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Add output examples and multion travel agent notebook (#1594)

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Dev Khant
2024-07-26 23:35:21 +05:30
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@@ -6,42 +6,48 @@ Build personal browser agent remembers user preferences and automates web tasks.
## Overview
In this example, we will create a Browser based AI Agent that searches [arxiv.org](https://arxiv.org) for research papers relevant to user's research interests.
In this guide, we'll explore two examples of creating Browser-based AI Agents:
1. An agent that searches [arxiv.org](https://arxiv.org) for research papers relevant to user's research interests.
2. A travel agent that provides personalized travel information based on user preferences.
## Setup and Configuration
Install necessary libraries:
```bash
pip install mem0ai multion
pip install mem0ai multion openai
```
First, we'll import the necessary libraries and set up our configurations.
```python
import os
from mem0 import Memory
from mem0 import Memory, MemoryClient
from multion.client import MultiOn
from openai import OpenAI
# Configuration
OPENAI_API_KEY = 'sk-xxx' # Replace with your actual OpenAI API key
MULTION_API_KEY = 'your-multion-key' # Replace with your actual MultiOn API key
USER_ID = "deshraj"
MEM0_API_KEY = 'your-mem0-key' # Replace with your actual Mem0 API key
USER_ID = "your-user-id"
# Set up OpenAI API key
os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
# Initialize Mem0 and MultiOn
memory = Memory()
memory = Memory() # For local usage
memory_client = MemoryClient(api_key=MEM0_API_KEY) # For API usage
multion = MultiOn(api_key=MULTION_API_KEY)
```
## Add memories to Mem0
## Example 1: Research Paper Search Agent
Next, we'll define our user data and add it to Mem0.
### Add memories to Mem0
Define user data and add it to Mem0.
```python
# Define user data
USER_DATA = """
About me
- I'm Deshraj Yadav, Co-founder and CTO at Mem0, interested in AI and ML Infrastructure.
@@ -50,17 +56,15 @@ About me
- Outside of work, I enjoy playing cricket in two leagues in the San Francisco.
"""
# Add user data to memory
memory.add(USER_DATA, user_id=USER_ID)
print("User data added to memory.")
```
## Retrieving Relevant Memories
### Retrieving Relevant Memories
Now, we'll define our search command and retrieve relevant memories from Mem0.
Define search command and retrieve relevant memories from Mem0.
```python
# Define search command and retrieve relevant memories
command = "Find papers on arxiv that I should read based on my interests."
relevant_memories = memory.search(command, user_id=USER_ID, limit=3)
@@ -69,22 +73,139 @@ print(f"Relevant memories:")
print(relevant_memories_text)
```
## Browsing arXiv
### Browsing arXiv
Finally, we'll use MultiOn to browse arXiv based on our command and relevant memories.
Use MultiOn to browse arXiv based on the command and relevant memories.
```python
# Create prompt and browse arXiv
prompt = f"{command}\n My past memories: {relevant_memories_text}"
browse_result = multion.browse(cmd=prompt, url="https://arxiv.org/")
print(browse_result)
```
## Example 2: Travel Agent
### Get Travel Information
Add conversation to Mem0 and create a function to get travel information based on user's question and optionally their preferences from memory.
<CodeGroup>
```python Code
def get_travel_info(question, use_memory=True):
if use_memory:
previous_memories = memory_client.search(question, user_id=USER_ID)
relevant_memories_text = ""
if previous_memories:
print("Using previous memories to enhance the search...")
relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
command = "Find travel information based on my interests:"
prompt = f"{command}\n Question: {question} \n My preferences: {relevant_memories_text}"
else:
command = "Find travel information based on my interests:"
prompt = f"{command}\n Question: {question}"
print("Searching for travel information...")
browse_result = multion.browse(cmd=prompt)
return browse_result.message
# Example usage
question = "Show me flight details for it."
answer_without_memory = get_travel_info(question, use_memory=False)
answer_with_memory = get_travel_info(question, use_memory=True)
print("Answer without memory:", answer_without_memory)
print("Answer with memory:", answer_with_memory)
# Another example
question = "What is the best place to eat there?"
answer_without_memory = get_travel_info(question, use_memory=False)
answer_with_memory = get_travel_info(question, use_memory=True)
print("Answer without memory:", answer_without_memory)
print("Answer with memory:", answer_with_memory)
```
```json Conversation
# Add conversation to Mem0
conversation = [
{
"role": "user",
"content": "What are the best travel destinations in the world?"
},
{
"role": "assistant",
"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."
},
{
"role": "user",
"content": "Sure, I want to travel to San Francisco."
},
{
"role": "assistant",
"content": """
Based on the information gathered from TripAdvisor, here are some popular attractions, activities, and travel tips for San Francisco: \
1. **Golden Gate Bridge**: A must-see iconic landmark. \
2. **Alcatraz Island**: Famous former prison offering tours. \
3. **Fisherman's Wharf**: Popular tourist area with shops, restaurants, and sea lions. \
4. **Chinatown**: The largest Chinatown outside of Asia. \
5. **Golden Gate Park**: Large urban park with gardens, museums, and recreational activities. \
6. **Cable Cars**: Historic streetcars offering a unique way to see the city. \
7. **Exploratorium**: Interactive science museum. \
8. **San Francisco Museum of Modern Art (SFMOMA)**: Modern and contemporary art museum. \
9. **Lombard Street**: Known for its steep, one-block section with eight hairpin turns. \
10. **Union Square**: Major shopping and cultural hub. \
Travel Tips: \
- **Weather**: San Francisco has a mild climate, but it can be foggy and windy. Dress in layers. \
- **Transportation**: Use public transportation like BART, Muni, and cable cars to get around. \
- **Safety**: Be aware of your surroundings, especially in crowded tourist areas. \
- **Dining**: Try local specialties like sourdough bread, seafood, and Mission-style burritos. \
"""
},
{
"role": "user",
"content": "Show me hotels around Golden Gate Bridge."
},
{
"role": "assistant",
"content": """The search results for hotels around Golden Gate Bridge in San Francisco include: \
1. Hilton Hotels In San Francisco - Hotel Near Fishermans Wharf (hilton.com) \
2. The 10 Closest Hotels to Golden Gate Bridge (tripadvisor.com) \
3. Hotels near Golden Gate Bridge (expedia.com) \
4. Hotels near Golden Gate Bridge (hotels.com) \
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 \
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 \
7. Hotel Zephyr San Francisco $159 (3.8K) 4-star hotel Golden Gate Bridge • 3.7 mi Nautical-themed lodging with bay views \
8. Lodge at the Presidio \
9. The Inn Above Tide \
10. Cavallo Point \
11. Casa Madrona Hotel and Spa \
12. Cow Hollow Inn and Suites \
13. Samesun San Francisco \
14. Inn on Broadway \
15. Coventry Motor Inn \
16. HI San Francisco Fisherman's Wharf Hostel \
17. Loews Regency San Francisco Hotel \
18. Fairmont Heritage Place Ghirardelli Square \
19. Hotel Drisco Pacific Heights \
20. Travelodge by Wyndham Presidio San Francisco \
"""
}
]
```
</CodeGroup>
## Conclusion
By integrating Mem0 with MultiOn, you've created a personalized browser agent that remembers user preferences and automates web tasks. For more details and advanced usage, refer to the full [cookbook here](https://github.com/mem0ai/mem0/blob/main/cookbooks/mem0-multion.ipynb).
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.
These examples illustrate how combining memory management with web browsing capabilities can create powerful, context-aware AI agents for various applications.
## Help
- For more details and advanced usage, refer to the full [cookbooks here](https://github.com/mem0ai/mem0/blob/main/cookbooks).
- Feel free to visit our [Github](https://github.com/mem0ai/mem0) or [Mem0 Platform](https://app.mem0.ai/).
- For any questions or assistance, please reach out to `taranjeetio` on [Discord](https://mem0.ai/discord).