212 lines
8.5 KiB
Plaintext
212 lines
8.5 KiB
Plaintext
---
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title: MultiOn
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---
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Build a personal browser agent that remembers user preferences and automates web tasks. It integrates Mem0 for memory management with MultiOn for executing browser actions, enabling personalized and efficient web interactions.
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## Overview
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In this guide, we'll explore two examples of creating Browser-based AI Agents:
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1. An agent that searches [arxiv.org](https://arxiv.org) for research papers relevant to user's research interests.
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2. A travel agent that provides personalized travel information based on user preferences. Refer to the [notebook](https://github.com/MULTI-ON/cookbook/blob/main/personalized-travel-agent/mem0_travel_agent.ipynb) for detailed code.
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## Setup and Configuration
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Install necessary libraries:
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```bash
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pip install mem0ai multion openai
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```
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First, we'll import the necessary libraries and set up our configurations.
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```python
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import os
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from mem0 import Memory, MemoryClient
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from multion.client import MultiOn
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from openai import OpenAI
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# Configuration
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OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
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MULTION_API_KEY = 'your-multion-key' # Replace with your actual MultiOn API key
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MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key
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USER_ID = "your-user-id"
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# Set up OpenAI API key
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os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
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# Initialize Mem0 and MultiOn
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memory = Memory() # For local usage
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memory_client = MemoryClient(api_key=MEM0_API_KEY) # For API usage
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multion = MultiOn(api_key=MULTION_API_KEY)
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```
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## Example 1: Research Paper Search Agent
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### Add memories to Mem0
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Define user data and add it to Mem0.
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```python
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USER_DATA = """
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About me
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- I'm Deshraj Yadav, Co-founder and CTO at Mem0, interested in AI and ML Infrastructure.
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- Previously, I was a Senior Autopilot Engineer at Tesla, leading the AI Platform for Autopilot.
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- I built EvalAI at Georgia Tech, an open-source platform for evaluating ML algorithms.
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- Outside of work, I enjoy playing cricket in two leagues in the San Francisco.
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"""
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memory.add(USER_DATA, user_id=USER_ID)
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print("User data added to memory.")
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```
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### Retrieving Relevant Memories
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Define search command and retrieve relevant memories from Mem0.
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```python
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command = "Find papers on arxiv that I should read based on my interests."
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relevant_memories = memory.search(command, user_id=USER_ID, limit=3)
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relevant_memories_text = '\n'.join(mem['text'] for mem in relevant_memories)
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print(f"Relevant memories:")
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print(relevant_memories_text)
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```
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### Browsing arXiv
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Use MultiOn to browse arXiv based on the command and relevant memories.
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```python
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prompt = f"{command}\n My past memories: {relevant_memories_text}"
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browse_result = multion.browse(cmd=prompt, url="https://arxiv.org/")
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print(browse_result)
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```
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## Example 2: Travel Agent
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### Get Travel Information
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Add conversation to Mem0 and create a function to get travel information based on user's question and optionally their preferences from memory.
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<CodeGroup>
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```python Code
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def get_travel_info(question, use_memory=True):
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if use_memory:
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previous_memories = memory_client.search(question, user_id=USER_ID)
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relevant_memories_text = ""
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if previous_memories:
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print("Using previous memories to enhance the search...")
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relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
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command = "Find travel information based on my interests:"
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prompt = f"{command}\n Question: {question} \n My preferences: {relevant_memories_text}"
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else:
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command = "Find travel information based on my interests:"
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prompt = f"{command}\n Question: {question}"
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print("Searching for travel information...")
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browse_result = multion.browse(cmd=prompt)
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return browse_result.message
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# Example usage
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question = "Show me flight details for it."
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answer_without_memory = get_travel_info(question, use_memory=False)
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answer_with_memory = get_travel_info(question, use_memory=True)
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print("Answer without memory:", answer_without_memory)
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print("Answer with memory:", answer_with_memory)
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# Another example
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question = "What is the best place to eat there?"
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answer_without_memory = get_travel_info(question, use_memory=False)
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answer_with_memory = get_travel_info(question, use_memory=True)
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print("Answer without memory:", answer_without_memory)
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print("Answer with memory:", answer_with_memory)
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```
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```json Conversation
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# Add conversation to Mem0
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conversation = [
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{
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"role": "user",
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"content": "What are the best travel destinations in the world?"
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},
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{
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"role": "assistant",
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"content": "Could you please specify your interests or the type of travel information you are looking for? This will help me find the most relevant information for you."
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},
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{
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"role": "user",
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"content": "Sure, I want to travel to San Francisco."
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},
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{
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"role": "assistant",
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"content": """
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Based on the information gathered from TripAdvisor, here are some popular attractions, activities, and travel tips for San Francisco: \
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1. **Golden Gate Bridge**: A must-see iconic landmark. \
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2. **Alcatraz Island**: Famous former prison offering tours. \
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3. **Fisherman's Wharf**: Popular tourist area with shops, restaurants, and sea lions. \
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4. **Chinatown**: The largest Chinatown outside of Asia. \
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5. **Golden Gate Park**: Large urban park with gardens, museums, and recreational activities. \
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6. **Cable Cars**: Historic streetcars offering a unique way to see the city. \
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7. **Exploratorium**: Interactive science museum. \
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8. **San Francisco Museum of Modern Art (SFMOMA)**: Modern and contemporary art museum. \
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9. **Lombard Street**: Known for its steep, one-block section with eight hairpin turns. \
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10. **Union Square**: Major shopping and cultural hub. \
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Travel Tips: \
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- **Weather**: San Francisco has a mild climate, but it can be foggy and windy. Dress in layers. \
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- **Transportation**: Use public transportation like BART, Muni, and cable cars to get around. \
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- **Safety**: Be aware of your surroundings, especially in crowded tourist areas. \
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- **Dining**: Try local specialities like sourdough bread, seafood, and Mission-style burritos. \
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"""
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},
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{
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"role": "user",
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"content": "Show me hotels around Golden Gate Bridge."
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},
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{
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"role": "assistant",
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"content": """The search results for hotels around Golden Gate Bridge in San Francisco include: \
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1. Hilton Hotels In San Francisco - Hotel Near Fishermans Wharf (hilton.com) \
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2. The 10 Closest Hotels to Golden Gate Bridge (tripadvisor.com) \
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3. Hotels near Golden Gate Bridge (expedia.com) \
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4. Hotels near Golden Gate Bridge (hotels.com) \
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5. Holiday Inn Express & Suites San Francisco Fishermans Wharf, an IHG Hotel $146 (1.8K) 3-star hotel Golden Gate Bridge • 3.5 mi DEAL 19% less than usual \
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6. Holiday Inn San Francisco-Golden Gateway, an IHG Hotel $151 (3.5K) 3-star hotel Golden Gate Bridge • 3.7 mi Casual hotel with dining, a bar & a pool \
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7. Hotel Zephyr San Francisco $159 (3.8K) 4-star hotel Golden Gate Bridge • 3.7 mi Nautical-themed lodging with bay views \
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8. Lodge at the Presidio \
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9. The Inn Above Tide \
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10. Cavallo Point \
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11. Casa Madrona Hotel and Spa \
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12. Cow Hollow Inn and Suites \
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13. Samesun San Francisco \
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14. Inn on Broadway \
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15. Coventry Motor Inn \
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16. HI San Francisco Fisherman's Wharf Hostel \
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17. Loews Regency San Francisco Hotel \
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18. Fairmont Heritage Place Ghirardelli Square \
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19. Hotel Drisco Pacific Heights \
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20. Travelodge by Wyndham Presidio San Francisco \
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"""
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}
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]
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```
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</CodeGroup>
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## Conclusion
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By integrating Mem0 with MultiOn, you've created personalized browser agents that remember user preferences and automate web tasks. The first example demonstrates a research-focused agent, while the second example shows a travel agent capable of providing personalized recommendations.
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These examples illustrate how combining memory management with web browsing capabilities can create powerful, context-aware AI agents for various applications.
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## Help
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- For more details and advanced usage, refer to the full [cookbooks here](https://github.com/mem0ai/mem0/blob/main/cookbooks).
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- Feel free to visit our [Github](https://github.com/mem0ai/mem0) or [Mem0 Platform](https://app.mem0.ai/).
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- For any questions or assistance, please reach out to `taranjeetio` on [Discord](https://mem0.dev/DiD).
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