In this tutorial, you'll learn how to build a production-ready AI agent that can answer questions using both structured data from a PostgreSQL database and unstructured content from PDF files. We'll use MindsDB, an open-source platform designed to integrate LLM-powered agents with databases and external knowledge sources like documents.

Whether you're developing internal tools, research assistants, or customer support bots, this step-by-step guide will show you how to combine retrieval-augmented generation (RAG), SQL, and language models efficiently.

Tools Used

  • MindsDB – agent orchestration and SQL-based pipeline
  • PostgreSQL – structured data source
  • PDF documents – unstructured data
  • OpenAI GPT / Hugging Face Transformers – response generation

Objective

We’ll build an AI agent that can:

  • Answer natural language questions by converting them into SQL queries
  • Retrieve and summarize content from uploaded PDFs
  • Integrate with platforms such as Slack or web interfaces

Step 1: Install MindsDB

You can run MindsDB locally using Docker or use the cloud-hosted version.

Run with Docker:

docker run -p 47334:47334 mindsdb/mindsdb

This provides access to the built-in SQL Editor, REST APIs, and external connectors.

Step 2: Create a Conversational Model

We start by creating a language model capable of natural dialogue, powered by OpenAI or Hugging Face via LangChain.

CREATE MODEL conversational_model
PREDICT answer
USING
    engine = 'langchain',
    openai_api_key = 'YOUR_OPENAI_API_KEY',
    model_name = 'gpt-4',
    mode = 'conversational',
    user_column = 'question',
    assistant_column = 'answer',
    prompt_template = 'Answer the user input in a helpful way',
    max_tokens = 100,
    temperature = 0,
    verbose = true;

To verify the model is active:

DESCRIBE conversational_model;

Step 3: Add Skills to Your AI Agent

3.1: Text-to-SQL Skill for Structured Data

Connect MindsDB to your PostgreSQL database:

CREATE DATABASE datasource
WITH ENGINE = "postgres",
PARAMETERS = {
    "user": "demo_user",
    "password": "demo_password",
    "host": "samples.mindsdb.com",
    "port": "5432",
    "database": "demo",
    "schema": "demo_data"
};

Define a skill for querying this data:

CREATE SKILL text2sql_skill
USING
    type = 'text2sql',
    database = 'datasource',
    tables = ['house_sales'],
    description = 'Contains US house sales data from 2007 to 2015';

3.2: Knowledge Base Skill for PDFs

Step 1: Create an embedding model:

CREATE MODEL embedding_model_hf
PREDICT embedding
USING
    engine = 'langchain_embedding',
    class = 'HuggingFaceEmbeddings',
    input_columns = ["content"];

Step 2: Create a knowledge base:

CREATE KNOWLEDGE BASE my_knowledge_base
USING
    model = embedding_model_hf;

Step 3: Insert PDF content:

INSERT INTO my_knowledge_base
SELECT * FROM files.my_file_name;

Step 4: Create a skill linked to the knowledge base:

CREATE SKILL kb_skill
USING
    type = 'knowledge_base',
    source = 'my_knowledge_base',
    description = 'PDF report with analysis on house pricing trends';

Step 4: Create the AI Agent

Now, connect the conversational model with the two skills.

CREATE AGENT ai_agent
USING
    model = 'conversational_model',
    skills = ['text2sql_skill', 'kb_skill'];

Test the agent:

SELECT question, answer
FROM ai_agent
WHERE question = 'How many houses were sold in 2015?';

Optional: Deploy the Agent to Slack 

CREATE DATABASE mindsdb_slack
WITH ENGINE = 'slack',
PARAMETERS = {
    "token": "xoxb-xxx",
    "app_token": "xapp-xxx"
};

CREATE CHATBOT ai_chatbot
USING
    database = 'mindsdb_slack',
    agent = 'ai_agent';

Your AI agent is now live on Slack and can be interacted with directly by your team.

Automating Knowledge Base Updates

To refresh your knowledge base regularly:

CREATE JOB update_kb_hourly AS (
    INSERT INTO my_knowledge_base (
        SELECT * FROM data_source WHERE id > LAST
    )
) EVERY hour;

Example Questions You Can Ask

  • "How many houses were sold in California in 2012?"
  • "Summarize the key insights from the PDF report."
  • "What is the average price of houses sold per state in 2015?"

Use Cases

  • Internal AI assistants for querying data systems
  • Customer support bots with product manuals in PDF format
  • AI dashboards for real-time analytics and summaries
  • Slack bots that act as knowledge agents for your team

SEO Keywords (Technical)

  • Build AI agent with MindsDB
  • Natural language to SQL chatbot
  • LangChain agent integration
  • RAG PDF chatbot with SQL
  • PostgreSQL GPT chatbot
  • Hugging Face Embeddings MindsDB
  • MindsDB Slack integration
  • Enterprise LLM agent orchestration

Conclusion

By combining MindsDB with OpenAI or Hugging Face models, you can rapidly develop intelligent agents that understand both structured and unstructured data sources. With just a few SQL-like commands, we’ve created an end-to-end AI workflow that can be deployed across business-critical applications.

For enterprise use cases, this stack scales well for document understanding, sales analytics, research summarization, and beyond.