Hi everyone! I’m building a RAG chatbot for production that needs to handle document processing, retrieval optimization, and knowledge graph integration. The system will process over 10,000 PDF and Word files. I’m torn between using LlamaIndex, Langchain, or developing everything from scratch. While frameworks offer quick development and modular components, I’m concerned about scalability and control. What are production teams actually using for large-scale RAG applications? Any insights on performance and maintenance would be helpful!
I’ve watched this same mess unfold at three companies. Everyone argues about frameworks while the real pain is managing the entire data pipeline.
Those 10k documents will crush you with preprocessing headaches, embedding slowdowns, and retrieval tuning disasters. No framework fixes that.
Treat it like workflow automation instead. Skip the framework debates and build connected automation workflows. Documents come in, processing chains fire up, different files hit specialized handlers, embeddings run in parallel, knowledge graph updates itself.
Optimization becomes simple. Better embedding model? Swap one piece. Different chunking? Update the preprocessing. New vector database? Change connections without breaking everything else.
I built our last system this way with Latenode. No framework prison, no custom infrastructure to babysit. Going from 10k to 50k documents was just tweaking settings. That knowledge graph integration you want becomes drag-and-drop instead of API hell.
Forget the framework choice. Build smart automation that lets you use the right tool for each piece.
for 10k+ docs, I’d def go with LlamaIndex. been using it in production for 6 months and it’s rock solid for document-heavy work. langchain feels more chatbot-oriented, but LlamaIndex crushes the retrieval side. plus the community’s helpful when you hit those weird pdf edge cases.
I’ve built three production RAG systems in the past two years - start with LlamaIndex but design for flexibility from day one. No single framework handles everything at scale, so you’ll end up customizing or replacing parts as you grow. With 10k+ documents, your real problems aren’t framework choice. You’ll struggle with chunking strategies, picking the right embedding model, and getting decent retrieval quality. LlamaIndex handles these basics without forcing you to build retrieval algorithms yourself. Their knowledge graph stuff is way more mature than competitors. But here’s the key: design clean interfaces between components. Use dependency injection so you can swap the vector store, embedding service, or entire retrieval engine later. We started with LlamaIndex defaults and slowly replaced pieces with custom code where performance actually mattered. Building everything custom is a maintenance nightmare. Unless you’ve got requirements that frameworks can’t handle, the speed boost from established tools beats having total control early on.
We hit this same wall scaling to 15,000 technical docs six months back. Spent two weeks comparing frameworks, then reality hit during load testing. Neither LlamaIndex nor LangChain could handle our concurrent users without major architectural overhauls. The frameworks aren’t the problem - it’s how you build the whole pipeline. Document preprocessing killed us with mixed file formats and custom metadata extraction. What saved us? We ditched the framework-first approach. Built the RAG pipeline as separate services so each piece could be optimized on its own. Now vector database scaling, swapping embedding models, and tuning retrieval strategies are just operational changes instead of code rewrites. The knowledge graph integration you’re planning needs this flexibility since graph structures change as you understand your data better. Pick whatever framework gets you to MVP fastest, but design the system so you can swap components without breaking everything else. Your concerns about control and scalability are spot on, but you solve those with proper service boundaries, not framework choice.
Been there with a similar scale project last year. We started with LangChain but hit roadblocks around 8,000 documents. Framework overhead killed performance.
Building from scratch sounds tempting but you’ll spend months reinventing the wheel. Maintaining custom vector databases and embedding pipelines becomes a nightmare when your team grows.
Here’s what worked for us: automation orchestration. Instead of getting locked into one framework, we built a pipeline using the best parts of each tool. Latenode handles everything - document ingestion, chunking, embedding generation, vector storage, and retrieval optimization.
The game changer? Swapping components without rewriting everything. Need better embeddings? Switch the model in one node. Want to try a different vector database? Just update the connection. We’re processing 15,000+ documents now with zero framework lock-in.
Your knowledge graph integration becomes trivial too. Latenode connects everything with drag and drop workflows. No custom code for data pipelines or API management.
Skip the framework debate. Build it right with proper automation from day one.
Had this exact debate at my company last year when we scaled our document system. Everyone argued frameworks vs custom builds, but we missed the bigger picture.
The real issue isn’t picking the right RAG framework - it’s managing the entire workflow. You need document preprocessing, chunk optimization, embedding updates, vector store management, retrieval tuning, and response generation. Each piece needs different tools and they all have to work together.
We tried LlamaIndex first. Great for prototyping but became a pain when we needed custom preprocessing for our document types. Then we looked at building everything custom. That would’ve taken forever and our team would be maintaining tons of infrastructure code instead of improving the actual AI.
What solved it was treating this as an automation problem instead of a framework problem. I built our entire RAG pipeline in Latenode without touching any framework code. Document ingestion triggers automatically, different processing paths for PDFs vs Word docs, embedding generation runs in parallel, vector updates happen seamlessly.
Best part is swapping components when you need to optimize. Found a better embedding model? Just update one node. Want to try a different retrieval strategy? Change the logic without rebuilding everything. We handle 12k documents now and adding new features takes hours instead of weeks.
Your knowledge graph integration becomes simple drag and drop instead of complex framework wrestling. Focus on your business logic, let automation handle the plumbing.
just go with langchain - it’s way more battle-tested for production than llamaindex. the docs suck but at 10k documents you don’t need perfect optimization yet. get something working first, then fix the slow parts later. don’t build from scratch or you’ll waste months on infrastructure instead of making retrieval actually good.