?? Leveraging Prompt Chaining and Function Calling in Text-to-SQL Conversion with ??
Today, we delve into an advanced implementation of AI-driven Text-to-SQL conversion, utilizing Large Language Models (LLMs) to facilitate seamless interaction between natural language queries (NLQs) and database systems. This approach integrates sophisticated techniques such as ???????????? ???????????????? and ???????????????? ?????????????? to enhance accuracy, efficiency, and adaptability in generating SQL queries from NLQs.
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The core of our system is built on the foundation of vectorized representations of database schemas (DDLs), comprehensive business documentation, and a repository of ground truth SQL queries. This repository acts as a knowledge base, enabling the AI to understand and navigate the database schema effectively.
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Initially, DDL statements, business documentation, and SQL query pairs are processed to create a high-dimensional vector space index. This index facilitates efficient semantic search capabilities within the database, allowing the LLM to retrieve relevant schema information and documentation based on the user's NLQ.
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The process begins with the LLM crafting an initial prompt based on the NLQ. This prompt aims to extract specific schema-related information and relevant documentation from the vector database. Utilizing prompt chaining, the process iterates through a series of refined prompts, each building upon the output of the previous:
?????????????? ????????????: Generates a rudimentary SQL draft and identifies key schema elements relevant to the NLQ.
???????????????????? ????????????: Compares the draft against business rules and database constraints, identifying potential errors or optimizations.
???????????????????? ????????????: Adjusts the SQL draft based on feedback from the validation step, optimizing query structure and ensuring adherence to business logic.
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Function calling is employed to extend the capabilities of the LLM beyond its pre-trained knowledge base. Through predefined functions, the LLM can interact with external APIs or perform specific tasks such as:
?????????????? ???????????? ??????????????: Retrieve and integrate the latest schema changes or updates from the database.
????????-???????? ???????? ????????????????: Call external data sources(business documentations) or APIs to enrich the query with up-to-date information.
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?????? ?????????????????? ?????? ????????????????????: Run the generated SQL query against a sandboxed version of the database to validate its correctness and performance.
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Upon successful generation and validation of the SQL query, the system executes the query against the target database. The retrieved data is then processed for summarization and visualization, converting raw outputs into insightful, easy-to-understand graphical representations.
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???????????? ??????????????????????: Careful design of prompts is crucial to guide the LLM through the prompt chaining process effectively, ensuring that each step contributes to refining the SQL query towards the desired outcome.
???????????????? ??????????????????????: Functions called by the LLM must be well-defined, with clear parameters and expected outputs to facilitate seamless integration and interaction with external tools and APIs.
?????????? ???????????????? ?????? ???????????????? ??????????: Mechanisms for capturing and analyzing errors during the SQL generation and validation phases are essential for continuous improvement of the system's accuracy and reliability.
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By integrating prompt chaining and function calling techniques with LLMs in the context of Text-to-SQL conversion, we unlock a new paradigm in natural language database interactions. This approach not only simplifies the process of querying databases but also enhances the adaptability and intelligence of AI-driven data retrieval systems, paving the way for more intuitive and efficient data analysis workflows.
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#AI #DataScience #MachineLearning #TextToSQL #NaturalLanguageProcessing #LLM #PromptEngineering #FunctionCalling
To Learn More:https://www.promptingguide.ai/techniques/prompt_chaining
AI Engineer @State Street
9 个月Thank you for the inputs MSP Raja