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Add Oracle Vector Store Integration (#5358)

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Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>
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Elif Sema Balcioglu
2026-07-23 19:00:01 +03:00
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commit d6d89c987b
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docs/components/vectordbs/config.mdx
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@@ -7,7 +7,7 @@ description: "Reference for vector database configuration options in Mem0, inclu
The `config` is defined as an object with two main keys:
- `vector_store`: Specifies the vector database provider and its configuration
- `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey")
- `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search", "valkey", "oracledb")
- `config`: A nested dictionary containing provider-specific settings
@@ -95,6 +95,11 @@ Here's a comprehensive list of all parameters that can be used across different
| `connection_string` | PostgreSQL connection string (for Supabase/PGVector) |
| `index_method` | Vector index method (for Supabase) |
| `index_measure` | Distance measure for similarity search (for Supabase) |
| `connection_params` | Connection settings for Oracle AI Vector Search |
| `use_connection_pool` | Create an Oracle connection pool from `connection_params` |
| `distance_metric` | Distance metric for Oracle vector indexing and search |
| `index_type` | Oracle vector index type: `HNSW` or `IVF` |
| `index_parameters` | Oracle vector-index parameters for the selected index type |
</Tab>
<Tab title="TypeScript">
| Parameter | Description |
docs/components/vectordbs/dbs/oracledb.mdx
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---
title: "Oracle AI Vector Search"
description: "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` 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](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` |
<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`](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": [...]}` |
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}},
)
```
docs/components/vectordbs/overview.mdx
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---
title: Overview
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, and more."
description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
---
Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
@@ -21,6 +21,7 @@ See the list of supported vector databases below.
<Card title="Milvus" icon="/images/provider-icons/milvus.svg" href="/components/vectordbs/dbs/milvus"></Card>
<Card title="Pinecone" icon="/images/provider-icons/pinecone.svg" href="/components/vectordbs/dbs/pinecone"></Card>
<Card title="MongoDB" icon="/images/provider-icons/mongodb.svg" href="/components/vectordbs/dbs/mongodb"></Card>
<Card title="Oracle AI Vector Search" icon="/images/provider-icons/oracle.svg" href="/components/vectordbs/dbs/oracledb"></Card>
<Card title="Azure" icon="/images/provider-icons/azure-color.svg" href="/components/vectordbs/dbs/azure"></Card>
<Card title="Redis" icon="/images/provider-icons/redis.svg" href="/components/vectordbs/dbs/redis"></Card>
<Card title="Valkey" icon="/images/provider-icons/valkey.svg" href="/components/vectordbs/dbs/valkey"></Card>
docs/docs.json
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@@ -207,6 +207,7 @@
"components/vectordbs/dbs/milvus",
"components/vectordbs/dbs/pinecone",
"components/vectordbs/dbs/mongodb",
"components/vectordbs/dbs/oracledb",
"components/vectordbs/dbs/azure",
"components/vectordbs/dbs/azure_mysql",
"components/vectordbs/dbs/redis",
docs/images/provider-icons/oracle.svg
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<svg fill="#8F74E0" role="img" viewBox="0 0 93.9 59.4" xmlns="http://www.w3.org/2000/svg"><title>Oracle</title><path d="M30.5,59.4H65c16.4-0.4,29.3-14.1,28.9-30.4C93.5,13.1,80.7,0.4,65,0H30.5C14.1-0.4,0.4,12.5,0,28.9s12.5,30,28.9,30.4C29.4,59.4,29.9,59.4,30.5,59.4 M64.2,48.9h-33c-10.6-0.3-18.9-9.2-18.6-19.8C13,19,21.1,10.8,31.2,10.5h33c10.6-0.3,19.5,8,19.8,18.6c0.3,10.6-8,19.5-18.6,19.8C65,48.9,64.6,48.9,64.2,48.9"/></svg>
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docs/llms.txt
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@@ -472,6 +472,7 @@ Everything below is OSS-only provider configuration. Skip this entire section wh
- [Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus) [OSS]: Use for large-scale Milvus deployments.
- [Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone) [OSS]: Use when the user is on Pinecone managed.
- [MongoDB](https://docs.mem0.ai/components/vectordbs/dbs/mongodb) [OSS]: Use when Mongo Atlas Vector Search is the backing store.
- [Oracle AI Vector Search](https://docs.mem0.ai/components/vectordbs/dbs/oracledb) [OSS]: Use when Oracle Database AI Vector Search is the backing store.
- [Azure AI Search](https://docs.mem0.ai/components/vectordbs/dbs/azure) [OSS]: Use when the user is on Azure AI Search.
- [Azure MySQL](https://docs.mem0.ai/components/vectordbs/dbs/azure_mysql) [OSS]: Use when vector search runs on Azure Database for MySQL.
- [Redis](https://docs.mem0.ai/components/vectordbs/dbs/redis) [OSS]: Use when Redis Stack is the backing store.