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How to record every request to a SQLite log for analysis with LLM Visuals

Start the dashboard with a log database so request rows are saved for later review.

Who it is forA developer comparing model setups who wants saved request data rather than screenshots of a live screen.

The problemLive dashboards disappear when closed, so runs with different models or settings cannot be compared afterwards.

The outcomeA SQLite file containing one row per logged request that can be opened with any SQLite tool.

What you need first

  • 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

Steps

  1. Install a Rust toolchain (1.75 or newer), clone the repo with git clone https://github.com/DingoOz/llm-visuals, and cd llm-visuals.
  2. 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)
  3. Send several prompts to the running llama-server so rows are written.
  4. Open llm.db with any SQLite client and check that rows exist for the requests you sent.

Prompt for your agent

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.

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.

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