> ## Documentation Index > Fetch the complete documentation index at: https://mem0-feature-memo-claude-plugin-v1.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Oracle AI Vector Search > Use Oracle Database AI Vector Search as a vector store in Mem0 for semantic and relational queries. [Oracle AI Vector Search](https://www.oracle.com/database/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` or `node-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required. ```bash Python theme={null} pip install oracledb ``` ```bash TypeScript theme={null} npm install oracledb ``` ### Usage ```python Python theme={null} 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"}) ``` ```typescript TypeScript theme={null} 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" }, }); ``` To reuse a connection or pool you already manage, pass it as `client` instead of the connection parameters: ```python Python theme={null} import oracledb pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1") config = { "vector_store": { "provider": "oracledb", "config": {"client": pool}, } } ``` ```typescript TypeScript theme={null} 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 }, }, }; ``` ### Config Here are the parameters available for configuring Oracle AI Vector Search: | 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 | `_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 ` | `None` | 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. ### Vector indexes Set the index type with `index_type` and tune it with `index_parameters`: ```python Python theme={null} 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, } } } ``` ```typescript TypeScript theme={null} 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, }, }, }; ``` 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 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": [...]}`, also `$and`, `$or`, `$not` | Multiple fields at the top level are combined with `AND`: ```python Python theme={null} m.search( "movie recommendations", user_id="alice", filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}}, ) ``` ```typescript TypeScript theme={null} await memory.search("movie recommendations", { userId: "alice", filters: { category: { in: ["movies", "books"] }, rating: { gte: 4 } }, }); ```