AWS Bedrock Integration and spell checks (#3124)

docs/changelog.mdx

@@ -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">

docs/docs.json

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

docs/examples/ai_companion.mdx

@@ -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

docs/examples/llama-index-mem0.mdx

@@ -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>

docs/examples/multimodal-demo.mdx

@@ -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.
+- **Image Understanding**: Share and discuss images with AI assistants while maintaining context.
-- **🔍 Smart Visual Context**: Automatically capture and reference visual elements in conversations.
+- **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-Modal Memory**: Link visual and textual information seamlessly in your memory layer.
-- **📌 Cross-Session Recall**: Reference previously discussed visual content across different conversations.
+- **Cross-Session Recall**: Reference previously discussed visual content across different conversations.
-- **⚡ Seamless Integration**: Works naturally with existing chat interfaces for a smooth experience.
+- **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.
+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.
+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.
+3. **Memory Integration**: Visual context is automatically stored and linked with your conversation history.
-4. **🔄 Persistent Recall**: Retrieve and reference past visual content effortlessly.
+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.
 

docs/faqs.mdx

@@ -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?">

docs/features.mdx

@@ -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
 
 
 

docs/integrations/agno.mdx

@@ -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
+1. Store and retrieve memories from Mem0 within Agno agents
-2. 🖼️ Support for multimodal interactions (text and images)
+2. Support for multimodal interactions (text and images)
-3. 🔍 Semantic search for relevant past conversations
+3. Semantic search for relevant past conversations
-4. 🌐 Personalized responses based on user history
+4. Personalized responses based on user history
 
 ## Prerequisites
 

docs/integrations/aws-bedrock.mdx

@@ -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" />
+

docs/integrations/langchain.mdx

@@ -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",

docs/integrations/llama-index.mdx

@@ -103,7 +103,7 @@ memory_from_config = Mem0Memory.from_config(
 )
 ```
 
-Intilaize the LLM
+Initialize the LLM
 
 ```python
 import os