Refactor knowledge base documentation: consolidate S3 vectors guide into main docs and update template

Author: Bob Strahan

docs/knowledge-base.md

@@ -7,10 +7,11 @@ The GenAIIDP solution includes an integrated Document Knowledge Base query featu
 
 ## How It Works
 
-1. **Document Indexing**
+1. **Document Processing & Indexing**
    - Processed documents are automatically indexed in a vector database
    - Documents are chunked into semantic segments for efficient retrieval
    - Each chunk maintains reference to its source document
+   - **Ingestion Schedule**: Documents are ingested into the knowledge base every 30 minutes, so newly processed documents may not be immediately available for querying
 
 2. **Interactive Query Interface**
    - Access through the Web UI via the "Knowledge Base" section
@@ -33,6 +34,25 @@ The GenAIIDP solution includes an integrated Document Knowledge Base query featu
 - **Markdown Formatting**: Responses support rich formatting for better readability
 - **Real-time Processing**: Get answers in seconds, even across large document collections
 
+## Architecture & Vector Storage Options
+
+The Knowledge Base feature supports two vector storage backends to optimize for different performance and cost requirements:
+
+### Vector Store Comparison
+
+| Aspect | OpenSearch Serverless | S3 Vectors |
+|--------|----------------------|------------|
+| **Query Latency** | Sub-millisecond | Sub-second |
+| **Pricing Model** | Always On (continuous capacity costs) | On Demand (pay-per-query) |
+| **Storage Cost** | Higher | 40-60% lower |
+| **Best For** | Real-time applications | Cost-sensitive deployments |
+| **Features** | Full-text search, advanced filtering | Native S3 integration |
+
+### Choosing Your Vector Store
+
+- **OpenSearch Serverless** (Default): Choose for applications requiring ultra-fast retrieval and real-time performance
+- **S3 Vectors**: Choose for cost optimization when query latency is acceptable
+
 ## Configuration
 
 The Document Knowledge Base Query feature can be configured during stack deployment:
@@ -46,14 +66,29 @@ ShouldUseDocumentKnowledgeBase:
     - "false"
   Description: Enable/disable the Document Knowledge Base feature
 
+KnowledgeBaseVectorStore:
+  Type: String
+  Default: "OPENSEARCH_SERVERLESS"
+  AllowedValues:
+    - "OPENSEARCH_SERVERLESS"
+    - "S3_VECTORS"
+  Description: Vector storage backend for the knowledge base
+
 DocumentKnowledgeBaseModel:
   Type: String
   Default: "us.amazon.nova-pro-v1:0"
   Description: Bedrock model to use for knowledge base queries (e.g., "us.anthropic.claude-3-7-sonnet-20250219-v1:0")
 ```
 
+### Supported Embedding Models
+
+Both vector store types support the same embedding models:
+- `amazon.titan-embed-text-v2:0` (default)
+- `cohere.embed-english-v3`  (disabled by default)
+- `cohere.embed-multilingual-v3` (disabled by default)
+
 When the feature is enabled, the solution:
-- Creates necessary OpenSearch resources for document indexing
+- Creates the selected vector storage resources (OpenSearch or S3 Vectors)
 - Configures API endpoints for querying the knowledge base
 - Adds the query interface to the Web UI
 
@@ -111,3 +146,14 @@ The Knowledge Base feature maintains the security controls of the overall soluti
 - Document visibility respects user permissions
 - Questions and answers are processed securely within your AWS account
 - No data is sent to external services beyond the configured Bedrock models
+
+## Future Enhancements
+
+### Potential Improvements & Community Contributions
+- **CloudFormation Support**: When S3 Vectors gains native CloudFormation support
+- **Migration Tools**: Utilities to migrate between vector store types
+- **Hybrid Deployment**: Support for multiple Knowledge Bases with different vector stores
+- **Document Chunking Options**: The system currently uses default chunking strategies, with additional chunking methods available for optimization based on document types and use cases
+- Performance optimization suggestions
+- Additional embedding model support
+- Enhanced monitoring and alerting

docs/s3-vectors-knowledge-base.md

@@ -936,7 +936,7 @@ Resources:
                 Action:
                   - "bedrock:StartIngestionJob"
                 Resource:
-                  - !Sub "arn:${AWS::Partition}:bedrock:*:${AWS::AccountId}:knowledge-base/${KnowledgeBase}"
+                  - !Sub "arn:${AWS::Partition}:bedrock:*:${AWS::AccountId}:knowledge-base/${KnowledgeBase.Value}"
           PolicyName: BedrockPolicy
 
   StartIngestionJobFunction: