Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Nikola Gigić — Tue, 02 Dec 2025 13:06:31 +0000

Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Building a production-ready RAG (Retrieval-Augmented Generation) system typically takes 6-12 weeks. But here's the thing: most of that time isn't spent on the actual RAG logic—it's spent on infrastructure.

You spend weeks on:

Setting up vector databases
Building embedding pipelines
Writing orchestration code
Debugging chunking strategies
Managing API integrations

The actual RAG logic? That's maybe 20% of the work.

That's why we built ShinRAG — a visual RAG platform that lets you build and deploy production RAG systems in days instead of weeks.

What is ShinRAG?

ShinRAG is an all-in-one RAG platform that combines:

Visual pipeline builder for drag-and-drop RAG workflows
Managed vector database (powered by Qdrant)
RAG agents with dataset assignment
Full REST API for production use
TypeScript SDK for easy integration
Usage tracking and monitoring

The Problem It Solves

Most teams building RAG systems face the same challenges:

Infrastructure complexity: Setting up vector databases, embedding services, and orchestration layers
Time investment: 6-12 weeks to go from idea to production
Maintenance overhead: Constant debugging and optimization
Vendor lock-in: Hard to switch between tools and providers

ShinRAG solves these by providing a managed platform where you focus on your application logic, not infrastructure.

Getting Started with ShinRAG

Installation

npm install @shinrag/sdk
# or
pnpm add @shinrag/sdk

Basic Usage

import { ShinRAGClient } from '@shinrag/sdk';

const client = new ShinRAGClient({
  apiKey: 'sk_your_api_key_here',
});

Query an Agent

The simplest way to use ShinRAG is through RAG agents. Create an agent, assign datasets, and start querying:

const result = await client.queryAgent('agent_1234567890abcdef', {
  question: 'What are the key features mentioned in the documentation?',
  maxResults: 5,
  temperature: 0.7
});

console.log('Answer:', result.answer);
console.log('Sources:', result.sources);
console.log('Tokens used:', result.tokensUsed);

The agent automatically:

Searches your assigned datasets
Retrieves relevant context
Generates an answer with citations
Tracks token usage

Execute a Pipeline

For more complex workflows, use visual pipelines:

const pipelineResult = await client.queryPipeline('pipeline-id', {
  input: 'Process this query through my custom pipeline'
});

if (pipelineResult.status === 'completed') {
  console.log('Output:', pipelineResult.output);
  console.log('Total tokens:', pipelineResult.totalTokensUsed);

  // Inspect individual node results
  pipelineResult.nodeResults.forEach(node => {
    console.log(`Node ${node.nodeId}: ${node.status}`);
    if (node.output) {
      console.log(`  Output: ${node.output}`);
    }
  });
}

Search Datasets Directly

You can also perform semantic search on your datasets:

const searchResult = await client.queryDataset({
  datasetId: 'dataset_1234567890abcdef',
  queryText: 'What are the key features?',
  limit: 10
});

if (searchResult.success) {
  console.log(`Found ${searchResult.results.length} results`);
  searchResult.results.forEach(item => {
    console.log(`Score: ${item.score}, Content: ${item.payload.text}`);
  });
}

Key Features

1. Visual Pipeline Builder

Build complex RAG workflows visually. Drag and drop nodes, configure connections, and deploy without writing orchestration code.

2. Bring Your Own API Keys

Use your own OpenAI, Anthropic, or other LLM API keys. You keep control of costs and providers.

3. Advanced Filtering

Filter search results using metadata filters:

const result = await client.queryAgent('agent-id', {
  question: 'Find information about TypeScript',
  filter: {
    must: [
      { key: 'category', match: { value: 'programming' } },
      { key: 'difficulty', range: { gte: 1, lte: 3 } }
    ]
  }
});

4. TypeScript-First SDK

Full TypeScript support with type safety and IntelliSense:

interface QueryAgentResponse {
  answer: string | null;
  sources: Array<{
    dataset: string;
    score: number;
    text: string;
  }>;
  tokensUsed: number;
  model: string;
  warning?: string;
}

5. Error Handling

The SDK provides typed errors for better debugging:

import { ShinRAGClient, ShinRAGError } from '@shinrag/sdk';

try {
  const result = await client.queryAgent('agent-id', {
    question: 'Your question here'
  });
} catch (error) {
  if (error instanceof ShinRAGError) {
    console.error('API Error:', error.message);
    console.error('Status Code:', error.statusCode);
  }
}

Real-World Use Cases

Internal Knowledge Base

Build a knowledge base for your team:

Upload company documentation
Create an agent assigned to the dataset
Query via API or integrate into Slack/Discord

Customer Support Bot

Create a support agent:

Ingest support documentation and FAQs
Create an agent with appropriate filters
Deploy via API to your support system

Document Q&A System

Build a Q&A system for technical documentation:

Upload technical docs
Create multiple agents for different topics
Use filter templates to route queries

Why Choose ShinRAG Over Building Custom?

Custom Build	ShinRAG
6-12 weeks development	3 days to production
Infrastructure management	Fully managed
Complex orchestration code	Visual builder
Manual debugging	Built-in monitoring
High maintenance	Low maintenance

Architecture Highlights

Built with NestJS for the API
Uses Qdrant for vector storage
Supports multiple LLM providers
TypeScript SDK for type safety
RESTful API design
Usage tracking and analytics

Getting Started

Sign up at shinrag.com (or use your own instance)
Create your first dataset
Build an agent and assign datasets
Install the SDK: npm install @shinrag/sdk
Start querying!

What's Next?

We're actively developing:

More pipeline node types
Streaming responses
Additional SDK languages (Python, Go)
Enhanced monitoring and analytics
Integration marketplace

Conclusion

RAG shouldn't require a PhD in distributed systems. ShinRAG lets you focus on building your application while it handles the infrastructure.

Whether you're building an internal knowledge base, a customer support bot, or a document Q&A system, ShinRAG can help you ship faster.

Try it out and let us know what you think. We're building in public and would love your feedback.

Tags: #rag #ai #typescript #machinelearning #developer #api #nlp #vectordatabase

Have questions or feedback? Drop a comment below or reach out on Twitter

Introducing GitCase: Secure Portfolio Creation for Developers

Nikola Gigić — Thu, 07 Nov 2024 22:47:46 +0000

Hey DEV Community! 👋

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Watch the Demo Video:
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DEV Community: Nikola Gigić

Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

Building Production RAG Systems in Days, Not Weeks: Introducing ShinRAG

What is ShinRAG?

The Problem It Solves

Getting Started with ShinRAG

Installation

Basic Usage

Query an Agent

Execute a Pipeline

Search Datasets Directly

Key Features

1. Visual Pipeline Builder

2. Bring Your Own API Keys

3. Advanced Filtering

4. TypeScript-First SDK

5. Error Handling

Real-World Use Cases

Internal Knowledge Base

Customer Support Bot

Document Q&A System

Why Choose ShinRAG Over Building Custom?

Architecture Highlights

Getting Started

What's Next?

Conclusion

Introducing GitCase: Secure Portfolio Creation for Developers