Personal Assistant LLM-based AI Agent

Overview

This project implements a personal assistant AI agent using Langchain, designed to assist users with various tasks by leveraging a wide range of capabilities. The assistant is built to interact with different tools and services, making it a versatile solution for personal and professional needs.

Features

• Email Management: Integrates with Gmail to manage emails, including sending, deleting, and flagging messages.
• Calendar Integration: Allows users to create and fetch calendar events seamlessly.
• Web Browsing: Utilizes Playwright for web browsing capabilities, enabling the assistant to gather information from the web.
• Academic Research: Supports querying academic databases like PubMed, Semantic Scholar, and arXiv for research papers.
• File Management: Provides tools for managing and manipulating files.
• Real-time Interaction: Built with Streamlit for a user-friendly interface, allowing real-time chat interactions with the assistant.
• Memory Management: Maintains a conversation history and user memories to provide personalized responses.

Project Structure

• customtools/: Contains custom tools and utilities that enhance the assistant's capabilities.
• personal_assistant.py: The main module that defines the PersonalAssistant class, which initializes the assistant and manages its operations.
• app.py: The Streamlit application that serves as the user interface for interacting with the assistant.
• ready_tools.py: Defines various tools that the assistant can use to perform specific tasks, such as searching for information or retrieving the current date and time.

Getting Started

To get started with the project, clone the repository and install the required dependencies. You can then run the Streamlit app to interact with your personal assistant.

Example Use cases

Email Management

• Forwarding Emails: "Forward the last email you got from Joe Doe to [email protected]."
• Summarizing Emails: "Summarize the last few emails I got from Joe Doe."

File System Interaction

• Organizing Files: "Create a folder in the Downloads directory named PDFS and move all .pdf files from the desktop to it."
• Content Summarization: "Summarize all the file content in directory X, create a new file called summary.txt with the summary, and send it to [email protected]."

Internet Research Tasks

• Information Gathering: "Get everything you can learn about George Kour, write a poem about it, and create a file on the desktop with this information."
• Academic Research: "Find the latest research papers on quantum computing, summarize the key findings, and compile them into a report."

Calendar and Scheduling

• Event Creation: "Schedule a meeting with the team for next Monday at 10 AM, including a video conference link."
• Event Retrieval: "List all my meetings for this week, highlighting any that overlap or conflict."

Real-time Assistance

• Task Automation: "Set a reminder to call John at 3 PM tomorrow and send a follow-up email if the call is missed."
• Information Retrieval: "What's the weather forecast for today, and do I need an umbrella?"

Academic and Professional Development

• Research Assistance: "Identify key trends in AI research over the past year and prepare a presentation."
• Skill Enhancement: "Find online courses to improve my Python programming skills and schedule study sessions."

Personal Productivity

• Daily Planning: "Create a daily schedule that includes work tasks, breaks, and personal time."
• Goal Tracking: "Set weekly goals and track progress, providing motivational quotes to keep me inspired."

Installation

# Clone the repository
git clone <repository-url>

# Navigate to the project directory
cd PersonalAssistant

# Install dependencies
pip install -r requirements.txt

Usage

Run the Streamlit app:

streamlit run app.py

Future Plans:

1. Use other vendors of LLMs.
2. Use local LLMs like deepseek.
3. adding proprietry knowledge and usign RAG.