rob thijssen fc9a8c42a3
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feat(neuron): extract <tool_call> blocks to structured tool_calls deltas
Closes #6.

Same model-agnostic seam as #8 but for tool-call markers
(`<tool_call>` / `</tool_call>` on Qwen3-Coder, Hermes-format,
DeepSeek-Coder, gpt-oss, …). Lets Zed's tool-use feature and any
other vanilla OpenAI chat client get structured `tool_calls` deltas
out of cortex without having to parse markers themselves.

## Implementation

1. **Tokenizer probe at load time** (`detect_tool_call_token_pair`
   in `wire::event`) — same shape as the reasoning-marker probe
   from #8. Both open AND close must resolve to single token ids;
   non-tool-use models get `None` and pass through unchanged.
   Stored on `LoadedModel.tool_call_tokens` and the TP analogue.

2. **New `InferenceEvent::ToolCall` variant** — carries `index`
   (call slot, per-turn counter), generated `id` (`call_<hex>_<idx>`),
   `name`, and the complete `arguments` JSON string. One event per
   parsed call.

3. **Token-level state machine** in all three streaming paths
   (CPU `run_inference_streaming`, CUDA single-GPU
   `stream_inference_via_worker`, CUDA TP `chat_completion_tp_stream`)
   layered on top of #8's reasoning routing:
   - `<tool_call>` token → enter buffering state, clear buffer.
   - Tokens while buffering → accumulate into `tool_call_buf`
     via the decoder (so multi-byte UTF-8 still buffers correctly)
     without emitting anything visible.
   - `</tool_call>` token → take the buffer, parse with
     `parse_tool_call_body` (extract `name` + `arguments`),
     emit a structured `ToolCall` event with a fresh `call_<hex>`
     id and the parsed fields.
   - On parse failure → fall back to re-emitting the original
     `<tool_call>{buf}</tool_call>` block as plain text content
     so helexa-acp's existing `ToolCallParser` repair passes still
     have a chance to recover the call.

4. **OpenAI chat projector** emits the OpenAI streaming
   `tool_calls` delta shape on `InferenceEvent::ToolCall` —
   `{tool_calls: [{index, id, type:"function",
   function:{name, arguments}}]}`. One chunk per call slot.

5. **OpenAI Responses projector** drops `ToolCall` events for
   now (Responses-side function_call event family routing tracked
   under #7); the chat path is what unblocks Zed's tool use today.

## Acceptance

- Vanilla OpenAI chat clients (Zed's tool-use feature, any other
  OpenAI-compatible tool-call consumer) get structured tool_calls
  deltas against cortex+neuron without having to parse `<tool_call>`
  markers in content.
- helexa-acp continues to work — when neuron parses cleanly, it
  consumes the structured deltas through its existing decoder.
  When the model emits malformed JSON, neuron falls back to text
  pass-through and helexa-acp's `ToolCallParser` recovers via the
  same path it always did.
- Models without tool-call markers in their tokenizer pass through
  unchanged.
- No hardcoded model knowledge — entirely driven by tokenizer
  metadata.

## Tests

2 new detection tests in `wire::event` (Qwen3-style marker
detection, no-marker case). The streaming paths themselves stay
covered by the existing chat-completions integration tests; full
end-to-end exercise of the new path requires GPU-loaded models
and lives outside the CI test surface.

215 workspace tests pass; clippy + fmt clean across the
workspace.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-31 23:26:31 +03:00

cortex

A Rust reverse-proxy and fleet management layer for multi-node GPU inference clusters. Cortex sits in front of one or more neuron daemons (each running candle-based inference on a local GPU host) and presents a unified OpenAI + Anthropic compatible API surface.

Problem

Running local LLMs across multiple GPU nodes (different VRAM tiers, different model affinities) requires a unified API surface that:

  • Presents a single /v1/models catalogue merging every model that can be served by any neuron in the fleet.
  • Routes requests to the correct node based on where a model is loaded (or can be loaded), handling cold-load and eviction transparently.
  • Manages model lifecycle — load on demand, unload cold models, pin critical ones — by calling each neuron's /models/{load,unload} API.
  • Translates between OpenAI and Anthropic request/response envelopes so every client speaks whichever dialect it prefers.
  • Captures per-request metrics (tokens, tok/s, TTFT, latency) and exposes them as Prometheus counters/histograms.

Architecture

┌──────────────┐  ┌──────────┐  ┌────────────┐  ┌────────────┐
│ Claude Code  │  │ Zed/IDE  │  │ Tidal / mm │  │ curl / etc │
└──────┬───────┘  └─────┬────┘  └──────┬─────┘  └──────┬─────┘
       │                │              │               │
       └────────────────┴──────┬───────┴───────────────┘
                               │
                    ┌──────────▼──────────┐
                    │      cortex         │
                    │  (cortex-gateway)   │
                    │                     │
                    │  Router · Metrics   │
                    │  Evictor · Translate│
                    └──┬──────┬────────┬──┘
                       │      │        │
            ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
            │  neuron   │  │ neuron │  │  neuron   │
            │  :13131   │  │ :13131 │  │  :13131   │
            │  candle   │  │ candle │  │  candle   │
            └───────────┘  └────────┘  └───────────┘
                  private network (.internal)

Crates

Crate Purpose
cortex-core Shared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types
cortex-gateway Axum HTTP server: proxy, router, evictor, poller, metrics exporter
neuron Per-node daemon: GPU discovery, in-process candle inference, model lifecycle API
cortex-cli CLI entrypoint (cortex serve, cortex status, etc.)

Node setup

Each GPU node runs neuron (listening on :13131). Neuron uses huggingface/candle for in-process inference — there is no external inference subprocess to manage.

Inside the daemon, every CUDA device gets one dedicated OS thread (named cuda-dev-N) that owns the device's CUDA context for the daemon's lifetime. Model loads, forward passes, KV-cache resets, NCCL collectives, VRAM queries, and unloads all route through that thread via a job channel; tensors never escape it alive. This pins context binding to a known thread, makes the CUDA Drop contract structurally safe, and isolates driver-error poisoning to one worker rather than the whole process. See CLAUDE.md for the design rationale and crates/neuron/src/harness/device_worker/ for the code.

The neuron RPM (helexa-neuron) ships a systemd unit:

dnf copr enable helexa/helexa
dnf install helexa-neuron
systemctl enable --now neuron

Gateway config

# /etc/cortex/cortex.toml
[gateway]
listen = "0.0.0.0:31313"
metrics_listen = "0.0.0.0:31314"

[eviction]
strategy = "lru"        # lru | priority
defrag_after_cycles = 50

[[neurons]]
name = "beast"
endpoint = "http://beast.internal:13131"

[[neurons]]
name = "benjy"
endpoint = "http://benjy.internal:13131"

Model placement profiles live in models.toml — see models.example.toml.

Building

cargo build --release

CI

Every push triggers format, lint, and test checks. Ensure these pass locally before pushing:

cargo fmt --check --all                    # must be clean
cargo clippy --workspace -- -D warnings   # warnings are errors
cargo test --workspace                     # all tests must pass

Tagged releases (v*) additionally build SRPMs for both cortex and helexa-neuron and publish to COPR.

Running

# start the gateway
cortex serve --config /etc/cortex/cortex.toml

# check fleet status
cortex status

# list all models across nodes
curl http://localhost:31313/v1/models

License

GPL-3.0

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