## OpenSearch OpenSearch is an enterprise-grade search and observability suite that brings order to unstructured data at scale. OpenSearch supports k-NN (k-Nearest Neighbors) and allows you to store and retrieve high-dimensional vector embeddings efficiently. ### Installation OpenSearch support requires an additional client library. Install the one for your SDK: #### Python ``` pip install opensearch-py ``` #### TypeScript ``` npm install @opensearch-project/opensearch ``` ### Prerequisites Before using OpenSearch with Mem0, you need to set up a collection in AWS OpenSearch Service. #### AWS OpenSearch Service You can create a collection through the AWS Console: - Navigate to [OpenSearch Service Console](https://console.aws.amazon.com/aos/home) - Click “Create collection” - Select “Serverless collection” and then enable “Vector search” capabilities - Once created, note the endpoint URL (host) for your configuration ### Usage #### Python ```python import os from mem0 import Memory import boto3 from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth # For AWS OpenSearch Service with IAM authentication region = 'us-west-2' service = 'aoss' credentials = boto3.Session().get_credentials() auth = AWSV4SignerAuth(credentials, region, service) config = { "vector_store": { "provider": "opensearch", "config": { "collection_name": "mem0", "host": "your-domain.us-west-2.aoss.amazonaws.com", "port": 443, "http_auth": auth, "embedding_model_dims": 1024, "connection_class": RequestsHttpConnection, "pool_maxsize": 20, "use_ssl": True, "verify_certs": True } } } ``` #### TypeScript ```typescript import { Memory } from 'mem0ai/oss'; // Basic self-hosted OpenSearch. For AWS OpenSearch Serverless, build an // @opensearch-project/opensearch Client with AwsSigv4Signer and pass it as // `client` instead of host/port/user/password. const config = { vectorStore: { provider: 'opensearch', config: { collectionName: 'mem0', embeddingModelDims: 1024, host: 'localhost', port: 9200, user: 'admin', password: 'admin', useSSL: false, verifyCerts: false, }, }, }; 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" } }); ``` ### Configuration Options | Parameter | Type | Default | Description | |-------------------------|---------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------| | `collection_name` | string | required| Name of the OpenSearch index | | `host` | string | required| OpenSearch endpoint URL | | `port` | int | 9200 | Port number | | `http_auth` | object | None | Authentication credentials (e.g., AWSV4SignerAuth) | | `embedding_model_dims` | int | 1536 | Dimension of embedding vectors | | `use_ssl` | bool | False | Enable SSL/TLS connection | | `verify_certs` | bool | False | Verify SSL certificates | | `auto_refresh` | bool | False | Automatically refresh index after insert. OpenSearch refreshes every ~1 second by default, so this is rarely needed. | | Parameter | Type | Default | Description | |-------------------------|---------|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------| | `collectionName` | string | required | Name of the OpenSearch index | | `embeddingModelDims` | number | 1536 | Dimension of embedding vectors | | `host` | string | `localhost`| OpenSearch endpoint host | | `port` | number | 9200 | Port number | | `httpAuth` | object | None | Authentication credentials, an object or `[user, password]` tuple | | `user` | string | None | Username for basic auth (used together with `password`) | | `password` | string | None | Password for basic auth (used together with `user`) | | `useSSL` | boolean | false | Enable SSL/TLS connection | | `verifyCerts` | boolean | false | Verify SSL certificates | | `autoRefresh` | boolean | false | Refresh the index after each write so new memories are searchable immediately. Not supported on AWS Serverless. | | `client` | object | None | Preconfigured OpenSearch client, e.g. one built with AwsSigv4Signer for AWS auth | The defaults above match a local OpenSearch instance. The AWS OpenSearch Serverless example earlier on this page intentionally overrides them with `port=443`, `use_ssl=True`, and `verify_certs=True`, which are required when connecting to a Serverless collection. For **AWS OpenSearch Serverless**, keep `auto_refresh=False` (the default). The `indices.refresh()` API is not supported on Serverless collections. ### Add Memories ``` 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"}) ``` ### Search Memories ``` results = m.search("What kind of movies does Alice like?", filters={"user_id": "alice"}) ``` ### Features - Fast and Efficient Vector Search - Can be deployed on-premises, in containers, or on cloud platforms like AWS OpenSearch Service - Multiple authentication and security methods (Basic Authentication, API Keys, LDAP, SAML, and OpenID Connect) - Automatic index creation with optimized mappings for vector search - Memory optimization through disk-based vector search and quantization - Real-time analytics and observability