> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Smart Travel Assistant
> Plan itineraries that remember traveler preferences across trips.
Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
## Overview
The Personalized AI Travel Assistant uses Mem0 to store and retrieve information across interactions, enabling a tailored travel planning experience. It integrates with OpenAI's GPT-4 model to provide detailed and context-aware responses to user queries.
## Setup
Install the required dependencies using pip:
```bash theme={null}
pip install openai mem0ai
```
## Full Code Example
Here's the complete code to create and interact with a Personalized AI Travel Assistant using Mem0:
```python After v1.1 theme={null}
import os
from openai import OpenAI
from mem0 import Memory
# Set the OpenAI API key
os.environ['OPENAI_API_KEY'] = "sk-xxx"
config = {
"llm": {
"provider": "openai",
"config": {
"model": "gpt-5-mini",
"temperature": 0.1,
"max_tokens": 2000,
}
},
"embedder": {
"provider": "openai",
"config": {
"model": "text-embedding-3-large"
}
},
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "test",
"embedding_model_dims": 3072,
}
},
}
class PersonalTravelAssistant:
def __init__(self):
self.client = OpenAI()
self.memory = Memory.from_config(config)
self.messages = [{"role": "system", "content": "You are a personal AI Assistant."}]
def ask_question(self, question, user_id):
# Fetch previous related memories
previous_memories = self.search_memories(question, user_id=user_id)
# Build the prompt
system_message = "You are a personal AI Assistant."
if previous_memories:
prompt = f"{system_message}\n\nUser input: {question}\nPrevious memories: {', '.join(previous_memories)}"
else:
prompt = f"{system_message}\n\nUser input: {question}"
# Generate response using Responses API
response = self.client.responses.create(
model="gpt-5-mini",
input=prompt
)
# Extract answer from the response
answer = response.output[0].content[0].text
# Store the question in memory
self.memory.add(question, user_id=user_id)
return answer
def get_memories(self, user_id):
memories = self.memory.get_all(filters={"user_id": user_id})
return [m['memory'] for m in memories['results']]
def search_memories(self, query, user_id):
memories = self.memory.search(query, filters={"user_id": user_id})
return [m['memory'] for m in memories['results']]
# Usage example
user_id = "traveler_123"
ai_assistant = PersonalTravelAssistant()
def main():
while True:
question = input("Question: ")
if question.lower() in ['q', 'exit']:
print("Exiting...")
break
answer = ai_assistant.ask_question(question, user_id=user_id)
print(f"Answer: {answer}")
memories = ai_assistant.get_memories(user_id=user_id)
print("Memories:")
for memory in memories:
print(f"- {memory}")
print("-----")
if __name__ == "__main__":
main()
```
```python Before v1.1 theme={null}
import os
from openai import OpenAI
from mem0 import Memory
# Set the OpenAI API key
os.environ['OPENAI_API_KEY'] = 'sk-xxx'
class PersonalTravelAssistant:
def __init__(self):
self.client = OpenAI()
self.memory = Memory()
self.messages = [{"role": "system", "content": "You are a personal AI Assistant."}]
def ask_question(self, question, user_id):
# Fetch previous related memories
previous_memories = self.search_memories(question, user_id=user_id)
prompt = question
if previous_memories:
prompt = f"User input: {question}\n Previous memories: {previous_memories}"
self.messages.append({"role": "user", "content": prompt})
# Generate response using gpt-4.1-nano
response = self.client.chat.completions.create(
model="gpt-5-mini",
messages=self.messages
)
answer = response.choices[0].message.content
self.messages.append({"role": "assistant", "content": answer})
# Store the question in memory
self.memory.add(question, user_id=user_id)
return answer
def get_memories(self, user_id):
memories = self.memory.get_all(filters={"user_id": user_id})
return [m['memory'] for m in memories.get('results', [])]
def search_memories(self, query, user_id):
memories = self.memory.search(query, filters={"user_id": user_id})
return [m['memory'] for m in memories.get('results', [])]
# Usage example
user_id = "traveler_123"
ai_assistant = PersonalTravelAssistant()
def main():
while True:
question = input("Question: ")
if question.lower() in ['q', 'exit']:
print("Exiting...")
break
answer = ai_assistant.ask_question(question, user_id=user_id)
print(f"Answer: {answer}")
memories = ai_assistant.get_memories(user_id=user_id)
print("Memories:")
for memory in memories:
print(f"- {memory}")
print("-----")
if __name__ == "__main__":
main()
```
## Key Components
* **Initialization**: The `PersonalTravelAssistant` class is initialized with the OpenAI client and Mem0 memory setup.
* **Asking Questions**: The `ask_question` method sends a question to the AI, incorporates previous memories, and stores new information.
* **Memory Management**: The `get_memories` and search\_memories methods handle retrieval and searching of stored memories.
## Usage
1. Set your OpenAI API key in the environment variable.
2. Instantiate the `PersonalTravelAssistant`.
3. Use the `main()` function to interact with the assistant in a loop.
## Conclusion
This Personalized AI Travel Assistant leverages Mem0's memory capabilities to provide context-aware responses. As you interact with it, the assistant learns and improves, offering increasingly personalized travel advice and information.
***
Use categories to organize travel preferences, destinations, and user context.
Build an educational companion that remembers learning progress and preferences.