chore: merge origin/main into mintlify/af82bda4 and resolve conflicts
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mintlify[bot]
@@ -1,6 +1,7 @@
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---
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title: Configurations
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description: "Reference for Mem0 vector store configuration, including provider selection, connection settings, and shared options across Qdrant, Chroma, and pgvector."
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title: "Vector Store Configuration Reference"
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sidebarTitle: "Configurations"
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description: "Reference for vector database configuration options in Mem0, including provider selection and connection settings."
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---
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## How to define configurations?
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@@ -123,10 +124,10 @@ Here's a comprehensive list of all parameters that can be used across different
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Each vector database has its own specific configuration requirements. To customize the config for your chosen vector store:
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1. Identify the vector database you want to use from [supported vector databases](./dbs).
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1. Identify the vector database you want to use from [supported vector databases](./overview).
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2. Refer to the `Config` section in the respective vector database's documentation.
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3. Include only the relevant parameters for your chosen database in the `config` dictionary.
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## Supported Vector Databases
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For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./dbs) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
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For detailed information on configuring specific vector databases, please visit the [Supported Vector Databases](./overview) section. There you'll find individual pages for each supported vector store with provider-specific usage examples and configuration details.
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@@ -1,6 +1,7 @@
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---
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title: LangChain
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description: "Use LangChain as a unified vector store provider in Mem0 to access many vector databases like Chroma, FAISS, and Pinecone through one API."
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title: "LangChain as Vector Store Provider"
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sidebarTitle: "LangChain"
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description: "Use LangChain as a unified vector store provider in Mem0 to access multiple vector databases through one interface."
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---
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Mem0 supports LangChain as a provider for vector store integration. LangChain provides a unified interface to various vector databases, making it easy to integrate different vector store providers through a consistent API.
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@@ -3,17 +3,25 @@ title: "Oracle AI Vector Search"
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description: "Use Oracle Database AI Vector Search as a vector store in Mem0 with the native VECTOR type to combine semantic memory search and relational queries."
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---
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{/* Copyright (c) 2026, Oracle and/or its affiliates. */}
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[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.
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### Requirements
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- Oracle Database 23.4 or later, with a user that can create tables and vector indexes
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- The `python-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
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- The `python-oracledb` or `node-oracledb` driver. In thick mode, Oracle Client 23.4 or later is also required.
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```bash
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pip install oracledb
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<CodeGroup>
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```bash Python
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pip install mem0ai
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```
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```bash TypeScript
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npm install oracledb
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```
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</CodeGroup>
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### Usage
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<CodeGroup>
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@@ -47,11 +55,58 @@ messages = [
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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```typescript TypeScript
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import { Memory } from "mem0ai/oss";
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const config = {
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vectorStore: {
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provider: "oracledb",
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config: {
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collectionName: "mem0",
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embeddingModelDims: 1536,
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connectionParams: {
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user: "mem0_user",
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password: "your-password",
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connectString: "localhost:1521/FREEPDB1",
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},
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},
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},
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};
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const memory = new Memory(config);
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const messages = [
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{
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role: "user",
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content: "I'm planning to watch a movie tonight. Any recommendations?",
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},
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{
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role: "assistant",
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content: "How about thriller movies? They can be quite engaging.",
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},
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{
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role: "user",
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content: "I'm not a big fan of thriller movies but I love sci-fi movies.",
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},
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{
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role: "assistant",
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content:
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"Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
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},
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];
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await memory.add(messages, {
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userId: "alice",
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metadata: { category: "movies" },
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});
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```
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</CodeGroup>
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To reuse a connection or pool you already manage, pass it as `client` instead of `connection_params`:
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To reuse a connection or pool you already manage, pass it as `client` instead of the connection parameters:
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```python
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<CodeGroup>
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```python Python
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import oracledb
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pool = oracledb.create_pool(user="mem0_user", password="your-password", dsn="localhost:1521/FREEPDB1")
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@@ -64,33 +119,54 @@ config = {
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}
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```
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```typescript TypeScript
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import oracledb from "oracledb";
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const pool = await oracledb.createPool({
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user: "mem0_user",
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password: "your-password",
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connectString: "localhost:1521/FREEPDB1",
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});
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const config = {
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vectorStore: {
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provider: "oracledb",
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config: { client: pool },
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},
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};
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```
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</CodeGroup>
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### Config
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Here are the parameters available for configuring Oracle AI Vector Search:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `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` |
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| `use_connection_pool` | Create a connection pool from `connection_params` instead of a single connection | `True` |
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| `client` | An existing `oracledb.Connection` or `oracledb.ConnectionPool` to use instead of building one from `connection_params` | `None` |
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| `collection_name` | Name of the Oracle table that stores vectors and payloads | `mem0` |
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| `embedding_model_dims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
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| `distance_metric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
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| `do_create_index` | Whether to create a vector index on the collection | `True` |
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| `index_type` | Vector index type: `HNSW` or `IVF` | `HNSW` |
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| `index_name` | Name of the vector index | `<collection_name>_VEC_IDX` |
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| `index_parameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
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| `index_accuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
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Provide either `connection_params`/`connectionParams` or an existing connection or pool as `client`.
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| Python | TypeScript | Description | Default Value |
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| --- | --- | --- | --- |
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| `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` |
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| `use_connection_pool` | `useConnectionPool` | Create a connection pool from the connection parameters instead of a single connection | `True` |
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| `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` |
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| `collection_name` | `collectionName` | Name of the Oracle table that stores vectors and payloads | `mem0` |
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| `embedding_model_dims` | `embeddingModelDims` | Dimension of your embedding vectors, must be greater than 0 | `1536` |
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| `distance_metric` | `distanceMetric` | Distance function used for indexing and search: `COSINE`, `EUCLIDEAN`, `EUCLIDEAN_SQUARED`, `DOT`, `HAMMING` or `MANHATTAN` | `COSINE` |
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| `do_create_index` | `doCreateIndex` | Whether to create a vector index on the collection | `True` |
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| `index_type` | `indexType` | Vector index type: `HNSW` or `IVF` | `HNSW` |
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| `index_name` | `indexName` | Name of the vector index | `<collection_name>_VEC_IDX` |
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| `index_parameters` | `indexParameters` | Index tuning parameters. For `HNSW`: `neighbors`, `efconstruction`. For `IVF`: `neighbor partitions`, `samples_per_partition`, `min_vectors_per_partition`. | `None` |
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| `index_accuracy` | `indexAccuracy` | Target index accuracy from 1 to 100, applied as `WITH TARGET ACCURACY <n>` | `None` |
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<Note>
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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.
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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.
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</Note>
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### Vector indexes
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Set the index type with `index_type` and tune it with `index_parameters`:
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```python
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<CodeGroup>
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```python Python
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config = {
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"vector_store": {
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"provider": "oracledb",
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@@ -104,6 +180,25 @@ config = {
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}
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```
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```typescript TypeScript
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const config = {
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vectorStore: {
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provider: "oracledb",
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config: {
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connectionParams: {
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user: "mem0_user",
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password: "your-password",
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connectString: "localhost:1521/FREEPDB1",
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},
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indexType: "HNSW",
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indexParameters: { neighbors: 32, efconstruction: 200 },
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indexAccuracy: 95,
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},
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},
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};
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```
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</CodeGroup>
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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.
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### Search scores
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@@ -121,14 +216,29 @@ Filters run against the JSON `payload` column and support:
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| Comparison | `{"score": {"gte": 0.5}}`, also `eq`, `ne`, `gt`, `lt`, `lte` |
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| Membership | `{"category": {"in": ["movies", "books"]}}`, also `nin` |
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| String matching | `{"title": {"contains": "sci-fi"}}`, also `icontains` for case-insensitive |
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| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}` |
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| Logical groups | `{"AND": [...]}`, `{"OR": [...]}`, `{"NOT": [...]}`, also `$and`, `$or`, `$not` |
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Multiple fields at the top level are combined with `AND`:
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```python
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<CodeGroup>
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```python Python
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m.search(
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"movie recommendations",
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user_id="alice",
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filters={"category": {"in": ["movies", "books"]}, "rating": {"gte": 4}},
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filters={
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"user_id": "alice",
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"category": {"in": ["movies", "books"]},
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"rating": {"gte": 4},
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},
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)
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```
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```typescript TypeScript
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await memory.search("movie recommendations", {
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filters: {
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user_id: "alice",
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category: { in: ["movies", "books"] },
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rating: { gte: 4 },
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},
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});
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```
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</CodeGroup>
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@@ -102,7 +102,7 @@ Here are the parameters available for configuring Upstash Vector:
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| `url` | URL for the Upstash Vector index | `None` |
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| `token` | Token for the Upstash Vector index | `None` |
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| `client` | An `upstash_vector.Index` instance | `None` |
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| `collection_name` | The default namespace used | `""` |
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| `collection_name` | The default namespace used | `"mem0"` |
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| `enable_embeddings` | Whether to use Upstash embeddings | `False` |
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<Note>
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@@ -1,6 +1,7 @@
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---
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title: Overview
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description: "Explore Mem0 vector database integrations including Qdrant, Chroma, Pinecone, pgvector, Milvus, Weaviate, Redis, and Oracle for memory storage."
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title: "Vector Store Providers Overview"
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sidebarTitle: "Overview"
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description: "Overview of all supported vector databases in Mem0, including Qdrant, Chroma, PGVector, Pinecone, Oracle, and more."
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---
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Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.
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@@ -10,7 +11,7 @@ Mem0 includes built-in support for various popular databases. Memory can utilize
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See the list of supported vector databases below.
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<Note>
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The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
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The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Oracle AI Vector Search, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.
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</Note>
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<CardGroup cols={3}>
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@@ -28,12 +29,12 @@ See the list of supported vector databases below.
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<Card title="Elasticsearch" icon="/images/provider-icons/elasticsearch.svg" href="/components/vectordbs/dbs/elasticsearch"></Card>
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<Card title="OpenSearch" icon="/images/provider-icons/opensearch.svg" href="/components/vectordbs/dbs/opensearch"></Card>
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<Card title="Supabase" icon="/images/provider-icons/supabase.svg" href="/components/vectordbs/dbs/supabase"></Card>
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<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex_ai"></Card>
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<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/vectordbs/dbs/vertex-ai"></Card>
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<Card title="Weaviate" icon="circle-nodes" href="/components/vectordbs/dbs/weaviate"></Card>
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<Card title="FAISS" icon="layer-group" href="/components/vectordbs/dbs/faiss"></Card>
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<Card title="LangChain" icon="/images/provider-icons/langchain-color.svg" href="/components/vectordbs/dbs/langchain"></Card>
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<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3_vectors"></Card>
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<Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune_analytics"></Card>
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<Card title="Amazon S3 Vectors" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/s3-vectors"></Card>
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<Card title="Neptune Analytics" icon="/images/provider-icons/aws-color.svg" href="/components/vectordbs/dbs/neptune-analytics"></Card>
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<Card title="Databricks" icon="/images/provider-icons/databricks.svg" href="/components/vectordbs/dbs/databricks"></Card>
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<Card title="Turbopuffer" icon="/images/provider-icons/turbopuffer.svg" href="/components/vectordbs/dbs/turbopuffer"></Card>
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</CardGroup>
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