AWS QnABot is a multi-channel, multi-language conversational interface (chatbot) that responds to your customer's questions, answers, and feedback. The solution allows you to deploy a fully functional chatbot across multiple channels including chat, voice, SMS and Amazon Alexa.
Integration with Guardrails for Amazon Bedrock and Amazon Bedrock Knowledge Base Integration (see documentation)
Ability to customize prompt template for RAG using Amazon Bedrock Knowledge Base through setting KNOWLEDGE_BASE_PROMPT_TEMPLATE (see documentation).
Ability to customize inference parameters for LLM specified in BedrockKnowledgeBaseModel inference parameters for BedrockKnowledgeBaseModel through setting KNOWLEDGE_BASE_MODEL_PARAMS (see documentation)
Ability to customize search type (e.g. SEMANTIC orHYBRID) for how data sources in the knowledge base are queried through setting KNOWLEDGE_BASE_SEARCH_TYPE (see documentation)
Ability to customize metadata and filters for RAG using Amazon Bedrock Knowledge through setting KNOWLEDGE_BASE_METADATA_FILTERS (see documentation)
Added an option to specify the retention period for log groups through cloudformation parameter LogRetentionPeriod
Anonymized operational metrics for some designer settings
Changed
Improved fault tolerance of Testall, Export, Import functionalities and added ContentDesignerOutputBucket
Added Amazon Titan Text Embeddings V2 as an additional option to the list of embedding models provided through cloudformation parameter EmbeddingsBedrockModelId
Added Amazon Titan Text Premier as an additional option to the list LLM models provided through cloudformation parameters LLMBedrockModelId and BedrockKnowledgeBaseModel. Issue 746
Changed Sagemaker LLM image to latest
Changed CustomQnABotSettings parameter store to Advanced Tier to accommodate storing additional custom settings
Removed
Removed Amazon Lex V1 resources
Removed Canvas LMS integration
Fixed
Fixed import settings in content designer for double byte characters
Fixed an edge case where the Knowledge Base could return a context starting with # characters, causing font differences in the returned text due to Markdown formatting
Fixed session attribute qnabot_gotanswer not being set to true after receiving hits from Knowledge Base
[6.1.0] - 2024-08-29
Added
KNOWLEDGE_BASE_PROMPT_TEMPLATE(see documentation).BedrockKnowledgeBaseModelinference parameters forBedrockKnowledgeBaseModelthrough settingKNOWLEDGE_BASE_MODEL_PARAMS(see documentation)SEMANTICorHYBRID) for how data sources in the knowledge base are queried through settingKNOWLEDGE_BASE_SEARCH_TYPE(see documentation)KNOWLEDGE_BASE_MAX_NUMBER_OF_RETRIEVED_RESULTS(see documentation).KNOWLEDGE_BASE_METADATA_FILTERS(see documentation)LogRetentionPeriodChanged
CustomQnABotSettingsparameter store to Advanced Tier to accommodate storing additional custom settingsRemoved
Fixed
#characters, causing font differences in the returned text due to Markdown formattingqnabot_gotanswernot being set totrueafter receiving hits from Knowledge BaseSecurity
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.