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feat(ts-oss): add Oracle AI Vector Search vector store (#6690)

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Kartik
2026-08-05 10:55:33 +05:30
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docs/components/vectordbs/dbs/oracledb.mdx
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@@ -8,12 +8,18 @@ description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for
### Requirements
- Oracle Database 23.4 or later, with a user that can create tables and vector indexes
- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
- The `python-oracledb` or `node-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
```bash
<CodeGroup>
```bash Python
pip install oracledb
```
```bash TypeScript
npm install oracledb
```
</CodeGroup>
### Usage
<CodeGroup>
@@ -47,11 +53,58 @@ messages = [
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```
```typescript TypeScript
import { Memory } from "mem0ai/oss";
const config = {
vectorStore: {
provider: "oracledb",
config: {
collectionName: "mem0",
embeddingModelDims: 1536,
connectionParams: {
user: "mem0_user",
password: "your-password",
connectString: "localhost:1521/FREEPDB1",
},
},
},
};
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 thriller movies? 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>
To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
To reuse a connection or pool you already manage, pass it as `client` instead of the connection parameters:
```python
<CodeGroup>
```python Python
import oracledb
pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
@@ -64,33 +117,52 @@ config = {
}
```
```typescript TypeScript
import oracledb from "oracledb";
const pool = await oracledb.createPool({
user: "mem0_user",
password: "your-password",
connectString: "localhost:1521/FREEPDB1",
});
const config = {
vectorStore: {
provider: "oracledb",
config: { client: pool },
},
};
```
</CodeGroup>
### Config
Here are the parameters available for configuring Oracle AI Vector Search:
| Parameter | Description | Default Value |
| --- | --- | --- |
| `connection_params` | Connection settings passed to `python-oracledb`, such as `user`, `password` and `dsn`. See the [connection handling guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html). | `None` |
| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
| `do_create_index` | Whether to create a vector index on the collection | `True` |
| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
| Python | TypeScript | Description | Default Value |
| --- | --- | --- | --- |
| `connection_params` | `connectionParams` | Connection settings passed to the Oracle driver, such as `user`, `password` and `dsn` (`connectString` in TypeScript). See the [Python](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html) or [Node.js](https://node-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html) connection handling guide. | `None` |
| `use_connection_pool` | `useConnectionPool` | Create a connection pool from the connection parameters instead of a single connection | `True` |
| `client` | `client` | An existing Oracle connection or pool to use instead of building one from the connection parameters | `None` |
| `collection_name` | `collectionName` | Name of the Oracle table that stores vectors and payloads | `mem0` |
| `embedding_model_dims` | `embeddingModelDims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
| `distance_metric` | `distanceMetric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
| `do_create_index` | `doCreateIndex` | Whether to create a vector index on the collection | `True` |
| `index_type` | `indexType` | Vector index type: `HNSW` or `IVF` | `HNSW` |
| `index_name` | `indexName` | Name of the vector index | `<collection_name>_VEC_IDX` |
| `index_parameters` | `indexParameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
| `index_accuracy` | `indexAccuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
<Note>
When you pass a pre-built `client`, Mem0 uses it as-is and ignores `connection_params` and `use_connection_pool`. Mem0 does not close a client it did not create.
When you pass a pre-built `client`, Mem0 uses it as-is and ignores the connection parameters and pooling options. Mem0 does not close a client it did not create.
</Note>
### Vector indexes
Set the index type with `index_type` and tune it with `index_parameters`:
```python
<CodeGroup>
```python Python
config = {
"vector_store": {
"provider": "oracledb",
@@ -104,6 +176,25 @@ config = {
}
```
```typescript TypeScript
const config = {
vectorStore: {
provider: "oracledb",
config: {
connectionParams: {
user: "mem0_user",
password: "your-password",
connectString: "localhost:1521/FREEPDB1",
},
indexType: "HNSW",
indexParameters: { neighbors: 32, efconstruction: 200 },
indexAccuracy: 95,
},
},
};
```
</CodeGroup>
For the full list of supported options, see the Oracle [`CREATE VECTOR INDEX`](https://docs.oracle.com/en/database/oracle/oracle-database/26/sqlrf/create-vector-index.html) reference.
### Search scores
@@ -121,14 +212,23 @@ Filters run against the JSON `payload` column and support:
| Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
| Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
| String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}`, also `$and`, `$or`, `$not` |
Multiple fields at the top level are combined with `AND`:
```python
<CodeGroup>
```python Python
m.search(
"movie recommendations",
user_id="alice",
filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
)
```
```typescript TypeScript
await memory.search("movie recommendations", {
userId: "alice",
filters: { category: { in: ["movies", "books"] }, rating: { gte: 4 } },
});
```
</CodeGroup>