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Enterprise-grade natural language to SQL generation using LLMs: Balancing accuracy, latency, and scale

Flipboard

These tables house complex domain-specific schemas, with instances of nested tables and multi-dimensional data that require complex database queries and domain-specific knowledge for data retrieval.

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MongoRAG: Leveraging MongoDB Atlas as a Vector Database with Databricks-Deployed Embedding Model and LLMs for Retrieval-Augmented Generation

Towards AI

Retrieval Augmented Generation generally consists of Three major steps, I will explain them briefly down below – Information Retrieval The very first step involves retrieving relevant information from a knowledge base, database, or vector database, where we store the embeddings of the data from which we will retrieve information.

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

AWS Machine Learning Blog

With the right underlying embedding model, capable of producing accurate semantic representations of the input document chunks and the input questions, and an efficient semantic search module, this solution is able to answer questions that require retrieving existent information in a database of documents.

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

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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. For Project 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. Database name : Enter dev. Database user : Enter awsuser. 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). Deploy the Cloudformation template to your account.