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
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
1729 lines
62 KiB
Python
1729 lines
62 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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"""Tests for Anthropic Messages API schemas and translation layer (no server/GPU)."""
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import sys
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import os
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import json
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import threading
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import httpx
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import pytest
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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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from models.inference import (
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AnthropicMessagesRequest,
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AnthropicMessagesResponse,
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AnthropicMessage,
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AnthropicTextBlock,
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AnthropicToolUseBlock,
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AnthropicToolResultBlock,
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AnthropicTool,
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AnthropicUsage,
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AnthropicResponseTextBlock,
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AnthropicResponseToolUseBlock,
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)
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from core.inference.anthropic_compat import (
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anthropic_messages_to_openai,
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anthropic_tools_to_openai,
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build_anthropic_sse_event,
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AnthropicStreamEmitter,
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AnthropicPassthroughEmitter,
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)
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from core.inference.api_monitor import ApiMonitor
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from routes.inference import (
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_build_tool_action_nudge,
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_normalize_anthropic_openai_images,
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_select_anthropic_server_tools,
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_anthropic_requested_studio_tools,
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_anthropic_passthrough_stream,
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_anthropic_tool_non_streaming,
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_monitor_anthropic_sse_line,
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anthropic_messages,
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)
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from state.tool_policy import reset_tool_policy, set_tool_policy
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from fastapi import HTTPException
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import asyncio
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import base64 as _b64
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from io import BytesIO as _BytesIO
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from types import SimpleNamespace
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def test_streamed_anthropic_tool_use_records_api_monitor_reply(monkeypatch):
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import routes.inference as inf_mod
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monitor = ApiMonitor(max_entries = 3)
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monkeypatch.setattr(inf_mod, "api_monitor", monitor)
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monitor_id = monitor.start(
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endpoint = "/v1/messages",
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method = "POST",
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model = "m",
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prompt = "hi",
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)
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for payload in (
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{
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"type": "content_block_start",
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"index": 0,
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"content_block": {
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"type": "tool_use",
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"id": "toolu_1",
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"name": "lookup",
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"input": {},
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},
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},
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{
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"type": "content_block_delta",
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"index": 0,
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"delta": {
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"type": "input_json_delta",
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"partial_json": '{"query":"weather"}',
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},
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},
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{"type": "content_block_stop", "index": 0},
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):
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_monitor_anthropic_sse_line(monitor_id, f"data: {json.dumps(payload)}")
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entry = monitor.get(monitor_id)
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assert entry is not None
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assert entry["reply"] == 'Tool call: lookup\nInput: {"query":"weather"}'
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# =====================================================================
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# Tool nudge tests
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# =====================================================================
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class TestToolActionNudge:
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def test_balanced_nudge_uses_expanded_web_and_code_tips(self):
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nudge = _build_tool_action_nudge(
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tools = [
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{"type": "function", "function": {"name": "web_search"}},
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{"type": "function", "function": {"name": "python"}},
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],
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model_name = "Llama-3.1-70B-Instruct",
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)
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assert nudge.startswith("The current date is ")
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assert "Tools are available when they materially improve" in nudge
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assert "prefer using tools rather than answering from memory" not in nudge
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assert "fetch its full content by calling web_search with the url parameter" in nudge
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assert "Use code execution for math" in nudge
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assert "render_html" not in nudge
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def test_balanced_nudge_preserves_compact_web_tip_and_canvas_gate(self):
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nudge = _build_tool_action_nudge(
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tools = [
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{"type": "function", "function": {"name": "web_search"}},
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{"type": "function", "function": {"name": "render_html"}},
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],
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model_name = "Llama-3.1-8B-Instruct",
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)
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assert "When using web_search, do not repeat the same search query." in nudge
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assert "fetch its full content" not in nudge
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assert "call render_html once" in nudge
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def test_balanced_nudge_empty_without_known_tool_categories(self):
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assert _build_tool_action_nudge(tools = [], model_name = "Llama-3.1-8B-Instruct") == ""
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# =====================================================================
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# Pydantic model tests
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# =====================================================================
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class TestAnthropicModels:
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def test_minimal_request(self):
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req = AnthropicMessagesRequest(
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messages = [{"role": "user", "content": "Hi"}],
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)
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assert req.max_tokens is None
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assert req.model == "default"
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assert req.stream is False
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def test_max_tokens_optional(self):
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req = AnthropicMessagesRequest(
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max_tokens = 100,
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messages = [{"role": "user", "content": "Hi"}],
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)
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assert req.max_tokens == 100
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def test_system_as_string(self):
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req = AnthropicMessagesRequest(
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max_tokens = 50,
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messages = [{"role": "user", "content": "Hi"}],
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system = "You are helpful.",
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)
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assert req.system == "You are helpful."
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def test_system_role_message_normalized_to_system_field(self):
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req = AnthropicMessagesRequest(
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max_tokens = 50,
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messages = [
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{"role": "system", "content": "You are helpful."},
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{"role": "user", "content": "Hi"},
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],
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)
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assert req.system == "You are helpful."
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assert len(req.messages) == 1
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assert req.messages[0].role == "user"
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def test_system_role_message_merges_with_existing_system_field(self):
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req = AnthropicMessagesRequest(
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max_tokens = 50,
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system = "Base instructions.",
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messages = [
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{"role": "user", "content": "Hi"},
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{"role": "system", "content": "Additional instructions."},
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{"role": "assistant", "content": "Hello."},
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],
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)
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assert req.system == "Base instructions.\n\nAdditional instructions."
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assert [msg.role for msg in req.messages] == ["user", "assistant"]
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def test_system_role_message_with_null_content_ignored(self):
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req = AnthropicMessagesRequest(
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max_tokens = 50,
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system = "Base.",
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messages = [
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{"role": "system", "content": None},
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{
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"role": "system",
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"content": [
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None,
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{"type": "text", "text": "Use short answers."},
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],
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},
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{"role": "user", "content": "Hi"},
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],
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)
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assert req.system == "Base.\n\nUse short answers."
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assert "None" not in str(req.system)
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assert [msg.role for msg in req.messages] == ["user"]
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def test_tools_field_parses(self):
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req = AnthropicMessagesRequest(
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max_tokens = 100,
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messages = [{"role": "user", "content": "Hi"}],
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tools = [{"name": "web_search", "input_schema": {"type": "object"}}],
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)
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assert len(req.tools) == 1
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assert req.tools[0].name == "web_search"
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def test_server_tool_field_parses(self):
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req = AnthropicMessagesRequest(
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max_tokens = 100,
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messages = [{"role": "user", "content": "Hi"}],
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tools = [{"type": "web_fetch_20250910", "name": "web_fetch"}],
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)
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assert len(req.tools) == 1
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assert req.tools[0].type == "web_fetch_20250910"
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assert req.tools[0].name == "web_fetch"
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assert req.tools[0].input_schema is None
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def test_extra_fields_accepted(self):
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req = AnthropicMessagesRequest(
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max_tokens = 100,
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messages = [{"role": "user", "content": "Hi"}],
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some_future_field = "hello",
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)
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assert req.max_tokens == 100
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def test_stream_defaults_false(self):
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req = AnthropicMessagesRequest(
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max_tokens = 100,
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messages = [{"role": "user", "content": "Hi"}],
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)
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assert req.stream is False
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def test_enable_tools_shorthand(self):
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req = AnthropicMessagesRequest(
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messages = [{"role": "user", "content": "Hi"}],
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enable_tools = True,
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enabled_tools = ["web_search", "python"],
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session_id = "my-session",
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)
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assert req.enable_tools is True
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assert req.enabled_tools == ["web_search", "python"]
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assert req.session_id == "my-session"
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def test_extension_fields_default_none(self):
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req = AnthropicMessagesRequest(
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messages = [{"role": "user", "content": "Hi"}],
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)
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assert req.enable_tools is None
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assert req.enabled_tools is None
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assert req.session_id is None
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def test_response_model_defaults(self):
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resp = AnthropicMessagesResponse()
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assert resp.type == "message"
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assert resp.role == "assistant"
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assert resp.id.startswith("msg_")
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assert resp.content == []
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assert resp.usage.input_tokens == 0
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# =====================================================================
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# Message translation tests
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# =====================================================================
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class TestAnthropicMessagesToOpenAI:
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def test_simple_user_message(self):
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msgs = [{"role": "user", "content": "Hello"}]
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result = anthropic_messages_to_openai(msgs)
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assert result == [{"role": "user", "content": "Hello"}]
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def test_system_string_prepended(self):
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msgs = [{"role": "user", "content": "Hello"}]
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result = anthropic_messages_to_openai(msgs, system = "Be brief.")
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assert result[0] == {"role": "system", "content": "Be brief."}
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assert result[1] == {"role": "user", "content": "Hello"}
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def test_top_level_system_request_translates_unchanged(self):
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req = AnthropicMessagesRequest(
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messages = [{"role": "user", "content": "Hello"}],
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system = "Be brief.",
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)
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result = anthropic_messages_to_openai(
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[m.model_dump() for m in req.messages],
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req.system,
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)
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assert result == [
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{"role": "system", "content": "Be brief."},
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{"role": "user", "content": "Hello"},
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]
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def test_system_as_block_list(self):
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system = [
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{"type": "text", "text": "Be brief."},
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{"type": "text", "text": "Be accurate."},
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]
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msgs = [{"role": "user", "content": "Hello"}]
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result = anthropic_messages_to_openai(msgs, system = system)
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assert result[0]["role"] == "system"
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assert "Be brief." in result[0]["content"]
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assert "Be accurate." in result[0]["content"]
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def test_multi_turn_conversation(self):
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msgs = [
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{"role": "user", "content": "Hi"},
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{"role": "assistant", "content": "Hello!"},
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{"role": "user", "content": "How are you?"},
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]
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result = anthropic_messages_to_openai(msgs)
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assert len(result) == 3
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assert result[0]["role"] == "user"
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assert result[1]["role"] == "assistant"
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assert result[2]["role"] == "user"
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def test_assistant_tool_use_maps_to_tool_calls(self):
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msgs = [
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": "Let me search."},
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{
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"type": "tool_use",
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"id": "tu_1",
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"name": "web_search",
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"input": {"query": "test"},
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},
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],
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}
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]
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result = anthropic_messages_to_openai(msgs)
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assert len(result) == 1
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m = result[0]
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assert m["role"] == "assistant"
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assert m["content"] == "Let me search."
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assert len(m["tool_calls"]) == 1
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tc = m["tool_calls"][0]
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assert tc["id"] == "tu_1"
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assert tc["function"]["name"] == "web_search"
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assert json.loads(tc["function"]["arguments"]) == {"query": "test"}
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def test_tool_result_maps_to_tool_role(self):
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msgs = [
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{
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": "tu_1",
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"content": "Result text",
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},
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],
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}
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]
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result = anthropic_messages_to_openai(msgs)
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assert len(result) == 1
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assert result[0]["role"] == "tool"
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assert result[0]["tool_call_id"] == "tu_1"
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assert result[0]["content"] == "Result text"
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def test_mixed_text_and_tool_use_blocks(self):
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msgs = [
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{
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"role": "assistant",
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"content": [
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{"type": "text", "text": "Thinking..."},
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{
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"type": "tool_use",
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"id": "tu_1",
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"name": "python",
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"input": {"code": "1+1"},
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},
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{
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"type": "tool_use",
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"id": "tu_2",
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"name": "terminal",
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"input": {"command": "ls"},
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},
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],
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}
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]
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result = anthropic_messages_to_openai(msgs)
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assert len(result) == 1
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m = result[0]
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assert m["content"] == "Thinking..."
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assert len(m["tool_calls"]) == 2
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def test_tool_result_with_list_content(self):
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msgs = [
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{
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": "tu_1",
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"content": [
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{"type": "text", "text": "Line 1"},
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{"type": "text", "text": "Line 2"},
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],
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},
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],
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}
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]
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result = anthropic_messages_to_openai(msgs)
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assert result[0]["content"] == "Line 1 Line 2"
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def test_image_base64_block_becomes_multimodal_part(self):
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msgs = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "What is this?"},
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": "image/jpeg",
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"data": "AAAA",
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},
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},
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],
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}
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]
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result = anthropic_messages_to_openai(msgs)
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assert len(result) == 1
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assert result[0]["role"] == "user"
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parts = result[0]["content"]
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assert isinstance(parts, list)
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assert parts[0] == {"type": "text", "text": "What is this?"}
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assert parts[1]["type"] == "image_url"
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assert parts[1]["image_url"]["url"] == "data:image/jpeg;base64,AAAA"
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|
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def test_image_url_block_forwarded_as_url(self):
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msgs = [
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{
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"role": "user",
|
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"content": [
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{"type": "text", "text": "Describe it"},
|
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{
|
|
"type": "image",
|
|
"source": {"type": "url", "url": "https://x/y.png"},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
result = anthropic_messages_to_openai(msgs)
|
|
parts = result[0]["content"]
|
|
assert parts[1] == {"type": "image_url", "image_url": {"url": "https://x/y.png"}}
|
|
|
|
def test_image_only_user_message_emits_no_text_part(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/png",
|
|
"data": "ZZ",
|
|
},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
result = anthropic_messages_to_openai(msgs)
|
|
parts = result[0]["content"]
|
|
assert len(parts) == 1
|
|
assert parts[0]["type"] == "image_url"
|
|
|
|
def test_image_default_media_type_when_missing(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image",
|
|
"source": {"type": "base64", "data": "BB"},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
result = anthropic_messages_to_openai(msgs)
|
|
parts = result[0]["content"]
|
|
assert parts[0]["image_url"]["url"].startswith("data:image/jpeg;base64,")
|
|
|
|
def test_image_text_order_preserved(self):
|
|
# [text1, image1, text2, image2] must not collapse to
|
|
# [text1+text2, image1, image2].
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "before"},
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/png",
|
|
"data": "AA",
|
|
},
|
|
},
|
|
{"type": "text", "text": "after"},
|
|
{
|
|
"type": "image",
|
|
"source": {"type": "url", "url": "https://x/y.png"},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
result = anthropic_messages_to_openai(msgs)
|
|
parts = result[0]["content"]
|
|
assert [p["type"] for p in parts] == ["text", "image_url", "text", "image_url"]
|
|
assert parts[0]["text"] == "before"
|
|
assert parts[2]["text"] == "after"
|
|
assert parts[1]["image_url"]["url"] == "data:image/png;base64,AA"
|
|
assert parts[3]["image_url"]["url"] == "https://x/y.png"
|
|
|
|
def test_malformed_image_block_is_skipped(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "Hi"},
|
|
{"type": "image", "source": {"type": "base64"}},
|
|
{"type": "image", "source": {"type": "url"}},
|
|
],
|
|
}
|
|
]
|
|
result = anthropic_messages_to_openai(msgs)
|
|
# No image parts emitted; message falls back to plain text.
|
|
assert result[0] == {"role": "user", "content": "Hi"}
|
|
|
|
|
|
# =====================================================================
|
|
# Tool translation tests
|
|
# =====================================================================
|
|
|
|
|
|
class TestAnthropicToolsToOpenAI:
|
|
def test_single_tool(self):
|
|
tools = [
|
|
{
|
|
"name": "web_search",
|
|
"description": "Search",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"query": {"type": "string"}},
|
|
},
|
|
}
|
|
]
|
|
result = anthropic_tools_to_openai(tools)
|
|
assert len(result) == 1
|
|
assert result[0]["type"] == "function"
|
|
assert result[0]["function"]["name"] == "web_search"
|
|
assert result[0]["function"]["parameters"]["type"] == "object"
|
|
|
|
def test_multiple_tools(self):
|
|
tools = [
|
|
{"name": "a", "description": "Tool A", "input_schema": {}},
|
|
{"name": "b", "description": "Tool B", "input_schema": {}},
|
|
]
|
|
result = anthropic_tools_to_openai(tools)
|
|
assert len(result) == 2
|
|
assert result[0]["function"]["name"] == "a"
|
|
assert result[1]["function"]["name"] == "b"
|
|
|
|
def test_empty_list(self):
|
|
assert anthropic_tools_to_openai([]) == []
|
|
|
|
def test_server_tools_are_not_converted_to_openai_functions(self):
|
|
tools = [
|
|
{"type": "web_fetch_20250910", "name": "web_fetch"},
|
|
{"type": "web_search_20250305", "name": "web_search"},
|
|
]
|
|
assert anthropic_tools_to_openai(tools) == []
|
|
|
|
def test_server_tool_selection_merges_enabled_tools_extension(self):
|
|
all_tools = [
|
|
{"type": "function", "function": {"name": "web_search"}},
|
|
{"type": "function", "function": {"name": "python"}},
|
|
{"type": "function", "function": {"name": "terminal"}},
|
|
]
|
|
|
|
result = _select_anthropic_server_tools(
|
|
all_tools,
|
|
requested_studio_tools = {"web_search"},
|
|
enabled_tools = ["python"],
|
|
)
|
|
|
|
assert [tool["function"]["name"] for tool in result] == ["web_search", "python"]
|
|
|
|
def test_pydantic_model_input(self):
|
|
tool = AnthropicTool(name = "test", description = "desc", input_schema = {"type": "object"})
|
|
result = anthropic_tools_to_openai([tool])
|
|
assert result[0]["function"]["name"] == "test"
|
|
|
|
|
|
# =====================================================================
|
|
# SSE event helper tests
|
|
# =====================================================================
|
|
|
|
|
|
class TestBuildAnthropicSSEEvent:
|
|
def test_basic_event(self):
|
|
result = build_anthropic_sse_event("message_start", {"type": "message_start"})
|
|
assert result.startswith("event: message_start\n")
|
|
assert "data: " in result
|
|
assert result.endswith("\n\n")
|
|
|
|
def test_data_is_valid_json(self):
|
|
result = build_anthropic_sse_event("test", {"key": "value"})
|
|
data_line = result.split("\n")[1]
|
|
payload = json.loads(data_line.removeprefix("data: "))
|
|
assert payload == {"key": "value"}
|
|
|
|
|
|
# =====================================================================
|
|
# Stream emitter tests
|
|
# =====================================================================
|
|
|
|
|
|
class TestAnthropicStreamEmitter:
|
|
def test_start_emits_message_start_and_content_block_start(self):
|
|
e = AnthropicStreamEmitter()
|
|
events = e.start("msg_123", "test-model")
|
|
assert len(events) == 2
|
|
assert "message_start" in events[0]
|
|
assert "content_block_start" in events[1]
|
|
assert '"type": "text"' in events[1]
|
|
|
|
def test_content_delta_emits_text_delta(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
events = e.feed({"type": "content", "text": "Hello"})
|
|
assert len(events) == 1
|
|
parsed = json.loads(events[0].split("data: ")[1])
|
|
assert parsed["delta"]["type"] == "text_delta"
|
|
assert parsed["delta"]["text"] == "Hello"
|
|
|
|
def test_cumulative_content_diffs_correctly(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed({"type": "content", "text": "Hel"})
|
|
events = e.feed({"type": "content", "text": "Hello"})
|
|
parsed = json.loads(events[0].split("data: ")[1])
|
|
assert parsed["delta"]["text"] == "lo"
|
|
|
|
def test_empty_content_diff_no_event(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed({"type": "content", "text": "Hi"})
|
|
events = e.feed({"type": "content", "text": "Hi"})
|
|
assert events == []
|
|
|
|
def test_tool_start_closes_text_opens_tool_block(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed({"type": "content", "text": "Thinking"})
|
|
events = e.feed(
|
|
{
|
|
"type": "tool_start",
|
|
"tool_name": "web_search",
|
|
"tool_call_id": "tc_1",
|
|
"arguments": {"query": "test"},
|
|
}
|
|
)
|
|
# content_block_stop + content_block_start(tool_use) + content_block_delta(input_json)
|
|
assert len(events) == 3
|
|
assert "content_block_stop" in events[0]
|
|
assert "tool_use" in events[1]
|
|
assert "input_json_delta" in events[2]
|
|
|
|
def test_duplicate_tool_start_merges_into_open_tool_block(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
first_events = e.feed(
|
|
{
|
|
"type": "tool_start",
|
|
"tool_name": "render_html",
|
|
"tool_call_id": "call_0",
|
|
"arguments": {},
|
|
}
|
|
)
|
|
second_events = e.feed(
|
|
{
|
|
"type": "tool_start",
|
|
"tool_name": "render_html",
|
|
"tool_call_id": "call_0",
|
|
"arguments": {"code": "<!doctype html><html></html>"},
|
|
}
|
|
)
|
|
|
|
first_payloads = [json.loads(event.split("data: ")[1]) for event in first_events]
|
|
second_payloads = [json.loads(event.split("data: ")[1]) for event in second_events]
|
|
|
|
tool_starts = [
|
|
payload
|
|
for payload in first_payloads + second_payloads
|
|
if payload["type"] == "content_block_start"
|
|
and payload["content_block"]["type"] == "tool_use"
|
|
]
|
|
assert len(tool_starts) == 1
|
|
assert tool_starts[0]["content_block"]["id"].startswith("toolu_")
|
|
assert second_payloads == [
|
|
{
|
|
"type": "content_block_delta",
|
|
"index": tool_starts[0]["index"],
|
|
"delta": {
|
|
"type": "input_json_delta",
|
|
"partial_json": json.dumps({"code": "<!doctype html><html></html>"}),
|
|
},
|
|
}
|
|
]
|
|
|
|
def test_tool_end_closes_tool_opens_new_text_block(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
start_events = e.feed(
|
|
{
|
|
"type": "tool_start",
|
|
"tool_name": "t",
|
|
"tool_call_id": "tc_1",
|
|
"arguments": {},
|
|
}
|
|
)
|
|
start_payload = next(
|
|
json.loads(event.split("data: ")[1])
|
|
for event in start_events
|
|
if "content_block_start" in event
|
|
)
|
|
tool_use_id = start_payload["content_block"]["id"]
|
|
assert tool_use_id.startswith("toolu_")
|
|
events = e.feed(
|
|
{
|
|
"type": "tool_end",
|
|
"tool_name": "t",
|
|
"tool_call_id": "tc_1",
|
|
"result": "done",
|
|
}
|
|
)
|
|
# content_block_stop (tool) + tool_result + content_block_start (new text)
|
|
assert len(events) == 3
|
|
assert "content_block_stop" in events[0]
|
|
assert "tool_result" in events[1]
|
|
parsed = json.loads(events[1].split("data: ")[1])
|
|
assert parsed["content"] == "done"
|
|
assert parsed["tool_use_id"] == tool_use_id
|
|
assert "content_block_start" in events[2]
|
|
assert '"type": "text"' in events[2]
|
|
|
|
def test_finish_emits_stop_events(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
events = e.finish("end_turn")
|
|
# content_block_stop + message_delta + message_stop
|
|
assert len(events) == 3
|
|
assert "content_block_stop" in events[0]
|
|
assert "message_delta" in events[1]
|
|
assert "end_turn" in events[1]
|
|
assert "message_stop" in events[2]
|
|
|
|
def test_metadata_captured_in_finish_usage(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed(
|
|
{
|
|
"type": "metadata",
|
|
"usage": {"prompt_tokens": 10, "completion_tokens": 20},
|
|
}
|
|
)
|
|
events = e.finish("end_turn")
|
|
delta_event = [ev for ev in events if "message_delta" in ev][0]
|
|
parsed = json.loads(delta_event.split("data: ")[1])
|
|
assert parsed["usage"]["output_tokens"] == 20
|
|
|
|
def test_status_events_ignored(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
events = e.feed({"type": "status", "text": "Searching..."})
|
|
assert events == []
|
|
|
|
def test_no_tool_calls_simple_text_flow(self):
|
|
e = AnthropicStreamEmitter()
|
|
start_events = e.start("msg_1", "m")
|
|
content_events = e.feed({"type": "content", "text": "Hello world"})
|
|
meta_events = e.feed(
|
|
{"type": "metadata", "usage": {"prompt_tokens": 5, "completion_tokens": 2}}
|
|
)
|
|
end_events = e.finish("end_turn")
|
|
|
|
assert len(start_events) == 2
|
|
assert len(content_events) == 1
|
|
assert meta_events == []
|
|
assert len(end_events) == 3
|
|
|
|
def test_block_index_increments(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
assert e.block_index == 0
|
|
e.feed(
|
|
{
|
|
"type": "tool_start",
|
|
"tool_name": "t",
|
|
"tool_call_id": "tc_1",
|
|
"arguments": {},
|
|
}
|
|
)
|
|
assert e.block_index == 1
|
|
e.feed(
|
|
{
|
|
"type": "tool_end",
|
|
"tool_name": "t",
|
|
"tool_call_id": "tc_1",
|
|
"result": "ok",
|
|
}
|
|
)
|
|
assert e.block_index == 2
|
|
|
|
def test_text_after_tool_resets_prev_text(self):
|
|
e = AnthropicStreamEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed({"type": "content", "text": "Before tool"})
|
|
e.feed(
|
|
{
|
|
"type": "tool_start",
|
|
"tool_name": "t",
|
|
"tool_call_id": "tc_1",
|
|
"arguments": {},
|
|
}
|
|
)
|
|
e.feed(
|
|
{
|
|
"type": "tool_end",
|
|
"tool_name": "t",
|
|
"tool_call_id": "tc_1",
|
|
"result": "ok",
|
|
}
|
|
)
|
|
# After tool_end, prev_text should be reset
|
|
events = e.feed({"type": "content", "text": "After tool"})
|
|
parsed = json.loads(events[0].split("data: ")[1])
|
|
assert parsed["delta"]["text"] == "After tool"
|
|
|
|
|
|
# =====================================================================
|
|
# Non-streaming tool response tests
|
|
# =====================================================================
|
|
|
|
|
|
class TestAnthropicToolNonStreaming:
|
|
def test_duplicate_tool_start_replaces_provisional_tool_block(self):
|
|
def _run_gen():
|
|
yield {
|
|
"type": "tool_start",
|
|
"tool_name": "render_html",
|
|
"tool_call_id": "call_0",
|
|
"arguments": {},
|
|
}
|
|
yield {
|
|
"type": "tool_start",
|
|
"tool_name": "render_html",
|
|
"tool_call_id": "call_0",
|
|
"arguments": {"code": "<!doctype html><html></html>"},
|
|
}
|
|
yield {
|
|
"type": "tool_end",
|
|
"tool_name": "render_html",
|
|
"tool_call_id": "call_0",
|
|
"result": "Rendered HTML canvas.",
|
|
}
|
|
|
|
response = asyncio.run(_anthropic_tool_non_streaming(_run_gen, "msg_1", "m"))
|
|
body = json.loads(response.body)
|
|
tool_blocks = [block for block in body["content"] if block["type"] == "tool_use"]
|
|
|
|
assert len(tool_blocks) == 1
|
|
assert tool_blocks[0]["type"] == "tool_use"
|
|
assert tool_blocks[0]["id"].startswith("toolu_")
|
|
assert tool_blocks[0]["name"] == "render_html"
|
|
assert tool_blocks[0]["input"] == {"code": "<!doctype html><html></html>"}
|
|
|
|
def test_display_strip_gates_on_declared_tools(self):
|
|
# A final answer containing NAME[ARGS]{json} is gated on the declared tools: undeclared
|
|
# ``foo`` markup is prose and survives, the declared web_search rehearsal strips.
|
|
def _run_gen():
|
|
yield {
|
|
"type": "content",
|
|
"text": 'Try foo[ARGS]{"x": 1} but not web_search[ARGS]{"q": "hi"} here.',
|
|
}
|
|
|
|
tools = [{"type": "function", "function": {"name": "web_search", "parameters": {}}}]
|
|
response = asyncio.run(
|
|
_anthropic_tool_non_streaming(_run_gen, "msg_1", "m", openai_tools = tools)
|
|
)
|
|
body = json.loads(response.body)
|
|
text = "".join(b["text"] for b in body["content"] if b["type"] == "text")
|
|
assert 'foo[ARGS]{"x": 1}' in text # inactive name preserved as prose
|
|
assert "web_search[ARGS]" not in text # active name stripped from display
|
|
|
|
|
|
# =====================================================================
|
|
# Pass-through emitter tests (client-side tool execution path)
|
|
# =====================================================================
|
|
|
|
|
|
class TestAnthropicPassthroughEmitter:
|
|
def _parse(self, event_str):
|
|
return json.loads(event_str.split("data: ")[1])
|
|
|
|
def test_start_emits_message_start_only(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
events = e.start("msg_1", "test-model")
|
|
assert len(events) == 1
|
|
assert "message_start" in events[0]
|
|
parsed = self._parse(events[0])
|
|
assert parsed["message"]["id"] == "msg_1"
|
|
assert parsed["message"]["model"] == "test-model"
|
|
|
|
def test_text_chunk_opens_text_block_and_emits_delta(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
chunk = {"choices": [{"delta": {"content": "Hello"}}]}
|
|
events = e.feed_chunk(chunk)
|
|
# content_block_start + content_block_delta
|
|
assert len(events) == 2
|
|
assert "content_block_start" in events[0]
|
|
assert '"type": "text"' in events[0]
|
|
delta = self._parse(events[1])
|
|
assert delta["delta"]["type"] == "text_delta"
|
|
assert delta["delta"]["text"] == "Hello"
|
|
|
|
def test_sequential_text_chunks_single_block(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
events1 = e.feed_chunk({"choices": [{"delta": {"content": "Hello"}}]})
|
|
events2 = e.feed_chunk({"choices": [{"delta": {"content": " world"}}]})
|
|
# First chunk opens the block, second only emits delta
|
|
assert len(events1) == 2
|
|
assert len(events2) == 1
|
|
assert self._parse(events2[0])["delta"]["text"] == " world"
|
|
|
|
def test_tool_call_opens_tool_use_block(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
chunk = {
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "call_1",
|
|
"type": "function",
|
|
"function": {"name": "Bash", "arguments": ""},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
events = e.feed_chunk(chunk)
|
|
assert len(events) == 1
|
|
parsed = self._parse(events[0])
|
|
assert parsed["type"] == "content_block_start"
|
|
assert parsed["content_block"]["type"] == "tool_use"
|
|
assert parsed["content_block"]["id"].startswith("toolu_")
|
|
assert parsed["content_block"]["name"] == "Bash"
|
|
|
|
def test_tool_call_arguments_streamed_as_input_json_delta(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
# Open the tool call
|
|
e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "c1",
|
|
"type": "function",
|
|
"function": {"name": "Bash", "arguments": ""},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
)
|
|
# Stream argument fragments
|
|
events1 = e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{"delta": {"tool_calls": [{"index": 0, "function": {"arguments": '{"cmd'}}]}}
|
|
]
|
|
}
|
|
)
|
|
events2 = e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{"delta": {"tool_calls": [{"index": 0, "function": {"arguments": '": "ls"}'}}]}}
|
|
]
|
|
}
|
|
)
|
|
parsed1 = self._parse(events1[0])
|
|
parsed2 = self._parse(events2[0])
|
|
assert parsed1["delta"]["type"] == "input_json_delta"
|
|
assert parsed1["delta"]["partial_json"] == '{"cmd'
|
|
assert parsed2["delta"]["partial_json"] == '": "ls"}'
|
|
|
|
def test_text_then_tool_closes_text_block(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed_chunk({"choices": [{"delta": {"content": "Let me check."}}]})
|
|
events = e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "c1",
|
|
"type": "function",
|
|
"function": {"name": "Bash", "arguments": ""},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
)
|
|
# Should close text block and open tool_use block
|
|
assert "content_block_stop" in events[0]
|
|
assert "content_block_start" in events[1]
|
|
assert '"type": "tool_use"' in events[1]
|
|
|
|
def test_finish_reason_tool_calls_sets_tool_use_stop(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "c1",
|
|
"type": "function",
|
|
"function": {"name": "Bash", "arguments": "{}"},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
)
|
|
e.feed_chunk({"choices": [{"delta": {}, "finish_reason": "tool_calls"}]})
|
|
events = e.finish()
|
|
delta_event = [ev for ev in events if "message_delta" in ev][0]
|
|
parsed = self._parse(delta_event)
|
|
assert parsed["delta"]["stop_reason"] == "tool_use"
|
|
|
|
def test_finish_reason_stop_sets_end_turn(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed_chunk({"choices": [{"delta": {"content": "Hi"}}]})
|
|
e.feed_chunk({"choices": [{"delta": {}, "finish_reason": "stop"}]})
|
|
events = e.finish()
|
|
delta_event = [ev for ev in events if "message_delta" in ev][0]
|
|
parsed = self._parse(delta_event)
|
|
assert parsed["delta"]["stop_reason"] == "end_turn"
|
|
|
|
def test_finish_reason_length_sets_max_tokens(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed_chunk({"choices": [{"delta": {"content": "Hi"}}]})
|
|
e.feed_chunk({"choices": [{"delta": {}, "finish_reason": "length"}]})
|
|
events = e.finish()
|
|
delta_event = [ev for ev in events if "message_delta" in ev][0]
|
|
parsed = self._parse(delta_event)
|
|
assert parsed["delta"]["stop_reason"] == "max_tokens"
|
|
|
|
def test_finish_closes_current_block(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed_chunk({"choices": [{"delta": {"content": "Hi"}}]})
|
|
events = e.finish()
|
|
assert "content_block_stop" in events[0]
|
|
assert "message_delta" in events[1]
|
|
assert "message_stop" in events[2]
|
|
|
|
def test_usage_chunk_captured(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
e.feed_chunk({"choices": [{"delta": {"content": "Hi"}}]})
|
|
e.feed_chunk(
|
|
{
|
|
"choices": [],
|
|
"usage": {"prompt_tokens": 10, "completion_tokens": 5},
|
|
}
|
|
)
|
|
events = e.finish()
|
|
delta_event = [ev for ev in events if "message_delta" in ev][0]
|
|
parsed = self._parse(delta_event)
|
|
assert parsed["usage"]["output_tokens"] == 5
|
|
|
|
def test_empty_chunk_returns_no_events(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
events = e.feed_chunk({"choices": []})
|
|
assert events == []
|
|
|
|
def test_no_blocks_at_all_still_produces_valid_finish(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
events = e.finish()
|
|
# No content_block_stop because no block was opened
|
|
assert not any("content_block_stop" in ev for ev in events)
|
|
assert any("message_delta" in ev for ev in events)
|
|
assert any("message_stop" in ev for ev in events)
|
|
|
|
def test_multiple_tool_calls_distinct_blocks(self):
|
|
e = AnthropicPassthroughEmitter()
|
|
e.start("msg_1", "m")
|
|
# First tool call
|
|
e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 0,
|
|
"id": "c1",
|
|
"type": "function",
|
|
"function": {"name": "Bash", "arguments": "{}"},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
)
|
|
# Second tool call (different index)
|
|
events = e.feed_chunk(
|
|
{
|
|
"choices": [
|
|
{
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": 1,
|
|
"id": "c2",
|
|
"type": "function",
|
|
"function": {"name": "Read", "arguments": "{}"},
|
|
}
|
|
]
|
|
}
|
|
}
|
|
]
|
|
}
|
|
)
|
|
# Should close block 0, open block 1
|
|
assert "content_block_stop" in events[0]
|
|
assert "content_block_start" in events[1]
|
|
parsed = self._parse(events[1])
|
|
assert parsed["content_block"]["name"] == "Read"
|
|
assert parsed["content_block"]["id"].startswith("toolu_")
|
|
|
|
|
|
class TestAnthropicPassthroughStreamAdapter:
|
|
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)
|
|
]
|
|
|
|
def test_stream_requests_usage_for_final_message_delta(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": {"content": "hi"}}]},
|
|
{
|
|
"choices": [],
|
|
"usage": {
|
|
"prompt_tokens": 2,
|
|
"completion_tokens": 4,
|
|
"total_tokens": 6,
|
|
},
|
|
},
|
|
]
|
|
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)
|
|
backend = SimpleNamespace(
|
|
base_url = "http://llama.test",
|
|
context_length = 4096,
|
|
count_chat_tokens = lambda *args, **kwargs: 2,
|
|
)
|
|
|
|
async def run():
|
|
response = await _anthropic_passthrough_stream(
|
|
self._Request(),
|
|
threading.Event(),
|
|
backend,
|
|
[{"role": "user", "content": "hi"}],
|
|
[
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "lookup",
|
|
"parameters": {"type": "object"},
|
|
},
|
|
}
|
|
],
|
|
0.7,
|
|
0.95,
|
|
20,
|
|
16,
|
|
"msg_1",
|
|
"test-model",
|
|
)
|
|
return await self._collect(response)
|
|
|
|
lines = asyncio.run(run())
|
|
|
|
assert captured["body"]["stream_options"] == {"include_usage": True}
|
|
message_delta = self._payloads(lines, "message_delta")[0]
|
|
assert message_delta["usage"]["input_tokens"] == 2
|
|
assert message_delta["usage"]["output_tokens"] == 4
|
|
|
|
|
|
# =====================================================================
|
|
# Vision guard + PNG normalization (/v1/messages)
|
|
# =====================================================================
|
|
|
|
|
|
def _jpeg_data_url() -> str:
|
|
from PIL import Image
|
|
|
|
img = Image.new("RGB", (2, 2), (255, 0, 0))
|
|
buf = _BytesIO()
|
|
img.save(buf, format = "JPEG")
|
|
b64 = _b64.b64encode(buf.getvalue()).decode("ascii")
|
|
return f"data:image/jpeg;base64,{b64}"
|
|
|
|
|
|
class TestNormalizeAnthropicOpenAIImages:
|
|
def test_noop_when_no_images(self):
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
has_image = _normalize_anthropic_openai_images(msgs, is_vision = False)
|
|
assert has_image is False
|
|
assert msgs == [{"role": "user", "content": "hi"}]
|
|
|
|
def test_returns_true_when_image_present(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "image_url", "image_url": {"url": _jpeg_data_url()}},
|
|
],
|
|
}
|
|
]
|
|
assert _normalize_anthropic_openai_images(msgs, is_vision = True) is True
|
|
|
|
def test_rejects_image_when_model_not_vision(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "?"},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": _jpeg_data_url()},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
with pytest.raises(HTTPException) as exc:
|
|
_normalize_anthropic_openai_images(msgs, is_vision = False)
|
|
assert exc.value.status_code == 400
|
|
|
|
def test_reencodes_jpeg_data_url_to_png(self):
|
|
original_url = _jpeg_data_url()
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "?"},
|
|
{"type": "image_url", "image_url": {"url": original_url}},
|
|
],
|
|
}
|
|
]
|
|
_normalize_anthropic_openai_images(msgs, is_vision = True)
|
|
new_url = msgs[0]["content"][1]["image_url"]["url"]
|
|
assert new_url.startswith("data:image/png;base64,")
|
|
assert new_url != original_url
|
|
|
|
def test_remote_url_left_unchanged(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": "https://x.example/y.png"},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
_normalize_anthropic_openai_images(msgs, is_vision = True)
|
|
assert msgs[0]["content"][0]["image_url"]["url"] == "https://x.example/y.png"
|
|
|
|
def test_bad_base64_raises_400(self):
|
|
msgs = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": "data:image/jpeg;base64,!!!not-b64!!!"},
|
|
},
|
|
],
|
|
}
|
|
]
|
|
with pytest.raises(HTTPException) as exc:
|
|
_normalize_anthropic_openai_images(msgs, is_vision = True)
|
|
assert exc.value.status_code == 400
|
|
|
|
|
|
# =====================================================================
|
|
# Studio-tool alias detection (/v1/messages tool routing)
|
|
# =====================================================================
|
|
|
|
|
|
class TestAnthropicRequestedStudioTools:
|
|
def test_recognizes_server_tool_by_type(self):
|
|
tools = [{"type": "web_search_20250305", "name": "web_search"}]
|
|
assert _anthropic_requested_studio_tools(tools) == {"web_search"}
|
|
|
|
def test_bare_name_without_type_is_not_treated_as_server_tool(self):
|
|
# Anthropic dispatches server tools by `type`; bare-name matching
|
|
# would let a malformed client tool (missing input_schema) silently
|
|
# flip the request into server-execution mode.
|
|
tools = [{"name": "python"}]
|
|
assert _anthropic_requested_studio_tools(tools) == set()
|
|
|
|
def test_client_tool_named_python_is_not_misclassified(self):
|
|
# input_schema is the client-tool discriminator; its presence must
|
|
# prevent the name from being treated as a Studio alias.
|
|
tools = [
|
|
{
|
|
"name": "python",
|
|
"description": "user's own python",
|
|
"input_schema": {"type": "object"},
|
|
}
|
|
]
|
|
assert _anthropic_requested_studio_tools(tools) == set()
|
|
|
|
def test_mixed_request_only_extracts_server_tools(self):
|
|
tools = [
|
|
{"type": "web_search_20250305", "name": "web_search"},
|
|
{"name": "custom_tool", "input_schema": {"type": "object"}},
|
|
]
|
|
assert _anthropic_requested_studio_tools(tools) == {"web_search"}
|
|
|
|
def test_pydantic_model_input(self):
|
|
tools = [
|
|
AnthropicTool(type = "web_fetch_20250910", name = "web_fetch"),
|
|
AnthropicTool(name = "x", input_schema = {"type": "object"}),
|
|
]
|
|
assert _anthropic_requested_studio_tools(tools) == {"web_search"}
|
|
|
|
def test_empty_and_none(self):
|
|
assert _anthropic_requested_studio_tools(None) == set()
|
|
assert _anthropic_requested_studio_tools([]) == set()
|
|
|
|
|
|
# =====================================================================
|
|
# Route-level tool routing (/v1/messages)
|
|
# =====================================================================
|
|
|
|
|
|
def _mock_backend(monkeypatch, **overrides):
|
|
"""Install a minimal stub backend on routes.inference.
|
|
|
|
Generation methods record which path the route entered, then yield one
|
|
content event so the route can complete normally.
|
|
"""
|
|
import routes.inference as inf_mod
|
|
|
|
calls = []
|
|
|
|
def _gen_plain(**kwargs):
|
|
calls.append(("plain", kwargs))
|
|
yield "ok"
|
|
|
|
def _gen_tools(**kwargs):
|
|
calls.append(("tools", kwargs))
|
|
yield {"type": "content", "text": "ok"}
|
|
|
|
backend = SimpleNamespace(
|
|
is_loaded = True,
|
|
is_vision = False,
|
|
supports_tools = True,
|
|
model_identifier = "test-model",
|
|
context_length = 4096,
|
|
count_chat_tokens = lambda *args, **kwargs: 2,
|
|
generate_chat_completion = _gen_plain,
|
|
generate_chat_completion_with_tools = _gen_tools,
|
|
calls = calls,
|
|
)
|
|
backend.__dict__.update(overrides)
|
|
monkeypatch.setattr(inf_mod, "get_llama_cpp_backend", lambda: backend)
|
|
return backend
|
|
|
|
|
|
def _drive(coro):
|
|
return asyncio.new_event_loop().run_until_complete(coro)
|
|
|
|
|
|
def _basic_payload(**fields) -> AnthropicMessagesRequest:
|
|
base = {
|
|
"max_tokens": 16,
|
|
"messages": [{"role": "user", "content": "hi"}],
|
|
}
|
|
base.update(fields)
|
|
return AnthropicMessagesRequest(**base)
|
|
|
|
|
|
@pytest.fixture(autouse = True)
|
|
def _reset_policy():
|
|
reset_tool_policy()
|
|
yield
|
|
reset_tool_policy()
|
|
|
|
|
|
class TestAnthropicMessagesToolRouting:
|
|
class _Request:
|
|
state = SimpleNamespace()
|
|
url = SimpleNamespace(path = "/v1/messages")
|
|
method = "POST"
|
|
|
|
async def is_disconnected(self):
|
|
return False
|
|
|
|
@staticmethod
|
|
def _consume_response(response):
|
|
async def _consume():
|
|
chunks = []
|
|
async for chunk in response.body_iterator:
|
|
chunks.append(chunk)
|
|
return chunks
|
|
|
|
return _drive(_consume())
|
|
|
|
def test_plain_non_streaming_records_api_monitor_entry(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
_mock_backend(monkeypatch, context_length = 2048)
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
payload = _basic_payload()
|
|
|
|
response = _drive(anthropic_messages(payload, request = self._Request(), current_subject = "t"))
|
|
|
|
assert response.status_code == 200
|
|
[entry] = monitor.snapshot()
|
|
assert entry["endpoint"] == "/v1/messages"
|
|
assert entry["status"] == "completed"
|
|
assert entry["model"] == "test-model"
|
|
assert entry["prompt_preview"] == "user: hi"
|
|
assert entry["reply_preview"] == "ok"
|
|
assert entry["context_length"] == 2048
|
|
assert monitor.active_count() == 0
|
|
|
|
def test_tool_use_non_streaming_records_api_monitor_reply(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
def _gen_tools(**_kwargs):
|
|
yield {
|
|
"type": "tool_start",
|
|
"tool_call_id": "call_1",
|
|
"tool_name": "lookup",
|
|
"arguments": {"query": "weather"},
|
|
}
|
|
|
|
_mock_backend(
|
|
monkeypatch,
|
|
context_length = 2048,
|
|
generate_chat_completion_with_tools = _gen_tools,
|
|
)
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
payload = _basic_payload(
|
|
enable_tools = True,
|
|
tools = [{"type": "web_search_20250305", "name": "web_search"}],
|
|
)
|
|
|
|
response = _drive(anthropic_messages(payload, request = self._Request(), current_subject = "t"))
|
|
|
|
assert response.status_code == 200
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply_preview"] == 'Tool call: lookup({"query": "weather"})'
|
|
|
|
def test_plain_streaming_records_active_and_completed_monitor_entry(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
_mock_backend(monkeypatch, context_length = 2048)
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
payload = _basic_payload(stream = True)
|
|
|
|
response = _drive(anthropic_messages(payload, request = self._Request(), current_subject = "t"))
|
|
|
|
assert monitor.active_count() == 1
|
|
self._consume_response(response)
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "completed"
|
|
assert entry["reply_preview"] == "ok"
|
|
assert entry["prompt_tokens"] == 2
|
|
assert entry["context_length"] == 2048
|
|
assert monitor.active_count() == 0
|
|
|
|
def test_plain_streaming_pre_response_cancel_finalizes_monitor(self, monkeypatch):
|
|
import routes.inference as inf_mod
|
|
|
|
async def _cancelled_before_response(*_args, **_kwargs):
|
|
raise asyncio.CancelledError()
|
|
|
|
_mock_backend(monkeypatch, context_length = 2048)
|
|
monitor = ApiMonitor(max_entries = 3)
|
|
monkeypatch.setattr(inf_mod, "api_monitor", monitor)
|
|
monkeypatch.setattr(inf_mod, "_anthropic_plain_stream", _cancelled_before_response)
|
|
payload = _basic_payload(stream = True)
|
|
|
|
with pytest.raises(asyncio.CancelledError):
|
|
_drive(anthropic_messages(payload, request = self._Request(), current_subject = "t"))
|
|
|
|
[entry] = monitor.snapshot()
|
|
assert entry["status"] == "cancelled"
|
|
assert monitor.active_count() == 0
|
|
|
|
def test_mixed_server_and_client_tools_rejected_with_400(self, monkeypatch):
|
|
_mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [
|
|
{"type": "web_search_20250305", "name": "web_search"},
|
|
{"name": "custom", "input_schema": {"type": "object"}},
|
|
],
|
|
)
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "Mixing Anthropic server tools" in exc.value.detail
|
|
|
|
def test_mixed_rejected_when_client_tool_name_collides_with_server_alias(self, monkeypatch):
|
|
# Regression: a client tool sharing a name with a mapped server tool
|
|
# (e.g. a custom "web_search") must still trigger the mixed-mode 400;
|
|
# otherwise the post-name filter drops the client tool and silently
|
|
# routes to server-only.
|
|
_mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [
|
|
{"type": "web_search_20250305", "name": "web_search"},
|
|
{"name": "web_search", "input_schema": {"type": "object"}},
|
|
],
|
|
)
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "Mixing Anthropic server tools" in exc.value.detail
|
|
|
|
def test_client_tool_missing_input_schema_rejected_with_400(self, monkeypatch):
|
|
_mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [{"name": "my_tool", "description": "oops, schema typo"}],
|
|
)
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "input_schema" in exc.value.detail
|
|
|
|
def test_client_tool_missing_name_rejected_with_400(self, monkeypatch):
|
|
# Regression: AnthropicTool.name was relaxed to Optional for server
|
|
# tools, so a client-tool payload with input_schema but no `name`
|
|
# (typo) now parses but would be silently dropped by
|
|
# anthropic_tools_to_openai, leaving tool calling disabled. Reject at
|
|
# the boundary instead.
|
|
_mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [{"input_schema": {"type": "object"}}],
|
|
)
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "name" in exc.value.detail
|
|
|
|
def test_client_tool_empty_name_rejected_with_400(self, monkeypatch):
|
|
# Same silent-disable class as missing-name: `name: ""` passes the
|
|
# isinstance check but is dropped by anthropic_tools_to_openai's
|
|
# `if not name` guard. Reject at the boundary so the typo shows.
|
|
_mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [{"name": "", "input_schema": {"type": "object"}}],
|
|
)
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "name" in exc.value.detail
|
|
|
|
def test_alias_named_client_tool_without_schema_rejected_with_400(self, monkeypatch):
|
|
# Regression: a typo'd client tool whose name collides with a Studio
|
|
# alias (e.g. a custom "python" tool missing input_schema) must
|
|
# surface a 400, not silently switch into Studio's built-in python
|
|
# execution.
|
|
_mock_backend(monkeypatch)
|
|
payload = _basic_payload(tools = [{"name": "python"}])
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "input_schema" in exc.value.detail
|
|
|
|
def test_unrecognized_server_tool_accepted_as_noop(self, monkeypatch):
|
|
backend = _mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [{"type": "code_execution_20250825", "name": "code_execution"}],
|
|
)
|
|
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert backend.calls[0][0] == "plain"
|
|
|
|
def test_disable_tools_policy_overrides_server_tool_alias(self, monkeypatch):
|
|
# CLI `unsloth run --disable-tools` sets policy=False. A request with
|
|
# a Studio server-tool alias must NOT enter the agentic loop then.
|
|
backend = _mock_backend(monkeypatch)
|
|
set_tool_policy(False)
|
|
payload = _basic_payload(
|
|
tools = [{"type": "web_search_20250305", "name": "web_search"}],
|
|
)
|
|
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert backend.calls[0][0] == "plain"
|
|
|
|
def test_server_tool_alias_enters_tool_path_when_policy_unset(self, monkeypatch):
|
|
# Mirror of the previous test for the default (None) policy.
|
|
backend = _mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
tools = [{"type": "web_search_20250305", "name": "web_search"}],
|
|
)
|
|
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert backend.calls[0][0] == "tools"
|
|
|
|
def test_confirm_tool_calls_rejected_for_server_tools(self, monkeypatch):
|
|
backend = _mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
confirm_tool_calls = True,
|
|
tools = [{"type": "web_search_20250305", "name": "web_search"}],
|
|
)
|
|
|
|
with pytest.raises(HTTPException) as exc:
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert exc.value.status_code == 400
|
|
assert "confirm_tool_calls is not supported" in exc.value.detail["error"]["message"]
|
|
assert backend.calls == []
|
|
|
|
def test_per_request_enable_tools_false_blocks_server_tool_alias(self, monkeypatch):
|
|
backend = _mock_backend(monkeypatch)
|
|
payload = _basic_payload(
|
|
enable_tools = False,
|
|
tools = [{"type": "web_search_20250305", "name": "web_search"}],
|
|
)
|
|
|
|
_drive(anthropic_messages(payload, request = None, current_subject = "t"))
|
|
assert backend.calls[0][0] == "plain"
|