Oracle AI Vector Search - Mem0

Oracle AI Vector Search stores embeddings in an Oracle table using the native VECTOR data type, so you can combine semantic search over unstructured data with relational queries over business data in a single database.

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.

bash

pip install oracledb

Usage

<CodeGroup> ```python Python import os from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = { "vector_store": { "provider": "oracledb", "config": { "collection_name": "mem0", "embedding_model_dims": 1536, "connection_params": { "user": "mem0_user", "password": "your-password", "dsn": "localhost:1521/FREEPDB1", }, } } }

m = Memory.from_config(config) 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."} ] m.add(messages, user_id="alice", metadata={"category": "movies"})

</CodeGroup>

To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:

```python
import oracledb

pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")

config = {
    "vector_store": {
        "provider": "oracledb",
        "config": {"client": pool},
    }
}

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.	`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`

<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. </Note>

Vector indexes

Set the index type with index_type and tune it with index_parameters:

python

config = {
    "vector_store": {
        "provider": "oracledb",
        "config": {
            "connection_params": {"user": "mem0_user", "password": "your-password", "dsn": "localhost:1521/FREEPDB1"},
            "index_type": "HNSW",
            "index_parameters": {"neighbors": 32, "efconstruction": 200},
            "index_accuracy": 95,
        }
    }
}

For the full list of supported options, see the Oracle CREATE VECTOR INDEX reference.

Search scores

Oracle returns a distance from VECTOR_DISTANCE, which Mem0 converts to a score where higher means more similar. COSINE and the other non-negative metrics produce scores in the range [0, 1]. DOT returns the inner product, which can fall outside that range.

Metadata filters

Filters run against the JSON payload column and support:

Filter type	Examples
Scalar equality	`{"user_id": "alice"}`
Field existence	`{"agent_id": "*"}`
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": [...]}`

Multiple fields at the top level are combined with AND:

python

m.search(
    "movie recommendations",
    user_id="alice",
    filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
)