f109e7f0e6* Studio: parse Mistral [TOOL_CALLS] and rehearsal tool-call shapes
Extends the rescue parsers in core/tool_healing.py and
core/inference/tool_call_parser.py to recognise two extra serialisations
local models commonly emit when bypassing native function calling:
* [TOOL_CALLS]name{json_args} (Devstral-Small-2, Mistral-Small-3.x).
* name[ARGS]{json_args} (reasoning-model rehearsal).
Both extractors use a brace-balance scan that honours escapes and
quoted strings so nested JSON args stay intact.
Also pre-strips <think>...</think> and [THINK]...[/THINK] blocks before
matching so calls emitted after a reasoning preamble are recognised
regardless of position.
Streaming gates (TOOL_XML_SIGNALS, llama_cpp.py _TOOL_XML_SIGNALS) and
the SSE strip regex (routes/inference.py _TOOL_XML_RE) gain the new
sentinels so the parser is actually invoked and the raw markup never
leaks to the UI.
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* Strip unclosed think blocks and catch rehearsal [ARGS] mid-buffer
The pre-existing ``_THINK_TAG_RE`` only matched closed thinking
blocks (``<think>...</think>`` or ``[THINK]...[/THINK]``). During
streaming the model is still inside the open block when the parser
runs, so any tool-shaped markup the model is REHEARSING inside that
block survived the strip and could be executed as a real call.
Switch both copies of the regex (parser + healing) to accept the
trailing block being terminated by end-of-string in addition to
the explicit closer.
The ``_TOOL_XML_SIGNALS`` list on the llama_cpp streaming buffer
included ``[ARGS]`` to catch rehearsal syntax, but the gate used a
``startswith`` check against the buffer head -- rehearsal is shaped
``name[ARGS]{json}``, so the buffer never STARTS with ``[ARGS]``
and the signal had no effect. Add a substring fallback for the
bracket-style signals so the BUFFERING window can still divert the
stream into DRAINING when rehearsal markup arrives mid-buffer.
Adds three regression tests covering rehearsal inside unclosed
``<think>`` / ``[THINK]`` blocks (must yield no calls) and the
positive case after a closed think block (still parsed).
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* Studio: harden bracket-tag tool-call parsing and streaming strip
Address review findings on the Mistral [TOOL_CALLS] / rehearsal [ARGS] paths:
- Accept hyphenated tool names in the bracket parsers and strip patterns.
_MISTRAL_BRACKET_RE and _REHEARSAL_RE used \w+, which dropped or truncated
MCP function names containing dashes (mcp__srv__list-issues). Use [\w-]+ to
match the XML and Gemma parsers.
- Strip a partial bracket marker streamed before its opening brace. The
trailing-unclosed patterns required the {, so a [TOOL_CALLS]web_search or
python[ARGS] split across deltas leaked the raw marker to the UI. Match the
bare marker to end-of-text, mirroring how the bare open tags are stripped.
Closed pairs are unchanged so in-progress markup stays buffered until parsed.
- Strip a truncated bracket tail in the route-level display regex. _TOOL_XML_RE
required a balanced JSON object; a tool call truncated by EOS now strips up
to \Z, like the orphan-opening XML shapes. Complete calls still strip only
their balanced JSON so following prose survives.
Add regression tests for hyphenated names, the streaming partial-marker strip,
and the unclosed-tail route strip.
* Studio: preserve XML parameter indentation in tool_healing
The chat template emits <parameter=k>\nVALUE\n</parameter>; the parameter-start
regex consumed the wrapping newline AND the value's first-line indentation via a
trailing \s*, then str.strip() removed the rest, corrupting code/diff arguments.
Narrow the trailing class to horizontal whitespace and trim exactly one wrapping
newline (_trim_param_value), preserving indentation. Matches SGLang's qwen3_coder
detector and the same fix on the multi-format parser. Add a regression test.
* Studio: tighten Mistral/rehearsal tool-call comments
Compress the comments in the Mistral [TOOL_CALLS] / rehearsal [ARGS] healing shim
and its callers to one or two lines, keeping the bracket-tag stripping rationale,
the thinking-block handling note, and the forge attribution intact.
Comment-only: no code or behavior change (verified with comment_tools.py check
--strip-docstrings; tests green).
* Studio: fix think-strip arg corruption and nested bracket-JSON strip
Review follow-up for the Mistral/rehearsal healing shim:
- The <think>/[THINK] strip ran unconditionally over the whole content before
parsing, so a real tool argument that legitimately contained a <think> /
[THINK] literal was silently corrupted. Don't delete the blocks: compute the
reasoning-block spans and skip any tool-call candidate that STARTS inside one,
across all parse paths (JSON, Gemma, XML, bracket, rehearsal). A rehearsed call
inside reasoning is still ignored; a real call after </think> still parses.
- The bracket-tag display strip used a fixed one-level-nesting regex, so a call
with two-level-nested JSON args either leaked raw markup or, in final mode, let
the catch-all eat the trailing prose. Add a balanced-brace
_strip_bracket_tag_calls pass (any nesting depth) used by strip_tool_call_markup
and the route display strip.
Add regressions: <think>/[THINK] literal inside a real argument, rehearsal-inside-
think with a real call after, and two-level-nested bracket/rehearsal strip keeping
trailing prose.
* Studio: correct think-block comments to match span-skip behavior
The think-strip fix replaced the unconditional think-block strip with a
span-skip (the block is kept and any tool-call candidate starting inside it is
ignored), but two comments still described the old strip-first behavior. Update
the _THINK_TAG_RE comment and the parse_tool_calls_from_text docstring.
* Studio: parse Mistral arrays and call-ids, unify bracket parse/strip, keep it linear
- Parse the canonical Mistral array form (TOOL_CALLS followed by a JSON list of
calls) and emit every call; parse the v11 shape that carries an opaque CALL_ID
token between the name and ARGS (the function name is the token after
TOOL_CALLS, never the call-id); and parse a Mistral call plus a rehearsal call
in one message (the second was dropped yet still stripped from display).
- One shared balanced forward scan (_iter_bracket_spans) backs both the parser
and the strip path, so they no longer diverge. It is linear: each regex is
re-searched only once its cached match falls behind the cursor, replacing the
per-match full-tail re-scan that was O(n^2) (O(n^3) over a stream). A length cap
before the scan is a backstop.
- strip_tool_call_markup preserves think/reasoning blocks verbatim (the parser
skips tool markup inside them), stripping only the visible text around them.
- _in_think uses bisect over the sorted think spans (was a linear scan per
candidate).
- GGUF streaming strip runs the balanced bracket pre-pass before the regex
patterns so nested-arg calls do not leak or eat trailing prose, and the
BUFFERING ARGS detector requires the rehearsal name-ARGS shape.
- Tests: canonical array, array string-args, array strip keeps prose, Mistral
plus rehearsal multi-call, v11 call-id name, think-rehearsal strip
preservation, and bracket-strip linearity.
* Studio: preserve reasoning blocks in the route and streaming strip paths too
Addresses Gemini/Codex review: making strip_tool_call_markup preserve think
blocks left the route display strip and the GGUF streaming strip inconsistent,
so a rehearsed call inside a reasoning block was still deleted from the visible
text on those paths.
- Extract the think-block segmentation into one shared helper (strip_outside_think)
and route all three strip paths through it: strip_tool_call_markup,
_strip_tool_xml_for_display, and the GGUF _strip_tool_markup_streaming closure.
- Add a route-strip regression test that a rehearsal inside a reasoning block is
preserved while a real call outside it is still stripped.
* Studio: fix bracket-tag strip/buffer review findings
Address the live code-review findings on the Mistral bracket-tag / rehearsal
tool-call rescue path:
- tool_healing: a literal think block inside a tool-call argument is no longer
treated as a reasoning block. strip_outside_think now excludes think spans
that sit inside a complete tool-call span, so the call is stripped whole
instead of the split hiding its open/close pair and leaking the raw call.
- tool_healing: the rehearsal trailing-strip pattern requires a following brace
or end-of-text, so prose that merely mentions name[ARGS] is not truncated as
a phantom call. The bracket strip patterns are aligned with the parser
regexes (whitespace, v11 [CALL_ID]/[ARGS] metadata, and the [CALL_ID]
lookbehind).
- routes: strip a truncated canonical Mistral array ([TOOL_CALLS] [{... with no
closing bracket) that the balanced scan cannot remove, align the display
regex with the parser regexes, and apply the same rehearsal-prose guard.
- safetensors loop: mirror the GGUF [ARGS] rehearsal-substring check during
BUFFERING so a rehearsal name does not stream before its [ARGS] arrives.
Adds regression tests for each; existing parser suite stays green.
* Studio: hold split rehearsal tool-name prefix in both streaming loops
A reasoning-model rehearsal call can stream the tool name and its [ARGS] arm in
separate chunks (web_search then [ARGS]{...}). The buffering detector only
recognised the rehearsal once [ARGS] was present, so the bare tool name was
emitted as visible content before the call drained and executed.
Add _is_rehearsal_prefix (mirrored in the safetensors loop and the GGUF loop):
when a no-signal buffer is a bare active-tool name -- or a partial prefix of
NAME[ARGS] -- hold it as a prefix instead of streaming it, so the next chunk's
[ARGS] flips it to a drain. A whitespace in the buffer means prose, not a split
call, so ordinary text still streams.
Adds regression tests for the split rehearsal in both loops and a guard that a
plain non-tool word still streams.
* Studio: route Anthropic tool-call cleanup through the protected display strip
The Anthropic stream, non-stream, and passthrough paths cleaned content with raw
_TOOL_XML_RE.sub instead of _strip_tool_xml_for_display, so a rehearsal call
inside <think> was deleted from the reasoning and a nested [TOOL_CALLS] call
dropped its trailing prose (the OpenAI-compatible paths already use the helper).
Route all four sites (prior-assistant cleanup, streaming content events,
non-stream aggregation, passthrough conversion) through the protected helper, and
add a source-level guard test so raw _TOOL_XML_RE.sub stays confined to the
helper itself.
* Studio: stop split rehearsal tool names leaking once streaming, uncapped, or unrestricted
The split-rehearsal guard (NAME in one chunk, [ARGS]{...} in the next) only held
the name in the initial BUFFERING state. Three gaps remained where the bare tool
name still streamed as visible content before the call drained:
- STREAMING: after prose had already streamed, both loops emitted a trailing
active-tool-name token (and the GGUF/safetensors [ARGS] boundary was not pulled
back over the name). Hold the trailing rehearsal token and release it on the
next chunk, with an end-of-stream flush so a plain answer that merely ends on a
tool-name word is never dropped.
- Buffer cap: a realistic MCP name longer than the 32-char _MAX_BUFFER_CHARS cap
defeated the BUFFERING hold. A rehearsal prefix is self-bounding (it stops
matching once it grows past NAME[ARGS]), so the generic cap no longer applies to
it.
- Unrestricted mode (tools=[]): with no declared tool list, any bare identifier
may be a NAME[ARGS] rehearsal, so the prefix check now recognises one instead of
leaking the name and mis-parsing the call.
Regression tests cover the streaming, long-name, and unrestricted cases plus the
plain-prose paths that must not be held or corrupted.
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* Studio tools: protect think blocks in safetensors streaming, hold split rehearsal on initial flush, advertise Mistral tools
Pass-3 review follow-ups on the Mistral [TOOL_CALLS] / rehearsal [ARGS] work:
- Safetensors streaming display strip now preserves think / [THINK] reasoning
verbatim (routes through strip_outside_think like the GGUF path). A call
rehearsed inside a reasoning block was stripped mid-stream and then restored by
the final strip, a non-monotonic shrink/grow that corrupted append-by-length
stream consumers and the visible reasoning.
- The first flush out of BUFFERING (safetensors and GGUF) now applies the same
trailing-name hold the STREAMING branch uses, so a split rehearsal (prose plus a
trailing active tool name in one chunk, [ARGS]{...} in the next) no longer leaks
the bare name before the call drains.
- Safetensors capability gate no longer suppresses tools for Mistral [TOOL_CALLS]
templates, which the shared bracket-tag parser now handles end to end. Llama
python_tag stays suppressed (still unparseable).
- Route display strip applies the open-ended / bare-marker tail arms only on the
segment after the last reasoning block (closed-only regex before it), matching
strip_tool_call_markup, so a bare foo[ARGS] before a reasoning block is preserved
while complete calls are still removed in every segment.
Adds regression tests for each and updates the now-stale Mistral capability test.
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* Fix tool-call think-marker and bracket-wrapper edge cases
Round-1 review follow-ups on the Mistral/rehearsal tool-call healing:
- tool_healing: a reasoning marker that opens INSIDE a tool call's
arguments is argument data, not a reasoning block. Add
_think_spans_outside_tool_markup (start-inside test) and use it in
both parse_tool_calls_from_text and strip_outside_think so a literal
marker in one call's args no longer hides a later call (parse) or
leaks the raw markup (strip) when the greedy match runs past the
call's closer.
- tool_healing: strip the orphan Mistral v11 [/TOOL_CALLS] closer left
behind after the balanced scan removes the call body. Add a route arm
for the same closer in _TOOL_XML_RE / _TOOL_XML_CLOSED_RE.
- safetensors + llama_cpp streaming strip: run the open-ended (EOS
anchored) tail patterns only on the last segment; segments before a
reasoning block use the closed-only patterns, matching the final
strip and the route strip. A bare foo[ARGS] before a reasoning block
is prose, not a truncated call.
- safetensors streaming detector: validate each [ARGS] hit before
draining. A bare foo[ARGS] in prose (no active tool name in front)
no longer drains the rest of the turn; a later real NAME[ARGS] call
is still found and the prose in between is preserved.
Regression tests added for each case across the parser, strip helpers,
and both streaming loops.
* Strip incomplete-XML tool markup with literal think tags; widen render-html detector
Round-2 review follow-ups.
- tool_healing: an UNCLOSED <tool_call> / <function= call that the parser still
executes via allow_incomplete leaked its markup when an argument contained a
literal think marker. _tool_call_markup_spans only covered closed calls, so the
literal was treated as a reasoning block to preserve. Extend it to the
open-ended XML tail forms (shared as _TOOL_OPEN_XML_TAIL_PATS) so a think marker
inside an unclosed call is argument data and the call's markup is stripped. A
complete call's opener stays bounded to its closed span, and a real reasoning
block with no tool call is still preserved.
- safetensors render-html provisional card: _detect_render_html_tool_start was
XML-only, so a Mistral [TOOL_CALLS]render_html or rehearsal render_html[ARGS]
call executed but skipped the early card. Detect the earliest tool-call marker
across every serialization the loop executes and fire when it is render_html.
Regression tests added for both.
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* Studio tools: gate [ARGS] on active tools and skip think-block render_html rehearsal
Round 3 review fixes for the Mistral / rehearsal tool-call parsing path. Both are
asymmetric-fix bugs where one code path applied a guard the analogous paths did not.
- [ARGS] active-tool gating: the streaming state already validates a rehearsal
NAME[ARGS] against the active tool list before draining, but the BUFFERING
detection and the end-of-stream safety-net checks (safetensors and GGUF) treated
any word[ARGS] substring as a tool boundary. An answer containing a literal
foo[ARGS]{...} in prose, where foo is not an enabled tool, was drained, parsed into
a disabled foo no-op, and forced an extra generation turn. Gate those checks on the
active tool name too (unrestricted mode still accepts any name), so inactive-name
prose is neither drained nor parsed. Adds a shared _has_genuine_tool_signal helper
(safetensors) and _gguf_rehearsal_signal_pos / _gguf_has_genuine_tool_signal (GGUF).
- render_html provisional card vs think blocks: the parser skips tool candidates that
start inside a <think>/[THINK] reasoning block, but the provisional render_html
detector scanned raw content. A render_html rehearsed inside <think> followed by a
real non-render_html call emitted a provisional render_html tool_start (reusing the
later call's id) that the loop never executed. Drop candidates that start inside a
think span and use the first marker of each shape outside the blocks. Also resolve
the [TOOL_CALLS] [{...}] array shape through the parser so a nested "name" argument
key no longer fires a false provisional card ahead of the real top-level tool name.
Adds regression tests for both loops: inactive-name foo[ARGS]{...} is not drained into
a disabled no-op or a retry turn, a think-block render_html rehearsal emits no
provisional card, and the array top-level name is read correctly.
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* Gate ambiguous bare-rehearsal parse and strip on the active tool list
A bare NAME[ARGS]{json} is a genuine rehearsal call only when NAME is an
active tool; otherwise it is prose. The earlier round gated only detection
(so an inactive foo[ARGS] no longer drained the buffer or forced a retry
turn), but the parse and strip stayed unrestricted, which produced two
regressions:
1. An inactive foo[ARGS]{...} placed immediately before a real
web_search[ARGS]{...} in the same content span made the real call fail
to execute (parse consumed the phantom foo call).
2. An inactive foo[ARGS]{...} in a prose answer had its markup stripped
from the visible text, corrupting the sentence to " is just syntax."
Thread enabled_tool_names through the shared parser/strip so parse and
strip apply the SAME active-tool gate as detection:
- core/tool_healing.py: _iter_bracket_spans skips an inactive rehearsal
span; parse_tool_calls_from_text, _strip_bracket_tag_calls,
_strip_markup_segment and strip_tool_call_markup accept and thread the
gate; apply_tool_strip_patterns keeps an inactive rehearsal match.
- core/inference/tool_call_parser.py: wrappers forward the gate.
- core/inference/safetensors_agentic.py and core/inference/llama_cpp.py:
compute the gate from the active tool list (None when unrestricted, to
keep the legacy strip-all behavior) and thread it into every parse and
streaming/final strip site.
- routes/inference.py: _strip_tool_xml_for_display accepts the gate and
keeps an inactive rehearsal via a capture group on its rehearsal arm, so
the display cleanup does not re-strip the already-correct loop output.
The [TOOL_CALLS] control-token arms still strip unconditionally. Wire
the current turn's active tool names into the GGUF and safetensors
content-display sites.
Tests: parse and strip gate coverage in test_tool_call_parser_strict.py,
test_tool_xml_strip.py and test_safetensors_tool_loop.py; end-to-end GGUF
coverage for the real-call-after-inactive-rehearsal case and a
strengthened assertion that the inactive rehearsal prose survives intact.
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* Studio: render the reasoning block for safetensors and MLX like GGUF
enable_thinking chat templates (Qwen3/Qwen3.5/GLM) prefill an unclosed <think>
into the generation prompt, so the model emits only the closing </think> then
the answer. The safetensors/MLX chat stream emitted that as plain content, so
the reasoning showed inline with no collapsible thinking block, while GGUF
(which surfaces reasoning via reasoning_content) rendered one. This brings
safetensors and MLX to parity.
- _ResponsesReasoningExtractor gains a reasoning_prefilled mode that starts
inside the reasoning block and splits on the first </think>; default False
keeps GGUF and every existing caller byte-identical. It suppresses a stray
re-emitted <think> and holds partial markers back across chunk boundaries.
- _sf_reasoning_prefill_mode gates the mode on reasoning being enabled for the
request, an enable_thinking or enable_thinking_effort style, and the template
actually using the standard <think>/</think> markers. Models with a bespoke
reasoning channel (e.g. gemma's <|think|>/<|channel>) are excluded so their
answer is never swallowed; gpt-oss (Harmony) and thinking-off requests are
excluded too.
- sf_tool_stream and stream_chunks (the latter also serves MLX) feed text
through the extractor, emitting reasoning_content then content deltas, with a
per-turn reset in the tool loop and a flush before each tool_start; only the
visible delta reaches the monitor reply. The two non-streaming drains split
reasoning_content the same way.
- Tests: extractor prefilled mode (streaming and edge cases), the gate matrix
including the gemma-style exclusion, and a route-replay of the tool-loop
reasoning stream.
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* studio: skip tool calls rehearsed in prefilled reasoning
Reasoning models (Qwen3.5 enable_thinking) open <think> in the prompt, so the
generated text starts inside the thought and emits only a closing </think> with
no opener. _think_spans_outside_tool_markup only found spans with an explicit
opener, so a NAME[ARGS]{...} or [TOOL_CALLS] call rehearsed in that leading
thought was parsed and executed as a real call.
Add a leading think span (offset 0 through the first close marker) when the
content opens with a bare close, so the rehearsed call is skipped and the
reasoning is preserved by strip_outside_think. Guarded by the existing call-span
check: a literal </think> inside a real call's arguments does not trigger the
span, so a genuine leading call still fires. Tests for both cases.
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* studio: do not start prefilled reasoning mode when reasoning_effort is none
enable_thinking_effort models (e.g. GLM-5.2) express thinking-off via
reasoning_effort="none" rather than enable_thinking=False, but
_sf_reasoning_prefill_mode only looked at enable_thinking, so such a request
started the extractor in prefilled mode. With thinking off the model never emits
</think>, so the whole answer was captured as reasoning_content and the visible
content/stream came back empty. Thread reasoning_effort through and return False
when it is "none". Tests for none vs a real effort level.
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* studio: only treat a leading bare </think> as prefilled reasoning when a real call follows
The prefilled-reasoning virtual span fired on any unmatched leading close marker,
so a non-prefilled turn that emits a real call before a stray </think> (for
example "Now web_search[ARGS]{...}</think> answer") had the call swallowed by the
span and dropped. Require that a real tool call also appear after the close (the
actual turn that follows the thought) before adding the span, so a stray close in
a normal answer no longer suppresses a genuine leading call. The rehearse-then-
call case still skips the rehearsal. Test for the stray-close case.
* Studio: trim redundant comments (comment-only, AST-verified)
* studio: keep tool_healing importable on Python 3.9
_balanced_json_span was annotated -> int | None. With no
from __future__ import annotations, that PEP 604 union is evaluated at
import time, so on Python 3.9 (which the package still supports,
requires-python >=3.9, and where external inference servers import this
module standalone) the def raises TypeError and the whole module fails
to import before any parsing runs.
Add from __future__ import annotations so annotations stay lazy strings,
matching the prevailing convention across studio/backend. No behavior
change: the module has no runtime annotation introspection.
* Studio: gate the Anthropic tool-stream display strip on declared tools
The Anthropic streaming and non-streaming tool paths called
_strip_tool_xml_for_display without enabled_tool_names, so with the default
strip-all behavior a final answer that literally contains an inactive-name
NAME[ARGS]{json} (prose, not a call) lost those bytes in the delivered text.
The GGUF and safetensors paths already pass _display_tool_name_gate(tools);
these two sites were missed when that gate was threaded through.
Compute the gate from the declared tools and pass it at both sites (threading
openai_tools into _anthropic_tool_non_streaming and its caller), so an
inactive-name rehearsal survives while an active-name one is still stripped.
Add a regression test.
* Studio: hold a split unrestricted rehearsal prefix at the bracket
In unrestricted tool mode (tools=[]) the rehearsal-prefix regex required
[A after the bracket, so a chunk boundary landing right after NAME[ (e.g.
web_search[ then ARGS]{...}) failed the prefix check and streamed the
partial tool markup web_search[ to the client before the call drained.
Restricted mode already holds this via a startswith check. Make the bracket
and each ARGS letter individually optional so NAME[ is held too, matching
the documented intent. Add a regression test.
* Studio: gate rehearsal detection and history strip on the original tool set
Two display/loop gate fixes so a spent one-shot tool is handled consistently:
- Rehearsal DETECTION (safetensors and GGUF loops) now uses the ORIGINAL tool
list, matching the strip gate, instead of the post-removal active_tools. After a
one-shot tool (render_html) runs it is dropped from active_tools; a repeat
render_html[ARGS]{...} while another tool is still active was stripped from
display yet never detected, so it was not routed to the render_html_repeat no-op
and the turn ended as a blank continuation. Detection now fires for it.
- The GGUF assistant-history sanitiser forwards the enabled-tool-name gate (like
the live-response strip), so a prior turn documenting an inactive foo[ARGS]{...}
shape is preserved in the replayed prompt context instead of being deleted.
Add regression tests for both loops and the history strip.
* Studio: thread the tool-name gate through the remaining rehearsal/history sites
Follow-up to the rehearsal-detection and history-strip gate fixes, covering the
sibling sites that were missed:
- GGUF loop: the rehearsal-prefix and trailing-name hold checks now use the
original tool list (_detect_tools) like the detection path, so a spent one-shot's
split repeat (bare render_html then [ARGS]{...}) is held instead of flushed as
visible text.
- The safetensors and Anthropic assistant-history sanitisers and the Anthropic
non-streaming passthrough now forward the enabled-tool-name gate to
_strip_tool_xml_for_display, matching the GGUF history sanitiser and the live
strips, so a prior turn documenting an inactive foo[ARGS]{...} example is
preserved in the replayed prompt / final text instead of deleted.
Add regression tests.
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* Tile bracket-call spans per array item and include the v11 closer
Two with_spans fixes for the Mistral bracket parser, both hit through the
client-tool passthrough healers:
- A multi-call [TOOL_CALLS] array carried its whole markup span on the first
call and zero-width spans after, so a consumer that filters promotions by
the declared tool set either re-emitted the full raw array as text next to
the promoted call or silently dropped a filtered call's bytes. The region is
now tiled across the call-producing items (each call's span covers its own
JSON object plus the separator bytes before it; the last span runs to the
region end), so promoted markup strips exactly once and a skipped call's
bytes stay visible.
- The v11 wrapper closer [/TOOL_CALLS] sat outside the reported span and
leaked as stray text after promotion; the region now extends over an
immediately-following closer.
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* Address review: decouple healer signals from the loop signal set
The passthrough healer buffered on every TOOL_XML_SIGNALS entry, so the bare
[ARGS] rehearsal marker this branch adds for the loops (where it is gated on
active tool names) put legitimate prose like 'Use foo[ARGS] in templates'
into the holding state and stalled the stream until finalization. The healer
can never promote a bare rehearsal call, so it now buffers only on formats
its parser promotes: <tool_call>, <|tool_call>, <function=, [TOOL_CALLS].
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* Condense comments in the Mistral tool-call rescue to contract essentials
* verify_import_hoist: exempt __future__ imports and same-diff relocations
Two false positives fired on this PR's refactor. A from __future__ import
is a compiler directive whose name never appears as a runtime load, so
HOISTED-IMPORT-UNUSED can never see it used, yet the file requires it for
PEP 604 annotations on Python 3.9. TARGET-CHANGED flagged the deliberate
move of the strip-pattern constants into core.inference.tool_call_parser
as a silent re-point even though the old module-level target was removed
and the new one added in the same diff. Both get narrow exemptions; a
re-point to a pre-existing target is still caught, and the self-test
negative controls all pass unchanged.
* Drain the whole Mistral [TOOL_CALLS] array in streaming passthrough healing
StreamToolCallHealer._drain promoted only the first parsed call per pass and
dropped the rest of the buffer past that one span. For a well-formed Mistral
parallel-tool-call array streamed through client-tool passthrough
([TOOL_CALLS][{...},{...}]), the per-item spans are contiguous, so after the
first call was promoted the residue began with ,{...}] (no leading signal) and
was flushed as raw text: every call after the first was lost.
_drain now walks the contiguous run of parsed calls (adjacent tiled spans =
one array), promoting each declared call and relaying undeclared ones as data,
and stops at the first gap (prose) or incomplete trailing block so separate
blocks still stream incrementally in document order. This mirrors the
non-streaming heal_openai_message / finalize promote-or-flush loop and the
server-side safetensors loop, which already handled multi-call arrays.
Added regression tests: 2-call array in one feed and char-by-char, an
undeclared middle call kept as text, and an array followed by trailing prose.
* Drain comma-less Mistral tool-call arrays and normalize null arguments
The array branch fed the whole body to a single json.loads, which rejects the
comma-less multi-call form the repo's own Mistral/Ollama templates render (the
range loop in ollama_template_mappers.py emits the objects with no separator) and
so dropped every call. Decode elements individually with the existing
comma-tolerant raw_decode helper, now _decode_array_items, which also returns the
objects, so all calls are recovered while the span tiling is unchanged.
Also normalize a non-object array argument such as arguments null to an empty
object, matching the wrapped tool_call path, instead of serializing None to the
string "null" that auto-heal would turn into a bogus query of "null".
* Gate safetensors reasoning prefill on the rendered generation prompt
reasoning_always_on fires on any paired <think></think> in the template,
including markup that only renders PAST assistant history (Kimi-K2-Thinking)
while the generation prompt opens no <think>. Starting the reasoning extractor
in prefilled mode there captured a normal answer entirely as reasoning_content
and returned blank visible content. Prefill only when rendering the generation
prompt actually leaves <think> open (DeepSeek-R1 / QwQ / Qwen3-Thinking);
history-only templates start the extractor in normal mode and parse the model's
own <think>...</think>. Adds a Kimi-shape regression test.
* Keep bare scalar Mistral array arguments raw instead of double-encoding
A scalar string argument in the canonical Mistral [TOOL_CALLS] array
(for example [TOOL_CALLS][{"name":"web_search","arguments":"weather"}])
was run through json.dumps, turning weather into the JSON string
"weather". The downstream argument healer then wrapped that quoted
form, so a single-string tool like web_search searched for the literal
"weather" with quotes. The <tool_call> path already keeps a scalar
argument raw; mirror it here so only a dict is serialized. Add a
regression test asserting both paths yield the same healed arguments.
* Tighten tool-call rescue and reasoning-prefill comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2281 lines
87 KiB
Python
2281 lines
87 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
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"""
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Tests for the OpenAI /v1/responses client-side function-calling pass-through.
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Covers:
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- ResponsesRequest accepts Responses-shape `tools`, `tool_choice`,
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`parallel_tool_calls`, and `function_call` / `function_call_output`
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input items for multi-turn tool loops.
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- _translate_responses_tools_to_chat(): flat Responses tool shape ->
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nested Chat Completions shape, drops non-function built-in tools,
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returns None for empty lists.
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- _translate_responses_tool_choice_to_chat(): passes string choices
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through, converts {type:function,name:X} to the nested shape.
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- _normalise_responses_input(): maps function_call_output items to
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role="tool" ChatMessages with tool_call_id, and function_call items to
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assistant messages with tool_calls.
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- _chat_tool_calls_to_responses_output(): keeps call_id, drops
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non-function tool calls.
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- ResponsesOutputFunctionCall / ResponsesResponse round-trip tool-call
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outputs without losing fields.
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No running server or GPU required.
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"""
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import os
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import sys
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import asyncio
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from types import SimpleNamespace
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_backend = os.path.join(os.path.dirname(__file__), "..")
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sys.path.insert(0, _backend)
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import json
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import httpx
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import pytest
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from fastapi import HTTPException
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from fastapi.responses import JSONResponse
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from pydantic import ValidationError
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from core.inference.api_monitor import ApiMonitor
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from models.inference import (
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ChatMessage,
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ResponsesFunctionCallInputItem,
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ResponsesFunctionCallOutputInputItem,
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ResponsesFunctionTool,
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ResponsesInputMessage,
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ResponsesOutputFunctionCall,
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ResponsesOutputMessage,
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ResponsesOutputReasoning,
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ResponsesOutputTextContent,
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ResponsesOutputTextPart,
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ResponsesRequest,
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ResponsesResponse,
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ResponsesUnknownContentPart,
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ResponsesUnknownInputItem,
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ResponsesUsage,
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)
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from routes.inference import (
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_ResponsesReasoningExtractor,
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_SameTaskStreamingResponse,
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_build_chat_request,
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_chat_tool_calls_to_responses_output,
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_extract_responses_reasoning,
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_normalise_responses_input,
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_responses_tool_output_content,
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_responses_non_streaming,
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_responses_stream,
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_translate_responses_tool_choice_to_chat,
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_translate_responses_tools_to_chat,
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)
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# =====================================================================
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# Request model — tools / tool_choice / parallel_tool_calls
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# =====================================================================
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class TestResponsesRequestTools:
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def test_flat_function_tool_accepted(self):
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req = ResponsesRequest(
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input = "hi",
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tools = [
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{
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"type": "function",
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"name": "get_weather",
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"description": "Get the weather for a city.",
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"parameters": {
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"type": "object",
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"properties": {"city": {"type": "string"}},
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"required": ["city"],
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},
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"strict": True,
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}
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],
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)
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assert req.tools is not None
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assert req.tools[0]["name"] == "get_weather"
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assert req.tools[0]["type"] == "function"
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assert req.tools[0]["strict"] is True
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def test_tool_choice_string_values(self):
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for choice in ("auto", "required", "none"):
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req = ResponsesRequest(input = "hi", tool_choice = choice)
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assert req.tool_choice == choice
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def test_tool_choice_forcing_object(self):
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req = ResponsesRequest(
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input = "hi",
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tool_choice = {"type": "function", "name": "get_weather"},
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)
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assert req.tool_choice == {"type": "function", "name": "get_weather"}
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def test_parallel_tool_calls(self):
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req = ResponsesRequest(input = "hi", parallel_tool_calls = True)
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assert req.parallel_tool_calls is True
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def test_builtin_tool_type_passes_validation(self):
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"""Non-function built-in tools (web_search, file_search, mcp, ...)
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must not raise at validation so SDKs that default to them don't
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fail on Studio; they're filtered out during translation."""
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req = ResponsesRequest(
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input = "hi",
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tools = [{"type": "web_search_preview"}],
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)
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assert req.tools == [{"type": "web_search_preview"}]
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def test_function_tool_model_direct(self):
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tool = ResponsesFunctionTool(
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type = "function",
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name = "send_email",
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parameters = {"type": "object", "properties": {}},
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)
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assert tool.name == "send_email"
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assert tool.description is None
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def test_function_tool_rejects_other_type(self):
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with pytest.raises(ValidationError):
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ResponsesFunctionTool(type = "web_search", name = "x")
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# =====================================================================
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# Request model — function_call / function_call_output input items
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# =====================================================================
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class TestResponsesMultiTurnInput:
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def test_function_call_input_item(self):
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req = ResponsesRequest(
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input = [
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{"role": "user", "content": "Weather in Paris?"},
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{
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"type": "function_call",
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"id": "fc_abc",
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"call_id": "call_abc",
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"name": "get_weather",
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"arguments": '{"city": "Paris"}',
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},
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{
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"type": "function_call_output",
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"call_id": "call_abc",
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"output": '{"temp": 12}',
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},
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],
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)
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assert len(req.input) == 3
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assert isinstance(req.input[1], ResponsesFunctionCallInputItem)
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assert req.input[1].call_id == "call_abc"
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assert isinstance(req.input[2], ResponsesFunctionCallOutputInputItem)
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assert req.input[2].call_id == "call_abc"
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assert req.input[2].output == '{"temp": 12}'
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def test_function_call_output_missing_call_id_rejected(self):
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with pytest.raises(ValidationError):
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ResponsesFunctionCallOutputInputItem(type = "function_call_output", output = "x")
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def test_function_call_output_accepts_content_array(self):
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item = ResponsesFunctionCallOutputInputItem(
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type = "function_call_output",
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call_id = "call_1",
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output = [{"type": "output_text", "text": "done"}],
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)
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assert isinstance(item.output, list)
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# =====================================================================
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# Translators — tools, tool_choice
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# =====================================================================
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class TestToolsTranslation:
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def test_flat_to_nested(self):
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tools = [
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{
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"type": "function",
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"name": "get_weather",
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"description": "Returns weather.",
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"parameters": {"type": "object"},
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"strict": True,
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}
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]
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out = _translate_responses_tools_to_chat(tools)
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assert out == [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Returns weather.",
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"parameters": {"type": "object"},
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"strict": True,
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},
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}
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]
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def test_builtin_tools_dropped(self):
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out = _translate_responses_tools_to_chat(
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[
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{"type": "web_search_preview"},
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{"type": "file_search"},
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{
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"type": "function",
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"name": "search",
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"parameters": {"type": "object"},
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},
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]
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)
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assert len(out) == 1
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assert out[0]["function"]["name"] == "search"
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def test_empty_returns_none(self):
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assert _translate_responses_tools_to_chat(None) is None
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assert _translate_responses_tools_to_chat([]) is None
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def test_only_builtin_tools_returns_none(self):
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assert _translate_responses_tools_to_chat([{"type": "web_search_preview"}]) is None
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def test_description_optional(self):
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out = _translate_responses_tools_to_chat(
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[
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{
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"type": "function",
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"name": "noop",
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"parameters": {"type": "object"},
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}
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]
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)
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assert "description" not in out[0]["function"]
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class TestToolChoiceTranslation:
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def test_string_passthrough(self):
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for v in ("auto", "required", "none"):
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assert _translate_responses_tool_choice_to_chat(v) == v
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def test_none_passthrough(self):
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assert _translate_responses_tool_choice_to_chat(None) is None
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def test_forcing_object_converted(self):
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assert _translate_responses_tool_choice_to_chat(
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{"type": "function", "name": "get_weather"}
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) == {"type": "function", "function": {"name": "get_weather"}}
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def test_already_chat_nested_shape_passes_through(self):
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"""A client sending the Chat Completions nested shape isn't
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double-wrapped."""
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already_nested = {"type": "function", "function": {"name": "get_weather"}}
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assert _translate_responses_tool_choice_to_chat(already_nested) == already_nested
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def test_unknown_shape_passes_through(self):
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obj = {"type": "allowed_tools", "tools": [{"type": "function", "name": "x"}]}
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assert _translate_responses_tool_choice_to_chat(obj) == obj
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class TestBuildChatRequest:
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def test_parallel_tool_calls_false_is_preserved_for_passthrough_caps(self):
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payload = ResponsesRequest(
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input = "hi",
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tools = [
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{
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"type": "function",
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"name": "lookup",
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"parameters": {"type": "object"},
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}
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],
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parallel_tool_calls = False,
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)
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messages = [ChatMessage(role = "user", content = "hi")]
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chat_req = _build_chat_request(payload, messages, stream = True)
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assert chat_req.parallel_tool_calls is False
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def test_chat_template_kwargs_enable_thinking_true_is_lifted(self):
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payload = ResponsesRequest(
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input = "hi",
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chat_template_kwargs = {"enable_thinking": True},
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)
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messages = [ChatMessage(role = "user", content = "hi")]
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chat_req = _build_chat_request(payload, messages, stream = False)
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assert chat_req.enable_thinking is True
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def test_chat_template_kwargs_enable_thinking_false_is_lifted(self):
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payload = ResponsesRequest(
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input = "hi",
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chat_template_kwargs = {"enable_thinking": False},
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)
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messages = [ChatMessage(role = "user", content = "hi")]
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chat_req = _build_chat_request(payload, messages, stream = False)
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assert chat_req.enable_thinking is False
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def test_reasoning_effort_high_enables_local_thinking(self):
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payload = ResponsesRequest(input = "hi", reasoning = {"effort": "high"})
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messages = [ChatMessage(role = "user", content = "hi")]
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chat_req = _build_chat_request(payload, messages, stream = False)
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assert chat_req.reasoning_effort == "high"
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assert chat_req.enable_thinking is True
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def test_reasoning_effort_none_disables_local_thinking(self):
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payload = ResponsesRequest(input = "hi", reasoning = {"effort": "none"})
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messages = [ChatMessage(role = "user", content = "hi")]
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chat_req = _build_chat_request(payload, messages, stream = False)
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assert chat_req.reasoning_effort == "none"
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assert chat_req.enable_thinking is False
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def test_explicit_enable_thinking_false_disables_reasoning_effort(self):
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payload = ResponsesRequest(
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input = "hi",
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reasoning = {"effort": "high"},
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chat_template_kwargs = {"enable_thinking": False},
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)
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messages = [ChatMessage(role = "user", content = "hi")]
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chat_req = _build_chat_request(payload, messages, stream = False)
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assert chat_req.reasoning_effort == "none"
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assert chat_req.enable_thinking is False
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# =====================================================================
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# _normalise_responses_input — multi-turn tool mapping
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# =====================================================================
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class TestNormaliseResponsesInputWithTools:
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def test_function_call_output_maps_to_tool_role(self):
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payload = ResponsesRequest(
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input = [
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{"role": "user", "content": "Weather?"},
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{
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"type": "function_call",
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"call_id": "call_1",
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"name": "get_weather",
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"arguments": "{}",
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},
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{
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"type": "function_call_output",
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"call_id": "call_1",
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"output": '{"temp": 20}',
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},
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],
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)
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msgs = _normalise_responses_input(payload)
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assert len(msgs) == 3
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assert msgs[0].role == "user"
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assert msgs[1].role == "assistant"
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assert msgs[1].tool_calls is not None
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assert msgs[1].tool_calls[0]["id"] == "call_1"
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assert msgs[1].tool_calls[0]["function"]["name"] == "get_weather"
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assert msgs[2].role == "tool"
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assert msgs[2].tool_call_id == "call_1"
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assert msgs[2].content == '{"temp": 20}'
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def test_instructions_plus_developer_message_are_merged(self):
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"""Codex CLI sends `instructions` (system prompt) AND a developer
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message in `input`. Strict chat templates (harmony / gpt-oss,
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Qwen3, ...) raise "System message must be at the beginning" on two
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separate system-role messages, so we emit exactly one merged
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system message at the top.
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"""
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payload = ResponsesRequest(
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instructions = "Base instructions.",
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input = [
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{"role": "developer", "content": "Developer override."},
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{"role": "user", "content": "Hi"},
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],
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)
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msgs = _normalise_responses_input(payload)
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system_roles = [m for m in msgs if m.role == "system"]
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assert len(system_roles) == 1
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assert "Base instructions." in system_roles[0].content
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assert "Developer override." in system_roles[0].content
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# System must be the first message for strict templates.
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assert msgs[0].role == "system"
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assert msgs[1].role == "user"
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def test_developer_message_after_user_is_still_hoisted(self):
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"""A developer message appearing after user turns must still
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produce a single leading system message, not a mid-conversation
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system that strict templates reject."""
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payload = ResponsesRequest(
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input = [
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{"role": "user", "content": "Hello"},
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{"role": "assistant", "content": "Hi!"},
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{"role": "developer", "content": "Updated rules."},
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{"role": "user", "content": "Continue"},
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],
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)
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msgs = _normalise_responses_input(payload)
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assert msgs[0].role == "system"
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assert "Updated rules." in msgs[0].content
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for m in msgs[1:]:
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assert m.role != "system", "no trailing system message permitted"
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|
|
|
def test_no_system_output_when_no_system_input(self):
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payload = ResponsesRequest(input = "Hi")
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msgs = _normalise_responses_input(payload)
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assert all(m.role != "system" for m in msgs)
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|
|
|
def test_multiple_system_messages_in_input_are_merged(self):
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payload = ResponsesRequest(
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input = [
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{"role": "system", "content": "A"},
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|
{"role": "system", "content": "B"},
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{"role": "user", "content": "Hi"},
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|
],
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|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert sum(1 for m in msgs if m.role == "system") == 1
|
|
assert "A" in msgs[0].content and "B" in msgs[0].content
|
|
|
|
def test_content_array_text_output_flattens_to_tool_text(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [{"type": "input_text", "text": "ok"}],
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].role == "tool"
|
|
assert msgs[0].content == "ok"
|
|
|
|
def test_content_array_image_output_becomes_multimodal_tool_content(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [
|
|
{"type": "input_text", "text": "see image"},
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "data:image/png;base64,AAA",
|
|
"detail": "high",
|
|
},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].role == "tool"
|
|
assert msgs[0].tool_call_id == "call_1"
|
|
assert msgs[0].model_dump(exclude_none = True)["content"] == [
|
|
{"type": "text", "text": "see image"},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": "data:image/png;base64,AAA",
|
|
"detail": "high",
|
|
},
|
|
},
|
|
]
|
|
|
|
chat_req = _build_chat_request(payload, msgs, stream = False)
|
|
assert chat_req.model_dump(exclude_none = True)["messages"][0]["content"] == [
|
|
{"type": "text", "text": "see image"},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": "data:image/png;base64,AAA",
|
|
"detail": "high",
|
|
},
|
|
},
|
|
]
|
|
|
|
def test_content_array_image_output_allows_original_detail(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "https://example.com/screenshot.png",
|
|
"detail": "original",
|
|
},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].model_dump(exclude_none = True)["content"] == [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": "https://example.com/screenshot.png",
|
|
"detail": "original",
|
|
},
|
|
},
|
|
]
|
|
|
|
def test_content_array_file_id_image_output_rejected_clearly(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [
|
|
{"type": "input_text", "text": "see image"},
|
|
{"type": "input_image", "file_id": "file_abc"},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
with pytest.raises(HTTPException) as exc:
|
|
_normalise_responses_input(payload)
|
|
assert exc.value.status_code == 400
|
|
assert "file_id" in str(exc.value.detail)
|
|
|
|
def test_content_array_file_output_rejected_clearly(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [
|
|
{"type": "input_text", "text": "see file"},
|
|
{
|
|
"type": "input_file",
|
|
"file_data": "data:application/pdf;base64,AAA",
|
|
"filename": "report.pdf",
|
|
},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
with pytest.raises(HTTPException) as exc:
|
|
_normalise_responses_input(payload)
|
|
assert exc.value.status_code == 400
|
|
assert "input_file" in str(exc.value.detail)
|
|
|
|
def test_content_array_malformed_image_output_rejected_clearly(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [{"type": "input_image", "detail": "high"}],
|
|
}
|
|
],
|
|
)
|
|
with pytest.raises(HTTPException) as exc:
|
|
_normalise_responses_input(payload)
|
|
assert exc.value.status_code == 400
|
|
assert "image_url" in str(exc.value.detail)
|
|
|
|
def test_empty_function_call_output_gets_no_output_sentinel(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": "",
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].role == "tool"
|
|
assert msgs[0].tool_call_id == "call_1"
|
|
assert msgs[0].content == "(no output)"
|
|
ChatMessage(**msgs[0].model_dump(exclude_none = True))
|
|
|
|
def test_whitespace_function_call_output_gets_no_output_sentinel(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": " \n\t",
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].content == "(no output)"
|
|
|
|
def test_empty_content_array_output_gets_no_output_sentinel(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [],
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].content == "(no output)"
|
|
|
|
def test_image_content_array_tool_output_is_serialised(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": [
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/png",
|
|
"data": "iVBORw0KGgo=",
|
|
},
|
|
}
|
|
],
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].role == "tool"
|
|
assert json.loads(msgs[0].content)[0]["type"] == "image"
|
|
|
|
def test_image_payload_outside_output_gets_no_output_sentinel(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": "",
|
|
"content": [
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/png",
|
|
"data": "iVBORw0KGgo=",
|
|
},
|
|
}
|
|
],
|
|
}
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].role == "tool"
|
|
assert msgs[0].tool_call_id == "call_1"
|
|
assert msgs[0].content == "(no output)"
|
|
|
|
def test_tool_output_serializer_preserves_non_empty_text(self):
|
|
assert _responses_tool_output_content("done") == "done"
|
|
assert _responses_tool_output_content(" done ") == " done "
|
|
|
|
|
|
# =====================================================================
|
|
# Response mapping — tool_calls → function_call output items
|
|
# =====================================================================
|
|
|
|
|
|
class TestChatToolCallsToResponsesOutput:
|
|
def test_basic_mapping(self):
|
|
items = _chat_tool_calls_to_responses_output(
|
|
[
|
|
{
|
|
"id": "call_abc",
|
|
"type": "function",
|
|
"function": {
|
|
"name": "get_weather",
|
|
"arguments": '{"city":"Paris"}',
|
|
},
|
|
}
|
|
]
|
|
)
|
|
assert len(items) == 1
|
|
assert items[0]["type"] == "function_call"
|
|
assert items[0]["call_id"] == "call_abc"
|
|
assert items[0]["name"] == "get_weather"
|
|
assert items[0]["arguments"] == '{"city":"Paris"}'
|
|
assert items[0]["status"] == "completed"
|
|
assert items[0]["id"].startswith("fc_")
|
|
|
|
def test_multiple_tool_calls_preserved(self):
|
|
items = _chat_tool_calls_to_responses_output(
|
|
[
|
|
{
|
|
"id": "call_1",
|
|
"type": "function",
|
|
"function": {"name": "a", "arguments": "{}"},
|
|
},
|
|
{
|
|
"id": "call_2",
|
|
"type": "function",
|
|
"function": {"name": "b", "arguments": "{}"},
|
|
},
|
|
]
|
|
)
|
|
assert [it["call_id"] for it in items] == ["call_1", "call_2"]
|
|
|
|
def test_non_function_tool_call_dropped(self):
|
|
items = _chat_tool_calls_to_responses_output([{"id": "x", "type": "retrieval"}])
|
|
assert items == []
|
|
|
|
def test_missing_arguments_coerced_to_empty_string(self):
|
|
items = _chat_tool_calls_to_responses_output(
|
|
[{"id": "call_1", "type": "function", "function": {"name": "x"}}]
|
|
)
|
|
assert items[0]["arguments"] == ""
|
|
|
|
|
|
# =====================================================================
|
|
# Non-streaming Responses adapter
|
|
# =====================================================================
|
|
|
|
|
|
class TestResponsesNonStreamingAdapter:
|
|
class _Request:
|
|
pass
|
|
|
|
@staticmethod
|
|
def _run_with_message(
|
|
monkeypatch,
|
|
message,
|
|
payload = None,
|
|
llama_backend = None,
|
|
):
|
|
import routes.inference as inf_mod
|
|
|
|
async def fake_chat_completions(chat_req, request):
|
|
return JSONResponse(
|
|
content = {
|
|
"model": "test-model",
|
|
"choices": [{"message": message}],
|
|
"usage": {"prompt_tokens": 2, "completion_tokens": 3},
|
|
}
|
|
)
|
|
|
|
monkeypatch.setattr(inf_mod, "openai_chat_completions", fake_chat_completions)
|
|
if llama_backend is not None:
|
|
monkeypatch.setattr(inf_mod, "get_llama_cpp_backend", lambda: llama_backend)
|
|
payload = payload or ResponsesRequest(input = "hi")
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_non_streaming(
|
|
payload, messages, TestResponsesNonStreamingAdapter._Request()
|
|
)
|
|
return json.loads(response.body.decode())
|
|
|
|
return asyncio.run(run())
|
|
|
|
def test_think_block_becomes_reasoning_item_before_message(self, monkeypatch):
|
|
payload = ResponsesRequest(input = "hi", reasoning = {"effort": "high"})
|
|
body = self._run_with_message(
|
|
monkeypatch,
|
|
{"content": "<think>plan</think>33"},
|
|
payload = payload,
|
|
)
|
|
|
|
assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
|
|
assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "plan"}]
|
|
assert body["output"][0]["summary"] == []
|
|
assert body["output"][1]["content"][0]["text"] == "33"
|
|
assert "<think>" not in body["output"][1]["content"][0]["text"]
|
|
assert "</think>" not in body["output"][1]["content"][0]["text"]
|
|
|
|
def test_unclosed_think_block_extracts_as_reasoning(self):
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"<think>partial plan",
|
|
parse_think_markers = True,
|
|
)
|
|
|
|
assert reasoning == "partial plan"
|
|
assert visible == ""
|
|
|
|
def test_monitor_records_translated_visible_text(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
import routes.inference as inf_mod
|
|
|
|
async def fake_chat_completions(chat_req, request):
|
|
assert request.state.skip_api_monitor is True
|
|
return JSONResponse(
|
|
content = {
|
|
"model": "test-model",
|
|
"choices": [{"message": {"content": "<think>plan</think>answer"}}],
|
|
"usage": {"prompt_tokens": 2, "completion_tokens": 3},
|
|
}
|
|
)
|
|
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "openai_chat_completions", fake_chat_completions)
|
|
payload = ResponsesRequest(input = "hi", reasoning = {"effort": "high"})
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
request = SimpleNamespace(
|
|
state = SimpleNamespace(),
|
|
url = SimpleNamespace(path = "/v1/responses"),
|
|
method = "POST",
|
|
)
|
|
|
|
async def run():
|
|
response = await _responses_non_streaming(payload, messages, request)
|
|
return json.loads(response.body.decode())
|
|
|
|
body = asyncio.run(run())
|
|
|
|
assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "plan"}]
|
|
assert body["output"][1]["content"][0]["text"] == "answer"
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply"] == "answer"
|
|
assert entry["prompt_tokens"] == 2
|
|
assert entry["completion_tokens"] == 3
|
|
assert request.state.skip_api_monitor is False
|
|
|
|
def test_monitor_records_tool_only_reply(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
async def fake_chat_completions(chat_req, request):
|
|
assert request.state.skip_api_monitor is True
|
|
return JSONResponse(
|
|
content = {
|
|
"model": "test-model",
|
|
"choices": [
|
|
{
|
|
"message": {
|
|
"content": "",
|
|
"tool_calls": [
|
|
{
|
|
"id": "call_1",
|
|
"type": "function",
|
|
"function": {
|
|
"name": "lookup",
|
|
"arguments": '{"query":"weather"}',
|
|
},
|
|
}
|
|
],
|
|
}
|
|
}
|
|
],
|
|
"usage": {"prompt_tokens": 2, "completion_tokens": 3},
|
|
}
|
|
)
|
|
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "openai_chat_completions", fake_chat_completions)
|
|
payload = ResponsesRequest(
|
|
input = "hi",
|
|
tools = [{"type": "function", "name": "lookup"}],
|
|
)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
request = SimpleNamespace(
|
|
state = SimpleNamespace(),
|
|
url = SimpleNamespace(path = "/v1/responses"),
|
|
method = "POST",
|
|
)
|
|
|
|
async def run():
|
|
response = await _responses_non_streaming(payload, messages, request)
|
|
return json.loads(response.body.decode())
|
|
|
|
body = asyncio.run(run())
|
|
|
|
assert body["output"][0]["type"] == "function_call"
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply"] == 'Tool call: lookup({"query":"weather"})'
|
|
assert request.state.skip_api_monitor is False
|
|
|
|
def test_cancelled_chat_completion_finalizes_monitor(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
async def fake_chat_completions(chat_req, request):
|
|
assert request.state.skip_api_monitor is True
|
|
raise asyncio.CancelledError()
|
|
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "openai_chat_completions", fake_chat_completions)
|
|
payload = ResponsesRequest(input = "hi")
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
request = SimpleNamespace(
|
|
state = SimpleNamespace(),
|
|
url = SimpleNamespace(path = "/v1/responses"),
|
|
method = "POST",
|
|
)
|
|
|
|
async def run():
|
|
with pytest.raises(asyncio.CancelledError):
|
|
await _responses_non_streaming(payload, messages, request)
|
|
|
|
asyncio.run(run())
|
|
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "cancelled"
|
|
assert monitor.active_count() == 0
|
|
assert request.state.skip_api_monitor is False
|
|
|
|
def test_literal_think_tags_remain_visible_without_reasoning_request(self, monkeypatch):
|
|
body = self._run_with_message(monkeypatch, {"content": "show <think>x</think> tags"})
|
|
|
|
assert [item["type"] for item in body["output"]] == ["message"]
|
|
assert body["output"][0]["content"][0]["text"] == "show <think>x</think> tags"
|
|
|
|
def test_non_reasoning_gguf_keeps_literal_think_tags_visible(self, monkeypatch):
|
|
payload = ResponsesRequest(input = "hi", reasoning = {"effort": "high"})
|
|
body = self._run_with_message(
|
|
monkeypatch,
|
|
{"content": "show <think>x</think> tags"},
|
|
payload = payload,
|
|
llama_backend = SimpleNamespace(
|
|
is_loaded = True,
|
|
reasoning_always_on = False,
|
|
supports_reasoning = False,
|
|
),
|
|
)
|
|
|
|
assert [item["type"] for item in body["output"]] == ["message"]
|
|
assert body["output"][0]["content"][0]["text"] == "show <think>x</think> tags"
|
|
|
|
def test_reasoning_capable_gguf_parses_think_tags_by_default(self, monkeypatch):
|
|
body = self._run_with_message(
|
|
monkeypatch,
|
|
{"content": "<think>plan</think>answer"},
|
|
llama_backend = SimpleNamespace(
|
|
is_loaded = True,
|
|
reasoning_always_on = False,
|
|
supports_reasoning = True,
|
|
),
|
|
)
|
|
|
|
assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
|
|
assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "plan"}]
|
|
assert body["output"][1]["content"][0]["text"] == "answer"
|
|
|
|
def test_reasoning_capable_gguf_sanitizes_think_tags_when_disabled(self, monkeypatch):
|
|
payload = ResponsesRequest(input = "hi", reasoning = {"effort": "none"})
|
|
body = self._run_with_message(
|
|
monkeypatch,
|
|
{"content": "<think>leaked</think>answer"},
|
|
payload = payload,
|
|
llama_backend = SimpleNamespace(
|
|
is_loaded = True,
|
|
reasoning_always_on = False,
|
|
supports_reasoning = True,
|
|
),
|
|
)
|
|
|
|
assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
|
|
assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "leaked"}]
|
|
assert body["output"][1]["content"][0]["text"] == "answer"
|
|
|
|
def test_structured_reasoning_content_extracts_text_parts(self, monkeypatch):
|
|
body = self._run_with_message(
|
|
monkeypatch,
|
|
{
|
|
"content": "33",
|
|
"reasoning_content": [
|
|
{"type": "reasoning_text", "text": "plan"},
|
|
{"type": "reasoning_text", "text": " next"},
|
|
],
|
|
},
|
|
)
|
|
|
|
assert [item["type"] for item in body["output"]] == ["reasoning", "message"]
|
|
assert body["output"][0]["content"] == [{"type": "reasoning_text", "text": "plan next"}]
|
|
assert body["output"][1]["content"][0]["text"] == "33"
|
|
|
|
def test_plain_content_remains_message_only(self, monkeypatch):
|
|
body = self._run_with_message(monkeypatch, {"content": "33"})
|
|
|
|
assert [item["type"] for item in body["output"]] == ["message"]
|
|
assert body["output"][0]["content"][0]["text"] == "33"
|
|
|
|
def test_reasoning_only_stays_out_of_visible_message_text(self, monkeypatch):
|
|
payload = ResponsesRequest(input = "hi", reasoning = {"effort": "high"})
|
|
body = self._run_with_message(
|
|
monkeypatch,
|
|
{"content": "<think>plan</think>"},
|
|
payload = payload,
|
|
)
|
|
|
|
assert [item["type"] for item in body["output"]] == ["reasoning"]
|
|
assert body["output"][0]["content"][0]["text"] == "plan"
|
|
|
|
|
|
# =====================================================================
|
|
# Streaming Responses adapter
|
|
# =====================================================================
|
|
|
|
|
|
class TestResponsesStreamAdapter:
|
|
class _Request:
|
|
async def is_disconnected(self):
|
|
return False
|
|
|
|
@staticmethod
|
|
async def _collect(response):
|
|
chunks = []
|
|
async for chunk in response.body_iterator:
|
|
chunks.append(chunk.decode() if isinstance(chunk, bytes) else chunk)
|
|
return chunks
|
|
|
|
@staticmethod
|
|
def _payloads(lines, event_name):
|
|
prefix = f"event: {event_name}\n"
|
|
return [
|
|
json.loads(line.split("data: ", 1)[1].strip())
|
|
for line in lines
|
|
if line.startswith(prefix)
|
|
]
|
|
|
|
@staticmethod
|
|
def _install_stream_mock(
|
|
monkeypatch,
|
|
chunks,
|
|
*,
|
|
supports_reasoning = True,
|
|
reasoning_always_on = False,
|
|
):
|
|
import routes.inference as inf_mod
|
|
|
|
def handler(request: httpx.Request) -> httpx.Response:
|
|
content = "".join(f"data: {json.dumps(chunk)}\n\n" for chunk in chunks)
|
|
content += "data: [DONE]\n\n"
|
|
return httpx.Response(
|
|
200,
|
|
content = content.encode(),
|
|
headers = {"content-type": "text/event-stream"},
|
|
)
|
|
|
|
transport = httpx.MockTransport(handler)
|
|
real_async_client = httpx.AsyncClient
|
|
|
|
def _client(*args, **kwargs):
|
|
return real_async_client(
|
|
transport = transport,
|
|
timeout = kwargs.get("timeout", 600),
|
|
)
|
|
|
|
monkeypatch.setattr(inf_mod.httpx, "AsyncClient", _client)
|
|
monkeypatch.setattr(
|
|
inf_mod,
|
|
"get_llama_cpp_backend",
|
|
lambda: SimpleNamespace(
|
|
is_loaded = True,
|
|
is_vision = False,
|
|
context_length = 4096,
|
|
base_url = "http://llama.test",
|
|
supports_reasoning = supports_reasoning,
|
|
reasoning_always_on = reasoning_always_on,
|
|
_request_reasoning_kwargs = (
|
|
lambda enable_thinking = None, reasoning_effort = None, preserve_thinking = None: None
|
|
),
|
|
),
|
|
)
|
|
|
|
def test_stream_response_avoids_legacy_receive_watcher(self, monkeypatch):
|
|
self._install_stream_mock(
|
|
monkeypatch,
|
|
[{"choices": [{"delta": {"content": "33"}}]}],
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
assert isinstance(response, _SameTaskStreamingResponse)
|
|
|
|
sent = []
|
|
|
|
async def receive():
|
|
raise AssertionError("Responses streams poll disconnects in the generator")
|
|
|
|
async def send(message):
|
|
sent.append(message)
|
|
|
|
await response({"type": "http", "asgi": {"spec_version": "2.3"}}, receive, send)
|
|
return sent
|
|
|
|
sent = asyncio.run(run())
|
|
|
|
assert sent[0]["type"] == "http.response.start"
|
|
body = b"".join(message.get("body", b"") for message in sent).decode()
|
|
assert "response.output_text.delta" in body
|
|
assert '"delta":"33"' in body.replace(" ", "")
|
|
|
|
def test_split_think_markers_stream_as_reasoning_and_visible_text(self, monkeypatch):
|
|
chunks = [
|
|
{"choices": [{"delta": {"content": "<thi"}}]},
|
|
{"choices": [{"delta": {"content": "nk>pla"}}]},
|
|
{"choices": [{"delta": {"content": "n</th"}}]},
|
|
{"choices": [{"delta": {"content": "ink>33"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True, reasoning = {"effort": "high"})
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
|
|
assert "".join(event["delta"] for event in text_deltas) == "33"
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert [item["type"] for item in completed["response"]["output"]] == [
|
|
"reasoning",
|
|
"message",
|
|
]
|
|
assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
|
|
assert completed["response"]["output"][1]["content"][0]["text"] == "33"
|
|
|
|
def test_usage_only_chunk_updates_monitor(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
chunks = [
|
|
{"choices": [{"delta": {"content": "33"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monitor_id = monitor.start(
|
|
endpoint = "/v1/responses",
|
|
method = "POST",
|
|
model = "m",
|
|
prompt = "hi",
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload,
|
|
messages,
|
|
self._Request(),
|
|
monitor_id = monitor_id,
|
|
)
|
|
return await self._collect(response)
|
|
|
|
asyncio.run(run())
|
|
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply"] == "33"
|
|
assert entry["prompt_tokens"] == 2
|
|
assert entry["completion_tokens"] == 3
|
|
assert entry["total_tokens"] == 5
|
|
assert entry["context_length"] == 4096
|
|
|
|
def test_function_call_chunk_updates_monitor_reply(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
chunks = [
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "call_1",
|
|
"function": {
|
|
"name": "lookup",
|
|
"arguments": '{"query":"weather"}',
|
|
},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monitor_id = monitor.start(
|
|
endpoint = "/v1/responses",
|
|
method = "POST",
|
|
model = "m",
|
|
prompt = "hi",
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload,
|
|
messages,
|
|
self._Request(),
|
|
monitor_id = monitor_id,
|
|
)
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
assert self._payloads(lines, "response.output_item.done")[-1]["item"]["name"] == "lookup"
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply"] == 'Tool call: lookup({"query":"weather"})'
|
|
|
|
def test_preheader_cancel_finalizes_monitor(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
self._install_stream_mock(monkeypatch, [])
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monitor_id = monitor.start(
|
|
endpoint = "/v1/responses",
|
|
method = "POST",
|
|
model = "m",
|
|
prompt = "hi",
|
|
)
|
|
|
|
async def fake_send(*_args, **_kwargs):
|
|
return None
|
|
|
|
monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload,
|
|
messages,
|
|
self._Request(),
|
|
monitor_id = monitor_id,
|
|
)
|
|
return await self._collect(response)
|
|
|
|
asyncio.run(run())
|
|
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "cancelled"
|
|
assert monitor.active_count() == 0
|
|
|
|
def test_stream_task_cancel_finalizes_monitor(self, monkeypatch):
|
|
async def _run():
|
|
import routes.inference as inf_mod
|
|
|
|
async def fake_send(*_args, **_kwargs):
|
|
return httpx.Response(200, content = b"")
|
|
|
|
async def fake_items(*_args, **_kwargs):
|
|
yield 'data: {"choices":[{"delta":{"content":"hello"}}]}'
|
|
await asyncio.sleep(3600)
|
|
|
|
self._install_stream_mock(monkeypatch, [])
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "_send_stream_with_preheader_cancel", fake_send)
|
|
monkeypatch.setattr(inf_mod, "_aiter_llama_stream_items", fake_items)
|
|
monitor_id = monitor.start(
|
|
endpoint = "/v1/responses",
|
|
method = "POST",
|
|
model = "m",
|
|
prompt = "hi",
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
response = await _responses_stream(
|
|
payload,
|
|
messages,
|
|
self._Request(),
|
|
monitor_id = monitor_id,
|
|
)
|
|
iterator = response.body_iterator
|
|
first = ""
|
|
for _ in range(8):
|
|
first = await anext(iterator)
|
|
if "hello" in first:
|
|
break
|
|
else:
|
|
pytest.fail("stream did not emit text delta")
|
|
|
|
pending = asyncio.create_task(anext(iterator))
|
|
await asyncio.sleep(0)
|
|
pending.cancel()
|
|
with pytest.raises(asyncio.CancelledError):
|
|
await pending
|
|
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "cancelled"
|
|
assert entry["reply"] == "hello"
|
|
assert monitor.active_count() == 0
|
|
|
|
asyncio.run(_run())
|
|
|
|
def test_final_visible_text_updates_monitor(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
class FakeExtractor:
|
|
def __init__(self, **_kwargs):
|
|
pass
|
|
|
|
def feed(
|
|
self,
|
|
_content,
|
|
_reasoning_content = None,
|
|
):
|
|
return "", ""
|
|
|
|
def finish(self):
|
|
return "", "tail"
|
|
|
|
self._install_stream_mock(monkeypatch, [{"choices": [{"delta": {"content": "<tai"}}]}])
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "_ResponsesReasoningExtractor", FakeExtractor)
|
|
monitor_id = monitor.start(
|
|
endpoint = "/v1/responses",
|
|
method = "POST",
|
|
model = "m",
|
|
prompt = "hi",
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload,
|
|
messages,
|
|
self._Request(),
|
|
monitor_id = monitor_id,
|
|
)
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
assert self._payloads(lines, "response.output_text.delta")[-1]["delta"] == "tail"
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply"] == "tail"
|
|
|
|
def test_reasoning_only_stream_does_not_update_visible_monitor_reply(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
class FakeExtractor:
|
|
def __init__(self, **_kwargs):
|
|
pass
|
|
|
|
def feed(
|
|
self,
|
|
_content,
|
|
_reasoning_content = None,
|
|
):
|
|
return "", ""
|
|
|
|
def finish(self):
|
|
return "plan", ""
|
|
|
|
self._install_stream_mock(monkeypatch, [{"choices": [{"delta": {"content": "<think>"}}]}])
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "_ResponsesReasoningExtractor", FakeExtractor)
|
|
monitor_id = monitor.start(
|
|
endpoint = "/v1/responses",
|
|
method = "POST",
|
|
model = "m",
|
|
prompt = "hi",
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload,
|
|
messages,
|
|
self._Request(),
|
|
monitor_id = monitor_id,
|
|
)
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
assert self._payloads(lines, "response.output_text.delta") == []
|
|
assert self._payloads(lines, "response.reasoning_text.delta")[-1]["delta"] == "plan"
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply"] == ""
|
|
|
|
def test_reasoning_capable_gguf_stream_parses_think_tags_by_default(self, monkeypatch):
|
|
chunks = [
|
|
{"choices": [{"delta": {"content": "<thi"}}]},
|
|
{"choices": [{"delta": {"content": "nk>plan</think>answer"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
|
|
assert "".join(event["delta"] for event in text_deltas) == "answer"
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert [item["type"] for item in completed["response"]["output"]] == [
|
|
"reasoning",
|
|
"message",
|
|
]
|
|
assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
|
|
assert completed["response"]["output"][1]["content"][0]["text"] == "answer"
|
|
|
|
def test_non_reasoning_gguf_stream_keeps_literal_think_tags_visible(self, monkeypatch):
|
|
chunks = [
|
|
{"choices": [{"delta": {"content": "show <thi"}}]},
|
|
{"choices": [{"delta": {"content": "nk>x</think> tags"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks, supports_reasoning = False)
|
|
payload = ResponsesRequest(input = "hi", stream = True, reasoning = {"effort": "high"})
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert reasoning_deltas == []
|
|
assert "".join(event["delta"] for event in text_deltas) == "show <think>x</think> tags"
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert [item["type"] for item in completed["response"]["output"]] == ["message"]
|
|
assert completed["response"]["output"][0]["content"][0]["text"] == (
|
|
"show <think>x</think> tags"
|
|
)
|
|
|
|
def test_reasoning_only_stream_stays_out_of_visible_message_text(self, monkeypatch):
|
|
chunks = [
|
|
{"choices": [{"delta": {"content": "<think>plan</think>"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True, reasoning = {"effort": "high"})
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
|
|
assert text_deltas == []
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert [item["type"] for item in completed["response"]["output"]] == ["reasoning"]
|
|
assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
|
|
|
|
def test_unclosed_think_stream_stays_out_of_visible_message_text(self, monkeypatch):
|
|
chunks = [
|
|
{"choices": [{"delta": {"content": "<thi"}}]},
|
|
{"choices": [{"delta": {"content": "nk>plan"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True, reasoning = {"effort": "high"})
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
|
|
assert text_deltas == []
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert [item["type"] for item in completed["response"]["output"]] == ["reasoning"]
|
|
assert completed["response"]["output"][0]["content"][0]["text"] == "plan"
|
|
|
|
def test_structured_reasoning_content_streams_as_reasoning(self, monkeypatch):
|
|
chunks = [
|
|
{"choices": [{"delta": {"reasoning_content": "plan"}}]},
|
|
{"choices": [{"delta": {"content": "33"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert "".join(event["delta"] for event in reasoning_deltas) == "plan"
|
|
assert "".join(event["delta"] for event in text_deltas) == "33"
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert completed["response"]["output"][0]["type"] == "reasoning"
|
|
assert completed["response"]["output"][1]["type"] == "message"
|
|
|
|
def test_structured_reasoning_content_parts_stream_as_reasoning(self, monkeypatch):
|
|
chunks = [
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"reasoning_content": {
|
|
"content": [
|
|
{"type": "reasoning_text", "text": "plan"},
|
|
{"type": "reasoning_text", "text": " next"},
|
|
]
|
|
}
|
|
}
|
|
}
|
|
]
|
|
},
|
|
{"choices": [{"delta": {"content": "33"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
reasoning_deltas = self._payloads(lines, "response.reasoning_text.delta")
|
|
text_deltas = self._payloads(lines, "response.output_text.delta")
|
|
assert "".join(event["delta"] for event in reasoning_deltas) == "plan next"
|
|
assert "".join(event["delta"] for event in text_deltas) == "33"
|
|
assert "reasoning_text" not in "".join(event["delta"] for event in reasoning_deltas)
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert completed["response"]["output"][0]["content"][0]["text"] == "plan next"
|
|
assert completed["response"]["output"][1]["content"][0]["text"] == "33"
|
|
|
|
def test_tool_first_stream_closes_items_in_output_index_order(self, monkeypatch):
|
|
chunks = [
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "call_0",
|
|
"type": "function",
|
|
"function": {"name": "lookup", "arguments": "{}"},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
},
|
|
{"choices": [{"delta": {"content": "done"}}]},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
self._install_stream_mock(monkeypatch, chunks)
|
|
payload = ResponsesRequest(input = "hi", stream = True)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
done_events = self._payloads(lines, "response.output_item.done")
|
|
assert [event["output_index"] for event in done_events] == [0, 1]
|
|
assert [event["item"]["type"] for event in done_events] == ["function_call", "message"]
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert [item["type"] for item in completed["response"]["output"]] == [
|
|
"function_call",
|
|
"message",
|
|
]
|
|
|
|
def test_requests_usage_and_caps_parallel_tool_calls(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
captured = {}
|
|
|
|
def handler(request: httpx.Request) -> httpx.Response:
|
|
captured["body"] = json.loads(request.content.decode())
|
|
chunks = [
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "call_0",
|
|
"type": "function",
|
|
"function": {"name": "first", "arguments": "{}"},
|
|
},
|
|
{
|
|
"index": 1,
|
|
"id": "call_1",
|
|
"type": "function",
|
|
"function": {"name": "second", "arguments": "{}"},
|
|
},
|
|
]
|
|
}
|
|
}
|
|
]
|
|
},
|
|
{"choices": [], "usage": {"prompt_tokens": 2, "completion_tokens": 3}},
|
|
]
|
|
content = "".join(f"data: {json.dumps(chunk)}\n\n" for chunk in chunks)
|
|
content += "data: [DONE]\n\n"
|
|
return httpx.Response(
|
|
200,
|
|
content = content.encode(),
|
|
headers = {"content-type": "text/event-stream"},
|
|
)
|
|
|
|
transport = httpx.MockTransport(handler)
|
|
real_async_client = httpx.AsyncClient
|
|
|
|
def _client(*args, **kwargs):
|
|
return real_async_client(
|
|
transport = transport,
|
|
timeout = kwargs.get("timeout", 600),
|
|
)
|
|
|
|
monkeypatch.setattr(inf_mod.httpx, "AsyncClient", _client)
|
|
monkeypatch.setattr(
|
|
inf_mod,
|
|
"get_llama_cpp_backend",
|
|
lambda: SimpleNamespace(
|
|
is_loaded = True,
|
|
is_vision = False,
|
|
context_length = 4096,
|
|
base_url = "http://llama.test",
|
|
# Non-reasoning template: the real backend returns None here.
|
|
_request_reasoning_kwargs = (
|
|
lambda enable_thinking = None, reasoning_effort = None, preserve_thinking = None: None
|
|
),
|
|
),
|
|
)
|
|
|
|
payload = ResponsesRequest(
|
|
input = "hi",
|
|
stream = True,
|
|
parallel_tool_calls = False,
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"name": "first",
|
|
"parameters": {"type": "object"},
|
|
},
|
|
{
|
|
"type": "function",
|
|
"name": "second",
|
|
"parameters": {"type": "object"},
|
|
},
|
|
],
|
|
)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(payload, messages, self._Request())
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
assert captured["body"]["stream_options"] == {"include_usage": True}
|
|
joined = "".join(lines)
|
|
assert "call_0" in joined
|
|
assert "call_1" not in joined
|
|
completed = self._payloads(lines, "response.completed")[0]
|
|
assert completed["response"]["usage"] == {
|
|
"input_tokens": 2,
|
|
"output_tokens": 3,
|
|
"total_tokens": 5,
|
|
}
|
|
|
|
|
|
# =====================================================================
|
|
# Response model — ResponsesOutputFunctionCall / mixed output
|
|
# =====================================================================
|
|
|
|
|
|
class TestResponsesOutputFunctionCall:
|
|
def test_reasoning_output_item_serialises_full_reasoning_content(self):
|
|
item = ResponsesOutputReasoning(content = [{"type": "reasoning_text", "text": "plan"}])
|
|
d = item.model_dump()
|
|
assert d["type"] == "reasoning"
|
|
assert d["id"].startswith("rs_")
|
|
assert d["status"] == "completed"
|
|
assert d["summary"] == []
|
|
assert d["content"] == [{"type": "reasoning_text", "text": "plan"}]
|
|
|
|
def test_direct_construction(self):
|
|
fc = ResponsesOutputFunctionCall(
|
|
call_id = "call_1",
|
|
name = "get_weather",
|
|
arguments = '{"city":"Paris"}',
|
|
)
|
|
d = fc.model_dump()
|
|
assert d["type"] == "function_call"
|
|
assert d["call_id"] == "call_1"
|
|
assert d["status"] == "completed"
|
|
assert d["id"].startswith("fc_")
|
|
|
|
def test_response_with_tool_call_output(self):
|
|
resp = ResponsesResponse(
|
|
model = "test",
|
|
output = [
|
|
ResponsesOutputFunctionCall(
|
|
call_id = "call_1",
|
|
name = "get_weather",
|
|
arguments = "{}",
|
|
)
|
|
],
|
|
usage = ResponsesUsage(input_tokens = 1, output_tokens = 1, total_tokens = 2),
|
|
)
|
|
d = json.loads(resp.model_dump_json())
|
|
assert d["output"][0]["type"] == "function_call"
|
|
assert d["output"][0]["call_id"] == "call_1"
|
|
|
|
def test_response_with_mixed_output(self):
|
|
resp = ResponsesResponse(
|
|
model = "test",
|
|
output = [
|
|
ResponsesOutputMessage(
|
|
content = [ResponsesOutputTextContent(text = "Calling...")],
|
|
),
|
|
ResponsesOutputFunctionCall(
|
|
call_id = "call_1",
|
|
name = "get_weather",
|
|
arguments = '{"city":"Paris"}',
|
|
),
|
|
],
|
|
)
|
|
d = resp.model_dump()
|
|
assert d["output"][0]["type"] == "message"
|
|
assert d["output"][1]["type"] == "function_call"
|
|
|
|
|
|
# =====================================================================
|
|
# Regression: ChatMessage validator still accepts mapped tool messages
|
|
# =====================================================================
|
|
|
|
|
|
class TestCodexStyleRequestShapes:
|
|
"""Regression tests for the request shapes OpenAI Codex CLI sends."""
|
|
|
|
def test_assistant_replay_output_text_accepted(self):
|
|
"""Codex replays prior assistant turns with `output_text` content;
|
|
this used to 422 on every turn after the first."""
|
|
req = ResponsesRequest(
|
|
input = [
|
|
{"role": "user", "content": "Hi"},
|
|
{
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"content": [
|
|
{
|
|
"type": "output_text",
|
|
"text": "Hello!",
|
|
"annotations": [],
|
|
"logprobs": [],
|
|
}
|
|
],
|
|
},
|
|
{"role": "user", "content": "Continue"},
|
|
],
|
|
)
|
|
assert len(req.input) == 3
|
|
parts = req.input[1].content
|
|
assert isinstance(parts, list)
|
|
assert isinstance(parts[0], ResponsesOutputTextPart)
|
|
assert parts[0].text == "Hello!"
|
|
|
|
def test_reasoning_item_accepted_as_unknown(self):
|
|
"""`reasoning` items replayed from prior o-series turns must not
|
|
fail validation — Codex keeps them in multi-turn."""
|
|
req = ResponsesRequest(
|
|
input = [
|
|
{"role": "user", "content": "Hi"},
|
|
{
|
|
"type": "reasoning",
|
|
"id": "rs_1",
|
|
"summary": [],
|
|
"encrypted_content": "opaque",
|
|
},
|
|
{"role": "assistant", "content": "Hello!"},
|
|
],
|
|
)
|
|
assert len(req.input) == 3
|
|
assert isinstance(req.input[1], ResponsesUnknownInputItem)
|
|
|
|
def test_emitted_reasoning_item_replay_is_dropped_for_local_chat(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{"role": "user", "content": "Hi"},
|
|
{
|
|
"type": "reasoning",
|
|
"id": "rs_1",
|
|
"summary": [],
|
|
"content": [{"type": "reasoning_text", "text": "plan"}],
|
|
},
|
|
{"role": "assistant", "content": "33"},
|
|
{"role": "user", "content": "Continue"},
|
|
],
|
|
)
|
|
|
|
msgs = _normalise_responses_input(payload)
|
|
|
|
assert [m.role for m in msgs] == ["user", "assistant", "user"]
|
|
assert all("plan" not in (m.content or "") for m in msgs if isinstance(m.content, str))
|
|
|
|
def test_unknown_content_part_type_accepted(self):
|
|
"""Unknown content-part types (e.g. future input_audio) validate as
|
|
ResponsesUnknownContentPart so the request doesn't 422."""
|
|
req = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "input_text", "text": "See:"},
|
|
{"type": "input_audio", "audio": {"data": "..."}},
|
|
],
|
|
}
|
|
],
|
|
)
|
|
parts = req.input[0].content
|
|
assert isinstance(parts[1], ResponsesUnknownContentPart)
|
|
assert parts[1].type == "input_audio"
|
|
|
|
def test_codex_full_shape_roundtrip(self):
|
|
"""End-to-end: developer + user + assistant(output_text) +
|
|
function_call + function_call_output + reasoning in one request."""
|
|
payload = ResponsesRequest(
|
|
instructions = "Base instructions.",
|
|
input = [
|
|
{
|
|
"type": "message",
|
|
"role": "developer",
|
|
"content": [{"type": "input_text", "text": "Dev override."}],
|
|
},
|
|
{
|
|
"type": "message",
|
|
"role": "user",
|
|
"content": [{"type": "input_text", "text": "Weather?"}],
|
|
},
|
|
{
|
|
"type": "reasoning",
|
|
"id": "rs_1",
|
|
"summary": [],
|
|
},
|
|
{
|
|
"type": "function_call",
|
|
"call_id": "call_1",
|
|
"name": "get_weather",
|
|
"arguments": "{}",
|
|
},
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": '{"temp":20}',
|
|
},
|
|
{
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"content": [
|
|
{
|
|
"type": "output_text",
|
|
"text": "It's 20°C.",
|
|
"annotations": [],
|
|
"logprobs": [],
|
|
}
|
|
],
|
|
},
|
|
{"role": "user", "content": "And tomorrow?"},
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
# One leading merged system; no mid-conversation system.
|
|
assert msgs[0].role == "system"
|
|
assert sum(1 for m in msgs if m.role == "system") == 1
|
|
assert "Base instructions." in msgs[0].content
|
|
assert "Dev override." in msgs[0].content
|
|
|
|
roles = [m.role for m in msgs[1:]]
|
|
# Reasoning dropped. Order: user, assistant(tool_calls), tool,
|
|
# assistant(text), user.
|
|
assert roles == ["user", "assistant", "tool", "assistant", "user"]
|
|
assert msgs[2].tool_calls is not None
|
|
assert msgs[3].role == "tool"
|
|
assert msgs[3].tool_call_id == "call_1"
|
|
assert msgs[4].content == "It's 20°C."
|
|
|
|
def test_single_output_text_part_flattens_to_string(self):
|
|
"""ChatMessage assistant role prefers plain string content — we
|
|
don't forward a single-part array that would force legacy chat
|
|
templates into multimodal handling."""
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"role": "assistant",
|
|
"content": [{"type": "output_text", "text": "ok", "annotations": []}],
|
|
},
|
|
{"role": "user", "content": "next"},
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
assert msgs[0].role == "assistant"
|
|
assert msgs[0].content == "ok"
|
|
|
|
|
|
class TestTranslatedMessagesValidate:
|
|
"""Messages from _normalise_responses_input satisfy ChatMessage's
|
|
role-shape validator so the downstream /v1/chat/completions
|
|
pass-through doesn't reject them."""
|
|
|
|
def test_round_trip_multi_turn(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{"role": "user", "content": "Weather in Paris?"},
|
|
{
|
|
"type": "function_call",
|
|
"call_id": "call_1",
|
|
"name": "get_weather",
|
|
"arguments": '{"city": "Paris"}',
|
|
},
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_1",
|
|
"output": '{"temp": 20}',
|
|
},
|
|
{"role": "user", "content": "Thanks!"},
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
for m in msgs:
|
|
# Building a fresh ChatMessage from the dump round-trips the
|
|
# role-shape validator — the passthrough's key invariant.
|
|
ChatMessage(**m.model_dump(exclude_none = True))
|
|
|
|
def test_empty_tool_output_round_trips_through_chat_message_validator(self):
|
|
payload = ResponsesRequest(
|
|
input = [
|
|
{
|
|
"type": "function_call_output",
|
|
"call_id": "call_empty",
|
|
"output": "",
|
|
},
|
|
],
|
|
)
|
|
msgs = _normalise_responses_input(payload)
|
|
for m in msgs:
|
|
ChatMessage(**m.model_dump(exclude_none = True))
|
|
|
|
|
|
# reasoning_prefilled: enable_thinking templates prefill an unclosed <think>, so
|
|
# generation begins inside the block; the extractor must start in reasoning.
|
|
class TestReasoningPrefilledExtractor:
|
|
def test_prefilled_single_feed_splits_lone_close(self):
|
|
# T1: reasoning...</think>answer with a prefilled (unseen) open tag.
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"plan</think>answer",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = True,
|
|
)
|
|
assert reasoning == "plan"
|
|
assert visible == "answer"
|
|
|
|
def test_prefilled_never_closed_is_all_reasoning(self):
|
|
# T2: truncated mid-thought (no </think>) -> all reasoning (GGUF parity).
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"still thinking with no close",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = True,
|
|
)
|
|
assert reasoning == "still thinking with no close"
|
|
assert visible == ""
|
|
|
|
def test_prefilled_close_split_across_feeds(self):
|
|
# T3: </think> straddles two feed() calls; holdback resolves it.
|
|
ex = _ResponsesReasoningExtractor(parse_think_markers = True, reasoning_prefilled = True)
|
|
r1, v1 = ex.feed("plan</th")
|
|
r2, v2 = ex.feed("ink>ans")
|
|
fr, fv = ex.finish()
|
|
assert (r1 + r2 + fr) == "plan"
|
|
assert (v1 + v2 + fv) == "ans"
|
|
|
|
def test_prefilled_close_split_one_char_per_feed(self):
|
|
# T4: every char in its own feed still splits correctly.
|
|
ex = _ResponsesReasoningExtractor(parse_think_markers = True, reasoning_prefilled = True)
|
|
reasoning, visible = "", ""
|
|
for ch in "plan</think>x":
|
|
r, v = ex.feed(ch)
|
|
reasoning += r
|
|
visible += v
|
|
fr, fv = ex.finish()
|
|
assert (reasoning + fr) == "plan"
|
|
assert (visible + fv) == "x"
|
|
|
|
def test_prefilled_empty_generation(self):
|
|
# T5: nothing generated.
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = True,
|
|
)
|
|
assert reasoning == ""
|
|
assert visible == ""
|
|
|
|
def test_prefilled_whitespace_after_close_is_visible(self):
|
|
# T6: Qwen commonly emits </think>\n\n before the answer.
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"plan</think>\n\nanswer",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = True,
|
|
)
|
|
assert reasoning == "plan"
|
|
assert visible == "\n\nanswer"
|
|
|
|
def test_prefilled_stray_open_tag_is_suppressed(self):
|
|
# T7: a re-emitted literal <think> inside prefilled reasoning is dropped,
|
|
# not leaked into the drawer (covers enable_thinking_effort full-tag output).
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"a<think>b</think>c",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = True,
|
|
)
|
|
assert reasoning == "ab"
|
|
assert visible == "c"
|
|
assert "<think>" not in reasoning
|
|
|
|
def test_prefilled_close_at_start_empty_reasoning(self):
|
|
# T8: model closed immediately (empty reasoning) then answered.
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"</think>hi",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = True,
|
|
)
|
|
assert reasoning == ""
|
|
assert visible == "hi"
|
|
|
|
def test_not_prefilled_lone_close_preserves_current_behavior(self):
|
|
# T9: without prefilled, a lone close tag keeps the pre-fix behavior (parity guard).
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"reasoning</think>ans",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = False,
|
|
)
|
|
assert reasoning == ""
|
|
assert visible == "reasoningans"
|
|
|
|
def test_not_prefilled_full_pair_still_splits(self):
|
|
# T10: normal explicit <think>..</think> (GGUF / Harmony) unchanged.
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"<think>r</think>v",
|
|
parse_think_markers = True,
|
|
reasoning_prefilled = False,
|
|
)
|
|
assert reasoning == "r"
|
|
assert visible == "v"
|
|
|
|
def test_prefilled_ignored_when_markers_not_parsed(self):
|
|
# T11: a non-reasoning model passes text through even with reasoning_prefilled False.
|
|
reasoning, visible = _extract_responses_reasoning(
|
|
"just an answer",
|
|
parse_think_markers = False,
|
|
reasoning_prefilled = False,
|
|
)
|
|
assert reasoning == ""
|
|
assert visible == "just an answer"
|
|
|
|
|
|
# =====================================================================
|
|
# Streaming passthrough healing — text-form calls promoted in order
|
|
# =====================================================================
|
|
|
|
|
|
class TestResponsesStreamHealing:
|
|
"""Route-level healing on the /v1/responses stream: text-form tool calls
|
|
are promoted through the same per-call item state machinery as structured
|
|
deltas, and healer events keep their order (text around a healed call must
|
|
not move relative to the function_call item)."""
|
|
|
|
_XML = '<tool_call>{"name":"lookup","arguments":{"q":"x"}}</tool_call>'
|
|
_TOOL = {"type": "function", "name": "lookup", "parameters": {"type": "object"}}
|
|
|
|
@staticmethod
|
|
def _ordered_events(lines):
|
|
events = []
|
|
for line in lines:
|
|
if not line.startswith("event: "):
|
|
continue
|
|
name, _, rest = line.partition("\n")
|
|
payload = json.loads(rest.split("data: ", 1)[1].strip())
|
|
events.append((name[len("event: ") :], payload))
|
|
return events
|
|
|
|
def _run_stream(self, monkeypatch, content, **payload_kwargs):
|
|
TestResponsesStreamAdapter._install_stream_mock(
|
|
monkeypatch, [{"choices": [{"delta": {"content": content}}]}]
|
|
)
|
|
payload = ResponsesRequest(input = "hi", stream = True, tools = [self._TOOL], **payload_kwargs)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload, messages, TestResponsesStreamAdapter._Request()
|
|
)
|
|
return await TestResponsesStreamAdapter._collect(response)
|
|
|
|
return self._ordered_events(asyncio.run(run()))
|
|
|
|
def test_text_around_healed_call_keeps_order(self, monkeypatch):
|
|
events = self._run_stream(monkeypatch, f"before {self._XML} after.")
|
|
pos_before = pos_item = pos_after = None
|
|
for i, (name, payload) in enumerate(events):
|
|
if name == "response.output_text.delta":
|
|
if "before" in payload["delta"] and pos_before is None:
|
|
pos_before = i
|
|
if "after" in payload["delta"]:
|
|
pos_after = i
|
|
if (
|
|
name == "response.output_item.added"
|
|
and payload["item"]["type"] == "function_call"
|
|
and pos_item is None
|
|
):
|
|
pos_item = i
|
|
assert payload["item"]["name"] == "lookup"
|
|
assert pos_before is not None and pos_item is not None and pos_after is not None
|
|
assert pos_before < pos_item < pos_after
|
|
|
|
def test_call_before_trailing_text_claims_lower_output_index(self, monkeypatch):
|
|
events = self._run_stream(monkeypatch, f"{self._XML} done.")
|
|
item_added = [
|
|
(name, payload) for name, payload in events if name == "response.output_item.added"
|
|
]
|
|
# The call came first in the model output, so its item is added first
|
|
# and claims the lower output_index; the trailing text's message item
|
|
# follows.
|
|
assert [payload["item"]["type"] for _, payload in item_added] == [
|
|
"function_call",
|
|
"message",
|
|
]
|
|
call_idx = item_added[0][1]["output_index"]
|
|
msg_idx = item_added[1][1]["output_index"]
|
|
assert call_idx < msg_idx
|
|
text = "".join(
|
|
payload["delta"] for name, payload in events if name == "response.output_text.delta"
|
|
)
|
|
assert "done." in text
|
|
assert "<tool_call>" not in text
|
|
|
|
def test_tool_choice_none_streams_raw_text(self, monkeypatch):
|
|
events = self._run_stream(monkeypatch, self._XML, tool_choice = "none")
|
|
assert not any(
|
|
payload["item"]["type"] == "function_call"
|
|
for name, payload in events
|
|
if name == "response.output_item.added"
|
|
)
|
|
text = "".join(
|
|
payload["delta"] for name, payload in events if name == "response.output_text.delta"
|
|
)
|
|
assert text == self._XML
|
|
|
|
def test_healed_call_splits_message_items(self, monkeypatch):
|
|
# Text on both sides of a healed call becomes TWO message items: the
|
|
# healed function_call closes the first, trailing text opens a fresh
|
|
# one with a later output index (native Responses stream shape).
|
|
events = self._run_stream(monkeypatch, f"before {self._XML} after.")
|
|
added = [
|
|
(payload["output_index"], payload["item"]["type"], payload["item"].get("id"))
|
|
for name, payload in events
|
|
if name == "response.output_item.added"
|
|
]
|
|
assert [item_type for _, item_type, _ in added] == [
|
|
"message",
|
|
"function_call",
|
|
"message",
|
|
]
|
|
assert [idx for idx, _, _ in added] == sorted(idx for idx, _, _ in added)
|
|
assert added[0][2] != added[2][2] # distinct message item ids
|
|
# Text deltas attribute to their OWN message item.
|
|
deltas = [
|
|
(payload["item_id"], payload["delta"])
|
|
for name, payload in events
|
|
if name == "response.output_text.delta"
|
|
]
|
|
assert [d for i, d in deltas if i == added[0][2]] == ["before "]
|
|
assert [d for i, d in deltas if i == added[2][2]] == [" after."]
|
|
# The completed snapshot lists all three items with per-item text.
|
|
completed = [payload for name, payload in events if name == "response.completed"]
|
|
output = completed[0]["response"]["output"]
|
|
assert [item["type"] for item in output] == ["message", "function_call", "message"]
|
|
assert output[0]["content"][0]["text"] == "before "
|
|
assert output[2]["content"][0]["text"] == " after."
|
|
|
|
def test_parallel_cap_drops_native_after_healed(self, monkeypatch):
|
|
# parallel_tool_calls=false: a healed call consumed the single allowed
|
|
# slot; a later native structured call (index 0, so it survives
|
|
# _drop_parallel_tool_call_deltas) must not open a second
|
|
# function_call item.
|
|
TestResponsesStreamAdapter._install_stream_mock(
|
|
monkeypatch,
|
|
[
|
|
{"choices": [{"delta": {"content": self._XML}}]},
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "call_up",
|
|
"function": {"name": "lookup", "arguments": "{}"},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
},
|
|
],
|
|
)
|
|
payload = ResponsesRequest(
|
|
input = "hi",
|
|
stream = True,
|
|
tools = [self._TOOL],
|
|
parallel_tool_calls = False,
|
|
)
|
|
messages = [ChatMessage(role = "user", content = "hi")]
|
|
|
|
async def run():
|
|
response = await _responses_stream(
|
|
payload, messages, TestResponsesStreamAdapter._Request()
|
|
)
|
|
return await TestResponsesStreamAdapter._collect(response)
|
|
|
|
events = self._ordered_events(asyncio.run(run()))
|
|
calls = [
|
|
payload
|
|
for name, payload in events
|
|
if name == "response.output_item.added" and payload["item"]["type"] == "function_call"
|
|
]
|
|
assert len(calls) == 1
|
|
assert calls[0]["item"]["name"] == "lookup"
|