All classes are under active development and subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.
This construct library provides a resource that creates a vector index on an Amazon OpenSearch Domain. It currently only supports Amazon OpenSearch Serverless.
See the API documentation.
The VectorIndex
resource connects to OpenSearch and creates an index suitable for use with Amazon Bedrock Knowledge Bases.
TypeScript
import {
opensearchserverless,
opensearch_vectorindex,
} from '@cdklabs/generative-ai-cdk-constructs';
const vectorStore = new opensearchserverless.VectorCollection(
this,
'VectorCollection'
);
new opensearch_vectorindex.VectorIndex(this, 'VectorIndex', {
collection: vectorStore,
indexName: 'bedrock-knowledge-base-default-index',
vectorField: 'bedrock-knowledge-base-default-vector',
vectorDimensions: 1536,
precision: 'float',
distanceType: 'l2',
mappings: [
{
mappingField: 'AMAZON_BEDROCK_TEXT_CHUNK',
dataType: 'text',
filterable: true,
},
{
mappingField: 'AMAZON_BEDROCK_METADATA',
dataType: 'text',
filterable: false,
},
],
analyzer: {
characterFilters: [opensearchserverless.CharacterFilterType.ICU_NORMALIZER],
tokenizer: opensearchserverless.TokenizerType.KUROMOJI_TOKENIZER,
tokenFilters: [
opensearchserverless.TokenFilterType.KUROMOJI_BASEFORM,
opensearchserverless.TokenFilterType.JA_STOP,
],
},
});
Python
from cdklabs.generative_ai_cdk_constructs import (
opensearchserverless,
opensearch_vectorindex,
)
vectorCollection = opensearchserverless.VectorCollection(self, "VectorCollection")
vectorIndex = opensearch_vectorindex.VectorIndex(self, "VectorIndex",
vector_dimensions= 1536,
collection=vectorCollection,
index_name='bedrock-knowledge-base-default-index',
vector_field='bedrock-knowledge-base-default-vector',
precision='float',
distance_type='l2',
mappings= [
opensearch_vectorindex.MetadataManagementFieldProps(
mapping_field='AMAZON_BEDROCK_TEXT_CHUNK',
data_type='text',
filterable=True
),
opensearch_vectorindex.MetadataManagementFieldProps(
mapping_field='AMAZON_BEDROCK_METADATA',
data_type='text',
filterable=False
)
],
analyzer=opensearchserverless.AnalyzerProps(
character_filters=[opensearchserverless.CharacterFilterType.ICU_NORMALIZER],
tokenizer=opensearchserverless.TokenizerType.KUROMOJI_TOKENIZER,
token_filters=[
opensearchserverless.TokenFilterType.KUROMOJI_BASEFORM,
opensearchserverless.TokenFilterType.JA_STOP,
],
)
)
Behind the scenes, the custom resource creates a k-NN vector in the OpenSearch index, allowing to perform different kinds of k-NN search. The knn_vector field is highly configurable and can serve many different k-NN workloads. It is created as follows:
Python
"properties": {
vector_field: {
"type": "knn_vector",
"dimension": dimensions,
"data_type": precision,
"method": {
"engine": "faiss",
"space_type": distance_type,
"name": "hnsw",
"parameters": {},
},
},
"id": {
"type": "text",
"fields": {"keyword": {"type": "keyword", "ignore_above": 256}},
},
},
Users can currently configure the vector_field
, dimension
, data_type
, and distance_type
fields through the construct interface.
For details on the different settings, you can refer to the Knn plugin documentation.