{"slug": "perforvisor-a-5-review-said-needs-to-be-more-clean-open-models-caught-it", "title": "Perforvisor: a 5★ review said \"Needs to be more clean.\" Open models caught it.", "summary": "A developer built Perforvisor, an open-source review-analysis tool that uses a self-hosted Qwen3-4B model on vLLM to extract claims from customer reviews, merge them into a knowledge graph, and surface a weekly list of fixes for single-location businesses. Testing on a live café in Indore, the tool flagged a five-star review reading \"Needs to be more clean\" as a complaint, with a Trust page marking it \"Star rating contradicts the text\" so it cannot inflate the praise list or advice. The stack runs on Vercel with LangGraph.js orchestration, MongoDB Atlas for records and graph data, and Tiger Data for pgvector and time-series storage, with both models sharing one L4 GPU on a GKE Spot node.", "body_md": "*This is a submission for the [Hacktoberfest Open-Source AI Challenge: Week 2 - Side Hustle](https://dev.to/challenges/hf26).*\n\nA small restaurant owner has hundreds of reviews and no time to read them. The star average says 4.1. It doesn't say what to fix this week.\n\nI built **Perforvisor** to answer that one question: *what should I fix first?* It reads every review with a small open-weight model, turns what customers say into a knowledge graph and gives the owner a verdict, up to three concrete actions and the reviews behind each one.\n\nWhile testing on a real café in Indore (Indian Coffee House, DAVV, synced live from Google Maps), Perforvisor flagged this one:\n\n★★★★★ \"Needs to be more clean.\"\n\nFive stars. One complaint. A star-average dashboard counts that as a happy customer.\n\nPerforvisor reads the words, not the stars. The model read the text as negative and the Trust page flagged it as *\"Star rating contradicts the text\"*. Because it's flagged, it can't inflate the praise list or the advice either.\n\nI had tested this on purpose earlier: I added a 5★ review that was really a complaint and asked *\"What do users complain about most?\"* The answer counted it as a complaint, with the review cited. Seeing the same thing happen on a real review, unprompted, is when I knew the idea worked.\n\nConnect a business (search Google Maps, load a sample or paste reviews from any platform, one per line with an optional rating) and Perforvisor:\n\nThe buyer is the owner of one location: a restaurant, café, salon, clinic or shop with a few hundred reviews. They know reviews matter. They don't read all of them every week and reputation-management suites are priced and built for chains.\n\nWhat they get for the money is a weekly to-do list grounded in their own customers' words, plus an early warning when something changes.\n\n**Pricing (a proposal, not live yet):**\n\n| Plan | Price | What's in it | \n|---|---|---|\n| Free | $0 | One business, samples or pasted reviews, manual sync | \n| Owner | $9/month (around ₹499 in India) | Daily sync from Google Maps, up to 3 competitors, Trust page, Ask | \n\n*Vercel runs the app, the API and background syncs. Two vLLM servers share one L4 GPU on GKE. MongoDB Atlas holds the records and the knowledge graph; Tiger Data holds vectors and time series.*\n\n| Part | What runs it | \n|---|---|\n| Review extraction, claim-merge judge, advice, answers | **Qwen3-4B** (official AWQ 4-bit build) on**vLLM** , self-hosted, thinking mode off | \n| Embeddings for retrieval and claim matching | **EmbeddingGemma 2** on vLLM, self-hosted, 768 dimensions | \n| Both models | One NVIDIA **L4** on a**GKE** Spot node, split with GPU time-sharing (60% / 15% of the 24 GB) | \n| Orchestration | **LangGraph.js** : an analyse graph (extract → aggregate → summarise) and an ask graph (index → retrieve → answer) | \n| Records and knowledge graph | **MongoDB Atlas** | \n| Vector search and time series | **Tiger Data** : pgvector plus a TimescaleDB hypertable | \n| Google Maps search and reviews | **SerpApi** , behind a cache and a monthly budget guard | \n| App, API, background sync | **Vercel** : Vite frontend, Fastify backend, Vercel Queues + a jobs service, daily Vercel Cron | \n\nOne sync of a business goes: fetch new reviews → store in MongoDB and embed into Tiger Data → extract each review → merge claims into the graph → rebuild the report. The app streams progress the whole way.\n\n**1. A claim takes its aspect's sentiment, not the review's.** \"Taste was fine\" inside an angry review about the wait isn't a complaint about taste. Each claim is tagged with an aspect and it inherits the sentiment the model gave *that* aspect. The review's overall sentiment is only the fallback.\n\n**2. Claim merging is two-stage, because one threshold can't work.** I measured EmbeddingGemma 2 on pairs of claims: same-meaning pairs scored 0.867 to 0.957, different-meaning pairs 0.772 to 0.912. Those ranges overlap, so any single cut-off either merges different things or misses real matches. So:\n\nThe cheap path handles the clear cases and the model only judges the grey zone. In testing, the judge leaned towards keeping claims apart, which is the safer mistake for counts.\n\n**3. I removed the reranker after measuring it.** My first design had a small LLM reranker. On my labelled set it *lowered* rerank score from 0.86 to 0.63. It scored \"overheated on a flight\" 2/10 for the question \"Does it overheat?\". Now retrieval is plain cosine top-8 over EmbeddingGemma 2 and Qwen3-4B, while writing the answer, cites only the reviews that actually support it. Citations are checked against what the model was shown, so it can't cite a review it never saw.\n\n| Ranking (12 labelled questions) | Reranked score | MRR | Similarity based score | \n|---|---|---|---|\n| Keyword overlap (my first version) | 0.56 | 0.69 | 0.60 | \n| Cosine similarity, EmbeddingGemma 2 | 0.86 | 0.89 | 0.98 | \n\nAnswers: citation precision 0.79, recall 0.79, median 1.0 s per answer (measured on my laptop's RTX 4060). It's a small test set, so I treat it as a direction, not a benchmark. You can rerun it with `pnpm --filter @perforvisor/backend eval:retrieval`.\n\n**4. Only extreme mismatches count as suspicious.** On real reviews, 4★ with \"the gravy could be better\" is normal. So a review is flagged only at the extremes: 5★ with negative text, or 1★ with positive text. Copy-paste detection compares word 3-grams (Jaccard ≥ 0.7) and skips reviews under 5 words, because lots of genuine people just write \"Nice place\".\n\n**5. Code decides the facts, the model writes the words.** Small models mixed up strengths and weaknesses when I asked them to list both. Now strengths and weaknesses come straight from the classified claims in code and the model only writes the verdict and actions, with the rule \"never suggest fixing something customers praise\". Thinking mode is off for every call: each one is schema-constrained JSON and thinking would multiply latency for little gain.\n\n**Serverless time limits.** A first sync reads dozens of reviews with a model and a Vercel function stops at 300 s. Each run stops starting new reviews at 240 s and queues a continuation. Every step skips finished work (analysed reviews, reviews already in the graph), so the next run picks up exactly where the last one stopped. Failed runs retry with backoff and an idempotency key per business per minute collapses double-clicks and overlapping crons into one sync.\n\n**EmbeddingGemma 2 crashed vLLM on the L4.** vLLM sizes its prefill attention tiles for A100/H100 shared memory. The L4 has about 99 KB per block, so the embedding server died at startup. I patched the kernel file and mount it into the container with a ConfigMap until it's fixed upstream.\n\n**The cost has to be flat, because the product re-reads everything.** Every review gets extracted, every new review gets embedded, every grey-zone claim pair gets a judge call and every question gets an answer. With per-token pricing, each of those is a line on the bill and the bill grows with every owner and every sync. With my own models, the GPU is a fixed cost that every business shares. The only thing left to do for me is to make sure that models are available and can handle requests from the growing clientele.\n\nHere is the real arithmetic, from public prices (estimates, October 2026, us-central1):\n\n| Cost | Price | Per month | \n|---|---|---|\n| L4 GPU, `g2-standard-4` Spot | $0.423/hour | ~$309 if always on; I scale it to zero when I'm not using it | \n| GKE load balancer (one forwarding rule) | $0.025/hour | ~$18 | \n| SerpApi, daily re-sync (1 review page/day) | $25 per 1,000 searches (Starter plan) | ~$0.75 per business | \n\nSo at the proposed $9/month, even an always-on GPU and load balancer are covered by about 40 paying owners and each owner after that mostly adds SerpApi cost. The GPU doesn't charge me more when the model reads more. I'll be honest about the other side: with only a few businesses, a cheap hosted API would cost less than an idle GPU. The open setup wins once the volume is steady and it keeps the cost predictable either way.\n\n**Small models are enough when the job is narrow.** Pulling aspects, sentiment and claims out of one review is a well-defined task. A 4B model with schema-constrained output does it and a 0.74B embedding model handles retrieval and claim matching. Both fit on one 24 GB GPU with room to spare.\n\n**I control the knobs.** I could switch Qwen3's thinking mode off, pin exact model versions, patch vLLM's kernel for my GPU and use EmbeddingGemma 2's task prefixes and Matryoshka truncation. Chat, extraction and embeddings are separate roles with their own URL and model name, so swapping the extraction model for a fine-tuned one later is a config change.\n\n**No model provider sees the data.** Reviews and owners' questions go to models running on infrastructure I control. No third-party model provider gets them and no one can deprecate my model out from under me.\n\n**EmbeddingGemma 2** is the retrieval and matching layer, served with vLLM on GKE (Google Cloud):\n\n`task: question answering | query:` for questions, `title: … | text:` for reviews and `task: sentence similarity` for claim-to-claim comparison. Vectors are 768-d, with Matryoshka truncation built in for smaller sizes.\nAtlas is the source of truth and the knowledge graph behind an app built on open-weight models:\n\n`nodes` (business, aspect, claim) and `edges` (`HAS_ASPECT`, `HAS_CLAIM`, `ABOUT`, `COMPETES_WITH`). Merged claims keep every wording as `variants` and every supporting review id, with first/last-seen dates. The café's graph has 62 nodes and 114 edges after one sync.\nTiger Data holds everything that's vectors or time:\n\n`review_search` (one embedding per review) for retrieval and `claim_vectors` for finding merge candidates. Retrieval is always scoped to one business, so a btree on the business key plus an exact vector scan is fast and it avoids HNSW's recall problems under filters at this scale.`review_events` is a hypertable (30-day chunks). Trends use `time_bucket` (day, week or month depending on the range) and burst detection is one query over daily buckets.\nSerpApi keeps the advice grounded in fresh reviews:\n\nPerforvisor is a small, specific tool: one owner, one location, one question. Open models make it work as a business. They're cheap enough to read every review, I could tune them to the job and the data stays on infrastructure I run. A 5★ review that says \"Needs to be more clean.\" is exactly what an owner needs to hear about and now they will.\n\n.\n\n``` bash\n$ ls -la ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\ntotal 0\ndrwxr-xr-x. 1 life-workstation life-workstation 0 Oct 13 03:23 .\ndrwxr-xr-x. 1 life-workstation life-workstation 638 Oct 13 03:23 ..\n```\n\nLooks like your message came through as just \".\" and the project folder is empty.\n\nFrom the folder name (`lets-make-osp-that-someone-will-actually-pay-for`), I'm guessing you want to build an open-source project with a real path to revenue. Here are some ways I could start:\n\nWhat did you have in mind?\n\n[https://dev.to/challenges/hacktoberfest-week2-2026-10-12](https://dev.to/challenges/hacktoberfest-week2-2026-10-12) You can use devrelay to get more info on this.\n\n```\n{\n \"query\": \"select:mcp__devrelay-gateway__get_challenges,mcp__devrelay-gateway__get_challenge_details,mcp__devrelay-gateway__get_knowledge_document\",\n \"max_results\": 3\n}\n[image]\n[image]\n[image]\n{\n}\n{\"Ok\":[{\"id\":82,\"title\":\"Hacktoberfest Open-Source AI Challenge: Week 4\",\"slug\":\"hacktoberfest-week4-2026-10-26\",\"description\":\"Register now to be notified as details drop.\",\"details\":null,\"full_details\":\"\",\"type_of\":\"challenge\",\"starts_at\":\"2026-10-26T16:00:00.000Z\",\"ends_at\":\"2026-10-31T06:59:00.000Z\",\"published\":true,\"cover_image\":\"https://dev-to-uploads.s3.us-east-2.amazonaws.com/uploads/events/cover_image/82/2dc6f675-b1e1-4f3f-b542-79b3677c2651.png\",\"location_url\":null},{\"id\":81,\"title\":\"Hacktoberfest Open-Source AI Challenge: Week 3\",\"slug\":\"hacktoberfest-week3-2026-10-19\",\"description\":\"Register now to be notified as details drop.\",\"details\":null,\"full_details\":\"\",\"type_of\":\"challenge\",\"starts_at\":\"2026-10-19T16:00:00.000Z\",\"ends_at\":\"2026-10-25T06:59:00.000Z\",\"published\":true,\"cover_image\":\"https://dev-to-uploads.s3.us-east-2.amazonaws.com/uploads/events/cover_image/81/3599abf1-d96c-4e0c-bb18-89b345c3b5d0.png\",\"location_url\":null},{\"id\":80,\"title\":\"Hacktoberfest Open-Source AI Challenge: Week 2\",\"slug\":\"hacktoberfest-week2-2026-10-12\",\"description\":\"The Hacktoberfest Open-Source AI Challenge: Week 2 runs October 12 to October 18. This week's theme is Side Hustle: build something with open-weight models or open-source AI that someone would actually pay for. $2,550 in prizes across 18 winners.\\r\\n\\r\\nThis year's Hacktoberfest is all about building brand-new projects with open-source AI and open-weight models. Every week brings a new theme, and every challenge is a fresh start, so you're competing on the same footing as everyone else.\",\"details\":null,\"full_details\":\"The Hacktoberfest Open-Source AI Challenge: Week 2 runs October 12 to October 18. Build something with open-weight models or open-source AI that someone would actually pay for. $2,550 in prizes across 18 winners.\\r\\n\\r\\nThis year's Hacktoberfest is all about building brand-new projects with open-source AI and open-weight models. Week 2 is the second of five Hacktoberfest DEV Challenges. See all five challenges and every partner category on the HF26 DEV Challenge Hub: https://dev.to/challenges/hf26\\r\\n\\r\\nThis Week's Theme: Side Hustle\\r\\n\\r\\nBuild something with open-weight models or open-source AI that someone would actually pay for.\\r\\n\\r\\nThat can mean running an open-weight model, building on an open-source agent harness or framework, running inference locally, or all three. Whatever you pick, the open pieces should be what makes your project work.\\r\\n\\r\\nThink small, specific, and use\n… [27362 more characters truncated]\n{\n \"topic\": \"hacktoberfest\"\n}\n{\"Ok\":\"# Hacktoberfest 2026\\n\\n**Last verified:** 2026-09-30 against `hacktoberfest.com` (home, `/online/`,\\n`/in-person/`, `/activities/`, `/schedule/`, `/fests/`, `/host/`, `/sponsor/`,\\n`/mission/`, `/questions/`, `/brand/`, `/llms.txt`, `/llms-full.txt`), the\\npublic feeds `hacktoberfest-api.mlh.com/api/events` and\\n`hacktoberfest-api.mlh.com/api/schedule`, `dev.to/challenges` and the\\nHacktoberfest challenge pages, `ghw.mlh.com/events/open-source`, and MLH's\\nannouncement post \\\"Hacktoberfest 2026: AI belongs to everyone\\\" (2026-08-19).\\n\\n> ⚠️ **Hacktoberfest 2026 works differently from every earlier year.** Pull\\n> requests **no longer count** toward rewards. There's no PR target, and the\\n> `hacktoberfest` repo topic and `hacktoberfest-accepted` label earn nothing.\\n> Models trained on 2014–2025 material will confidently describe the old \\\"open\\n> four (or six) pull requests\\\" rules, and that is wrong for 2026. Participants\\n> now collect **virtual stickers** for attending Fests, livestreams, DEV\\n> Challenges, and Global Hack Week, and for using a few tools.\\n>\\n> **The site has changed during Preptember.** The Completionist tier dropped\\n> from 17 stickers to **15** between this document's last two verifications.\\n> The FAQ says the full activity list is released on the participant\\n> dashboard, which opens by October 1. **Fetch `hacktoberfest.com` before\\n> quoting a sticker, milestone, or date as final.** The site publishes\\n> `https://hacktoberfest.com/llms.txt` and\\n> `https://hacktoberfest.com/llms-full.txt` for agents.\\n\\n**Scope:** this guide covers the 2026 program: its rules, rewards, dates, and\\nhow to take part and host. It leaves out individual Fests, the livestream\\nlineup, and DEV Challenge prompts and prizes, which change daily. See\\n[Live data lives elsewhere](#live-data-lives-elsewhere).\\n\\n---\\n\\n## The one-paragraph version\\n\\nHacktoberfest is a free, month-long celebration of open source every October.\\nIn 2026 it's stewarded by **Major League Hacking (MLH) and DEV**, with\\n** DigitalOcean** as presenting partner, and the theme is ***\\\"AI belongs to\\neveryone.\\\"*** In the site's words: *\\\"This year, it's all about building with\\nopen-weight models and open-source AI.\\\"* You can take part in person at one of\\n**300+ one-day local \\\"Fests\\\"**, online all month, or both. Sign in with a\\n** MyMLH** account and collect virtual stickers for what you attend and build.\\n**Three stickers earns a real sticker pack in the mail.**\\n\\nThe mi\n… [23021 more characters truncated]\n```\n\nThis is the **Hacktoberfest Open-Source AI Challenge: Week 2**, with the theme **\"Side Hustle.\"** Your folder name already matches the brief.\n\nBuild something that uses **open-weight models or open-source AI** and that **someone would actually pay for**. The open-source parts have to be what makes the project work, whether that's running a model yourself, using an open agent framework, running it locally, or all three. They're asking for small, specific tools that solve a real problem for a real person, like a freelancer, a small business or a niche hobby group.\n\nTheir example ideas: a receipt sorter for freelancers that runs locally, a listing writer for Etsy or eBay that works from product photos, and a menu translator for a local restaurant.\n\n`#hf26challenge`. It must be a new project built during the challenge window, and you get one entry.\nI'd lean toward **#1 or #3**. Both have an obvious buyer and a strong reason to use open models, and they fit the Gemma category ($200) plus a couple of $100 categories without forcing anything.\n\nWant to pick one, or bring your own idea? I can then scaffold the project and plan the build so you have time left for the write-up, since that's what's judged most heavily.\n\nCut the prize but this tie, I realy want to use as many cloud platforms as possible. Not making a locally running app this time\n\n```\n{\n \"query\": \"select:mcp__devrelay-gateway__search_dev_to_semantic\",\n \"max_results\": 1\n}\n[image]\n{\n \"query\": \"serving open-weight LLM in the cloud for a paid SaaS side project\",\n \"per_page\": 6\n}\n{\"Ok\":[{\"id\":4805262,\"title\":\"The Real Post-Mortem: Serving Open LLMs on AWS (SageMaker vLLM vs. Bedrock Custom Models)\",\"description\":\"Running an open-source LLM on AWS sounds straightforward at first. Pick a model, deploy it, send...\",\"tags\":[\"ai\",\"machinelearning\",\"llm\",\"aws\"],\"path\":\"/rahul_r15/the-real-post-mortem-serving-open-llms-on-aws-sagemaker-vllm-vs-bedrock-custom-models-216b\",\"url\":\"https://dev.to/rahul_r15/the-real-post-mortem-serving-open-llms-on-aws-sagemaker-vllm-vs-bedrock-custom-models-216b\",\"score\":1.0},{\"id\":3651259,\"title\":\"What I learned from going 'cloud-optional' with my side projects.\",\"description\":\"My AI is local, my code is hand-rolled: Why I'm falling in love with building small again It was 2...\",\"tags\":[\"cloud\",\"sideprojects\",\"showdev\",\"ai\"],\"path\":\"/rexthony/what-i-learned-from-going-cloud-optional-with-my-side-projects-1af7\",\"url\":\"https://dev.to/rexthony/what-i-learned-from-going-cloud-optional-with-my-side-projects-1af7\",\"score\":1.0},{\"id\":3491819,\"title\":\"The Developer's Guide to Running LLMs Locally: Ollama, Gemma 4, and Why Your Side Projects Don't Need an API Key\",\"description\":\"Every tutorial about building with LLMs starts the same way: \\\"First, get your OpenAI API key.\\\" But...\",\"tags\":[\"ai\",\"llm\",\"sideprojects\",\"tutorial\"],\"path\":\"/kennedyraju55/the-developers-guide-to-running-llms-locally-ollama-gemma-4-and-why-your-side-projects-dont-54oe\",\"url\":\"https://dev.to/kennedyraju55/the-developers-guide-to-running-llms-locally-ollama-gemma-4-and-why-your-side-projects-dont-54oe\",\"score\":1.0},{\"id\":4773770,\"title\":\"The LLM Portability Drill: Redeploy an Open Model on a Second GPU Cloud\",\"description\":\"Access to open weights gives a team the right to operate a model in its own environment. I test...\",\"tags\":[\"ai\",\"cloud\",\"infrastructure\",\"llm\"],\"path\":\"/ihopkins/the-llm-portability-drill-redeploy-an-open-model-on-a-second-gpu-cloud-439g\",\"url\":\"https://dev.to/ihopkins/the-llm-portability-drill-redeploy-an-open-model-on-a-second-gpu-cloud-439g\",\"score\":1.0},{\"id\":4596279,\"title\":\"Comparing Open-Source LLM Gateways in 2026 to Run Enterprise AI at Scale\",\"description\":\"If your applications are already using multiple AI providers, the next problem is usually not model...\",\"tags\":[\"opensource\",\"llm\",\"ai\",\"devops\"],\"path\":\"/elsie-rainee/comparing-open-source-llm-gateways-in-2026-to-run-enterprise-ai-at-scale-4h4p\",\"url\":\"https://dev.to/elsie-rainee/comparing-open-source-llm-gateways-in-2026-to-run-enterprise-ai-at-scale-4h4p\"\n… [428 more characters truncated]\n```\n\nGot it: no more prize talk, and a cloud-hosted app this time. The rules still need the AI to be **open-weight models or open-source AI**, so the plan is to run open models on cloud infrastructure rather than calling a closed API like OpenAI's.\n\nRestaurants, salons and dentists know they should answer every Google review, but they rarely have time. ReplyDesk:\n\n1. Pulls in new reviews every day.\n\n2. Drafts replies in the owner's own voice.\n\n3. Flags angry or urgent reviews.\n\n4. Sends the owner a short weekly briefing they can listen to.\n\nA plausible price is $15–30 a month per location. That's a real market (Birdeye and Podium charge far more), and you could pitch it to an actual local business for the \"first user\" bonus.\n\n**Why open models matter:** at a few dollars a month, closed-API costs would eat the margin, while cheap open-model inference keeps it profitable. You can also fine-tune a small open model on each owner's past replies so the drafts sound like them, and your pricing isn't tied to another company's.\n\n| Layer | Platform | What it does | \n|---|---|---|\n| Model hosting | **DigitalOcean** (serverless inference or a GPU server) | Runs the open-weight model (Gemma or Llama) | \n| Voice fine-tuning | **Tinker** | Trains a small adapter on each owner's past replies | \n| Agent logic | **Mastra** | Agent that sorts reviews (sentiment, urgency) and drafts replies | \n| Memory | **Backboard** | Remembers each business's tone and facts (hours, staff names, policies) | \n| Background jobs | **Temporal Cloud** | Daily review check, draft → owner approval → post, and retries | \n| Review data | **SerpApi** | Pulls Google Maps reviews | \n| Main database | **MongoDB Atlas** | Businesses, reviews, drafts | \n| Analytics | **Tiger Data** (Timescale) | Ratings and sentiment over time for the dashboard | \n| Audio | **ElevenLabs** | Weekly spoken briefing (\"3 new reviews, 1 needs you\") | \n| Monitoring | **Sentry** | Traces each agent run (model calls, tool use, failures) | \n| Web app | **Render** | Hosts the dashboard and API | \n| Domain | **.tech** | replydesk.tech | \n\nThat's 12 platforms, and none of them is there just to be on the list.\n\n**The risk:** with 6 days left, 12 integrations can fail in a lot of places. I'd build it in rings and only add the next ring once the previous one works:\n\n- **Ring 1 (must work):** SerpApi → model on DigitalOcean → MongoDB → web app on Render.\n\n- **Ring 2:** Temporal jobs, Mastra agent, Sentry tracing.\n\n- **Ring 3:** Tinker fine-tuning, Backboard memory, Tiger Data analytics, ElevenLabs briefing.\n\nShould I go with ReplyDesk, or do you want me to pitch a different idea on the same stack? Also, **TypeScript or Python?** Mastra is TypeScript, so I'd go with TypeScript unless you have a strong preference.\n\nFailure is not an issue. I can debug and solve in no time. And I am aware of hosting an AI on a cloud service. I have hosted one on GCP vertex AI and messed everything up tbh. So, hopefully, i will use that expereince to make AI part better at least ;)\n\n``` bash\n$ node -v; npm -v; pnpm -v 2>/dev/null; bun -v 2>/dev/null; git --version; python3 --version; gcloud --version 2>/dev/null | head -1; doctl version 2>/dev/null; docker --version 2>/dev/null\nv20.19.4\n10.8.2\n1.4.2\ngit version 2.55.0\nPython 3.14.6\nDocker version 29.6.2, build 1.fc44\nbash\n$ for p in @mastra/core @temporalio/client mongodb @sentry/node @elevenlabs/elevenlabs-js serpapi next; do echo \"$p: $(npm view $p version engines --json 2>/dev/null | tr -d '\\n' | head -c 200)\"; done\n@mastra/core: { \"version\": \"1.76.0\", \"engines\": { \"node\": \">=22.13.0\" }}\n@temporalio/client: { \"version\": \"1.24.0\", \"engines\": { \"node\": \">= 20.3.0\" }}\nmongodb: { \"version\": \"7.7.0\", \"engines\": { \"node\": \">=20.19.0\" }}\n@sentry/node: { \"version\": \"11.7.0\", \"engines\": { \"node\": \">=20.19.0 <22.0.0 || >=22.12.0 <23.0.0 || >=23.2.0\" }}\n@elevenlabs/elevenlabs-js: { \"version\": \"2.71.0\", \"engines\": { \"node\": \">=18.0.0\" }}\nserpapi: \"2.2.1\"\nnext: { \"version\": \"16.4.0\", \"engines\": { \"node\": \">=20.9.0\" }}\n{\n \"file_path\": \"~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory/user-cloud-builder.md\",\n \"content\": \"---\\nname: user-cloud-builder\\ndescription: User is a confident debugger who wants cloud-heavy builds; has prior (messy) GCP Vertex AI model-hosting experience\\nmetadata:\\n type: user\\n---\\n\\n- Says failure risk isn't a concern: \\\"I can debug and solve in no time\\\". Don't pad plans with risk caveats or \\\"build in rings\\\" hedging.\\n- Has hosted a model on GCP Vertex AI before and \\\"messed everything up\\\"; wants to apply that lesson to do the AI-hosting part better.\\n- For this project, explicitly wants as many cloud platforms as possible and NOT a locally running app.\\n- Doesn't want prize/competition talk foregrounded (\\\"cut the prize bit\\\").\\n\\nRelated: [[hf26-week2-project]]\\n\"\n}\nFile created successfully at: ~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory/user-cloud-builder.md\n{\n \"file_path\": \"~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory/hf26-week2-project.md\",\n \"content\": \"---\\nname: hf26-week2-project\\ndescription: This repo is an entry for DEV Hacktoberfest Open-Source AI Challenge Week 2 (\\\"Side Hustle\\\"), due 2026-10-18 11:59 PM PDT\\nmetadata:\\n type: project\\n---\\n\\nBuilding a cloud-hosted product using open-weight models that someone would pay for, for the DEV challenge (id 80, tag #hf26challenge, submit via DEV post with the Side Hustle template). Deadline 2026-10-18 23:59 PDT. Writing quality is judged most heavily; a real first user is a bonus.\\n\\nProposed idea (2026-10-13): \\\"ReplyDesk\\\" — review-reply agent for small local businesses, built across many cloud platforms (Vertex AI/DigitalOcean for model hosting, Tinker, Mastra, Backboard, Temporal, SerpApi, MongoDB Atlas, Tiger Data, ElevenLabs, Sentry, Render, .tech domain).\\n\\n**Why:** user wants max cloud platforms, no local app — see [[user-cloud-builder]].\\n**How to apply:** keep the open-weight model central to the story; check the user has confirmed the idea/stack before assuming it's final.\\n\"\n}\nFile created successfully at: ~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory/hf26-week2-project.md\nbash\n$ cat > ~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory/MEMORY.md <<'EOF'\n- [User: cloud builder](user-cloud-builder.md) — confident debugger, wants max cloud platforms, prior Vertex AI hosting experience\n- [HF26 Week 2 project](hf26-week2-project.md) — DEV Hacktoberfest \"Side Hustle\" entry, due 2026-10-18; ReplyDesk idea\nEOF\n(Bash completed with no output)\n```\n\nFair enough, I'll drop the caution and build the full thing. Since you've hosted on Vertex AI before, let's add **Google Cloud** to the list. That's one more platform and a chance at a cleaner run on Vertex this time.\n\nThis gives the post a strong \"why open\" section that a closed API can't match: the same weights run on two clouds, and you can switch between them whenever you like. If you remember what went wrong on Vertex last time (quota, endpoint scaling, region, auth?), tell me and I'll plan around it.\n\nThat brings the stack to **13 platforms:** Vertex AI, DigitalOcean, Tinker, Mastra, Backboard, Temporal, SerpApi, MongoDB Atlas, Tiger Data, ElevenLabs, Sentry, Render and a .tech domain.\n\n`fnm` or `nvm`, or do you want to upgrade it yourself?\nI will use node 24. You can set default to 24 now using nvm\n\n``` bash\n$ export NVM_DIR=\"$HOME/.nvm\"; if [ ! -s \"$NVM_DIR/nvm.sh\" ]; then echo \"nvm not installed\"; exit 0; fi; . \"$NVM_DIR/nvm.sh\"; nvm --version; nvm ls 2>&1 | head -20\nphp\n0.40.3\n[0;32m-> v20.19.4[0m\n[0;34m v22.21.1[0m\n[0;34m v22.23.3[0m\n[0;34m v24.21.0[0m\n[0;33m system[0m\n[0;32mdefault[0m [0;90m->[0m [0;32m20[0m ([0;90m->[0m [0;32mv20.19.4[0m)\n[0;31miojs[0m [0;90m->[0m [0;31mN/A[0m [0;37m(default)[0m\n[0;31munstable[0m [0;90m->[0m [0;31mN/A[0m [0;37m(default)[0m\n[0;34mnode[0m [0;90m->[0m [0;34mstable[0m ([0;90m->[0m [0;34mv24.21.0[0m) [0;37m(default)[0m\n[0;34mstable[0m [0;90m->[0m [0;34m24.21[0m ([0;90m->[0m [0;34mv24.21.0[0m) [0;37m(default)[0m\n[1;33mlts/*[0m [0;90m->[0m [1;33mlts/krypton[0m ([0;90m->[0m [0;34mv24.21.0[0m)\n[1;33mlts/argon[0m [0;90m->[0m [0;31mv4.9.1[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/boron[0m [0;90m->[0m [0;31mv6.17.1[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/carbon[0m [0;90m->[0m [0;31mv8.17.0[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/dubnium[0m [0;90m->[0m [0;31mv10.24.1[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/erbium[0m [0;90m->[0m [0;31mv12.22.12[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/fermium[0m [0;90m->[0m [0;31mv14.21.3[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/gallium[0m [0;90m->[0m [0;31mv16.20.2[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/hydrogen[0m [0;90m->[0m [0;31mv18.20.8[0m ([0;90m->[0m [0;31mN/A[0m)\n[1;33mlts/iron[0m [0;90m->[0m [0;31mv20.20.2[0m ([0;90m->[0m [0;31mN/A[0m)\n```\n\nNode 24.21.0 is already installed, so I just need to make it the default.\n\n``` bash\n$ export NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\"; nvm alias default 24 && nvm use default >/dev/null && node -v && npm -v && echo \"24\" > ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/.nvmrc && bash -lc 'node -v'\nphp\n[0;34mdefault[0m [0;90m->[0m [0;34m24[0m ([0;90m->[0m [0;34mv24.21.0[0m)\nv24.21.0\n12.0.2\nv24.21.0\n```\n\nNode 24 is now your default (v24.21.0, with npm 12.0.2). A fresh login shell picks it up, so new terminals will use it. I also added a `.nvmrc` with `24` to the project folder, so running `nvm use` there selects the same version.\n\nYour already-open terminals are still on Node 20 until you run `nvm use 24` or open a new one.\n\nShould I go ahead and scaffold **ReplyDesk** in TypeScript? That's the Next.js dashboard on Render, the Temporal worker, the Mastra agent, and the model router for Vertex AI and DigitalOcean. Or did you want a different idea?\n\nReply desk is a great idea. I actually have a better idea than auto reply. I reserached and came to know that google and amazon exposes apis to get the reveiw and customer feedback data. We can make an app that processes this feedback and review data and our AI processes this data to give insights about how the product is performing. In case of google map, it will be restaurants review, for amazon, a prodcut psoted by the seller will be the one. On a broader level, we can make an app that can connect to any service having review system specially if they expose the feedback or review api for automation and we can actually automate the process of assessment of our restaurants, our grocery shop, product being sold on amazon, flipkart, blinkit like platforms. Even amazon can use this to get customer's rating for a particular product or seller to analyse and review fake products and their delisting, removing fraud sellers. Wow. I am liking it. Its name should be different. Reply desk is good for the original idea but not for something i have in my mind right now.\n\n```\n{\n \"query\": \"select:WebSearch\",\n \"max_results\": 1\n}\n[image]\n{\n \"query\": \"Amazon Selling Partner API Customer Feedback API reviews brand owners\",\n \"mode\": \"standard\"\n}\nWeb search results for query: \"Amazon Selling Partner API Customer Feedback API reviews brand owners\"\n\nLinks: [{\"title\":\"Customer Feedback API\",\"url\":\"https://developer-docs.amazon.com/sp-api/docs/customer-feedback-api\"},{\"title\":\"Customer Feedback API\",\"url\":\"https://developer-docs.amazon.com/sp-api/docs/customer-feedback-api-v2024-06-01-use-case-guide\"},{\"title\":\"Select your preferred language\",\"url\":\"https://developer.amazonservices.com/start/customer-feedback-insights-api\"},{\"title\":\"Select your preferred language\",\"url\":\"https://developer.amazonservices.com/start/customer-feedback-api\"},{\"title\":\"New: Introducing the Customer Feedback API v2024-06-01\",\"url\":\"https://developer-docs.amazon.com/sp-api/changelog/new-introducing-the-customer-feedback-api-v2024-06-01\"},{\"title\":\"New: Introducing the Customer Feedback API v2024-06-01\",\"url\":\"https://developer-docs.amazon/sp-api/lang-us/changelog/new-introducing-the-customer-feedback-api-v2024-06-01\"},{\"title\":\"Selling Partner API BlogVideos\",\"url\":\"https://developer-docs.amazon.com/sp-api-blog/docs/enhancing-user-reviews-and-feedback\"},{\"title\":\"New: Introducing the Customer Feedback API v2024-06-01\",\"url\":\"https://developer-docs.amazon/sp-api/changelog/new-introducing-the-customer-feedback-api-v2024-06-01\"},{\"title\":\"New: Introducing the Customer Feedback API v2024-06-01\",\"url\":\"https://developer-docs.amazon/sp-api/lang-US/changelog/new-introducing-the-customer-feedback-api-v2024-06-01\"},{\"title\":\"new blog post enhancing user reviews and feedback\",\"url\":\"https://developer-docs.amazon.com/sp-api/v2.0/changelog/new-blog-post-enhancing-user-reviews-and-feedback\"}]\n\nHere are the search results for Amazon Selling Partner API Customer Feedback API reviews brand owners:\n\n## Key Information:\n\nThe Selling Partner API for Customer Feedback (Customer Feedback API) provides insights from customer reviews and returns. This API provides the same information as the Product Opportunity Explorer (OX) on Seller Central and Vendor Central.\n\n### Functionality and Features:\n\nReview insights are available at the ASIN and browse node level, while return insights are only available at the browse node level. The Customer Feedback API gives developers programmatic access to customer signals, enabling you to build tools that help sellers understand what customers love, what frustrates them, and where to focus next, whether you're building a product analytics dashboard, a listing optimization tool, or an automated quality monitoring s\n… [489 more characters truncated]\n{\n \"query\": \"Google Business Profile API list reviews accounts.locations.reviews owner\",\n \"mode\": \"standard\"\n}\nWeb search results for query: \"Google Business Profile API list reviews accounts.locations.reviews owner\"\n\nLinks: [{\"title\":\"Google Business Reviews\",\"url\":\"https://docs.upload-post.com/api/google-business-reviews\"},{\"title\":\"truto.one\",\"url\":\"https://truto.one/integrations/detail/googlereviews.md\"},{\"title\":\"truto.one\",\"url\":\"https://truto.one/integrations/detail/googlereviews/\"},{\"title\":\"Google Business Profile एपीआई में\",\"url\":\"https://developers.google.cn/my-business/reference/rest/v4/accounts.locations.reviews/list?hl=hi\"},{\"title\":\"REST Resource: accounts.locations.reviews\",\"url\":\"https://developers.google.com/my-business/reference/rest/v4/accounts.locations.reviews\"},{\"title\":\"For Agents\",\"url\":\"https://jentic.com/apis/googleapis.com/my-business\"},{\"title\":\"REST Resource: accounts.locations.reviews\",\"url\":\"https://developers.google.com/my-business/reference/rest/v4/accounts.locations.reviews?authuser=1\"},{\"title\":\"How to Reply to Google Business Reviews via API\",\"url\":\"https://postproxy.dev/how-to/reply-google-business-reviews-api/\"},{\"title\":\"Google Business Reviews API\",\"url\":\"https://bundle.social/google-business-profile-reviews-api\"}]\n\nBased on the search results, here's what I found about the Google Business Profile API's `accounts.locations.reviews.list` endpoint:\n\n## Overview\n\nThe `accounts.locations.reviews.list` method returns the paginated list of reviews for the specified location.\n\n## Response Structure\n\nA successful response returns a JSON object containing a list of `reviews`, `averageRating`, `totalReviewCount`, and optionally `nextPageToken` for pagination.\n\n## Review Resource\n\nA Review resource has the form `accounts/{accountId}/locations/{locationId}/reviews/{reviewId}` and includes fields like name, reviewId, reviewer object, starRating, comment, createTime, updateTime, and reviewReply.\n\n## Review Data\n\nThe API provides:\n- The author of the review (reviewer object)\n- The star rating of the review\n- The body of the review as plain text with markups (comment)\n- The owner/manager of the location's reply to the review (reviewReply)\n\nThe documentation indicates that authorization requires specific OAuth scopes, but the exact scopes aren't detailed in these search results.\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.\n{\n \"query\": \"Flipkart seller API product reviews ratings endpoint\",\n \"mode\": \"standard\"\n}\nWeb search results for query: \"Flipkart seller API product reviews ratings endpoint\"\n\nLinks: [{\"title\":\"Flipkart API\",\"url\":\"https://www.openwebninja.com/request-apis/flipkart\"},{\"title\":\"docs.anysite.io\",\"url\":\"https://docs.anysite.io/api-reference/flipkart/flipkartproductsreviews\"},{\"title\":\"# Flipkart Scraper - Products, Prices, Specs & Reviews (`scrapesage/flipkart-scraper`) Actor\",\"url\":\"https://apify.com/scrapesage/flipkart-scraper.md\"},{\"title\":\"docs.anysite.io\",\"url\":\"https://docs.anysite.io/api-reference/flipkart/flipkartsellers\"},{\"title\":\"# Flipkart Reviews (`ikmal_suzali_atr/flipkart-reviews`) Actor\",\"url\":\"https://apify.com/ikmal_suzali_atr/flipkart-reviews.md\"},{\"title\":\"www.itechguides.com\",\"url\":\"https://www.itechguides.com/?p=863860\"},{\"title\":\"api.apify.com\",\"url\":\"https://api.apify.com/v2/acts/OKiJ0vq9IJeGhBbzP/builds/NwY9w3qA5F3ciwVel/openapi.json\"},{\"title\":\"# Flipkart Reviews Scraper (`solidcode/flipkart-scraper`) Actor\",\"url\":\"https://apify.com/solidcode/flipkart-scraper.md\"},{\"title\":\"> ## Documentation Index\",\"url\":\"https://docs.anysite.io/api-reference/flipkart/flipkartsellers.md\"},{\"title\":\"# Flipkart Product Scraper (`datascrapers/flipkart-scraper`) Actor\",\"url\":\"https://apify.com/datascrapers/flipkart-scraper.md\"}]\n\nBased on the search results, here's information about Flipkart seller API product reviews and ratings endpoints:\n\n## Official Flipkart Seller API\n\nFor authorized seller integration, use the authenticated Marketplace Seller API, whose documented product-search operation can return seller-listing names, identifiers, prices, dimensions, and weight—but the response reference does not list ratings. Ratings are not listed in the documented response fields, and the endpoint documentation does not establish rating access.\n\n## Third-Party API Solutions\n\nIf you need product reviews and ratings data, there are third-party APIs available:\n\n**Anysite.io API:**\nThe `/flipkart/products/reviews` endpoint is a POST request that takes a Flipkart product item ID or URL and returns reviews with rating, title, text, author, date, location, certified-buyer status, helpful counts, and images.\n\nThere's also a `/flipkart/sellers` endpoint that returns seller information including name, rating, years with Flipkart, products sold, quality scores, and service-quality ratings.\n\n**General Flipkart Data APIs:**\nA reviews endpoint returns individual reviews with rating, title, text, and certified-buyer status.\n\n## Important Note\n\nFlipkart's publis\n… [297 more characters truncated]\n```\n\nThat's a stronger product than auto-reply. Review insights are something a business keeps paying for every month, while auto-reply is a feature competitors can copy easily. Before we lock the plan, here's what I checked on your API research, because it changes the design.\n\n| Platform | Official API? | What you actually get | \n|---|---|---|\n| **Google Maps (Business Profile API)** | Yes | Full review text, star rating, reviewer and the owner's reply. **Only for locations the user owns or manages** , and Google requires you to apply for API access. | \n| **Google Maps (any place)** | Partly | The Places API returns at most 5 reviews per place. SerpApi's Google Maps Reviews endpoint returns the full list, which is useful for comparing against competitors. | \n| **Amazon (Customer Feedback API)** | Yes | **Aggregated insights, not raw reviews:** topics customers mention, sentiment and trends per product, refreshed weekly. It requires the Brand Analytics role, which effectively means brand-registered sellers, and covers only the US, UK, France, Italy, Germany, Spain and Japan.**India is not on that list.** | \n| **Flipkart** | No reviews | The official seller API doesn't return ratings. Third-party scrapers exist, but Flipkart's terms forbid scraping. | \n| **Blinkit, Swiggy, Zomato** | None I know of | Data would have to come from the seller's own exports. | \n\nSo the \"connect to any review system\" idea works, but the app needs a **connector layer with three kinds of source**:\n\n1. **Official APIs:** Google Business Profile and the Amazon Customer Feedback API.\n\n2. **Data providers:** SerpApi for public Google Maps and Amazon reviews, mainly to compare against competitors.\n\n3. **Universal import:** CSV upload and a webhook for any platform without an API, such as Flipkart and Blinkit seller exports, Shopify or survey tools.\n\nAll three feed into **one standard review format**, and the AI works only on that format. That's what lets you say \"any platform\" honestly.\n\nBuilding a detector that Amazon itself would buy is too much for this week, but **fake-review detection for the business owner** is buildable and a strong selling point. It would flag:\n\n- **Bursts:** 40 five-star reviews in two days.\n\n- **Coordinated attacks:** a sudden run of one-star reviews, possibly from a competitor.\n\n- **Copy-paste reviews:** near-identical text across reviews.\n\n- **Rating–text mismatches:** a one-star rating with praising text, or the reverse.\n\nTiger Data (Timescale) fits naturally here because burst detection is a time-series problem. Marketplace-scale fraud detection, such as getting sellers delisted, works as the \"where this goes next\" section of your post.\n\nTwo decisions for you:\n\nSources:\n\n- [Amazon SP-API: Customer Feedback API](https://developer-docs.amazon.com/sp-api/docs/customer-feedback-api)\n\n- [Customer Feedback API v2024-06-01 use case guide](https://developer-docs.amazon.com/sp-api/docs/customer-feedback-api-v2024-06-01-use-case-guide)\n\n- [Google Business Profile API: accounts.locations.reviews](https://developers.google.com/my-business/reference/rest/v4/accounts.locations.reviews)\n\n- [Anysite Flipkart API docs (official seller API lacks ratings; Flipkart terms on scraping)](https://docs.anysite.io/api-reference/flipkart/flipkartproductsreviews)\n\n```\n{\n \"questions\": [\n {\n \"question\": \"What should the product be called?\",\n \"header\": \"Name\",\n \"multiSelect\": false,\n \"options\": [\n {\n \"label\": \"Verdikt\",\n \"description\": \"The verdict on your product or shop, taken from every platform. Short, and works as a .tech domain (verdikt.tech).\"\n },\n {\n \"label\": \"Chorus\",\n \"description\": \"Every customer voice, from every platform, in one place. Friendly and easy to remember.\"\n },\n {\n \"label\": \"ReviewRadar\",\n \"description\": \"Says exactly what it does. Fits the anomaly and fraud-detection angle.\"\n },\n {\n \"label\": \"Sift\",\n \"description\": \"Sifts real feedback from noise and fake reviews. Minimal and modern.\"\n }\n ]\n },\n {\n \"question\": \"Which connectors should the first version ship with? (The universal CSV/webhook import is always included.)\",\n \"header\": \"Connectors\",\n \"multiSelect\": true,\n \"options\": [\n {\n \"label\": \"Google Maps via SerpApi\",\n \"description\": \"Public reviews for any restaurant or shop. Works for the demo with no approval wait.\"\n },\n {\n \"label\": \"Google Business Profile\",\n \"description\": \"The official owner API. Needs Google to approve API access, which may not arrive by Oct 18.\"\n },\n {\n \"label\": \"Amazon via SerpApi\",\n \"description\": \"Public product reviews for any listing (ASIN). Good for the demo.\"\n },\n {\n \"label\": \"Amazon Customer Feedback API\",\n \"description\": \"The official seller API. Needs a brand-registered seller account in a supported marketplace.\"\n }\n ]\n }\n ]\n}\nThe user answered: \"What should the product be called?\"=\"I feel more like, its will act as an advisor. Isnt it? I also want to have a consumer facing interface too. where consumers can ask about anything that exists. For this reason, I need a graph DB as well to store a lot of unique information blocks. Also, I am dropping the idea for auto reply from this. For now, lets list down the requriements. Start by lsitign down, what can be done within 3 days first.\", \"Which connectors should the first version ship with? (The universal CSV/webhook import is always included.)\"=\"1 and 3 sounds great. serpAPI can acatually help in extending this from amazon and google to other products as well.\". Read the answers carefully — they may request clarification, changes, or that you not proceed — and follow what they actually say.\n{\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/docs/REQUIREMENTS.md\",\n \"content\": \"# Requirements\\n\\nWorking name: TBD (advisor-themed). Challenge deadline: Sun 2026-10-18, 11:59 PM PDT.\\n\\n## Product in one paragraph\\n\\nAn AI advisor built on open-weight models that reads customer reviews and feedback from any platform with reviews (Google Maps and Amazon first, via SerpApi), turns them into a knowledge graph, and gives advice to two kinds of user:\\n\\n- **Business owners and sellers**: how is my restaurant, shop or product actually doing, what are customers praising or complaining about, what is changing, which reviews look fake, and what should I fix first?\\n- **Consumers**: ask about anything that has reviews (\\\"Is the biryani at X worth it?\\\", \\\"Do people say this power bank overheats?\\\", \\\"Are this seller's 5-star reviews real?\\\") and get an answer that cites the evidence.\\n\\nOut of scope: auto-replying to reviews.\\n\\n## Users\\n\\n| ID | User | Main need |\\n|---|---|---|\\n| U1 | Business owner or seller (restaurant, grocery shop, Amazon seller) | Ongoing insight and advice about their own listings, and how they compare with competitors |\\n| U2 | Consumer | Trustworthy answers about any place or product before buying or visiting |\\n\\n## Scope tiers\\n\\n- **P0, first 3 days (Oct 14–16):** a working end-to-end product, deployed.\\n- **P1, days 4–5 (Oct 17–18):** remaining platform integrations, polish, first real user, DEV post.\\n- **P2, after the challenge:** the vision section of the post.\\n\\n---\\n\\n## P0: what can be done in 3 days\\n\\n### Data ingestion\\n| ID | Requirement |\\n|---|---|\\n| ING-\\n… [7480 more characters truncated]\"\n}\nFile created successfully at: ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/docs/REQUIREMENTS.md (file state is current in your context — no need to Read it back)\nbash\n$ cd ~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory && python3 - <<'EOF'\np='hf26-week2-project.md'\ns=open(p).read()\ns=s.replace(\"\"\"Proposed idea (2026-10-13): \"ReplyDesk\" — review-reply agent for small local businesses, built across many cloud platforms (Vertex AI/DigitalOcean for model hosting, Tinker, Mastra, Backboard, Temporal, SerpApi, MongoDB Atlas, Tiger Data, ElevenLabs, Sentry, Render, .tech domain).\"\"\",\n\"\"\"Idea pivoted 2026-10-14 (user's own idea): AI *advisor* over review/feedback data from any platform (Google Maps + Amazon via SerpApi first; CSV import for others), with an owner-facing advisor AND a consumer-facing \"ask about anything\" Q&A, backed by a graph DB (knowledge graph of entities/claims/aspects) plus fake-review trust signals. Auto-reply explicitly dropped. Name TBD, should be advisor-themed (ReplyDesk rejected). Requirements live in docs/REQUIREMENTS.md (P0 = first 3 days Oct 14–16). Cloud stack: Vertex AI + DigitalOcean (Gemma), Tinker, Mastra, Backboard, Temporal, SerpApi, MongoDB Atlas, Neo4j AuraDB, Tiger Data, ElevenLabs, Sentry, Render, .tech domain. Node 24 via nvm.\"\"\")\nopen(p,'w').write(s)\nEOF\nsed -i 's/ReplyDesk idea/review-advisor idea (owner + consumer, graph DB)/' MEMORY.md\nShell cwd was reset to ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\n```\n\nThe full requirements list is in `docs/REQUIREMENTS.md`. Auto-reply is out, the product is now an **advisor** with two kinds of user, and a **graph database** sits at the center.\n\n| Day | Goal | \n|---|---|\n| **Day 1** | Data flows end to end. SerpApi pulls Google Maps and Amazon reviews, they're converted to one standard review record and stored in MongoDB Atlas, Gemma on Vertex AI pulls out what each review discusses (food, service, quality…), how positive or negative it is, and the specific claims it makes, and the results go into the **Neo4j AuraDB** graph. | \n| **Day 2** | Both interfaces work. **Owner dashboard:** rating trend, positives and negatives per topic, top 3 things to fix and top 3 to promote, comparison with competitors, and an \"ask the advisor\" chat.**Consumer search:** paste a link or search for anything, get a verdict with pros and cons, and ask questions answered from the graph with cited reviews. Also CSV import. | \n| **Day 3** | Production features. Review trust signals (rating bursts, copy-paste reviews, star ratings that contradict the text, and a trust score per place or product), automatic failover from Vertex AI to DigitalOcean, scheduled background syncing with Temporal, trend data in Tiger Data, Sentry tracing, and deployment to Render. | \n\n**Days 4–5:** Tinker fine-tuning, Backboard memory, ElevenLabs audio briefing, the .tech domain, more SerpApi sources (Walmart, Yelp, app stores…), your first real user, and the DEV post.\n\nOnce you've reviewed the list, I can start Day 1 by scaffolding the project and setting up a free connection to each service. You'd then add the API keys for SerpApi, MongoDB Atlas, Neo4j AuraDB and Google Cloud.\n\nAre there any free or opensource alternatives to serp api?\n\n```\n{\n \"query\": \"SerpApi free plan searches per month 2026\",\n \"mode\": \"standard\"\n}\nWeb search results for query: \"SerpApi free plan searches per month 2026\"\n\nLinks: [{\"title\":\"free serp api\",\"url\":\"https://www.octoparse.com/blog/free-serp-api\"},{\"title\":\"serpapi pricing\",\"url\":\"https://scrapegraphai.com/blog/serpapi-pricing\"},{\"title\":\"All SerpApi Plans & Pricing\",\"url\":\"https://costbench.com/software/web-scraping/serpapi/\"},{\"title\":\"SerpApi Free Plan 2026: What's Included & What's Not\",\"url\":\"https://costbench.com/software/web-scraping/serpapi/free-plan/\"},{\"title\":\"SerpApi Free Tier 2026 stable\",\"url\":\"https://agentdeals.dev/vendor/serpapi\"},{\"title\":\"SerpApi Pricing Explained (2026): What 1,000 Searches Really Cost\",\"url\":\"https://apiserpent.com/blog/serpapi-pricing-explained\"},{\"title\":\"Free Google Search API in 2026: 7 Free Tiers Tested Honestly\",\"url\":\"https://apiserpent.com/blog/free-google-search-api-tested\"},{\"title\":\"SerpAPI Pricing 2026\",\"url\":\"https://costbench.com/software/ai-search-apis/serpapi-search/\"},{\"title\":\"toolradar.com\",\"url\":\"https://toolradar.com/tools/serpapi/pricing\"}]\n\nBased on the search results, SerpApi's free plan includes 250 searches per month. This information has been verified from multiple sources as of 2026, with pricing verified as of August 2026.\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.\n{\n \"query\": \"open source Google Maps reviews scraper github gosom\",\n \"mode\": \"standard\"\n}\nWeb search results for query: \"open source Google Maps reviews scraper github gosom\"\n\nLinks: [{\"title\":\"google maps scraper\",\"url\":\"https://ossinsight.io/analyze/gosom/google-maps-scraper\"},{\"title\":\"Ecosyste.ms: Awesome\",\"url\":\"https://awesome.ecosyste.ms/topics/google-maps?keyword=cx\"},{\"title\":\"google-maps-scraper  by gosom\",\"url\":\"https://www.sourcepulse.org/projects/1419906\"},{\"title\":\"trendshift.io\",\"url\":\"https://trendshift.io/repositories/5640\"},{\"title\":\"google maps scraper\",\"url\":\"https://gittrend.io/repo/gosom/google-maps-scraper\"},{\"title\":\"Toggle navigation\",\"url\":\"https://www.jsdelivr.com/package/npm/gm-review-scraper\"},{\"title\":\"Toggle navigation\",\"url\":\"https://www.jsdelivr.com/package/npm/gm-review-scraper?tab=files\"},{\"title\":\"google maps scraper github guide\",\"url\":\"https://www.thunderbit.com/pt/blog/google-maps-scraper-github-guide\"},{\"title\":\"github.com%2Fgaspa93%2Fgooglemaps scraper\",\"url\":\"https://awesome.ecosyste.ms/projects/github.com%2Fgaspa93%2Fgooglemaps-scraper\"},{\"title\":\"Google Maps reviews scraping\",\"url\":\"https://gittrend.io/repo/gaspa93/googlemaps-scraper\"}]\n\nHere are the search results for \"open source Google Maps reviews scraper github gosom\":\n\n## Main Project: gosom/google-maps-scraper\n\ngosom/google-maps-scraper is a Go open source project on GitHub for scraping data from Google Maps. It extracts data such as the name, address, phone number, website URL, rating, reviews number, latitude and longitude, reviews, email and more for each place.\n\n### Key Features:\n\n- This is a free, open-source tool for scraping data from Google Maps that offers both command-line and web UI interfaces, allowing users to extract detailed business information such as names, addresses, ratings, reviews, contact details, and geographic coordinates\n- The scraper utilizes the scrapemate web crawling framework and can be run via Docker or as a compiled binary\n- It supports configurable concurrency, proxy usage (SOCKS5/HTTP/HTTPS), and offers a \"Fast Mode\" for quicker data retrieval\n- For scalability, it integrates with PostgreSQL via docker-compose and supports deployment on Kubernetes, enabling distributed scraping\n- Supports CSV, JSON, and PostgreSQL output formats, with custom exporter plugin capability\n\n### Project Stats:\nThe project has 3.9k stars and 567 forks on GitHub, and is licensed under the MIT License.\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.\n{\n \"query\": \"Amazon Reviews 2023 dataset McAuley lab open dataset\",\n \"mode\": \"standard\"\n}\nWeb search results for query: \"Amazon Reviews 2023 dataset McAuley lab open dataset\"\n\nLinks: [{\"title\":\"Amazon Reviews 2023\",\"url\":\"https://huggingface.co/datasets/minto1234/Amazon-Reviews-2023\"},{\"title\":\"Amazon Reviews 2023\",\"url\":\"https://huggingface.co/datasets/Pratik4570/Amazon-Reviews-2023\"},{\"title\":\"hyper.ai\",\"url\":\"https://hyper.ai/en/datasets/30267\"},{\"title\":\"Amazon Reviews 2023\",\"url\":\"https://huggingface.co/datasets/Kkkkksy/Amazon-Reviews-2023\"},{\"title\":\"Amazon Reviews 2023\",\"url\":\"https://huggingface.co/datasets/VoidZai/Amazon-Reviews-2023\"},{\"title\":\"Amazon Reviews 2023\",\"url\":\"https://huggingface.co/datasets/du09/Amazon-Reviews-2023\"},{\"title\":\"Amazon Reviews 2023\",\"url\":\"https://huggingface.co/datasets/Ahmadhaiwala/Amazon-Reviews-2023\"},{\"title\":\"Hugging Face's logo Hugging Face\",\"url\":\"https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023/tree/main/benchmark\"},{\"title\":\"Hugging Face's logo Hugging Face\",\"url\":\"https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023/tree/8625e87e5ec693ccba0a00931016df5e8884cab7\"}]\n\nBased on the search results, here's what I found about the Amazon Reviews 2023 dataset from McAuley Lab:\n\n## Overview\n\nThis is a large-scale Amazon Reviews dataset collected in 2023 by McAuley Lab that includes rich features such as user reviews (ratings, text, helpfulness votes, etc.), item metadata (descriptions, price, raw image, etc.), and links (user-item / bought together graphs).\n\n## Dataset Scale\n\nThe dataset contains more than 570 million reviews and 48 million products, covering 33 different categories.\n\n## Key Features\n\nCompared with the previous version, the data volume of this version has increased by 245.2%, the interaction data has been updated to September 2023, the product metadata is more detailed, the timestamp accuracy has been improved to the second level, and standard data partitioning is performed to promote the benchmarking of recommendation systems.\n\n## Access\n\nThe dataset is available on Hugging Face under the McAuley-Lab organization, and you can load it using the datasets library. For more details, loading scripts, and preprocessed benchmark files, you can visit amazon-reviews-2023.github.io/.\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.\n{\n \"query\": \"free alternatives to SerpApi for scraping Google Maps and Amazon reviews\",\n \"per_page\": 6\n}\n{\"Ok\":[{\"id\":4834722,\"title\":\"Introducing Review Audit: An open-source tool to check how real a Google Maps rating is\",\"description\":\"Review Audit reads a Google Maps place's newest reviews and its reviewers' history, and shows how much of the rating the place's own reviewers can explain. Open source, runs locally on your SerpApi key.\",\"tags\":[\"analysis\",\"github\",\"opensource\",\"tools\"],\"path\":\"/serpapi/introducing-review-audit-an-open-source-tool-to-check-how-real-a-google-maps-rating-is-2p65\",\"url\":\"https://dev.to/serpapi/introducing-review-audit-an-open-source-tool-to-check-how-real-a-google-maps-rating-is-2p65\",\"score\":1.0},{\"id\":3627766,\"title\":\"How to scrape Google Maps without code: a REST API tutorial\",\"description\":\"Disclosure: I run basedonb, the API I'm using in this tutorial. The technique works with any Google...\",\"tags\":[\"api\",\"google\",\"tutorial\",\"webscraping\"],\"path\":\"/ilyas_yldrm_72e3216f68/how-to-scrape-google-maps-without-code-a-rest-api-tutorial-p67\",\"url\":\"https://dev.to/ilyas_yldrm_72e3216f68/how-to-scrape-google-maps-without-code-a-rest-api-tutorial-p67\",\"score\":1.0},{\"id\":3081651,\"title\":\"Best SERP API Comparison 2025: SerpAPI vs Exa vs Tavily vs ScrapingDog vs ScrapingBee\",\"description\":\"We tested five SERP APIs with standardized benchmarks to find which performs best for different use...\",\"tags\":[\"ai\",\"api\",\"tooling\"],\"path\":\"/ritza/best-serp-api-comparison-2025-serpapi-vs-exa-vs-tavily-vs-scrapingdog-vs-scrapingbee-2jci\",\"url\":\"https://dev.to/ritza/best-serp-api-comparison-2025-serpapi-vs-exa-vs-tavily-vs-scrapingdog-vs-scrapingbee-2jci\",\"score\":1.0},{\"id\":4493636,\"title\":\"How to Scrape App Store & Google Play Reviews (with Sentiment Analysis) 2026\",\"description\":\"If you are trying to scrape app reviews - from the Apple App Store, Google Play, or both - to feed a...\",\"tags\":[\"webscraping\",\"python\",\"javascript\",\"api\"],\"path\":\"/robertokerber/how-to-scrape-app-store-google-play-reviews-with-sentiment-analysis-2026-27e2\",\"url\":\"https://dev.to/robertokerber/how-to-scrape-app-store-google-play-reviews-with-sentiment-analysis-2026-27e2\",\"score\":1.0},{\"id\":3410250,\"title\":\"Google Maps Scraping: Extract Places, Reviews, and Business Hours in 2026\",\"description\":\"Google Maps is one of the richest sources of local business data on the web. Whether you're building...\",\"tags\":[\"python\",\"webdev\",\"tutorial\",\"webscraping\"],\"path\":\"/agenthustler/google-maps-scraping-extract-places-reviews-and-business-hours-in-2026-cfp\",\"url\":\"https://dev.to/agenthu\n… [487 more characters truncated]\n```\n\nNot a true drop-in replacement. Google and Amazon don't offer free, complete review APIs, which is why paid services like SerpApi exist. A mix of free sources gets close, though.\n\n| Option | Type | Covers | Catch | \n|---|---|---|---|\n| **SerpApi free plan** | Paid service, free tier | Google Maps, Amazon and more | 250 searches a month. That's enough if we cache everything and only call it for things not already stored. | \n| **gosom/google-maps-scraper** | Open source (MIT, about 3.9k GitHub stars) | Google Maps places and reviews; outputs CSV, JSON or Postgres; Docker image; supports proxies | Breaks Google's terms. It'll get blocked faster from a cloud server than from your laptop, so expect to need proxies. | \n| **Google Places API** | Official | Place details for any place | Only 5 reviews per place. Good for details like name, hours and rating, not for analysis. | \n| **Amazon Reviews 2023 dataset** (McAuley Lab, UCSD) | Open dataset | 570M+ reviews, 48M products, 33 categories, with ratings, text, helpful votes, product details and \"bought together\" links | It's a snapshot up to September 2023 and licensed for research, so you can't build a paid product on it. | \n| **Yelp Open Dataset** | Open dataset | Restaurant and local-business reviews | Non-commercial license, so the same limitation. | \n| Self-hosted Amazon scraping | Open source tools (Playwright, Crawlee) | Amazon | Amazon blocks scrapers aggressively. I wouldn't spend any of your 3 days on this. | \n| **SearXNG** | Open source | Web search only | No review data, so it doesn't help here. | \n\nAll three still feed the same standard review format, so the rest of the app doesn't care where a review came from.\n\nFrom DEV: SerpApi recently published an open-source tool, *Introducing Review Audit* ([https://dev.to/serpapi/introducing-review-audit-an-open-source-tool-to-check-how-real-a-google-maps-rating-is-2p65](https://dev.to/serpapi/introducing-review-audit-an-open-source-tool-to-check-how-real-a-google-maps-rating-is-2p65)). It checks how genuine a Google Maps rating is by looking at the reviewers' history. That overlaps with our review trust signals, so it's worth reading for ideas and to see what we'd do differently. *Best SERP API Comparison 2025* by ritza ([https://dev.to/ritza/best-serp-api-comparison-2025-serpapi-vs-exa-vs-tavily-vs-scrapingdog-vs-scrapingbee-2jci](https://dev.to/ritza/best-serp-api-comparison-2025-serpapi-vs-exa-vs-tavily-vs-scrapingdog-vs-scrapingbee-2jci)) compares the paid options in case you outgrow the free plan.\n\nShould I add these as data sources in `docs/REQUIREMENTS.md`: the open datasets as a seed and evaluation source, and the gosom scraper as an optional connector?\n\nSources:\n\n- [SerpApi Free Plan 2026 (costbench)](https://costbench.com/software/web-scraping/serpapi/free-plan/)\n\n- [gosom/google-maps-scraper (OSS Insight)](https://ossinsight.io/analyze/gosom/google-maps-scraper)\n\n- [McAuley-Lab/Amazon-Reviews-2023 on Hugging Face](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023/tree/main/benchmark)\n\nTech stack to use: Nodejs for backend, React for frontend, Langgraph for agent runtime(This flow cannot be handled solely by n AI call), docker for containers and kubernetes for scaling the deployed containers(I want to start small here. I am not hosting and scaling on day 1 but I need to prepare a base version beforehand) and lastly, an AI model opensource that I will find and let you know. We will use vLLM for model inference and serving engine. Anything that is missing?\n\n``` bash\n$ for p in @langchain/langgraph @langchain/langgraph-checkpoint-mongodb @langchain/langgraph-checkpoint-postgres @langchain/openai neo4j-driver fastify bullmq; do echo \"$p: $(npm view $p version 2>/dev/null)\"; done; corepack --version 2>/dev/null || echo \"no corepack\"; pnpm -v 2>/dev/null || echo \"no pnpm\"; bun -v 2>/dev/null\n@langchain/langgraph: 1.5.0\n@langchain/langgraph-checkpoint-mongodb: 1.5.0\n@langchain/langgraph-checkpoint-postgres: 1.1.0\n@langchain/openai: 1.6.2\nneo4j-driver: 6.2.0\nfastify: 5.12.7\nbullmq: 6.3.15\n0.32.0\nno pnpm\n1.4.2\n{\n \"query\": \"deploying vLLM on Kubernetes GPU autoscaling lessons learned\",\n \"per_page\": 5\n}\n{\"Ok\":[{\"id\":4572316,\"title\":\"Scale Before the Spike: Predictive Autoscaling for GPU Workloads on Kubernetes\",\"description\":\"Also published on the CNCF blog. Cross-post with canonical link to the CNCF version. The 3...\",\"tags\":[\"kubernetes\",\"autoscaling\",\"gpu\",\"devops\"],\"path\":\"/ramkumar_nagaraj_38541938/scale-before-the-spike-predictive-autoscaling-for-gpu-workloads-on-kubernetes-5eok\",\"url\":\"https://dev.to/ramkumar_nagaraj_38541938/scale-before-the-spike-predictive-autoscaling-for-gpu-workloads-on-kubernetes-5eok\",\"score\":1.0},{\"id\":4525035,\"title\":\"Standing Up a GPU Cluster on AKS for vLLM\",\"description\":\"This article is Part 1 of a 3-Part series on running vLLM on AKS and walks through creating an AKS...\",\"tags\":[\"azure\",\"devops\",\"kubernetes\",\"llm\"],\"path\":\"/josef_doornink_930b2caf1c/standing-up-a-gpu-cluster-on-aks-for-vllm-bif\",\"url\":\"https://dev.to/josef_doornink_930b2caf1c/standing-up-a-gpu-cluster-on-aks-for-vllm-bif\",\"score\":1.0},{\"id\":4515453,\"title\":\"Self-Hosting vLLM on Cloud GPUs in 2026: Sub-180ms LLM Inference for Autonomous AI Agents (Full Production Guide)\",\"description\":\"A comprehensive 2026 production guide to deploying self-hosted vLLM with EAGLE-3 speculative decoding, PagedAttention v2, and prefix caching on RunPod/Vast.ai to achieve sub-180ms TTFT and cut LLM inference costs by 45-74%.\",\"tags\":[\"ai\",\"llm\",\"mlops\",\"python\"],\"path\":\"/shubhanshu_shrimali/how-i-self-host-vllm-on-cloud-gpus-for-sub-180ms-inference-and-saved-45-on-costs-4cm5\",\"url\":\"https://dev.to/shubhanshu_shrimali/how-i-self-host-vllm-on-cloud-gpus-for-sub-180ms-inference-and-saved-45-on-costs-4cm5\",\"score\":1.0},{\"id\":4596101,\"title\":\"Inside vLLM: Following One Request from the API to GPU Execution\",\"description\":\"Follow one vLLM V1 request through scheduling, paged KV-cache management, GPU execution, and output delivery.\",\"tags\":[\"vllm\",\"llm\",\"inference\",\"python\"],\"path\":\"/yuan_lei_e631e36865ed370b/inside-vllm-following-one-request-from-the-api-to-gpu-execution-1ja6\",\"url\":\"https://dev.to/yuan_lei_e631e36865ed370b/inside-vllm-following-one-request-from-the-api-to-gpu-execution-1ja6\",\"score\":1.0},{\"id\":4355869,\"title\":\"Self-Hosting Your First LLM: What the Tutorials Skip About GPU Memory\",\"description\":\"Why a model that \\\"fits\\\" in your GPU still OOMs — the KV cache, overhead, and quantization math self-hosting tutorials leave out.\",\"tags\":[\"llm\",\"ai\",\"machinelearning\",\"devops\"],\"path\":\"/libme/self-hosting-your-first-llm-what-the-tutorials-skip-about-gpu-memo\n… [126 more characters truncated]\n{\n \"query\": \"LangGraph.js production agent Node.js checkpointer persistence\",\n \"per_page\": 5\n}\n{\n \"Ok\": [\n {\n \"id\": 4591743,\n \"title\": \"Adding AgentInspect to LangGraph Without Changing Production\",\n \"description\": \"What I learned from adding an environment-gated local trace to a NestJS, LangGraph, and...\",\n \"tags\": [\n \"agents\",\n \"debugging\",\n \"llm\",\n \"typescript\"\n ],\n \"path\": \"/abhilaksharora/adding-agentinspect-to-langgraph-without-changing-production-3p7p\",\n \"url\": \"https://dev.to/abhilaksharora/adding-agentinspect-to-langgraph-without-changing-production-3p7p\",\n \"score\": 1.0\n },\n {\n \"id\": 4822466,\n \"title\": \"Pause a LangGraph Agent Mid-Run for Human Approval with interrupt and a Checkpointer\",\n \"description\": \"A hands-on guide to stopping a LangGraph agent in the middle of a node, waiting for a human decision, and resuming exactly where it left off.\",\n \"tags\": [\n \"ai\",\n \"agents\",\n \"python\",\n \"tutorial\"\n ],\n \"path\": \"/royalpinto007/pause-a-langgraph-agent-mid-run-for-human-approval-with-interrupt-and-a-checkpointer-536g\",\n \"url\": \"https://dev.to/royalpinto007/pause-a-langgraph-agent-mid-run-for-human-approval-with-interrupt-and-a-checkpointer-536g\",\n \"score\": 1.0\n },\n {\n \"id\": 4333254,\n \"title\": \"Checkpointing Multi-Step Agents in Node So a Crash Doesn't Cost You\",\n \"description\": \"Book: AI That Plans The series: AI in TypeScript — 5 books, from your first LLM call to agents in...\",\n \"tags\": [\n \"typescript\",\n \"node\",\n \"agents\",\n \"ai\"\n ],\n \"path\": \"/gabrielanhaia/checkpointing-multi-step-agents-in-node-so-a-crash-doesnt-cost-you-3icf\",\n \"url\": \"https://dev.to/gabrielanhaia/checkpointing-multi-step-agents-in-node-so-a-crash-doesnt-cost-you-3icf\",\n \"score\": 1.0\n },\n {\n \"id\": 4023818,\n \"title\": \"Why my LangGraph agent throws away its checkpoint on every request\",\n \"description\": \"The problem Wingman is a worker-evaluator agent. A worker LLM tries to satisfy a success...\",\n \"tags\": [\n \"agents\",\n \"architecture\",\n \"aws\",\n \"serverless\"\n ],\n \"path\": \"/akashpersetti/why-my-langgraph-agent-throws-away-its-checkpoint-on-every-request-21ff\",\n \"url\": \"https://dev.to/akashpersetti/why-my-langgraph-agent-throws-away-its-checkpoint-on-every-request-21ff\",\n \"score\": 1.0\n },\n {\n \"id\": 4611375,\n \"title\": \"How to Catch and Replay Production Agent Failures: Trace-Native CI/CD in Practice\",\n \"description\": \"How to Catch and Replay Production Agent Failures: Trace-Native CI/CD in Practice ...\",\n \"tags\": [\n \"ai\",\n \"agenticai\",\n \"machinelearning\",\n \"llm\"\n ],\n \"path\": \"/priyeshdave6/how-to-catch-and-replay-production-agent-failures-trace-native-cicd-in-practice-335k\",\n \"url\": \"https://dev.to/priyeshdave6/how-to-catch-and-replay-production-agent-failures-trace-native-cicd-in-practice-335k\",\n \"score\": 1.0\n }\n ]\n}\n```\n\nThat's a solid core. LangGraph is the right call: ingestion, then extraction, then building the graph, then deciding whether to fetch more reviews before answering, is a multi-step, branching flow, not a single AI call. It also replaces Mastra in the earlier plan. Here's what I'd add.\n\n**Language and project layout**\n\n- **TypeScript** across the backend, frontend and shared types. LangGraph.js (v1.5) is fully typed.\n\n- **One repo with npm workspaces:** `apps/api`, `apps/web`, `apps/worker`, and `packages/shared` for the standard review format and Zod schemas used by both sides. pnpm isn't installed; npm workspaces on Node 24 work fine.\n\n**Backend**\n\n- **Fastify** as the API framework. It's fast, has built-in schema validation, and handles streaming well.\n\n- **Server-sent events (SSE)** to stream agent progress to the browser (\"fetching reviews… analysing 214 reviews…\"), which the consumer flow for things not yet in the database needs.\n\n- **Zod** to validate every model output before it touches a database.\n\n**Agent runtime**\n\n- **A LangGraph checkpointer** to save each run's state. Without one, a crashed or restarted pod loses the run halfway. `@langchain/langgraph-checkpoint-mongodb` already exists, so it can reuse MongoDB Atlas.\n\n- **A job queue for background work** (scheduled re-syncs, batch extraction). LangGraph runs the agent flow but doesn't schedule or retry jobs. Options:\n\n - **BullMQ + Redis:** simple, and it scales cleanly on Kubernetes with KEDA, which adds workers based on queue length.\n\n - **Temporal:** the earlier plan; more robust, more setup.\n\nI'd go with BullMQ unless you want Temporal as one of the cloud platforms.\n\n- **Redis**, which BullMQ needs anyway. It can also cache SerpApi responses and rate-limit the public consumer endpoint, which protects your 250 free searches a month.\n\n**AI serving**\n\n- **An embedding model, also served by vLLM**, for merging duplicate claims and for search over the graph. vLLM serves one model per server, so that's two deployments: the chat model and the embedding model.\n\n- **What to look for when you pick the model.** Things to check:\n\n - Its license allows commercial use.\n\n - It supports tool calling and JSON-schema output in vLLM, which LangGraph and our extraction step depend on.\n\n - It fits your GPU's memory, including the KV cache that vLLM reserves for running requests.\n\n - A context window of about 16k tokens is enough for batches of reviews.\n\n- **The app talks to vLLM through `@langchain/openai`** pointed at vLLM's OpenAI-compatible endpoint. Switching models or providers then means changing a URL, not code.\n\n- **Where the GPU runs.** vLLM needs one. The base Kubernetes setup should put vLLM on its own GPU node pool (on GKE or DigitalOcean's Kubernetes) so the rest of the cluster can stay on cheap CPU nodes.\n\n**Data stores (from the earlier plan; confirm you're keeping them)**\n\n- **MongoDB Atlas:** stores reviews, places and products, users, and LangGraph's saved state.\n\n- **Neo4j AuraDB:** the graph. It has built-in vector indexes, so **no separate vector database** is needed.\n\n- **Tiger Data:** time series, for trends and burst detection.\n\n**Frontend**\n\n- **Vite + React + TypeScript** for the app itself.\n\n- **TanStack Query** for fetching and caching API data.\n\n- **React Router** for pages.\n\n- **Tailwind + shadcn/ui** for styling and components.\n\n- **Recharts** for the trend charts.\n\n- **Auth for owners:** Better Auth (open source, runs in your API) or a hosted service like Clerk. Consumers stay anonymous.\n\n**Containers and Kubernetes (a base version you can build on later)**\n\n- **Docker Compose for local development:** the API, worker, web app and Redis, plus an optional vLLM container if you have a GPU.\n\n- **Kubernetes manifests with Kustomize** (simpler than Helm to start): a `base/` folder plus `overlays/dev` and `overlays/prod`. It would include:\n\n - Deployments, Services and an Ingress\n\n - A Horizontal Pod Autoscaler (HPA) to scale the API on load\n\n - A KEDA scaler to add workers as the queue grows\n\n - Health checks and resource limits\n\n - The vLLM Deployment with a GPU node selector\n\n- **Kind** to test the manifests on your machine before deploying anywhere.\n\n- **A container registry** such as GitHub Container Registry (GHCR) or Google's Artifact Registry.\n\n- **GitHub Actions** to lint, test, build and push the images.\n\n**Observability**\n\n- **Sentry** for errors.\n\n- **Langfuse** for LangGraph traces. It's open source, which suits the open-AI theme of the post.\n\n- **vLLM's built-in `/metrics` endpoint**, scraped later by Prometheus.\n\n**Testing**\n\n- **Vitest** for unit and integration tests.\n\n- **A small labelled evaluation set** (about 100 reviews from the Amazon Reviews 2023 dataset) to measure how well your chosen model pulls out topics and claims.\n\nFrom DEV, two posts worth reading before you pick the model:\n\n- *Self-Hosting Your First LLM: What the Tutorials Skip About GPU Memory* by libme ([https://dev.to/libme/self-hosting-your-first-llm-what-the-tutorials-skip-about-gpu-memory-50pc](https://dev.to/libme/self-hosting-your-first-llm-what-the-tutorials-skip-about-gpu-memory-50pc)) explains why a model that \"fits\" on paper still runs out of GPU memory under vLLM because of the KV cache. That's likely what bit you on Vertex.\n\n- *Why my LangGraph agent throws away its checkpoint on every request* by akashpersetti ([https://dev.to/akashpersetti/why-my-langgraph-agent-throws-away-its-checkpoint-on-every-request-21ff](https://dev.to/akashpersetti/why-my-langgraph-agent-throws-away-its-checkpoint-on-every-request-21ff)) covers how agents lose their saved state across requests, which is the reason for the checkpointer above.\n\nShould I add this as a Tech Stack section in `docs/REQUIREMENTS.md` once you've answered the three questions?\n\nTypescript it is. Install pnpm. we will use that as the package manager. Fastify for backend, SSE can be used for streaming and Definately Zod for schema validation. OpenAI officially encourages in its docs. We can use it for our AI as well. Also, rename the workspaces as backend(for backend), frontend(consumer for consumer and client for seller/owner/admin view: Separate hyper minimalist themes for both (Dark and light mode). Refer [https://plane.so](https://plane.so) for reference), plugins for external dynamic dependencies. common for anything that will be used by everythng from FE, BE, plugins or in general anything like simple utilities or anything for that matter. Any questions and you ask me right away\n\n``` bash\n$ export NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null; corepack enable pnpm && COREPACK_ENABLE_DOWNLOAD_PROMPT=0 corepack prepare pnpm@latest --activate && pnpm -v && which pnpm && bash -lc 'pnpm -v'\nPreparing pnpm@latest for immediate activation...\nDownloading the pnpm 12.11.2 binary for linux-x64...\n12.11.2\n~/.nvm/versions/node/v24.21.0/bin/pnpm\n12.11.2\n{\n \"questions\": [\n {\n \"question\": \"How should plugins/ be organized?\",\n \"header\": \"Plugins\",\n \"multiSelect\": false,\n \"options\": [\n {\n \"label\": \"One package per plugin (Recommended)\",\n \"description\": \"plugins/serpapi-google-maps, plugins/serpapi-amazon, plugins/csv-import, plugins/neo4j, plugins/vllm… Each one implements a shared Plugin interface defined in common, and the backend loads the enabled ones from config at startup. Adding a platform means adding a folder.\"\n },\n {\n \"label\": \"Single plugins package\",\n \"description\": \"One @app/plugins package with a module per integration. Simpler, but every plugin's dependencies get installed together and you can't version or disable them independently.\"\n },\n {\n \"label\": \"Runtime-installable\",\n \"description\": \"Plugins are published as separate npm packages and loaded dynamically by name at runtime (like a marketplace). Most flexible, but more machinery than the first version needs.\"\n }\n ]\n },\n {\n \"question\": \"Where should the background worker live? It runs ingestion and extraction jobs and the long LangGraph runs.\",\n \"header\": \"Worker\",\n \"multiSelect\": false,\n \"options\": [\n {\n \"label\": \"Inside backend (Recommended)\",\n \"description\": \"Same package and code, built into one Docker image with two start commands (api and worker). In Kubernetes they become two Deployments that scale independently.\"\n },\n {\n \"label\": \"Its own workspace\",\n \"description\": \"A separate worker/ package. Cleaner separation, but it duplicates config and has to import the backend's job logic through common.\"\n }\n ]\n },\n {\n \"question\": \"Which background job system should the worker use?\",\n \"header\": \"Jobs\",\n \"multiSelect\": false,\n \"options\": [\n {\n \"label\": \"BullMQ + Redis (Recommended)\",\n \"description\": \"Simple, Node-native, and scales on Kubernetes with KEDA based on queue length. Redis also handles SerpApi caching and rate limiting.\"\n },\n {\n \"label\": \"Temporal\",\n \"description\": \"Durable workflows with built-in retries and scheduling. More robust for long-running jobs, but needs a Temporal server or Temporal Cloud and has a steeper setup.\"\n }\n ]\n },\n {\n \"question\": \"Where should shared React code live, such as UI primitives and the theme system that both frontends use?\",\n \"header\": \"Shared UI\",\n \"multiSelect\": false,\n \"options\": [\n {\n \"label\": \"common split into packages (Recommended)\",\n \"description\": \"common/core (types, Zod schemas, utilities, used everywhere) and common/ui (React components and theme tokens, used only by the frontends). The backend never pulls in React.\"\n },\n {\n \"label\": \"One common package\",\n \"description\": \"Everything in a single @app/common package, including the React code. Simplest layout, but the backend's dependency tree would include React.\"\n },\n {\n \"label\": \"No shared UI\",\n \"description\": \"Each frontend owns all of its components. Fits 'separate themes for each', at the cost of duplicating basic components like buttons and inputs.\"\n }\n ]\n }\n ]\n}\nYour questions have been answered: \"How should plugins/ be organized?\"=\"One package per plugin (Recommended)\", \"Where should the background worker live? It runs ingestion and extraction jobs and the long LangGraph runs.\"=\"Inside backend (Recommended)\", \"Which background job system should the worker use?\"=\"BullMQ + Redis (Recommended)\", \"Where should shared React code live, such as UI primitives and the theme system that both frontends use?\"=\"common split into packages (Recommended)\". You can now continue with these answers in mind.\n```\n\nLocking in: one package per plugin, the worker inside `backend`, BullMQ + Redis, and `common/core` plus `common/ui`. I'll check Plane's design before setting up the theme tokens.\n\n```\n{\n \"query\": \"select:WebFetch\",\n \"max_results\": 1\n}\n[image]\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/docs 2>/dev/null; curl -sL https://plane.so -A \"Mozilla/5.0\" -o f= wc -c $f; grep -oE 'font-family:[^;\"}]{1,80}' $f | sort | uniq -c | sort -rn | head; grep -oE '#[0-9a-fA-F]{6}\\b' $f | sort | uniq -c | sort -rn | head -25; grep -oE 'fonts\\.googleapis[^\"]+|/_next/static/media/[^\")]+\\.woff2' $f | head\n357624 \n 35 #CACDCE\n 28 #E4E6E7\n 28 #1D1F20\n 22 #eaebeb\n 18 #36393A\n 18 #006399\n 14 #f7f7f8\n 14 #EAEBEB\n 12 #FAFAFA\n 12 #0F0F10\n 10 #181A1B\n 8 #F7F7F8\n 8 #4e5355\n 7 #2C2E30\n 6 #2893CC\n 2 #ffffff\n 2 #E4ECFB\n 2 #CFD2D3\n 2 #AFB3B6\n 2 #959A9D\n 2 #3b82f6\n 2 #2060DF\n 1 #141415\n 1 #0f0f10\nbash\n$ f= grep -oE 'href=\"[^\"]+\\.css[^\"]*\"' $f | head -3; css=$(grep -oE '/_next/static/[^\"]+\\.css' $f | head -2); for c in $css; do curl -sL \"https://plane.so$c\" | grep -oE \"font-family:[^;}]{1,60}|--[a-z-]*(radius|font)[a-z-]*:[^;}]{1,40}\" | sort | uniq -c | sort -rn | head -8; done\nhref=\"https://dev.to/_next/static/immutable/chunks/3gdm8huclmn6o.css\"\nhref=\"https://dev.to/_next/static/immutable/chunks/28kh9tzk_f6fd.css\"\nhref=\"https://dev.to/_next/static/immutable/chunks/069m3khzwax_t.css\"\n 12 font-family:sourceSerifPro\n 10 font-family:IBM Plex Mono\n 2 font-family:satoshi\n 2 font-family:inter\n 1 --font-source-serif-pro:\"sourceSerifPro\", \"sourceSerifPro Fallba\n 1 --font-satoshi:\"satoshi\", \"satoshi Fallback\"\n 1 --font-inter:\"inter\", \"inter Fallback\"\n 1 --font-ibm-plex-mono:\"IBM Plex Mono\", \"IBM Plex Mono Fallback\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nmkdir -p $R/{backend/src/{routes,lib,jobs,plugins},frontend/{consumer,client}/src,plugins/serpapi-google-maps/src,common/{core,ui}/src}\ncd $R && git init -q 2>&1; git -C $R status --short | head -3; ls $R\n?? .nvmrc\n?? docs/\nbackend\ncommon\ndocs\nfrontend\nplugins\n```\n\nNow the root config files.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\ncat > package.json <<'EOF'\n{\n \"name\": \"app\",\n \"private\": true,\n \"type\": \"module\",\n \"packageManager\": \"pnpm@12.11.2\",\n \"engines\": {\n \"node\": \">=24\"\n },\n \"scripts\": {\n \"dev\": \"pnpm -r --parallel --filter \\\"./backend\\\" --filter \\\"./frontend/*\\\" dev\",\n \"dev:api\": \"pnpm --filter @app/backend dev:api\",\n \"dev:worker\": \"pnpm --filter @app/backend dev:worker\",\n \"dev:consumer\": \"pnpm --filter @app/consumer dev\",\n \"dev:client\": \"pnpm --filter @app/client dev\",\n \"build\": \"pnpm -r build\",\n \"typecheck\": \"pnpm -r typecheck\",\n \"test\": \"vitest run\",\n \"infra:up\": \"docker compose up -d\",\n \"infra:down\": \"docker compose down\"\n }\n}\nEOF\ncat > pnpm-workspace.yaml <<'EOF'\npackages:\n - backend\n - frontend/*\n - plugins/*\n - common/*\nEOF\ncat > tsconfig.base.json <<'EOF'\n{\n \"compilerOptions\": {\n \"target\": \"ES2023\",\n \"lib\": [\"ES2023\"],\n \"module\": \"ESNext\",\n \"moduleResolution\": \"Bundler\",\n \"resolveJsonModule\": true,\n \"strict\": true,\n \"noUncheckedIndexedAccess\": true,\n \"noImplicitOverride\": true,\n \"exactOptionalPropertyTypes\": false,\n \"verbatimModuleSyntax\": true,\n \"isolatedModules\": true,\n \"skipLibCheck\": true,\n \"noEmit\": true\n }\n}\nEOF\ncat > .gitignore <<'EOF'\nnode_modules/\ndist/\n.env\n.env.*\n!.env.example\n*.log\n.DS_Store\ncoverage/\n.vite/\nEOF\ncat > docker-compose.yml <<'EOF'\n# Local development infrastructure. Managed services (MongoDB Atlas, Neo4j AuraDB,\n# Tiger Data) are reached over the network; only Redis runs locally.\nservices:\n redis:\n image: redis:8-alpine\n ports:\n - \"6379:6379\"\n volumes:\n - redis-data:/data\n command: [\"redis-server\", \"--appendonly\", \"yes\"]\n\nvolumes:\n redis-data:\nEOF\ncat > .env.example <<'EOF'\n# Backend\nNODE_ENV=development\nAPI_PORT=4000\nLOG_LEVEL=info\nCORS_ORIGINS=http://localhost:5173,http://localhost:5174\nREDIS_URL=redis://localhost:6379\n\n# Comma-separated plugin ids to load (folder names under plugins/)\nPLUGINS=serpapi-google-maps\n\n# plugins/serpapi-google-maps\nSERPAPI_API_KEY=\nEOF\necho ok\nok\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/common/core\ncat > package.json <<'EOF'\n{\n \"name\": \"@app/core\",\n \"version\": \"0.0.0\",\n \"private\": true,\n \"type\": \"module\",\n \"exports\": {\n \".\": \"./src/index.ts\"\n },\n \"scripts\": {\n \"typecheck\": \"tsc -p tsconfig.json\"\n }\n}\nEOF\ncat > tsconfig.json <<'EOF'\n{\n \"extends\": \"../../tsconfig.base.json\",\n \"compilerOptions\": {\n \"types\": [\"node\"]\n },\n \"include\": [\"src\"]\n}\nEOF\ncat > src/index.ts <<'EOF'\nexport * from \"./schemas/entity.ts\";\nexport * from \"./schemas/review.ts\";\nexport * from \"./schemas/stream.ts\";\nexport * from \"./plugin.ts\";\nexport * from \"./utils/hash.ts\";\nexport * from \"./utils/result.ts\";\nEOF\nmkdir -p src/schemas src/utils\ncat > src/schemas/entity.ts <<'EOF'\nimport { z } from \"zod\";\n\n/** A thing that receives reviews: a place (restaurant, shop) or a product. */\nexport const EntityKind = z.enum([\"place\", \"product\"]);\nexport type EntityKind = z.infer;\n\n/** Points at an entity on one source platform, e.g. a Google Maps place or an Amazon ASIN. */\nexport const EntityRef = z.object({\n source: z.string().min(1),\n externalId: z.string().min(1),\n});\nexport type EntityRef = z.infer;\n\nexport const Entity = z.object({\n ref: EntityRef,\n kind: EntityKind,\n name: z.string(),\n url: z.url().optional(),\n category: z.string().optional(),\n address: z.string().optional(),\n brand: z.string().optional(),\n seller: z.string().optional(),\n rating: z.number().min(0).max(5).optional(),\n reviewCount: z.number().int().nonnegative().optional(),\n location: z.object({ lat: z.number(), lng: z.number() }).optional(),\n /** The source's original payload, kept for re-processing. */\n raw: z.unknown().optional(),\n});\nexport type Entity = z.infer;\nEOF\ncat > src/schemas/review.ts <<'EOF'\nimport { z } from \"zod\";\nimport { EntityRef } from \"./entity.ts\";\n\n/** The standard review record. Every connector converts its source's reviews into this shape. */\nexport const Review = z.object({\n source: z.string().min(1),\n externalId: z.string().min(1),\n entity: EntityRef,\n rating: z.number().min(0).max(5).nullable(),\n title: z.string().optional(),\n text: z.string(),\n /** Hash of the reviewer's id on the source; the raw identity is never stored. */\n authorHash: z.string().optional(),\n createdAt: z.iso.datetime(),\n verified: z.boolean().optional(),\n helpfulCount: z.number().int().nonnegative().optional(),\n language: z.string().optional(),\n raw: z.unknown().optional(),\n});\nexport type Review = z.infer;\n\nexport const ReviewPage = z.object({\n reviews: z.array(Review),\n /** Opaque cursor for the next page; absent when there are no more pages. */\n nextCursor: z.string().optional(),\n});\nexport type ReviewPage = z.infer;\nEOF\ncat > src/schemas/stream.ts <<'EOF'\nimport { z } from \"zod\";\n\n/** Events sent over SSE from the backend to both frontends. */\nexport const StreamEvent = z.discriminatedUni\n… [2813 more characters truncated]\nok\n```\n\n`common/core` must work in the browser too, so the Node-only hash helper moves to a separate `@app/core/node` entry point.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/common/core\nsed -i '/utils\\/hash.ts/d' src/index.ts\ncat > src/node.ts <<'EOF'\n// Node-only helpers. Import from \"@app/core/node\"; never from frontend code.\nexport * from \"./utils/hash.ts\";\nEOF\npython3 - <<'EOF'\nimport json\np='package.json'; d=json.load(open(p))\nd['exports']={\".\":\"./src/index.ts\",\"./node\":\"./src/node.ts\"}\nopen(p,'w').write(json.dumps(d,indent=2)+\"\\n\")\np='../../tsconfig.base.json'; d=json.load(open(p))\nd['compilerOptions']['allowImportingTsExtensions']=True\nopen(p,'w').write(json.dumps(d,indent=2)+\"\\n\")\nEOF\ncat src/index.ts\nexport * from \"./schemas/entity.ts\";\nexport * from \"./schemas/review.ts\";\nexport * from \"./schemas/stream.ts\";\nexport * from \"./plugin.ts\";\nexport * from \"./utils/result.ts\";\n```\n\nNow `common/ui`. It holds the theme mechanics and basic components. Each app owns its own color and font tokens, so the two themes stay separate.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/common/ui\nmkdir -p src/components src/theme\ncat > package.json <<'EOF'\n{\n \"name\": \"@app/ui\",\n \"version\": \"0.0.0\",\n \"private\": true,\n \"type\": \"module\",\n \"exports\": {\n \".\": \"./src/index.ts\",\n \"./base.css\": \"./src/base.css\"\n },\n \"scripts\": {\n \"typecheck\": \"tsc -p tsconfig.json\"\n }\n}\nEOF\ncat > tsconfig.json <<'EOF'\n{\n \"extends\": \"../../tsconfig.base.json\",\n \"compilerOptions\": {\n \"lib\": [\"ES2023\", \"DOM\", \"DOM.Iterable\"],\n \"jsx\": \"react-jsx\"\n },\n \"include\": [\"src\"]\n}\nEOF\ncat > src/index.ts <<'EOF'\nexport * from \"./theme/ThemeProvider.tsx\";\nexport * from \"./components/Button.tsx\";\nexport * from \"./components/Card.tsx\";\nexport * from \"./components/Input.tsx\";\nexport * from \"./components/ThemeToggle.tsx\";\nEOF\ncat > src/theme/ThemeProvider.tsx <<'EOF'\nimport { createContext, useCallback, useContext, useEffect, useState, type ReactNode } from \"react\";\n\nexport type ThemePreference = \"light\" | \"dark\" | \"system\";\nexport type ResolvedTheme = \"light\" | \"dark\";\n\ninterface ThemeContextValue {\n preference: ThemePreference;\n resolved: ResolvedTheme;\n setPreference(preference: ThemePreference): void;\n}\n\nconst ThemeContext = createContext(null);\n\nconst darkQuery = () => window.matchMedia(\"(prefers-color-scheme: dark)\");\n\nfunction readStored(key: string): ThemePreference {\n try {\n const value = localStorage.getItem(key);\n if (value === \"light\" || value === \"dark\" || value === \"system\") return value;\n } catch {\n // Storage can be unavailable (private mode, blocked site data).\n }\n return \"system\";\n}\n\n/** Sets data-theme on ; each app's theme CSS defines tokens for both values. */\nexport function ThemeProvider({ children, storageKey = \"theme\" }: { children: ReactNode; storageKey?: string }) {\n const [preference, setPreferenceState] = useState(() => readStored(storageKey));\n const [systemDark, setSystemDark] = useState(() => darkQuery().matches);\n\n useEffect(() => {\n const query = darkQuery();\n const onChange = (e: MediaQueryListEvent) => setSystemDark(e.matches);\n query.addEventListener(\"change\", onChange);\n return () => query.removeEventListener(\"change\", onChange);\n }, []);\n\n const resolved: ResolvedTheme = preference === \"system\" ? (systemDark ? \"dark\" : \"light\") : preference;\n\n useEffect(() => {\n document.documentElement.dataset.theme = resolved;\n document.documentElement.style.colorScheme = resolved;\n }, [resolved]);\n\n const setPreference = useCallback(\n (next: ThemePreference) => {\n setPreferenceState(next);\n try {\n localStorage.setItem(storageKey, next);\n } catch {\n // Preference just won't persist.\n }\n },\n [storageKey],\n );\n\n return {children};\n}\n\nexport function useTheme(): ThemeContextValue {\n const value = useContext(ThemeContext)\n… [4314 more characters truncated]\nok\n```\n\nNow the first plugin, the SerpApi Google Maps connector. It's the first real data source on Day 1, and it shows that the plugin interface works.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/plugins/serpapi-google-maps\ncat > package.json <<'EOF'\n{\n \"name\": \"@app/plugin-serpapi-google-maps\",\n \"version\": \"0.0.0\",\n \"private\": true,\n \"type\": \"module\",\n \"exports\": {\n \".\": \"./src/index.ts\"\n },\n \"scripts\": {\n \"typecheck\": \"tsc -p tsconfig.json\"\n }\n}\nEOF\ncat > tsconfig.json <<'EOF'\n{\n \"extends\": \"../../tsconfig.base.json\",\n \"compilerOptions\": {\n \"types\": [\"node\"]\n },\n \"include\": [\"src\"]\n}\nEOF\ncat > src/serpapi.ts <<'EOF'\nimport { z } from \"zod\";\n\n// Only the fields we use are declared; SerpApi responses carry many more,\n// which loose objects keep (they end up in each record's `raw`).\n\nexport const SerpPlace = z.looseObject({\n place_id: z.string(),\n data_id: z.string().optional(),\n title: z.string(),\n address: z.string().optional(),\n type: z.string().optional(),\n rating: z.number().optional(),\n reviews: z.number().optional(),\n website: z.string().optional(),\n gps_coordinates: z.object({ latitude: z.number(), longitude: z.number() }).optional(),\n});\nexport type SerpPlace = z.infer;\n\nexport const SerpSearchResponse = z.looseObject({\n local_results: z.array(SerpPlace).optional(),\n place_results: SerpPlace.optional(),\n});\n\nexport const SerpReview = z.looseObject({\n review_id: z.string(),\n rating: z.number().optional(),\n snippet: z.string().optional(),\n extracted_snippet: z.object({ original: z.string().optional() }).optional(),\n iso_date: z.string(),\n likes: z.number().optional(),\n user: z.looseObject({ name: z.string().optional(), contributor_id: z.string().optional(), link: z.string().optional() }).optional(),\n});\nexport type SerpReview = z.infer;\n\nexport const SerpReviewsResponse = z.looseObject({\n reviews: z.array(SerpReview).optional(),\n serpapi_pagination: z.object({ next_page_token: z.string().optional() }).optional(),\n});\n\nexport const SerpError = z.object({ error: z.string() });\n\nexport async function callSerpApi(\n fetchImpl: typeof fetch,\n apiKey: string,\n params: Record,\n schema: z.ZodType,\n): Promise {\n const url = new URL(\"https://serpapi.com/search.json\");\n for (const [key, value] of Object.entries(params)) url.searchParams.set(key, value);\n url.searchParams.set(\"api_key\", apiKey);\n\n const res = await fetchImpl(url);\n const body: unknown = await res.json();\n const apiError = SerpError.safeParse(body);\n if (!res.ok || apiError.success) {\n throw new Error(`SerpApi ${params.engine} failed (${res.status}): ${apiError.success ? apiError.data.error : res.statusText}`);\n }\n return schema.parse(body);\n}\nEOF\ncat > src/index.ts <<'EOF'\nimport { z } from \"zod\";\nimport { defineConnector, type Entity, type EntityRef, type Review } from \"@app/core\";\nimport { hashIdentity } from \"@app/core/node\";\nimport { callSerpApi, SerpReviewsResponse, SerpSearchResponse, type SerpPlace, type SerpReview } from \"./serpapi.ts\";\n\nconst SOURCE = \"google_maps\";\n\nfunction \n… [3171 more characters truncated]\nok\n```\n\nNow the backend: the Fastify API, the BullMQ worker, a plugin loader, SSE streaming, and environment config validated with Zod.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\ncat > package.json <<'EOF'\n{\n \"name\": \"@app/backend\",\n \"version\": \"0.0.0\",\n \"private\": true,\n \"type\": \"module\",\n \"scripts\": {\n \"dev:api\": \"tsx watch --env-file-if-exists=../.env src/api.ts\",\n \"dev:worker\": \"tsx watch --env-file-if-exists=../.env src/worker.ts\",\n \"dev\": \"concurrently -n api,worker -c blue,magenta \\\"pnpm dev:api\\\" \\\"pnpm dev:worker\\\"\",\n \"build\": \"tsup\",\n \"start:api\": \"node dist/api.js\",\n \"start:worker\": \"node dist/worker.js\",\n \"typecheck\": \"tsc -p tsconfig.json\"\n }\n}\nEOF\ncat > tsconfig.json <<'EOF'\n{\n \"extends\": \"../tsconfig.base.json\",\n \"compilerOptions\": {\n \"types\": [\"node\"]\n },\n \"include\": [\"src\", \"tsup.config.ts\"]\n}\nEOF\ncat > tsup.config.ts <<'EOF'\nimport { defineConfig } from \"tsup\";\n\nexport default defineConfig({\n entry: [\"src/api.ts\", \"src/worker.ts\"],\n format: \"esm\",\n platform: \"node\",\n target: \"node24\",\n splitting: true,\n clean: true,\n sourcemap: true,\n // Workspace packages ship TypeScript source, so they are bundled in;\n // third-party dependencies stay external and are installed in the image.\n noExternal: [/^@app\\//],\n});\nEOF\ncat > src/config.ts <<'EOF'\nimport { z } from \"zod\";\n\nconst commaList = z\n .string()\n .default(\"\")\n .transform((value) => value.split(\",\").map((item) => item.trim()).filter(Boolean));\n\nconst Env = z.object({\n NODE_ENV: z.enum([\"development\", \"test\", \"production\"]).default(\"development\"),\n API_PORT: z.coerce.number().int().positive().default(4000),\n LOG_LEVEL: z.enum([\"fatal\", \"error\", \"warn\", \"info\", \"debug\", \"trace\"]).default(\"info\"),\n CORS_ORIGINS: commaList,\n REDIS_URL: z.url().default(\"redis://localhost:6379\"),\n PLUGINS: commaList,\n WORKER_CONCURRENCY: z.coerce.number().int().positive().default(4),\n});\n\nexport type Config = z.infer;\n\nexport function loadConfig(env: NodeJS.ProcessEnv = process.env): Config {\n const parsed = Env.safeParse(env);\n if (!parsed.success) {\n throw new Error(`Invalid environment:\\n${z.prettifyError(parsed.error)}`);\n }\n return parsed.data;\n}\nEOF\ncat > src/lib/logger.ts <<'EOF'\nimport { pino, type LoggerOptions } from \"pino\";\nimport type { Config } from \"../config.ts\";\n\nexport function loggerOptions(config: Config): LoggerOptions {\n return {\n level: config.LOG_LEVEL,\n ...(config.NODE_ENV === \"development\" && { transport: { target: \"pino-pretty\", options: { singleLine: true } } }),\n };\n}\n\nexport const createLogger = (config: Config) => pino(loggerOptions(config));\nEOF\ncat > src/plugins/registry.ts <<'EOF'\nimport type { Plugin } from \"@app/core\";\n\n/**\n * Every plugin the backend can load, keyed by its folder name under plugins/.\n * Imports are lazy, so only plugins listed in PLUGINS are loaded at runtime.\n * Adding a plugin: create plugins/, add it to backend's dependencies, add one line here.\n */\nexport const pluginRegistry: Record Promise<{ default: Plugin }>> = {\n \"serpapi-google-maps\": \n… [10658 more characters truncated]\nok\n```\n\nNow the two frontends. The client app gets Plane's dense, neutral app-shell look, and the consumer app gets a calmer, more editorial theme. Both support light and dark.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nmk() { # $1=app dir name, $2=port, $3=title, $4=storage key\nd=$R/frontend/$1; mkdir -p $d/src\ncat > $d/package.json < $d/tsconfig.json <<'EOF'\n{\n \"extends\": \"../../tsconfig.base.json\",\n \"compilerOptions\": {\n \"lib\": [\"ES2023\", \"DOM\", \"DOM.Iterable\"],\n \"jsx\": \"react-jsx\",\n \"types\": [\"vite/client\"]\n },\n \"include\": [\"src\", \"vite.config.ts\"]\n}\nEOF\ncat > $d/vite.config.ts <<'EOF'\nimport { defineConfig } from \"vite\";\nimport react from \"@vitejs/plugin-react\";\n\nexport default defineConfig({\n plugins: [react()],\n server: {\n // Same-origin API calls in development; production routes /v1 through the ingress.\n proxy: { \"/v1\": \"http://localhost:4000\", \"/health\": \"http://localhost:4000\" },\n },\n});\nEOF\ncat > $d/index.html <\n\n \n \n \n $3\n \n \n \n
\n \n \n\nEOF\ncat > $d/src/main.tsx <\n \n \n \n ,\n);\nEOF\n}\nmk consumer 5173 \"Ask about anything\" \"consumer-theme\"\nmk client 5174 \"Advisor\" \"client-theme\"\n\n# ---------- client (sellers / owners / admin): Plane-like, dense, neutral ----------\nC=$R/frontend/client/src\ncat > $C/fonts.ts <<'EOF'\nimport \"@fontsource-variable/inter\";\nimport \"@fontsource/ibm-plex-mono/400.css\";\nEOF\ncat > $C/theme.css <<'EOF'\n/* Client theme: dense, neutral, one restrained blue accent (after plane.so). */\n:root {\n --font-sans: \"Inter Variable\", system-ui, sans-serif;\n --font-display: var(--font-sans);\n --font-mono: \"IBM Plex Mono\", ui-monospace, monospace;\n --text-size: 13px;\n --radius: 6px;\n}\n:root[data-theme=\"light\"] {\n --bg: #f7f7f8;\n --surface: #ffffff;\n --surface-hover: #f2f3f3;\n --border: #e4e6e7;\n --border-strong: #cacdce;\n --text: #1d1f20;\n\n… [7010 more characters truncated]\nok\n```\n\nNow install the dependencies with pnpm.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nset -e\npnpm add -w -D typescript @types/node vitest 2>&1 | tail -3\npnpm --filter @app/core add zod 2>&1 | tail -2\npnpm --filter @app/core add -D @types/node 2>&1 | tail -1\npnpm --filter @app/ui add react 2>&1 | tail -1\npnpm --filter @app/ui add -D @types/react 2>&1 | tail -1\npnpm --filter @app/plugin-serpapi-google-maps add zod @app/core@workspace:* 2>&1 | tail -1\npnpm --filter @app/backend add fastify @fastify/cors @fastify/sensible fastify-type-provider-zod zod bullmq pino @app/core@workspace:* @app/plugin-serpapi-google-maps@workspace:* 2>&1 | tail -2\npnpm --filter @app/backend add -D tsx tsup pino-pretty concurrently @types/node 2>&1 | tail -2\nfor app in consumer client; do\n pnpm --filter @app/$app add react react-dom @app/ui@workspace:* @app/core@workspace:* @fontsource-variable/inter @fontsource/ibm-plex-mono 2>&1 | tail -1\n pnpm --filter @app/$app add -D vite @vitejs/plugin-react @types/react @types/react-dom 2>&1 | tail -1\ndone\npnpm --filter @app/consumer add @fontsource-variable/source-serif-4 2>&1 | tail -1\n+ vitest 5.0.4\n\nDone in 11.1s using pnpm v12.11.2\nProgress: resolved 1, reused 0, downloaded 1, added 1, done\nDone in 912ms using pnpm v12.11.2\nDone in 87ms using pnpm v12.11.2\nDone in 2.7s using pnpm v12.11.2\nDone in 548ms using pnpm v12.11.2\nDone in 117ms using pnpm v12.11.2\n to run scripts.\n\n to run scripts.\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nfor p in backend frontend/consumer frontend/client; do echo \"== $p\"; node -e \"const p=require('./$p/package.json');console.log(Object.keys({...p.dependencies,...p.devDependencies}).join(' '))\"; done\npnpm --filter @app/backend add -D tsx 2>&1 | tail -15; cat pnpm-workspace.yaml\n== backend\n@app/core @app/plugin-serpapi-google-maps @fastify/cors @fastify/sensible bullmq fastify fastify-type-provider-zod pino zod @types/node concurrently pino-pretty tsup tsx\n== frontend/consumer\n@app/core @app/ui @fontsource-variable/inter @fontsource-variable/source-serif-4 @fontsource/ibm-plex-mono react react-dom @types/react @types/react-dom @vitejs/plugin-react vite\n== frontend/client\n@app/core @app/ui @fontsource-variable/inter @fontsource/ibm-plex-mono react react-dom @types/react @types/react-dom @vitejs/plugin-react vite\n✓ Lockfile passes supply-chain policies (verified 6s ago)\nLockfile is up to date, resolution step is skipped\nAlready up to date\nError: ERR_PNPM_IGNORED_BUILDS\n\n × adding a new package\n ╰─▶ Ignored build scripts: esbuild@0.27.7, esbuild@0.28.2, msgpackr-\n extract@3.0.4\n help: Run \"pnpm approve-builds\" to pick which dependencies should be allowed\n to run scripts.\n\npackages:\n - backend\n - frontend/*\n - plugins/*\n - common/*\n```\n\nAll dependencies are installed. pnpm 12 blocks packages' install scripts by default, so I'll explicitly allow esbuild (used by Vite and tsup) and msgpackr-extract (a native speed-up for BullMQ).\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ncat >> pnpm-workspace.yaml <<'EOF'\n\nallowBuilds:\n esbuild: true\n msgpackr-extract: true\nEOF\npnpm install 2>&1 | tail -6\nbash\n.../esbuild@0.28.2/node_modules/esbuild postinstall$ node install.js\n.../esbuild@0.27.7/node_modules/esbuild postinstall$ node install.js\n.../esbuild@0.28.2/node_modules/esbuild postinstall: Done\n.../esbuild@0.27.7/node_modules/esbuild postinstall: Done\n.../node_modules/msgpackr-extract install: Done\nDone in 283ms using pnpm v12.11.2\n```\n\nNow typecheck everything.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\npnpm typecheck 2>&1 | grep -vE \"^\\s*$\" | tail -40\nbash\n$ pnpm -r typecheck\ncommon/ui typecheck$ tsc -p tsconfig.json\ncommon/core typecheck$ tsc -p tsconfig.json\ncommon/core typecheck: Done\nplugins/serpapi-google-maps typecheck$ tsc -p tsconfig.json\ncommon/ui typecheck: Done\nfrontend/client typecheck$ tsc -p tsconfig.json\nfrontend/consumer typecheck$ tsc -p tsconfig.json\nfrontend/consumer typecheck: Done\nfrontend/client typecheck: Done\nplugins/serpapi-google-maps typecheck: Done\nbackend typecheck$ tsc -p tsconfig.json\nbackend typecheck: Done\n```\n\nTypecheck passes everywhere. Now I'll run the API for real: health check, the plugin loading, and SSE streaming. With a dummy SerpApi key, the search should come back as a clean error.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nLOG=\nPLUGINS=serpapi-google-maps SERPAPI_API_KEY=dummy API_PORT=4123 NODE_ENV=production npx tsx src/api.ts > $LOG 2>&1 &\nPID=$!\nfor i in $(seq 1 30); do curl -s localhost:4123/health >/dev/null && break; sleep 0.5; done\necho \"--- health\"; curl -s localhost:4123/health; echo\necho \"--- sources\"; curl -s localhost:4123/v1/sources; echo\necho \"--- search (dummy key)\"; curl -s \"localhost:4123/v1/search?source=google_maps&q=pizza\"; echo\necho \"--- bad source\"; curl -s \"localhost:4123/v1/search?source=nope&q=pizza\"; echo\necho \"--- validation\"; curl -s \"localhost:4123/v1/search?source=google_maps&q=a\"; echo\necho \"--- sse\"; curl -sN -H \"Origin: http://localhost:5173\" -D - \"localhost:4123/v1/reviews/stream?source=google_maps&id=ChIJabc\" | head -20\nkill $PID; wait $PID 2>/dev/null; echo \"--- log\"; head -c 1500 $LOG\n--- health\n{\"status\":\"ok\"}\n--- sources\n[{\"source\":\"google_maps\",\"name\":\"Google Maps (SerpApi)\"}]\n--- search (dummy key)\n{\"statusCode\":500,\"error\":\"Internal Server Error\",\"message\":\"SerpApi google_maps failed (401): Invalid API key. Your API key should be here: https://serpapi.com/manage-api-key\"}\n--- bad source\n{\"statusCode\":400,\"error\":\"Bad Request\",\"message\":\"Unknown or disabled source \\\"nope\\\"\"}\n--- validation\n{\"statusCode\":400,\"code\":\"FST_ERR_VALIDATION\",\"error\":\"Bad Request\",\"message\":\"querystring/q Too small: expected string to have >=2 characters\"}\n--- sse\nHTTP/1.1 200 OK\nContent-Type: text/event-stream; charset=utf-8\nCache-Control: no-cache, no-transform\nConnection: keep-alive\nX-Accel-Buffering: no\nAccess-Control-Allow-Origin: http://localhost:5173\nVary: Origin\nDate: Wed, 14 Oct 2026 12:56:20 GMT\nTransfer-Encoding: chunked\n\nevent: progress\ndata: {\"type\":\"progress\",\"stage\":\"fetch\",\"message\":\"Fetching reviews…\"}\n\nevent: error\ndata: {\"type\":\"error\",\"message\":\"SerpApi google_maps_reviews failed (401): Invalid API key. Your API key should be here: https://serpapi.com/manage-api-key\"}\n\n--- log\nnpm notice run @app/backend@0.0.0 npx\nnpm notice run 'tsx' src/api.ts\n{\"level\":30,\"time\":1791982580145,\"pid\":855292,\"hostname\":\"fedora\",\"plugin\":\"serpapi-google-maps\",\"source\":\"google_maps\",\"msg\":\"plugin loaded\"}\n{\"level\":30,\"time\":1791982580162,\"pid\":855292,\"hostname\":\"fedora\",\"msg\":\"Server listening at http://127.0.0.1:4123\"}\n{\"level\":30,\"time\":1791982580162,\"pid\":855292,\"hostname\":\"fedora\",\"msg\":\"Server listening at http://192.168.29.195:4123\"}\n{\"level\":30,\"time\":1791982580162,\"pid\":855292,\"hostname\":\"fedora\",\"msg\":\"Server listening at http://172.20.0.1:4123\"}\n{\"level\":30,\"time\":1791982580226,\"pid\":855292,\"hostname\":\"fedora\",\"reqId\":\"req-1\",\"req\":{\"method\":\"GET\",\"url\":\"/health\",\"host\":\"localhost:4123\",\"remoteAddress\":\"127.0.0.1\",\"remotePort\":35176},\"msg\":\"incoming request\"}\n{\"level\":30,\"time\":1791982580233,\"pid\":855292,\"hostname\":\"fedora\",\"reqId\":\"req-1\",\"res\":{\"statusCode\":200},\"responseTime\":6.882140999999933,\"msg\":\"request completed\"}\n{\"level\":30,\"time\":1791982580245,\"pid\":855292,\"hostname\":\"fedora\",\"reqId\":\"req-2\",\"req\":{\"method\":\"GET\",\"url\":\"/health\",\"host\":\"localhost:4123\",\"remoteAddress\":\"127.0.0.1\",\"remotePort\":35186},\"msg\":\"incoming request\"}\n{\"level\":30,\"time\":1791982580246,\"pid\":855292,\"hostname\":\"fedora\",\"reqId\":\"req-2\",\"res\":{\"statusCode\":200},\"responseTime\":0.5730419999999867,\"msg\":\"request completed\"}\n{\"level\":30,\"time\":1791982580259,\"pi\n… [123 more characters truncated]\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ndocker compose up -d 2>&1 | tail -2\nSP=\ncd backend\nPLUGINS=serpapi-google-maps SERPAPI_API_KEY=dummy NODE_ENV=production npx tsx src/worker.ts > $SP/worker.log 2>&1 &\nPID=$!; sleep 3\ncat > $SP/enqueue.ts <<'EOF'\nimport { Queue } from \"bullmq\";\nconst q = new Queue(\"ingest\", { connection: { host: \"localhost\", port: 6379 } });\nconst job = await q.add(\"ingest\", { entity: { source: \"google_maps\", externalId: \"ChIJabc\" }, maxPages: 1 }, { attempts: 1 });\nconsole.log(\"enqueued\", job.id);\nawait q.close();\nEOF\ncp $SP/enqueue.ts ./enqueue-tmp.ts && npx tsx enqueue-tmp.ts; rm enqueue-tmp.ts\nsleep 3; kill $PID; wait $PID 2>/dev/null\ngrep -oE '\"msg\":\"[^\"]+\"|\"message\":\"[^\"]{0,80}' $SP/worker.log | head\necho \"--- build backend\"; pnpm build 2>&1 | grep -E \"Build success|error|dist/\" | head\ncd $R; for a in consumer client; do echo \"--- build $a\"; pnpm --filter @app/$a build 2>&1 | grep -E \"built in|error|dist/assets/index.*\\.(js|css)\" ; done\nContainer lets-make-osp-that-someone-will-actually-pay-for-redis-1 Starting \nError response from daemon: failed to set up container networking: driver failed programming external connectivity on endpoint lets-make-osp-that-someone-will-actually-pay-for-redis-1 (8776d54c7e46d9da99beaaefd206a88536f418a40c91a3f33771b111e694d395): failed to bind host port 0.0.0.0:6379/tcp: address already in use\nnpm notice run @app/backend@0.0.0 npx\nnpm notice run 'tsx' enqueue-tmp.ts\nError: BullMQ could not load the optional 'ioredis' package. Install it with `npm install ioredis`, or provide a different Redis client instance (e.g. node-redis) via the connection option. In a native ESM environment, pass an already-constructed client instance instead of connection options.\n at loadIORedis (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/classes/redis-connection.js:54:11)\n at RedisConnection.init (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/classes/redis-connection.js:260:37)\n at new RedisConnection (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/classes/redis-connection.js:153:34)\n at Queue.createRedisBackend [as backendFactory] (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/utils/create-backend.js:55:24)\n at Queue.createBackend (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/classes/queue-base.js:62:29)\n at new QueueBase (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/classes/queue-base.js:29:14)\n at new Queue (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/bullmq@6.3.14/node_modules/bullmq/dist/cjs/classes/queue.js:39:9)\n at (~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/enqueue-tmp.ts:2:11)\n at ModuleJob.run (node:internal/modules/esm/module_job:561:25)\n at async node:internal/modules/esm/loader:647:26\nnode:internal/modules/run_main:107\n triggerUncaughtException(\n ^\n\nError: BullMQ could not load the optional 'ioredis' package. Install it with `npm install io\n… [3741 more characters truncated]\nbash\n$ ss -ltnp 2>/dev/null | grep -E ':6379\\b'; docker ps --format '{{.Names}}\\t{{.Image}}\\t{{.Ports}}' | grep -i 6379; R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; docker compose -f $R/docker-compose.yml ps -a --format '{{.Name}} {{.State}}'\nLISTEN 0 511 127.0.0.1:6379 0.0.0.0:* \nLISTEN 0 511 [::1]:6379 [::]:* \njob_scheduler-redis-1 redis:7-alpine 0.0.0.0:32776->6379/tcp, [::]:32776->6379/tcp\nlets-make-osp-that-someone-will-actually-pay-for-redis-1 created\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nsed -i 's/\"6379:6379\"/\"6380:6379\" # host 6380: 6379 is often taken by a system Redis/' docker-compose.yml\nsed -i 's#REDIS_URL=redis://localhost:6379#REDIS_URL=#' .env.example\nsed -i 's#default(\"redis://localhost:6379\")#default(\"\")#' backend/src/config.ts\npnpm --filter @app/backend add ioredis 2>&1 | tail -1\ncat > backend/src/lib/queue.ts <<'EOF'\nimport { Queue } from \"bullmq\";\nimport { Redis } from \"ioredis\";\nimport type { Config } from \"../config.ts\";\n\nexport const QueueName = {\n ingest: \"ingest\",\n} as const;\n\n/**\n * BullMQ 6 needs a ready-made client under ESM. maxRetriesPerRequest must be null\n * so workers' blocking commands are never cut off.\n */\nexport const createRedis = (config: Config) => new Redis(config.REDIS_URL, { maxRetriesPerRequest: null });\n\nexport const createIngestQueue = (config: Config) => new Queue(QueueName.ingest, { connection: createRedis(config) });\nEOF\npython3 - <<'EOF'\np='backend/src/worker.ts'; s=open(p).read()\ns=s.replace('import { QueueName, redisConnection } from \"./lib/queue.ts\";','import { QueueName, createRedis } from \"./lib/queue.ts\";')\ns=s.replace('connection: redisConnection(config),','connection: createRedis(config),')\nopen(p,'w').write(s)\nEOF\ndocker compose up -d 2>&1 | tail -1; grep -n \"6380\\|redis\" docker-compose.yml | head -3\nDone in 1.3s using pnpm v12.11.2\n Container lets-make-osp-that-someone-will-actually-pay-for-redis-1 Started \n4: redis:\n5: image: redis:8-alpine\n7: - \"6380:6379\" # host 6380: 6379 is often taken by a system Redis\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nSP=\nPLUGINS=serpapi-google-maps SERPAPI_API_KEY=dummy NODE_ENV=production npx tsx src/worker.ts > $SP/worker.log 2>&1 &\nPID=$!; sleep 3\ncat > enqueue-tmp.ts <<'EOF'\nimport { loadConfig } from \"./src/config.ts\";\nimport { createIngestQueue } from \"./src/lib/queue.ts\";\nconst q = createIngestQueue(loadConfig());\nconst job = await q.add(\"ingest\", { entity: { source: \"google_maps\", externalId: \"ChIJabc\" }, maxPages: 1 }, { attempts: 1 });\nconsole.log(\"enqueued job\", job.id);\nawait q.close();\nEOF\nnpx tsx enqueue-tmp.ts 2>&1 | grep -v notice; rm enqueue-tmp.ts\nsleep 3; kill $PID; wait $PID 2>/dev/null\ngrep -oE '\"msg\":\"[^\"]+\"|\"job\":\"[^\"]+\"|\"message\":\"[^\"]{0,70}' $SP/worker.log\ncd $R && pnpm typecheck 2>&1 | grep -E \"error|backend typecheck: Done\"\nCommand did not complete within its 120s timeout and was moved to the background (ID: bejw4l2s4). Output is being written to: You will be notified when it completes. If it is still running after 30m in the background, it will be stopped and you will be notified. To check interim output, use Read on that file path.\nSession cwd remains ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; directory changes made by the backgrounded command do not apply to subsequent commands.\nbash\n$ cat echo ---; cat | cut -c1-300; echo; ps aux | grep -E \"tsx|worker.ts|enqueue\" | grep -v grep | awk '{print $2, $11, $12, $13, $14}'\n---\nnpm notice run @app/backend@0.0.0 npx\nnpm notice run 'tsx' src/worker.ts\n{\"level\":30,\"time\":1791982645286,\"pid\":858110,\"hostname\":\"fedora\",\"plugin\":\"serpapi-google-maps\",\"source\":\"google_maps\",\"msg\":\"plugin loaded\"}\n{\"level\":30,\"time\":1791982645290,\"pid\":858110,\"hostname\":\"fedora\",\"queue\":\"ingest\",\"concurrency\":4,\"msg\":\"worker started\"}\n{\"level\":50,\"time\":1791982649219,\"pid\":858110,\"hostname\":\"fedora\",\"job\":\"1\",\"err\":{\"type\":\"Error\",\"message\":\"SerpApi google_maps_reviews failed (401): Invalid API key. Your API key should be here: https://serpapi.com/manage-api-key\",\"stack\":\"Error: SerpApi google_maps_reviews failed (401): Invalid A\n\n858070 npm exec tsx src/worker.ts\n858092 node ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/node_modules/.bin/../../../node_modules/.pnpm/tsx@4.23.15/node_modules/tsx/dist/cli.mjs src/worker.ts \n858110 ~/.nvm/versions/node/v24.21.0/bin/node --require ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/tsx@4.23.15/node_modules/tsx/dist/preflight.cjs --import\n858170 npm exec tsx enqueue-tmp.ts\n858184 node ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/node_modules/.bin/../../../node_modules/.pnpm/tsx@4.23.15/node_modules/tsx/dist/cli.mjs enqueue-tmp.ts \n858203 ~/.nvm/versions/node/v24.21.0/bin/node --require ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/tsx@4.23.15/node_modules/tsx/dist/preflight.cjs --import\nbash\n$ kill 858070 858092 858110 858170 858184 858203 2>/dev/null; sleep 1\nR=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\ncat > src/lib/queue.ts <<'EOF'\nimport { Queue } from \"bullmq\";\nimport { Redis } from \"ioredis\";\nimport type { Config } from \"../config.ts\";\n\nexport const QueueName = {\n ingest: \"ingest\",\n} as const;\n\n/**\n * BullMQ 6 needs a ready-made client under ESM. maxRetriesPerRequest must be null\n * so workers' blocking commands are never cut off. BullMQ does not close clients\n * it was given, so callers must quit() them on shutdown.\n */\nexport const createRedis = (config: Config) => new Redis(config.REDIS_URL, { maxRetriesPerRequest: null });\n\nexport function createIngestQueue(config: Config) {\n const redis = createRedis(config);\n const queue = new Queue(QueueName.ingest, { connection: redis });\n return {\n queue,\n async close() {\n await queue.close();\n await redis.quit();\n },\n };\n}\nEOF\npython3 - <<'EOF'\np='src/worker.ts'; s=open(p).read()\ns=s.replace(\"\"\"const worker = new Worker(QueueName.ingest, ingestProcessor(connectors), {\n connection: createRedis(config),\"\"\",\"\"\"const redis = createRedis(config);\nconst worker = new Worker(QueueName.ingest, ingestProcessor(connectors), {\n connection: redis,\"\"\")\ns=s.replace(\"\"\" await worker.close();\n\"\"\",\"\"\" await worker.close();\n await redis.quit();\n\"\"\")\nopen(p,'w').write(s)\nEOF\ngrep -n \"redis\" src/worker.ts\njs\n12:const redis = createRedis(config);\n14: connection: redis,\n26: await redis.quit();\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nSP=\nTSX=$R/node_modules/.bin/tsx; [ -x $TSX ] || TSX=./node_modules/.bin/tsx\nPLUGINS=serpapi-google-maps SERPAPI_API_KEY=dummy NODE_ENV=production timeout 20 $TSX src/worker.ts > $SP/worker.log 2>&1 &\nPID=$!; sleep 3\ncat > enqueue-tmp.ts <<'EOF'\nimport { loadConfig } from \"./src/config.ts\";\nimport { createIngestQueue } from \"./src/lib/queue.ts\";\nconst { queue, close } = createIngestQueue(loadConfig());\nconst job = await queue.add(\"ingest\", { entity: { source: \"google_maps\", externalId: \"ChIJabc\" }, maxPages: 1 }, { attempts: 1 });\nconsole.log(\"enqueued job\", job.id);\nawait close();\nEOF\ntimeout 20 $TSX enqueue-tmp.ts; echo \"enqueue exit: $?\"; rm enqueue-tmp.ts\nsleep 2; kill -TERM $PID; wait $PID; echo \"worker exit: $?\"\ngrep -oE '\"msg\":\"[^\"]+\"|\"job\":\"[^\"]+\"' $SP/worker.log\ncd $R && timeout 100 pnpm typecheck 2>&1 | grep -E \"error|: Done\"\nenqueued job 2\nenqueue exit: 0\nworker exit: 0\n\"msg\":\"plugin loaded\"\n\"msg\":\"worker started\"\n\"job\":\"2\"\n\"msg\":\"ingest job failed\"\n\"msg\":\"shutting down worker\"\ncommon/core typecheck: Done\ncommon/ui typecheck: Done\nfrontend/consumer typecheck: Done\nfrontend/client typecheck: Done\nplugins/serpapi-google-maps typecheck: Done\nbackend typecheck: Done\n```\n\nEverything passes. Now I'll record the stack decisions in the requirements doc and in my notes.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\npython3 - <<'EOF'\np='docs/REQUIREMENTS.md'; s=open(p).read()\nstack = \"\"\"## Tech stack (decided 2026-10-14)\n\n| Area | Choice |\n|---|---|\n| Language | TypeScript everywhere, Node 24 (`.nvmrc`) |\n| Package manager | pnpm 12 workspaces |\n| Backend | Fastify 5 + `fastify-type-provider-zod`; SSE for streaming (` backend/src/lib/sse.ts`) |\n| Validation | Zod 4, for requests, env config, connector payloads and model outputs |\n| Background jobs | BullMQ + Redis; the worker is a second entry point in `backend` (`src/worker.ts`) |\n| Agent runtime | LangGraph.js |\n| Model serving | vLLM (OpenAI-compatible API); open-weight model to be chosen by the user |\n| Frontend | React + Vite: `frontend/consumer` and `frontend/client`, each with its own minimal light and dark theme (after plane.so) |\n| Containers | Docker; Kubernetes base manifests prepared, not deployed on day 1 |\n\nWorkspace layout:\n\n```\nbackend/ Fastify API + BullMQ worker (two start commands, one image)\nfrontend/consumer/ consumer app (@app/consumer)\nfrontend/client/ owner, seller and admin app (@app/client)\nplugins// one package per external integration (@app/plugin-), loaded from the PLUGINS env var\ncommon/core/ types, Zod schemas, plugin interface (browser-safe; Node-only helpers at @app/core/node)\ncommon/ui/ shared React primitives and the theme system; colors and fonts are set by each app\n```\n\n---\n\n\"\"\"\ns=s.replace(\"## Users\\n\", stack + \"## Users\\n\",1)\nopen(p,'w').write(s)\nEOF\nM=~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory\ncat >> $M/hf26-week2-project.md <<'EOF'\n\nStack decided 2026-10-14 (user's choices): TypeScript, pnpm workspaces (backend, frontend/consumer, frontend/client, plugins/ one package per integration, common/core + common/ui), Fastify + SSE + Zod, BullMQ + Redis (worker inside backend), LangGraph.js, vLLM serving (user will pick the open model), Docker + Kubernetes base (not deployed day 1). LangGraph replaced Mastra. Each frontend has its own hyper-minimal light/dark theme referencing plane.so. Local Redis runs on host port 6380 because the user's machine already has a system Redis on 6379.\nEOF\necho ok\nok\n```\n\nThe project is set up and tested with your stack: TypeScript, pnpm 12, Fastify with SSE, Zod, BullMQ + Redis, and two React apps. Typecheck and production builds pass for every workspace. I also ran the API and the background worker against a local Redis, using a dummy SerpApi key.\n\n```\nbackend/ Fastify API + BullMQ worker (one image, two start commands)\nfrontend/consumer/ consumer app: calm, editorial look with serif headings\nfrontend/client/ owner/seller/admin app: Plane-style dense, neutral sidebar layout\nplugins/serpapi-google-maps/ first connector (search, place details, reviews by page)\ncommon/core/ Zod schemas, the plugin interface, shared utilities\ncommon/ui/ theme system and basic components (Button, Input, Card, ThemeToggle)\n```\n\n`PLUGINS` environment variable and loaded only when enabled. Each plugin validates its own settings with Zod, so a missing API key fails at startup with a clear message.`pnpm dev` and open localhost:5173 (consumer) and 5174 (client).`.env.example` uses that port.`ioredis` installed separately`esbuild` and `msgpackr-extract` in `pnpm-workspace.yaml`.` git init` but made no commit.`docs/REQUIREMENTS.md`.`@app/*` package names and the \"Advisor\" placeholder in both apps.`.env`, I can test the Google Maps connector against real data and start on storage (MongoDB, then Neo4j).\nLeave serp api step for now. Keep it in backlog. For openweight model, use lightest version that can be quickly downloaded and tested. use any qwen model for now and vllm will run on most likely GKE. thats my goal for now, so build accordingly. Name of app is \"Perforvisor\"\n\nStarting with your machine's hardware and tools, plus the current Qwen models and vLLM releases, so I pick a model that actually runs here.\n\n``` bash\n$ nvidia-smi --query-gpu=name,memory.total --format=csv 2>&1 | head -3; lspci 2>/dev/null | grep -iE \"vga|3d\" ; nproc; free -g | head -2; lscpu | grep -E \"Model name|Flags\" | cut -c1-120 | head -2; lscpu | grep -o -E \"avx512f|avx2\" | sort -u; for t in kubectl kind k3d minikube gcloud helm kustomize; do printf \"%s: \" $t; command -v $t || echo missing; done; df -h ~ | tail -1\nname, memory.total [MiB]\nNVIDIA GeForce RTX 4060 Laptop GPU, 8188 MiB\n00:02.0 VGA compatible controller: Intel Corporation Raptor Lake-P [UHD Graphics] (rev 04)\n01:00.0 VGA compatible controller: NVIDIA Corporation AD107M [GeForce RTX 4060 Max-Q / Mobile] (rev a1)\n16\n total used free shared buff/cache available\nMem: 15 9 3 0 3 5\nModel name: 13th Gen Intel(R) Core(TM) i7-13620H\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush\navx2\nkubectl: missing\nkind: missing\nk3d: missing\nminikube: missing\ngcloud: missing\nhelm: missing\nkustomize: missing\n/dev/nvme0n1p7 324G 194G 127G 61% /home\nbash\n$ curl -s \"https://huggingface.co/api/models?author=Qwen&sort=createdAt&direction=-1&limit=60\" | python3 -c \"\nimport json,sys\nfor m in json.load(sys.stdin):\n i=m['id']\n if any(s in i for s in ['0.5B','0.6B','0.8B','1B','1.5B','1.7B','2B']) and not any(x in i for x in ['GGUF','AWQ','GPTQ','MLX','FP8','Embedding','Reranker','VL','Omni','Coder','Math','Guard','TTS','ASR','Audio','Base','Image']): print(i, m.get('createdAt','')[:10])\n\"; echo ---; curl -s https://pypi.org/pypi/vllm/json | python3 -c \"import json,sys;d=json.load(sys.stdin);print('vllm', d['info']['version'])\"; curl -s \"https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags?page_size=5&ordering=last_updated\" | python3 -c \"import json,sys;print([t['name'] for t in json.load(sys.stdin)['results']])\"; curl -s \"https://hub.docker.com/v2/repositories/vllm/vllm-openai-cpu/tags?page_size=5&ordering=last_updated\" | python3 -c \"import json,sys;d=json.load(sys.stdin);print('cpu:',[t['name'] for t in d.get('results',[])] or d)\"\nQwen/Qwen3-ForcedAligner-0.6B-hf 2026-06-26\nQwen/Qwen3.5-0.8B 2026-02-28\nQwen/Qwen3.5-2B 2026-02-28\nQwen/Qwen3.5-122B-A10B 2026-02-24\nQwen/WebWorld-32B 2026-02-13\n---\nvllm 0.31.0\n['cu134-nightly-77154ca6116ae34cbfe314beef1016bde9b63989', 'cu134-nightly', 'cu134-nightly-aarch64', 'cu134-nightly-x86_64', 'cu129-nightly-77154ca6116ae34cbfe314beef1016bde9b63989']\ncpu: ['nightly-77154ca6116ae34cbfe314beef1016bde9b63989', 'nightly', 'nightly-arm64', 'nightly-x86_64', 'nightly-5555a2d8f7116b2b4877f66516f4128176cf722a']\nbash\n$ for m in Qwen/Qwen3.5-0.8B Qwen/Qwen3-0.6B; do echo \"== $m\"; curl -s \"https://huggingface.co/api/models/$m?expand[]=safetensors&expand[]=cardData&expand[]=config&expand[]=pipeline_tag\" | python3 -c \"\nimport json,sys;d=json.load(sys.stdin)\nprint('license:',(d.get('cardData') or {}).get('license'),'| pipeline:',d.get('pipeline_tag'))\nprint('arch:',(d.get('config') or {}).get('architectures'))\nst=d.get('safetensors') or {}; print('params:',st.get('total'),'dtypes:',st.get('parameters'))\"; done\ncurl -s https://huggingface.co/Qwen/Qwen3.5-0.8B/raw/main/README.md | grep -iE -m12 \"vllm serve|reasoning-parser|tool-call-parser|enable-thinking|language-model-only|context|multimodal|vision\"\necho \"--- docker gpu\"; docker info 2>/dev/null | grep -iE \"runtimes|nvidia|cdi\" ; command -v nvidia-ctk; ls /etc/cdi 2>/dev/null\nExit code 2\n== Qwen/Qwen3.5-0.8B\nlicense: apache-2.0 | pipeline: image-text-to-text\narch: ['Qwen3_5ForConditionalGeneration']\nparams: 873438784 dtypes: {'F32': 2592, 'BF16': 873436192}\n== Qwen/Qwen3-0.6B\nlicense: apache-2.0 | pipeline: text-generation\narch: ['Qwen3ForCausalLM']\nparams: 751632384 dtypes: {'BF16': 751632384}\nOver recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.\n- **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.\n- **Next-Generation Training Infrastructure**: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.\n- Type: Causal Language Model with Vision Encoder\n- Context Length: 262,144 natively\nLong Context (Thinking）\n### Vision Language\n* MathVision: our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \\boxed{}.” For other models, we report the higher score between runs with and without the \\boxed{} formatting.
\n> The model has a default context length of 262,144 tokens.\n> If you encounter out-of-memory (OOM) errors, consider reducing the context window. \n[SGLang](https://github.com/sgl-project/sglang) is a fast serving framework for large language models and vision language models.\n- **Standard Version**: The following command can be used to create an API endpoint with maximum context length 262,144 tokens using tensor parallel on 8 GPUs.\n--- docker gpu\n CDI spec directories:\n /etc/cdi\n /var/run/cdi\n Runtimes: io.containerd.runc.v2 runc\nbash\n$ curl -s https://huggingface.co/Qwen/Qwen3.5-0.8B/raw/main/README.md | grep -E -A3 \"^vllm serve|vllm serve Qwen\" | head -40; echo ---; ls /etc/cdi /var/run/cdi 2>&1; rpm -qa 2>/dev/null | grep -iE \"nvidia-container|nvidia-ctk|akmod-nvidia|xorg-x11-drv-nvidia-[0-9]\" | head; cat /proc/driver/nvidia/version 2>/dev/null | head -1\nvllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 \n ```\n\n- **Tool Call**: To support tool use, you can use the following command.\n--\n vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder \n ```\n\n- **Multi-Token Prediction (MTP)**: The following command is recommended for MTP:\n--\n vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --speculative-config '{\"method\":\"qwen3_next_mtp\",\"num_speculative_tokens\":2}'\n ```\n\n- **Text-Only**: The following command skips the vision encoder and multimodal profiling to free up memory for additional KV cache:\n--\n vllm serve Qwen/Qwen3.5-0.8B --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --language-model-only\n ```\n\n#### KTransformers\n---\nls: cannot access '/etc/cdi': No such file or directory\nls: cannot access '/var/run/cdi': No such file or directory\nxorg-x11-drv-nvidia-610.43.03-1.fc44.x86_64\nakmod-nvidia-610.43.03-1.fc44.x86_64\nNVRM version: NVIDIA UNIX Open Kernel Module for x86_64 610.43.03 Release Build (dvs-builder@U22-I3-AD05-33-5) Tue Jun 30 23:47:20 UTC 2026\nbash\n$ curl -s \"https://hub.docker.com/v2/repositories/vllm/vllm-openai/tags?page_size=10&name=v0.3\" | python3 -c \"import json,sys;print([(t['name'],round(t['full_size']/1e9,1)) for t in json.load(sys.stdin)['results']])\"; curl -s https://huggingface.co/Qwen/Qwen3-0.6B/raw/main/README.md | grep -E -i \"vllm serve|reasoning-parser|enable_thinking|presence\" | head -8; command -v uv\n[('v0.31.0-cu129', 11.8), ('v0.31.0-aarch64-cu129', 11.8), ('v0.31.0-cu129-ubuntu2404', 11.8), ('v0.31.0-aarch64-cu129-ubuntu2404', 11.8), ('v0.31.0-x86_64-zstd', 7.2), ('v0.31.0-ubuntu2404', 10.1), ('v0.31.0-aarch64-ubuntu2404', 10.1), ('v0.31.0', 10.1), ('v0.31.0-aarch64', 10.1), ('v0.31.0-x86_64-cu129', 11.0)]\n> If you encounter significant endless repetitions, please refer to the [Best Practices](#best-practices) section for optimal sampling parameters, and set the ``presence_penalty`` to 1.5.\n enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.\n python -m sglang.launch_server --model-path Qwen/Qwen3-0.6B --reasoning-parser qwen3\n vllm serve Qwen/Qwen3-0.6B --enable-reasoning --reasoning-parser deepseek_r1\n> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. \n### `enable_thinking=True`\nBy default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.\n enable_thinking=True # True is the default value for enable_thinking\n~/.local/bin/uv\n```\n\nStep 1: rename the project to **Perforvisor** and move SerpApi to the backlog.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nfiles=$(grep -rlE '@app/|@app\\\\/' --include='*.json' --include='*.ts' --include='*.tsx' --include='*.md' --include='*.yaml' --include='*.html' . --exclude-dir=node_modules --exclude-dir=dist --exclude=pnpm-lock.yaml)\nsed -i -e 's#@app/#@perforvisor/#g' -e 's#@app\\\\/#@perforvisor\\\\/#g' $files\nsed -i 's/\"name\": \"app\"/\"name\": \"perforvisor\"/' package.json\nsed -i 's/>AdvisorPerforvisorAdvisor#Perforvisor#; s#Ask about anything#Perforvisor#' frontend/*/index.html\nsed -i 's/\"consumer-theme\"/\"perforvisor-consumer-theme\"/g' frontend/consumer/index.html frontend/consumer/src/main.tsx\nsed -i 's/\"client-theme\"/\"perforvisor-client-theme\"/g' frontend/client/index.html frontend/client/src/main.tsx\nsed -i 's/^Working name: TBD (advisor-themed)\\./Product name: **Perforvisor**./' docs/REQUIREMENTS.md\nsed -i 's/^1\\. Product name (advisor-themed)\\.$/1. ~~Product name~~: Perforvisor./' docs/REQUIREMENTS.md\n# SerpApi -> backlog: no plugins enabled by default\nsed -i 's/^PLUGINS=serpapi-google-maps$/PLUGINS=/' .env.example\nsed -i 's/^# plugins\\/serpapi-google-maps$/# plugins\\/serpapi-google-maps (backlog: enable with PLUGINS=serpapi-google-maps)/' .env.example\ngrep -rn \"@app/\\|Advisor<\\|\\\"app\\\"\" --include='*.json' --include='*.ts' --include='*.tsx' --include='*.html' . --exclude-dir=node_modules | grep -v pnpm-lock | head; echo \"remaining: $?\"\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null; pnpm install 2>&1 | tail -1\nremaining: 0\nDone in 236ms using pnpm v12.11.2\n```\n\nStep 2: vLLM. For local runs it goes in Docker Compose, using the same image and settings as on GKE.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\ncat > docker-compose.yml <<'EOF'\n# Local development infrastructure. Managed services (MongoDB Atlas, Neo4j AuraDB,\n# Tiger Data) are reached over the network; Redis and the model server run here.\n#\n# docker compose up -d Redis only\n# docker compose --profile llm up -d Redis + vLLM (needs the NVIDIA Container Toolkit)\n\nservices:\n redis:\n image: redis:8-alpine\n ports:\n - \"6380:6379\" # host 6380: 6379 is often taken by a system Redis\n volumes:\n - redis-data:/data\n command: [\"redis-server\", \"--appendonly\", \"yes\"]\n\n # Mirrors deploy/k8s/base/vllm: same image and flags, tuned for an 8 GB laptop GPU.\n vllm:\n profiles: [\"llm\"]\n image: vllm/vllm-openai:v0.31.0\n ports:\n - \"8000:8000\"\n ipc: host\n environment:\n HF_TOKEN: ${HF_TOKEN:-}\n volumes:\n - hf-cache:/root/.cache/huggingface\n command:\n - --model=${LLM_MODEL:-Qwen/Qwen3-0.6B}\n - --max-model-len=8192\n - --gpu-memory-utilization=0.75\n - --reasoning-parser=qwen3\n - --enable-auto-tool-choice\n - --tool-call-parser=hermes\n - --generation-config=auto\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n count: 1\n capabilities: [gpu]\n healthcheck:\n test: [\"CMD\", \"curl\", \"-fsS\", \"http://localhost:8000/health\"]\n interval: 10s\n timeout: 5s\n retries: 60\n start_period: 60s\n\nvolumes:\n redis-data:\n hf-cache:\nEOF\ncat >> .env.example <<'EOF'\n\n# Model server (vLLM, OpenAI-compatible). Local: docker compose --profile llm up -d\nLLM_BASE_URL=\nLLM_MODEL=Qwen/Qwen3-0.6B\n# Only needed if vLLM is started with --api-key\nLLM_API_KEY=\n# Optional; Qwen models are public\nHF_TOKEN=\nEOF\ndocker compose config -q && echo \"compose ok\"\ncompose ok\n```\n\nNow the Kubernetes manifests: a Kustomize base for vLLM, plus a GKE overlay that schedules it on an NVIDIA L4 GPU node.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nmkdir -p deploy/k8s/base/vllm deploy/k8s/overlays/gke deploy/gke\ncat > deploy/k8s/base/kustomization.yaml <<'EOF'\napiVersion: kustomize.config.k8s.io/v1beta1\nkind: Kustomization\nnamespace: perforvisor\nlabels:\n - pairs:\n app.kubernetes.io/part-of: perforvisor\n includeSelectors: false\nresources:\n - namespace.yaml\n - vllm\nEOF\ncat > deploy/k8s/base/namespace.yaml <<'EOF'\napiVersion: v1\nkind: Namespace\nmetadata:\n name: perforvisor\nEOF\ncat > deploy/k8s/base/vllm/kustomization.yaml <<'EOF'\napiVersion: kustomize.config.k8s.io/v1beta1\nkind: Kustomization\nresources:\n - pvc.yaml\n - deployment.yaml\n - service.yaml\nconfigMapGenerator:\n # Model and engine settings; overlays merge in their own values.\n - name: vllm-config\n literals:\n - LLM_MODEL=Qwen/Qwen3-0.6B\n - VLLM_MAX_MODEL_LEN=8192\n - VLLM_GPU_MEMORY_UTILIZATION=0.85\n - VLLM_REASONING_PARSER=qwen3\n - VLLM_TOOL_CALL_PARSER=hermes\nEOF\ncat > deploy/k8s/base/vllm/pvc.yaml <<'EOF'\n# Hugging Face cache, so restarts don't re-download the model.\napiVersion: v1\nkind: PersistentVolumeClaim\nmetadata:\n name: vllm-hf-cache\nspec:\n accessModes: [\"ReadWriteOnce\"]\n resources:\n requests:\n storage: 30Gi\nEOF\ncat > deploy/k8s/base/vllm/deployment.yaml <<'EOF'\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: vllm\n labels:\n app.kubernetes.io/name: vllm\n app.kubernetes.io/component: model-server\nspec:\n replicas: 1\n # One GPU and a ReadWriteOnce cache volume: the old pod must stop before the new one starts.\n strategy:\n type: Recreate\n selector:\n matchLabels:\n app.kubernetes.io/name: vllm\n template:\n metadata:\n labels:\n app.kubernetes.io/name: vllm\n app.kubernetes.io/component: model-server\n spec:\n terminationGracePeriodSeconds: 60\n containers:\n - name: vllm\n image: vllm/vllm-openai:v0.31.0\n # Kubernetes expands $(VAR) in args from the container's env.\n args:\n - --model=$(LLM_MODEL)\n - --port=8000\n - --max-model-len=$(VLLM_MAX_MODEL_LEN)\n - --gpu-memory-utilization=$(VLLM_GPU_MEMORY_UTILIZATION)\n - --reasoning-parser=$(VLLM_REASONING_PARSER)\n - --enable-auto-tool-choice\n - --tool-call-parser=$(VLLM_TOOL_CALL_PARSER)\n - --generation-config=auto\n envFrom:\n - configMapRef:\n name: vllm-config\n env:\n - name: HF_TOKEN\n valueFrom:\n secretKeyRef:\n name: hf-token\n key: token\n optional: true\n ports:\n - name: http\n containerPort: 8000\n resources:\n requests:\n cpu: \"2\"\n memory: 8Gi\n limits:\n memory: 16Gi\n nvidia.com/gpu: \"1\"\n # First start downloads the model; allow up to 20 \n… [2120 more characters truncated]\nok\n```\n\nNow I'll validate the manifests. kustomize and kubeconform aren't installed, so I'll download standalone copies into the scratchpad.\n\n``` bash\n$ SP= mkdir -p $SP/bin; cd $SP/bin\nKV=$(curl -s https://api.github.com/repos/kubernetes-sigs/kustomize/releases | python3 -c \"import json,sys;print(next(r['tag_name'] for r in json.load(sys.stdin) if r['tag_name'].startswith('kustomize/')))\"); echo $KV\ncurl -sL \"https://github.com/kubernetes-sigs/kustomize/releases/download/${KV/\\//%2F}/kustomize_${KV#kustomize/}_linux_amd64.tar.gz\" | tar xz\ncurl -sL https://github.com/yannh/kubeconform/releases/latest/download/kubeconform-linux-amd64.tar.gz | tar xz\n./kustomize version; ./kubeconform -v\nR=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nfor o in base overlays/gke; do echo \"== $o\"; ./kustomize build $R/deploy/k8s/$o > $SP/$(basename $o).yaml && ./kubeconform -strict -summary -ignore-missing-schemas $SP/$(basename $o).yaml; done\ngrep -A3 \"kind: ConfigMap\" -m1 $SP/gke.yaml; grep -E \"VLLM_MAX|GPU_MEM|gke-accelerator|configMapRef\" -A1 $SP/gke.yaml | head -20\nkustomize/v5.8.3\nv5.8.3\nv0.8.0\n== base\nSummary: 5 resources found in 1 file - Valid: 5, Invalid: 0, Errors: 0, Skipped: 0\n== overlays/gke\nSummary: 5 resources found in 1 file - Valid: 5, Invalid: 0, Errors: 0, Skipped: 0\nkind: ConfigMap\nmetadata:\n labels:\n app.kubernetes.io/part-of: perforvisor\n VLLM_GPU_MEMORY_UTILIZATION: \"0.90\"\n VLLM_MAX_MODEL_LEN: \"32768\"\n VLLM_REASONING_PARSER: qwen3\n--\n - --max-model-len=$(VLLM_MAX_MODEL_LEN)\n - --gpu-memory-utilization=$(VLLM_GPU_MEMORY_UTILIZATION)\n - --reasoning-parser=$(VLLM_REASONING_PARSER)\n--\n - configMapRef:\n name: vllm-config-cdk56mdtgf\n--\n cloud.google.com/gke-accelerator: nvidia-l4\n terminationGracePeriodSeconds: 60\nShell cwd was reset to ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\n```\n\nBoth manifest sets build and pass schema validation, and the GKE overlay's settings override the base values correctly. Now the GKE setup guide.\n\n```\n{\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/deploy/gke/README.md\",\n \"content\": \"# vLLM on GKE\\n\\nRuns the model server (`deploy/k8s/overlays/gke`) on a GKE Standard cluster with one NVIDIA L4 GPU node (Spot VM).\\n\\n## Prerequisites\\n\\n- `gcloud`, `kubectl` and the GKE auth plugin (`gcloud components install kubectl gke-gcloud-auth-plugin`)\\n- A project with billing enabled, and **GPU quota for L4 in your region** (IAM & Admin → Quotas → \\\"NVIDIA L4 GPUs\\\" and \\\"Preemptible NVIDIA L4 GPUs\\\"). New projects often have 0; request 1.\\n\\n## 1. Create the cluster\\n\\n``` bash\\nexport PROJECT_ID=your-project\\nexport REGION=us-central1 # pick a region with L4 capacity\\nexport ZONE=us-central1-a\\nexport CLUSTER=perforvisor\\n\\ngcloud config set project $PROJECT_ID\\ngcloud services enable container.googleapis.com\\n\\n# Small CPU pool for system pods (and later the API, worker and frontends)\\ngcloud container clusters create $CLUSTER \\\\\\n --zone $ZONE \\\\\\n --machine-type e2-standard-4 \\\\\\n --num-nodes 1 \\\\\\n --release-channel regular\\n\\n# GPU pool: one L4, Spot pricing, Google-installed drivers, scales to zero when idle\\ngcloud container node-pools create gpu-l4 \\\\\\n --cluster $CLUSTER \\\\\\n --zone $ZONE \\\\\\n --machine-type g2-standard-8 \\\\\\n --accelerator type=nvidia-l4,count=1,gpu-driver-version=latest \\\\\\n --spot \\\\\\n --num-nodes 1 \\\\\\n --enable-autoscaling --min-nodes 0 --max-nodes 1\\n\\ngcloud container clusters get-credentials $CLUSTER --zone $ZONE\\n```\\n\\n## 2. Deploy vLLM\\n\\n``` bash\\nkubectl apply -k deploy/k8s/overlays/gke\\n\\n# Optional: only needed for gated models\\nkubectl -n perforvisor create secret g\\n… [1403 more characters truncated]\"\n}\nFile created successfully at: ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/deploy/gke/README.md (file state is current in your context — no need to Read it back)\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\npnpm --filter @perforvisor/backend add @langchain/core @langchain/openai @langchain/langgraph 2>&1 | grep -E \"^\\+|Done|ERR|WARN\" | head\ncd backend; grep -n \"modelKwargs\" node_modules/@langchain/openai/dist/chat_models/base.d.ts 2>/dev/null | head -3 || grep -rn \"modelKwargs?\" node_modules/@langchain/openai/dist/*.d.ts | head -3\ngrep -rln \"writer\" node_modules/@langchain/langgraph/dist/pregel/types.d.ts 2>/dev/null; grep -n \"writer\" node_modules/@langchain/langgraph/dist/pregel/types.d.ts | head -3\ngrep -rn \"zod/v4\\|\\\"zod\\\"\" node_modules/@langchain/core/package.json | head -3\n+++++++++++++++++++++++\nDone in 10.3s using pnpm v12.11.2\n179: modelKwargs?: OpenAIChatInput[\"modelKwargs\"];\nnode_modules/@langchain/core/package.json:25: \"zod\": \"^3.25.76 || ^4\"\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/node_modules/@langchain/langgraph; ls dist | head -40; grep -rln \"writer\" dist --include=*.d.ts | head; grep -rn \"writer?:\" dist --include=*.d.ts | head -3; grep -rn \"export declare const Annotation\\|export declare class StateGraph\\|StateSchema\" dist/index.d.ts dist/web.d.ts 2>/dev/null | head\ncallbacks.cjs\ncallbacks.cjs.map\ncallbacks.d.cts\ncallbacks.d.cts.map\ncallbacks.d.ts\ncallbacks.d.ts.map\ncallbacks.js\ncallbacks.js.map\nchannels\nconstants.cjs\nconstants.cjs.map\nconstants.d.cts\nconstants.d.cts.map\nconstants.d.ts\nconstants.d.ts.map\nconstants.js\nconstants.js.map\nerrors.cjs\nerrors.cjs.map\nerrors.d.cts\nerrors.d.cts.map\nerrors.d.ts\nerrors.d.ts.map\nerrors.js\nerrors.js.map\nfunc\ngraph\nhash.cjs\nhash.cjs.map\nhash.js\nhash.js.map\nindex.cjs\nindex.cjs.map\nindex.d.cts\nindex.d.ts\nindex.js\nindex.js.map\ninterrupt.cjs\ninterrupt.cjs.map\ninterrupt.d.cts\ndist/graph/state.d.ts\ndist/writer.d.ts\ndist/index.d.ts\ndist/pregel/utils/config.d.ts\ndist/pregel/read.d.ts\ndist/func/index.d.ts\ndist/graph/graph.d.ts\ndist/pregel/index.d.ts\ndist/pregel/runnable_types.d.ts\ndist/pregel/utils/timeout.d.ts\ndist/graph/types.d.ts:56: writer?: WriterType;\ndist/index.d.ts:35:import { AnyStateSchema, InferStateSchemaUpdate, InferStateSchemaValue, StateSchema, StateSchemaField, StateSchemaFieldToChannel, StateSchemaFields, StateSchemaFieldsToStateDefinition } from \"./state/schema.js\";\ndist/index.d.ts:40:import { CompiledStateGraph, NodePolicyOptions, StateGraph, StateGraphAddNodeOptions, StateGraphArgs, StateGraphArgsWithInputOutputSchemas, StateGraphArgsWithStateSchema, StateGraphNodeSpec } from \"./graph/state.js\";\ndist/index.d.ts:50:export { AddNodeOptions, AgentStatus, Annotation, AnnotationRoot, AnyStateSchema, AnyValue, AsyncBatchedStore, BaseChannel, BaseCheckpointSaver, BaseLangGraphError, BaseLangGraphErrorFields, BaseStore, BinaryOperator, BinaryOperatorAggregate, COMMAND_SYMBOL, CachePolicy, ChatModelStream, ChatModelStreamImpl, Checkpoint, CheckpointMetadata, CheckpointTuple, Command, CommandInstance, CommandParams, CompiledGraph, CompiledGraphType, CompiledStateGraph, ConditionalEdgeRouter, ConditionalEdgeRouterTypes, ContextSchemaInit, ConvertToProtocolEventOptions, CreateGraphRunStreamOptions, DeltaChannel, DeltaReducer, DeltaValue, DeltaValueInit, DynamicBarrierValue, END, EmptyChannelError, EmptyInputError, EntrypointOptions, EphemeralValue, StreamChannel as EventLog, ExecutionInfo, ExtractStateType, ExtractUpdateType, GetOperation, GetStateOptions, Graph, GraphBubbleUp, GraphCallbackHandler, GraphDrained, GraphInterrupt, GraphInterruptEvent, GraphLifecycleEvent, GraphLifecycleStatus, GraphNode, GraphNodeReturnValue, GraphNodeTypes, GraphRecursionError, GraphResumeEvent, GraphRunStream, GraphValueError, INTERRUPT, InMemoryStore, InferExtensions, InferInterruptInputType, Infer\n… [7380 more characters truncated]\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\ncat > common/core/src/schemas/analysis.ts <<'EOF'\nimport { z } from \"zod\";\n\n/** Fixed list so small models stay consistent and results aggregate across reviews. */\nexport const Aspect = z.enum([\n \"quality\",\n \"value\",\n \"service\",\n \"staff\",\n \"food\",\n \"taste\",\n \"ambience\",\n \"cleanliness\",\n \"wait_time\",\n \"delivery\",\n \"packaging\",\n \"durability\",\n \"performance\",\n \"authenticity\",\n \"other\",\n]);\nexport type Aspect = z.infer;\n\nexport const Sentiment = z.enum([\"positive\", \"neutral\", \"negative\"]);\nexport type Sentiment = z.infer;\n\n/** What the model extracts from one review. */\nexport const ReviewExtraction = z.object({\n sentiment: Sentiment.describe(\"Overall sentiment of the review text\"),\n aspects: z\n .array(z.object({ aspect: Aspect, sentiment: Sentiment }))\n .max(6)\n .describe(\"Aspects the review talks about, each with its own sentiment\"),\n claims: z\n .array(z.string().max(120))\n .max(5)\n .describe(\"Short, specific factual claims made by the reviewer, e.g. 'battery overheats after an hour'\"),\n});\nexport type ReviewExtraction = z.infer;\n\n/** A review as the analysis flow receives it. */\nexport const AnalysisReviewInput = z.object({\n id: z.string(),\n text: z.string().min(1).max(4000),\n rating: z.number().min(0).max(5).optional(),\n createdAt: z.iso.datetime().optional(),\n});\nexport type AnalysisReviewInput = z.infer;\n\nexport const ReviewAnalysis = ReviewExtraction.extend({\n reviewId: z.string(),\n /** Set when the star rating and the text's sentiment disagree. */\n ratingMismatch: z.boolean(),\n});\nexport type ReviewAnalysis = z.infer;\n\nexport const AspectStat = z.object({\n aspect: Aspect,\n mentions: z.number().int(),\n positive: z.number().int(),\n neutral: z.number().int(),\n negative: z.number().int(),\n /** (positive - negative) / mentions, from -1 to 1. */\n score: z.number(),\n});\nexport type AspectStat = z.infer;\n\nexport const AnalysisStats = z.object({\n reviewCount: z.number().int(),\n analysedCount: z.number().int(),\n failedCount: z.number().int(),\n averageRating: z.number().nullable(),\n sentiment: z.object({ positive: z.number().int(), neutral: z.number().int(), negative: z.number().int() }),\n aspects: z.array(AspectStat),\n topClaims: z.array(z.object({ claim: z.string(), count: z.number().int() })),\n ratingMismatches: z.array(z.string()).describe(\"Review ids whose rating contradicts their text\"),\n duplicateGroups: z.array(z.array(z.string())).describe(\"Groups of review ids with near-identical text\"),\n});\nexport type AnalysisStats = z.infer;\n\n/** What the model writes for the owner, grounded in the aggregated stats. */\nexport const AdvisorSummary = z.object({\n verdict: z.string().max(300).describe(\"One or two sentences on how this place or product is performing\"),\n strength\n… [694 more characters truncated]\nexport * from \"./schemas/entity.ts\";\nexport * from \"./schemas/review.ts\";\nexport * from \"./schemas/stream.ts\";\nexport * from \"./schemas/analysis.ts\";\nexport * from \"./plugin.ts\";\nexport * from \"./utils/result.ts\";\n```\n\nNow the backend AI code: the model client, the statistics step, and the LangGraph flow.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\ncat > common/core/src/utils/concurrency.ts <<'EOF'\n/** Maps over items with at most `limit` promises in flight, preserving order. */\nexport async function mapWithConcurrency(items: readonly T[], limit: number, fn: (item: T, index: number) => Promise): Promise {\n const results = new Array(items.length);\n let next = 0;\n const run = async () => {\n while (next < items.length) {\n const index = next++;\n results[index] = await fn(items[index]!, index);\n }\n };\n await Promise.all(Array.from({ length: Math.min(limit, items.length) }, run));\n return results;\n}\nEOF\nsed -i 's#export \\* from \"./utils/result.ts\";#export * from \"./utils/result.ts\";\\nexport * from \"./utils/concurrency.ts\";#' common/core/src/index.ts\n\nmkdir -p backend/src/ai/graphs\npython3 - <<'EOF'\np='backend/src/config.ts'; s=open(p).read()\ns=s.replace(\"\"\" WORKER_CONCURRENCY: z.coerce.number().int().positive().default(4),\n\"\"\",\"\"\" WORKER_CONCURRENCY: z.coerce.number().int().positive().default(4),\n LLM_BASE_URL: z.url().default(\"\"),\n LLM_MODEL: z.string().min(1).default(\"Qwen/Qwen3-0.6B\"),\n // vLLM accepts any key unless started with --api-key.\n LLM_API_KEY: z.string().default(\"\").transform((key) => key || \"EMPTY\"),\n LLM_CONCURRENCY: z.coerce.number().int().positive().default(8),\n\"\"\")\nopen(p,'w').write(s)\nEOF\ncat > backend/src/ai/model.ts <<'EOF'\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport type { Config } from \"../config.ts\";\n\n/**\n * Chat model served by vLLM through its OpenAI-compatible API.\n * Qwen3's thinking mode is switched off: the extraction and summary steps are\n * schema-constrained, and thinking would multiply latency for little gain.\n */\nexport function createChatModel(config: Config, options: { temperature?: number; maxTokens?: number } = {}) {\n return new ChatOpenAI({\n model: config.LLM_MODEL,\n apiKey: ,\n configuration: { baseURL: config.LLM_BASE_URL },\n temperature: options.temperature ?? 0.2,\n maxTokens: options.maxTokens ?? 512,\n maxRetries: 2,\n modelKwargs: { chat_template_kwargs: { enable_thinking: false } },\n });\n}\n\nexport type ModelStatus = { ok: true; models: string[] } | { ok: false; error: string };\n\nexport async function checkModelServer(config: Config): Promise {\n try {\n const res = await fetch(`${config.LLM_BASE_URL}/models`, {\n headers: { Authorization: `Bearer ${config.LLM_API_KEY}` },\n signal: AbortSignal.timeout(3000),\n });\n if (!res.ok) return { ok: false, error: `HTTP ${res.status}` };\n const body = (await res.json()) as { data?: { id: string }[] };\n return { ok: true, models: (body.data ?? []).map((m) => m.id) };\n } catch (error) {\n return { ok: false, error: error instanceof Error ? error.message : String(error) };\n }\n}\nEOF\ncat > backend/src/ai/stats.ts <<'EOF'\nimport type { AnalysisReviewInput, AnalysisStats, Aspect, AspectStat, ReviewAnalysis,\n… [12464 more characters truncated]\ncommon/core typecheck: Done\ncommon/ui typecheck: Done\nfrontend/client typecheck: Done\nfrontend/consumer typecheck: Done\nplugins/serpapi-google-maps typecheck: Done\nbackend typecheck: src/routes/ai.ts(45,21): error TS2352: Conversion of type 'StateType<{ entityName: { (annotation: SingleReducer): BaseChannel, unknown>; (): LastValue; Root: (sd: S) => AnnotationRoot<...>; }; reviews: { ...; }; analyses: { ...; }; stats: { ...; }; summary: { ...; }; }>' to type 'Partial<{ entityName: string; stats: { reviewCount: number; analysedCount: number; failedCount: number; averageRating: number | null; sentiment: { positive: number; neutral: number; negative: number; }; aspects: { ...; }[]; topClaims: { ...; }[]; ratingMismatches: string[]; duplicateGroups: string[][]; }; summary: {...' may be a mistake because neither type sufficiently overlaps with the other. If this was intentional, convert the expression to 'unknown' first.\n```\n\nOne type error, from my clumsy casting in the route. The graph's stream already types its final state, so I'll use that type directly.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\npython3 - <<'EOF'\np='backend/src/ai/graphs/analyze.ts'; s=open(p).read()\ns=s.replace('export type AnalyzeInput = Pick;',\n'export type AnalyzeStateValue = typeof AnalyzeState.State;\\nexport type AnalyzeInput = Pick;')\nopen(p,'w').write(s)\np='backend/src/routes/ai.ts'; s=open(p).read()\ns=s.replace('import { buildAnalyzeGraph, type AnalyzeProgress } from \"../ai/graphs/analyze.ts\";','import { buildAnalyzeGraph, type AnalyzeProgress, type AnalyzeStateValue } from \"../ai/graphs/analyze.ts\";')\nold_start=s.index(' let final: Partial = {};')\nold_end=s.index(' stream.send({ type: \"result\", data: result });')\ns=s[:old_start]+''' let final: AnalyzeStateValue | undefined;\n const events = await analyzeGraph.stream(request.body, { streamMode: [\"custom\", \"values\"], signal: abort.signal });\n for await (const [mode, chunk] of events) {\n if (mode === \"custom\") {\n const progress = chunk as AnalyzeProgress;\n const percent = progress.stage === \"extract\" ? Math.round((progress.done / progress.total) * 80) : progress.stage === \"aggregate\" ? 85 : 90;\n stream.send({ type: \"progress\", stage: progress.stage, message: progressMessage(progress), percent });\n } else {\n final = chunk as AnalyzeStateValue;\n }\n }\n if (!final?.stats) throw new Error(\"Analysis finished without results\");\n const result: AnalysisResult = { entityName: final.entityName, stats: final.stats, summary: final.summary ?? null, reviews: final.analyses ?? [] };\n'''+s[old_end:]\nopen(p,'w').write(s)\nEOF\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ntimeout 120 pnpm --filter @perforvisor/backend typecheck 2>&1 | grep -E \"error|Done\"\n(Bash completed with no output)\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ntimeout 120 npx tsc -p tsconfig.json; echo \"tsc exit $?\"\nnpm notice run @perforvisor/backend@0.0.0 npx\nnpm notice run 'tsc' -p tsconfig.json\ntsc exit 0\n```\n\nTypecheck is clean. Next: unit tests for the deterministic statistics, and a sample review set so the flow can be tested without SerpApi.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\nmkdir -p fixtures\ncat > fixtures/sample-reviews.json <<'EOF'\n{\n \"entityName\": \"Spice Route Kitchen (sample data)\",\n \"reviews\": [\n { \"id\": \"r1\", \"rating\": 5, \"text\": \"Best butter chicken in the area. Generous portions and the naan comes out hot. Staff were friendly.\" },\n { \"id\": \"r2\", \"rating\": 2, \"text\": \"Waited 50 minutes for our food on a Friday night. The biryani was good but cold by the time it arrived.\" },\n { \"id\": \"r3\", \"rating\": 4, \"text\": \"Great food, a bit pricey for the portion size of the starters. Paneer tikka was excellent.\" },\n { \"id\": \"r4\", \"rating\": 1, \"text\": \"Delivery order arrived with the curry leaking all over the bag. Packaging needs serious work.\" },\n { \"id\": \"r5\", \"rating\": 5, \"text\": \"Amazing place!!! Best restaurant in the city, everyone must visit, five stars, highly recommended!!!\" },\n { \"id\": \"r6\", \"rating\": 5, \"text\": \"Amazing place!! Best restaurant in the city, everyone must visit, five stars, highly recommended!\" },\n { \"id\": \"r7\", \"rating\": 3, \"text\": \"Food is tasty but the dining area was not clean, sticky tables and the restroom was dirty.\" },\n { \"id\": \"r8\", \"rating\": 5, \"text\": \"Terrible experience. Rude waiter, cold food and we were overcharged on the bill.\" },\n { \"id\": \"r9\", \"rating\": 4, \"text\": \"Lovely ambience and music. Long wait for a table even with a reservation, around 30 minutes.\" },\n { \"id\": \"r10\", \"rating\": 2, \"text\": \"Ordered via delivery, the container cracked and half the dal spilled. Taste was fine.\" },\n { \"id\": \"r11\", \"rating\": 5, \"text\": \"The mango lassi and the butter chicken are must-tries. Quick service at lunch.\" },\n { \"id\": \"r12\", \"rating\": 3, \"text\": \"Decent food, but service was slow and we had to ask twice for water.\" }\n ]\n}\nEOF\ncat > src/ai/stats.test.ts <<'EOF'\nimport { describe, expect, it } from \"vitest\";\nimport type { AnalysisReviewInput, ReviewAnalysis } from \"@perforvisor/core\";\nimport { computeStats, findDuplicateGroups, isRatingMismatch } from \"./stats.ts\";\n\nconst review = (id: string, text: string, rating?: number): AnalysisReviewInput => ({ id, text, rating });\n\ndescribe(\"isRatingMismatch\", () => {\n it(\"flags high ratings with negative text and low ratings with positive text\", () => {\n expect(isRatingMismatch(5, \"negative\")).toBe(true);\n expect(isRatingMismatch(1, \"positive\")).toBe(true);\n });\n\n it(\"accepts consistent or unrated reviews\", () => {\n expect(isRatingMismatch(5, \"positive\")).toBe(false);\n expect(isRatingMismatch(3, \"negative\")).toBe(false);\n expect(isRatingMismatch(undefined, \"negative\")).toBe(false);\n });\n});\n\ndescribe(\"findDuplicateGroups\", () => {\n it(\"groups near-identical reviews and ignores distinct ones\", () => {\n const groups = findDuplicateGroups([\n review(\"a\", \"Amazing place!!! Best restaurant in the city, everyone must visit, five stars\"),\n review(\"b\", \"Amazing place! Best restaurant in the city, \n… [1783 more characters truncated]\nbash\n$ vitest run\n\n RUN v5.0.4 ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\n\n Test Files 1 passed (1)\n Tests 4 passed (4)\n Start at 19:45:00\n Duration 117ms (transform 60%, import 22%, tests 10%, worker 8%)\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nSP=\ncat > $SP/mock-llm.mjs <<'EOF'\nimport { createServer } from \"node:http\";\nimport { writeFileSync } from \"node:fs\";\nlet first = {};\nlet n = 0;\ncreateServer(async (req, res) => {\n if (req.url.endsWith(\"/models\")) { res.end(JSON.stringify({ data: [{ id: \"Qwen/Qwen3-0.6B\" }] })); return; }\n let body = \"\"; for await (const c of req) body += c;\n const r = JSON.parse(body);\n const name = r.response_format?.json_schema?.name;\n if (!first[name]) { first[name] = r; writeFileSync(process.argv[2], JSON.stringify(first, null, 2)); }\n n++;\n const text = r.messages.at(-1).content;\n let content;\n if (name === \"review_extraction\") {\n if (text.includes(\"leaking\")) content = \"not json at all\"; // simulate one malformed output\n else {\n const neg = /cold|rude|terrible|leak|cracked|slow|dirty|wait/i.test(text);\n content = JSON.stringify({ sentiment: neg ? \"negative\" : \"positive\", aspects: [{ aspect: \"food\", sentiment: neg ? \"negative\" : \"positive\" }], claims: neg ? [\"Food arrives cold\"] : [\"Butter chicken is excellent\"] });\n }\n } else {\n content = JSON.stringify({ verdict: \"Food is loved; delivery and waits hurt ratings.\", strengths: [\"Butter chicken\"], weaknesses: [\"Cold food\"], actions: [\"Fix delivery packaging\"] });\n }\n res.setHeader(\"content-type\", \"application/json\");\n res.end(JSON.stringify({ id: \"x\" + n, object: \"chat.completion\", created: 0, model: r.model, choices: [{ index: 0, finish_reason: \"stop\", message: { role: \"assistant\", content } }], usage: { prompt_tokens: 1, completion_tokens: 1, total_tokens: 2 } }));\n}).listen(8011);\nEOF\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\nnode $SP/mock-llm.mjs $SP/llm-requests.json & MOCK=$!\ncd $R/backend\nLLM_BASE_URL=http://localhost:8011/v1 API_PORT=4124 NODE_ENV=production LOG_LEVEL=warn timeout 60 ./node_modules/.bin/tsx src/api.ts > $SP/api2.log 2>&1 & API=$!\nfor i in $(seq 1 40); do curl -s localhost:4124/health >/dev/null && break; sleep 0.5; done\necho \"--- ai health\"; curl -s localhost:4124/v1/ai/health; echo\necho \"--- analyze (SSE)\"; curl -sN -X POST localhost:4124/v1/analyze -H 'content-type: application/json' --data @fixtures/sample-reviews.json > $SP/sse.txt\ngrep '^event:' $SP/sse.txt | sort | uniq -c\ngrep -o '\"message\":\"[^\"]*\"' $SP/sse.txt | tail -4\ngrep '^data: {\"type\":\"result\"' $SP/sse.txt | sed 's/^data: //' | python3 -c \"\nimport json,sys; d=json.load(sys.stdin)['data']; s=d['stats']\nprint('analysed',s['analysedCount'],'failed',s['failedCount'],'avg',s['averageRating'],'sentiment',s['sentiment'])\nprint('mismatches',s['ratingMismatches'],'dupes',s['duplicateGroups'])\nprint('topClaims',s['topClaims']); print('summary',d['summary'])\"\necho \"--- request sent to model\"; python3 -c \"\nimport json; d=json.load(open('$SP/llm-requests.json'))\nfor k,v in d.items(): print(k,'| model:',v['model'],'| response_format:',v['response_format']['type'],'| chat_templat\n… [275 more characters truncated]\nCommand did not complete within its 120s timeout and was moved to the background (ID: btht1f0ab). Output is being written to: You will be notified when it completes. If it is still running after 30m in the background, it will be stopped and you will be notified. To check interim output, use Read on that file path.\nSession cwd remains ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; directory changes made by the backgrounded command do not apply to subsequent commands.\nbash\n$ SP= cat $SP/../tasks/btht1f0ab.output; echo \"--- sse so far\"; head -c 1500 $SP/sse.txt; echo; echo \"--- api log\"; tail -5 $SP/api2.log | cut -c1-400; ls -la $SP/llm-requests.json 2>&1\nExit code 2\n--- ai health\n{\"model\":\"Qwen/Qwen3-0.6B\",\"baseUrl\":\"http://localhost:8011/v1\",\"ok\":true,\"models\":[\"Qwen/Qwen3-0.6B\"]}\n--- analyze (SSE)\n--- sse so far\n\n--- api log\nls: cannot access '': No such file or directory\nbash\n$ pkill -f \"mock-llm.mjs\" ; pkill -f \"tsx src/api.ts\"; sleep 1\nR=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\npython3 - <<'EOF'\np='src/lib/sse.ts'; s=open(p).read()\ns=s.replace(\"\"\"export interface SseStream {\n send(event: StreamEvent): void;\n /** True once the client has disconnected; long-running work should stop. */\n readonly closed: boolean;\n end(): void;\n}\"\"\",\"\"\"export interface SseStream {\n send(event: StreamEvent): void;\n /** True once the client has disconnected; long-running work should stop. */\n readonly closed: boolean;\n /** Aborts when the client disconnects; pass it to model calls and graph runs. */\n readonly signal: AbortSignal;\n end(): void;\n}\"\"\")\ns=s.replace(\"\"\" let closed = false;\n const heartbeat = setInterval(() => res.write(\": ping\\\\n\\\\n\"), 15_000);\n const close = () => {\n if (closed) return;\n closed = true;\n clearInterval(heartbeat);\n };\n request.raw.on(\"close\", close);\n\"\"\",\"\"\" let closed = false;\n const abort = new AbortController();\n const heartbeat = setInterval(() => res.write(\": ping\\\\n\\\\n\"), 15_000);\n // Listen on the response, not the request: a request emits \"close\" as soon as\n // its body has been read, which would end every POST stream immediately.\n res.on(\"close\", () => {\n closed = true;\n clearInterval(heartbeat);\n abort.abort();\n });\n\"\"\")\ns=s.replace(\"\"\" get closed() {\n return closed;\n },\n end() {\n if (!closed) res.end();\n close();\n },\"\"\",\"\"\" get closed() {\n return closed;\n },\n signal: abort.signal,\n end() {\n if (!closed) res.end();\n },\"\"\")\nopen(p,'w').write(s)\np='src/routes/ai.ts'; s=open(p).read()\ns=s.replace(\"\"\" const stream = openSse(request, reply);\n const abort = new AbortController();\n request.raw.on(\"close\", () => abort.abort());\n\"\"\",\"\"\" const stream = openSse(request, reply);\n\"\"\")\ns=s.replace(\"signal: abort.signal });\",\"signal: stream.signal });\")\ns=s.replace(\"if (!abort.signal.aborted) {\",\"if (!stream.closed) {\")\nopen(p,'w').write(s)\nEOF\ngrep -n \"abort\\|close\" src/lib/sse.ts src/routes/ai.ts\nExit code 144\n```\n\nThe `pkill` matched the command line of the shell it was running in and killed it, so I need to check whether the edits went through.\n\n``` bash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend; grep -n \"abort\\|close\" src/lib/sse.ts src/routes/ai.ts; ps aux | grep -E \"mock-llm|src/api.ts\" | grep -v grep | awk '{print $2, $12, $13, $14}'\njs\nsrc/lib/sse.ts:7: readonly closed: boolean;\nsrc/lib/sse.ts:24: let closed = false;\nsrc/lib/sse.ts:26: const close = () => {\nsrc/lib/sse.ts:27: if (closed) return;\nsrc/lib/sse.ts:28: closed = true;\nsrc/lib/sse.ts:31: request.raw.on(\"close\", close);\nsrc/lib/sse.ts:35: if (!closed) res.write(`event: ${event.type}\\ndata: ${JSON.stringify(event)}\\n\\n`);\nsrc/lib/sse.ts:37: get closed() {\nsrc/lib/sse.ts:38: return closed;\nsrc/lib/sse.ts:41: if (!closed) res.end();\nsrc/lib/sse.ts:42: close();\nsrc/routes/ai.ts:33: const abort = new AbortController();\nsrc/routes/ai.ts:34: request.raw.on(\"close\", () => abort.abort());\nsrc/routes/ai.ts:38: const events = await analyzeGraph.stream(request.body, { streamMode: [\"custom\", \"values\"], signal: abort.signal });\nsrc/routes/ai.ts:53: if (!abort.signal.aborted) {\n895519 60 ./node_modules/.bin/tsx src/api.ts\n895521 ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/node_modules/.bin/../../../node_modules/.pnpm/tsx@4.23.15/node_modules/tsx/dist/cli.mjs src/api.ts \n895548 --require ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.pnpm/tsx@4.23.15/node_modules/tsx/dist/preflight.cjs --import\n```\n\nThe edits didn't apply. I'll kill the leftover test processes by PID this time, then rewrite the SSE helper properly.\n\n``` bash\n$ kill 895519 895521 895548 2>/dev/null; sleep 1; ps aux | grep -E \"mock-llm\" | grep -v grep | awk '{print $2}' | xargs -r kill\ncd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend\ncat > src/lib/sse.ts <<'EOF'\nimport type { FastifyReply, FastifyRequest } from \"fastify\";\nimport type { StreamEvent } from \"@perforvisor/core\";\n\nexport interface SseStream {\n send(event: StreamEvent): void;\n /** True once the client has disconnected; long-running work should stop. */\n readonly closed: boolean;\n /** Aborts when the client disconnects; pass it to model calls and graph runs. */\n readonly signal: AbortSignal;\n end(): void;\n}\n\n/** Takes over the response and streams StreamEvents as server-sent events. */\nexport function openSse(request: FastifyRequest, reply: FastifyReply): SseStream {\n reply.hijack();\n const res = reply.raw;\n res.writeHead(200, {\n \"Content-Type\": \"text/event-stream; charset=utf-8\",\n \"Cache-Control\": \"no-cache, no-transform\",\n Connection: \"keep-alive\",\n // Stops proxies such as nginx ingress from buffering the stream.\n \"X-Accel-Buffering\": \"no\",\n ...corsHeaders(request),\n });\n\n let closed = false;\n const abort = new AbortController();\n const heartbeat = setInterval(() => res.write(\": ping\\n\\n\"), 15_000);\n // Listen on the response, not the request: a request emits \"close\" as soon as\n // its body has been read, which would end every POST stream immediately.\n res.on(\"close\", () => {\n closed = true;\n clearInterval(heartbeat);\n abort.abort();\n });\n\n return {\n send(event) {\n if (!closed) res.write(`event: ${event.type}\\ndata: ${JSON.stringify(event)}\\n\\n`);\n },\n get closed() {\n return closed;\n },\n signal: abort.signal,\n end() {\n if (!closed) res.end();\n },\n };\n}\n\n// hijack() skips Fastify's onSend hooks, so @fastify/cors headers must be copied over.\nfunction corsHeaders(request: FastifyRequest): Record {\n const origin = request.headers.origin;\n return origin ? { \"Access-Control-Allow-Origin\": origin, Vary: \"Origin\" } : {};\n}\nEOF\npython3 - <<'EOF'\np='src/routes/ai.ts'; s=open(p).read()\ns=s.replace(\"\"\" const stream = openSse(request, reply);\n const abort = new AbortController();\n request.raw.on(\"close\", () => abort.abort());\n\"\"\",\"\"\" const stream = openSse(request, reply);\n\"\"\")\ns=s.replace(\"signal: abort.signal });\",\"signal: stream.signal });\")\ns=s.replace(\"if (!abort.signal.aborted) {\",\"if (!stream.closed) {\")\nopen(p,'w').write(s)\nEOF\ngrep -n \"stream.signal\\|stream.closed\" src/routes/ai.ts src/routes/entities.ts; ./node_modules/.bin/tsc -p tsconfig.json && echo tsc ok\nExit code 127\nsrc/routes/entities.ts:42: if (stream.closed) return;\nsrc/routes/ai.ts:36: const events = await analyzeGraph.stream(request.body, { streamMode: [\"custom\", \"values\"], signal: stream.signal });\nsrc/routes/ai.ts:51: if (!stream.closed) {\n/bin/bash: line 71: ./node_modules/.bin/tsc: No such file or directory\n```\n\nThe edits applied (the failure was just a wrong path to `tsc`). Rerunning the typecheck and the full test against the fake server, with timeouts this time:\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nSP=\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ncd $R/backend; $R/node_modules/.bin/tsc -p tsconfig.json && echo \"tsc ok\"\nrm -f $SP/llm-requests.json\nnode $SP/mock-llm.mjs $SP/llm-requests.json & MOCK=$!\nLLM_BASE_URL=http://localhost:8011/v1 API_PORT=4124 NODE_ENV=production LOG_LEVEL=warn timeout 60 $R/node_modules/.bin/tsx src/api.ts > $SP/api2.log 2>&1 & API=$!\nfor i in $(seq 1 40); do curl -s localhost:4124/health >/dev/null && break; sleep 0.5; done\ncurl -sN --max-time 30 -X POST localhost:4124/v1/analyze -H 'content-type: application/json' --data @fixtures/sample-reviews.json > $SP/sse.txt; echo \"curl exit $?\"\ngrep '^event:' $SP/sse.txt | sort | uniq -c\ngrep -o '\"message\":\"[^\"]*\"' $SP/sse.txt | tail -3\ngrep '^data: {\"type\":\"result\"' $SP/sse.txt | sed 's/^data: //' | python3 -c \"\nimport json,sys; d=json.load(sys.stdin)['data']; s=d['stats']\nprint('analysed',s['analysedCount'],'failed',s['failedCount'],'avg',s['averageRating'],'sentiment',s['sentiment'])\nprint('mismatches',s['ratingMismatches'],'dupes',s['duplicateGroups'])\nprint('topClaims',s['topClaims']); print('summary',d['summary'])\"\npython3 -c \"\nimport json; d=json.load(open('$SP/llm-requests.json'))\nfor k,v in d.items(): print(k,'| model:',v['model'],'| response_format:',v['response_format']['type'],'| chat_template_kwargs:',v.get('chat_template_kwargs'),'| temperature:',v.get('temperature'))\"\nkill $API $MOCK 2>/dev/null; wait 2>/dev/null; grep -ciE '\"level\":(50|40)' $SP/api2.log\ntsc ok\ncurl exit 7\nTraceback (most recent call last):\n File \"\", line 2, in \n import json,sys; d=json.load(sys.stdin)['data']; s=d['stats']\n ~~~~~~~~~^^^^^^^^^^^\n File \"/usr/lib64/python3.14/json/__init__.py\", line 298, in load\n return loads(fp.read(),\n cls=cls, object_hook=object_hook,\n parse_float=parse_float, parse_int=parse_int,\n parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n File \"/usr/lib64/python3.14/json/__init__.py\", line 352, in loads\n return _default_decoder.decode(s)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^\n File \"/usr/lib64/python3.14/json/decoder.py\", line 345, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib64/python3.14/json/decoder.py\", line 363, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\nTraceback (most recent call last):\n File \"\", line 2, in \n import json; d=json.load(open(''))\n ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: ''\n0\nbash\n$ SP= cut -c1-600 $SP/api2.log | head -20; ss -ltnp | grep -E \"4124|8011\"\ntimeout: failed to run command ‘~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/node_modules/.bin/tsx’: No such file or directory\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nSP=\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ncd $R/backend; TSX=$R/backend/node_modules/.bin/tsx; ls $TSX >/dev/null || exit 1\nrm -f $SP/llm-requests.json\nnode $SP/mock-llm.mjs $SP/llm-requests.json & MOCK=$!\nLLM_BASE_URL=http://localhost:8011/v1 API_PORT=4124 NODE_ENV=production LOG_LEVEL=warn timeout 60 $TSX src/api.ts > $SP/api2.log 2>&1 & API=$!\nfor i in $(seq 1 40); do curl -s localhost:4124/health >/dev/null && break; sleep 0.5; done\ncurl -sN --max-time 30 -X POST localhost:4124/v1/analyze -H 'content-type: application/json' --data @fixtures/sample-reviews.json > $SP/sse.txt; echo \"curl exit $?\"\ngrep '^event:' $SP/sse.txt | sort | uniq -c\ngrep -o '\"message\":\"[^\"]*\"' $SP/sse.txt | tail -3\ngrep '^data: {\"type\":\"result\"' $SP/sse.txt | sed 's/^data: //' | python3 -c \"\nimport json,sys; d=json.load(sys.stdin)['data']; s=d['stats']\nprint('analysed',s['analysedCount'],'failed',s['failedCount'],'avg',s['averageRating'],'sentiment',s['sentiment'])\nprint('mismatches',s['ratingMismatches'],'dupes',s['duplicateGroups'])\nprint('topClaims',s['topClaims']); print('summary',d['summary'])\"\npython3 -c \"\nimport json; d=json.load(open('$SP/llm-requests.json'))\nfor k,v in d.items(): print(k,'| model:',v['model'],'| response_format:',v['response_format']['type'],'| chat_template_kwargs:',v.get('chat_template_kwargs'),'| temperature:',v.get('temperature'))\"\nkill $API $MOCK 2>/dev/null; wait 2>/dev/null; echo \"api errors/warnings: $(grep -cE '\"level\":(50|40)' $SP/api2.log)\"\ncurl exit 0\n 1 event: done\n 14 event: progress\n 1 event: result\n\"message\":\"Analysed 12 of 12 reviews (1 failed)\"\n\"message\":\"Aggregating results\"\n\"message\":\"Writing advice\"\nanalysed 11 failed 1 avg 3.67 sentiment {'positive': 5, 'neutral': 0, 'negative': 6}\nmismatches ['r8', 'r9'] dupes [['r5', 'r6']]\ntopClaims [{'claim': 'Food arrives cold', 'count': 6}, {'claim': 'Butter chicken is excellent', 'count': 5}]\nsummary {'verdict': 'Food is loved; delivery and waits hurt ratings.', 'strengths': ['Butter chicken'], 'weaknesses': ['Cold food'], 'actions': ['Fix delivery packaging']}\nreview_extraction | model: Qwen/Qwen3-0.6B | response_format: json_schema | chat_template_kwargs: {'enable_thinking': False} | temperature: 0\nadvisor_summary | model: Qwen/Qwen3-0.6B | response_format: json_schema | chat_template_kwargs: {'enable_thinking': False} | temperature: 0.3\napi errors/warnings: 0\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\npython3 - <<'EOF'\np='docs/REQUIREMENTS.md'; s=open(p).read()\ns=s.replace(\"| Model serving | vLLM (OpenAI-compatible API); open-weight model to be chosen by the user |\",\n\"| Model serving | vLLM 0.31 (OpenAI-compatible API) on GKE with an NVIDIA L4 (`deploy/k8s/overlays/gke`, setup in `deploy/gke/README.md`); locally via `docker compose --profile llm` |\\n| Model | `Qwen/Qwen3-0.6B` for development (lightest Qwen, ~1.5 GB); swap via `LLM_MODEL` |\")\ns=s.replace(\"## P1: days 4–5\\n\",\"\"\"## Backlog\n\n| ID | Item |\n|---|---|\n| BL-1 | SerpApi connectors (ING-1, ING-2, ING-5): the Google Maps plugin exists in `plugins/serpapi-google-maps` but is disabled (`PLUGINS=`) and untested against real data. Until then, reviews come in through `POST /v1/analyze` and fixtures. |\n\n## P1: days 4–5\n\"\"\",1)\ns=s.replace(\"3. Which model size on Vertex: Gemma 4B or 12B? Trade cost and speed against extraction quality.\",\"3. ~~Model~~: Qwen3-0.6B for development; revisit size once extraction quality is measured.\")\nopen(p,'w').write(s)\nEOF\nM=~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory\npython3 - </dev/null; nvm use 24 >/dev/null\ntimeout 150 pnpm typecheck 2>&1 | grep -cE \": Done\"; timeout 60 pnpm test 2>&1 | grep -E \"Tests|failed\"; timeout 120 pnpm build 2>&1 | grep -E \"error|built in|Build success\" | head\n6\n Tests 4 passed (4)\nbackend build: ESM ⚡️ Build success in 18ms\nfrontend/client build: ✓ built in 330ms\nfrontend/consumer build: ✓ built in 334ms\n```\n\nThe project is now Perforvisor, and vLLM serving with Qwen is built and wired into the backend. I haven't run a real model yet: Docker can't reach your GPU until the NVIDIA Container Toolkit is installed (steps at the end). Everything else is checked: typecheck passes in all 6 workspaces, the 4 unit tests pass, the builds succeed, the Kubernetes manifests pass schema validation, and the AI flow works end to end against a fake model server.\n\n`@perforvisor/*`, and both apps show \"Perforvisor\".` PLUGINS=`). For now, reviews come in through the API or the sample file.`Qwen/Qwen3-0.6B`. It's the lightest text-only Qwen (about a 1.5 GB download, Apache-2.0 license). The newer Qwen3.5-0.8B is slightly bigger and includes a vision encoder we don't need. Changing models is one setting, `LLM_MODEL`.` docker-compose.yml`):` docker compose --profile llm up -d` starts vLLM 0.31 on port 8000. It uses up to 75% of your 4060's 8 GB and caps the context at 8k tokens. The model download is cached between restarts.`deploy/k8s`):` deploy/gke/README.md`:\n`POST /v1/analyze` streams its progress over SSE. Each review goes through three steps:\n\n1. **Extract:** Qwen pulls out sentiment, topics and claims, constrained to a Zod schema.\n\n2. **Aggregate:** plain code with no model: average rating, per-topic scores, the most common claims, star ratings that contradict the text, and near-duplicate reviews.\n\n3. **Summarize:** Qwen writes the owner advice from those numbers only.\n\nIt's skipped when every extraction failed.\n\nAlso added:\n\n- `GET /v1/ai/health` checks the model server.\n\n- `backend/fixtures/sample-reviews.json` has 12 made-up restaurant reviews, including planted duplicates and a misleading 5-star rating.\n\nThe SSE helper decided the client had disconnected on every POST request. Node fires the request's `close` event as soon as the body has been read, not when the client leaves. So every analysis was cancelled immediately and the request hung. It now watches the response instead, and cancels the LangGraph run only when the client really disconnects. Earlier GET tests didn't catch it because GETs have no body.\n\nInstalling the toolkit needs sudo. Run these with the `!` prefix so the output shows up here:\n\n! curl -sL https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo | sudo tee /etc/yum.repos.d/nvidia-container-toolkit.repo\n\n! sudo dnf install -y nvidia-container-toolkit\n\n! sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker\n\n! docker run --rm --gpus all ubuntu nvidia-smi\n\nIf the last command prints your RTX 4060, tell me. I'll then start vLLM (about a 10 GB image pull the first time), run the sample reviews through real Qwen, and see how well a 0.6B model handles the extraction.\n\nUse CPU for now with option to change to GPU later on if required. There is some AMD container connection issue with my GPU. Unable to permit docker to use it as of now\n\n``` bash\n$ curl -s \"https://hub.docker.com/v2/repositories/vllm/vllm-openai-cpu/tags?page_size=25&ordering=last_updated\" | python3 -c \"import json,sys;print([(t['name'],round(t['full_size']/1e9,1)) for t in json.load(sys.stdin)['results'] if not t['name'].startswith('nightly-')])\"; curl -s \"https://hub.docker.com/v2/repositories/vllm/vllm-openai-cpu/tags?page_size=10&name=v0.3\" | python3 -c \"import json,sys;print([(t['name'],round(t['full_size']/1e9,1)) for t in json.load(sys.stdin)['results']])\"; curl -sL https://raw.githubusercontent.com/vllm-project/vllm/main/docs/getting_started/installation/cpu.x86.inc.md | grep -iE -m15 \"avx|requirement|docker|image|VLLM_CPU\"\n[('nightly', 1.2), ('v0.31.0', 1.1), ('latest', 1.1), ('v0.31.0-arm64', 1.1), ('latest-arm64', 1.1), ('v0.31.0-x86_64', 1.6), ('latest-x86_64', 1.6), ('v0.30.0', 1.3), ('v0.30.0-arm64', 1.3), ('v0.30.0-x86_64', 2.3)]\n[('v0.31.0', 1.1), ('v0.31.0-arm64', 1.1), ('v0.31.0-x86_64', 1.6), ('v0.30.0', 1.3), ('v0.30.0-arm64', 1.3), ('v0.30.0-x86_64', 2.3)]\n--8<-- [start:requirements]\n- CPU flags: `avx512f` (Recommended), `avx2` (Limited features)\n--8<-- [end:requirements]\nPre-built vLLM wheels for x86 with AVX512/AVX2 are available since version 0.17.0. To install release wheels:\nuv pip install -r requirements/build/cpu.txt --torch-backend cpu --index-strategy unsafe-best-match\nuv pip install -r requirements/cpu.txt --torch-backend cpu --index-strategy unsafe-best-match\n pip install -v -r requirements/build/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu\n pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu\n - `AMD` requires at least 4th gen processors (Zen 4/Genoa) or higher to support [AVX512](https://www.phoronix.com/review/amd-zen4-avx512) to run vLLM on CPU.\n - If you receive an error such as: `Could not find a version that satisfies the requirement torch==X.Y.Z+cpu+cpu`, consider updating [pyproject.toml](https://github.com/vllm-project/vllm/blob/main/pyproject.toml) to help pip resolve the dependency.\n--8<-- [start:pre-built-images]\nYou can pull the latest available CPU image from Docker Hub:\ndocker pull vllm/vllm-openai-cpu:latest-x86_64\nTo pull an image for a specific vLLM version:\ndocker pull vllm/vllm-openai-cpu:v${VLLM_VERSION}-x86_64\nbash\n$ curl -sL https://raw.githubusercontent.com/vllm-project/vllm/main/docs/getting_started/installation/cpu.md | grep -E -A2 \"VLLM_CPU_KVCACHE_SPACE|VLLM_CPU_OMP_THREADS_BIND|VLLM_CPU_NUM_OF_RESERVED_CPU|docker run|--shm-size|dtype|security-opt|cap-add\" | head -60\n- `VLLM_CPU_KVCACHE_SPACE`: specify the KV Cache size (e.g, `VLLM_CPU_KVCACHE_SPACE=40` means 40 GiB space for KV cache), larger setting will allow vLLM to run more requests in parallel. This parameter should be set based on the hardware configuration and memory management pattern of users. Default value is `0`.\n- `VLLM_CPU_OMP_THREADS_BIND`: specify the CPU cores dedicated to the OpenMP threads, can be set as CPU id lists, `auto` (by default), or `nobind` (to disable binding to individual CPU cores and to inherit user-defined OpenMP variables). For example, `VLLM_CPU_OMP_THREADS_BIND=0-31` means there will be 32 OpenMP threads bound on 0-31 CPU cores. `VLLM_CPU_OMP_THREADS_BIND=0-31|32-63` means there will be 2 tensor parallel processes, 32 OpenMP threads of rank0 are bound on 0-31 CPU cores, and the OpenMP threads of rank1 are bound on 32-63 CPU cores. By setting to `auto`, the OpenMP threads of each rank are bound to the CPU cores in each NUMA node respectively. If set to `nobind`, the number of OpenMP threads is determined by the standard `OMP_NUM_THREADS` environment variable.\n- `VLLM_CPU_NUM_OF_RESERVED_CPU`: specify the total number of CPU cores which are not dedicated to OpenMP threads. The variable only takes effect when `VLLM_CPU_OMP_THREADS_BIND` is set to `auto`. By default, x86, ARM, and RISC-V reserve `local_world_size` CPUs in total, while PowerPC and S390X retain a one-CPU default. When KV transfer is enabled, one additional CPU is reserved per local rank. Reserved CPUs are distributed across the local ranks.\n- `CPU_VISIBLE_MEMORY_NODES`: specify visible NUMA memory nodes for vLLM CPU workers, similar to ``` CUDA_VISIBLE_DEVICES```. The variable only takes effect when VLLM_CPU_OMP_THREADS_BIND is set to `auto`. The variable provides more control for the auto thread-binding feature, such as masking nodes and changing nodes binding sequence.\n- `VLLM_ZENTORCH_WEIGHT_PREPACK` (AMD Zen only): when `ZenCpuPlatform` is active, eagerly prepack linear weights into ZenDNN's blocked layout at model load time, eliminating per-inference layout conversion overhead. Default is `1` (enabled). See [AMD Zen optimizations](#amd-zen-optimizations).\n\n--\n### Which `dtype` should be used?\n\n- Currently, vLLM CPU uses model default settings as `dtype`. However, due to unstable float16 support in torch CPU, it is recommended to explicitly set `dtype=bfloat16` if there are any performance or accuracy problem. \n- On AMD Zen CPUs (`ZenCpuPlatform`), `float16` is **not*\n… [2366 more characters truncated]\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\ncat > docker-compose.yml <<'EOF'\n# Local development infrastructure. Managed services (MongoDB Atlas, Neo4j AuraDB,\n# Tiger Data) are reached over the network; Redis and the model server run here.\n#\n# docker compose up -d Redis only\n# docker compose --profile llm-cpu up -d Redis + vLLM on CPU (default for now)\n# docker compose --profile llm-gpu up -d Redis + vLLM on an NVIDIA GPU (needs the NVIDIA Container Toolkit)\n#\n# Both model servers listen on :8000 with the same model and flags, so the backend\n# doesn't change when switching. Run one profile at a time.\n\nx-vllm-common: &vllm-common\n ports:\n - \"8000:8000\"\n environment:\n HF_TOKEN: ${HF_TOKEN:-}\n volumes:\n - hf-cache:/root/.cache/huggingface\n healthcheck:\n test: [\"CMD\", \"curl\", \"-fsS\", \"http://localhost:8000/health\"]\n interval: 10s\n timeout: 5s\n retries: 90\n start_period: 60s\n\n# Flags shared by both devices; mirrors deploy/k8s/base/vllm.\nx-vllm-args: &vllm-args\n - --model=${LLM_MODEL:-Qwen/Qwen3-0.6B}\n - --port=8000\n - --max-model-len=${VLLM_MAX_MODEL_LEN:-4096}\n - --reasoning-parser=qwen3\n - --enable-auto-tool-choice\n - --tool-call-parser=hermes\n - --generation-config=auto\n\nservices:\n redis:\n image: redis:8-alpine\n ports:\n - \"6380:6379\" # host 6380: 6379 is often taken by a system Redis\n volumes:\n - redis-data:/data\n command: [\"redis-server\", \"--appendonly\", \"yes\"]\n\n vllm-cpu:\n <<: *vllm-common\n profiles: [\"llm-cpu\"]\n image: vllm/vllm-openai-cpu:v0.31.0-x86_64\n # vLLM's CPU backend pins threads to cores and NUMA nodes.\n cap_add: [SYS_NICE]\n security_opt: [\"seccomp=unconfined\"]\n shm_size: 2g\n mem_limit: ${VLLM_CPU_MEM_LIMIT:-8g}\n entrypoint: [\"vllm\", \"serve\"]\n command:\n - --model=${LLM_MODEL:-Qwen/Qwen3-0.6B}\n - --port=8000\n - --max-model-len=${VLLM_MAX_MODEL_LEN:-4096}\n - --reasoning-parser=qwen3\n - --enable-auto-tool-choice\n - --tool-call-parser=hermes\n - --generation-config=auto\n # CPU-only flags\n - --dtype=bfloat16\n - --kv-cache-memory-bytes=${VLLM_CPU_KV_CACHE_BYTES:-2147483648}\n\n vllm-gpu:\n <<: *vllm-common\n profiles: [\"llm-gpu\"]\n image: vllm/vllm-openai:v0.31.0\n ipc: host\n command:\n - --model=${LLM_MODEL:-Qwen/Qwen3-0.6B}\n - --port=8000\n - --max-model-len=${VLLM_MAX_MODEL_LEN:-4096}\n - --reasoning-parser=qwen3\n - --enable-auto-tool-choice\n - --tool-call-parser=hermes\n - --generation-config=auto\n # GPU-only flags\n - --gpu-memory-utilization=0.75\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n count: 1\n capabilities: [gpu]\n\nvolumes:\n redis-data:\n hf-cache:\nEOF\n# x-vllm-args anchor unused (compose can't merge lists); drop it to avoid confusion\npython3 - <<'EOF'\np='docker-compose.yml'; s=open(p).read()\nstart=s.\n… [470 more characters truncated]\ncompose ok\n - --dtype=bfloat16\n - --kv-cache-memory-bytes=2147483648\n```\n\nNow pulling the CPU image and starting vLLM. The image is about 1.6 GB and the model about 1.5 GB.\n\n``` bash\n$ docker compose --profile llm-cpu up -d 2>&1 | tail -4\nCommand running in background with ID: by60fkquf. Output is being written to: You will be notified when it completes. To check interim output, use Read on that file path.\n```\n\nWhile that downloads, I'll restructure the Kubernetes manifests into a device-agnostic base plus CPU and GPU components.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/deploy/k8s\ngit -C $R mv deploy/k8s/overlays/gke deploy/k8s/overlays/gke-gpu 2>/dev/null || mv overlays/gke overlays/gke-gpu\nmkdir -p components/vllm-cpu components/vllm-gpu overlays/gke-cpu\ncat > base/vllm/kustomization.yaml <<'EOF'\napiVersion: kustomize.config.k8s.io/v1beta1\nkind: Kustomization\n# Device-agnostic vLLM. Not deployable alone: an overlay must add exactly one of\n# components/vllm-cpu or components/vllm-gpu, which set the image and device flags.\nresources:\n - pvc.yaml\n - deployment.yaml\n - service.yaml\nconfigMapGenerator:\n # Model and engine settings; components and overlays merge in their own values.\n - name: vllm-config\n literals:\n - LLM_MODEL=Qwen/Qwen3-0.6B\n - VLLM_MAX_MODEL_LEN=4096\n - VLLM_REASONING_PARSER=qwen3\n - VLLM_TOOL_CALL_PARSER=hermes\nEOF\npython3 - <<'EOF'\np='base/vllm/deployment.yaml'; s=open(p).read()\ns=s.replace(\"\"\" # One GPU and a ReadWriteOnce cache volume: the old pod must stop before the new one starts.\"\"\",\"\"\" # ReadWriteOnce cache volume (and, on GPU, one GPU): the old pod must stop before the new one starts.\"\"\")\ns=s.replace(\"\"\" image: vllm/vllm-openai:v0.31.0\n # Kubernetes expands $(VAR) in args from the container's env.\"\"\",\"\"\" # Placeholder; the CPU or GPU component sets the real image.\n image: vllm-server\n # Kubernetes expands $(VAR) in args from the container's env.\n # Components append device-specific flags to this list.\"\"\")\ns=s.replace(\"\"\" - --gpu-memory-utilization=$(VLLM_GPU_MEMORY_UTILIZATION)\n\"\"\",\"\")\ns=s.replace(\"\"\" resources:\n requests:\n cpu: \"2\"\n memory: 8Gi\n limits:\n memory: 16Gi\n nvidia.com/gpu: \"1\"\n\"\"\",\"\"\" resources:\n requests:\n cpu: \"2\"\n memory: 4Gi\n limits:\n memory: 8Gi\n\"\"\")\nopen(p,'w').write(s)\nEOF\ncat > components/vllm-cpu/kustomization.yaml <<'EOF'\napiVersion: kustomize.config.k8s.io/v1alpha1\nkind: Component\n# vLLM on CPU. Best on nodes with AVX-512 (and AMX for bf16), e.g. GKE C3 or N2;\n# AVX2-only CPUs work with reduced performance.\nimages:\n - name: vllm-server\n newName: vllm/vllm-openai-cpu\n newTag: v0.31.0-x86_64\nconfigMapGenerator:\n - name: vllm-config\n behavior: merge\n literals:\n # KV cache lives in RAM on CPU; keep it inside the memory limit below.\n - VLLM_CPU_KV_CACHE_BYTES=4294967296\npatches:\n - target:\n kind: Deployment\n name: vllm\n patch: |-\n - op: add\n path: /spec/template/spec/containers/0/args/-\n value: --dtype=bfloat16\n - op: add\n path: /spec/template/spec/containers/0/args/-\n value: --kv-cache-memory-bytes=$(VLLM_CPU_KV_CACHE_BYTES)\n - op: replace\n path: /spec/template/spec/containers/0/resources\n value:\n requests:\n cpu: \"6\"\n memory:\n… [2625 more characters truncated]\n== gke-cpu\nSummary: 5 resources found in 1 file - Valid: 5, Invalid: 0, Errors: 0, Skipped: 0\n VLLM_MAX_MODEL_LEN: \"8192\"\n - --dtype=bfloat16\n - --kv-cache-memory-bytes=$(VLLM_CPU_KV_CACHE_BYTES)\n image: vllm/vllm-openai-cpu:v0.31.0-x86_64\n - SYS_NICE\n cloud.google.com/gke-nodepool: llm-cpu\n== gke-gpu\nSummary: 5 resources found in 1 file - Valid: 5, Invalid: 0, Errors: 0, Skipped: 0\n VLLM_MAX_MODEL_LEN: \"32768\"\n - --gpu-memory-utilization=$(VLLM_GPU_MEMORY_UTILIZATION)\n image: vllm/vllm-openai:v0.31.0\n nvidia.com/gpu: \"1\"\n cloud.google.com/gke-accelerator: nvidia-l4\n key: nvidia.com/gpu\n```\n\nBoth overlays build and validate, and each gets the right image, device flags and node placement. Checking that the configuration values merged correctly, and how the download is going:\n\n``` bash\n$ SP= for o in gke-cpu gke-gpu; do echo \"== $o\"; python3 -c \"\nimport yaml,sys\nfor d in yaml.safe_load_all(open('$SP/$o.yaml')):\n if d['kind']=='ConfigMap': print(d['data'])\" 2>/dev/null || grep -A8 \"^data:\" $SP/$o.yaml; done; echo; cat $SP/../tasks/by60fkquf.output; docker ps -a --format '{{.Names}}\\t{{.Status}}' | grep -i perfor\\\\\\|lets-make\n== gke-cpu\n{'LLM_MODEL': 'Qwen/Qwen3-0.6B', 'VLLM_CPU_KV_CACHE_BYTES': '4294967296', 'VLLM_MAX_MODEL_LEN': '8192', 'VLLM_REASONING_PARSER': 'qwen3', 'VLLM_TOOL_CALL_PARSER': 'hermes'}\n== gke-gpu\n{'LLM_MODEL': 'Qwen/Qwen3-0.6B', 'VLLM_GPU_MEMORY_UTILIZATION': '0.85', 'VLLM_MAX_MODEL_LEN': '32768', 'VLLM_REASONING_PARSER': 'qwen3', 'VLLM_TOOL_CALL_PARSER': 'hermes'}\n\nlets-make-osp-that-someone-will-actually-pay-for-redis-1 Up 7 hours\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/deploy/k8s\nsed -i 's/ - VLLM_GPU_MEMORY_UTILIZATION=0.85/ - VLLM_GPU_MEMORY_UTILIZATION=0.90/' components/vllm-gpu/kustomization.yaml\npython3 - <<'EOF'\np='overlays/gke-gpu/kustomization.yaml'; s=open(p).read()\ns=s.replace(\"\"\" # An L4 has 24 GB, so the small model gets a much longer context than on a laptop.\n - name: vllm-config\n behavior: merge\n literals:\n - VLLM_MAX_MODEL_LEN=32768\n - VLLM_GPU_MEMORY_UTILIZATION=0.90\n\"\"\",\"\"\" # An L4 has 24 GB, so the small model gets a much longer context than on a laptop.\n # Note: values a component sets win over the same keys here (components merge last).\n - name: vllm-config\n behavior: merge\n literals:\n - VLLM_MAX_MODEL_LEN=32768\n\"\"\")\nopen(p,'w').write(s)\nEOF\nSP=\n$SP/bin/kustomize build overlays/gke-gpu > $SP/gke-gpu.yaml && $SP/bin/kubeconform -strict -summary -ignore-missing-schemas $SP/gke-gpu.yaml && grep -E \"VLLM_GPU_MEMORY|VLLM_MAX\" $SP/gke-gpu.yaml\nSummary: 5 resources found in 1 file - Valid: 5, Invalid: 0, Errors: 0, Skipped: 0\n VLLM_GPU_MEMORY_UTILIZATION: \"0.90\"\n VLLM_MAX_MODEL_LEN: \"32768\"\n - --max-model-len=$(VLLM_MAX_MODEL_LEN)\n - --gpu-memory-utilization=$(VLLM_GPU_MEMORY_UTILIZATION)\n```\n\nNext I'll rewrite the GKE guide to put CPU first, with GPU as the upgrade path.\n\n```\n{\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/deploy/gke/README.md\",\n \"content\": \"# vLLM on GKE\\n\\nThe model server runs on CPU for now, with a GPU path ready when it's needed. Both use the same base (`deploy/k8s/base`) and differ only in the component they include:\\n\\n| Overlay | Component | Runs on | Context |\\n|---|---|---|---|\\n| `deploy/k8s/overlays/gke-cpu` | `components/vllm-cpu` | `llm-cpu` node pool (C3: AVX-512 + AMX) | 8k tokens |\\n| `deploy/k8s/overlays/gke-gpu` | `components/vllm-gpu` | `gpu-l4` node pool (NVIDIA L4, Spot) | 32k tokens |\\n\\nBoth expose the same Service (`vllm.perforvisor.svc.cluster.local:8000`), so the backend doesn't change when switching.\\n\\n## Prerequisites\\n\\n- `gcloud`, `kubectl` and the GKE auth plugin (`gcloud components install kubectl gke-gcloud-auth-plugin`)\\n- A project with billing enabled\\n\\n## 1. Create the cluster\\n\\n``` bash\\nexport PROJECT_ID=your-project\\nexport ZONE=us-central1-a\\nexport CLUSTER=perforvisor\\n\\ngcloud config set project $PROJECT_ID\\ngcloud services enable container.googleapis.com\\n\\n# Small pool for system pods (and later the API, worker and frontends)\\ngcloud container clusters create $CLUSTER \\\\\\n --zone $ZONE \\\\\\n --machine-type e2-standard-4 \\\\\\n --num-nodes 1 \\\\\\n --release-channel regular\\n\\ngcloud container clusters get-credentials $CLUSTER --zone $ZONE\\n```\\n\\n## 2. CPU model server (current setup)\\n\\nC3 machines have AVX-512 and AMX, which vLLM's CPU backend uses for bf16; they are several times faster than AVX2-only machines for this.\\n\\n``` bash\\ngcloud container node-pools create llm-cpu \\\\\\n --cluster $CLUSTER \\\\\\n --zone \\n… [2323 more characters truncated]\"\n}\nThe file ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/deploy/gke/README.md has been updated successfully. (file state is current in your context — no need to Read it back)\nbash\n$ cat docker ps -a --format '{{.Names}}\\t{{.Status}}' | grep vllm; docker images --format '{{.Repository}}:{{.Tag}} {{.Size}}' | grep vllm\n(Bash completed with no output)\n```\n\nThe image is still downloading. Meanwhile I'll update the config notes and the docs for running on CPU.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\npython3 - <<'EOF'\np='.env.example'; s=open(p).read()\ns=s.replace(\"\"\"# Model server (vLLM, OpenAI-compatible). Local: docker compose --profile llm up -d\nLLM_BASE_URL=\nLLM_MODEL=Qwen/Qwen3-0.6B\"\"\",\"\"\"# Model server (vLLM, OpenAI-compatible)\n# CPU (default): docker compose --profile llm-cpu up -d\n# GPU: docker compose --profile llm-gpu up -d\nLLM_BASE_URL=\nLLM_MODEL=Qwen/Qwen3-0.6B\n# Parallel model calls per analysis. vLLM batches them; on CPU, 4 is a good start.\nLLM_CONCURRENCY=4\n\n# vLLM container tuning (docker compose only)\nVLLM_MAX_MODEL_LEN=4096\nVLLM_CPU_MEM_LIMIT=8g\nVLLM_CPU_KV_CACHE_BYTES=2147483648\"\"\")\nopen(p,'w').write(s)\np='docs/REQUIREMENTS.md'; s=open(p).read()\ns=s.replace(\"| Model serving | vLLM 0.31 (OpenAI-compatible API) on GKE with an NVIDIA L4 (`deploy/k8s/overlays/gke`, setup in `deploy/gke/README.md`); locally via `docker compose --profile llm` |\",\n\"| Model serving | vLLM 0.31 (OpenAI-compatible API) on **CPU for now**, GPU-ready. Local: `docker compose --profile llm-cpu` (or `llm-gpu`). GKE: `deploy/k8s/overlays/gke-cpu` (C3 node pool) or `gke-gpu` (L4); see `deploy/gke/README.md` |\")\nopen(p,'w').write(s)\nEOF\ngrep -n \"Model serving\" docs/REQUIREMENTS.md | cut -c1-80\nM=~/.claude/projects/-home-life-workstation-Personal-Projects-lets-make-osp-that-someone-will-actually-pay-for/memory\ncat >> $M/hf26-week2-project.md <<'EOF'\n2026-10-15: user can't give Docker GPU access (container/GPU issue on their machine), so vLLM runs on CPU (vllm/vllm-openai-cpu image, compose profile llm-cpu; k8s overlay gke-cpu on a C3 pool). GPU path kept ready (profile llm-gpu, overlay gke-gpu) — don't push the user to fix GPU access.\nEOF\n24:| Model serving | vLLM 0.31 (OpenAI-compatible API) on **CPU for now**, GPU-r\nbash\n$ sed -i 's/LLM_CONCURRENCY: z.coerce.number().int().positive().default(8),/LLM_CONCURRENCY: z.coerce.number().int().positive().default(4),/' backend/src/config.ts && grep -n LLM_CONCURRENCY backend/src/config.ts; cat \n20: LLM_CONCURRENCY: z.coerce.number().int().positive().default(4),\n```\n\nConfig is updated. The image is still downloading, so I'll wait for it to finish.\n\n```\n{\n \"query\": \"select:Monitor\",\n \"max_results\": 1\n}\n[image]\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nuntil curl -sf localhost:8000/health >/dev/null; do\n st=$(docker inspect -f '{{.State.Status}}' $(docker compose ps -aq vllm-cpu 2>/dev/null) 2>/dev/null)\n if [ \"$st\" = \"exited\" ] || [ \"$st\" = \"dead\" ]; then echo \"vllm container $st\"; docker compose logs --tail 40 vllm-cpu; exit 1; fi\n sleep 5\ndone\necho \"vllm healthy\"; docker compose logs vllm-cpu 2>&1 | grep -iE \"avx|amx|isa|kv cache|memory|loading took|model weights|maximum concurrency|warn\" | tail -15\nCommand running in background with ID: bqglj8zy0. Output is being written to: You will be notified when it completes. To check interim output, use Read on that file path.\nSession cwd remains ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; directory changes made by the backgrounded command do not apply to subsequent commands.\n```\n\nWhile I wait for vLLM to start, I'll run the typecheck and unit tests again after the config change.\n\n``` bash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; export NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null; timeout 150 pnpm typecheck 2>&1 | grep -cE \": Done\"; timeout 60 pnpm test 2>&1 | grep -E \"Tests\"\n6\n Tests 4 passed (4)\n```\n\nTypecheck and tests still pass. The vLLM CPU image is downloading in the background. Once it's up and healthy, I'll run the 12 sample reviews through real Qwen3-0.6B on CPU and report the speed and how good the extraction is.\n\n\n\nby60fkquf\n\ntoolu_01Qucuhn8ESWsVEWmcGjYL1Z\n\n\n\ncompleted\n\nBackground command \"Pull CPU vLLM image and start it with Redis\" completed (exit code 0)\n\n\n\n``` bash\n$ cat cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; docker compose ps -a --format '{{.Name}} {{.Status}}'; docker compose logs --tail 15 vllm-cpu 2>&1 | cut -c1-250\nContainer lets-make-osp-that-someone-will-actually-pay-for-vllm-cpu-1 Creating \n Container lets-make-osp-that-someone-will-actually-pay-for-vllm-cpu-1 Created \n Container lets-make-osp-that-someone-will-actually-pay-for-vllm-cpu-1 Starting \n Container lets-make-osp-that-someone-will-actually-pay-for-vllm-cpu-1 Started \n\n[exited with code 0]\nlets-make-osp-that-someone-will-actually-pay-for-redis-1 Up 7 hours\nlets-make-osp-that-someone-will-actually-pay-for-vllm-cpu-1 Up 5 seconds (health: starting)\n```\n\nThe container started and is now loading the model. The health watcher will notify me once vLLM is serving.\n\n\n\nbqglj8zy0\n\ntoolu_011FLef2QAJxRvpXtUxXFSSN\n\n\n\ncompleted\n\nBackground command \"Wait for vLLM CPU server to become healthy or fail\" completed (exit code 0)\n\n\n\n``` bash\n$ cat | cut -c1-260; curl -s localhost:8000/v1/models | head -c 300; echo; time curl -s localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"Qwen/Qwen3-0.6B\",\"messages\":[{\"role\":\"user\",\"content\":\"Say hi in five words.\"}],\"chat_template_kwargs\":{\"enable_thinking\":false},\"max_tokens\":30}' | python3 -c \"import json,sys;d=json.load(sys.stdin);print(d['choices'][0]['message']['content'],'|',d['usage'])\"\nvllm healthy\nvllm-cpu-1 | (EngineCore pid=191) INFO 10-14 19:40:22 [core.py:129] Initializing a V1 LLM engine (v0.31.0) with config: model='Qwen/Qwen3-0.6B', speculative_config=None, tokenizer='Qwen/Qwen3-0.6B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=mai\nvllm-cpu-1 | (VllmWorker-0 pid=253) WARNING 10-14 19:40:28 [indexer_topk.py:35] Failed to import the DeepSelect extension (vllm._deepselect_C): No module named 'vllm._deepselect_C'\nvllm-cpu-1 | [W1014 19:40:28.570881714 utils.cpp:68] Warning: NUMA binding: Using MEMBIND policy for memory allocation on the NUMA nodes (0). Memory allocations will be strictly bound to these NUMA nodes. (function init_cpu_memory_env)\nvllm-cpu-1 | (Worker pid=253) INFO 10-14 19:46:09 [weight_utils.py:1086] Auto-prefetch is disabled because the filesystem (BTRFS) is not a recognized network FS (NFS/Lustre). If you want to force prefetching, start vLLM with --safetensors-load-strategy=prefet\nvllm-cpu-1 | (Worker pid=253) WARNING 10-14 19:46:13 [utils.py:579] Failed to create oneDNN linear, fallback to torch linear. Exception: could not create a primitive descriptor for the matmul primitive. Run workload with environment variable ONEDNN_VERBOSE=al\nvllm-cpu-1 | (Worker pid=253) INFO 10-14 19:46:14 [model_runner.py:407] Model loading took 2.84 GiB memory and 345.650455 seconds\nvllm-cpu-1 | (Worker pid=253) WARNING 10-14 19:46:14 [torch_utils.py:274] OMP_NUM_THREADS=9 is set; leaving Torch threads at 9 for serving. Multi-threaded torch CPU ops during serving can degrade performance through spin-wait contention and cgroup CPU-quota t\nvllm-cpu-1 | (EngineCore pid=191) INFO 10-14 19:46:14 [utils.py:343] Using LBHNC KV cache layout.\nvllm-cpu-1 | (EngineCore pid=191) INFO 10-14 19:47:15 [shm_broadcast.py:812] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation, weight/kv \nvllm-cpu-1 | (Worker pid=253) INFO 10-14 19:48:03 [cpu_worker.py:260] Explicitly set (2.0/8.0) GiB for KV cache on node 0.\nvllm-cpu-1 | (EngineCore pid=191) INFO 10-14 19:48:03 [kv_cache_utils.py:2464] CPU KV cache size: 18,688 tokens, Maximum concurrency for 4,096 tokens per request: 4.56x\nvllm-cpu-1 | (EngineCore pid=191) INFO 10-14 19:48:03 [core.py:380] init engine (profile, create kv cache, warmup model) took 109.52 s\nvllm-cpu-1 | (EngineCore pid=191) WARNING 10-14 19:48:06 [vllm.py:1750] Inductor compilation was disab\n… [1040 more characters truncated]\nbash\n$ for i in 1 2; do /usr/bin/time -f \"%es\" curl -s localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"Qwen/Qwen3-0.6B\",\"messages\":[{\"role\":\"user\",\"content\":\"List three fruits.\"}],\"chat_template_kwargs\":{\"enable_thinking\":false},\"max_tokens\":60}' | python3 -c \"import json,sys;d=json.load(sys.stdin);print(repr(d['choices'][0]['message']['content'][:80]),d['usage']['completion_tokens'],'tokens')\"; done\n1.20s\n'1. **Apples** \\n2. **Bananas** \\n3. **Oranges**' 21 tokens\n1.30s\n'Sure! Here are three fruits:\\n\\n1. **Apple** \\n2. **Orange** \\n3. **Lemon**' 26 tokens\n```\n\nOnce warm, a short answer takes about 1.2 seconds, which is usable. Now the real test: the 12 sample reviews through the full backend flow, with Qwen on CPU.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nSP=\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ncd $R/backend\nAPI_PORT=4125 NODE_ENV=production LOG_LEVEL=warn timeout 400 ./node_modules/.bin/tsx src/api.ts > $SP/api3.log 2>&1 & API=$!\nfor i in $(seq 1 40); do curl -s localhost:4125/health >/dev/null && break; sleep 0.5; done\ncurl -s localhost:4125/v1/ai/health; echo\nstart=$(date +%s.%N)\ncurl -sN --max-time 300 -X POST localhost:4125/v1/analyze -H 'content-type: application/json' --data @fixtures/sample-reviews.json > $SP/sse-real.txt; echo \"curl exit $? in $(python3 -c \"print(round($(date +%s.%N)-$start,1))\")s\"\ngrep '^event:' $SP/sse-real.txt | sort | uniq -c\ngrep '^data: {\"type\":\"error\"' $SP/sse-real.txt\ngrep '^data: {\"type\":\"result\"' $SP/sse-real.txt | sed 's/^data: //' > $SP/result-real.json\nkill $API 2>/dev/null; wait 2>/dev/null; echo \"api warn/err lines: $(grep -cE '\"level\":(50|40)' $SP/api3.log)\"\n{\"model\":\"Qwen/Qwen3-0.6B\",\"baseUrl\":\"\",\"ok\":true,\"models\":[\"Qwen/Qwen3-0.6B\"]}\ncurl exit 0 in 145.7s\n 1 event: done\n 14 event: progress\n 1 event: result\napi warn/err lines: 0\n```\n\nThe whole flow ran on real Qwen: 12 reviews in **146 seconds**, with no errors. That's slow, so before tuning I'll look at the output quality and where the time went.\n\n``` python\n$ SP=\npython3 - < {r['sentiment']} {[(a['aspect'],a['sentiment']) for a in r['aspects']]} {r['claims']}\")\nEOF\ngrep -o '\"message\":\"Analysed[^\"]*\"' $SP/sse-real.txt | head -3; cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; docker compose logs --since 5m vllm-cpu 2>&1 | grep -iE \"throughput|xgrammar|guided|structured|compil\" | tail -5 | cut -c1-220\nanalysed 12 failed 0 avg 3.67 sentiment {'positive': 6, 'neutral': 0, 'negative': 6}\nmismatches ['r8'] | dupes [['r5', 'r6']]\naspects [('food', 9, 0.22), ('service', 7, 0.14), ('delivery', 4, -1), ('quality', 1, 1), ('packaging', 1, -1), ('ambience', 1, 1), ('wait_time', 1, 0)]\ntopClaims [('The restaurant is highly recommended.', 2), ('Everyone must visit the restaurant.', 2), ('Best butter chicken in the area.', 1), ('Generous portions and the naan comes out hot.', 1), ('Staff were friendly.', 1), ('The biryani was good but cold by the time it arrived.', 1)]\nSUMMARY {\n \"verdict\": \"The restaurant is highly recommended (2 claims), everyone must visit (2 claims), and the butter chicken is excellent (1 claim). However, the food was delivered 50 minutes late (1 claim), the portions were not generous (1 claim), and the naan was cold (1 claim). The ambience is good, but the wait is \",\n \"strengths\": [\n \"The food was delivered 50 minutes late\",\n \"Generous portions and the naan comes out hot\",\n \"The biryani was good but cold by the time it arrived\"\n ],\n \"weaknesses\": [\n \"The restaurant is highly recommended\",\n \"Everyone must visit the restaurant\",\n \"Best butter chicken in the area\"\n ],\n \"actions\": [\n \"Fix the delivery time issue\",\n \"Ensure generous portions and hot naan are available\",\n \"Address cold biryani by ensuring timely delivery\"\n ]\n}\n\nr1 (5*) Best butter chicken in the area. Generous portions and the naan comes \n -> positive [('quality', 'positive'), ('service', 'positive'), ('food', 'positive')] ['Best butter chicken in the area.', 'Generous portions and the naan comes out hot.', 'Staff were friendly.']\n\nr2 (2*) Waited 50 minutes for our food on a Friday night. The biryani was good\n -> negative [('food', 'negative'), ('delivery', 'negative')] ['The biryani was good but cold by the time it arrived.', 'The food was delivered 50 minutes late.']\n\nr3 (4*) Great food, a bit pricey for the portion size of the starters. Paneer \n -> positive [('food', 'positive'), ('food', 'positive'), ('food', 'positive'), ('food', 'positive'), ('food', 'positive')] ['Great food', 'a bit pricey for the portion size of the starters', 'Paneer tikka was excellent']\n\nr4 (1*) Delivery order arrived with the curry leaking all over the bag. Packag\n -> negative [('delivery', 'negative'), ('packaging', 'negative')] ['Delivery order arrived with the curry leaking all over the bag.', 'Packaging needs serious work.']\n\nr5 (5*) Amazing place!!! Best restaurant in the city, ever\n… [3039 more characters truncated]\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R\npython3 - <<'EOF'\nimport re\n# ---- schemas\np='common/core/src/schemas/analysis.ts'; s=open(p).read()\ns=s.replace(\"\"\" topClaims: z.array(z.object({ claim: z.string(), count: z.number().int() })),\n ratingMismatches\"\"\",\"\"\" topClaims: z.array(ClaimCount),\n /** Claims from positive and negative reviews, excluding suspicious ones. */\n praise: z.array(ClaimCount),\n complaints: z.array(ClaimCount),\n /** Reviews left out of praise and complaints: rating mismatches and all but one of each duplicate group. */\n suspiciousReviewIds: z.array(z.string()),\n ratingMismatches\"\"\")\ns=s.replace(\"\"\"export const AnalysisStats = z.object({\"\"\",\"\"\"export const ClaimCount = z.object({ claim: z.string(), count: z.number().int() });\nexport type ClaimCount = z.infer;\n\nexport const AnalysisStats = z.object({\"\"\")\ns=s.replace(\"\"\"/** What the model writes for the owner, grounded in the aggregated stats. */\nexport const AdvisorSummary = z.object({\n verdict: z.string().max(300).describe(\"One or two sentences on how this place or product is performing\"),\n strengths: z.array(z.string().max(160)).max(3),\n weaknesses: z.array(z.string().max(160)).max(3),\n actions: z.array(z.string().max(200)).max(3).describe(\"Concrete things the owner should do next, most important first\"),\n});\nexport type AdvisorSummary = z.infer;\"\"\",\"\"\"/**\n * What the model writes. Kept small on purpose: deciding what counts as a strength\n * or weakness is done in code, which small models get wrong.\n */\nexport const AdvisorDraft = z.object({\n verdict: z.string().describe(\"One or two sentences, under 40 words, on how this place or product is performing\"),\n actions: z.array(z.string()).max(3).describe(\"Concrete things the owner should do next, most important first\"),\n});\nexport type AdvisorDraft = z.infer;\n\nexport const AdvisorSummary = AdvisorDraft.extend({\n strengths: z.array(z.string()),\n weaknesses: z.array(z.string()),\n});\nexport type AdvisorSummary = z.infer;\"\"\")\nopen(p,'w').write(s)\n\n# ---- stats\np='backend/src/ai/stats.ts'; s=open(p).read()\ns=s.replace('import type { AnalysisReviewInput, AnalysisStats, Aspect, AspectStat, ReviewAnalysis, ReviewExtraction, Sentiment } from \"@perforvisor/core\";',\n'import type { AnalysisReviewInput, AnalysisStats, Aspect, AspectStat, ClaimCount, ReviewAnalysis, ReviewExtraction, Sentiment } from \"@perforvisor/core\";')\ns=s.replace(\"\"\"export function computeStats(reviews: AnalysisReviewInput[], analyses: ReviewAnalysis[]): AnalysisStats {\n const ratings = reviews.map((r) => r.rating).filter((r): r is number => r !== undefined);\n const sentiment = { positive: 0, neutral: 0, negative: 0 };\n const aspects = new Map();\n const claims = new Map();\n\"\"\",\"\"\"function countClaims(analyses: ReviewAnalysis[], limit: number): ClaimCount[] {\n const claim\n… [7732 more characters truncated]\ncommon/core typecheck: Done\ncommon/ui typecheck: Done\nfrontend/client typecheck: Done\nfrontend/consumer typecheck: Done\nplugins/serpapi-google-maps typecheck: Done\nbackend typecheck: Done\n⎯⎯⎯⎯⎯⎯⎯ Failed Tests 2 ⎯⎯⎯⎯⎯⎯⎯\n FAIL backend/src/ai/stats.test.ts > computeStats suspicious reviews > keeps the first review of a duplicate group and drops the copies from praise\nAssertionError: expected [] to deeply equal [ { …(2) } ]\n FAIL backend/src/ai/stats.test.ts > computeStats > aggregates sentiment, aspects and claims\nAssertionError: expected [] to deeply equal [ { claim: 'Service is slow', …(1) } ]\n Tests 2 failed | 3 passed (5)\n```\n\nTwo of the new test assertions fail: `praise` comes back empty. I'll look at what the code actually does now.\n\n```\n{\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/ai/stats.ts\",\n \"offset\": 58\n}\n58 \n59 export function computeStats(reviews: AnalysisReviewInput[], analyses: ReviewAnalysis[]): AnalysisStats {\n60 const ratings = reviews.map((r) => r.rating).filter((r): r is number => r !== undefined);\n61 const sentiment = { positive: 0, neutral: 0, negative: 0 };\n62 const aspects = new Map();\n63 \n64 const duplicateGroups = findDuplicateGroups(reviews);\n65 const ratingMismatches = analyses.filter((a) => a.ratingMismatch).map((a) => a.reviewId);\n66 // Keep the first review of each duplicate group; the copies are suspicious.\n67 const suspicious = new Set([...ratingMismatches, ...duplicateGroups.flatMap((group) => group.slice(1))]);\n68 const trusted = analyses.filter((a) => !suspicious.has(a.reviewId));\n69 \n70 for (const analysis of analyses) {\n71 sentiment[analysis.sentiment]++;\n72 \n73 // Count each aspect once per review, even if the model listed it twice.\n74 const seen = new Set();\n75 for (const { aspect, sentiment: s } of analysis.aspects) {\n76 if (seen.has(aspect)) continue;\n77 seen.add(aspect);\n78 const stat = aspects.get(aspect) ?? { aspect, mentions: 0, positive: 0, neutral: 0, negative: 0, score: 0 };\n79 stat.mentions++;\n80 stat[s]++;\n81 aspects.set(aspect, stat);\n82 }\n83 }\n84 \n85 for (const stat of aspects.values()) stat.score = Number(((stat.positive - stat.negative) / stat.mentions).toFixed(2));\n86 \n87 return {\n88 reviewCount: reviews.length,\n89 analysedCount: analyses.length,\n90 failedCount: reviews.length - analyses.length,\n91 averageRating: ratings.length ? Number((ratings.reduce((a, b) => a + b, 0) / ratings.length).toFixed(2)) : null,\n92 sentiment,\n93 aspects: [...aspects.values()].sort((a, b) => b.mentions - a.mentions),\n94 topClaims: countClaims(analyses, 10),\n95 praise: countClaims(trusted.filter((a) => a.sentiment === \"positive\"), 5),\n96 complaints: countClaims(trusted.filter((a) => a.sentiment === \"negative\"), 5),\n97 suspiciousReviewIds: [...suspicious],\n98 ratingMismatches,\n99 duplicateGroups,\n100 };\n101 }\n102\n```\n\nThe `computeStats` body looks right, so the problem is likely in `countClaims` itself.\n\n```\n{\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/ai/stats.ts\",\n \"offset\": 40,\n \"limit\": 18\n}\njs\n40 for (let j = i + 1; j < reviews.length; j++) {\n41 if (jaccard(sets[i]!, sets[j]!) >= threshold) parent[find(j)] = find(i);\n42 }\n43 }\n44 \n45 const groups = new Map();\n46 reviews.forEach((review, i) => {\n47 const root = find(i);\n48 groups.set(root, [...(groups.get(root) ?? []), review.id]);\n49 });\n50 return [...groups.values()].filter((group) => group.length > 1);\n51 }\n52 \n53 function countClaims(analyses: ReviewAnalysis[], limit: number): ClaimCount[] {\n54 const claims = new Map();\n55 for (const analysis of analyses) { }\n56 return [...claims.values()].sort((a, b) => b.count - a.count).slice(0, limit);\n57 }\n{\n \"replace_all\": false,\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/ai/stats.ts\",\n \"old_string\": \" for (const analysis of analyses) { }\\n return [...claims.values()]\",\n \"new_string\": \" for (const analysis of analyses) {\\n for (const claim of analysis.claims) {\\n const key = normalizeClaim(claim);\\n if (!key) continue;\\n const entry = claims.get(key) ?? { claim, count: 0 };\\n entry.count++;\\n claims.set(key, entry);\\n }\\n }\\n return [...claims.values()]\"\n}\nThe file ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/ai/stats.ts has been updated successfully.\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; export NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null; timeout 60 pnpm test 2>&1 | grep -E \"Tests|FAIL|AssertionError\" | head; timeout 100 pnpm --filter @perforvisor/backend typecheck 2>&1 | grep -E \"error|Done\"\nTests 5 passed (5)\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend; export NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null; timeout 100 ../node_modules/.bin/tsc -p tsconfig.json 2>&1 | head; echo \"tsc exit ${PIPESTATUS[0]}\"\ntsc exit 0\n```\n\nAll 5 tests pass and the typecheck is clean. Rerunning the sample reviews through real Qwen on CPU:\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nSP=\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ncd $R/backend\nAPI_PORT=4126 NODE_ENV=production LOG_LEVEL=warn timeout 400 ./node_modules/.bin/tsx src/api.ts > $SP/api4.log 2>&1 & API=$!\nfor i in $(seq 1 40); do curl -s localhost:4126/health >/dev/null && break; sleep 0.5; done\nstart=$(date +%s.%N)\ncurl -sN --max-time 300 -X POST localhost:4126/v1/analyze -H 'content-type: application/json' --data @fixtures/sample-reviews.json > $SP/sse-real2.txt; echo \"curl exit $? in $(python3 -c \"print(round($(date +%s.%N)-$start,1))\")s\"\nkill $API 2>/dev/null; wait 2>/dev/null\ngrep '^data: {\"type\":\"error\"' $SP/sse-real2.txt\ngrep '^data: {\"type\":\"result\"' $SP/sse-real2.txt | sed 's/^data: //' > $SP/result-real2.json\npython3 - </dev/null\nmkdir -p fixtures/samples\npython3 - <<'EOF'\nimport json\nd=json.load(open('fixtures/sample-reviews.json'))\nd={\"id\":\"spice-route-kitchen\",\"kind\":\"place\",\"entityName\":\"Spice Route Kitchen\",\"category\":\"Indian restaurant\",**{k:v for k,v in d.items() if k==\"reviews\"}}\njson.dump(d,open('fixtures/samples/spice-route-kitchen.json','w'),indent=2)\nEOF\nrm fixtures/sample-reviews.json\ncat > fixtures/samples/voltmax-power-bank.json <<'EOF'\n{\n \"id\": \"voltmax-power-bank\",\n \"kind\": \"product\",\n \"entityName\": \"VoltMax 20000mAh Power Bank\",\n \"category\": \"Electronics\",\n \"reviews\": [\n { \"id\": \"p1\", \"rating\": 5, \"text\": \"Charges my phone four times on a single charge. Fast charging works with my Pixel. Solid build.\" },\n { \"id\": \"p2\", \"rating\": 2, \"text\": \"Gets really hot while charging my laptop, too hot to hold after about an hour. Worried about safety.\" },\n { \"id\": \"p3\", \"rating\": 4, \"text\": \"Great capacity for the price. A bit heavy to carry in a pocket, but fine in a backpack.\" },\n { \"id\": \"p4\", \"rating\": 1, \"text\": \"Stopped holding charge after two months. Now it dies within a day even when not in use.\" },\n { \"id\": \"p5\", \"rating\": 3, \"text\": \"Works fine, but it gets warm when charging two devices at once. The LED display is handy.\" },\n { \"id\": \"p6\", \"rating\": 5, \"text\": \"Best power bank ever!!! Must buy, five stars, amazing product, highly recommended to everyone!!!\" },\n { \"id\": \"p7\", \"rating\": 5, \"text\": \"Best power bank ever!! Must buy, five stars, amazing product, highly recommended to everyone!\" },\n { \"id\": \"p8\", \"rating\": 4, \"text\": \"USB-C port is fast and the cable included is good quality. Takes about 6 hours to recharge itself.\" },\n { \"id\": \"p9\", \"rating\": 2, \"text\": \"The casing cracked after a small drop from my desk. Charging still works but it feels flimsy.\" },\n { \"id\": \"p10\", \"rating\": 5, \"text\": \"Overheated badly on a flight and the attendant asked me to put it away. Never buying again.\" }\n ]\n}\nEOF\nls fixtures/samples\n?? .env.example\n?? .gitignore\n?? .nvmrc\n?? backend/\n?? common/\n?? deploy/\n?? docker-compose.yml\n?? docs/\n?? frontend/\n?? package.json\nspice-route-kitchen.json\nvoltmax-power-bank.json\n```\n\nNow the shared schemas for samples and Q&A, plus a browser-safe client for SSE over POST. The browser's built-in `EventSource` only supports GET.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/common/core\ncat > src/schemas/sample.ts <<'EOF'\nimport { z } from \"zod\";\nimport { EntityKind } from \"./entity.ts\";\nimport { AnalysisReviewInput } from \"./analysis.ts\";\n\n/** A built-in set of reviews for one place or product, used until live connectors are enabled. */\nexport const SampleDataset = z.object({\n id: z.string(),\n kind: EntityKind,\n entityName: z.string(),\n category: z.string(),\n reviews: z.array(AnalysisReviewInput).min(1),\n});\nexport type SampleDataset = z.infer;\n\nexport const SampleSummary = SampleDataset.omit({ reviews: true }).extend({ reviewCount: z.number().int() });\nexport type SampleSummary = z.infer;\nEOF\ncat > src/schemas/ask.ts <<'EOF'\nimport { z } from \"zod\";\nimport { AnalysisReviewInput } from \"./analysis.ts\";\n\nexport const AskRequest = z.object({\n question: z.string().trim().min(3).max(500),\n entityName: z.string().min(1).max(200),\n reviews: z.array(AnalysisReviewInput).min(1).max(500),\n});\nexport type AskRequest = z.infer;\n\n/** What the model writes; citations are checked against the reviews it was shown. */\nexport const AskDraft = z.object({\n answer: z.string().describe(\"Direct answer to the question in 2-4 sentences, based only on the reviews\"),\n citedReviewIds: z.array(z.string()).max(5).describe(\"Ids of the reviews that support the answer\"),\n});\nexport type AskDraft = z.infer;\n\nexport const AskResult = z.object({\n question: z.string(),\n answer: z.string(),\n citations: z.array(AnalysisReviewInput),\n /** How many reviews were relevant enough to show the model. */\n consideredCount: z.number().int(),\n});\nexport type AskResult = z.infer;\nEOF\ncat > src/utils/sse-client.ts <<'EOF'\nimport { StreamEvent } from \"../schemas/stream.ts\";\n\n/**\n * POSTs JSON and reads the server-sent events in the response.\n * EventSource only supports GET, so this parses the stream with fetch.\n */\nexport async function postSse(url: string, body: unknown, onEvent: (event: StreamEvent) => void, signal?: AbortSignal): Promise {\n const res = await fetch(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", Accept: \"text/event-stream\" },\n body: JSON.stringify(body),\n signal,\n });\n\n // Validation errors come back as plain JSON before any stream starts.\n if (!res.ok || !res.body) {\n const detail = await res.json().catch(() => null);\n const message = (detail as { message?: string } | null)?.message ?? `Request failed (${res.status})`;\n onEvent({ type: \"error\", message });\n return;\n }\n\n const reader = res.body.pipeThrough(new TextDecoderStream()).getReader();\n let buffer = \"\";\n for (;;) {\n const { value, done } = await reader.read();\n if (done) break;\n buffer += value;\n let boundary: number;\n while ((boundary = buffer.indexOf(\"\\n\\n\")) !== -1) {\n const frame = buffer.slice(0, boundary);\n buf\n… [963 more characters truncated]\nok\n```\n\nNow the backend: sample datasets, simple keyword retrieval with tests, the Q&A LangGraph flow, and its routes.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\ncat > src/samples.ts <<'EOF'\nimport { SampleDataset, type SampleSummary } from \"@perforvisor/core\";\nimport spiceRouteKitchen from \"../fixtures/samples/spice-route-kitchen.json\" with { type: \"json\" };\nimport voltmaxPowerBank from \"../fixtures/samples/voltmax-power-bank.json\" with { type: \"json\" };\n\n/** Built-in datasets, served until live connectors (SerpApi, backlog BL-1) are enabled. */\nexport const samples: SampleDataset[] = [spiceRouteKitchen, voltmaxPowerBank].map((sample) => SampleDataset.parse(sample));\n\nexport const sampleSummaries: SampleSummary[] = samples.map(({ reviews, ...rest }) => ({ ...rest, reviewCount: reviews.length }));\nEOF\ncat > src/ai/retrieve.ts <<'EOF'\nimport type { AnalysisReviewInput } from \"@perforvisor/core\";\n\nconst STOPWORDS = new Set(\n \"a an and are as at be but by do does did for from has have how i if in is it its of on or people say says so that the their them they this to was were what when where which who why will with you your about any there\".split(\" \"),\n);\n\nconst terms = (text: string) =>\n text\n .toLowerCase()\n .split(/[^\\p{L}\\p{N}]+/u)\n .filter((word) => word.length > 2 && !STOPWORDS.has(word));\n\n// Crude stemming: \"overheats\", \"overheated\" and \"overheating\" share \"overhea\".\nconst stem = (word: string) => word.slice(0, Math.max(4, Math.min(7, word.length - 1)));\n\n/**\n * Ranks reviews by keyword overlap with the question. A stand-in until the\n * knowledge graph's vector search (KG-4) exists.\n */\nexport function rankReviews(question: string, reviews: AnalysisReviewInput[], limit = 12): { review: AnalysisReviewInput; score: number }[] {\n const queryStems = [...new Set(terms(question).map(stem))];\n return reviews\n .map((review, index) => {\n const words = terms(review.text);\n const score = queryStems.filter((q) => words.some((w) => w.startsWith(q))).length;\n return { review, score, index };\n })\n .sort((a, b) => b.score - a.score || a.index - b.index)\n .slice(0, limit)\n .map(({ review, score }) => ({ review, score }));\n}\nEOF\ncat > src/ai/retrieve.test.ts <<'EOF'\nimport { describe, expect, it } from \"vitest\";\nimport { rankReviews } from \"./retrieve.ts\";\n\nconst reviews = [\n { id: \"a\", text: \"Great capacity and fast charging.\" },\n { id: \"b\", text: \"It overheated badly on a flight.\" },\n { id: \"c\", text: \"Gets really hot and overheats while charging my laptop.\" },\n];\n\ndescribe(\"rankReviews\", () => {\n it(\"ranks reviews matching the question's terms first, including word variants\", () => {\n const ranked = rankReviews(\"Do people say this power bank overheats?\", reviews);\n expect(ranked.map((r) => r.review.id)).toEqual([\"b\", \"c\", \"a\"]);\n expect(ranked[0]!.score).toBeGreaterThan(0);\n expect(ranked[2]!.score).toBe(0);\n });\n\n it(\"respects the limit\", () => {\n expect(rankReviews(\"charging\", reviews, 1)).toHaveLength(1);\n });\n});\nEOF\ncat > src/ai/graphs/ask.ts <<'EOF'\nimport { Ann\n… [2791 more characters truncated]\nok\n```\n\nNow the routes. Both flows stream the same way, so the streaming loop moves into one shared helper.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/backend\ncat > src/routes/ai.ts <<'EOF'\nimport type { FastifyPluginAsyncZod } from \"fastify-type-provider-zod\";\nimport type { FastifyReply, FastifyRequest } from \"fastify\";\nimport { z } from \"zod\";\nimport { AnalysisReviewInput, AskRequest, SampleDataset, SampleSummary, type AnalysisResult, type StreamEvent } from \"@perforvisor/core\";\nimport type { Config } from \"../config.ts\";\nimport { checkModelServer } from \"../ai/model.ts\";\nimport { buildAnalyzeGraph, type AnalyzeProgress, type AnalyzeStateValue } from \"../ai/graphs/analyze.ts\";\nimport { buildAskGraph, type AskProgress, type AskStateValue } from \"../ai/graphs/ask.ts\";\nimport { openSse } from \"../lib/sse.ts\";\nimport { samples, sampleSummaries } from \"../samples.ts\";\n\ntype Progress = Extract;\n\n/**\n * Runs a compiled graph over SSE: its custom events become progress events,\n * and the final state is turned into the result.\n */\nasync function streamGraph(\n request: FastifyRequest,\n reply: FastifyReply,\n run: (signal: AbortSignal) => Promise>,\n toProgress: (custom: unknown) => Progress,\n toResult: (state: State) => unknown,\n) {\n const stream = openSse(request, reply);\n try {\n let final: State | undefined;\n for await (const [mode, chunk] of await run(stream.signal)) {\n if (mode === \"custom\") stream.send(toProgress(chunk));\n else final = chunk as State;\n }\n if (!final) throw new Error(\"Run finished without a result\");\n stream.send({ type: \"result\", data: toResult(final) });\n stream.send({ type: \"done\" });\n } catch (error) {\n if (!stream.closed) {\n request.log.error({ err: error }, \"graph run failed\");\n stream.send({ type: \"error\", message: error instanceof Error ? error.message : \"Unexpected error\" });\n }\n } finally {\n stream.end();\n }\n}\n\nconst analyzeProgress = (chunk: unknown): Progress => {\n const p = chunk as AnalyzeProgress;\n if (p.stage === \"extract\") {\n return { type: \"progress\", stage: p.stage, message: `Analysed ${p.done} of ${p.total} reviews${p.failed ? ` (${p.failed} failed)` : \"\"}`, percent: Math.round((p.done / p.total) * 80) };\n }\n return p.stage === \"aggregate\"\n ? { type: \"progress\", stage: p.stage, message: \"Aggregating results\", percent: 85 }\n : { type: \"progress\", stage: p.stage, message: \"Writing advice\", percent: 90 };\n};\n\nconst askProgress = (chunk: unknown): Progress => {\n const p = chunk as AskProgress;\n return p.stage === \"retrieve\"\n ? { type: \"progress\", stage: p.stage, message: `Reading ${p.considered} relevant reviews`, percent: 30 }\n : { type: \"progress\", stage: p.stage, message: \"Writing the answer\", percent: 60 };\n};\n\nexport const aiRoutes = (config: Config): FastifyPluginAsyncZod => async (app) => {\n const analyzeGraph = buildAnalyzeGraph(config);\n const askGraph = buildAskGraph(config);\n\n app.get(\"/ai/health\", async (_request, reply) => {\n\n… [2032 more characters truncated]\ntsc 0\n Tests 7 passed (7)\n```\n\nThe backend typechecks and all 7 tests pass. Before building the frontends, I'll try `/ask` against real Qwen.\n\n``` bash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for\nSP=\ncurl -sf localhost:8000/health && echo \"vllm up\" || { echo \"vllm down\"; docker compose -f $R/docker-compose.yml ps -a --format '{{.Name}} {{.Status}}'; }\nexport NVM_DIR=\"$HOME/.nvm\"; . \"$NVM_DIR/nvm.sh\" >/dev/null; nvm use 24 >/dev/null\ncd $R/backend\nAPI_PORT=4127 NODE_ENV=production LOG_LEVEL=warn timeout 200 ./node_modules/.bin/tsx src/api.ts > $SP/api5.log 2>&1 & API=$!\nfor i in $(seq 1 40); do curl -s localhost:4127/health >/dev/null && break; sleep 0.5; done\ncurl -s localhost:4127/v1/samples; echo\ncurl -s localhost:4127/v1/samples/voltmax-power-bank | python3 -c \"\nimport json,sys; d=json.load(sys.stdin); json.dump({'entityName':d['entityName'],'reviews':d['reviews'],'question':'Do people say this power bank overheats?'},open('$SP/ask.json','w'))\"\nstart=$(date +%s.%N); curl -sN --max-time 120 -X POST localhost:4127/v1/ask -H 'content-type: application/json' --data @$SP/ask.json > $SP/ask-sse.txt; echo \"ask in $(python3 -c \"print(round($(date +%s.%N)-$start,1))\")s\"\ngrep -o '\"message\":\"[^\"]*\"' $SP/ask-sse.txt\ngrep '^data: {\"type\":\"result\"' $SP/ask-sse.txt | sed 's/^data: //' | python3 -c \"import json,sys;d=json.load(sys.stdin)['data'];print('ANSWER:',d['answer']);print('CITED:',[c['id'] for c in d['citations']],'considered',d['consideredCount'])\"\ncurl -s -X POST localhost:4127/v1/ask -H 'content-type: application/json' -d '{\"question\":\"x\",\"entityName\":\"a\",\"reviews\":[]}'; echo\nkill $API; wait 2>/dev/null\nvllm down\nlets-make-osp-that-someone-will-actually-pay-for-redis-1 Up 10 minutes\nlets-make-osp-that-someone-will-actually-pay-for-vllm-cpu-1 Exited (0) 11 hours ago\n[{\"id\":\"spice-route-kitchen\",\"kind\":\"place\",\"entityName\":\"Spice Route Kitchen\",\"category\":\"Indian restaurant\",\"reviewCount\":12},{\"id\":\"voltmax-power-bank\",\"kind\":\"product\",\"entityName\":\"VoltMax 20000mAh Power Bank\",\"category\":\"Electronics\",\"reviewCount\":10}]\nask in 4.5s\n\"message\":\"Reading 10 relevant reviews\"\n\"message\":\"Writing the answer\"\n\"message\":\"Connection error.\"\nTraceback (most recent call last):\n File \"\", line 1, in \n import json,sys;d=json.load(sys.stdin)['data'];print('ANSWER:',d['answer']);print('CITED:',[c['id'] for c in d['citations']],'considered',d['consideredCount'])\n ~~~~~~~~~^^^^^^^^^^^\n File \"/usr/lib64/python3.14/json/__init__.py\", line 298, in load\n return loads(fp.read(),\n cls=cls, object_hook=object_hook,\n parse_float=parse_float, parse_int=parse_int,\n parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n File \"/usr/lib64/python3.14/json/__init__.py\", line 352, in loads\n return _default_decoder.decode(s)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^\n File \"/usr/lib64/python3.14/json/decoder.py\", line 345, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/lib64/python3.14/json/decoder.py\", line 363, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n{\"statusCode\":400,\"code\":\"FST_ERR_VALIDATION\",\"error\":\"Bad Request\",\"message\":\"body/question Too small: expected string to have >=3 characters, body/reviews Too small: expected array to have >=1 items\"}\nbash\n$ cd ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for && docker compose --profile llm-cpu up -d vllm-cpu 2>&1 | tail -1 && until curl -sf localhost:8000/health >/dev/null; do st=$(docker inspect -f '{{.State.Status}}' $(docker compose ps -aq vllm-cpu)); [ \"$st\" = exited ] && { echo \"vllm exited\"; docker compose logs --tail 20 vllm-cpu; exit 1; }; sleep 5; done; echo \"vllm healthy\"\nCommand running in background with ID: buk1j7pgj. Output is being written to: You will be notified when it completes. To check interim output, use Read on that file path.\nSession cwd remains ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend; directory changes made by the backgrounded command do not apply to subsequent commands.\n{\n \"replace_all\": false,\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/routes/ai.ts\",\n \"old_string\": \" } catch (error) {\\n if (!stream.closed) {\\n request.log.error({ err: error }, \\\"graph run failed\\\");\\n stream.send({ type: \\\"error\\\", message: error instanceof Error ? error.message : \\\"Unexpected error\\\" });\\n }\",\n \"new_string\": \" } catch (error) {\\n if (!stream.closed) {\\n request.log.error({ err: error }, \\\"graph run failed\\\");\\n stream.send({ type: \\\"error\\\", message: describeError(error) });\\n }\"\n}\nThe file ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/routes/ai.ts has been updated successfully.\n{\n \"replace_all\": false,\n \"file_path\": \"~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/routes/ai.ts\",\n \"old_string\": \"const analyzeProgress = (chunk: unknown): Progress => {\",\n \"new_string\": \"function describeError(error: unknown): string {\\n const message = error instanceof Error ? error.message : \\\"Unexpected error\\\";\\n // The OpenAI client reports an unreachable model server as a bare \\\"Connection error.\\\"\\n if (/connection error|ECONNREFUSED|fetch failed/i.test(message)) {\\n return \\\"The AI model server isn't reachable. Start it with `docker compose --profile llm-cpu up -d` and try again in a minute.\\\";\\n }\\n return message;\\n}\\n\\nconst analyzeProgress = (chunk: unknown): Progress => {\"\n}\nThe file ~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for/backend/src/routes/ai.ts has been updated successfully.\nbash\n$ R=~/Personal_Projects/lets-make-osp-that-someone-will-actually-pay-for; cd $R/common/ui\ncat > src/hooks/useStream.ts 2>/dev/null || { mkdir -p src/hooks; }\ncat > src/hooks/useStream.ts <<'EOF'\nimport { useCallback, useEffect, useRef, useState } from \"react\";\nimport { postSse } from \"@perforvisor/core\";\nimport type { z } from \"zod\";\n\nexport type StreamStatus = \"idle\" | \"running\" | \"done\" | \"error\";\n\nexport interface StreamState {\n status: StreamStatus;\n message: string;\n percent: number;\n result: T | null;\n error: string | null;\n}\n\nconst initial = { status: \"idle\", message: \"\", percent: 0, result: null, error: null } as const;\n\n/** Runs a backend SSE flow (POST) and tracks its progress, result and errors. */\nexport function useStream(schema: z.ZodType) {\n const [state, setState] = useState>(initial);\n const controller = useRef(null);\n\n useEffect(() => () => controller.current?.abort(), []);\n\n const run = useCallback(\n async (url: string, body: unknown) => {\n controller.current?.abort();\n const abort = new AbortController();\n controller.current = abort;\n setState({ ...initial, status: \"running\", message: \"Starting…\" });\n\n try {\n await postSse(\n url,\n body,\n (event) => {\n if (event.type === \"progress\") {\n setState((s) => ({ ...s, message: event.message, percent: event.percent ?? s.percent }));\n } else if (event.type === \"result\") {\n const parsed = schema.safeParse(event.data);\n setState((s) =>\n parsed.success ? { ...s, status: \"done\", percent: 100, result: parsed.data } : { ...s, status: \"error\", error: \"The server sent a result in an unexpected format.\" },\n );\n } else if (event.type === \"error\") {\n setState((s) => ({ ...s, status: \"error\", error: event.message }));\n }\n },\n abort.signal,\n );\n // A stream that ends without a result or error was cut off.\n setState((s) => (s.status === \"running\" ? { ...s, status: \"error\", error: \"The connection closed before the result arrived.\" } : s));\n } catch (error) {\n if (abort.signal.aborted) return;\n setState((s) => ({ ...s, status: \"error\", error: error instanceof Error ? `Can't reach the server: ${error.message}` : \"Can't reach the server.\" }));\n }\n },\n [schema],\n );\n\n const reset = useCallback(() => {\n controller.current?.abort();\n setState(initial);\n }, []);\n\n return { ...state, run, reset };\n}\nEOF\ncat > src/components/Textarea.tsx <<'EOF'\nimport type { TextareaHTMLAttributes } from \"react\";\n\nexport function Textarea({ className, ...props }: TextareaHTMLAttributes) {\n return