RAG Document Pipeline CLI - Startup Idea

Problem (Pain Score: 7/10)

Building a RAG (Retrieval-Augmented Generation) pipeline requires combining multiple tools from document conversion to vector DB loading—a tedious process.

Real Examples:

• Separate tool for converting PDFs to markdown
• Implementing chunking logic from scratch
• Writing embedding API call code
• Designing pgvector schema and writing load scripts
• Maintaining glue code connecting each step

Frequency: Every RAG project start (frequently)

For indie hackers or small teams adding RAG-based AI features, days are spent just setting up the pipeline before actual development begins.

Target Market

Primary Targets:

• AI/LLM app developers
• RAG system building startups
• Enterprise internal document search teams
• AI agent developers

Market Size:

• TAM: $82.1B (LLM market, projected 2033)
• RAG/Vector DB market: 30%+ annual growth
• Edge AI deployment: 27.25% CAGR

Customer Characteristics:

• Familiar with LLM/AI technology
• Prefers rapid prototyping
• Wants minimal infrastructure management
• Often already using Postgres

Proposed Solution

Core Features:

1. One-Step Pipeline

   ragpipe ingest ./docs --db postgres://... --embed openai
   

   • Automates: Document → Markdown → Chunking → Embedding → DB Load

2. Multiple Document Format Support

   • PDF, DOCX, HTML, Notion export
   • Code files (comment extraction)
   • Web page crawling

3. Flexible Configuration

   • Chunking strategy selection (paragraph, token, semantic)
   • Embedding model selection (OpenAI, Cohere, local)
   • Custom metadata

4. Postgres/pgvector Optimization

   • Auto schema generation
   • Index optimization
   • Incremental update support

Competitive Analysis

Differentiation:

• Complete pipeline with CLI only, no code required
• Postgres/pgvector native (no separate vector DB needed)
• Reproducible pipeline with single config file
• RAG system bootstrap in 5 minutes

MVP Development Plan

Timeline: 6 weeks

Week 1-2: Document Parsing

• PDF/DOCX parser integration (PyMuPDF, python-docx)
• Markdown normalization
• Metadata extraction

Week 3: Chunking Engine

• Token-based chunking
• Overlap configuration
• Semantic chunking (optional)

Week 4: Embedding Integration

• OpenAI API integration
• Local model support (sentence-transformers)
• Batch processing optimization

Week 5: DB Loading

• pgvector auto schema generation
• Incremental update logic
• Index optimization

Week 6: CLI & Launch

• CLI interface completion
• Config file format definition
• Documentation and examples

Tech Stack:

• Runtime: Python (Typer CLI)
• Parsing: Docling, PyMuPDF
• Embedding: OpenAI API, sentence-transformers
• DB: PostgreSQL + pgvector

Revenue Model

Pricing:

Revenue Projections:

• Year 1 target: $4K MRR
• 100 paid customers (avg $40/mo)
• Differentiate with cloud parsing service

Growth Strategy:

• AI/LLM community marketing
• RAG tutorial content creation
• Partnerships with Supabase, Neon, etc.

Risks & Challenges

Technical Risks:

• Parsing quality across diverse document formats
• Embedding API costs (user burden)

Market Risks:

• LlamaIndex, LangChain may release similar CLI
• Supabase etc. may offer built-in features

Operational Risks:

• Handling document parsing edge cases
• Compatibility across Postgres environments

Mitigation:

• Focus on CLI (differentiate from frameworks)
• Deep Postgres ecosystem integration
• Fast iteration

Why We Recommend This

Score: 89/100

1. Growing market: RAG/LLM market explosive growth
2. Clear pain point: Repetitive RAG pipeline setup work
3. Reasonable MVP timeline: Core features in 6 weeks
4. Preferred domain: data_mgmt, productivity
5. Postgres-friendly: Leverages existing DB
6. Global target: Global AI developer community

A practical tool lowering the barrier to RAG system construction.