SQL Agent with Memory
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A workflow that uses an AI agent to query an SQLite database with conversational memory.
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About This Workflow
Overview
This workflow demonstrates how to create an AI agent capable of querying an SQLite database. It leverages conversational memory to maintain context across multiple interactions, allowing for more sophisticated data analysis and question answering. The workflow first prepares a local SQLite database and then enables users to interact with it via a chat interface, with the AI agent processing queries and providing responses.
Key Features
- Integrates with OpenAI's chat models for AI capabilities.
- Utilizes a LangChain SQL Agent for database interaction.
- Supports conversational memory for context-aware queries.
- Automates the setup of a local SQLite database from a zip file.
- Allows interaction through a chat trigger.
How To Use
- Setup Local Database (Run Once): Execute the part of the workflow that downloads
chinook.zip, extractschinook.db, and saves it locally. - Configure Credentials: Set up your OpenAI API credentials.
- Initiate Chat: Trigger the workflow via the 'Chat Trigger' or directly if using a webhook.
- Ask Questions: Interact with the AI agent by asking questions about the
chinook.dbdata. Examples: "Please describe the database." or "What are the revenues by genre?".
Apps Used
Workflow JSON
{
"id": "5f430cc6-32c7-42a3-9ea1-a9bbef3f507f",
"name": "SQL Agent with Memory",
"nodes": 0,
"category": "AI & Machine Learning",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 5f430cc6-32c7...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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