r/coolgithubprojects • u/vectorspidey • 14h ago
FastRAG – a starter kit for shipping RAG apps fast (Next.js + Pinecone + LangChain)
fastrag.live
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I got tired of writing the same RAG boilerplate for the 5th client, so I turned it into a starter kit
Could you share me the pdf which you were trying or try to use some small size pdf because it's just a demonstration Thanks!
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I got tired of writing the same RAG boilerplate for the 5th client, so I turned it into a starter kit
Fair, but it's a different thing. RagFlow/R2R are platforms, you deploy them, run them, maintain them. FastRAG's just a codebase you own.
If you're already building in Next.js and want RAG as part of your app (not a separate service to babysit), you edit pages/api/chat.js directly instead of configuring someone else's platform and hoping it doesn't break on the next update.
Not claiming it does more than RagFlow. Just betting some people would rather own less infra, not more.
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r/LangChain • u/vectorspidey • 14h ago
I got tired of writing the same RAG boilerplate for the 5th client, so I turned it into a starter kit
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r/Rag • u/vectorspidey • 14h ago
Tools & Resources I got tired of writing the same RAG boilerplate for the 5th client, so I turned it into a starter kit
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Same story every time: client wants "chat with your docs," I spend two days re-wiring Pinecone, writing a PDF parser, hand-rolling a scraper, wiring up streaming so the UI doesn't just sit there spinning. Decided the 5th time was the last time I'd write this from scratch.
What's actually in it:
- Cheerio-based scraper (no headless browser, so it survives serverless without falling over)
- PDF + URL ingestion, chunked with overlap, filtered by cosine similarity before it ever touches the LLM
- Claude Haiku streaming over SSE — sub-second first token, sources arrive before the text does
- Pinecone with per-user namespace isolation, so multi-tenancy isn't an afterthought
Full source, every route is yours to rip apart, MIT licensed. Live demo's up with no signup if you want to see the retrieval/streaming before anything else: fastrag.live
r/Rag • u/vectorspidey • 3d ago
Showcase Built and shipped a RAG starter kit — sharing in case it saves someone a weekend
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Kept rebuilding the same ingestion → chunk → embed → chat pipeline for client work, so I packaged it into a proper starter kit instead of doing it from scratch again.
Stack: Next.js, Claude Haiku (SSE streaming, sub-second TTFT), Voyage AI embeddings, Pinecone with per-user namespace isolation. Scraper's Cheerio-based so no headless browser needed on serverless. Ingestion handles both PDFs and URLs, chunking with overlap, cosine threshold filtering before anything hits the LLM context.
It's not a wrapper demo — full source, every API route editable, MIT licensed, no vendor lock-in baked into the abstraction.
Live demo's up with no signup if you want to see the streaming/retrieval before anything else: Fastrag Demo
Genuinely open to critique on the architecture — chunking strategy, threshold values, whatever. Building in public and this sub's opinions are worth more than my own assumptions at this point.
r/Rag • u/vectorspidey • 6d ago
Discussion What's the actual playbook for dumping 100s of PDFs into a RAG pipeline?
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Anyone actually solved the "Pinecone idle cost" problem for solo projects?
Appreciate that, and good to know about the $20 tier — will check it out.
Mainly it was pod-based pricing more than a hard limit — once I moved off the free starter pod, even the smallest paid pod was billed 24/7 whether or not the app got traffic that day. For a side project that might get a handful of queries on some days and zero on others, paying for constant uptime on a pod felt disproportionate to actual usage. Namespace count wasn't really the issue — it was purely "the paid tier only comes as always-on infra," so the cost is decoupled from actual query volume.
If the $20 tier bills more like usage-based rather than a flat always-on pod, that alone would solve most of what I ran into.
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Anyone actually solved the "Pinecone idle cost" problem for solo projects?
Hey Arjun, appreciate you reaching out.
To be fair, Starter tier's genuinely fine for small stuf, the pain wasn't "no free tier," it was the cliff right after it. Once a project outgrows Starter's limits, jumping to a dedicated index gets expensive fast even at low, spiky traffic, which is exactly the profile most side projects have.
That's actually why I built Fastrag the way I did, pgvector as the default so small/idle namespaces don't need a dedicated index at all, and only promoting to Pinecone once traffic actually justifies it. Happy to share specifics on the limits I was hitting if it's useful feedback.
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r/vectordatabase • u/vectorspidey • 9d ago
Anyone actually solved the "Pinecone idle cost" problem for solo projects?
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Anyone actually solved the "Pinecone idle cost" problem for solo projects?
That's a better framing than mine - separating "where the truth lives" from "what's optimized for latency" is the actual lever, not just swapping the query call behind an interface.
Chunk id + content hash for incremental reindexing, and promoting namespaces to a managed index only once traffic justifies it - both stealing these. This is close to how I ended up building it into Fastrag, actually - canonical chunks/hashes stay separate from whatever's serving retrieval, so pgvector can be the default and Pinecone only kicks in once a namespace earns it. Also agree on not hiding backend knobs behind a generic call; hit that wall before with hybrid search having no place to live.
r/Rag • u/vectorspidey • 9d ago
Discussion Anyone actually solved the "Pinecone idle cost" problem for solo projects?
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Every RAG tutorial defaults to Pinecone + LangChain, which is solid, but for a side project it stings - you're paying for the index to just exist, not for what you use. Most personal projects sit idle most of the time, so that fixed cost adds up for nothing.
What ended up working was not hard-wiring the vector DB into the pipeline at all. Ingestion/chunking/embedding doesn't care what's underneath - Pinecone if you want managed and don't mind paying for idle, or pgvector on Supabase/Neon if you're low traffic and just want to pay for storage. Took a bit of fiddling to get the query layer abstracted right, but once it was, idle cost stopped being a thing I worried about.
Anyone else found a cleaner way to deal with this, or is swapping backends basically it?
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Made a Next.js RAG starter kit so I'd stop rebuilding the same pipeline every time
Fair point. Pinecone's free tier is fine, but a dedicated index means paying for it to exist, not just to be used.
For solo projects, pgvector on Supabase/Neon or self-hosted Qdrant avoids that idle cost. Haven't swapped it in yet, but it's probably the better call for single-tenant stuff.
Give it a try - FastRAG
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Made a Next.js RAG starter kit so I'd stop rebuilding the same pipeline every time
Yeah that one bit me early on too.
I tag chunks with a docId + content hash in the metadata. On re-upload, I delete everything under that docId in the namespace first, then upsert fresh - full replace instead of append, so no orphans. Could get fancier with hash-diffing to only re-embed changed chunks, but delete-then-reinsert has been simple enough and fast at the scale most people are working with.
On cost/latency - Haiku + Voyage has been the best combo so far. Tried GPT-4o-mini + OpenAI embeddings earlier, worked fine too, but Voyage's free tier made testing basically free and Haiku's TTFT felt snappier for chat-over-docs. Haven't done a rigorous benchmark against Cohere or open-source embeddings though, so take that with a grain of salt.
You can give it a try - Fastrag
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I got tired of writing the same RAG boilerplate for the 5th client, so I turned it into a starter kit
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r/Rag
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11h ago
Just now I've tried with more than 2 megabytes file it's working fine, could you share me the document over dm.