session_state:
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st.session_state['topic'] = ''
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# Streamlit sidebar for API keys
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with st.sidebar:
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st.title("API Keys Configuration")
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st.session_state['openai_api_key'] = st.text_input("Enter your OpenAI API Key", type="password").strip()
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st.session_state['composio_api_key'] = st.text_input("Enter your Composio API Key", type="password").strip()
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# Add info about terminal responses
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st.info("Note: You can also view detailed agent responses\nin your terminal after execution.")
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# Validate API keys
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if not st.session_state['openai_api_key'] or not st.session_state['composio_api_key']:
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st.error("Please enter both OpenAI and Composio API keys in the sidebar.")
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st.stop()
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# Set the OpenAI API key and Composio API key from session state
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os.environ["OPENAI_API_KEY"] = st.session_state['openai_api_key']
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try:
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composio_toolset = ComposioToolSet(api_key=st.session_state['composio_api_key'])
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google_docs_tool = composio_toolset.get_tools(actions=[Action.GOOGLEDOCS_CREATE_DOCUMENT])[0]
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google_docs_tool_update = composio_toolset.get_tools(actions=[Action.GOOGLEDOCS_UPDATE_EXISTING_DOCUMENT])[0]
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except Exception as e:
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st.error(f"Error initializing ComposioToolSet: {e}")
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st.stop()
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# Create the KnowledgeBuilder agent
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knowledge_agent = Agent(
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name="KnowledgeBuilder",
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role="Research and Knowledge Specialist",
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model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
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tools=[google_docs_tool],
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instructions=[
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"Create a comprehensive knowledge base that covers fundamental concepts, advanced topics, and current developments of the given topic.",
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"Include key terminology, core principles, and practical applications and make it as a detailed report that anyone who's starting out can read and get maximum value out of it.",
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"Make sure it is formatted in a way that is easy to read and understand. DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
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"Open a new Google Doc and write down the response of the agent neatly with great formatting and structure in it. **Include the Google Doc link in your response.**",
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],
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show_tool_calls=True,
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markdown=True,
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)
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# Create the RoadmapArchitect agent
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roadmap_agent = Agent(
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name="RoadmapArchitect",
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role="Learning Path Designer",
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model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
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tools=[google_docs_tool],
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instructions=[
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"Using the knowledge base for the given topic, create a detailed learning roadmap.",
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"Break down the topic into logical subtopics and arrange them in order of progression, a detailed report of roadmap that includes all the subtopics in order to be an expert in this topic.",
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"Include estimated time commitments for each section.",
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"Present the roadmap in a clear, structured format. DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
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"Open a new Google Doc and write down the response of the agent neatly with great formatting and structure in it. **Include the Google Doc link in your response.**",
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],
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show_tool_calls=True,
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markdown=True
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)
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# Create the ResourceCurator agent
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resource_agent = Agent(
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name="ResourceCurator",
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role="Learning Resource Specialist",
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model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
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tools=[google_docs_tool, ArxivToolkit(), DuckDuckGo(fixed_max_results=10)],
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instructions=[
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"Find and validate high-quality learning resources for the given topic.",
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"Use the DuckDuckGo search tool to find current and relevant learning materials.",
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"Include technical blogs, GitHub repositories, official documentation, video tutorials, and courses.",
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"Verify the credibility and relevance of each resource.",
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"Present the resources in a curated list with descriptions and quality assessments. DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
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"Open a new Google Doc and write down the response of the agent neatly with great formatting and structure in it. **Include the Google Doc link in your response.**",
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],
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show_tool_calls=True,
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markdown=True,
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)
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# Create the PracticeDesigner agent
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practice_agent = Agent(
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name="PracticeDesigner",
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role="Exercise Creator",
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model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
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tools=[google_docs_tool, DuckDuckGo(fixed_max_results=10)],
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instructions=[
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"Create comprehensive practice materials for the given topic.",
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"Use the DuckDuckGo search tool to find example problems and real-world applications.",
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"Include progressive exercises, quizzes, hands-on projects, and real-world application scenarios.",
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"Ensure the materials align with the roadmap progression.",
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"Provide detailed solutions and explanations for all practice materials.DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
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"Open a new Google Doc and write down the response of the agent neatly with great formatting and structure in it. **Include the Google Doc link in your response.**",
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],
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show_tool_calls=True,
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markdown=True,
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)
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# Streamlit main UI
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st.title("AI Learning Roadmap Agent")
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st.markdown("Enter a topic to generate a detailed learning path and resources")
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# Add info message about Google Docs
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st.info("📝 The agents will create detailed Google Docs for each section (Knowledge Base, Learning Roadmap, Resources, and Practice Materials). The links to these documents will be displayed below after processing.")
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# Query bar for topic input
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st.session_state['topic'] = st.text_input("Enter the topic you want to learn about:", placeholder="e.g., Machine Learning, LoRA, etc.")
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# Start button
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if st.button("Start"):
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if not st.session_state['topic']:
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st.error("Please enter a topic.")
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else:
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# Display loading animations while generating responses
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with st.spinner("Generating Knowledge Base..."):
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knowledge_response: RunResponse = knowledge_agent.run(
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f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
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stream=False
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)
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with st.spinner("Generating Learning Roadmap..."):
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roadmap_response: RunResponse = roadmap_agent.run(
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f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
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stream=False
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)
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with st.spinner("Curating Learning Resources..."):
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resource_response: RunResponse = resource_agent.run(
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f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
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stream=False
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)
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with st.spinner("Creating Practice Materials..."):
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practice_response: RunResponse = practice_agent.run(
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f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
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stream=False
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)
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# Extract Google Doc links from the responses
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def extract_google_doc_link(response_content):
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# Assuming the Google Doc link is embedded in the response content
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# You may need to adjust this logic based on the actual response format
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if "https://docs.google.com" in response_content:
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return response_content.split("https://docs.google.com")[1].split()[0]
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return None
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knowledge_doc_link = extract_google_doc_link(knowledge_response.content)
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roadmap_doc_link = extract_google_doc_link(roadmap_response.content)
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resource_doc_link = extract_google_doc_link(resource_response.content)
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practice_doc_link = extract_google_doc_link(practice_response.content)
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# Display Google Doc links at the top of the Streamlit UI
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st.markdown("### Google Doc Links:")
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if knowledge_doc_link:
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st.markdown(f"- **KnowledgeBuilder Document:** [View Document](https://docs.google.com{knowledge_doc_link})")
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if roadmap_doc_link:
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st.markdown(f"- **RoadmapArchitect Document:** [View Document](https://docs.google.com{roadmap_doc_link})")
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if resource_doc_link:
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st.markdown(f"- **ResourceCurator Document:** [View Document](https://docs.google.com{resource_doc_link})")
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if practice_doc_link:
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st.markdown(f"- **PracticeDesigner Document:** [View Document](https://docs.google.com{practice_doc_link})")
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# Display responses in the Streamlit UI using pprint_run_response
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st.markdown("### KnowledgeBuilder Response:")
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st.markdown(knowledge_response.content)
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pprint_run_response(knowledge_response, markdown=True)
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st.divider()
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st.markdown("### RoadmapArchitect Response:")
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st.markdown(roadmap_response.content)
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pprint_run_response(roadmap_response, markdown=True)
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st.divider()
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st.markdown("### ResourceCurator Response:")
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st.markdown(resource_response.content)
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pprint_run_response(resource_response, markdown=True)
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st.divider()
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st.markdown("### PracticeDesigner Response:")
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st.markdown(practice_response.content)
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pprint_run_response(practice_response, markdown=True)
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st.divider()
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# Information about the agents
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st.markdown("---")
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st.markdown("### About the Agents:")
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st.markdown("""
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- **KnowledgeBuilder**: Researches the topic and creates a detailed knowledge base.
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- **RoadmapArchitect**: Designs a structured learning roadmap for the topic.
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- **ResourceCurator**: Curates high-quality learning resources.
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- **PracticeDesigner**: Creates practice materials, exercises, and projects.
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""")
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streamlit==1.41.1
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openai==1.58.1
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duckduckgo-search==6.4.1
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typing-extensions>=4.5.0
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phidata==2.7.3
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composio-phidata==0.6.9
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composio_core
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composio==0.1.1
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