Oracle AI Vector Search

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 or node-oracledb driver. In thick mode, Oracle Client 23.4 or later is also required.

npm install oracledb

Usage

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
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:

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

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

```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 },
  },
};

Config

Here are the parameters available for configuring Oracle AI Vector Search:

Vector indexes

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

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 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:

Multiple fields at the top level are combined with AND:

await memory.search("movie recommendations", {
  userId: "alice",
  filters: { category: { in: ["movies", "books"] }, rating: { gte: 4 } },
});