Kokoro TTS

Tue, 20 Jan 2026 00:00:00 +0000

Text To Speech

Technology that enables text to be converted into speech sounds imitative of the human voice.

Run locally

https://github.com/remsky/Kokoro-FastAPI

docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:latest

And you can play with web ui:

http://localhost:8880/web

n8n workflow with Kokoro

I want to hear Qwen answer 😊

First I tested kokoro API with Bruno.

And then I added it to a new n8n workflow.

I had to remove all special characters.

RAG n8n

Thu, 15 Jan 2026 00:00:00 +0000

RAG with n8n

Overview

This guide explains how to implement a RAG (Retrieval Augmented Generation) on your laptop.

Embedded AI
Data sovereignty

Before you start

What’s RAG

RAG (retrieval augmented generation) is a technology that improves the responses of generative AI models by feeding them with knowledge from internal databases.

What’s you need

Before you put the RAG in place, ensure you already have:

Docker
Ollama
md files

Installation

n8n

n8n is a workflow automation platform that gives technical teams the flexibility of code with the speed of no-code.

Run locally

docker volume create n8n_data
docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n

Go to the web n8n Dashboard:

Qdrant

Qdrant (read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload Qdrant is tailored to extended filtering support.

Run localy

docker volume create qdrant_data
docker run -p 6333:6333 -v qdrant_data:/qdrant/storage qdrant/qdrant

qdrant Dashboard

Ollama

Ollama is the easiest way to get up and running with large language models such as gpt-oss, Gemma 3, DeepSeek-R1, Qwen3 and more.

RAG Workflow

The RAG is composed in 2 workflows.

Data ingestion

It starts with the file submission trigger, to upload CVs (in markdown format).

We add Qdrant connector to store the files in the vector database. We need an embed model to split the files into vectors.

Emebed model: mxbai-embed-large

Qdrant collections

When the Data Ingestion workflow is executed, you can go to Qdrant dashboard to see the collections.

Chatbot

Now the CVs are in the Qdrant vector database, we can chat to request some informations about the candidate.

We start with the Chat trigger connected to an AI agent, with Qwen3 model.

We create the tool to be able to search in our Qdrant collection and we had a simple prompt.

🔥 And finaly we test our chat by asking informations about a candidate. We can see that the agent called qdrant to retrieve the data and generate a nice answer.

N8n on Thomas