VibeOps

Imagine uploading your company’s handbook, product manuals, or research papers, and then simply asking questions like “What’s our vacation policy?” or “How do I troubleshoot error code 42?” Your AI assistant instantly finds the answer and explains it to you in plain English.

Imagine you want to do it on your private documents, without sending them in an unknown datacenter in you cloud provider.

We previously talked about ambedding our documents in this article, now we want to add a new piece: calculate the embeddings ONCE and then store them for further use.

🧭 Step 1: Get Ready

Before we jump in, check out this quick-start tutorial:
👉 Embedding Part 3: Running Your First AI Model with Docker — No Tech Degree Needed

That post gets your system set up. Once that’s done, you’ll already familiarized yourself with embeddings, models and docker images. Now we’ll take things further — giving your AI an actual memory so it can “remember” and “discuss” your documents.

All the scripts for this project are currently available here

Download them and move your vscode shell to the directory where the new repository is located

🧠 Step 2: Give Your AI a Brain (Qdrant)

Think of Qdrant as your AI’s memory vault — a place to store and recall everything it learns from your documents, like a super-smart filing cabinet for your documents. Instead of organizing papers alphabetically, it understands the meaning of your content.

First, we grab Qdrant (think of this as downloading a filing cabinet):

docker pull qdrant/qdrant

Then we set it up in our workspace (replace ${path_on_drive} with where you want to store your files):

docker run -p 6333:6333 -v ${path_on_drive}\qdrant:/qdrant/storage qdrant/qdrant

🧰 Step 3: Build Your AI’s Workspace

In our last tutorial, we saw how to extend a basic docker image with the libraries we need. This time we need to add the ability to talk to Qdrant.

Think of this like upgrading from a basic toolbox to a professional one:

create the dockerfile as below:

FROM python:3

WORKDIR /usr/src/app

RUN pip install requests

RUN pip install qdrant_client

and then run the below command to create a new docker image withe the qdrant client

docker build -t python-qdrant:latest .

This creates your upgraded workspace with all the tools you need to work with your documents.r AI’s workspace is ready — neat, clean, and efficient.

📚 Step 4: Feed Your AI Some Knowledge

Let’s give your AI something to think about! This step loads your own documents into its memory so it can “read” and “understand” them.

Just run:

docker run -it --rm -v "${pwd}:/usr/src/app" -w /usr/src/app python-qdrant:latest python embed.py

Behind the scenes, your AI turns those documents into embeddings — digital brain connections that help it grasp meanings and relationships between words and ideas, your AI is reading through all your documents and creating a mental map of the information. It’s like when you read a book and form memories about it – except your AI does this in seconds!

💬 Step 5: Chat With Your Data

Now for the exciting part — time to talk to your own files!

Run this:

docker run -it --rm -v "${pwd}:/usr/src/app" -w /usr/src/app python-qdrant:latest python chat.py

Then try asking questions like:

“What did my report say about Q3 results?”
“Summarize the key points from my project notes.”

Your AI will respond with clear, contextual answers drawn straight from your own content. It’s like having a super-smart assistant who actually knows your work.

🚀 Ready to Try It?

You’ve just learned how to give your computer a memory, load it with your knowledge, and chat with it — all without coding.

This is just the beginning of your AI creator journey. Whether you’re a writer, entrepreneur, or curious explorer, you now have the power to make your data talk back.

Keep discovering more easy, no-stress AI guides at vibeops.one — and keep building your AI superpowers!

repository available on https://github.com/notoriousrunner/qdrant