Added new demo

README.md

@@ -6,64 +6,95 @@
 
 <p align="center">
   <a href="https://www.linkedin.com/in/shubhamsaboo/">
-    <img src="https://img.shields.io/badge/-Follow%20Shubham Saboo-blue?logo=linkedin&style=flat-square" alt="LinkedIn">
+    <img src="https://img.shields.io/badge/-Follow%20Shubham%20Saboo-blue?logo=linkedin&style=flat-square" alt="LinkedIn">
   </a>
   <a href="https://twitter.com/Saboo_Shubham_">
-    <img src="https://img.shields.io/twitter/follow/Shubham Saboo" alt="Twitter">  </a>
+    <img src="https://img.shields.io/twitter/follow/Shubham_Saboo" alt="Twitter">
+  </a>
 </p>
+
 <hr/>
 
 # 🌟 Awesome LLM Apps
 A curated collection of awesome LLM apps built with RAG and AI agents. This repository features LLM apps that use models from OpenAI, Anthropic, Google, and even open-source models like LLaMA that you can run locally on your computer.
 
+## 📑 Table of Contents
+
+- [🤔 Why Awesome LLM Apps?](#-why-awesome-llm-apps)
+- [📂 Featured Projects](#-featured-projects)
+  - [💻 Local Lllama-3 with RAG](#-local-llama-3-with-rag)
+  - [🎯 Generative AI Web Search Assistant](#-generative-ai-web-search-assistant)
+  - [💬 Chat with GitHub Repo](#-chat-with-github-repo)
+  - [📈 AI Investment Agent](#-ai-investment-agent)
+  - [📰 Multi-Agent AI Researcher](#-multi-agent-ai-researcher)
+  - [📄 Chat with PDF](#-chat-with-pdf)
+  - [💻 Web Scraping AI Agent](#-web-scraping-ai-agent)
+  - [📨 Chat with Gmail](#-chat-with-gmail)
+  - [📽️ Chat with YouTube Videos](#-chat-with-youtube-videos)
+  - [🔎 Chat with Arxiv Research Papers](#-chat-with-arxiv-research-papers)
+  - [📝 Chat with Substack Newsletter](#-chat-with-substack-newsletter)
+- [🚀 Getting Started](#-getting-started)
+- [🤝 Contributing to Opensource](#-contributing-to-opensource)
+
 ## 🤔 Why Awesome LLM Apps?
 - 💡 Discover practical and creative ways LLMs can be applied across different domains, from code repositories to email inboxes and more.
-- 🔥 Explore apps that combines LLMs from OpenAI, Anthropic, Gemini, and open-source alternatives with RAG and AI Agents.
-- 🎓 Learn from well-documented projects and contribute to the growing opensource ecosystem of LLM-powered applications.
+- 🔥 Explore apps that combine LLMs from OpenAI, Anthropic, Gemini, and open-source alternatives with RAG and AI Agents.
+- 🎓 Learn from well-documented projects and contribute to the growing open-source ecosystem of LLM-powered applications.
 
 ## 📂 Featured Projects
 
 ### 💻 Local Lllama-3 with RAG
 Chat with any webpage using local Llama-3 and Retrieval Augmented Generation (RAG) in a Streamlit app. Enjoy 100% free and offline functionality.
 
+### 🎯 Generative AI Web Search Assistant
+Get pinpointed answers to your queries by combining search engines and LLMs using OpenAI's GPT-4 and the DuckDuckGo search engine for accurate responses.
+
 ### 💬 Chat with GitHub Repo
 Engage in natural conversations with your GitHub repositories using GPT-4. Uncover valuable insights and documentation effortlessly.
 
-### 📨 Chat with Gmail
-Interact with your Gmail inbox using natural language. Get accurate answers to your questions based on the content of your emails with Retrieval Augmented Generation (RAG).
+### 📈 AI Investment Agent
+AI investment agent that compares the performance of two stocks and generates detailed stock reports with company insights, news, and analyst recommendations to help you make smart investment choices.
 
-### 📝 Chat with Substack Newsletter
-Chat with a Substack newsletter using OpenAI's API and the Embedchain library in a Streamlit app. Leverage GPT-4 for precise answers based on newsletter content.
+### 📰 Multi-Agent AI Researcher
+Use a team of AI agents to research top HackerNews stories and users with GPT-4 to generate blog posts, reports, and social media content on autopilot.
 
 ### 📄 Chat with PDF
 Engage in intelligent conversation and question-answering based on the content of your PDF documents. Simply upload and start asking questions.
 
-### 📽️ Chat with YouTube Videos
-Dive into video content with interactive conversation and question-answering based on YouTube videos. Provide a URL and engage with the video's content through natural language.
-
 ### 💻 Web Scraping AI Agent
 Intelligently scrape websites using OpenAI API and the scrapegraphai library. Specify the URL and extraction requirements, and let the AI agent handle the rest.
 
+### 📨 Chat with Gmail
+Interact with your Gmail inbox using natural language. Get accurate answers to your questions based on the content of your emails with Retrieval Augmented Generation (RAG).
+
+### 📽️ Chat with YouTube Videos
+Dive into video content with interactive conversation and question-answering based on YouTube videos. Provide a URL and engage with the video's content through natural language.
+
+### 🔎 Chat with Arxiv Research Papers
+Explore the vast knowledge in arXiv research papers through interactive conversations using GPT-4 and unlock insights from millions of research papers.
+
+### 📝 Chat with Substack Newsletter
+Chat with a Substack newsletter using OpenAI's API and the Embedchain library in a Streamlit app. Leverage GPT-4 for precise answers based on newsletter content.
 
 ## 🚀 Getting Started
 
 1. Clone the repository 
 
-```bash 
-git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 
-```
+    ```bash 
+    git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git 
+    ```
 
 2. Navigate to the desired project directory
 
-```bash 
-cd awesome-llm-apps/chat_with_gmail 
-```
+    ```bash 
+    cd awesome-llm-apps/chat_with_gmail 
+    ```
 
 3. Install the required dependencies
 
-```bash
-pip install -r requirements.txt
-```
+    ```bash
+    pip install -r requirements.txt
+    ```
 
 4. Follow the project-specific instructions in each project's README.md file to set up and run the app.
 

chat_with_github/chat_github_llama3.py

@@ -5,9 +5,9 @@ from embedchain.loaders.github import GithubLoader
 import streamlit as st
 import os
 
-GITHUB_TOKEN = os.getenv("GITHUB_TOKEN")
+GITHUB_TOKEN = os.getenv("Your GitHub Token")
+
 def get_loader():
-    print("Creating GithubLoader")
     loader = GithubLoader(
         config={
             "token": GITHUB_TOKEN
@@ -22,7 +22,6 @@ loader = st.session_state.loader
 
 # Define the embedchain_bot function
 def embedchain_bot(db_path):
-    print("Creating Embedchain App")
     return App.from_config(
         config={
             "llm": {"provider": "ollama", "config": {"model": "llama3:instruct", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}},
@@ -36,7 +35,7 @@ def load_repo(git_repo):
     # Add the repo to the knowledge base
     print(f"Adding {git_repo} to knowledge base!")
     app.add("repo:" + git_repo + " " + "type:repo", data_type="github", loader=loader)
-#    st.success(f"Added {git_repo} to knowledge base!")
+    st.success(f"Added {git_repo} to knowledge base!")
 
 
 def make_db_path():

chat_with_research_papers/README.md

@@ -1,4 +1,4 @@
-## 🔎 Chat with Arxiv
+## 🔎 Chat with Arxiv Research Papers
 This Streamlit app enables you to engage in interactive conversations with arXiv, a vast repository of scholarly articles, using GPT-4o. With this RAG application, you can easily access and explore the wealth of knowledge contained within arXiv.
 
 ### Features

chat_with_research_papers/chat_arxiv.py

@@ -5,8 +5,8 @@ from phi.llm.openai import OpenAIChat
 from phi.tools.arxiv_toolkit import ArxivToolkit
 
 # Set up the Streamlit app
-st.title("Chat with Arxiv 🔎🤖")
-st.caption("This app allows you to chat with arXiv using OpenAI GPT-4o model.")
+st.title("Chat with Research Papers 🔎🤖")
+st.caption("This app allows you to chat with arXiv research papers using OpenAI GPT-4o model.")
 
 # Get OpenAI API key from user
 openai_access_token = st.text_input("OpenAI API Key", type="password")
@@ -19,7 +19,7 @@ if openai_access_token:
         model="gpt-4o",
         max_tokens=1024,
         temperature=0.9,
-        api_key=openai_access_token) , tools=[ArxivToolkit()], show_tool_calls=True
+        api_key=openai_access_token) , tools=[ArxivToolkit()]
     )
 
     # Get the search query from the user

chat_with_research_papers/chat_arxiv_llama3.py

@@ -0,0 +1,23 @@
+# Import the required libraries
+import streamlit as st
+from phi.assistant import Assistant
+from phi.llm.ollama import Ollama
+from phi.tools.arxiv_toolkit import ArxivToolkit
+
+# Set up the Streamlit app
+st.title("Chat with Research Papers 🔎🤖")
+st.caption("This app allows you to chat with arXiv research papers using Llama-3 running locally.")
+
+# Create an instance of the Assistant
+assistant = Assistant(
+llm=Ollama(
+    model="llama3:instruct") , tools=[ArxivToolkit()], show_tool_calls=True
+)
+
+# Get the search query from the user
+query= st.text_input("Enter the Search Query", type="default")
+
+if query:
+    # Search the web using the AI Assistant
+    response = assistant.run(query, stream=False)
+    st.write(response)

chat_with_research_papers/requirements.txt

@@ -0,0 +1,4 @@
+streamlit 
+phidata
+arxiv
+openai

investment_ai_agent/README.md

@@ -0,0 +1,41 @@
+## 📈 AI Investment Agent
+This Streamlit app is an AI-powered investment agent that compares the performance of two stocks and generates detailed reports. By using GPT-4o with Yahoo Finance data, this app provides valuable insights to help you make informed investment decisions.
+
+### Features
+- Compare the performance of two stocks
+- Retrieve comprehensive company information
+- Get the latest company news and analyst recommendations
+- Get the latest company news and analyst recommendations
+
+### How to get Started?
+
+1. Clone the GitHub repository
+
+```bash
+git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+```
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+3. Get your OpenAI API Key
+
+- Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key.
+
+4. Run the Streamlit App
+```bash
+streamlit run investment_agent.py
+```
+
+### How it Works?
+
+- Upon running the app, you will be prompted to enter your OpenAI API key. This key is used to authenticate and access the OpenAI language model.
+- Once you provide a valid API key, an instance of the Assistant class is created. This assistant utilizes the GPT-4 language model from OpenAI and the YFinanceTools for accessing stock data.
+- Enter the stock symbols of the two companies you want to compare in the provided text input fields.
+- The assistant will perform the following steps:
+    - Retrieve real-time stock prices and historical data using YFinanceTools
+    - Fetch the latest company news and analyst recommendations
+    - Gather comprehensive company information
+    - Generate a detailed comparison report using the GPT-4 language model
+- The generated report will be displayed in the app, providing you with valuable insights and analysis to guide your investment decisions.

investment_ai_agent/investment_agent.py

@@ -0,0 +1,30 @@
+# Import the required libraries
+import streamlit as st
+from phi.assistant import Assistant
+from phi.llm.openai import OpenAIChat
+from phi.tools.yfinance import YFinanceTools
+
+# Set up the Streamlit app
+st.title("AI Investment Agent 📈🤖")
+st.caption("This app allows you to compare the performance of two stocks and generate detailed reports.")
+
+# Get OpenAI API key from user
+openai_api_key = st.text_input("OpenAI API Key", type="password")
+
+if openai_api_key:
+    # Create an instance of the Assistant
+    assistant = Assistant(
+        llm=OpenAIChat(model="gpt-4o", api_key=openai_api_key),
+        tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
+        show_tool_calls=True,
+    )
+
+    # Input fields for the stocks to compare
+    stock1 = st.text_input("Enter the first stock symbol")
+    stock2 = st.text_input("Enter the second stock symbol")
+
+    if stock1 and stock2:
+        # Get the response from the assistant
+        query = f"Compare {stock1} to {stock2}. Use every tool you have."
+        response = assistant.run(query, stream=False)
+        st.write(response)

multi_agent_researcher/requirements.txt

@@ -0,0 +1,3 @@
+streamlit 
+phidata
+openai

multi_agent_researcher/research_agent.py

@@ -6,7 +6,7 @@ from phi.llm.openai import OpenAIChat
 
 # Set up the Streamlit app
 st.title("Multi-Agent AI Researcher 🔍🤖")
-st.caption("This app allows you to research top stories and users on HackerNews and write blog posts, reports and social posts on that.")
+st.caption("This app allows you to research top stories and users on HackerNews and write blogs, reports and social posts.")
 
 # Get OpenAI API key from user
 openai_api_key = st.text_input("OpenAI API Key", type="password")

web_search_ai_assistant/README.md

@@ -24,7 +24,7 @@ pip install -r requirements.txt
 
 4. Run the Streamlit App
 ```bash
-streamlit run ai_webagent.py
+streamlit run ai_websearch.py
 ```
 
 ### How It Works?

web_search_ai_assistant/ai_websearch.py

@@ -5,8 +5,8 @@ from phi.tools.duckduckgo import DuckDuckGo
 from phi.llm.openai import OpenAIChat
 
 # Set up the Streamlit app
-st.title("AI Search Assistant 🤖")
-st.caption("This app allows you to search the web using AI")
+st.title("AI Web Search Assistant 🤖")
+st.caption("This app allows you to search the web using GPT-4o")
 
 # Get OpenAI API key from user
 openai_access_token = st.text_input("OpenAI API Key", type="password")