Pre-Built RAG Infrastructure Starter Kit
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Ship AI-powered document chat apps in hours, not weeks, with pre-configured vector ingestion, embedding, and retrieval pipelines.
Added Mar 31, 2026
11 signals
The Problem
Developers building AI apps waste days configuring vector databases, writing chunking logic, handling embedding API calls, and debugging PDF/web parsers before they can even start on their actual product. This repetitive boilerplate—Pinecone setup, LangChain wiring, retrieval query tuning—kills momentum on weekend MVPs and side projects alike.
Potential Solution
Detailed solution approach available for premium members.
Why Now?
Market timing analysis available for premium members.
I got tired of setting up Pinecone pipelines for every tiny MVP, so I built a wrapper around pgvector.
Every time I start a new AI side project that needs long-term memory (RAG), I hit the same wall. I have to: Spin up a vector DB instance. Write the chunking logic. Handle the embedding API calls. Write the retrieval query. It’s just too much boilerplate when I’m trying to validate an idea in a weekend. So I built MemVault to abstract all that away. It’s basically "Memory-as-a-Service". The logic is simple: I send text to an endpoint -> The API handles chunking/embedding -> It stores it in Postgres. When I query it, it doesn't just do a cosine similarity search. It uses a hybrid score of Semantic Similarity + Recency + Importance. The Stack (for those interested): Backend: Node.js / Express (TypeScript) DB: PostgreSQL with pgvector (Running via Prisma) Hosting: Railway I also built a visualizer because debugging RAG is a nightmare when you can't "see" what's being retrieved. It shows the input -> embedding -> DB match in real-time. It's Open Source: Since we are all indie hackers here, I know you probably don't want to rely on a random API for critical infra. You can just grab the code and self-host it on a $5 VPS if you prefer. But if you want to save the setup time, I put it up on RapidAPI to test the waters. Would love some feedback on the retrieval logic if anyone here is deep into RAG. Repo: https://github.com/jakops88-hub/Long-Term-Memory-API Visualizer Demo: https://memvault-demo-g38n.vercel.app/ NPM: https://www.npmjs.com/package/memvault-sdk-jakops88 RapidAPI: https://rapidapi.com/jakops88/api/long-term-memory-api
Update: I launched my RAG Starter Kit on Saturday. Got my first customer and shipped v1.0.
On Saturday, I posted a "Smoke Test" landing page for a Next.js RAG Starter Kit because I was tired of setting up Pinecone and LangChain from scratch every time. I got some great roasting (and some actual interest), so I stayed up all weekend building the real thing. **What I Shipped (v1.0):** * ✅ **Multi-File Upload:** Ingest 5+ PDFs at once. * ✅ **Cost Optimization:** Configured for `text-embedding-3-small` (1024 dims) to save DB costs. * ✅ **Citations:** The AI tells you exactly which file and paragraph the answer came from. * ✅ **"Browser" UI:** Cleaned up the interface to look like a proper macOS window. **The Stack:** Next.js 14, LangChain, Pinecone, Vercel AI SDK. **The Offer:** I'm keeping the price at **$9** for the first 50 users (Launch Price will be $49). **Demo:** [https://rag-starter-kit.vercel.app/](https://rag-starter-kit.vercel.app/) Thanks to the user who asked about "Blog Scraping" functionality—that's coming in v1.1!
Update: I launched my RAG Starter Kit on Saturday. Got my first customer and shipped v1.0.
On Saturday, I posted a "Smoke Test" landing page for a Next.js RAG Starter Kit because I was tired of setting up Pinecone and LangChain from scratch every time. I got some great roasting (and some actual interest), so I stayed up all weekend building the real thing. **What I Shipped (v1.0):** * ✅ **Multi-File Upload:** Ingest 5+ PDFs at once. * ✅ **Cost Optimization:** Configured for `text-embedding-3-small` (1024 dims) to save DB costs. * ✅ **Citations:** The AI tells you exactly which file and paragraph the answer came from. * ✅ **"Browser" UI:** Cleaned up the interface to look like a proper macOS window. **The Stack:** Next.js 14, LangChain, Pinecone, Vercel AI SDK. **The Offer:** I'm keeping the price at **$9** for the first 50 users (Launch Price will be $49). **Demo:** [https://rag-starter-kit.vercel.app/](https://rag-starter-kit.vercel.app/) Thanks to the user who asked about "Blog Scraping" functionality—that's coming in v1.1!
Update: I launched my RAG Starter Kit on Saturday. Got my first customer and shipped v1.0.
On Saturday, I posted a "Smoke Test" landing page for a Next.js RAG Starter Kit because I was tired of setting up Pinecone and LangChain from scratch every time. I got some great roasting (and some actual interest), so I stayed up all weekend building the real thing. **What I Shipped (v1.0):** * ✅ **Multi-File Upload:** Ingest 5+ PDFs at once. * ✅ **Cost Optimization:** Configured for `text-embedding-3-small` (1024 dims) to save DB costs. * ✅ **Citations:** The AI tells you exactly which file and paragraph the answer came from. * ✅ **"Browser" UI:** Cleaned up the interface to look like a proper macOS window. **The Stack:** Next.js 14, LangChain, Pinecone, Vercel AI SDK. **The Offer:** I'm keeping the price at **$9** for the first 50 users (Launch Price will be $49). **Demo:** [https://rag-starter-kit.vercel.app/](https://rag-starter-kit.vercel.app/) Thanks to the user who asked about "Blog Scraping" functionality—that's coming in v1.1!
I started this as a weekend project on Saturday. 4 days later, I just shipped v1.2 based on your feedback (Multi-File RAG starter kit).
On Saturday, I posted a "Smoke Test" landing page here for **FastRAG** (a Next.js + Pinecone Starter Kit). I was tired of writing the same boilerplate code for vector ingestion, so I productized it. The feedback from this sub was brutal but fair: *"Single file upload is a toy. I need to chat with a whole folder of PDFs."* So, I got back to work. **What I Shipped Today (v1.2):** 1. **Multi-File Ingestion:** I rewrote the backend (`formidable` \+ loops) to handle array uploads. You can now ingest 5+ PDFs at once. 2. **Global Namespace Search:** The AI now scans across *all* uploaded files to find answers (and cites the specific filename in the response). 3. **UI Polish:** Revamped the interface to look like a clean macOS window (dark mode bubbles, clean typography). **The Stack:** * Next.js 14 (Pages Router for Edge compatibility) * Pinecone (Serverless) * OpenAI (`text-embedding-3-small` forced to **1024 dimensions** to save 33% on storage costs). **The Result:** I got my first few paying customers yesterday (huge milestone!). I'm keeping the "Early Bird" price at **$9** for a few more days before I hike it up for the v2.0 launch. **Live Demo:** [https://rag-starter-kit.vercel.app/](https://rag-starter-kit.vercel.app/) I'm hanging out in the comments if you want to know how I handled the vector metadata for multiple files!
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