Added new demo

README.md

@@ -31,6 +31,7 @@ A curated collection of awesome LLM apps built with RAG and AI agents. This repo
   - [🛫 AI Travel Agent](#-ai-travel-agent)
   - [🎬 AI Movie Production Agent](#-ai-movie-production-agent)
   - [📰 Multi-Agent AI Researcher](#-multi-agent-ai-researcher)
+  - [📚 AI Research Agent with Memory](#-ai-research-agent-with-memory)
   - [📄 Chat with PDF](#-chat-with-pdf)
   - [💻 Web Scraping AI Agent](#-web-scraping-ai-agent)
   - [📨 Chat with Gmail](#-chat-with-gmail)
@@ -74,6 +75,9 @@ AI-powered movie production assistant that helps bring your movie ideas to life
 ### 📰 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.
 
+### 📚 AI Research Agent with Memory
+AI Research Agent that helps user find research papers on Arxiv based on their interests and past interactions with LLMs. It maintains a memory of user interests and past interactions using Mem0 and Qdrant.
+
 ### 📄 Chat with PDF
 Engage in intelligent conversation and question-answering based on the content of your PDF documents. Simply upload and start asking questions.
 

ai_arxiv_agent_memory/README.md

@@ -0,0 +1,39 @@
+## 📚 AI Research Agent with Memory
+This Streamlit app implements an AI-powered research assistant that helps users search for academic papers on arXiv while maintaining a memory of user interests and past interactions. It utilizes OpenAI's GPT-4o-mini model for processing search results, MultiOn for web browsing, and Mem0 with Qdrant for maintaining user context.
+
+### Features
+
+- Search interface for querying arXiv papers
+- AI-powered processing of search results for improved readability
+- Persistent memory of user interests and past searches
+- Utilizes OpenAI's GPT-4o-mini model for intelligent processing
+- Implements memory storage and retrieval using Mem0 and Qdrant
+
+### 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. Ensure Qdrant is running:
+The app expects Qdrant to be running on localhost:6333. Adjust the configuration in the code if your setup is different.
+
+```bash
+docker pull qdrant/qdrant
+
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    qdrant/qdrant
+```
+
+4. Run the Streamlit App
+```bash
+streamlit run ai_arxiv_agent_memory.py
+```

ai_arxiv_agent_memory/ai_arxiv_agent_memory.py

@@ -0,0 +1,52 @@
+import streamlit as st
+import os
+from mem0 import Memory
+from multion.client import MultiOn
+from openai import OpenAI
+
+st.title("AI Research Agent with Memory 📚")
+
+api_keys = {k: st.text_input(f"{k.capitalize()} API Key", type="password") for k in ['openai', 'multion']}
+
+if all(api_keys.values()):
+    os.environ['OPENAI_API_KEY'] = api_keys['openai']
+    # Initialize Mem0 with Qdrant
+    config = {
+        "vector_store": {
+            "provider": "qdrant",
+            "config": {
+                "model": "gpt-4o-mini",
+                "host": "localhost",
+                "port": 6333,
+            }
+        },
+    }
+    memory, multion, openai_client = Memory.from_config(config), MultiOn(api_key=api_keys['multion']), OpenAI(api_key=api_keys['openai'])
+
+    user_id = st.sidebar.text_input("Enter your Username")
+    #user_interests = st.text_area("Research interests and background")
+
+    search_query = st.text_input("Research paper search query")
+
+    def process_with_gpt4(result):
+        prompt = f"""
+        Based on the following arXiv search result, provide a proper structured output in markdown that is readable by the users. 
+        Each paper should have a title, authors, abstract, and link.
+        Search Result: {result}
+        Output Format: Table with the following columns: [{{"title": "Paper Title", "authors": "Author Names", "abstract": "Brief abstract", "link": "arXiv link"}}, ...]
+        """
+        response = openai_client.chat.completions.create(model="gpt-4o-mini", messages=[{"role": "user", "content": prompt}], temperature=0.2)
+        return response.choices[0].message.content
+
+    if st.button('Search for Papers'):
+        with st.spinner('Searching and Processing...'):
+            relevant_memories = memory.search(search_query, user_id=user_id, limit=3)
+            prompt = f"Search for arXiv papers: {search_query}\nUser background: {' '.join(mem['text'] for mem in relevant_memories)}"
+            result = process_with_gpt4(multion.browse(cmd=prompt, url="https://arxiv.org/"))
+            st.markdown(result)
+
+    if st.sidebar.button("View Memory"):
+        st.sidebar.write("\n".join([f"- {mem['text']}" for mem in memory.get_all(user_id=user_id)]))
+
+else:
+    st.warning("Please enter your API keys to use this app.")

ai_arxiv_agent_memory/requirements.txt

@@ -0,0 +1,4 @@
+streamlit 
+openai
+mem0ai
+multion