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Chat with SQLite databases with a LangChain AI Agent and Rantir 🧠💬

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This rantir workflow shows how to create an AI agent using LangChain and SQLite. The agent understands natural language queries and interacts with a SQLite database to deliver precise answers. 💪

🚀 Setup

Run the top part of the workflow once to set up.
It downloads the sample SQLite database, extracts it from a ZIP file, and saves it locally as chinook.db.

🔍 Chatting with Your Data

Send a query in the chat window.
The locally saved SQLite database loads automatically.
The user’s input combines with binary data for processing.
The LangChain Agent node processes both data sources.

The AI Agent analyzes the user's message, executes SQL queries as needed, and generates a response based on the database content. ⏳

🌟 Example Queries

Try these examples to see the AI Agent in action:

"Please describe the database" - Get a high-level overview of the database structure, requiring only one or two queries.
"What are the revenues by genre?" - Retrieve revenue information by genre; the LangChain agent iterates multiple times to build the answer.

The AI Agent will store responses, enabling context-aware conversations. 💬

Read the full article here on our Discord here: 👉 https://discord.gg/qwcYvMVt

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