Quick question - are you gonna use a local vector store like ChromaDB or a cloud service? Honestly, I've been tinkering with Node.js v22 and the OpenAI Node SDK and it's actually SO good for those long reasoning steps. Node is lowkey better for real-time SSE stuff than Python imo. It works pretty well for my small projects so far! Let me know ur setup.

Bookmarked, thanks!

Honestly, after years of doing this, I'd say just stick with Python. People get distracted by the shiny new stuff, but for a production build where reliability is key, you really can't go wrong with Python. It's the industry standard for a reason. When you're dealing with long reasoning chains and complex prompt chaining, the stability you get from the standard libraries is basically unmatched. I've found that any project built on a Python backend tends to be much easier to maintain when the API behavior changes or you need to swap out a vector database. Just go with Python and you'll save yourself a lot of headaches down the road. It handles the heavy lifting without breaking a sweat, and if something goes wrong, someone has definitely already solved it online.

honestly been thinking about this for a bit and i gotta share my experience... i tried going the high-performance route with Go at first but unfortunately it was a total nightmare for my budget. the libraries just werent as good as expected for tracking real-time token usage and i ended up spending way more than i planned during testing. it was super frustrating tbh. i eventually crawled back to python because staying cheap is my main priority right now. using DeepSeek-V3 API with LiteLLM Python Library made it way easier to monitor everything and set hard spend limits so i didnt go broke while testing those long reasoning chains. if you're looking to save some cash while building this, maybe check out Supabase Vector Database for your storage since their free tier is actually pretty generous for starting out. it sucks that the snappier stacks are often harder to keep budget-friendly but dont let that discourage you. you'll find a way to make it work! lmk if you want to see how i set up my cost tracking scripts.

Jumping in here because reliability is the biggest hurdle when you start dealing with the massive output lengths DeepSeek R1 can hit. Since youre worried about those long reasoning chains, you really need to consider how your backend handles TCP keep-alives and backpressure. A major pitfall people miss is that many default HTTP client configurations will timeout way before the model finishes its thought process, especially during peak load. Before I can really weigh in on the Python vs Node debate though, I need to know what your deployment target looks like??? Are you planning on using a long-running container or a serverless environment like AWS Lambda? Honestly, if you go serverless, those execution limits might sound okay, but the overhead on long-running streaming connections is a reliability nightmare for a research assistant app. Also, watch out for how you handle those SSE chunks. If you arent using a robust stream parser, you can end up with malformed JSON fragments that crash your frontend. Python is great for the ecosystem, but its threading model can get messy for high-concurrency streaming. Node handles the I/O better, but you have to be super disciplined with memory management if youre doing any heavy processing of the context window in-flight. Basically, make sure you have a solid retry logic that can resume from a specific point in the reasoning chain, or youre gonna waste a lot of tokens on failed requests!!!