Document Knowledge Base Query

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. SPDX-License-Identifier: MIT-0

Document Knowledge Base Query

The GenAIIDP solution includes an integrated Document Knowledge Base query feature that enables you to interactively ask questions about your processed document collection using natural language. This feature leverages the processed data to create a searchable knowledge base.

How It Works

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
   • Ask natural language questions about your document collection
   • View responses with citations to source documents
   • Follow-up with contextual questions in a chat-like interface

3. AI-Powered Responses

   • LLM generates responses based on relevant document chunks
   • Responses include citations to source documents
   • Links to original documents for reference
   • Context-aware for follow-up questions

Query Features

• Natural Language Understanding: Ask questions in plain English rather than using keywords or query syntax
• Document Citations: Responses include references to the specific documents used to generate answers
• Contextual Follow-ups: Ask follow-up questions without repeating context
• Direct Document Links: Click on document references to view the original source
• 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

• S3 Vectors (Default): Choose for cost optimization with acceptable sub-second query latency
• OpenSearch Serverless: Choose for applications requiring ultra-fast retrieval and real-time performance

Configuration

The Document Knowledge Base Query feature can be configured during stack deployment:

ShouldUseDocumentKnowledgeBase:
  Type: String
  Default: "true"
  AllowedValues:
    - "true"
    - "false"
  Description: Enable/disable the Document Knowledge Base feature

KnowledgeBaseVectorStore:
  Type: String
  Default: "S3_VECTORS"
  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 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

Using the Knowledge Base

Accessing the Knowledge Base

1. Log in to the Web UI
2. Navigate to the “Knowledge Base” section in the main navigation
3. You’ll see a chat-like interface for querying your document collection

Asking Questions

1. Type your question in the input field at the bottom of the screen
2. Press Enter or click the send button
3. The system will process your question and return an answer
4. The answer will include:
   • A direct response to your question
   • Citations to the source documents
   • Links to view the original documents

Exploring Document Context

1. Click on document citations to view the original source
2. The system will highlight the relevant sections in the document
3. You can navigate to other parts of the document to explore the full context

Follow-up Questions

1. After receiving an answer, you can ask related follow-up questions
2. The system maintains context from previous questions
3. This allows for a natural conversation about your documents
4. You can start a new topic at any time by asking an unrelated question

Best Practices

1. Be specific: Clearly state what information you’re looking for
2. Start broad, then narrow: Begin with general questions before diving into specifics
3. Use follow-ups: Build on previous questions to explore topics in depth
4. Check citations: Verify information by consulting the source documents
5. Refine questions: If you don’t get the expected answer, try rephrasing your question

Performance Considerations

• Document Collection Size: Performance may vary with very large document collections
• Query Complexity: More complex queries may take longer to process
• Document Types: Some document types may be indexed more effectively than others
• Model Selection: Different Bedrock models offer different performance/accuracy tradeoffs

Security Considerations

The Knowledge Base feature maintains the security controls of the overall solution:

• Access is restricted to authenticated users
• 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