Chat with SQLite databases with a LangChain AI Agent and Rantir 🧠💬
Explore this Workflow. Built for next generation AI app & agent building.
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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