rob thijssen 1818dfb337
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feat(helexa-acp): openai-responses provider
Stage 6a. Implements the `Provider` trait for OpenAI's Responses
API surface, parallel to the existing `OpenAIChatProvider`. Lets a
helexa-acp endpoint configured with `wire_api = "openai-responses"`
drive a `/v1/responses` server (today: neuron through cortex; later:
OpenAI directly) using the same agent-loop machinery the chat
provider already supports.

## Encoder (CompletionRequest → Responses body)

- System messages collapse into a single top-level `instructions`
  string. Multiple system messages concatenate with blank lines so
  ordering is preserved.
- User messages become `{type:"message", role:"user", content:…}`
  input items. Text content stays a bare string; MultiPart content
  (text + images, post-Stage 5) becomes a
  `[{type:"input_text"}, {type:"input_image"}]` array with images
  encoded as `data:{mime};base64,{data}` URIs — exactly the shape
  neuron's `wire::openai_responses::request_to_chat` accepts.
- Assistant text turns become an `output_text` content part inside
  a `message` item.
- Assistant tool-call turns become `function_call` input items.
- Tool result turns become `function_call_output` input items.
- `max_tokens` translates to `max_output_tokens`.

## Decoder (Responses SSE → CompletionEvent)

Reads named events on the SSE `event:` line:

- `response.output_text.delta` → `CompletionEvent::TextDelta`
- `response.output_item.added` with `type:"function_call"` →
  `CompletionEvent::ToolCallStart` (and, when the upstream
  pre-buffers fully, a single `ToolCallArgsDelta`)
- `response.function_call_arguments.delta` →
  `CompletionEvent::ToolCallArgsDelta`, correlated back to the
  tool-call slot by output_index.
- `response.completed` → `CompletionEvent::Usage` (if present) +
  `CompletionEvent::Finish` with reason mapped from `status`:
  `"completed"` → `"stop"`, `"incomplete"` → `"length"`.
- Bookkeeping events (`response.created`, `response.in_progress`,
  `*.content_part.*`, `*.output_text.done`, `*.output_item.done`,
  `*.function_call_arguments.done`, reasoning_*) are skipped.

## Wiring

- `EndpointConfig::responses_url()` joins `{base_url}/responses`.
- `WireApi::OpenAiResponses` in `build_provider` constructs the new
  provider (was previously a "reserved for future" error).
- `provider::mod.rs` registers the new module.

## Cuts (carried over from neuron-side issues)

- The decoder's `ToolCall*` handling fires correctly when the
  upstream emits `function_call` items, but the neuron candle
  harness doesn't yet (Refs #6). Real tool-call testing against
  cortex+neuron stays on the chat path until #6 lands.
- Reasoning events (`response.reasoning_*`) are deliberately
  dropped today; once neuron emits `InferenceEvent::ReasoningDelta`
  (Refs #5) the projector on the neuron side will start firing the
  reasoning event family and this decoder will need a matching
  case to route them to `CompletionEvent::ReasoningDelta`.

13 new unit tests cover encoder (system collapse, multipart user
input, assistant output_text encoding, tool-call round-trip via
function_call items) and decoder (text streaming, empty deltas
dropped, length finish, function_call lifecycle, inline-arguments
shape, cancellation, malformed payload skip).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-31 11:30:25 +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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