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docs: fix Oracle vector store setup and search examples (#7111)

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This commit is contained in:
Elif Sema Balcioglu
2026-09-01 15:49:48 +02:00
committed by GitHub
parent 71fba8d464
commit c33ca27f5e
2 changed files with 16 additions and 8 deletions
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docs/components/vectordbs/dbs/oracledb.mdx
+15 -7
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@@ -14,7 +14,7 @@ description: "Use Oracle Database AI Vector Search as a vector store in Mem0 for
<CodeGroup>
```bash Python
pip install oracledb
pip install mem0ai
```
```bash TypeScript
@@ -141,11 +141,13 @@ const config = {
Here are the parameters available for configuring Oracle AI Vector Search:
Provide either `connection_params`/`connectionParams` or an existing connection or pool as `client`.
| 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` |
| `connection_params` | `connectionParams` | Connection settings passed to the Oracle driver, such as `user`, `password` and `dsn` (`connectString` in TypeScript). Required unless `client` is provided. 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` |
| `client` | `client` | An existing Oracle connection or pool to use instead of building one from the connection parameters. Required unless connection parameters are provided. | `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` |
@@ -222,15 +224,21 @@ Multiple fields at the top level are combined with `AND`:
```python Python
m.search(
"movie recommendations",
user_id="alice",
filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
filters={
"user_id": "alice",
"category": {"in": ["movies", "books"]},
"rating": {"gte": 4},
},
)
```
```typescript TypeScript
await memory.search("movie recommendations", {
userId: "alice",
filters: { category: { in: ["movies", "books"] }, rating: { gte: 4 } },
filters: {
user_id: "alice",
category: { in: ["movies", "books"] },
rating: { gte: 4 },
},
});
```
</CodeGroup>
tests/vector_stores/test_oracledb.py
+1 -1
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@@ -1081,7 +1081,7 @@ def test_documentation():
},
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
results = m.search("What movie to watch?", user_id="alice", limit=2)["results"]
results = m.search("What movie to watch?", filters={"user_id": "alice"}, top_k=2)["results"]
assert len(results) == 2
assert all(res["user_id"] == "alice" for res in results)
m.reset()