---
title: Personal AI Travel Assistant
---

<Snippet file="blank-notif.mdx" />

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
pip install openai mem0ai
```

## Full Code Example

Here's the complete code to create and interact with a Personalized AI Travel Assistant using Mem0:

<CodeGroup>

```python After v1.1
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-4o",
            "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,
        }
    },
    "version": "v1.1",
}

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-4o",
            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(user_id=user_id)
        return [m['memory'] for m in memories['results']]

    def search_memories(self, query, user_id):
        memories = self.memory.search(query, 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
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-4o
        response = self.client.chat.completions.create(
            model="gpt-4o",
            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(user_id=user_id)
        return [m['memory'] for m in memories['memories']]

    def search_memories(self, query, user_id):
        memories = self.memory.search(query, user_id=user_id)
        return [m['memory'] for m in memories['memories']]

# 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()
```
</CodeGroup>


## 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.