# Human to SQL Translator
The Human to SQL Translator is an innovative AI agent that simplifies the process of generating SQL queries from natural language inputs. It interprets user requests, inferring necessary database structure, including tables and relationships, and produces valid SQL code. If the user's request is unclear or requires further detail, the agent will solicit clarifications to ensure accurate query generation. With this tool, even users with minimal SQL expertise can interact with databases efficiently and effectively.
# Getting Started
To get started, make sure you have cloned the Human to SQL Translator. Follow Create from Template to clone and configure your AI agent.
# Configuration Instructions
# Knowledge
You can upload .pdf or .txt files to provide the agent with tailored context that meets your specific requirements.
# User Inputs
| Field Title | Description | Instruction |
|---|---|---|
| Request | A natural language statement or question that you want to translate into SQL. | Keep the input concise and clear, ideally phrased as a question or request related to data retrieval or manipulation in a hypothetical database. |
# AI Models
The default model is Llama 3.1 8B Instruct, but you can explore other supported AI models as well. If needed, refine your prompts for more tailored outputs.
TIP
For more detailed instructions on configuring an AI agent, please refer to Configure AI Agent
# Sample Inputs and Output
# Sample Inputs
- Request: List all employees who are managers in the sales department.
# Sample Output
-- This query retrieves the names of employees who are managers in the sales department.
SELECT name FROM employees
WHERE role = 'manager'
AND department = 'sales';
-- Assumptions:
-- 1. The table is named 'employees'.
-- 2. The 'name', 'role', and 'department' are column names in the 'employees' table.
# Tips for Effective Configuration
- Be Clear and Specific with Requests: Use precise language to ensure that the agent correctly interprets your intent.
- Provide Context When Possible: Mention any relevant database structure or previous queries to help the agent generate relevant SQL.
- Test with Varied Examples: Input different phrases to see how well the agent adapts; this can help refine your interactions.
- Utilize Ambiguity for Clarifications: If the agent asks for clarifications, take advantage of this to make sure that the final query meets your exact needs.
- Stay Updated with AI Capabilities: Regularly check for updates to the agent or AI models to enhance performance and capabilities.
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