generative-ai-cdk-constructs
Amazon OpenSearch Vector Index Construct Library
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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.
| Language | Package |
|---|---|
 TypeScript |
@cdklabs/generative-ai-cdk-constructs |
 Python |
cdklabs.generative_ai_cdk_constructs |
 Java |
io.github.cdklabs.generative_ai_cdk_constructs |
 .Net |
CdkLabs.GenerativeAICdkConstructs |
 Go |
github.com/cdklabs/generative-ai-cdk-constructs-go/generative-ai-cdk-constructs |
This construct library provides a resource that creates a vector index on an Amazon OpenSearch Domain. It currently only supports Amazon OpenSearch Serverless.
Table of contents
API
See the API documentation.
Vector Index
The VectorIndex resource connects to OpenSearch and creates an index suitable for use with Amazon Bedrock Knowledge Bases.
```typescript fixture=default-bedrock const vectorStore = new genaicdk.opensearchserverless.VectorCollection( this, ‘VectorCollection’ );
new genaicdk.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: [genaicdk.opensearchserverless.CharacterFilterType.ICU_NORMALIZER], tokenizer: genaicdk.opensearchserverless.TokenizerType.KUROMOJI_TOKENIZER, tokenFilters: [ genaicdk.opensearchserverless.TokenFilterType.KUROMOJI_BASEFORM, genaicdk.opensearchserverless.TokenFilterType.JA_STOP, ], }, });
## Default values
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
```py
"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.