Sure! Here’s the translation into American English:
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Developing robust text-to-SQL conversion capabilities presents a crucial challenge in the field of natural language processing (NLP) and database management. This complexity particularly increases when dealing with complex queries and intricate database structures. Recently, an innovative solution has been introduced that, along with custom implementation code, utilizes Amazon Bedrock and the Converse API to simplify this conversion.
The ability to translate natural language queries into SQL statements can radically transform how businesses and institutions interact with data. However, the variety and complexity of database schemas have historically made it difficult for users to obtain accurate answers. The new architecture presented allows an agent to understand the query, plan the execution, generate the corresponding SQL statements, self-correct in case of errors, and learn from its execution to improve over time. Over time, this agent develops a cohesive understanding of how to effectively respond to queries.
The solution is based on an AWS Lambda function that implements the agent’s logic, which communicates with Amazon DynamoDB for long-term memory retention and uses Anthropic’s Claude Model on Amazon Bedrock via the Converse API. Additionally, AWS Secrets Manager is used to manage the database connection details and the Amazon RDS service, which hosts a sample database called the HR Database. This Lambda function is connected to a virtual private cloud (VPC) and communicates via AWS PrivateLink endpoints, ensuring that traffic remains private.
Users can interact with the agent through the Lambda function by asking natural language questions such as, “How many employees are there in each department of each region?” or “What is the distribution of employees by gender in each region?”
The agent employs the Converse API to create conversational applications, enabling powerful features such as tool usage. Through the API, the agent can decide which tool to use based on the conversation’s context, facilitating the execution of SQL queries.
Another highlighted aspect of the agent is its planning loop, self-correction, and long-term learning. In the planning phase, the agent develops an execution scheme for the task, and if it forgets any steps, it can revitalize that plan by re-injecting it into the system section. When executing its plan, the agent can choose to use different available tools, and in case of errors in the SQL queries, it can self-correct and incorporate that learning into its hierarchical memory structure, storing lessons in DynamoDB.
Finally, implementing this solution offers businesses a more intuitive way to access and manage data, opening up new opportunities for productivity and data-driven decision-making. The methodology presented demonstrates how technology can help overcome the barriers between natural language and structured queries, potentially leading to significant advancements in the realm of business intelligence.
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Let me know if you need any more help!
via: MiMub in Spanish
