Turbovec + OpenClaw + Ollama - Local RAG Agent with 8x TurboQuant Compression

Added: 2026-05-05

[00:00:01]When OpenClaw meets TurboVec, great things happen. Today in this video, we are going to build a fully local, fully private AI agent that can answer questions about any document you give it. We are combining three tools.

[00:00:17]OpenClaw, which is an open-source AI agentic platform that runs locally and lets you chat with an AI through a terminal interface. I will be using an Ollama-based local model behind the scene and I will show you how.

[00:00:32]We're also going to use TurboVec, a brand new pip-installable implementation of Google's Turbo Cont algorithm, which we already have covered here in this video, and thank you for the huge response.

[00:00:44]Now, what it does is it compresses your vector search index eight times smaller with near-zero quality loss. And then we have our Ollama, our local inference engine running quant 3.6 right here on this system where I'm using I have this GPU card and I have this Ollama model, so you can see we are running quant 3.6. And for embedding, we are using Nomik Embed. Very quickly, the reason why I am using embedding model is because I will be providing it my own document, which is simply a text file with my own information. And then I will be using this Nomik Embed model to convert it into numbers because these models only understand numbers, not text. And then we will be using some sort of flow, which is this, and I will explain it to you very shortly what exactly is happening here. And then we will be knitting all of these together to create an end-to-end rag pipeline, which will be powering or providing more grounded, nuanced semantic context to OpenClaw. So, let's get right into it.

[00:01:55]And by the way, this is Fahad Mirza, and I very welcome you to the channel.

[00:02:00]Please also follow me on X if you're looking for AI updates, and goes without saying, please like the video and subscribe and hype it if you like.

[00:02:08]Also, if you're looking to rent a GPU or VM on very good price, you can find the link to Mass Compute in video's description with a discount coupon code of 50% for a range of GPUs.

[00:02:20]Okay, everything is done. You can see that our Ollama is already installed and running. If you don't know what Ollama is, how to run it, just search my channel. And then once Ollama is installed, just do Ollama pull model name, and then it is going to download the model for you. And you can go with any model of your choice.

[00:02:40]Okay, first up, I'm going to quickly install this OpenClaw on my local system, and I am going to integrate it with Ollama. I will quickly go through it and show you the final result, but if you want the detail as how to install it with Ollama, just go to my channel, search with OpenClaw with local, and watch this first video where I have shown you step-by-step, also shared the files and commands which you can use in order to get OpenClaw properly installed with Ollama.

[00:03:10]And I'm just going to select Ollama here, and let's go with local only.

[00:03:15]This is endpoint, the default one, and then I'm just going to select the model, which is already running on my system.

[00:03:23]And by default, it goes with Gemma.

[00:03:27]But I'm just going to select the quant 3.6, which we already have running here.

[00:03:32]And that should now install this OpenClaw.

[00:03:36]And OpenClaw is installed.

[00:03:39]Now, let's install TurboVec. I'm just going to quickly create a virtual environment with conda.

[00:03:46]And now let's install the TurboVec with the pip and all the other stuff which we'll be using like llama index and other stuff.

[00:03:53]As I said, if you want to know more about TurboVec, just watch the video which I shared earlier. I will also drop the link in video's description.

[00:04:03]And TurboVec is also installed.

[00:04:07]Now, before we run anything, let me explain in as simple words as possible to show you exactly what we are building.

[00:04:15]This diagram shows you the full flow. On the left, you have yourself. You type a question into OpenClaw's terminal interface. OpenClaw is your local AI agent. We are calling it Honey today.

[00:04:29]It is running quant 3.6, a 23 billion parameter model via Ollama right here on this machine.

[00:04:36]Now, when you ask Honey something about Fahad, Honey doesn't guess. Instead, it calls our TurboVec rag server running on Fast API on port 8811. I will shortly show you the code.

[00:04:49]That server has already loaded um the file which I'm going to use. Let me very quickly show you the file which I am also going to use. So, this is a file fahad.txt, which contains all the information about me, the personal information.

[00:05:05]Now, the model quant 3.6 or OpenClaw or TurboVec, they have no clue who I am and all my personal information, and that is where the rag will shine. So, open when we ask any questions about myself, OpenClaw is going to answer it from this TurboVec by using Ollama-based models.

[00:05:24]That is the whole flow.

[00:05:27]So, for that, I am using that TurboVec as a skill of this OpenClaw. Now, the skill file looks like something this.

[00:05:40]A skill in OpenClaw is a markdown file that tells the agent when and how to use a tool.

[00:05:47]This skill tells Honey, which is OpenClaw, whenever someone asks about Fahad, stop, call this curl command, hit our local rag server, and use whatever comes back as your answer. One file, plain markdown, and the agent knows exactly what to do.

[00:06:06]I will also drop the skill in my GitHub repo, and I will drop the link in the first pinned comment, so you can also check it out.

[00:06:15]Now, this is where I am creating the server. So, you see, this is where this is the same one which we used for TurboVec, so I'm not going to go into the detail. All it is doing, it is picking up that fahad.txt file, it is converting it into numbers, and then it is going to give us a vector store. So, whenever OpenClaw uses this skill, it primarily goes to TurboVec, get that similar data, and then appends it with a prompt and give it to quant 3.6, which gives us an answer. That is this is the whole flow which is being described in this diagram. Hopefully, this makes it clear how and why and where we are using and integrating all these three.

[00:07:02]Okay, so now you have seen the file. Let me um now give you a demo.

[00:07:08]So, in this new terminal, what we are doing, we are starting our Fast API rag server. Uvicorn is the web server.

[00:07:15]rag_server is our Python file which I just showed you, and we are binding it to local host port 8811, so that OpenClaw could can call it.

[00:07:28]So, you see it has loaded that document, it has created that Turbo Cont vector index, and rag server is ready at port 8811 on our local host. I will let it run, and I will just minimize this window, and we will work in this another terminal window.

[00:07:44]Let's also confirm our skills. I'm just going to list it and then grep it. There you go. So, our skill is ready, and you see the description is that always use this skill to answer any questions about Fahad, and I showed you that whole file where we have described it.

[00:08:01]Any questions about me, it should be redirected to this skill by the agent of OpenClaw.

[00:08:08]And now let's start it in my terminal user interface.

[00:08:15]It is connecting to that gateway service. All done.

[00:08:20]And it's not mandatory, but let's give it some personality. I'm telling it that your name is Honey, you're a sharp technical AI assistant. My name is Fahad, and save this and complete it.

[00:08:37]Okay, Honey is there, alive and attentive. Let's ask it. So, I'm just going to ask who is Fahad Mirza.

[00:08:44]And you can see that it is using that quant 3.6 with Ollama.

[00:08:49]And there you go, it has given me the answer, and you can see that it is talking about the 158k+ subscribers, um and then founder of Start Claw, by the way, fictitious company, nothing like that exists.

[00:09:02]Um I'll just quickly show you the file.

[00:09:04]So, you see, this is where it says 158, and should there you go. So, I just put it on purpose. So, because no model, including quant 3.6 or anything, uh has any clue what this Start Claw is and who I am and the exact number, but you can see that it is using that TurboVec skill to access that TurboVec vector score store, and then using that embedding model and Ollama-based, you know, LLM, it has returned us the answer.

[00:09:35]And you can just keep asking questions about, you know, whatever your topic is, it is going to go in and then give you the grounded and very, very targeted response from your own data.

[00:09:47]Now, of course, you can use it on your own data.

[00:09:50]Just make, you know, have as much data as possible. This is just a demo. I could just simply easily replace it with any amount of data corpus.

[00:10:02]And there you go, another um very grounded answer.

[00:10:06]So, you see, that's it. Uh we have this fully local AI agent powered by Qwen 3.6 running on Ollama with a Turbo Quant compressed vector index serving as her memory, built with Turbo Quant, the open source pip installable implementation of Google's Turbo Quant.

[00:10:24]Eight times smaller vectors and lot of other goodies which are happening. Let me know what do you think. Again, please hype the video, like it, subscribe to the channel, and please consider becoming a member as that helps a lot.

[00:10:36]Please also follow me on X if you're looking for AI updates. Thank you for all the support.