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.

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Installation

OpenSearch support requires additional dependencies. Install them with:

pip install opensearch-py

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Prerequisites

Before using OpenSearch with Zentry, you need to set up a collection in AWS OpenSearch Service.

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AWS OpenSearch Service

You can create a collection through the AWS Console:

  • Navigate to OpenSearch Service Console
  • Click “Create collection”
  • Select “Serverless collection” and then enable “Vector search” capabilities
  • Once created, note the endpoint URL (host) for your configuration

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Usage

import os
from Zentry 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": "Zentry",
            "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
        }
    }
}

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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 a 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"})

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Search Memories

results = m.search("What kind of movies does Alice like?", user_id="alice")

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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