SQL Agent with Memory
detail.loadingPreview
Interact with a SQLite database using natural language and maintain conversation history.
About This Workflow
This workflow enables a conversational interface to a SQLite database, powered by LangChain. It leverages an OpenAI chat model and window buffer memory to understand and execute SQL queries based on user input. The workflow first sets up a local chinook.db file for demonstration purposes, then allows users to query the database through a chat interface.
Key Features
- Natural Language to SQL: Translate natural language questions into SQL queries.
- Database Interaction: Directly query and retrieve information from a SQLite database.
- Conversation Memory: Maintains context across multiple user interactions using a window buffer.
- OpenAI Integration: Utilizes OpenAI's GPT-4 Turbo for language understanding and generation.
- Local Database Setup: Includes steps to download, extract, and load a sample SQLite database.
How To Use
- **Initial Setup (Run Once):
- Execute the
When clicking "Test workflow"node. - This will download
chinook.zip, extractchinook.db, and save it locally.
- Execute the
- **Chat Interaction:
- The
Chat Triggernode listens for incoming messages. - The
Load local chinook.dbnode loads the database. Combine chat input with the binaryprepares the input for the AI Agent.- The
AI Agentnode (usingsqlAgentandOpenAI Chat Model) processes the input, queries the database, and returns an answer. - The
Window Buffer Memorynode retains conversation history for context.
- The
Apps Used
Workflow JSON
{
"id": "95455b46-c657-4e83-9078-933de08accc1",
"name": "SQL Agent with Memory",
"nodes": 26,
"category": "AI",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 95455b46-c657...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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