{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "dingooz-llm-visuals--log-llm-requests-to-sqlite",
 "repo": "DingoOz/llm-visuals",
 "repo_url": "https://github.com/DingoOz/llm-visuals",
 "repo_slug": "dingooz-llm-visuals",
 "title": "Record every request to a SQLite log for analysis",
 "page_title": "How to record every request to a SQLite log for analysis with LLM Visuals",
 "summary": "Start the dashboard with a log database so request rows are saved for later review.",
 "who": "A developer comparing model setups who wants saved request data rather than screenshots of a live screen.",
 "problem": "Live dashboards disappear when closed, so runs with different models or settings cannot be compared afterwards.",
 "outcome": "A SQLite file containing one row per logged request that can be opened with any SQLite tool.",
 "steps": [
  "Install a Rust toolchain (1.75 or newer), clone the repo with `git clone https://github.com/DingoOz/llm-visuals`, and `cd llm-visuals`.",
  "Start the dashboard with `cargo run --release -- --log-db llm.db`, using the `--log-db llm.db` option the README describes. (inferred, check the README)",
  "Send several prompts to the running llama-server so rows are written.",
  "Open `llm.db` with any SQLite client and check that rows exist for the requests you sent."
 ],
 "agent_prompt": "Using the repo DingoOz/llm-visuals, start the dashboard with `cargo run --release -- --log-db llm.db` while a llama-server is running. Expected output: an llm.db file in the repo folder with one row per request. Verify the row count rises after you send a prompt.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "45 minutes",
 "basis": "readme",
 "caveat": "The README says rows are written to the database but does not describe the table layout, so check the schema yourself before writing queries.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "rust",
 "requirements": [
  "Rust toolchain 1.75 or newer",
  "A C compiler, needed by the bundled SQLite used for --log-db",
  "A locally listening llama-server for throughput panels (the demo mode needs none)",
  "nvidia-smi for NVIDIA GPU panels, xpu-smi for Intel, the amdgpu driver for AMD, or macmon on Apple silicon"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/dingooz-llm-visuals/log-llm-requests-to-sqlite/",
 "md": "https://dedupedai.com/use-cases/dingooz-llm-visuals/log-llm-requests-to-sqlite/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/dingooz-llm-visuals--log-llm-requests-to-sqlite.json",
 "api_version": "v1"
}