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AWS Bedrock Integration and spell checks (#3124)

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Antaripa Saha
2025-07-08 22:46:44 +05:30
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4
docs/changelog.mdx
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@@ -496,7 +496,7 @@ mode: "wide"
<Update label="2025-04-28" description="v2.1.20">
**Improvements:**
- **Client:** Fixed `organizationId` and `projectId` being asssigned to default in `ping` method
- **Client:** Fixed `organizationId` and `projectId` being assigned to default in `ping` method
</Update>
<Update label="2025-04-22" description="v2.1.19">
@@ -555,7 +555,7 @@ mode: "wide"
<Update label="2025-03-29" description="v2.1.13">
**Improvements:**
- **Introuced `ping` method to check if API key is valid and populate org/project id**
- **Introduced `ping` method to check if API key is valid and populate org/project id**
</Update>
<Update label="2025-03-29" description="AI SDK v1.0.0">

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docs/docs.json
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@@ -260,6 +260,7 @@
"integrations/livekit",
"integrations/pipecat",
"integrations/elevenlabs",
"integrations/aws-bedrock",
"integrations/flowise",
"integrations/langchain-tools",
"integrations/agentops",

2
docs/examples/ai_companion.mdx
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@@ -61,7 +61,7 @@ class Companion:
check_prompt = f"""
Analyze the given input and determine whether the user is primarily:
1) Talking about themselves or asking for personal advice. They may use words like "I" for this.
2) Inquiring about the AI companions's capabilities or characteristics They may use words like "you" for this.
2) Inquiring about the AI companion's capabilities or characteristics They may use words like "you" for this.
Respond with a single word:
- 'user' if the input is focused on the user

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docs/examples/llama-index-mem0.mdx
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@@ -80,7 +80,7 @@ agent = FunctionCallingAgent.from_tools(
```
Start the chat.
<Note> The agent will use the Mem0 to store the relavant memories from the chat. </Note>
<Note> The agent will use the Mem0 to store the relevant memories from the chat. </Note>
Input
```python
@@ -139,7 +139,7 @@ Added user message to memory: I am feeling hungry, order me something and send m
=== LLM Response ===
Please let me know your name and the dish you'd like to order, and I'll take care of it for you!
```
<Note> The agent is not able to remember the past prefernces that user shared in previous chats. </Note>
<Note> The agent is not able to remember the past preferences that user shared in previous chats. </Note>
### Using the agent WITH memory
Input
@@ -171,4 +171,4 @@ Emailing... David
=== LLM Response ===
I've ordered a pizza for you, and the bill has been sent to your email. Enjoy your meal! If there's anything else you need, feel free to let me know.
```
<Note> The agent is able to remember the past prefernces that user shared and use them to perform actions. </Note>
<Note> The agent is able to remember the past preferences that user shared and use them to perform actions. </Note>

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docs/examples/multimodal-demo.mdx
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@@ -6,28 +6,28 @@ title: Multimodal Demo with Mem0
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.
> 🎉 Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
> Experience the power of multimodal AI! Test out Mem0's image understanding capabilities at [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai)
## 🚀 Features
## Features
- **🖼️ Image Understanding**: Share and discuss images with AI assistants while maintaining context.
- **🔍 Smart Visual Context**: Automatically capture and reference visual elements in conversations.
- **🔗 Cross-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
- **📌 Cross-Session Recall**: Reference previously discussed visual content across different conversations.
- **⚡ Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
- **Image Understanding**: Share and discuss images with AI assistants while maintaining context.
- **Smart Visual Context**: Automatically capture and reference visual elements in conversations.
- **Cross-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
- **Cross-Session Recall**: Reference previously discussed visual content across different conversations.
- **Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
## 📖 How It Works
## How It Works
1. **📂 Upload Visual Content**: Simply drag and drop or paste images into your conversations.
2. **💬 Natural Interaction**: Discuss the visual content naturally with AI assistants.
3. **📚 Memory Integration**: Visual context is automatically stored and linked with your conversation history.
4. **🔄 Persistent Recall**: Retrieve and reference past visual content effortlessly.
1. **Upload Visual Content**: Simply drag and drop or paste images into your conversations.
2. **Natural Interaction**: Discuss the visual content naturally with AI assistants.
3. **Memory Integration**: Visual context is automatically stored and linked with your conversation history.
4. **Persistent Recall**: Retrieve and reference past visual content effortlessly.
## Demo Video
<iframe width="700" height="400" src="https://www.youtube.com/embed/2Md5AEFVpmg?si=rXXupn6CiDUPJsi3" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
## 🔥 Try It Out
## Try It Out
Visit [multimodal-demo.mem0.ai](https://multimodal-demo.mem0.ai) to experience Mem0's multimodal capabilities firsthand. Upload images and see how Mem0 understands and remembers visual context across your conversations.

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docs/faqs.mdx
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@@ -14,7 +14,7 @@ iconType: "solid"
When an AI agent or LLM needs to access memories, it employs the `search` method. Mem0 conducts a comprehensive search across these data stores, retrieving relevant information from each.
The retrieved memories can be seamlessly integrated into the LLM's prompt as required, enhancing the personalization and relevance of responses.
The retrieved memories can be seamlessly integrated into the system prompt as required, enhancing the personalization and relevance of responses.
</Accordion>
<Accordion title="What are the key features of Mem0?">
@@ -23,7 +23,7 @@ iconType: "solid"
- **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
- **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
- **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
- **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
- **Save Costs**: Saves costs by adding relevant memories instead of complete transcripts to context window
</Accordion>
<Accordion title="How Mem0 is different from traditional RAG?">

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docs/features.mdx
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@@ -13,7 +13,7 @@ iconType: "solid"
- **Developer-Friendly API**: Offers a straightforward API for seamless integration into various applications.
- **Platform Consistency**: Ensures consistent behavior and data across different platforms and devices.
- **Managed Service**: Provides a hosted solution for easy deployment and maintenance.
- **Save Costs**: Saves costs by adding relevent memories instead of complete transcripts to context window
- **Save Costs**: Saves costs by adding relevant memories instead of complete transcripts to context window

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docs/integrations/agno.mdx
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@@ -7,10 +7,10 @@ Integrate [**Mem0**](https://github.com/mem0ai/mem0) with [Agno](https://github.
## Overview
1. 🧠 Store and retrieve memories from Mem0 within Agno agents
2. 🖼️ Support for multimodal interactions (text and images)
3. 🔍 Semantic search for relevant past conversations
4. 🌐 Personalized responses based on user history
1. Store and retrieve memories from Mem0 within Agno agents
2. Support for multimodal interactions (text and images)
3. Semantic search for relevant past conversations
4. Personalized responses based on user history
## Prerequisites

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docs/integrations/aws-bedrock.mdx Normal file
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@@ -0,0 +1,132 @@
---
title: AWS Bedrock
---
<Snippet file="security-compliance.mdx" />
This integration demonstrates how to use **Mem0** with **AWS Bedrock** and **Amazon OpenSearch Service (AOSS)** to enable persistent, semantic memory in intelligent agents.
## Overview
In this guide, you'll:
1. Configure AWS credentials to enable Bedrock and OpenSearch access
2. Set up the Mem0 SDK to use Bedrock for embeddings and LLM
3. Store and retrieve memories using OpenSearch as a vector store
4. Build memory-aware applications with scalable cloud infrastructure
## Prerequisites
- AWS account with access to:
- Bedrock foundation models (e.g., Titan, Claude)
- OpenSearch Service with a configured domain
- Python 3.8+
- Valid AWS credentials (via environment or IAM role)
## Setup and Installation
Install required packages:
```bash
pip install mem0ai boto3 opensearch-py
```
Set environment variables:
Be sure to configure your AWS credentials using environment variables, IAM roles, or the AWS CLI.
```python
import os
os.environ['AWS_REGION'] = 'us-west-2'
os.environ['AWS_ACCESS_KEY_ID'] = 'AKIA...'
os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS...'
```
## Initialize Mem0 Integration
Import necessary modules and configure Mem0:
```python
import boto3
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
from mem0.memory.main import Memory
region = 'us-west-2'
service = 'aoss'
credentials = boto3.Session().get_credentials()
auth = AWSV4SignerAuth(credentials, region, service)
config = {
"embedder": {
"provider": "aws_bedrock",
"config": {
"model": "amazon.titan-embed-text-v2:0"
}
},
"llm": {
"provider": "aws_bedrock",
"config": {
"model": "anthropic.claude-3-5-haiku-20241022-v1:0",
"temperature": 0.1,
"max_tokens": 2000
}
},
"vector_store": {
"provider": "opensearch",
"config": {
"collection_name": "mem0",
"host": "your-opensearch-domain.us-west-2.es.amazonaws.com",
"port": 443,
"http_auth": auth,
"embedding_model_dims": 1024,
"connection_class": RequestsHttpConnection,
"pool_maxsize": 20,
"use_ssl": True,
"verify_certs": True
}
}
}
# Initialize memory system
m = Memory.from_config(config)
```
## Memory Operations
Use Mem0 with your Bedrock-powered LLM and OpenSearch storage backend:
```python
# Store conversational context
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller?"},
{"role": "user", "content": "I prefer sci-fi."},
{"role": "assistant", "content": "Noted! I'll suggest sci-fi movies next time."}
]
m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
# Search for memory
relevant = m.search("What kind of movies does Alice like?", user_id="alice")
# Retrieve all user memories
all_memories = m.get_all(user_id="alice")
```
## Key Features
1. **Serverless Memory Embeddings**: Use Titan or other Bedrock models for fast, cloud-native embeddings
2. **Scalable Vector Search**: Store and retrieve vectorized memories via OpenSearch
3. **Seamless AWS Auth**: Uses AWS IAM or environment variables to securely authenticate
4. **User-specific Memory Spaces**: Memories are isolated per user ID
5. **Persistent Memory Context**: Maintain and recall history across sessions
## Help
- [AWS Bedrock Documentation](https://docs.aws.amazon.com/bedrock/)
- [Amazon OpenSearch Service Docs](https://docs.aws.amazon.com/opensearch-service/)
- [Mem0 Platform](https://app.mem0.ai)
<Snippet file="get-help.mdx" />

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docs/integrations/langchain.mdx
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@@ -64,11 +64,11 @@ Create functions to handle context retrieval, response generation, and addition
def retrieve_context(query: str, user_id: str) -> List[Dict]:
"""Retrieve relevant context from Mem0"""
memories = mem0.search(query, user_id=user_id)
seralized_memories = ' '.join([mem["memory"] for mem in memories])
serialized_memories = ' '.join([mem["memory"] for mem in memories])
context = [
{
"role": "system",
"content": f"Relevant information: {seralized_memories}"
"content": f"Relevant information: {serialized_memories}"
},
{
"role": "user",

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docs/integrations/llama-index.mdx
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@@ -103,7 +103,7 @@ memory_from_config = Mem0Memory.from_config(
)
```
Intilaize the LLM
Initialize the LLM
```python
import os