Merge pull request #79 from Madhuvod/ai-personal-learning-phidata

Added new demo

ai_agent_tutorials/ai_personal_learning_agent/README.md

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+# AI Personal Learning Agent
+
+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.
+
+## Demo
+
+
+https://github.com/user-attachments/assets/67e81377-d80e-4221-b1f2-e25cffb71c93
+
+
+
+## Features
+
+- 🧠 Knowledge Building: Researches and creates comprehensive knowledge bases
+- 🗺️ Learning Roadmaps: Generates structured learning paths with time estimates
+- 📚 Resource Curation: Finds and validates high-quality learning materials
+- ✍️ Practice Materials: Creates progressive exercises and projects
+- 🔍 Internet Search Integration: Used a DuckDuckGo tool for real-time research
+- 📊 Live Terminal Output: Shows real-time agent interactions in terminal - also in streamlit UI
+
+## Agents
+
+1. **KnowledgeBuilder**: Research specialist that gathers and organizes information
+2. **RoadmapArchitect**: Curriculum designer that creates structured learning paths
+3. **ResourceCurator**: Resource specialist that finds and validates learning materials
+4. **PracticeDesigner**: Exercise creator that develops practice materials
+
+
+## How to Run
+
+1. Clone the repository
+  ```bash
+   # Clone the repository
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+   cd ai_agent_tutorials/ai_personal_learning_agent
+
+   # Install dependencies
+   pip install -r requirements.txt
+   ```
+
+## Configuration - IMPORTANT STEP
+
+1. Get your OpenAI API Key
+- Create an account on [OpenAI Platform](https://platform.openai.com/)
+- Navigate to API Keys section
+- Create a new API key
+
+2. Get your Composio API Key
+- Create an account on [Composio Platform](https://composio.ai/)
+- [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:  
+- composio add googledocs (IN THE TERMINAL) -> Create a new connection -> Select OAUTH2 -> Select Google Account and Done.
+- 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 )
+
+## Usage
+
+1. Start the Streamlit app
+```bash
+streamlit run ai_personal_learning_agent.py
+```
+
+2. Use the application
+- Enter your OpenAI API key in the sidebar (if not set in environment)
+- Enter your Composio API key in the sidebar 
+- Type a topic you want to learn about (e.g., "Python Programming", "Machine Learning")
+- Click "Generate Learning Plan"
+- Wait for the agents to generate your personalized learning plan
+- View the results and terminal output in the interface

ai_agent_tutorials/ai_personal_learning_agent/ai_personal_learning_agent.py

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+import streamlit as st
+from phi.agent import Agent, RunResponse
+from phi.model.openai import OpenAIChat
+from composio_phidata import Action, ComposioToolSet
+import os
+from phi.tools.arxiv_toolkit import ArxivToolkit
+from phi.utils.pprint import pprint_run_response
+from phi.tools.duckduckgo import DuckDuckGo
+
+# Set page configuration
+st.set_page_config(page_title="Learning Path Generator", layout="centered")
+
+# Initialize session state for API keys and topic
+if 'openai_api_key' not in st.session_state:
+    st.session_state['openai_api_key'] = ''
+if 'composio_api_key' not in st.session_state:
+    st.session_state['composio_api_key'] = ''
+if 'topic' not in st.session_state:
+    st.session_state['topic'] = ''
+
+# Streamlit sidebar for API keys
+with st.sidebar:
+    st.title("API Keys Configuration")
+    st.session_state['openai_api_key'] = st.text_input("Enter your OpenAI API Key", type="password").strip()
+    st.session_state['composio_api_key'] = st.text_input("Enter your Composio API Key", type="password").strip()
+    
+    # Add info about terminal responses
+    st.info("Note: You can also view detailed agent responses\nin your terminal after execution.")
+
+# Validate API keys
+if not st.session_state['openai_api_key'] or not st.session_state['composio_api_key']:
+    st.error("Please enter both OpenAI and Composio API keys in the sidebar.")
+    st.stop()
+
+# Set the OpenAI API key and Composio API key from session state
+os.environ["OPENAI_API_KEY"] = st.session_state['openai_api_key']
+
+try:
+    composio_toolset = ComposioToolSet(api_key=st.session_state['composio_api_key'])
+    google_docs_tool = composio_toolset.get_tools(actions=[Action.GOOGLEDOCS_CREATE_DOCUMENT])[0]
+    google_docs_tool_update = composio_toolset.get_tools(actions=[Action.GOOGLEDOCS_UPDATE_EXISTING_DOCUMENT])[0]
+except Exception as e:
+    st.error(f"Error initializing ComposioToolSet: {e}")
+    st.stop()
+
+# Create the KnowledgeBuilder agent
+knowledge_agent = Agent(
+    name="KnowledgeBuilder",
+    role="Research and Knowledge Specialist", 
+    model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
+    tools=[google_docs_tool],
+    instructions=[
+        "Create a comprehensive knowledge base that covers fundamental concepts, advanced topics, and current developments of the given topic.",
+        "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.",
+        "Make sure it is formatted in a way that is easy to read and understand. DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
+        "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.**",
+    ],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+# Create the RoadmapArchitect agent
+roadmap_agent = Agent(
+    name="RoadmapArchitect",
+    role="Learning Path Designer",
+    model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
+    tools=[google_docs_tool],
+    instructions=[
+        "Using the knowledge base for the given topic, create a detailed learning roadmap.",
+        "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.",
+        "Include estimated time commitments for each section.",
+        "Present the roadmap in a clear, structured format. DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
+        "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.**",
+
+    ],
+    show_tool_calls=True,
+    markdown=True
+)
+
+# Create the ResourceCurator agent
+resource_agent = Agent(
+    name="ResourceCurator",
+    role="Learning Resource Specialist",
+    model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
+    tools=[google_docs_tool, ArxivToolkit(), DuckDuckGo(fixed_max_results=10)],
+    instructions=[
+        "Find and validate high-quality learning resources for the given topic.",
+        "Use the DuckDuckGo search tool to find current and relevant learning materials.",
+        "Include technical blogs, GitHub repositories, official documentation, video tutorials, and courses.",
+        "Verify the credibility and relevance of each resource.",
+        "Present the resources in a curated list with descriptions and quality assessments. DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
+        "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.**",
+    ],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+# Create the PracticeDesigner agent
+practice_agent = Agent(
+    name="PracticeDesigner",
+    role="Exercise Creator",
+    model=OpenAIChat(id="gpt-4o", api_key=st.session_state['openai_api_key']),
+    tools=[google_docs_tool, DuckDuckGo(fixed_max_results=10)],
+    instructions=[
+        "Create comprehensive practice materials for the given topic.",
+        "Use the DuckDuckGo search tool to find example problems and real-world applications.",
+        "Include progressive exercises, quizzes, hands-on projects, and real-world application scenarios.",
+        "Ensure the materials align with the roadmap progression.",
+        "Provide detailed solutions and explanations for all practice materials.DONT FORGET TO CREATE THE GOOGLE DOCUMENT.",
+        "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.**",
+    ],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+# Streamlit main UI
+st.title("AI Learning Roadmap Agent")
+st.markdown("Enter a topic to generate a detailed learning path and resources")
+
+# Add info message about Google Docs
+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.")
+
+# Query bar for topic input
+st.session_state['topic'] = st.text_input("Enter the topic you want to learn about:", placeholder="e.g., Machine Learning, LoRA, etc.")
+
+# Start button
+if st.button("Start"):
+    if not st.session_state['topic']:
+        st.error("Please enter a topic.")
+    else:
+        # Display loading animations while generating responses
+        with st.spinner("Generating Knowledge Base..."):
+            knowledge_response: RunResponse = knowledge_agent.run(
+                f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
+                stream=False
+            )
+            
+        with st.spinner("Generating Learning Roadmap..."):
+            roadmap_response: RunResponse = roadmap_agent.run(
+                f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
+                stream=False
+            )
+            
+        with st.spinner("Curating Learning Resources..."):
+            resource_response: RunResponse = resource_agent.run(
+                f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
+                stream=False
+            )
+            
+        with st.spinner("Creating Practice Materials..."):
+            practice_response: RunResponse = practice_agent.run(
+                f"the topic is: {st.session_state['topic']},Don't forget to add the Google Doc link in your response.",
+                stream=False
+            )
+
+        # Extract Google Doc links from the responses
+        def extract_google_doc_link(response_content):
+            # Assuming the Google Doc link is embedded in the response content
+            # You may need to adjust this logic based on the actual response format
+            if "https://docs.google.com" in response_content:
+                return response_content.split("https://docs.google.com")[1].split()[0]
+            return None
+
+        knowledge_doc_link = extract_google_doc_link(knowledge_response.content)
+        roadmap_doc_link = extract_google_doc_link(roadmap_response.content)
+        resource_doc_link = extract_google_doc_link(resource_response.content)
+        practice_doc_link = extract_google_doc_link(practice_response.content)
+
+        # Display Google Doc links at the top of the Streamlit UI
+        st.markdown("### Google Doc Links:")
+        if knowledge_doc_link:
+            st.markdown(f"- **KnowledgeBuilder Document:** [View Document](https://docs.google.com{knowledge_doc_link})")
+        if roadmap_doc_link:
+            st.markdown(f"- **RoadmapArchitect Document:** [View Document](https://docs.google.com{roadmap_doc_link})")
+        if resource_doc_link:
+            st.markdown(f"- **ResourceCurator Document:** [View Document](https://docs.google.com{resource_doc_link})")
+        if practice_doc_link:
+            st.markdown(f"- **PracticeDesigner Document:** [View Document](https://docs.google.com{practice_doc_link})")
+
+        # Display responses in the Streamlit UI using pprint_run_response
+        st.markdown("### KnowledgeBuilder Response:")
+        st.markdown(knowledge_response.content)
+        pprint_run_response(knowledge_response, markdown=True)
+        st.divider()
+        st.markdown("### RoadmapArchitect Response:")
+        st.markdown(roadmap_response.content)
+        pprint_run_response(roadmap_response, markdown=True)
+        st.divider()
+
+        st.markdown("### ResourceCurator Response:")
+        st.markdown(resource_response.content)
+        pprint_run_response(resource_response, markdown=True)
+        st.divider()
+
+        st.markdown("### PracticeDesigner Response:")
+        st.markdown(practice_response.content)
+        pprint_run_response(practice_response, markdown=True)
+        st.divider()
+# Information about the agents
+st.markdown("---")
+st.markdown("### About the Agents:")
+st.markdown("""
+- **KnowledgeBuilder**: Researches the topic and creates a detailed knowledge base.
+- **RoadmapArchitect**: Designs a structured learning roadmap for the topic.
+- **ResourceCurator**: Curates high-quality learning resources.
+- **PracticeDesigner**: Creates practice materials, exercises, and projects.
+""")

ai_agent_tutorials/ai_personal_learning_agent/requirements.txt

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+streamlit==1.41.1
+openai==1.58.1
+duckduckgo-search==6.4.1
+typing-extensions>=4.5.0
+phidata==2.7.3
+composio-phidata==0.6.9
+composio_core
+composio==0.1.1