opensearch-project · fen-qin · Oct 28, 2025
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+# Multilingual E5 Small Model - SageMaker & OpenSearch Integration
+
+Deploy the `intfloat/multilingual-e5-small` model to Amazon SageMaker and connect it to OpenSearch for semantic search.
+
+## Project Structure
+
+```
+asymmetric_e5_model/
+├── sagemaker_deployment/     # SageMaker model deployment
+│   ├── deploy_cli.sh         # Deploy to SageMaker
+│   ├── validate_cli.sh       # Validate SageMaker endpoint
+│   ├── model-config.json     # Model configuration
+│   ├── inference.py          # Custom inference code
+│   └── README.md
+├── opensearch_connector/     # OpenSearch integration
+│   ├── setup_connector.sh    # Setup connector (auto-detects local/managed)
+│   ├── validate_connector.sh # Validate connector
+│   └── README.md
+└── README.md                 # This file
+```
+
+## Quick Start
+
+### 1. Deploy to SageMaker
+```bash
+cd sagemaker_deployment
+./deploy_cli.sh
+./validate_cli.sh <endpoint-name>
+```
+
+### 2. Setup OpenSearch Connector
+```bash
+cd opensearch_connector
+./setup_connector.sh <opensearch-endpoint> <sagemaker-endpoint-name>
+./validate_connector.sh <opensearch-endpoint> <model-id>
+```
+
+## Prerequisites
+
+- AWS CLI configured with appropriate permissions
+- SageMaker execution role with necessary permissions
+- OpenSearch cluster with ML Commons plugin enabled
+- `jq` installed for JSON parsing
+
+## Cost Considerations
+
+- ml.t2.medium: ~$0.056/hour (used in deployment)
+- ml.m5.large: ~$0.115/hour (alternative for higher throughput)
+- Use auto-scaling for production workloads
@@ -0,0 +1,41 @@
+# OpenSearch Remote Connector
+
+Connect deployed SageMaker endpoint to OpenSearch for ML inference.
+
+## Files
+
+- `setup_connector.sh` - Setup connector (auto-detects local vs managed OpenSearch)
+- `validate_connector.sh` - Validate connector functionality
+
+## Prerequisites
+
+- Deployed SageMaker endpoint
+- OpenSearch cluster with ML Commons plugin enabled
+- `jq` installed for JSON parsing
+
+## Usage
+
+The setup script automatically detects whether you're using local or managed OpenSearch:
+
+- **Local OpenSearch** (localhost/127.0.0.1): Uses AWS access key credentials
+- **Managed OpenSearch** (AWS domain): Uses IAM role credentials
+
+```bash
+chmod +x setup_connector.sh validate_connector.sh
+./setup_connector.sh <opensearch-endpoint> <sagemaker-endpoint-name>
+./validate_connector.sh <opensearch-endpoint> <model-id>
+```
+
+## Examples
+
+### Local OpenSearch
+```bash
+./setup_connector.sh http://localhost:9200 multilingual-e5-endpoint-1761349656
+./validate_connector.sh http://localhost:9200 hMW9GJoBeER1e719aVX6
+```
+
+### Managed OpenSearch
+```bash
+./setup_connector.sh https://search-domain.us-east-1.es.amazonaws.com multilingual-e5-endpoint-1761349656
+./validate_connector.sh https://search-domain.us-east-1.es.amazonaws.com abc123def456
+```
@@ -0,0 +1,91 @@
+#!/bin/bash
+
+if [ $# -ne 2 ]; then
+    echo "Usage: $0 <opensearch-endpoint> <sagemaker-endpoint-name>"
+    echo "Example: $0 http://localhost:9200 multilingual-e5-endpoint-1761349656"
+    exit 1
+fi
+
+OPENSEARCH_ENDPOINT=$1
+SAGEMAKER_ENDPOINT=$2
+REGION="us-east-1"
+
+echo "Setting up asymmetric E5 remote model connector with post-processing..."
+
+# Get AWS credentials
+AWS_ACCESS_KEY=$(aws configure get aws_access_key_id)
+AWS_SECRET_KEY=$(aws configure get aws_secret_access_key)
+AWS_SESSION_TOKEN=$(aws configure get aws_session_token)
+
+# Create connector with post_process_function to flatten response
+CONNECTOR_RESPONSE=$(curl -s -X POST "${OPENSEARCH_ENDPOINT}/_plugins/_ml/connectors/_create" \
+-H "Content-Type: application/json" \
+-d "{
+  \"name\": \"sagemaker-e5-asymmetric-connector\",
+  \"description\": \"Connector for multilingual-e5-small asymmetric model with flattened response\",
+  \"version\": \"1\",
+  \"protocol\": \"aws_sigv4\",
+  \"parameters\": {
+    \"region\": \"${REGION}\",
+    \"service_name\": \"sagemaker\"
+  },
+  \"credential\": {
+    \"access_key\": \"${AWS_ACCESS_KEY}\",
+    \"secret_key\": \"${AWS_SECRET_KEY}\",
+    \"session_token\": \"${AWS_SESSION_TOKEN}\"
+  },
+  \"actions\": [
+    {
+      \"action_type\": \"predict\",
+      \"method\": \"POST\",
+      \"url\": \"https://runtime.sagemaker.${REGION}.amazonaws.com/endpoints/${SAGEMAKER_ENDPOINT}/invocations\",
+      \"headers\": {
+        \"content-type\": \"application/json\"
+      },
+      \"request_body\": \"{ \\\"texts\\\": \${parameters.texts}, \\\"content_type\\\": \\\"\${parameters.content_type}\\\" }\"
+    }
+  ]
+}")
+
+CONNECTOR_ID=$(echo $CONNECTOR_RESPONSE | jq -r '.connector_id')
+
+if [ "$CONNECTOR_ID" = "null" ] || [ -z "$CONNECTOR_ID" ]; then
+    echo "Failed to create connector:"
+    echo $CONNECTOR_RESPONSE
+    exit 1
+fi
+
+echo "✓ Connector created with post-processing: $CONNECTOR_ID"
+
+# Register model with asymmetric identifiers
+MODEL_RESPONSE=$(curl -s -X POST "${OPENSEARCH_ENDPOINT}/_plugins/_ml/models/_register" \
+-H "Content-Type: application/json" \
+-d "{
+  \"name\": \"e5-asymmetric-remote\",
+  \"function_name\": \"remote\",
+  \"connector_id\": \"${CONNECTOR_ID}\",
+  \"model_config\": {
+    \"model_type\": \"text_embedding\",
+    \"embedding_dimension\": 384,
+    \"framework_type\": \"SENTENCE_TRANSFORMERS\",
+    \"additional_config\": {
+      \"space_type\": \"l2\",
+      \"is_asymmetric\": true,
+      \"model_family\": \"e5\",
+      \"query_prefix\": \"query: \",
+      \"passage_prefix\": \"passage: \"
+    }
+  }
+}")
+
+TASK_ID=$(echo $MODEL_RESPONSE | jq -r '.task_id')
+sleep 10
+MODEL_ID=$(curl -s -X GET "${OPENSEARCH_ENDPOINT}/_plugins/_ml/tasks/$TASK_ID" | jq -r '.model_id')
+
+# Deploy model
+curl -s -X POST "${OPENSEARCH_ENDPOINT}/_plugins/_ml/models/$MODEL_ID/_deploy" > /dev/null
+sleep 15
+
+echo "✓ Model deployed: $MODEL_ID"
+echo ""
+echo "Run validation: ./validate_connector.sh $OPENSEARCH_ENDPOINT $MODEL_ID"
@@ -0,0 +1,57 @@
+#!/bin/bash
+
+if [ $# -ne 2 ]; then
+    echo "Usage: $0 <opensearch-endpoint> <model-id>"
+    echo "Example: $0 http://localhost:9200 abc123"
+    exit 1
+fi
+
+OPENSEARCH_ENDPOINT=$1
+MODEL_ID=$2
+
+echo "Validating asymmetric remote model with OpenSearch ML Commons format..."
+
+# Check model config
+echo "Model configuration:"
+curl -s -X GET "${OPENSEARCH_ENDPOINT}/_plugins/_ml/models/$MODEL_ID" | jq '.model_config.additional_config'
+
+# Test query embedding
+echo -e "\nTesting query embedding..."
+QUERY_RESPONSE=$(curl -s -X POST "${OPENSEARCH_ENDPOINT}/_plugins/_ml/models/$MODEL_ID/_predict" \
+-H "Content-Type: application/json" \
+-d '{
+  "parameters": {
+    "texts": ["What is machine learning?"],
+    "content_type": "query"
+  }
+}')
+
+# With simplified format, response is wrapped in an array, so access response[0]
+QUERY_DIM=$(echo $QUERY_RESPONSE | jq -r '.inference_results[0].output[0].dataAsMap.response[0] | length' 2>/dev/null)
+
+# Test passage embedding  
+echo "Testing passage embedding..."
+PASSAGE_RESPONSE=$(curl -s -X POST "${OPENSEARCH_ENDPOINT}/_plugins/_ml/models/$MODEL_ID/_predict" \
+-H "Content-Type: application/json" \
+-d '{
+  "parameters": {
+    "texts": ["Machine learning is a subset of artificial intelligence."],
+    "content_type": "passage"
+  }
+}')
+
+PASSAGE_DIM=$(echo $PASSAGE_RESPONSE | jq -r '.inference_results[0].output[0].dataAsMap.response[0] | length' 2>/dev/null)
+
+# Validation results
+if [ "$QUERY_DIM" != "null" ] && [ "$PASSAGE_DIM" != "null" ] && [ "$QUERY_DIM" -gt 0 ] && [ "$PASSAGE_DIM" -gt 0 ]; then
+    echo -e "\n✓ Validation successful with flattened response!"
+    echo "✓ Query embedding dimension: $QUERY_DIM"
+    echo "✓ Passage embedding dimension: $PASSAGE_DIM"
+    echo "✓ Post-processing function working correctly (no processing needed)"
+    echo "✓ Asymmetric remote model ready for neural-search"
+else
+    echo -e "\n✗ Validation failed"
+    echo "Query response: $QUERY_RESPONSE"
+    echo "Passage response: $PASSAGE_RESPONSE"
+    exit 1
+fi
@@ -0,0 +1,37 @@
+# SageMaker Deployment
+
+Deploy the `intfloat/multilingual-e5-small` model to Amazon SageMaker.
+
+## Files
+
+- `deploy_cli.sh` - Deploy model to SageMaker endpoint
+- `validate_cli.sh` - Validate deployed endpoint
+- `model-config.json` - Model configuration template
+- `inference.py` - Custom inference code (for future use)
+
+## Usage
+
+### Deploy Model
+```bash
+chmod +x deploy_cli.sh
+./deploy_cli.sh
+```
+
+### Validate Deployment
+```bash
+chmod +x validate_cli.sh
+./validate_cli.sh <endpoint-name>
+```
+
+## Example
+```bash
+./deploy_cli.sh
+./validate_cli.sh multilingual-e5-endpoint-1761349656
+```
+
+## Cleanup
+```bash
+aws sagemaker delete-endpoint --endpoint-name <endpoint-name>
+aws sagemaker delete-endpoint-config --endpoint-config-name <config-name>
+aws sagemaker delete-model --model-name <model-name>
+```
@@ -0,0 +1,76 @@
+#!/bin/bash
+
+# Set variables
+TIMESTAMP=$(date +%s)
+MODEL_NAME="multilingual-e5-small-$TIMESTAMP"
+ENDPOINT_CONFIG_NAME="multilingual-e5-config-$TIMESTAMP"
+ENDPOINT_NAME="multilingual-e5-endpoint"
+ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
+ROLE_ARN="arn:aws:iam::${ACCOUNT_ID}:role/Admin"
+REGION="us-east-1"
+BUCKET="sagemaker-$REGION-$ACCOUNT_ID"
+
+# Create code package
+echo "Creating code package..."
+tar -czf model.tar.gz inference.py
+
+# Upload to S3
+echo "Uploading code to S3..."
+aws s3 cp model.tar.gz s3://$BUCKET/$MODEL_NAME/model.tar.gz
+
+# Create temporary model config
+sed "s/MODEL_TIMESTAMP/$TIMESTAMP/g; s|ROLE_ARN_PLACEHOLDER|$ROLE_ARN|g; s/ACCOUNT_ID/$ACCOUNT_ID/g" model-config.json > temp-model-config.json
+
+# Create model
+aws sagemaker create-model \
+    --region $REGION \
+    --cli-input-json file://temp-model-config.json
+
+if [ $? -ne 0 ]; then
+    echo "Failed to create model"
+    rm -f temp-model-config.json model.tar.gz
+    exit 1
+fi
+
+# Create endpoint configuration
+aws sagemaker create-endpoint-config \
+    --region $REGION \
+    --endpoint-config-name $ENDPOINT_CONFIG_NAME \
+    --production-variants VariantName=primary,ModelName=$MODEL_NAME,InitialInstanceCount=1,InstanceType=ml.m5.large,InitialVariantWeight=1
+
+if [ $? -ne 0 ]; then
+    echo "Failed to create endpoint config"
+    rm -f temp-model-config.json model.tar.gz
+    exit 1
+fi
+
+# Create endpoint
+aws sagemaker create-endpoint \
+    --region $REGION \
+    --endpoint-name $ENDPOINT_NAME \
+    --endpoint-config-name $ENDPOINT_CONFIG_NAME
+
+if [ $? -ne 0 ]; then
+    echo "Failed to create endpoint"
+    rm -f temp-model-config.json model.tar.gz
+    exit 1
+fi
+
+echo "Deployment initiated:"
+echo "Model: $MODEL_NAME"
+echo "Endpoint Config: $ENDPOINT_CONFIG_NAME" 
+echo "Endpoint: $ENDPOINT_NAME"
+
+# Wait for endpoint to be in service
+echo "Waiting for endpoint to be ready..."
+aws sagemaker wait endpoint-in-service --region $REGION --endpoint-name $ENDPOINT_NAME
+
+if [ $? -eq 0 ]; then
+    echo "Endpoint $ENDPOINT_NAME is ready!"
+    echo "You can now validate with: ./validate_cli.sh $ENDPOINT_NAME"
+else
+    echo "Endpoint deployment failed or timed out"
+fi
+
+# Cleanup
+rm -f temp-model-config.json model.tar.gz