Add new demo
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chat_with_arxiv/README.md
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chat_with_arxiv/README.md
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## 🔎 Chat with Arxiv
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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.
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### Features
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- Engage in conversational interactions with arXiv
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- Access and explore a vast collection of research papers
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- Utilize OpenAI GPT-4o for intelligent responses
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### How to get Started?
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1. Clone the GitHub repository
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```bash
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git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
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```
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2. Install the required dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Get your OpenAI API Key
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- Sign up for an [OpenAI account](https://platform.openai.com/) (or the LLM provider of your choice) and obtain your API key.
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4. Run the Streamlit App
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```bash
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streamlit run chat_arxiv.py
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```
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chat_with_arxiv/chat_arxiv.py
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chat_with_arxiv/chat_arxiv.py
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# Import the required libraries
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import streamlit as st
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from phi.assistant import Assistant
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from phi.llm.openai import OpenAIChat
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from phi.tools.arxiv_toolkit import ArxivToolkit
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# Set up the Streamlit app
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st.title("Chat with Arxiv 🔎🤖")
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st.caption("This app allows you to chat with arXiv using OpenAI GPT-4o model.")
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# Get OpenAI API key from user
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openai_access_token = st.text_input("OpenAI API Key", type="password")
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# If OpenAI API key is provided, create an instance of Assistant
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if openai_access_token:
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# Create an instance of the Assistant
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assistant = Assistant(
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llm=OpenAIChat(
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model="gpt-4o",
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max_tokens=1024,
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temperature=0.9,
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api_key=openai_access_token) , tools=[ArxivToolkit()], show_tool_calls=True
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)
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# Get the search query from the user
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query= st.text_input("Enter the Search Query", type="default")
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if query:
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# Search the web using the AI Assistant
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response = assistant.run(query, stream=False)
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st.write(response)
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chat_with_arxiv/requirements.txt
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chat_with_arxiv/requirements.txt
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streamlit
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phidata
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arxiv
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openai
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