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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. Typically, these analytical operations are done on structured data, using tools such as pandas or SQL engines.

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5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists

ODSC - Open Data Science

Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language , so important to learn? Let’s start with the first clause often learned by new SQL users, the WHERE clause.

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An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Flipboard

Data processing and SQL analytics Analyze, prepare, and integrate data for analytics and AI using Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources. Under Quick setup settings , for Name , enter a name (for example, demo).

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How healthcare payers and plans can empower members with generative AI

AWS Machine Learning Blog

From a broad perspective, the complete solution can be divided into four distinct steps: text-to-SQL generation, SQL validation, data retrieval, and data summarization. A pre-configured prompt template is used to call the LLM and generate a valid SQL query. The following diagram illustrates this workflow.

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Basic knowledge of a SQL query editor. A provisioned or serverless Amazon Redshift data warehouse. For this post we’ll use a provisioned Amazon Redshift cluster. A SageMaker domain. A QuickSight account (optional). Set up the Amazon Redshift cluster We’ve created a CloudFormation template to set up the Amazon Redshift cluster.

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CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

AWS Machine Learning Blog

The prompts are managed through Lambda functions to use OpenSearch Service and Anthropic Claude 2 on Amazon Bedrock to search the client’s database and generate an appropriate response to the client’s business analysis, including the response in plain English, the reasoning, and the SQL code.

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Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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