Build Your AI RAG / Chatbot in Minutes with Flowise and Stacktic

AI transforms user experiences, delivering powerful assistance and guidance. Yet, behind the scenes, Ops and Dev teams face increased complexity—new tools, additional stack components, and rising costs. Thankfully, the open-source community is thriving, addressing these challenges with smart, efficient solutions like:

• LangFlow – drag-and-drop chains and agents builder.

• Haystack – production-ready RAG pipelines.

• Chainlit & Open WebUI – AI chat interfaces.

  
  

Today, let’s explore Flowise, a standout open-source solution valued for its power and ease of use.

Why choose Flowise over managed services?

Managed platforms such as OpenAI Custom GPTs, Amazon Bedrock, or Google Vertex simplify data ingestion but restrict you with rigid pricing and limitations.

Flowise, on the other hand, provides full ownership and control of your AI stack. It's completely open-source, highly modular, scalable, and cost-effective—allowing easy switching between models (OpenAI, LLaMA) and vector databases.

Stacktic: automating integration, simplifying complexity

Stacktic unifies and automates your stack's relationships, while Flowise manages AI-specific integrations on top. Together, we deliver a fully automated AI Dev and Ops environment with minimal effort.

Quickly set up your AI chatbot environment

Simply assemble your stack components, link them together, and Stacktic will handle all the automation tasks. For example, when linking MinIO, Redis or PostgreSQL to Flowise, configuration maps, secrets, and integrations are automatically created. All components are fully managed, and the entire workflow, including dependencies, is automatically versioned—requiring minimal effort. Security: as AI development environments require robust security measures, we handle everything comprehensively—from securing the Flowise image and dependencies to managing connectors, authentication, and secrets. Security is built-in at every step on the way.

Essential Components:

• AI Model: OpenAI (for speed) or LLaMA (for flexibility and affordability).

• Database: PostgreSQL for structured data management.

• Vector DB: Qdrant, Kubernetes-optimized for fast and scalable searches.

• Orchestration: Kubernetes automates deployments, scaling, and real-time monitoring.

• MinIO envs feed the ingestion side—where your PDFs live.

• Redis envs feed the chat side—where user conversation history is stored.

• Many ohter options like on the table, this is just the base: Ollama or vLLM , Keycloak , Loki, ELK, etc....

  
  

  60 sec basic stack Demo Stacktic / Flowise

  
  
  
  

Why a Vector DB?

• Stores millions of vectors efficiently.

• Performs ultra fast similarity searches.

• Supports detailed metadata filtering and scaling beyond memory limits.

Two-Step Quick Start

Step 1: Data Ingestion Pipeline (ingest-stacktic)

• File Loader: Reads PDFs and Markdown.

• Text Splitter: Divides documents into concise passages.

• Embeddings: Converts text to searchable vectors.

• Storage: Stores vectors automatically in Qdrant.

Step 2: Chat Pipeline (stacktic-chat)

• Input: Captures user questions.

• Buffer Memory: Maintains conversational context.

• Qdrant: Quickly retrieves relevant information.

• ChatOpenAI & QA Chain: Delivers accurate answers with context and citations.

  
  
  
  

  

  
  
  
  

Why split into two pipelines?

• Pay Once, Use Forever – Ingest flow embeds and stores vectors a single time; chat queries reuse them, so no per-question embedding cost.

• Fast Chats – Chat flow only does a quick vector search and LLM call; no file parsing slows it down.

• Independent Scaling – Ingest runs as a batch or CronJob; chat runs 24×7 and can autoscale separately.

• Safe Reloads – You can re-ingest new documents without touching the live chatbot.

What's next?

Just upload your content in Markdown or text format, embed the chat into your app, and your chatbot is ready to engage. my guide.md for stacktic (upload the file to the file loader node):

# Stacktic Fast-Track Guide

## Introduction

Stacktic is a powerful framework capable of automating and integrating everything within the Kubernetes ecosystem. It unifies tools, frameworks, and open-source technologies into a seamless stack, including automation for operations, logic, and much more.

Key features:

- Non-opinionated open framework with zero vendor lock-in

- Full customization capabilities for your platform

- Comprehensive automation from Day 0 setup through Day 2 operations

This guide focuses on providing a basic method for implementing an app stack in just 5 minutes.

## Getting Started

### 1. Access and Login

1. Login to the Stacktic platform

2. from system, Create a New Stack (or use an existing sample)

3. from configuraiton of the stack , Provide necessary credentials:

- Container Registry (e.g., Docker)

- GitHub or other source control

Embed the chat into your app,

Summary

Flowise extends further, empowering you to build sophisticated solutions like API-aware support bots, voice-enabled AI assistants (Whisper ASR → ChatAI → ElevenLabs TTS), or dynamic Agentflows that can select tools for comprehensive responses.

Next, we'll explore building a LLaMA development environment, integrating Chainlit or Open WebUI with Flowise, providing a customizable frontend while Flowise handles orchestration...

Best...