Add neptune example notebook and documentation

docs/examples/aws_example.mdx

@@ -1,17 +1,17 @@
 ---
-title: AWS Bedrock and AOSS
+title: Amazon Stack: AWS Bedrock, AOSS, and Neptune Analytics
 ---
 
 <Snippet file="security-compliance.mdx" />
 
-This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **OpenSearch Service (AOSS)** for persistent memory capabilities in Python.
+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.
 
 ## Installation
 
-Install the required dependencies:
+Install the required dependencies to include the Amazon data stack, including **boto3**, **opensearch-py**, and **langchain-aws**:
 
 ```bash
-pip install mem0ai boto3 opensearch-py
+pip install "mem0ai[graph,extras]"
 ```
 
 ## Environment Setup
@@ -34,11 +34,15 @@ print(os.environ['AWS_SECRET_ACCESS_KEY'])
 
 ## Configuration and Usage
 
-This sets up Mem0 with AWS Bedrock for embeddings and LLM, and OpenSearch as the vector store.
+This sets up Mem0 with:
+- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
+- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
+- [OpenSearch as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/opensearch)
+- [Neptune Analytics as your graph store](https://docs.mem0.ai/open-source/graph_memory/overview#initialize-neptune-analytics).
 
 ```python
 import boto3
-from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
+from opensearchpy import RequestsHttpConnection, AWSV4SignerAuth
 from mem0.memory.main import Memory
 
 region = 'us-west-2'
@@ -56,7 +60,7 @@ config = {
     "llm": {
         "provider": "aws_bedrock",
         "config": {
-            "model": "anthropic.claude-3-5-haiku-20241022-v1:0",
+            "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
             "temperature": 0.1,
             "max_tokens": 2000
         }
@@ -68,21 +72,29 @@ config = {
             "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
+            "verify_certs": True,
+            "embedding_model_dims": 1024,
         }
-    }
+    },
+    "graph_store": {
+        "provider": "neptune",
+        "config": {
+            "endpoint": f"neptune-graph://my-graph-identifier",
+        },
+    },
 }
 
-# Initialize memory system
+# Initialize the memory system
 m = Memory.from_config(config)
 ```
 
 ## Usage
 
+Reference [Notebook example](https://github.com/mem0ai/mem0/blob/main/examples/graph-db-demo/neptune-example.ipynb)
+
 #### Add a memory:
 
 ```python
@@ -117,4 +129,4 @@ memory = m.get(memory_id)
 
 ## Conclusion
 
-With Mem0 and AWS services like Bedrock and OpenSearch, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.
+With Mem0 and AWS services like Bedrock, OpenSearch, and Neptune Analytics, you can build intelligent AI companions that remember, adapt, and personalize their responses over time. This makes them ideal for long-term assistants, tutors, or support bots with persistent memory and natural conversation abilities.

docs/open-source/graph_memory/overview.mdx

@@ -236,6 +236,8 @@ m = Memory.from_config(config_dict=config)
 
 Mem0 now supports Amazon Neptune Analytics as a graph store provider. This integration allows you to use Neptune Analytics for storing and querying graph-based memories.
 
+You can use Neptune Analytics as part of an Amazon tech stack [Setup AWS Bedrock, AOSS, and Neptune](https://docs.mem0.ai/examples/aws_example#aws-bedrock-and-aoss)
+
 #### Instance Setup
 
 Create an Amazon Neptune Analytics instance in your AWS account following the [AWS documentation](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/get-started.html).
@@ -266,12 +268,8 @@ The Neptune memory store uses AWS LangChain Python API to connect to Neptune ins
 ```python Python
 from mem0 import Memory
 
-# This example must connect to a neptune-graph instance with 1536 vector dimensions specified.
+# Provided neptune-graph instance must have the same vector dimensions as the embedder provider.
 config = {
-    "embedder": {
-        "provider": "openai",
-        "config": {"model": "text-embedding-3-large", "embedding_dims": 1536},
-    },
     "graph_store": {
         "provider": "neptune",
         "config": {