Update Docs (#3315)
This commit is contained in:
Deshraj Yadav
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title: AI Companion
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---
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<Snippet file="blank-notif.mdx" />
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You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
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## Overview
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The Personalized AI Companion leverages Mem0 to retain information across interactions, enabling a tailored learning experience. It creates separate memories for both the user and the companion. By integrating with OpenAI's GPT-4 model, the companion can provide detailed and context-aware responses to user queries.
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## Setup
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Before you begin, ensure you have the required dependencies installed. You can install the necessary packages using pip:
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```bash
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pip install openai mem0ai
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```
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## Full Code Example
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Below is the complete code to create and interact with an AI Companion using Mem0:
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```python
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from openai import OpenAI
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from mem0 import Memory
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import os
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# Set the OpenAI API key
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os.environ['OPENAI_API_KEY'] = 'sk-xxx'
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# Initialize the OpenAI client
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client = OpenAI()
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class Companion:
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def __init__(self, user_id, companion_id):
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"""
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Initialize the Companion with memory configuration, OpenAI client, and user IDs.
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:param user_id: ID for storing user-related memories
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:param companion_id: ID for storing companion-related memories
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"""
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config = {
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"vector_store": {
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"provider": "qdrant",
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"config": {
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"host": "localhost",
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"port": 6333,
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}
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},
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}
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self.memory = Memory.from_config(config)
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self.client = client
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self.app_id = "app-1"
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self.USER_ID = user_id
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self.companion_id = companion_id
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def analyze_question(self, question):
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"""
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Analyze the question to determine whether it's about the user or the companion.
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"""
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check_prompt = f"""
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Analyze the given input and determine whether the user is primarily:
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1) Talking about themselves or asking for personal advice. They may use words like "I" for this.
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2) Inquiring about the AI companion's capabilities or characteristics They may use words like "you" for this.
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Respond with a single word:
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- 'user' if the input is focused on the user
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- 'companion' if the input is focused on the AI companion
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If the input is ambiguous or doesn't clearly fit either category, respond with 'user'.
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Input: {question}
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"""
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response = self.client.chat.completions.create(
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model="gpt-4",
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messages=[{"role": "user", "content": check_prompt}]
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)
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return response.choices[0].message.content
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def ask(self, question):
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"""
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Ask a question to the AI and store the relevant facts in memory
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:param question: The question to ask the AI.
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"""
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check_answer = self.analyze_question(question)
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user_id_to_use = self.USER_ID if check_answer == "user" else self.companion_id
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previous_memories = self.memory.search(question, user_id=user_id_to_use)
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relevant_memories_text = ""
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if previous_memories and previous_memories.get('results'):
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relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results'])
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prompt = f"User input: {question}\nPrevious {check_answer} memories: {relevant_memories_text}"
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messages = [
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{
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"role": "system",
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"content": "You are the user's romantic companion. Use the user's input and previous memories to respond. Answer based on the context provided."
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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stream = self.client.chat.completions.create(
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model="gpt-4",
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stream=True,
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messages=messages
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)
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answer = ""
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for chunk in stream:
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if chunk.choices[0].delta.content is not None:
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content = chunk.choices[0].delta.content
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print(content, end="")
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answer += content
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# Store the question and answer in memory
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self.memory.add(question, user_id=self.USER_ID, metadata={"app_id": self.app_id})
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self.memory.add(answer, user_id=self.companion_id, metadata={"app_id": self.app_id})
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def get_memories(self, user_id=None):
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"""
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Retrieve all memories associated with the given user ID.
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:param user_id: Optional user ID to filter memories.
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:return: List of memories.
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"""
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return self.memory.get_all(user_id=user_id)
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# Example usage:
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user_id = "user"
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companion_id = "companion"
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ai_companion = Companion(user_id, companion_id)
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# Ask a question
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ai_companion.ask("Ive been missing you. What have you been up to off late?")
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```
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### Fetching Memories
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You can fetch all the memories at any point in time using the following code:
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```python
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def print_memories(user_id, label):
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print(f"\n{label} Memories:")
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memories = ai_companion.get_memories(user_id=user_id)
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if memories:
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for m in memories:
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print(f"- {m['memory']}")
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else:
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print("No memories found.")
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# Print user memories
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print_memories(user_id, "User")
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# Print companion memories
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print_memories(companion_id, "Companion")
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```
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### Key Points
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- **Initialization**: The Companion class is initialized with the necessary memory configuration and OpenAI client setup.
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- **Asking Questions**: The ask method sends a question to the AI and stores the relevant information in memory.
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- **Retrieving Memories**: The get_memories method fetches all stored memories associated with a user.
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### Conclusion
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As the conversation progresses, Mem0's memory automatically updates based on the interactions, providing a continuously improving personalized experience. This setup ensures that the AI Companion can offer contextually relevant and accurate responses, enhancing the user's experience.
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title: AI Companion in Node.js
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---
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<Snippet file="blank-notif.mdx" />
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You can create a personalised AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
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## Overview
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title: Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics
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---
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<Snippet file="blank-notif.mdx" />
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This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock**, **OpenSearch Service (AOSS)**, and **AWS Neptune Analytics** for persistent memory capabilities in Python.
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## Installation
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# Mem0 Chrome Extension
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<Snippet file="blank-notif.mdx" />
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Enhance your AI interactions with **Mem0**, a Chrome extension that introduces a universal memory layer across platforms like `ChatGPT`, `Claude`, and `Perplexity`. Mem0 ensures seamless context sharing, making your AI experiences more personalized and efficient.
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<Note>
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title: Multi-User Collaboration with Mem0
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---
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<Snippet file="blank-notif.mdx" />
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## Overview
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Build a multi-user collaborative chat or task management system with Mem0. Each message is attributed to its author, and all messages are stored in a shared project space. Mem0 makes it easy to track contributions, sort and group messages, and collaborate in real time.
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title: Customer Support AI Agent
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---
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<Snippet file="blank-notif.mdx" />
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You can create a personalized Customer Support AI Agent using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
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title: Eliza OS Character
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---
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<Snippet file="blank-notif.mdx" />
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You can create a personalised Eliza OS Character using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
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## Overview
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title: Email Processing with Mem0
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---
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<Snippet file="blank-notif.mdx" />
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This guide demonstrates how to build an intelligent email processing system using Mem0's memory capabilities. You'll learn how to store, categorize, retrieve, and analyze emails to create a smart email management solution.
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## Overview
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---
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title: LlamaIndex ReAct Agent
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---
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<Snippet file="blank-notif.mdx" />
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Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.
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title: Mem0 as an Agentic Tool
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---
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<Snippet file="blank-notif.mdx" />
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Integrate Mem0's memory capabilities with OpenAI's Agents SDK to create AI agents with persistent memory.
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You can create agents that remember past conversations and use that context to provide better responses.
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title: Mem0 Demo
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---
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<Snippet file="blank-notif.mdx" />
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You can create a personalized AI Companion using Mem0. This guide will walk you through the necessary steps and provide the complete setup instructions to get you started.
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<video
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@@ -3,7 +3,6 @@ title: 'Healthcare Assistant with Mem0 and Google ADK'
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description: 'Build a personalized healthcare agent that remembers patient information across conversations using Mem0 and Google ADK'
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---
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<Snippet file="blank-notif.mdx" />
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# Healthcare Assistant with Memory
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title: Mem0 with Mastra
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---
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<Snippet file="blank-notif.mdx" />
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In this example you'll learn how to use the Mem0 to add long-term memory capabilities to [Mastra's agent](https://mastra.ai/) via tool-use.
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This memory integration can work alongside Mastra's [agent memory features](https://mastra.ai/docs/agents/01-agent-memory).
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@@ -3,8 +3,6 @@ title: 'Mem0 with OpenAI Agents SDK for Voice'
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description: 'Integrate memory capabilities into your voice agents using Mem0 and OpenAI Agents SDK'
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---
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<Snippet file="blank-notif.mdx" />
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# Building Voice Agents with Memory using Mem0 and OpenAI Agents SDK
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This guide demonstrates how to combine OpenAI's Agents SDK for voice applications with Mem0's memory capabilities to create a voice assistant that remembers user preferences and past interactions.
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title: Mem0 with Ollama
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---
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<Snippet file="blank-notif.mdx" />
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## Running Mem0 Locally with Ollama
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Mem0 can be utilized entirely locally by leveraging Ollama for both the embedding model and the language model (LLM). This guide will walk you through the necessary steps and provide the complete code to get you started.
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---
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title: Memory-Guided Content Writing
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---
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<Snippet file="blank-notif.mdx" />
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This guide demonstrates how to leverage **Mem0** to streamline content writing by applying your unique writing style and preferences using persistent memory.
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title: Multimodal Demo with Mem0
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---
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<Snippet file="blank-notif.mdx" />
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Enhance your AI interactions with **Mem0**'s multimodal capabilities. Mem0 now supports image understanding, allowing for richer context and more natural interactions across supported AI platforms.
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> Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
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title: OpenAI Inbuilt Tools
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---
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<Snippet file="blank-notif.mdx" />
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Integrate Mem0’s memory capabilities with OpenAI’s Inbuilt Tools to create AI agents with persistent memory.
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## Getting Started
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title: Personalized AI Tutor
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---
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<Snippet file="blank-notif.mdx" />
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You can create a personalized AI Tutor using Mem0. This guide will walk you through the necessary steps and provide the complete code to get you started.
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## Overview
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title: Personal AI Travel Assistant
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---
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<Snippet file="blank-notif.mdx" />
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Create a personalized AI Travel Assistant using Mem0. This guide provides step-by-step instructions and the complete code to get you started.
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title: Personalized Deep Research
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---
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<Snippet file="blank-notif.mdx" />
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Deep Research is an intelligent agent that synthesizes large amounts of online data and completes complex research tasks, customized to your unique preferences and insights. Built on Mem0's technology, it enhances AI-driven online exploration with personalized memories.
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You can checkout GitHub repositry here: [Personalized Deep Research](https://github.com/mem0ai/personalized-deep-research/tree/mem0)
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title: YouTube Assistant Extension
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---
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<Snippet file="blank-notif.mdx" />
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Enhance your YouTube experience with Mem0's **YouTube Assistant**, a Chrome extension that brings AI-powered chat directly to your YouTube videos. Get instant, personalized answers about video content while leveraging your own knowledge and memories - all without leaving the page.
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## Features
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