How can I integrate my new assistant with a database for dynamic question answering?

Below is an overview of how you can connect an assistant to a database using the available runtimes:

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What the Docs Cover

The documentation primarily shows examples of using files or pre-defined data sources (like LangGraph or LangChain) with the assistant. While there isn’t a built-in feature for direct database connectivity, you can still integrate your database data into your assistant’s interactions.

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Approaches to Connect a Database

1. Custom REST API Runtime
   You can create a custom REST API that queries your database and returns the data you need. In your assistant’s logic, call this API, fetch the data, and then pass the results to the assistant. This approach puts control in your hands for authentication, error handling, and data transformation.

   Example: Node.js Express API

   JSCopied
   const express = require('express');
   const app = express();
   const port = process.env.PORT || 3000;
   
   // Example database query; replace with your actual DB connection and query logic
   async function queryDatabase(query) {
     // Replace this with your database querying logic
     return [{ id: 1, name: 'Example Data' }];
   }
   
   app.get('/api/data', async (req, res) => {
     try {
       const data = await queryDatabase('SELECT * FROM your_table');
       res.json({ data });
     } catch (error) {
       res.status(500).json({ error: 'Database query failed' });
     }
   });
   
   app.listen(port, () => {
     console.log(`API server running on port ${port}`);
   });

   In your assistant UI integration, you then can call the /api/data endpoint to get fresh information from your database.

2. External Store Runtime
   Some runtimes (like the External Store runtime) allow you to integrate external data sources more directly. This might involve implementing middleware that fetches data from your database and integrates it with your assistant’s message flow.

3. Middleware Integration
   You can also add a layer in your application that queries your database when needed and then feeds that data into the assistant’s conversation. This gives you full control over when and how data is retrieved and how it affects the dialogue.

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Summary

While the assistant UI docs focus on file-based inputs and integrations with frameworks like LangGraph and LangChain, connecting to a database is achievable by:

• Building a custom REST API that serves your database results.
• Integrating with an external store or middleware that fetches and processes your data before interacting with the assistant.

For more details on setting up custom integrations, see the documentation on the Custom REST API or explore examples with the Vercel AI SDK.

Feel free to ask if you need more specific guidance on any of these approaches!