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dd592d918d
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test(neuron): C2 — guard Responses→chat image translation contract
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The Responses request translator already emits the chat `image_url` Parts array Stage B5's vision path consumes, and the non-streaming (`chat_completion`) and streaming (`responses_stream` → `inference_stream`, Stage C1) Responses paths both route image content to the vision-aware prefill — so vision works end-to-end through `/v1/responses` with no translator change required. Add a multi-image test asserting order preservation and that the `detail` hint is tolerated (and dropped, since chat image_url has no analogue), locking the translator's output to the exact `image_url.url` shape `extract_images_from_request` walks. Closes part of #16 (Stage C2). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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44008358c5
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feat(neuron): emit response.in_progress between created and output_item.added
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Refs #7. OpenAI's Responses API spec emits `response.in_progress` between `response.created` and the first output-item event to mark "request validated, model is generating". Some Responses-API clients distinguish loading-spinner vs streaming-spinner UI based on which event arrived last; emitting both keeps the wire shape matched. Carries the same shell as `response.created` (status=in_progress, empty output, no usage yet) — both events are payload-light bookkeeping, distinguished only by the event name. The hosted-tool event families remaining in #7 (web_search_call, code_interpreter_call, file_search_call, image_generation_call) stay deferred until the underlying tools exist in neuron. Updated `full_stream_emits_expected_event_sequence` to assert the new event lands in position 1; downstream indexing shifted by one across the existing test assertions. CI green, fmt + clippy clean. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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fc9a8c42a3
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feat(neuron): extract <tool_call> blocks to structured tool_calls deltas
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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> |
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7733eecba5
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feat(neuron): strip reasoning from chat completions by default
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Closes #8. Reasoning-capable models (Qwen3, DeepSeek-R1, gpt-oss, Mistral Magistral, …) emit `<think>...</think>` blocks inline in their content stream. The chat-completions wire format has no slot for reasoning, so until this change every consumer either parsed the markers themselves (helexa-acp) or wrote the raw scratchpad content into their UI (Zed's commit-message generator — visible as the leaked reasoning block on every generated commit message against benjy's Qwen3-8B). ## Implementation, model-agnostic by design The neuron side now does token-level routing without any hardcoded model knowledge: 1. **At load time** (`detect_reasoning_token_pair` in `wire::event`), probe the tokenizer's vocabulary for a known reasoning-marker pair: `<think>` / `</think>` (Qwen3, DeepSeek-R1, gpt-oss), `[THINK]` / `[/THINK]` (Mistral Magistral), and a couple of derivatives. Each marker must resolve to a single token id; if both open and close resolve, stash on `LoadedModel.reasoning_tokens` (similarly `TpLoadedModel`). Non-reasoning models get `None` and pass through unchanged. 2. **At inference time**, the three streaming paths (`run_inference_streaming` CPU, `stream_inference_via_worker` CUDA single-GPU, `chat_completion_tp_stream` CUDA TP) now check each sampled token against the pair via the new `handle_reasoning_marker` helper before feeding it to the detokeniser. Open marker → set `in_reasoning = true`, drop the marker. Close marker → unset, drop. Other tokens go through `emit_delta(_blocking)` which now picks `ReasoningDelta` or `TextDelta` based on state. Markers never appear in the streamed output. 3. **In `wire::openai_chat`**, the projector splits into: - `project_chat_stream` (unchanged signature; default behaviour — drops `ReasoningDelta`) - `project_chat_stream_with(rx, …, ChatProjectionConfig)` — when `include_thinking: true` and `reasoning_markers: Some(_)`, re-wraps reasoning content with the literal open/close marker text and emits as content deltas. Preserves the on-the-wire shape that helexa-acp's `ThinkParser` expects. 4. **HTTP handler** reads `x-include-thinking: true` (case- insensitive `1`/`true`/`yes`) from the request headers and threads it into the projection config. cortex-gateway already forwards arbitrary headers verbatim, so the opt-in works end-to-end without gateway changes. 5. **helexa-acp's `openai_chat` provider** sets `x-include-thinking: true` on every request so its existing `ThinkParser` keeps receiving the marked content stream. `ThinkParser` itself is unchanged — needed for endpoints that aren't reasoning-aware (OpenRouter, OpenAI directly, etc.). ## Acceptance - Zed's commit-message generator (vanilla chat-completions client, no `x-include-thinking`) gets clean commit messages with no `<think>` block. - helexa-acp sessions continue to render thinking in Zed's thought UI via the opt-in path. - Models without reasoning tokens declared in their tokenizer pass through unchanged. - Implementation contains zero references to "qwen3" or any specific model — entirely driven by tokenizer metadata. ## Tests 9 new tests in `wire::event` (token-pair detection across 4 marker conventions, edge cases) and `wire::openai_chat` (default drop, opt-in re-wrap with multi-chunk reasoning, close-marker on Finish, fallback when markers absent, off-switch with markers present). All 213 workspace tests pass; fmt + clippy clean. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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957f704efa
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feat(neuron): OpenAI Responses API + ci cuda-check runner label
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Step 2 of the Responses rollout: native `/v1/responses` endpoint on
neuron that consumes the same InferenceEvent stream as
`/v1/chat/completions` but emits it as the Responses API's named
SSE event family. No gateway-side translation.
## Surface
- `cortex-core::responses` envelope types: `ResponsesRequest`,
`ResponsesInput` (text | items), `ResponsesInputItem` (message |
function_call | function_call_output | reasoning),
`ResponsesContentPart` (input_text | input_image | output_text),
`ResponsesResponse`, `ResponsesOutputItem`, `ResponsesUsage`. Plus
a `events::*` constant module so the projector and the wire shape
stay in sync without string-typos.
- `neuron::wire::openai_responses`:
- `request_to_chat(req)` flattens Responses input + instructions
into a `ChatCompletionRequest` the candle harness already
understands. Text-only Parts collapse to a string; mixed
text+image Parts go to chat's content-array shape; reasoning
items drop; function_call / function_call_output round-trip
via tool_calls / tool_call_id metadata so the surface is
consistent for the day the harness emits tool calls.
- `project_responses_stream(rx, meta)` reads InferenceEvents
and emits the eight named events that compose a Responses
stream: response.created → output_item.added → content_part.added
→ output_text.delta×N → output_text.done → content_part.done
→ output_item.done → response.completed. Synthesises start
frames if the producer skips Start (poisoned model, early
disconnect) so the stream stays coherent.
- `build_response(meta, text, reason, usage)` for the
non-streaming path.
- `CandleHarness::inference_stream(req)` extracted from
`chat_completion_stream`, returning a typed `InferenceStream`
(event receiver + id/created/model_id metadata). Both
`chat_completion_stream` and the new `responses_stream` are now
thin wrappers that pick their wire projection. TP path got the
same treatment (`chat_completion_tp_stream` → `inference_tp_stream`).
- `POST /v1/responses` route on neuron. Non-streaming returns one
buffered `ResponsesResponse`; streaming returns axum SSE with
both event names and JSON data per frame (Responses, unlike
chat completions, uses named `event:` lines). Reused
`inference_error_response` helper hoisted out so the chat and
responses handlers share the InferenceError → HTTP mapping.
## CI
Also bundles the `cuda-check` runner-label fix from feedback on
commit
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302ccfb982
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refactor(neuron): introduce InferenceEvent + wire projection layer
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Step 1 of the OpenAI Responses API rollout. Pure refactor — no new
endpoints, no behaviour change on the wire. Lays the seam for
emitting Responses-shaped streaming events from the same harness
output as chat completions in Step 2.
- New `neuron::wire` module tree:
- `wire::event::InferenceEvent` — format-agnostic enum
(Start, TextDelta, ReasoningDelta, Finish) the candle harness
now emits as its native streaming currency.
- `wire::event::FinishReason` — typed reason that maps cleanly
onto OpenAI `finish_reason`, OpenAI Responses `status`, and
Anthropic `stop_reason` strings.
- `wire::openai_chat::project_chat_stream` — async task that
consumes an InferenceEvent receiver and produces a
ChatCompletionChunk receiver, stamping per-request metadata
(id, created, model_id) onto every chunk. Output matches the
pre-refactor wire shape bit-for-bit.
- candle.rs refactored to emit InferenceEvent on its internal
channel through all three streaming paths (CPU
run_inference_streaming, CUDA single-GPU stream_inference_via_worker,
CUDA TP chat_completion_tp_stream). The streaming functions lost
their id/created/model_id parameters since wire-format metadata
now lives in the projector.
- emit_delta + emit_delta_blocking simplified to single-purpose
TextDelta emitters with no wire-format coupling.
- chat_completion_stream wraps the InferenceEvent receiver in
wire_chat::project_chat_stream before returning so the
/v1/chat/completions HTTP handler keeps consuming
ChatCompletionChunks unchanged. External signature preserved.
Also fixes a pre-existing helexa-acp test race (three modules each
declared their own static LOCK for HOME mutation, so cross-module
parallelism flaked tests that read HOME at runtime). Consolidated
onto a single crate-wide path_util::ENV_LOCK.
122 helexa-acp tests + 44 neuron tests pass (5 new wire projection
tests). fmt + clippy --workspace -- -D warnings clean. Ran helexa-acp
suite 3x to confirm the env race is closed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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