Neptune Analytics 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.
Installation
The Neptune Analytics provider needs the AWS Neptune Graph client. Install it alongside mem0ai:
pip install mem0ai[vector-stores]
npm install @aws-sdk/client-neptune-graph
Usage
Configure AWS credentials in your environment (environment variables, shared config file, an IAM role, or an instance profile). Both SDKs pick them up automatically through the standard AWS credential chain.
from mem0 import Memory
config = {
"vector_store": {
"provider": "neptune",
"config": {
"collection_name": "mem0",
"endpoint": "neptune-graph://g-abc123xyz0",
},
},
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
import { Memory } from 'mem0ai/oss';
const config = {
vectorStore: {
provider: 'neptune',
config: {
collectionName: 'mem0',
graphIdentifier: 'g-abc123xyz0',
// Any other key here (region, credentials, maxAttempts, ...) is
// forwarded to the underlying NeptuneGraphClient constructor.
region: 'us-east-1',
},
},
};
const memory = new Memory(config);
const messages = [
{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
{ role: "assistant", content: "How about a thriller movie? They can be quite engaging." },
{ role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
{ role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
];
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
Config
| Parameter | Description | Default Value |
|---|
collection_name | The name of the collection to store the vectors | mem0 |
endpoint | Connection URL for the Neptune Analytics service, must be neptune-graph://<graph-id> | Required |
| Parameter | Description | Default Value |
|---|
collectionName | The name of the collection to store the vectors | memories |
graphIdentifier | Graph ID, e.g. g-abc123xyz0. Takes priority over endpoint. | Required, unless endpoint supplies it |
endpoint | Either neptune-graph://<graph-id> (or a bare graph ID) to supply the graph ID, or an https:// service endpoint to override the AWS endpoint. An https:// value must be paired with graphIdentifier. | undefined |
dimension | Embedding vector dimension | Auto-detected from the embedder when omitted |
client | A pre-built NeptuneGraphClient to use instead of constructing one | undefined |
| any other key | Forwarded as-is to the NeptuneGraphClient constructor, e.g. region, credentials, maxAttempts | N/A |
MEM0_VECTOR_<collection_name>. Point them at the same
graph with the same collection_name — the defaults differ, mem0 in Python and memories in
TypeScript — and get(), list(), and delete() interoperate across SDKs.
search() is not currently cross-SDK compatible. The TypeScript provider filters on Neptune’s reserved
~label metafield, while the Python provider filters on a synthetic label property that only Python’s
own insert() writes. Python’s search() therefore cannot see nodes written by the TypeScript provider.
IAM Permissions
Your AWS identity (user or role) needs a policy that allows the ExecuteQuery actions used for reads, writes, and deletes:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"neptune-graph:ReadDataViaQuery",
"neptune-graph:WriteDataViaQuery",
"neptune-graph:DeleteDataViaQuery"
],
"Resource": "*"
}
]
}
