leetcrypt 69bce5ead8 feat(ai): stream agent replies token-by-token to the room

Closes the cross-language half of token streaming (perf-plan A3). On the
CPU-only box perceived latency is time-to-first-token, so showing the reply
as it generates makes a slow model feel live.

- Agent: OllamaProvider.stream() runs on a worker thread; bridge relays
  cumulative previews as throttled (~5/sec) `_ai:"stream"` control frames,
  then a `done` frame clears the preview as the final persisted chat message
  is posted. Providers without stream() fall back to blocking complete().
- Rust client: new Net::AiStream variant + parse_ai branch; App.ai_stream
  map holds the in-progress text per agent; draw_chat renders it as a dim,
  italic preview bubble below history. Cleared on done and on agent leave.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-06-02 22:42:08 -07:00

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AI agent: CPU-only performance & code-quality plan

Hardware reality: the box serving local models is CPU-only (Intel i5-8350U, 4c/8t, no GPU, 62 GB RAM), Ollama 0.3.9. So we optimize time-to-first-token (prefill is O(context)) and tokens/sec, not VRAM. GPU knobs (flash attention, KV-cache quant) are no-ops here.

Status

Tier A — high impact / low effort

qwen2.5-coder for the sandbox/code path. (done) qwen2.5-coder:1.5b pulled and wired as a separate code provider used only by !task; chat keeps the general model. Same speed, better shell/code. Auto-selected when the chat provider is Ollama and the coder model is present; override with --code-model.
Lower num_ctx to 4096 + expose num_thread. (done) OllamaProvider default num_ctx 8192→4096 (8192 was a GPU-mindset default that inflated TTFT on CPU); token_budget default 3000→2000 to fit. --num-ctx, --num-thread, --num-predict flags added. num_thread defaults to Ollama's own (= physical cores, 4 here); benchmark 4/6/8.
Token streaming. (done) OllamaProvider.stream() yields deltas from Ollama's stream=True chat endpoint; the agent relays them as throttled (~5/sec) cumulative _ai:"stream" frames off a worker thread, and the Rust client renders a dim in-progress preview bubble (cleared by a done frame when the final, persisted message lands). On CPU, perceived latency is TTFT — this makes a slow reply feel live.
Keep model warm + single-flight. (partial) keep_alive already 30m (prevents mid-session reload). OLLAMA_NUM_PARALLEL=1 is a server-side env read by ollama serve, not settable from the agent — set it where Ollama is launched (documented below).

Tier B — code-generation quality

Few-shot in SANDBOX_SYSTEM. (done) 1–2 request→shell exemplars to anchor the small model's output format.
GBNF constrained output. (blocked on #7) Ollama 0.3.9 only supports format: json, not custom grammars for fenced shell. Needs the upgrade; the existing _extract_commands parser + few-shot cover the gap meanwhile.

Tier C — infra / housekeeping

Upgrade Ollama 0.3.9 → current. (manual, user-run) System-wide action that restarts the daemon other projects share — not run automatically. Buys current coder builds, structured-output/grammar support (unblocks #6), bugfixes. CPU speed gains are incremental. Suggested: curl -fsSL https://ollama.com/install.sh | sh.
Matryoshka embedding truncation. (done) nomic-embed-text is MRL-trained; truncate vectors to 256-dim (--embed-dim) for faster pure-Python cosine and less RAM. Query + stored use the same dim, so cosine stays correct.

Server-side env (set where `ollama serve` runs, e.g. systemd unit or shell)

OLLAMA_NUM_PARALLEL=1      # single interactive user → all cores to one request
OLLAMA_KEEP_ALIVE=30m      # or -1 to pin forever (62 GB RAM is plenty)

Notes

All grounded in public sources + the Obsidian vault (research/2026-06-02-*): Q4_K_M is the CPU speed sweet spot, small num_ctx beats "context rot", and qwen2.5-coder beats the general model at equal size for code.

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