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Feat: Mem0 vector store backend integration for Neptune Analytics (#3453)
Signed-off-by: Andy Kwok <[email protected]>
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‎docs/components/vectordbs/dbs/neptune_analytics.mdx‎

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# Neptune Analytics Vector Store
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[Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html/) is a memory-optimized graph database engine for analytics. With Neptune Analytics, you can get insights and find trends by processing large amounts of graph data in seconds, including vector search.
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## Installation
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```bash
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pip install mem0ai[vector_stores]
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```
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## Usage
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```python
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config = {
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"vector_store": {
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"provider": "neptune",
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"config": {
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"collection_name": "mem0",
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"endpoint": f"neptune-graph://my-graph-identifier",
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},
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},
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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## Parameters
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Let's see the available parameters for the `neptune` config:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `collection_name` | The name of the collection to store the vectors | `mem0` |
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| `endpoint` | Connection URL for the Neptune Analytics service | `neptune-graph://my-graph-identifier` |

‎docs/docs.json‎

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"components/vectordbs/dbs/langchain",
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"components/vectordbs/dbs/baidu",
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"components/vectordbs/dbs/s3_vectors",
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"components/vectordbs/dbs/databricks"
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"components/vectordbs/dbs/databricks",
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"components/vectordbs/dbs/neptune_analytics"
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]
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}
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]
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"pages": [
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"examples",
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"examples/aws_example",
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"examples/aws_neptune_analytics_hybrid_store",
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"examples/mem0-demo",
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"examples/ai_companion_js",
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"examples/collaborative-task-agent",

‎docs/examples/aws_neptune_analytics_hybrid_store.mdx‎

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---
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title: "Amazon Stack - Neptune Analytics Hybrid Store: AWS Bedrock and Neptune Analytics"
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---
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This example demonstrates how to configure and use the `mem0ai` SDK with **AWS Bedrock** and **AWS Neptune Analytics** for persistent memory capabilities in Python.
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## Installation
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Install the required dependencies to include the Amazon data stack, including **boto3** and **langchain-aws**:
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```bash
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pip install "mem0ai[graph,extras]"
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```
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## Environment Setup
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Set your AWS environment variables:
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```python
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import os
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# Set these in your environment or notebook
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os.environ['AWS_REGION'] = 'us-west-2'
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os.environ['AWS_ACCESS_KEY_ID'] = 'AK00000000000000000'
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os.environ['AWS_SECRET_ACCESS_KEY'] = 'AS00000000000000000'
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# Confirm they are set
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print(os.environ['AWS_REGION'])
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print(os.environ['AWS_ACCESS_KEY_ID'])
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print(os.environ['AWS_SECRET_ACCESS_KEY'])
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```
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## Configuration and Usage
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This sets up Mem0 with:
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- [AWS Bedrock for LLM](https://docs.mem0.ai/components/llms/models/aws_bedrock)
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- [AWS Bedrock for embeddings](https://docs.mem0.ai/components/embedders/models/aws_bedrock#aws-bedrock)
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- [Neptune Analytics as the vector store](https://docs.mem0.ai/components/vectordbs/dbs/neptune_analytics)
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- [Neptune Analytics as the graph store](https://docs.mem0.ai/open-source/graph_memory/overview#initialize-neptune-analytics).
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```python
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import boto3
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from mem0.memory.main import Memory
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region = 'us-west-2'
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neptune_analytics_endpoint = 'neptune-graph://my-graph-identifier'
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config = {
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"embedder": {
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"provider": "aws_bedrock",
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"config": {
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"model": "amazon.titan-embed-text-v2:0"
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}
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},
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"llm": {
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"provider": "aws_bedrock",
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"config": {
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"model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
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"temperature": 0.1,
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"max_tokens": 2000
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}
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},
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"vector_store": {
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"provider": "neptune",
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"config": {
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"collection_name": "mem0",
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"endpoint": neptune_analytics_endpoint,
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},
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},
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"graph_store": {
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"provider": "neptune",
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"config": {
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"endpoint": neptune_analytics_endpoint,
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},
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},
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}
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# Initialize the memory system
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m = Memory.from_config(config)
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```
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## Usage
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Reference [Notebook example](https://github.com/mem0ai/mem0/blob/main/examples/graph-db-demo/neptune-example.ipynb)
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#### Add a memory:
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```python
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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# Store inferred memories (default behavior)
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result = m.add(messages, user_id="alice", metadata={"category": "movie_recommendations"})
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```
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#### Search a memory:
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```python
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relevant_memories = m.search(query, user_id="alice")
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```
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#### Get all memories:
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```python
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all_memories = m.get_all(user_id="alice")
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```
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#### Get a specific memory:
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```python
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memory = m.get(memory_id)
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```
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---
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## Conclusion
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With Mem0 and AWS services like Bedrock 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.

‎examples/graph-db-demo/neptune-example.ipynb‎

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"\n",
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"### 2. Connect to Amazon services\n",
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"\n",
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"For this sample notebook, configure `mem0ai` with [Amazon Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) as the graph store, [Amazon OpenSearch Serverless](https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless-overview.html) as the vector store, and [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) for generating embeddings.\n",
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"For this sample notebook, configure `mem0ai` with [Amazon Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) as the vector and graph store, and [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) for generating embeddings.\n",
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"\n",
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"Use the following guide for setup details: [Setup AWS Bedrock, AOSS, and Neptune](https://docs.mem0.ai/examples/aws_example#aws-bedrock-and-aoss)\n",
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"\n",
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"The Neptune Analytics instance must be created using the same vector dimensions as the embedding model creates. See: https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-index.html\n",
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"\n",
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"Your configuration should look similar to:\n",
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"\n",
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"```python\n",
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"config = {\n",
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" \"embedder\": {\n",
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" \"provider\": \"aws_bedrock\",\n",
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" \"config\": {\n",
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" \"model\": \"amazon.titan-embed-text-v2:0\"\n",
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" \"model\": \"amazon.titan-embed-text-v2:0\",\n",
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" \"embedding_dims\": 1024\n",
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" }\n",
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" },\n",
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" \"llm\": {\n",
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" }\n",
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" },\n",
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" \"vector_store\": {\n",
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" \"provider\": \"opensearch\",\n",
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" \"provider\": \"neptune\",\n",
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" \"config\": {\n",
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" \"collection_name\": \"mem0\",\n",
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" \"host\": \"your-opensearch-domain.us-west-2.es.amazonaws.com\",\n",
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" \"port\": 443,\n",
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" \"http_auth\": auth,\n",
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" \"connection_class\": RequestsHttpConnection,\n",
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" \"pool_maxsize\": 20,\n",
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" \"use_ssl\": True,\n",
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" \"verify_certs\": True,\n",
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" \"embedding_model_dims\": 1024,\n",
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" }\n",
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" \"endpoint\": f\"neptune-graph://my-graph-identifier\",\n",
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" },\n",
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" },\n",
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" \"graph_store\": {\n",
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" \"provider\": \"neptune\",\n",
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"import os\n",
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"import logging\n",
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"import sys\n",
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"import boto3\n",
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"from opensearchpy import RequestsHttpConnection, AWSV4SignerAuth\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()\n",
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"\n",
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"logging.getLogger(\"mem0.graphs.neptune.main\").setLevel(logging.DEBUG)\n",
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"logging.getLogger(\"mem0.graphs.neptune.base\").setLevel(logging.DEBUG)\n",
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"logging.getLogger(\"mem0.graphs.neptune.main\").setLevel(logging.INFO)\n",
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"logging.getLogger(\"mem0.graphs.neptune.base\").setLevel(logging.INFO)\n",
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"logger = logging.getLogger(__name__)\n",
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"logger.setLevel(logging.DEBUG)\n",
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"\n",
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"source": [
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"Setup the Mem0 configuration using:\n",
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"- Amazon Bedrock as the embedder\n",
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"- Amazon Neptune Analytics instance as a graph store\n",
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"- OpenSearch as the vector store"
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"- Amazon Neptune Analytics instance as a vector / graph store"
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]
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},
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{
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"\n",
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"graph_identifier = os.environ.get(\"GRAPH_ID\")\n",
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"\n",
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"opensearch_host = os.environ.get(\"OS_HOST\")\n",
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"opensearch_post = os.environ.get(\"OS_PORT\")\n",
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"\n",
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"credentials = boto3.Session().get_credentials()\n",
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"region = os.environ.get(\"AWS_REGION\")\n",
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"auth = AWSV4SignerAuth(credentials, region)\n",
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"\n",
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"config = {\n",
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" \"embedder\": {\n",
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" \"provider\": \"aws_bedrock\",\n",
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" \"config\": {\n",
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" \"model\": bedrock_embedder_model,\n",
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" \"embedding_dims\": embedding_model_dims\n",
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" }\n",
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" },\n",
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" \"llm\": {\n",
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" }\n",
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" },\n",
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" \"vector_store\": {\n",
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" \"provider\": \"opensearch\",\n",
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" \"provider\": \"neptune\",\n",
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" \"config\": {\n",
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" \"collection_name\": \"mem0ai_vector_store\",\n",
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" \"host\": opensearch_host,\n",
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" \"port\": opensearch_post,\n",
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" \"http_auth\": auth,\n",
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" \"embedding_model_dims\": embedding_model_dims,\n",
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" \"use_ssl\": True,\n",
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" \"verify_certs\": True,\n",
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" \"connection_class\": RequestsHttpConnection,\n",
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" \"endpoint\": f\"neptune-graph://{graph_identifier}\",\n",
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" },\n",
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" },\n",
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" \"graph_store\": {\n",
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"source": [
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"## Conclusion\n",
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"\n",
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"In this example we demonstrated how an AWS tech stack can be used to store and retrieve memory context. Bedrock LLM models can be used to interpret given conversations. OpenSearch can store text chunks with vector embeddings. Neptune Analytics can store the text chunks in a graph format with relationship entities."
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"In this example we demonstrated how an AWS tech stack can be used to store and retrieve memory context. Bedrock LLM models can be used to interpret given conversations. Neptune Analytics can store the text chunks in a graph format with relationship entities."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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"version": "3.13.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

‎mem0/configs/vector_stores/neptune.py‎

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"""
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Configuration for Amazon Neptune Analytics vector store.
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This module provides configuration settings for integrating with Amazon Neptune Analytics
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as a vector store backend for Mem0's memory layer.
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"""
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from pydantic import BaseModel, Field
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class NeptuneAnalyticsConfig(BaseModel):
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"""
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Configuration class for Amazon Neptune Analytics vector store.
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Amazon Neptune Analytics is a graph analytics engine that can be used as a vector store
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for storing and retrieving memory embeddings in Mem0.
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Attributes:
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collection_name (str): Name of the collection to store vectors. Defaults to "mem0".
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endpoint (str): Neptune Analytics graph endpoint URL or Graph ID for the runtime.
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"""
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collection_name: str = Field("mem0", description="Default name for the collection")
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endpoint: str = Field("endpoint", description="Graph ID for the runtime")
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model_config = {
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"arbitrary_types_allowed": False,
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}

‎mem0/utils/factory.py‎

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"langchain": "mem0.vector_stores.langchain.Langchain",
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"s3_vectors": "mem0.vector_stores.s3_vectors.S3Vectors",
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"baidu": "mem0.vector_stores.baidu.BaiduDB",
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"neptune": "mem0.vector_stores.neptune_analytics.NeptuneAnalyticsVector",
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}
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@classmethod

‎mem0/vector_stores/configs.py‎

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"mongodb": "MongoDBConfig",
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"milvus": "MilvusDBConfig",
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"baidu": "BaiduDBConfig",
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"neptune": "NeptuneAnalyticsConfig",
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"upstash_vector": "UpstashVectorConfig",
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"azure_ai_search": "AzureAISearchConfig",
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"redis": "RedisDBConfig",

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