Boost your AI workflow with Ollama, Mistral, and PostgreSQL

Are you looking to boost your AI workflow with powerful language models locally and combine their capabilities with the robustness of a relational database? In this post, I’ll explore how to use Ollama with Mistral—a recently introduced open-source large language model—and connect it all to a PostgreSQL database. By the end of this tutorial, you’ll have a working environment that can generate natural language outputs using Mistral via Ollama, and store or query results using PostgreSQL. You can easily exchange mistral with another LLM that suits your requirmenets best.

What is Ollama?

Ollama is a command-line tool and framework designed to run large language models locally on your machine. It simplifies installing, updating, and running LLMs without needing a heavyweight environment. Ollama supports various open-source models, including Mistral.

What is Mistral?

Mistral is an open-source large language model known for its compact size and competitive performance. Mistral can be integrated into various workflows for text generation, summarization, or more specialized tasks like chat interfaces or question-answering systems.

Setting Up the Environment

Installing Ollama

First, follow the installation instructions to install ollama:

Installing Mistral Through Ollama

Once Ollama is installed, adding Mistral (or any supported model) is straightforward. Check available models by running in the shell: ollama pull mistral

This pulls the Mistral model onto your machine. Alternatively, if a specific variant or quantization is available, you might use: ollama pull mistral-nemo

The site lists available models with a varienty of parameters: mistral, deepseek, llama, etc.

Create a table in PostgreSQL to capture the results.

CREATE TABLE generated_texts ( 

    id SERIAL PRIMARY KEY, 

    prompt TEXT NOT NULL, 

    response TEXT NOT NULL, 

    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP 
);

Connecting Ollama (with Mistral) to PostgreSQL

Below is a sample Python script demonstrating how to:

1. Send a prompt to Mistral via Ollama.
2. Capture the generated response.
3. Insert the prompt-response pair into PostgreSQL.

Prerequisites

• Python 3.7+
• psycopg-binary for Postgres connectivity
• requests (if you use Ollama’s REST API mode) or direct CLI calls using subprocess

pip install psycopg2-binary requests

import subprocess
import psycopg2
from datetime import datetime

pg_user = 'postgres'
pg_password = 'better get the password from .env'
pg_host = 'localhost'
pg_port = 5432

conn = psycopg2.

       connect(f"dbname='postgres' user={pg_user} password={pg_password} host={pg_host} port={pg_port}")

cursor = conn.cursor()

def generate_text(prompt):
    """
    Uses Ollama CLI to generate text from the Mistral model.
    """

    # Make sure you have the Mistral model pulled: ollama pull mistral
    cmd = ["ollama", "run", "mistral", prompt]

    # Run the command
    try:
        output = subprocess.check_output(cmd, stderr=subprocess.STDOUT, text=True)

        return output.strip()

    except subprocess.CalledProcessError as e:
        print("Error generating text:", e.output)
        return None

def store_response(prompt, response):
    """
    Stores the prompt and response in the PostgreSQL database.
    """

    if response is None:
        print("No response to store.")
        return

    insert_query = """
    INSERT INTO generated_texts (prompt, response)
    VALUES (%s, %s)
    """

    cursor.execute(insert_query, (prompt, response))
    conn.commit()

def main():
    sample_prompt = "Explain how to integrate Mistral with a PostgreSQL database using Ollama."

    generated_response = generate_text(sample_prompt)

    if generated_response:
        print("Generated Response:")
        print(generated_response)

        # Store in PostgreSQL
        store_response(sample_prompt, generated_response)
        print("Response stored in PostgreSQL successfully.")

if __name__ == "__main__":
   main()

# Close the connection after usage
cursor.close()
conn.close()

The following screenshot shows the output.

A Practical Use Case to boost your AI workflow: RAG (Retrieval Augmented Generation)

Let’s walk through a simple RAG scenario: You have an internal knowledge base (KB) of documentation and need to generate quick answers or summaries. Here’s how it might look:

1. Ingest Documents: Convert product documentation into text chunks and store them in a dedicated table, e.g., docs.
2. Query & Summarize: Retrieve relevant chunks from the docs table based on user queries (using an approach like full-text search or vector embeddings).
3. Use Mistral or another LLM via Ollama: Pass the retrieved text into Mistral or another LLM, prompting it to provide a concise summary.
4. Store Summaries: Insert the user prompt and Mistral’s summary into generated_texts.

You now have a historical log of what was asked and how the AI responded, all stored safely in PostgreSQL. Administrators and data analysts can then review usage patterns, refine prompts, or improve the knowledge base coverage.

By integrating Ollama and Mistral with PostgreSQL, you gain a potent setup for local AI workflows. You can quickly generate text or answer questions using the efficiency of Mistral while securely storing and organizing data in PostgreSQL. Whether you’re building an internal FAQ system, summarizing documents, or simply experimenting with LLMs, this setup offers flexibility, privacy, and performance—all on your own machine.

Next Steps:

• Experiment with other LLMs having more parameters or a smaller model that is more suited to your use case.
• Experiment with different prompts and advanced prompt-engineering techniques.
• Explore more advanced text retrieval and summarization pipelines by leveraging PostgreSQL’s full-text search or a vector database approach (such as pgvector).
• Scale up to more sophisticated workflows: multi-step reasoning, chat interfaces, or knowledge-graph-based queries.
• Proceed with agents or multi-agent applications.