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@comdaze comdaze released this 03 May 06:03
· 8 commits to main since this release
v1.9.2
a646ca5

Generative BI using RAG on AWS - Release Notes

版本 1.9.2 (2025年5月3日)

新功能

  1. 增强的嵌入模型管理

    • 新的嵌入模型管理系统，支持多种嵌入平台（Bedrock、SageMaker、BR Client API）
    • 添加了embedding.py模块，用于全面的嵌入模型管理
    • 通过dynamo_embedding.py集成DynamoDB存储嵌入模型配置
    • 全局设置管理，支持设定默认的LLM模型、嵌入模型和数据配置文件

  2. 改进的实体识别

    • 增强实体检索，添加分数阈值(>0.8)以提高准确性
    • 实体信息现在保存到answer对象中，用于知识搜索

  3. SQL自动纠错

    • 为代理任务添加SQL自动纠错逻辑
    • 使用错误信息自动尝试修复失败的SQL查询
    • 为失败的SQL执行提供详细的错误日志

错误修复

  1. 向量存储嵌入改进

    • 增强嵌入生成中的错误处理
    • 添加对不同嵌入响应格式的支持
    • 实现嵌入失败的回退机制
    • 改进嵌入相关操作的日志记录

  2. 状态机工作流修复

    • 通过正确过渡实体检索，修复了知识搜索工作流
    • 改进了带有列表类型查询的代理任务处理
    • 当所有代理任务失败时提供更好的错误处理

架构改进

  1. 强大的错误处理

    • 在嵌入和查询处理管道中全面处理错误
    • 当服务返回意外格式时优雅回退
    • 提供详细日志以便故障排除

  2. 增强的查询处理

    • 改进知识搜索意图检测和处理
    • 实体检索和知识搜索之间更好的集成

  3. 模型集成

    • 灵活的模型配置系统，适用于LLM和嵌入模型
    • 支持不同模型提供商的各种输入/输出格式

  4. 全局设置管理

    • 支持设置默认LLM模型，方便统一管理
    • 支持设置默认嵌入模型，确保一致的向量表示
    • 支持设置默认数据配置文件，简化多配置文件环境

  5. 中英文双语支持

    • 增强了对中文和英文查询的处理能力
    • 改进了中文实体识别和语义理解
    • 优化了中英文混合环境下的系统响应

Generative BI using RAG on AWS - Release Notes

Version 1.9.2 (May-3-2025)

New Features

  1. Enhanced Embedding Model Management

    • New embedding model management system with support for multiple embedding platforms (Bedrock, SageMaker, BR Client API)
    • Added embedding.py module for comprehensive embedding model management
    • DynamoDB integration for storing embedding model configurations via dynamo_embedding.py
    • Global settings management for default LLM models, embedding models, and data profiles

  2. Improved Entity Recognition

    • Enhanced entity retrieval with score threshold (>0.8) for better accuracy
    • Entity information now saved to answer object for use in knowledge search

  3. SQL Auto-correction

    • Added SQL auto-correction logic for agent tasks
    • Automatically attempts to fix failed SQL queries using error information
    • Detailed error logging for failed SQL executions

Bug Fixes

  1. Vector Store Embedding Improvements

    • Enhanced error handling in embedding generation
    • Added support for different embedding response formats
    • Implemented fallback mechanisms for embedding failures
    • Better logging for embedding-related operations

  2. State Machine Workflow Fixes

    • Fixed knowledge search workflow by properly transitioning through entity retrieval
    • Improved handling of agent tasks with list-type queries
    • Better error handling when all agent tasks fail

Architecture Improvements

  1. Robust Error Handling

    • Comprehensive error handling throughout the embedding and query processing pipeline
    • Graceful fallbacks when services return unexpected formats
    • Detailed logging for troubleshooting

  2. Enhanced Query Processing

    • Improved knowledge search intent detection and processing
    • Better integration between entity retrieval and knowledge search

  3. Model Integration

    • Flexible model configuration system for both LLM and embedding models
    • Support for various input/output formats across different model providers

  4. Global Settings Management

    • Support for setting default LLM model for unified management
    • Support for setting default embedding model for consistent vector representations
    • Support for setting default data profile for simplified multi-profile environments

  5. Bilingual Support (English and Chinese)

    • Enhanced processing capabilities for both Chinese and English queries
    • Improved Chinese entity recognition and semantic understanding
    • Optimized system responses in mixed Chinese-English environments
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