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# Introduction
It’s a narrative where artificial intelligence (AI) projects require months to deploy. The truth is, you can deploy an AI analyst that can answer complex business questions from your own structured query language (SQL) database in minutes if you know how to successfully connect the right language model (LLM) to your data source.
In this article, I’m going to break down how to deploy with an AI analyzer Bag of wordsan advanced AI data layer technology. You will learn practical, step-by-step processes that focus on SQL databases and the LL.M. Along the way, we’ll cover the struggles and ethical considerations of joint deployment that every professional should know.
# Understanding the bag of words
Bag of Words is an AI data layer platform that connects any LLM to almost any data source, including SQL databases such as postgresqlfor , for , for , for , . SQLfor , for , for , for , . Snowflakeand more. It helps you build your own data-driven AI analytics with these key features:
- It allows direct connectivity to your existing data infrastructure
- It controls which tables and views the AI ​​can access
- It improves the context of your data with metadata from tools Tableau or DBT
- It securely manages user access and permissions
- It is designed for fast, reliable and comprehensible insights
This approach simply means that users can “ask once, optimize, and get results you can define,” without huge engineering costs.
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# AI analyst deployment
Many organizations struggle to unlock the full potential of their data despite having powerful tools. The problem is mostly integration, which is complicated, and there is no clear way of integration. AI analysts powered by LLMS turn raw data into insights through natural language queries, but it’s critical to accurately connect these models to backup data.
The good news is that Bag of Words makes it possible to seamlessly integrate your SQL database and LLM without endless custom code. This reduces bottlenecks and speeds from weeks or months to minutes, empowering both data teams and business users.
# Deploying an AI analyst with a bag of words
act These technical steps To get an AI analyzer up and running quickly in Docker..
// Step 1: Preparing Your SQL Database
- Make sure of it Docker installed on your machine and properly configured before running the code below.
- Then run the following command:
docker run --pull always -d -p 3000:3000 bagofwords/bagofwords- If you are new you will need to sign up:
http://localhost:3000/users/sign-up.
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Follow the steps to complete the onboarding flow to configure your AI analyzer.
- Make sure you have your connection credentials for your SQL database (host, port, username, password).
- Click New Report. Then select a database of your choice. For this article, I’ll go with PostgreSQL.
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- Create and populate your database. I recommend Soup base For demo you can use any one you like. Also, make sure your database is accessible from the network where you will be deploying Wordbag.
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- Know which schemas, tables, and views hold the data you want the AI ​​analyst to query.
- Next is to give context to your analysis.
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This is where you need to give the AI ​​instructions on how you want to manage the data, and you can contact the table, DBT, Dataformand yours AGENTS.md files in git.
You can even set up a conversation where, with the click of a button, you have your answer ready with all the information you need.
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You can also configure your report and run it again. Reports on your data become autopilot.
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// Step 2: Test and refine questions
- Communicate with the AI ​​analyst through the bag of words interface.
- Start with simple natural language questions like “What were the total sales last quarter?” or “Show top products by product.”
- Refine indicators and instructions based on preliminary results to improve accuracy and relevance.
- Use debugging tools to understand how LLMS interprets SQL and adjust metadata if needed.
// Step 3: Deployment and Scaling
- Integrate AI Analytics into your business applications or reporting tools by embedding APIs or user interfaces (UI).
- Monitor usage metrics and query performance to identify bottlenecks.
- Iteratively increase database access or model creation as adoption increases.
# Challenges and solutions
What you might encounter when deploying AI analysts (and how a bag of words can help)
| Model | Train ACC | Well ACC | Gap | Maximum risk |
|---|---|---|---|---|
| Logistic Regression | 91.2% | 92.1% | -0.9% | low (negative space) |
| Hierarchy tree | 98.5% | 97.3% | 1.2% | less |
| Neural Network (5 nodes) | 90.7% | 89.8% | 0.9% | less |
| Neural Network (10 nodes) | 95.1% | 88.2% | 6.9% | High – Reject it |
| Neural Network (14 nodes) | 99.3% | 85.4% | 13.9% | Too high – reject it |
# wrap up
It is not possible to deploy AI Analyzer in minutes by connecting any LLM to your SQL database. This is to be expected in today’s data-driven world. Bag of Words offers an accessible, flexible and secure way to quickly transform your data into interactive, AI-powered insights. By following the steps outlined, both data professionals and business users can unlock new levels of productivity and clarity in decision making.
If you’ve been struggling to effectively deploy AI projects, now’s the time to ditch the process, embrace new tools, and build your own AI analytics with confidence.
Shito Olomide is a software engineer and technical writer passionate about leveraging modern technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. You can also get Shito Twitter.