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feat(ts-sdk): add AWS Neptune Analytics vector store (#5797)

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Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
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Rod Boev
2026-07-10 19:51:05 +05:30
committed by GitHub
co-authored by kartik-mem0
parent ba9054e0c8
commit 33a0ed7559
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docs/components/vectordbs/dbs/neptune_analytics.mdx
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@@ -2,26 +2,37 @@
title: "Neptune Analytics"
description: "Use AWS Neptune Analytics as a vector store in Mem0, combining graph analytics with vector search capabilities."
---
# Neptune Analytics Vector Store
[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.
[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.
### Installation
## Installation
The Neptune Analytics provider needs the AWS Neptune Graph client. Install it alongside `mem0ai`:
```bash
<CodeGroup>
```bash Python
pip install mem0ai[vector-stores]
```
## Usage
```bash TypeScript
npm install @aws-sdk/client-neptune-graph
```
</CodeGroup>
### 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.
<CodeGroup>
```python Python
from mem0 import Memory
```python
config = {
"vector_store": {
"provider": "neptune",
"config": {
"collection_name": "mem0",
"endpoint": f"neptune-graph://my-graph-identifier",
"endpoint": "neptune-graph://g-abc123xyz0",
},
},
}
@@ -29,18 +40,90 @@ config = {
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 movies? They can be quite engaging."},
{"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"})
```
## Parameters
```typescript TypeScript
import { Memory } from 'mem0ai/oss';
Let's see the available parameters for the `neptune` config:
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" } });
```
</CodeGroup>
### Config
<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collection_name` | The name of the collection to store the vectors | `mem0` |
| `endpoint` | Connection URL for the Neptune Analytics service | `neptune-graph://my-graph-identifier` |
| `endpoint` | Connection URL for the Neptune Analytics service, must be `neptune-graph://<graph-id>` | Required |
</Tab>
<Tab title="TypeScript">
| 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`](https://www.npmjs.com/package/@aws-sdk/client-neptune-graph) constructor, e.g. `region`, `credentials`, `maxAttempts` | N/A |
</Tab>
</Tabs>
Both SDKs store vectors on graph nodes labeled `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.
<Note>
`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.
</Note>
### IAM Permissions
Your AWS identity (user or role) needs a policy that allows the [`ExecuteQuery`](https://docs.aws.amazon.com/neptune-analytics/latest/apiref/API_ExecuteQuery.html) actions used for reads, writes, and deletes:
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"neptune-graph:ReadDataViaQuery",
"neptune-graph:WriteDataViaQuery",
"neptune-graph:DeleteDataViaQuery"
],
"Resource": "*"
}
]
}
```
For production, scope the resource ARN down to your specific graph.
docs/components/vectordbs/overview.mdx
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@@ -10,7 +10,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
See the list of supported vector databases below.
<Note>
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, and an in-memory store.
The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
</Note>
<CardGroup cols={3}>
@@ -32,6 +32,7 @@ See the list of supported vector databases below.
<Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
<Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
<Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune_analytics"></Card>
<Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
<Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
</CardGroup>