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Merge remote-tracking branch 'origin/main' into pr-5717

Browse source 
Resolves package.json conflict: keep main's biome 1->2 simplification
(`biome check` without trailing ".") and the test / test:watch scripts
this branch added for vitest.
This commit is contained in:
Daniel Han 2026-05-27 06:21:06 +00:00
commit b62e4d18cd
74 changed files with 10967 additions and 1234 deletions
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41
studio/backend/core/data_recipe/service.py
View file

@ -109,7 +109,7 @@ def _apply_data_designer_image_context_patch() -> None:
return
try:
from data_designer.config.models import ImageContext
from data_designer.config.models import ImageContext # pyright: ignore[reportMissingImports]
except ImportError:
return
@ -131,7 +131,7 @@ def _apply_data_designer_image_context_patch() -> None:
def build_model_providers(recipe: dict[str, Any]):
from data_designer.config.models import ModelProvider
from data_designer.config.models import ModelProvider # pyright: ignore[reportMissingImports]
providers: list[ModelProvider] = []
for provider in recipe.get("model_providers", []):
@ -174,7 +174,7 @@ def _validate_recipe_runtime_support(
def build_mcp_providers(
recipe: dict[str, Any],
) -> list:
from data_designer.config.mcp import LocalStdioMCPProvider, MCPProvider
from data_designer.config.mcp import LocalStdioMCPProvider, MCPProvider # pyright: ignore[reportMissingImports]
providers: list[MCPProvider | LocalStdioMCPProvider] = []
for provider in recipe.get("mcp_providers", []):
@ -214,16 +214,42 @@ def build_mcp_providers(
return providers
def _strip_frontend_model_config_metadata(recipe: dict[str, Any]) -> dict[str, Any]:
model_configs = recipe.get("model_configs")
if not isinstance(model_configs, list):
return recipe
changed = False
next_model_configs: list[Any] = []
for model_config in model_configs:
if isinstance(model_config, dict) and "gguf_variant" in model_config:
next_model_config = dict(model_config)
next_model_config.pop("gguf_variant", None)
next_model_configs.append(next_model_config)
changed = True
continue
next_model_configs.append(model_config)
if not changed:
return recipe
return {
**recipe,
"model_configs": next_model_configs,
}
def build_config_builder(recipe: dict[str, Any]):
_apply_data_designer_image_context_patch()
from data_designer.config import DataDesignerConfigBuilder
from data_designer.config.processors import ProcessorType
from data_designer.config import DataDesignerConfigBuilder # pyright: ignore[reportMissingImports]
from data_designer.config.processors import ProcessorType # pyright: ignore[reportMissingImports]
recipe_core = {
key: value
for key, value in recipe.items()
if key not in {"model_providers", "mcp_providers"}
}
recipe_core = _strip_frontend_model_config_metadata(recipe_core)
recipe_core, oxc_local_callable_specs = split_oxc_local_callable_validators(
recipe_core
)
@ -256,8 +282,9 @@ def create_data_designer(
artifact_path: str | None = None,
):
_apply_data_designer_image_context_patch()
from data_designer.interface.data_designer import DataDesigner
from data_designer.interface.data_designer import DataDesigner # pyright: ignore[reportMissingImports]
recipe = _strip_frontend_model_config_metadata(recipe)
model_providers = build_model_providers(recipe)
_validate_recipe_runtime_support(recipe, model_providers)
@ -265,7 +292,7 @@ def create_data_designer(
# when the pipeline contains no LLM columns. Supply a lightweight stub
# so sampler/expression-only recipes can run without a real provider.
if not model_providers:
from data_designer.config.models import ModelProvider
from data_designer.config.models import ModelProvider # pyright: ignore[reportMissingImports]
model_providers = [
ModelProvider(

960
studio/backend/core/inference/external_provider.py
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File diff suppressed because it is too large Load diff

4
studio/backend/core/inference/llama_cpp.py
View file

@ -60,7 +60,9 @@ _INTENT_SIGNAL = re.compile(
# Handles both straight and curly apostrophes.
# Excludes "I can", "I should", "I want to", "let's" which
# appear frequently in direct answers / explanations.
r"\b(i['\u2019](ll|m going to|m gonna)|i am (going to|gonna)|i will|i shall|let me|allow me)\b"
# Negative lookahead drops negated forms ("I will not", "I'll never")
# so a refusal doesn't trigger a re-prompt.
r"\b(i['\u2019](ll|m going to|m gonna)|i am (going to|gonna)|i will|i shall|let me|allow me)\b(?!\s+(?:not|never)\b)"
r"|"
# Step/plan framing: "First ...", "Step 1:", "Here's my plan"
r"\b(?:first\b|step \d+:?|here['\u2019]?s (?:my |the |a )?(?:plan|approach))"

137
studio/backend/core/inference/llama_server_args.py
View file

@ -1,46 +1,29 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Validator for user-supplied llama-server pass-through args.
"""Boundary validator for user-supplied llama-server pass-through args.
Studio runs llama-server as a managed subprocess and lets callers pass
extra flags directly (CLI: ``unsloth run ... --top-k 20``; HTTP:
``LoadRequest.llama_extra_args``). This module is the boundary that
rejects only flags Studio fundamentally cannot share with the user --
model identity, the auth key, and the network endpoint Studio's HTTP
proxy targets. Anything else passes through.
Reject only flags Studio manages (model identity, auth, network,
parallel slots). Everything else (sampling, ``-c``, ``-ngl``,
``--flash-attn``, ``--cache-type-*``, ``--spec-*``, ``--jinja``, ...)
is appended after Studio's auto-set flags so llama.cpp's last-wins
parser lets the user override.
User-supplied args are appended to ``cmd`` after Studio's auto-set
flags, so llama.cpp's last-wins CLI parsing makes the user's value
override the auto-set one. That covers tunable knobs the user might
reasonably want to override -- ``-c``/``--ctx-size``,
``-np``/``--parallel``, ``-fa``/``--flash-attn``,
``-ngl``/``--gpu-layers``, ``-t``/``--threads``, ``-fit``/``--fit*``,
``--cache-type-k/v``, ``--chat-template-file/-kwargs``,
``--spec-*``, ``--jinja``/``--no-jinja``,
``--no-context-shift``/``--context-shift``, sampling params, etc.
Reference: https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md
Ref: https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md
"""
from __future__ import annotations
from typing import Iterable, Optional
# Each group is the full set of aliases (short + long) for one
# hard-denied flag, taken from the llama-server README. If llama.cpp
# adds a new alias for an existing denied flag, extend the relevant
# group.
#
# Flags NOT in this list (e.g. -c, --parallel, --flash-attn, -ngl,
# -t/--threads, --jinja, --no-context-shift, --fit*, --cache-type-*,
# --chat-template-*, --spec-*) pass through and override Studio's
# auto-set version via llama.cpp's last-wins CLI parsing.
# Each group = every alias (short + long) of one hard-denied flag.
# Extend the matching group when llama.cpp adds a new alias.
_DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
# Model identity -- Studio resolves the model from LoadRequest and
# passes -m / mmproj after downloading from HF if needed. A second
# -m would point at a different model than the one Studio thinks
# is loaded.
# Parallel slots: owned by typer --parallel; a pass-through would
# desync app.state.llama_parallel_slots from llama-server.
frozenset({"-np", "--parallel", "--n-parallel"}),
# Model identity: Studio resolves it from LoadRequest; a second
# -m would load a different model than Studio thinks it loaded.
frozenset({"-m", "--model"}),
frozenset({"-mu", "--model-url"}),
frozenset({"-dr", "--docker-repo"}),
@ -51,28 +34,21 @@ _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
frozenset({"-hft", "--hf-token"}),
frozenset({"-mm", "--mmproj"}),
frozenset({"-mmu", "--mmproj-url"}),
# Networking -- Studio binds llama-server's port and reverse-proxies
# HTTP traffic to it. Retargeting host/port/path/prefix would
# orphan Studio's proxy and the UI would lose the server.
# Networking: Studio binds + proxies; retargeting orphans the proxy.
frozenset({"--host"}),
frozenset({"--port"}),
frozenset({"--path"}),
frozenset({"--api-prefix"}),
frozenset({"--reuse-port"}),
# Auth / TLS -- Studio terminates auth at its own layer; an
# upstream --api-key would shadow Studio's UNSLOTH_DIRECT_STREAM
# key, and TLS on llama-server would break the local proxy hop.
# Auth / TLS: Studio terminates auth; upstream --api-key / TLS
# shadows Studio's key and breaks the proxy hop.
frozenset({"--api-key"}),
frozenset({"--api-key-file"}),
frozenset({"--ssl-key-file"}),
frozenset({"--ssl-cert-file"}),
# Single-model server -- Studio runs one model per llama-server
# process and serves its own UI. Enabling multi-model loading or
# llama-server's built-in web UI changes the surface clients see.
# ``--webui``/``--no-webui`` are the legacy spelling; current
# upstream uses ``--ui``/``--no-ui`` + ``--ui-*`` companions.
# Keep both so the denylist matches old and new llama-server
# binaries (Studio's prebuilt vs system-llama.cpp).
# Built-in web UI. --webui/--no-webui is the legacy spelling;
# upstream renamed to --ui/--no-ui + --ui-*. Keep both so prebuilt
# and system llama.cpp binaries both match.
frozenset({"--webui", "--no-webui"}),
frozenset({"--ui", "--no-ui"}),
frozenset({"--ui-config"}),
@ -82,32 +58,46 @@ _DENYLIST_GROUPS: tuple[frozenset[str], ...] = (
frozenset({"--models-preset"}),
frozenset({"--models-max"}),
frozenset({"--models-autoload", "--no-models-autoload"}),
# Server-mode flips: --embedding / --rerank restrict llama-server to
# those endpoints, breaking Studio's /v1/chat/completions hop.
frozenset({"--embedding", "--embeddings"}),
frozenset({"--rerank", "--reranking"}),
# llama-server's own built-in tools flag would silently stack on top
# of Studio's --enable-tools / --disable-tools policy resolver.
frozenset({"--tools"}),
)
_DENYLIST: frozenset[str] = frozenset().union(*_DENYLIST_GROUPS)
def _flag_name(token: str) -> Optional[str]:
"""Return the flag name for a token, or None if it isn't a flag.
"""Flag name for ``token``, or None if it isn't a flag.
Peels ``--key=value`` to the bare ``--key``. Plain numeric values
like ``-1`` or ``-0.5`` (e.g. ``--seed -1``) are values, not flags;
llama-server short-form flags always start with a letter.
Peels `--key=value` to `--key`, treats `-1` / `-0.5` as values
(llama-server shorts always start with a letter), strips
whitespace, and normalises attached `-np8` / signed `-np-1` /
digit-prefix-junk `-np8x` to `-np`. Mirrors the CLI's
`_expand_attached_np_short`.
"""
token = token.strip()
if not token.startswith("-") or token in {"-", "--"}:
return None
if len(token) >= 2 and (token[1].isdigit() or token[1] == "."):
return None
return token.split("=", 1)[0]
name = token.split("=", 1)[0]
if len(name) > 3 and name.startswith("-np"):
suffix = name[3:]
if suffix[0].isdigit() or (
len(suffix) > 1 and suffix[0] in {"-", "+"} and suffix[1].isdigit()
):
return "-np"
return name
def validate_extra_args(args: Optional[Iterable[str]]) -> list[str]:
"""Validate user-supplied llama-server args.
Returns the args as a flat list ready to extend the llama-server
command. Raises ``ValueError`` (with the offending flag in the
message) the moment a token resolves to a Studio-managed flag.
"""
"""Validate user-supplied llama-server args. Returns a flat list
ready to extend the llama-server command; raises ``ValueError``
naming the offending flag on the first managed token."""
if not args:
return []
out: list[str] = []
@ -124,15 +114,15 @@ def validate_extra_args(args: Optional[Iterable[str]]) -> list[str]:
def is_managed_flag(flag: str) -> bool:
"""True if ``flag`` is a Studio-managed llama-server flag."""
return flag in _DENYLIST
"""True if ``flag`` is Studio-managed. Normalises via ``_flag_name``
so `-np8` / `--parallel=8` classify like the canonical tokens."""
normalised = _flag_name(flag)
return normalised is not None and normalised in _DENYLIST
# Pass-through flags that shadow first-class ``LoadRequest`` fields
# (max_seq_length, cache_type_kv, speculative_type,
# chat_template_override). Stripped from inherited extras so they
# can't last-wins-override an Apply that re-sets the same first-class
# field.
# Pass-through flags that shadow first-class LoadRequest fields;
# stripped from inherited extras so they can't last-wins-override an
# Apply that re-sets the same field.
_CONTEXT_FLAGS: frozenset[str] = frozenset({"-c", "--ctx-size"})
_CACHE_FLAGS: frozenset[str] = frozenset(
{"-ctk", "--cache-type-k", "-ctv", "--cache-type-v"}
@ -169,9 +159,8 @@ _SHADOWING_FLAGS: frozenset[str] = (
_CONTEXT_FLAGS | _CACHE_FLAGS | _SPEC_FLAGS | _TEMPLATE_FLAGS
)
# Boolean flags inside _SHADOWING_FLAGS that take no value. The
# value-consuming heuristic in strip_shadowing_flags must skip just the
# flag for these, never the following token.
# Shadowing flags that take no value -- strip the flag only, never the
# following token.
_BOOLEAN_SHADOWING_FLAGS: frozenset[str] = frozenset(
{"--spec-default", "--jinja", "--no-jinja"}
)
@ -187,14 +176,11 @@ def strip_shadowing_flags(
) -> list[str]:
"""Strip flags that shadow first-class Studio settings.
Used when the route inherits a previous load's ``llama_extra_args``
so that an inherited ``-c 4096`` cannot override the current
request's ``max_seq_length`` (and equivalents for cache /
speculative / chat template). Each ``strip_*`` flag controls one
group; the route only strips groups whose corresponding first-class
field was actually supplied by the caller, so an inherited
``--chat-template-file`` survives an Apply that omits both
``llama_extra_args`` and ``chat_template_override``.
Used when inheriting a previous load's ``llama_extra_args`` so an
inherited `-c 4096` can't override the current `max_seq_length`
(same for cache / spec / template). Each ``strip_*`` toggle
controls one group; the route only strips groups whose first-class
field the caller actually supplied.
"""
shadowing: set[str] = set()
if strip_context:
@ -216,9 +202,8 @@ def strip_shadowing_flags(
out.append(tok)
i += 1
continue
# Drop this token. Boolean shadowing flags never carry a value;
# other shadowing flags consume the next token when it isn't a
# flag and the value isn't already packed as ``--key=value``.
# Drop the flag; consume the next token too unless it's
# boolean, already inline (`-c=4096`), or another flag.
if flag in _BOOLEAN_SHADOWING_FLAGS or "=" in tok:
i += 1
elif i + 1 < n and _flag_name(tokens[i + 1]) is None:

245
studio/backend/core/inference/pricing.py
View file

@ -1,50 +1,24 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Static per-MTok pricing tables for external providers, plus a
``calculate_cost`` helper that turns an upstream ``usage`` block into
a USD figure for surfacing in the chat UI.
"""Static per-MTok pricing tables and ``calculate_cost`` helper for
turning an upstream ``usage`` block into a USD figure.
Neither the Anthropic Messages API nor the OpenAI Responses API
reports a ``cost`` field on the response. Both expose detailed token
counts (input, output, cache hits, server-tool invocations); pricing
multipliers live in the provider docs. We fold the docs into a static
table here, multiply by the usage block, and emit a per-turn cost +
running session total client-side.
Sources (verified live 2026-05-22):
- Anthropic models overview:
https://platform.claude.com/docs/en/about-claude/models/overview
- Anthropic prompt-caching multipliers (5m write 1.25x, 1h write 2x,
read 0.1x):
https://platform.claude.com/docs/en/build-with-claude/prompt-caching
- Anthropic web search ($10 / 1000 searches, code execution
free-with-paid when paired with the newer web tools):
https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool
https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool
- OpenAI pricing page (input / output per MTok per model family):
https://platform.openai.com/docs/pricing
Sources: Anthropic prompt-caching docs (5m write 1.25x, 1h write 2x,
read 0.1x), web search ($10/1000), code execution; OpenAI pricing page.
"""
from __future__ import annotations
from typing import Any, Optional
# Per-million-token base pricing. `cache_5m_write_mult`, `cache_1h_write_mult`,
# `cache_read_mult` are multipliers ON `input_per_mtok` -- not absolute prices --
# matching how Anthropic publishes them (5m write = 1.25x base, etc.).
#
# `input_per_mtok` and `output_per_mtok` are USD per 1,000,000 tokens.
# Per-MTok base pricing in USD. Cache multipliers are applied ON
# `input_per_mtok` (not absolute prices), matching Anthropic's docs.
ANTHROPIC_PRICING: dict[str, dict[str, float]] = {
"claude-opus-4-7": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
"claude-opus-4-6": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
# Canonical 4.5 ids are referenced from backend defaults (e.g.
# PROVIDER_REGISTRY['anthropic'].default_models) without the date
# suffix. The dated ids ARE the canonical names per Anthropic's
# models overview, but lookups for the bare id ("claude-opus-4-5")
# don't prefix-match the dated key the other way around, so we
# alias both forms here. Otherwise calculate_cost returns
# priced=False + zero cost for the common ids.
# Alias both the bare id and dated id: backend defaults reference
# the bare form, which won't prefix-match the dated key.
"claude-opus-4-5": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
"claude-opus-4-5-20251101": {"input_per_mtok": 5.0, "output_per_mtok": 25.0},
"claude-opus-4-1": {"input_per_mtok": 15.0, "output_per_mtok": 75.0},
@ -59,19 +33,9 @@ ANTHROPIC_PRICING: dict[str, dict[str, float]] = {
}
OPENAI_PRICING: dict[str, dict[str, float]] = {
# All values verified against developers.openai.com/api/docs/pricing
# 2026-05-22. Update against the live pricing page on every model launch.
# Initial commit underbilled every gpt-5.x family 2-6x -- fixed here
# after PR review caught it via doc cross-check.
#
# `long_context_input_per_mtok` / `long_context_output_per_mtok` /
# `long_context_threshold` are populated when OpenAI publishes a
# second pricing tier for prompts above N input tokens. gpt-5.5 and
# gpt-5.4 cross over at 272k input tokens; the long-context rates
# are double the headline input price (and ~1.5x on output). Other
# families currently ship with a single rate (no `long_context_*`
# keys = no tier crossover). Reference:
# https://developers.openai.com/api/docs/pricing
# Verified against developers.openai.com/api/docs/pricing.
# `long_context_*` keys apply once input exceeds the threshold
# (gpt-5.5/5.4: 272k); families without these keys ship a single rate.
"gpt-5.5": {
"input_per_mtok": 5.0,
"output_per_mtok": 30.0,
@ -91,43 +55,33 @@ OPENAI_PRICING: dict[str, dict[str, float]] = {
"gpt-5.4-mini": {"input_per_mtok": 0.75, "output_per_mtok": 4.5},
"gpt-5.4-nano": {"input_per_mtok": 0.20, "output_per_mtok": 1.25},
"gpt-5.3-codex": {"input_per_mtok": 1.75, "output_per_mtok": 14.0},
# chat-latest / gpt-5.3-chat-latest is an alias for the current
# ChatGPT model; same price as gpt-5.5.
# chat-latest aliases gpt-5.5.
"gpt-5.3-chat-latest": {"input_per_mtok": 5.0, "output_per_mtok": 30.0},
"chat-latest": {"input_per_mtok": 5.0, "output_per_mtok": 30.0},
# o-series and gpt-4.5: NOT currently listed on the pricing page.
# Removed to avoid silent-underbilling drift. Returning priced=False
# is honest; the UI can still render token counts. Restore with
# verified per-MTok rates if/when the page lists them again.
# o-series and gpt-4.5 are no longer on the pricing page; omit them
# so calculate_cost returns priced=False rather than silently $0.
}
# Shared multipliers (same across every Anthropic model).
ANTHROPIC_CACHE_5M_WRITE_MULT = 1.25
ANTHROPIC_CACHE_1H_WRITE_MULT = 2.0
ANTHROPIC_CACHE_READ_MULT = 0.1
# Anthropic fast-mode (Opus 4.6 / 4.7 only): 6x standard on input + output.
# https://platform.claude.com/docs/en/build-with-claude/fast-mode#pricing
ANTHROPIC_FAST_MODE_MULT = 6.0
# OpenAI: cache reads are 0.1x base input, cache writes are not billed
# separately (the first prefix-write request just pays normal input).
# OpenAI: cache reads 0.1x; cache writes pay normal input price.
OPENAI_CACHE_READ_MULT = 0.1
# Server-tool surcharges.
# Anthropic: $10 / 1000 web searches; code_execution is $0.05/hr after
# 50 free hours/day per org (no per-org visibility here, so the
# calculator reports the marginal rate).
# Server-tool surcharges. Anthropic code_exec is $0.05/hr marginal
# (50 free hours/day per org, not visible here).
ANTHROPIC_WEB_SEARCH_USD_PER_1K = 10.0
ANTHROPIC_CODE_EXEC_USD_PER_HOUR = 0.05
# OpenAI: web_search is billed at $10/1000 calls plus the model's
# token rate for the returned search content (already captured under
# input/output_tokens). The hosted shell tool bills per 20-minute
# session per container memory tier (1g/4g/16g/64g at
# $0.03/$0.12/$0.48/$1.92). Since Studio doesn't surface the memory
# tier in the cost ledger and most users land on the default 1g, we
# bill the 1g rate ($0.09/hour) and let the user inspect the OpenAI
# dashboard for the exact figure on heavier configs.
# Source: developers.openai.com/api/docs/pricing 2026-05-22.
# OpenAI container bills per memory tier; we report the 1g default
# ($0.09/hour) since the tier isn't surfaced to the cost ledger.
OPENAI_WEB_SEARCH_USD_PER_1K = 10.0
OPENAI_CONTAINER_USD_PER_HOUR = 0.09 # 1g default tier; 3 x $0.03 / 60min
OPENAI_CONTAINER_USD_PER_HOUR = 0.09 # 1g default tier
def _lookup(provider: str, model: str) -> Optional[dict[str, float]]:
@ -142,11 +96,13 @@ def _lookup(provider: str, model: str) -> Optional[dict[str, float]]:
return None
if model in table:
return table[model]
# Fall back to a prefix match so date-suffixed snapshots
# ("gpt-5.5-2026-04-23") inherit the canonical-id prices.
for key, val in table.items():
if model.startswith(key):
return val
# Longest-prefix match on a dash boundary: lets dated snapshots
# inherit canonical prices while preventing "claude-opus-4-15"
# from matching "claude-opus-4-1" or "gpt-5.5-prod" from matching
# "gpt-5.5-pro". Sort longest-first to pick the most specific row.
for key in sorted(table, key = len, reverse = True):
if model.startswith(key) and (len(model) == len(key) or model[len(key)] == "-"):
return table[key]
return None
@ -155,28 +111,11 @@ def calculate_cost(
model: str,
usage: dict[str, Any],
) -> dict[str, float]:
"""Return a per-turn USD cost breakdown.
Returns a dict with the per-bucket cost AND the totals so the
frontend can render either a single number or a "where did the
money go" tooltip without re-doing the math:
{
"input_usd": 0.0042,
"output_usd": 0.012,
"cache_write_usd": 0.0001,
"cache_read_usd": 0.0008,
"server_tools_usd": 0.01,
"total_usd": 0.0271,
"billable_input_tokens": 5023, # input + cache_create + cache_read
"billable_output_tokens": 480,
"model_priced": "claude-opus-4-7",
"priced": true,
}
When the model isn't in the static table (new family, custom base
URL), `priced` is False and every USD field is 0.0; the frontend
can still show the token counts.
"""Return a per-turn USD cost breakdown with per-bucket + total
fields so the frontend can render either a single number or a
tooltip without re-doing the math. When the model isn't in the
static table, ``priced`` is False and USD fields are 0.0 (token
counts still report).
"""
prices = _lookup(provider, model)
out: dict[str, float] = {
@ -192,34 +131,64 @@ def calculate_cost(
"priced": bool(prices),
}
input_tokens = int(usage.get("input_tokens") or 0)
output_tokens = int(usage.get("output_tokens") or 0)
cache_creation = int(usage.get("cache_creation_input_tokens") or 0)
cache_read = int(usage.get("cache_read_input_tokens") or 0)
# OpenAI Responses reports cached tokens under input_tokens_details
# but ALSO folds them into the top-level input_tokens, so we don't
# add cache_read into the billable total again below (Anthropic
# excludes cache buckets from input_tokens, OpenAI includes them --
# the two providers differ here and the calculator must match).
if provider == "openai":
details = usage.get("input_tokens_details") or {}
# Accept raw (input_tokens/output_tokens) and Studio chat-style
# (prompt_tokens/completion_tokens) envelopes. Cache buckets
# behave differently per envelope:
# raw Anthropic: input_tokens EXCLUDES cache buckets
# raw OpenAI: input_tokens INCLUDES cache_read
# Studio Anthropic: prompt_tokens INCLUDES cache_creation + cache_read
# Studio OpenAI: prompt_tokens == raw input_tokens
# Clamp tokens >=0 so corrupted payloads can't produce a negative bill.
cache_creation = max(0, int(usage.get("cache_creation_input_tokens") or 0))
cache_read_native_present = (
"cache_read_input_tokens" in usage
and usage.get("cache_read_input_tokens") is not None
)
cache_read = max(0, int(usage.get("cache_read_input_tokens") or 0))
# Fallback to mirrored prompt_tokens_details only when the native
# cache_read_input_tokens key is absent. An explicit native 0 is
# authoritative, so a stale mirrored block from a proxy can never
# inflate cache_read past the native count.
if not cache_read_native_present:
details = usage.get("prompt_tokens_details") or {}
if isinstance(details, dict):
cache_read = max(cache_read, int(details.get("cached_tokens") or 0))
# OpenAI: cache_read already counted inside input_tokens.
cache_read = max(0, int(details.get("cached_tokens") or 0))
has_input_tokens = "input_tokens" in usage and usage.get("input_tokens") is not None
if has_input_tokens:
input_tokens = max(0, int(usage.get("input_tokens") or 0))
else:
# Chat-style: peel cache buckets back out for Anthropic to
# recover the raw uncached prompt count.
prompt_tokens = max(0, int(usage.get("prompt_tokens") or 0))
if provider == "anthropic":
input_tokens = max(0, prompt_tokens - cache_creation - cache_read)
else:
input_tokens = prompt_tokens
# Prefer raw output_tokens even when 0 (an `or` fallback would
# silently pick a stale completion_tokens).
if "output_tokens" in usage and usage.get("output_tokens") is not None:
output_tokens = max(0, int(usage.get("output_tokens") or 0))
else:
output_tokens = max(0, int(usage.get("completion_tokens") or 0))
if provider == "openai":
# Cached tokens land on either input_tokens_details (raw
# Responses) or prompt_tokens_details (Studio chat-style).
for key in ("input_tokens_details", "prompt_tokens_details"):
details = usage.get(key) or {}
if isinstance(details, dict):
cache_read = max(cache_read, int(details.get("cached_tokens") or 0))
# OpenAI input_tokens already counts cache_read.
out["billable_input_tokens"] = input_tokens + cache_creation
else:
# Anthropic: input_tokens excludes cache_* buckets, add them all.
# Anthropic input_tokens excludes cache buckets; add them back.
out["billable_input_tokens"] = input_tokens + cache_creation + cache_read
out["billable_output_tokens"] = output_tokens
if not prices:
return out
# Long-context tier crossover (gpt-5.5 / gpt-5.4 today). OpenAI
# bills the whole turn at the long-context rate once the prompt
# crosses the threshold, NOT a per-token blend, so we pick a
# single (base, out_per) pair for this turn based on
# billable_input_tokens.
# Long-context tier: whole-turn flip (not per-token blend) once
# billable_input_tokens crosses the threshold.
lc_thresh = prices.get("long_context_threshold")
in_long_context_tier = (
lc_thresh is not None
@ -235,17 +204,27 @@ def calculate_cost(
base = prices["input_per_mtok"]
out_per = prices["output_per_mtok"]
# Anthropic fast-mode: 6x on input + output. Cache multipliers stack
# on top of fast-mode, so applying once to (base, out_per) propagates
# into the cache_*_usd buckets computed below.
if provider == "anthropic" and usage.get("speed") == "fast":
base *= ANTHROPIC_FAST_MODE_MULT
out_per *= ANTHROPIC_FAST_MODE_MULT
if out["model_priced"]:
out["model_priced"] = f"{out['model_priced']} (fast)"
out["input_usd"] = (input_tokens / 1_000_000.0) * base
out["output_usd"] = (output_tokens / 1_000_000.0) * out_per
if provider == "anthropic":
# Split cache_creation across 5m / 1h buckets when the
# response surfaces the breakdown.
cc_breakdown = usage.get("cache_creation") or {}
cc_5m = int(cc_breakdown.get("ephemeral_5m_input_tokens") or 0)
cc_1h = int(cc_breakdown.get("ephemeral_1h_input_tokens") or 0)
# Split cache_creation into 5m / 1h buckets when surfaced.
# Tolerate non-dict (some proxies fold to an int total).
cc_raw = usage.get("cache_creation")
cc_breakdown = cc_raw if isinstance(cc_raw, dict) else {}
cc_5m = max(0, int(cc_breakdown.get("ephemeral_5m_input_tokens") or 0))
cc_1h = max(0, int(cc_breakdown.get("ephemeral_1h_input_tokens") or 0))
if cc_5m + cc_1h == 0 and cache_creation > 0:
# Fall back: assume default 5m pool when no breakdown is given.
# No breakdown -- assume default 5m pool.
cc_5m = cache_creation
out["cache_write_usd"] = (
cc_5m / 1_000_000.0
@ -265,24 +244,17 @@ def calculate_cost(
+ code_exec_hours * ANTHROPIC_CODE_EXEC_USD_PER_HOUR
)
else:
# OpenAI: cache writes share the base input price (no premium).
# Only cache reads get the 0.1x multiplier; subtract those from
# the input_usd we already counted so we don't double-bill.
# Anthropic excludes cache buckets from input_tokens, but
# OpenAI folds them in, so the math differs.
# OpenAI: cache writes pay base input; only cache reads get
# 0.1x. Subtract cached from already-counted input_usd to
# avoid double-billing (OpenAI folds cache into input_tokens).
if cache_read > 0:
non_cached_input = max(0, input_tokens - cache_read)
out["input_usd"] = (non_cached_input / 1_000_000.0) * base
out["cache_read_usd"] = (
(cache_read / 1_000_000.0) * base * OPENAI_CACHE_READ_MULT
)
# Server-tool surcharges. OpenAI doesn't include these on its
# `usage` object directly -- web_search invocations are counted
# from `ResponseFunctionWebSearch` items in the output array,
# and container hours come from the SSE translator's shell-tool
# accounting. Studio surfaces both under a normalised
# `openai_tool_use` key on the usage dict the SSE finaliser
# hands to this calculator.
# OpenAI server-tool surcharges arrive under `openai_tool_use`
# (normalised by the SSE finaliser from output array items).
srv = usage.get("openai_tool_use") or {}
if isinstance(srv, dict):
web_searches = int(srv.get("web_search_requests") or 0)
@ -304,17 +276,14 @@ def calculate_cost(
def pricing_snapshot() -> dict[str, Any]:
"""Whole pricing table, for the /api/providers/pricing endpoint.
Returns a flat structure the frontend can hand to its cost
formatter without re-implementing the multipliers.
"""
"""Whole pricing table for the /api/providers/pricing endpoint."""
return {
"anthropic": {
"models": dict(ANTHROPIC_PRICING),
"cache_5m_write_mult": ANTHROPIC_CACHE_5M_WRITE_MULT,
"cache_1h_write_mult": ANTHROPIC_CACHE_1H_WRITE_MULT,
"cache_read_mult": ANTHROPIC_CACHE_READ_MULT,
"fast_mode_mult": ANTHROPIC_FAST_MODE_MULT,
"web_search_usd_per_1k": ANTHROPIC_WEB_SEARCH_USD_PER_1K,
"code_execution_usd_per_hour": ANTHROPIC_CODE_EXEC_USD_PER_HOUR,
},

109
studio/backend/main.py
View file

@ -49,6 +49,8 @@ import shutil
import warnings
from contextlib import asynccontextmanager
from importlib.metadata import PackageNotFoundError, version as package_version
from typing import Optional
from urllib.parse import urlparse
_STUDIO_INSTALL_ID_RE = _re.compile(r"^[0-9a-f]{64}$")
@ -728,10 +730,8 @@ def _strip_crossorigin(html_bytes: bytes) -> bytes:
def _inject_bootstrap(html_bytes: bytes, app: FastAPI):
"""Inject bootstrap credentials when password change is pending.
Returns ``(html_bytes, script_nonce_or_None)``. Callers must forward
the nonce via ``_CSP_SCRIPT_NONCE_HEADER`` so the inline script is
not blocked by CSP.
Returns ``(html_bytes, script_nonce_or_None)``; callers forward the
nonce via ``_CSP_SCRIPT_NONCE_HEADER`` so CSP allows the inline script.
"""
import json as _json
import secrets as _secrets
@ -756,6 +756,86 @@ def _inject_bootstrap(html_bytes: bytes, app: FastAPI):
return html.encode("utf-8"), nonce
_DEFAULT_PORTS = {"http": 80, "https": 443, "ws": 80, "wss": 443}
def _canonical_origin(scheme: str, netloc: str) -> Optional[tuple[str, str, int]]:
"""Canonicalise an Origin to ``(scheme, host, port)`` for equality.
Browsers strip default ports (RFC 6454 sec 6.1) and scheme/host are
case-insensitive (RFC 3986), so bare string compare misclassifies
same-origin requests as cross-origin. Returns ``None`` on unparseable
input so callers fall to the safer cross-origin default.
"""
scheme = (scheme or "").strip().lower()
if not scheme or not netloc:
return None
# Strip userinfo (RFC 3986); Origin never carries credentials.
if "@" in netloc:
netloc = netloc.rsplit("@", 1)[1]
# IPv6 hosts use brackets (RFC 3986 sec 3.2.2): ``[::1]:8902``. Bare
# ``partition(":")`` mis-parses these and breaks ``unsloth studio -H ::1``.
if netloc.startswith("["):
close = netloc.find("]")
if close == -1:
return None
host = netloc[1:close]
rest = netloc[close + 1 :]
if rest.startswith(":"):
port_str = rest[1:]
elif rest == "":
port_str = ""
else:
return None
else:
host, _, port_str = netloc.partition(":")
host = host.strip().lower()
if not host:
return None
if port_str:
try:
port = int(port_str)
except ValueError:
return None
else:
port = _DEFAULT_PORTS.get(scheme, 0)
return (scheme, host, port)
def _is_same_origin_request(request: Request) -> bool:
"""True when Origin is missing or matches request's scheme://host:port.
Top-level same-document GETs omit Origin, so missing counts as same-origin.
Callers must also emit ``Vary: Origin``. Both sides are canonicalised via
:func:`_canonical_origin` so default-port stripping and scheme/host case
do not misclassify same-origin requests as cross-origin.
"""
origin = request.headers.get("origin")
if origin is None:
# Missing header: top-level same-document GETs omit Origin.
return True
# Empty string is not a valid serialised origin (RFC 6454 sec 6.1).
if not origin:
return False
# "null" token (sandboxed iframes, file:// pages) is never same-origin.
if origin == "null":
return False
# ``urlparse`` raises ``ValueError`` on malformed IPv6 brackets; swallow
# so a garbage Origin doesn't 500 the SPA handler.
try:
parsed = urlparse(origin)
except ValueError:
return False
origin_canon = _canonical_origin(parsed.scheme, parsed.netloc)
if origin_canon is None:
return False
try:
self_canon = _canonical_origin(request.url.scheme, request.url.netloc)
except ValueError:
return False
if self_canon is None:
return False
return origin_canon == self_canon
def setup_frontend(app: FastAPI, build_path: Path):
"""Mount frontend static files (optional)"""
if not build_path.exists():
@ -766,11 +846,18 @@ def setup_frontend(app: FastAPI, build_path: Path):
if assets_dir.exists():
app.mount("/assets", StaticFiles(directory = assets_dir), name = "assets")
def _build_index_response() -> Response:
def _build_index_response(request: Request) -> Response:
content = (build_path / "index.html").read_bytes()
content = _strip_crossorigin(content)
content, nonce = _inject_bootstrap(content, app)
headers = {"Cache-Control": "no-cache, no-store, must-revalidate"}
# Bootstrap pw is same-origin only; Vary: Origin keeps caches honest.
if _is_same_origin_request(request):
content, nonce = _inject_bootstrap(content, app)
else:
nonce = None
headers = {
"Cache-Control": "no-cache, no-store, must-revalidate",
"Vary": "Origin",
}
if nonce:
headers[_CSP_SCRIPT_NONCE_HEADER] = nonce
return Response(
@ -780,11 +867,11 @@ def setup_frontend(app: FastAPI, build_path: Path):
)
@app.get("/")
async def serve_root():
return _build_index_response()
async def serve_root(request: Request):
return _build_index_response(request)
@app.get("/{full_path:path}")
async def serve_frontend(full_path: str):
async def serve_frontend(request: Request, full_path: str):
if full_path in {"api", "v1"} or full_path.startswith(("api/", "v1/")):
return {"error": "API endpoint not found"}
@ -798,6 +885,6 @@ def setup_frontend(app: FastAPI, build_path: Path):
return FileResponse(file_path)
# Serve index.html as bytes — avoids Content-Length mismatch
return _build_index_response()
return _build_index_response(request)
return True

52
studio/backend/models/inference.py
View file

@ -299,7 +299,11 @@ class InferenceStatusResponse(BaseModel):
"""Current inference backend status"""
active_model: Optional[str] = Field(
None, description = "Currently active model identifier"
None, description = "Currently active model display identifier"
)
model_identifier: Optional[str] = Field(
None,
description = "Loadable identifier for the active model.",
)
is_vision: bool = Field(
False, description = "Whether the active model is a vision model"
@ -471,6 +475,40 @@ class InputDocumentContentPart(BaseModel):
)
class OpenAIReasoningContentPart(BaseModel):
"""OpenAI Responses reasoning item paired with a tool output.
Reasoning models can require the previous ``reasoning`` output item
to be replayed immediately before an ``image_generation_call`` id
when manually managing Responses context. This part is OpenAI-only;
routes strip it for every other provider before proxying.
"""
type: Literal["reasoning"]
id: str = Field(..., description = "OpenAI reasoning output item id.")
summary: list[dict[str, Any]] = Field(default_factory = list)
status: Optional[Literal["in_progress", "completed", "incomplete"]] = None
class ImageGenerationCallContentPart(BaseModel):
"""OpenAI Responses image_generation call reference.
OpenAI accepts prior ``image_generation_call`` items in the next
Responses ``input`` array so follow-up prompts can edit or refine a
generated image without resending the base64 payload. The frontend
forwards this as a synthetic assistant content part when building
the next OpenAI Responses request; ``external_provider`` translates
it back to the provider-specific top-level input item.
"""
type: Literal["image_generation_call"]
id: str = Field(..., description = "OpenAI image_generation_call output item id.")
response_id: Optional[str] = Field(
None,
description = "OpenAI Responses response id to use as previous_response_id for follow-up edits.",
)
class CompactionContentPart(BaseModel):
"""Anthropic server-side compaction state, attached to an assistant
message for round-tripping on the next turn.
@ -504,6 +542,8 @@ ContentPart = Annotated[
Annotated[TextContentPart, Tag("text")],
Annotated[ImageContentPart, Tag("image_url")],
Annotated[InputDocumentContentPart, Tag("input_document")],
Annotated[OpenAIReasoningContentPart, Tag("reasoning")],
Annotated[ImageGenerationCallContentPart, Tag("image_generation_call")],
Annotated[CompactionContentPart, Tag("compaction")],
],
Discriminator(_content_part_discriminator),
@ -786,6 +826,16 @@ class ChatCompletionRequest(BaseModel):
"to auto-create."
),
)
fast_mode: Optional[bool] = Field(
None,
description = (
"[x-unsloth] Anthropic fast-mode toggle. On Claude Opus 4.6 / "
"4.7 adds the `fast-mode-2026-02-01` beta header and sends "
"`speed: 'fast'` for higher OTPS at premium pricing. Silently "
"ignored on every other model + provider. See "
"https://platform.claude.com/docs/en/build-with-claude/fast-mode"
),
)
@model_validator(mode = "after")
def _resolve_missing_tool_call_ids(self) -> "ChatCompletionRequest":

5
studio/backend/requirements/single-env/overrides-darwin-arm64.txt Normal file
View file

@ -0,0 +1,5 @@
# mlx-vlm / mlx-lm declare transformers>=5.x which conflicts with the
# main venv's constraints.txt pin transformers==4.57.6 and forces uv to
# backtrack unsloth. Relax to match the pin -- per-model 5.x routing
# happens at runtime via the side-car venvs.
transformers>=4.57.6

121
studio/backend/routes/data_recipe/jobs.py
View file

@ -95,6 +95,89 @@ def _used_llm_model_aliases(recipe: dict[str, Any]) -> set[str]:
return aliases
def _used_local_model_selections(
recipe: dict[str, Any], local_provider_names: set[str]
) -> dict[tuple[str, str], list[str]]:
used_aliases = _used_llm_model_aliases(recipe)
selections: dict[tuple[str, str], list[str]] = {}
for mc in recipe.get("model_configs", []):
if not isinstance(mc, dict):
continue
alias = mc.get("alias")
if not isinstance(alias, str) or alias not in used_aliases:
continue
provider = mc.get("provider")
if not isinstance(provider, str) or provider not in local_provider_names:
continue
model = mc.get("model")
target = model.strip() if isinstance(model, str) else ""
if not target or target.lower() == "local":
continue
variant = mc.get("gguf_variant")
gguf_variant = variant.strip() if isinstance(variant, str) else ""
selections.setdefault((target, gguf_variant), []).append(alias)
return selections
def _single_used_local_model_selection(
recipe: dict[str, Any], local_provider_names: set[str]
) -> tuple[str, str] | None:
selections = _used_local_model_selections(recipe, local_provider_names)
if not selections:
return None
if len(selections) > 1:
aliases = ", ".join(alias for values in selections.values() for alias in values)
raise ValueError(
"Recipes supports one active local model per run. "
f"Select the same local model and GGUF variant for: {aliases}."
)
return next(iter(selections))
def _loaded_local_model_identity() -> tuple[bool, str, str]:
from routes.inference import get_llama_cpp_backend
from core.inference import get_inference_backend
llama = get_llama_cpp_backend()
if llama.is_loaded:
model = str(getattr(llama, "model_identifier", "") or "").strip()
variant = str(getattr(llama, "hf_variant", "") or "").strip()
return True, model, variant
backend = get_inference_backend()
active_model = str(getattr(backend, "active_model_name", "") or "").strip()
if active_model:
return True, active_model, ""
return False, "", ""
def _ensure_selected_local_model_loaded(
recipe: dict[str, Any], local_provider_names: set[str]
) -> None:
model_loaded, active_model, active_variant = _loaded_local_model_identity()
if not model_loaded:
raise ValueError(
"No model loaded in Chat. Load a model first, then run the recipe."
)
selection = _single_used_local_model_selection(recipe, local_provider_names)
if selection is None:
return
target, gguf_variant = selection
variant_matches = not gguf_variant or active_variant == gguf_variant
if active_model.lower() != target.lower() or not variant_matches:
selected = f"{target} ({gguf_variant})" if gguf_variant else target
active = (
f"{active_model} ({active_variant})" if active_variant else active_model
)
raise ValueError(
"Selected local model is not loaded. "
f"Selected {selected}; active {active or 'none'}. "
"Load the selected model again, then run the recipe."
)
def _inject_local_structured_response_format(
recipe: dict[str, Any], local_provider_names: set[str]
) -> None:
@ -238,24 +321,12 @@ def _inject_local_providers(recipe: dict[str, Any], request: Request) -> Optiona
token = ""
internal_key_id: Optional[int] = None
if local_names & referenced_providers:
# Verify a model is loaded.
# NOTE: This is a point-in-time check (TOCTOU). The model could be unloaded
# or swapped after this check but before the recipe subprocess calls /v1.
# The inference endpoint returns a clear 400 in that case.
#
# Imports are deferred to avoid circular dependencies with inference modules.
from routes.inference import get_llama_cpp_backend
from core.inference import get_inference_backend
llama = get_llama_cpp_backend()
model_loaded = llama.is_loaded
if not model_loaded:
backend = get_inference_backend()
model_loaded = bool(backend.active_model_name)
if not model_loaded:
raise ValueError(
"No model loaded in Chat. Load a model first, then run the recipe."
)
# Verify the selected local model is loaded before minting a workflow
# key. This still remains a point-in-time singleton-backend check
# (TOCTOU): a future generation token should bind frontend load and
# job creation, and the inference endpoint returns a clear 400 if the
# model is later unloaded or swapped before the subprocess calls /v1.
_ensure_selected_local_model_loaded(recipe, local_names)
from auth import storage # deferred: avoids circular import
@ -287,12 +358,12 @@ def _inject_local_providers(recipe: dict[str, Any], request: Request) -> Optiona
providers[i].pop("extra_body", None)
# Force skip_health_check on any model_config that references a local
# provider. The local /v1/models endpoint only lists the real loaded
# model (e.g. "unsloth/llama-3.2-1b") and not the placeholder "local"
# that the recipe sends as the model id, so data_designer's pre-flight
# health check would otherwise fail before the first completion call.
# The backend route ignores the model id field in chat completions, so
# skipping the check is safe.
# provider. The frontend now sends the explicit selected local model id,
# but llama-server's /v1/models response can still differ from that id
# for local paths, cache aliases, and GGUF variant loads. The recipe run
# has already gated on a loaded local inference backend above, so the
# data_designer model-list health check would be redundant and can reject
# valid local selections.
for mc in recipe.get("model_configs", []):
if not isinstance(mc, dict):
continue
@ -319,7 +390,7 @@ def _inject_local_providers(recipe: dict[str, Any], request: Request) -> Optiona
tpl_kwargs = extra_body.get("chat_template_kwargs")
if not isinstance(tpl_kwargs, dict):
tpl_kwargs = {}
tpl_kwargs.setdefault("enable_thinking", False)
tpl_kwargs["enable_thinking"] = False
extra_body["chat_template_kwargs"] = tpl_kwargs
params["extra_body"] = extra_body

70
studio/backend/routes/inference.py
View file

@ -606,11 +606,16 @@ async def load_model(
backend = get_inference_backend()
llama_backend = get_llama_cpp_backend()
if request.gguf_variant:
is_direct_gguf_request = model_identifier.lower().endswith(".gguf")
if request.gguf_variant or is_direct_gguf_request:
gguf_variant_matches = is_direct_gguf_request or bool(
llama_backend.hf_variant
and request.gguf_variant
and llama_backend.hf_variant.lower() == request.gguf_variant.lower()
)
if (
llama_backend.is_loaded
and llama_backend.hf_variant
and llama_backend.hf_variant.lower() == request.gguf_variant.lower()
and gguf_variant_matches
and llama_backend.model_identifier
and llama_backend.model_identifier.lower() == model_identifier.lower()
# Match runtime settings too so Apply isn't dropped (#5401).
@ -619,7 +624,8 @@ async def load_model(
and getattr(llama_backend, "_audio_probed", True)
):
logger.info(
f"Model already loaded (GGUF): {model_log_label} variant={request.gguf_variant}, skipping reload"
"Model already loaded (GGUF): "
f"{model_log_label} variant={request.gguf_variant or llama_backend.hf_variant}, skipping reload"
)
inference_config = load_inference_config(llama_backend.model_identifier)
@ -1373,6 +1379,7 @@ async def get_status(
_audio_type = getattr(llama_backend, "_audio_type", None)
return InferenceStatusResponse(
active_model = _display_model_id,
model_identifier = None if _native_grant_backed else _model_id,
is_vision = llama_backend.is_vision,
is_gguf = True,
gguf_variant = llama_backend.hf_variant,
@ -1435,6 +1442,7 @@ async def get_status(
return InferenceStatusResponse(
active_model = backend.active_model_name,
model_identifier = backend.active_model_name,
is_vision = is_vision,
is_gguf = False,
is_audio = is_audio,
@ -1709,6 +1717,12 @@ def _build_external_messages(
see ``_INPUT_DOCUMENT_PROVIDERS``). For every other provider the
part is stripped so the unknown content type doesn't reach generic
/chat/completions passthrough and 400 the request.
- `reasoning`: OpenAI-only Responses reasoning item paired with a
prior tool output. Forwarded ONLY when provider_type=="openai"
so follow-up image edits can replay the required reasoning item.
- `image_generation_call`: OpenAI-only Responses image reference.
Forwarded ONLY when provider_type=="openai" so follow-up image
edits can reference prior generated images.
- `compaction`: Anthropic-only synthetic part (round-trips server-side
compaction state). Forwarded ONLY when provider_type=="anthropic";
stripped for every other provider so the unknown part doesn't
@ -1717,6 +1731,7 @@ def _build_external_messages(
"""
document_provider = provider_type in _INPUT_DOCUMENT_PROVIDERS
anthropic = provider_type == "anthropic"
openai = provider_type == "openai"
result = []
for msg in messages:
if isinstance(msg.content, str):
@ -1737,6 +1752,30 @@ def _build_external_messages(
"image_url": {"url": part.image_url.url},
}
)
elif (
part.type == "reasoning" and openai and msg.role == "assistant"
):
reasoning: dict[str, Any] = {
"type": "reasoning",
"id": part.id,
"summary": part.summary,
}
if part.status:
reasoning["status"] = part.status
parts.append(reasoning)
elif (
part.type == "image_generation_call"
and openai
and msg.role == "assistant"
):
# ExternalProviderClient maps this onto a top-level
# Responses input item after the current user prompt,
# or onto `previous_response_id` when response_id is
# available from the prior Responses turn.
image_ref = {"type": "image_generation_call", "id": part.id}
if getattr(part, "response_id", None):
image_ref["response_id"] = part.response_id
parts.append(image_ref)
elif part.type == "input_document" and document_provider:
# ExternalProviderClient maps this onto
# Anthropic's `document` or OpenAI Responses'
@ -1758,6 +1797,8 @@ def _build_external_messages(
# provider would 400 on the unknown part, so
# gate by provider_type.
parts.append({"type": "compaction", "content": part.content})
if msg.role == "assistant" and not parts:
continue
result.append({"role": msg.role, "content": parts})
else:
# Non-vision provider: strip images / documents, keep
@ -1769,8 +1810,28 @@ def _build_external_messages(
for p in msg.content:
if p.type == "text":
preserved.append({"type": "text", "text": p.text})
elif p.type == "reasoning" and openai and msg.role == "assistant":
reasoning: dict[str, Any] = {
"type": "reasoning",
"id": p.id,
"summary": p.summary,
}
if p.status:
reasoning["status"] = p.status
preserved.append(reasoning)
elif (
p.type == "image_generation_call"
and openai
and msg.role == "assistant"
):
image_ref = {"type": "image_generation_call", "id": p.id}
if getattr(p, "response_id", None):
image_ref["response_id"] = p.response_id
preserved.append(image_ref)
elif p.type == "compaction" and anthropic:
preserved.append({"type": "compaction", "content": p.content})
if msg.role == "assistant" and not preserved:
continue
if len(preserved) == 1 and preserved[0]["type"] == "text":
# Single text part collapses back to a string for
# providers that don't accept content arrays.
@ -1876,6 +1937,7 @@ async def _proxy_to_external_provider(
anthropic_code_exec_container_id = payload.anthropic_code_exec_container_id,
prompt_cache_ttl = payload.prompt_cache_ttl,
compaction_threshold = payload.compaction_threshold,
fast_mode = payload.fast_mode,
stream = payload.stream,
)
try:

157
studio/backend/run.py
View file

@ -9,6 +9,7 @@ Works independently and can be moved to any directory.
import os
import sys
from pathlib import Path
from typing import Optional
# Suppress annoying C-level dependency warnings globally (e.g. SwigPyPacked)
os.environ["PYTHONWARNINGS"] = "ignore"
@ -512,10 +513,94 @@ _server = None
_shutdown_event = None
_DEFAULT_FRONTEND_PATH = Path(__file__).resolve().parent.parent / "frontend" / "dist"
def _iter_frontend_fallback_candidates() -> "list[Path]":
"""Yield `studio/frontend/dist` paths to try when the default is missing.
Covers PATH-shadowed binaries whose __file__ resolves into a
site-packages tree that never received a vite build (e.g. plain
`pip install unsloth` from PyPI).
"""
import ast
import re
out: list[Path] = []
home_str = (
os.environ.get("UNSLOTH_STUDIO_HOME")
or os.environ.get("STUDIO_HOME")
or str(Path.home() / ".unsloth" / "studio")
)
venv_dir = Path(home_str).expanduser() / "unsloth_studio"
# Installer venv site-packages.
for pattern in (
"lib/python*/site-packages/studio/frontend/dist",
"Lib/site-packages/studio/frontend/dist",
):
out.extend(venv_dir.glob(pattern))
# Editable source roots referenced from the installer venv.
for sp_pattern in ("lib/python*/site-packages", "Lib/site-packages"):
for sp in venv_dir.glob(sp_pattern):
for finder in sp.glob("__editable___*_finder.py"):
try:
src = finder.read_text(encoding = "utf-8")
except OSError:
continue
# Tolerate single- or multi-line dict literals; [^}]* still
# rejects nested dicts, which the setuptools template never
# emits for editable installs.
m = re.search(
r"^MAPPING\s*(?::[^=]*)?=\s*(\{[^}]*\})", src, re.M | re.S
)
if not m:
continue
try:
mapping = ast.literal_eval(m.group(1))
except (SyntaxError, ValueError):
continue
# Defensive: literal_eval can return a set / list / None if the
# matched literal is not a dict (regex captures `{...}`).
if not isinstance(mapping, dict):
continue
studio_pkg = mapping.get("studio")
if studio_pkg:
out.append(Path(studio_pkg) / "frontend" / "dist")
return out
def _resolve_frontend_path(frontend_path: Path) -> tuple[Optional[Path], list[Path]]:
"""Pick a frontend dir that actually contains `index.html`.
Returns (chosen, attempted). `chosen` is None if nothing servable was
found; `attempted` is the full ordered list for diagnostics.
"""
attempted: list[Path] = []
seen: set[Path] = set()
def _try(p: Path) -> bool:
try:
key = p.resolve()
except OSError:
key = p
if key in seen:
return False
seen.add(key)
attempted.append(p)
return (p / "index.html").is_file()
if _try(Path(frontend_path)):
return attempted[-1], attempted
for alt in _iter_frontend_fallback_candidates():
if _try(alt):
return attempted[-1], attempted
return None, attempted
def run_server(
host: str = "127.0.0.1",
port: int = 8888,
frontend_path: Path = Path(__file__).resolve().parent.parent / "frontend" / "dist",
frontend_path: Path = _DEFAULT_FRONTEND_PATH,
silent: bool = False,
api_only: bool = False,
llama_parallel_slots: int = 1,
@ -584,14 +669,48 @@ def run_server(
print("=" * 50)
print("")
# Setup frontend if path provided (skip in api-only mode)
# Setup frontend if path provided (skip in api-only mode).
# Falls back through alternate locations if the default lacks a built
# dist; errors out loudly rather than silently serving 404 on `/`.
if frontend_path and not api_only:
if setup_frontend(app, frontend_path):
chosen, attempted = _resolve_frontend_path(Path(frontend_path))
if chosen is not None and setup_frontend(app, chosen):
if not silent:
print(f"[OK] Frontend loaded from {frontend_path}")
# Resolve so logs always show an absolute path for support.
try:
display = chosen.resolve()
except OSError:
display = chosen
print(f"[OK] Frontend loaded from {display}")
else:
if not silent:
print(f"[WARNING] Frontend not found at {frontend_path}")
home_str = (
os.environ.get("UNSLOTH_STUDIO_HOME")
or os.environ.get("STUDIO_HOME")
or str(Path.home() / ".unsloth" / "studio")
)
# Windows ships the user-facing shim at $STUDIO_HOME/bin/unsloth.exe
# (a hardlink to the venv exe); Linux/macOS use the venv binary
# at $STUDIO_HOME/unsloth_studio/bin/unsloth.
home = Path(home_str).expanduser()
if sys.platform == "win32":
installer_bin = home / "bin" / "unsloth.exe"
else:
installer_bin = home / "unsloth_studio" / "bin" / "unsloth"
tried_lines = "\n".join(f" - {p}" for p in attempted) or " (none)"
raise SystemExit(
"[ERROR] Studio frontend build not found.\n"
f"Tried:\n{tried_lines}\n"
"\n"
"Likely cause: another 'unsloth' on PATH is shadowing the "
"installer's binary and points at a site-packages tree with "
"no built dist.\n"
"\n"
"Fix one of:\n"
f" - run the installer's binary directly: {installer_bin} studio\n"
" - pass --frontend <path/to/studio/frontend/dist>\n"
" - pass --api-only to skip serving the web UI\n"
" - reinstall: curl -fsSL https://unsloth.ai/install.sh | sh"
)
# Resolve once; shared by the log rewrite and the banner.
display_host = _resolve_external_ip() if host == "0.0.0.0" else host
@ -718,7 +837,7 @@ if __name__ == "__main__":
parser.add_argument(
"--frontend",
type = str,
default = Path(__file__).resolve().parent.parent / "frontend" / "dist",
default = _DEFAULT_FRONTEND_PATH,
help = "Path to frontend build",
)
parser.add_argument("--silent", action = "store_true", help = "Suppress output")
@ -727,11 +846,33 @@ if __name__ == "__main__":
action = "store_true",
help = "API server only, no frontend (for Tauri)",
)
# Mirror unsloth_cli/commands/studio.py's _PARALLEL_*. Default 1
# applies only to direct backend launches; `unsloth studio run`
# always passes its own value (4) explicitly.
_PARALLEL_MIN = 1
_PARALLEL_MAX = 64
_PARALLEL_DEFAULT_PLAIN = 1
parser.add_argument(
"--parallel",
"--n-parallel",
type = int,
default = _PARALLEL_DEFAULT_PLAIN,
help = (
f"llama-server parallel decode slots ({_PARALLEL_MIN}..{_PARALLEL_MAX}). "
f"Default {_PARALLEL_DEFAULT_PLAIN}; `unsloth studio run` uses 4."
),
)
args = parser.parse_args()
if not _PARALLEL_MIN <= args.parallel <= _PARALLEL_MAX:
parser.error(f"--parallel must be between {_PARALLEL_MIN} and {_PARALLEL_MAX}")
kwargs = dict(
host = args.host, port = args.port, silent = args.silent, api_only = args.api_only
host = args.host,
port = args.port,
silent = args.silent,
api_only = args.api_only,
llama_parallel_slots = args.parallel,
)
if args.frontend is not None:
kwargs["frontend_path"] = Path(args.frontend)

353
studio/backend/tests/test_anthropic_citations.py Normal file
View file

@ -0,0 +1,353 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Tests for Anthropic ``citations_delta`` handling in the streaming proxy.
Verifies the proxy injects inline ``[N]`` markers after cited text,
dedupes by type-specific anchor (char_location, page_location,
content_block_location, search_result_location), forwards a synthetic
``document_citations`` tool_event at message_stop, and stays inert when
no citations_delta events fire. See
https://platform.claude.com/docs/en/build-with-claude/citations
"""
import asyncio
import json
import httpx
from core.inference import external_provider as ep_mod
from core.inference.external_provider import ExternalProviderClient
def _drive(coro):
return asyncio.new_event_loop().run_until_complete(coro)
def _make_client() -> ExternalProviderClient:
return ExternalProviderClient(
provider_type = "anthropic",
base_url = "https://api.anthropic.com/v1",
api_key = "sk-ant-test",
)
def _sse(events: list[dict]) -> bytes:
out = []
for e in events:
ev = e.get("type", "message")
out.append(f"event: {ev}\ndata: {json.dumps(e)}\n\n")
return "".join(out).encode("utf-8")
def _capture(monkeypatch, events: list[dict]) -> list[str]:
def handler(request: httpx.Request) -> httpx.Response:
return httpx.Response(
200,
content = _sse(events),
headers = {"content-type": "text/event-stream"},
)
monkeypatch.setattr(
ep_mod,
"_http_client",
httpx.AsyncClient(transport = httpx.MockTransport(handler)),
)
lines: list[str] = []
async def run():
client = _make_client()
try:
async for line in client.stream_chat_completion(
messages = [{"role": "user", "content": "what color is grass?"}],
model = "claude-opus-4-7",
max_tokens = 64,
):
lines.append(line)
finally:
await client.close()
_drive(run())
return lines
def _message_start() -> dict:
return {
"type": "message_start",
"message": {
"id": "m1",
"content": [],
"model": "claude-opus-4-7",
"role": "assistant",
"stop_reason": None,
"usage": {"input_tokens": 5, "output_tokens": 2},
},
}
def _content_block_start_text() -> dict:
return {
"type": "content_block_start",
"index": 0,
"content_block": {"type": "text", "text": ""},
}
def _text_delta(text: str, index: int = 0) -> dict:
return {
"type": "content_block_delta",
"index": index,
"delta": {"type": "text_delta", "text": text},
}
def _citations_delta(citation: dict, index: int = 0) -> dict:
return {
"type": "content_block_delta",
"index": index,
"delta": {"type": "citations_delta", "citation": citation},
}
def _content_block_stop(index: int = 0) -> dict:
return {"type": "content_block_stop", "index": index}
def _message_delta_end() -> dict:
return {"type": "message_delta", "delta": {"stop_reason": "end_turn"}}
def _message_stop() -> dict:
return {"type": "message_stop"}
def _joined(lines: list[str]) -> str:
return "\n".join(lines)
def test_no_citations_stream_unchanged(monkeypatch):
"""Plain text streams pass through with no inline markers and no
document_citations tool_event."""
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Grass is green."),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "Grass is green." in body
assert "document_citations" not in body
assert "[1]" not in body
def test_single_char_location_emits_inline_marker(monkeypatch):
cit = {
"type": "char_location",
"cited_text": "The grass is green.",
"document_index": 0,
"document_title": "Example",
"start_char_index": 0,
"end_char_index": 20,
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Grass is green."),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "Grass is green." in body
assert "[1]" in body, body
assert "document_citations" in body, body
assert '"document_index": 0' in body, body
assert "_key" not in body, body
def test_duplicate_citation_dedupes_to_same_number(monkeypatch):
cit = {
"type": "char_location",
"document_index": 0,
"document_title": "Example",
"start_char_index": 0,
"end_char_index": 20,
"cited_text": "The grass is green.",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Grass."),
_citations_delta(cit),
_text_delta(" Still green."),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert body.count("[1]") == 2, body
citation_blob = body[body.index("document_citations") :]
assert citation_blob.count('"start_char_index"') == 1, citation_blob
def test_distinct_sources_get_distinct_numbers(monkeypatch):
cit1 = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc A",
"start_char_index": 0,
"end_char_index": 5,
}
cit2 = {
"type": "page_location",
"document_index": 1,
"document_title": "Doc B",
"start_page_number": 3,
"end_page_number": 4,
}
cit3 = {
"type": "content_block_location",
"document_index": 2,
"document_title": "Doc C",
"start_block_index": 0,
"end_block_index": 1,
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("First"),
_citations_delta(cit1),
_text_delta(" Second"),
_citations_delta(cit2),
_text_delta(" Third"),
_citations_delta(cit3),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body and "[2]" in body and "[3]" in body, body
assert body.index("[1]") < body.index("[2]") < body.index("[3]")
def test_search_result_location_supported(monkeypatch):
cit = {
"type": "search_result_location",
"document_index": 0,
"document_title": "Anthropic Search Results",
"source": "https://example.com/doc.html",
"start_block_index": 0,
"end_block_index": 1,
"cited_text": "blah",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Some sourced fact."),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body
assert "search_result_location" in body
assert "example.com/doc.html" in body
def test_same_start_different_end_offsets_get_distinct_numbers(monkeypatch):
"""Same start_char_index + different end_char_index = distinct spans,
so they must get distinct footnote numbers (ranges use exclusive end)."""
cit_a = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 100,
"end_char_index": 150,
"cited_text": "first half",
}
cit_b = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 100,
"end_char_index": 250,
"cited_text": "wider span",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("A "),
_citations_delta(cit_a),
_text_delta(" and B "),
_citations_delta(cit_b),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body, body
assert "[2]" in body, body
def test_search_result_location_different_indices_get_distinct_numbers(monkeypatch):
"""Same source + different search_result_index = distinct footnotes
(matches the Anthropic search-result citation contract)."""
cit_a = {
"type": "search_result_location",
"search_result_index": 0,
"source": "https://example.com/result.html",
"title": "Result",
"start_block_index": 0,
"end_block_index": 1,
"cited_text": "first",
}
cit_b = {
"type": "search_result_location",
"search_result_index": 1,
"source": "https://example.com/result.html",
"title": "Result",
"start_block_index": 0,
"end_block_index": 1,
"cited_text": "second",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("A "),
_citations_delta(cit_a),
_text_delta(" and B "),
_citations_delta(cit_b),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body, body
assert "[2]" in body, body

690
studio/backend/tests/test_anthropic_citations_edge.py Normal file
View file

@ -0,0 +1,690 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Edge-case tests for Anthropic ``citations_delta`` handling.
Complements ``test_anthropic_citations.py``. Covers malformed payloads,
unusual orderings, mixed citation types, and the ``citations:
{enabled: true}`` opt-in attached to translated ``input_document``
blocks. See
https://platform.claude.com/docs/en/build-with-claude/citations and
https://platform.claude.com/docs/en/build-with-claude/search-results.
"""
import asyncio
import json
import httpx
from core.inference import external_provider as ep_mod
from core.inference.external_provider import ExternalProviderClient
# ── shared SSE harness ───────────────────────────────────────
def _drive(coro):
return asyncio.new_event_loop().run_until_complete(coro)
def _make_client() -> ExternalProviderClient:
return ExternalProviderClient(
provider_type = "anthropic",
base_url = "https://api.anthropic.com/v1",
api_key = "sk-ant-test",
)
def _sse(events: list[dict]) -> bytes:
out = []
for e in events:
ev = e.get("type", "message")
out.append(f"event: {ev}\ndata: {json.dumps(e)}\n\n")
return "".join(out).encode("utf-8")
def _capture(
monkeypatch,
events: list[dict],
*,
messages: list[dict] | None = None,
captured_body: dict | None = None,
) -> list[str]:
"""Drive ``stream_chat_completion`` against a mocked Anthropic
response and return the SSE lines. Pass ``captured_body`` to also
capture the outgoing request body for assertions on the translated
Anthropic shape.
"""
def handler(request: httpx.Request) -> httpx.Response:
if captured_body is not None:
try:
captured_body.update(json.loads(request.content.decode("utf-8")))
except Exception: # pragma: no cover -- diagnostic only
pass
return httpx.Response(
200,
content = _sse(events),
headers = {"content-type": "text/event-stream"},
)
monkeypatch.setattr(
ep_mod,
"_http_client",
httpx.AsyncClient(transport = httpx.MockTransport(handler)),
)
lines: list[str] = []
async def run():
client = _make_client()
try:
async for line in client.stream_chat_completion(
messages = messages
or [{"role": "user", "content": "what color is grass?"}],
model = "claude-opus-4-7",
max_tokens = 64,
):
lines.append(line)
finally:
await client.close()
_drive(run())
return lines
def _message_start() -> dict:
return {
"type": "message_start",
"message": {
"id": "m1",
"content": [],
"model": "claude-opus-4-7",
"role": "assistant",
"stop_reason": None,
"usage": {"input_tokens": 5, "output_tokens": 2},
},
}
def _content_block_start_text() -> dict:
return {
"type": "content_block_start",
"index": 0,
"content_block": {"type": "text", "text": ""},
}
def _text_delta(text: str, index: int = 0) -> dict:
return {
"type": "content_block_delta",
"index": index,
"delta": {"type": "text_delta", "text": text},
}
def _citations_delta(citation: dict, index: int = 0) -> dict:
return {
"type": "content_block_delta",
"index": index,
"delta": {"type": "citations_delta", "citation": citation},
}
def _content_block_stop(index: int = 0) -> dict:
return {"type": "content_block_stop", "index": index}
def _message_delta_end() -> dict:
return {"type": "message_delta", "delta": {"stop_reason": "end_turn"}}
def _message_stop() -> dict:
return {"type": "message_stop"}
def _joined(lines: list[str]) -> str:
return "\n".join(lines)
def _citation_payload(body: str) -> dict:
"""Pull the ``document_citations`` synthetic tool_event from the
SSE body and return its payload. Raises if absent."""
assert "document_citations" in body, body
for line in body.splitlines():
if not line.startswith("data: "):
continue
try:
payload = json.loads(line[len("data: ") :])
except json.JSONDecodeError:
continue
tool_event = payload.get("_toolEvent") if isinstance(payload, dict) else None
if (
isinstance(tool_event, dict)
and tool_event.get("type") == "document_citations"
):
return tool_event
raise AssertionError("document_citations event not parsed out of SSE body")
# ── edge cases ───────────────────────────────────────────────
def test_citation_with_no_preceding_text_still_emits_marker(monkeypatch):
"""citations_delta before any text_delta must not crash; marker
lands at the start of the block."""
cit = {
"type": "char_location",
"document_index": 0,
"document_title": "X",
"start_char_index": 0,
"end_char_index": 5,
"cited_text": "x",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_citations_delta(cit),
_text_delta("hello"),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body, body
assert "document_citations" in body, body
def test_citations_delta_with_non_dict_citation_is_ignored(monkeypatch):
"""Non-dict ``delta.citation`` must not crash, emit a marker, or
poison the document_citations list."""
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Hello."),
{
"type": "content_block_delta",
"index": 0,
"delta": {"type": "citations_delta", "citation": "not-a-dict"},
},
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "Hello." in body
assert "[1]" not in body
assert "document_citations" not in body
def test_citations_delta_with_missing_citation_field_is_ignored(monkeypatch):
"""Missing ``citation`` field is treated like a non-dict citation:
skip without crashing."""
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Hello."),
{
"type": "content_block_delta",
"index": 0,
"delta": {"type": "citations_delta"},
},
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "Hello." in body
assert "[1]" not in body
assert "document_citations" not in body
def test_char_location_with_reversed_indices_does_not_crash(monkeypatch):
"""Malformed char_location with reversed indices must not crash;
the dedup key accepts any int pair and still surfaces a footnote."""
cit = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 300,
"end_char_index": 50,
"cited_text": "?",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Weird."),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body, body
payload = _citation_payload(body)
assert payload["citations"][0]["start_char_index"] == 300
assert payload["citations"][0]["end_char_index"] == 50
def test_page_location_missing_document_index_does_not_crash(monkeypatch):
"""page_location missing ``document_index`` still produces a
footnote; dedup key falls back to ``None`` for the missing field."""
cit = {
"type": "page_location",
"document_title": "Untitled PDF",
"start_page_number": 1,
"end_page_number": 2,
"cited_text": "p1",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("From the PDF:"),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body, body
payload = _citation_payload(body)
assert payload["citations"][0].get("document_index") is None
def test_content_block_location_with_non_int_block_index_does_not_crash(monkeypatch):
"""content_block_location with string block indices must not crash;
dedup key tolerates non-int values."""
cit = {
"type": "content_block_location",
"document_index": 0,
"document_title": "Custom",
"start_block_index": "0",
"end_block_index": "1",
"cited_text": "anything",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Cite."),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body, body
payload = _citation_payload(body)
assert payload["citations"][0]["start_block_index"] == "0"
def test_unknown_citation_type_falls_back_to_stringified_key(monkeypatch):
"""Unknown citation ``type`` (forward-compat) still dedupes:
identical ones collapse, differing ones get distinct numbers."""
cit_a = {
"type": "future_shape_location",
"anchor": "abc",
"cited_text": "blah",
}
cit_b = {
"type": "future_shape_location",
"anchor": "xyz",
"cited_text": "blah",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("A"),
_citations_delta(cit_a),
_text_delta(" again"),
_citations_delta(cit_a),
_text_delta(" B"),
_citations_delta(cit_b),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
# cit_a dedupes onto [1], cit_b gets [2].
assert body.count("[1]") == 2, body
assert body.count("[2]") == 1, body
payload = _citation_payload(body)
assert len(payload["citations"]) == 2
def test_mixed_citation_types_same_document_get_distinct_keys(monkeypatch):
"""char_location and page_location on the same document_index are
distinct shapes; dedup key uses citation type as its first slot."""
cit_char = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 0,
"end_char_index": 10,
}
cit_page = {
"type": "page_location",
"document_index": 0,
"document_title": "Doc",
"start_page_number": 1,
"end_page_number": 2,
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("char-cite"),
_citations_delta(cit_char),
_text_delta(" page-cite"),
_citations_delta(cit_page),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body and "[2]" in body, body
payload = _citation_payload(body)
assert len(payload["citations"]) == 2
def test_cited_text_is_preserved_in_synthetic_event(monkeypatch):
"""``cited_text`` must survive into the synthetic event so the
Sources panel can render it as a tooltip. Anthropic does not bill
cited_text against output tokens, so preserving it is free."""
cit = {
"type": "char_location",
"document_index": 0,
"document_title": "Trustworthy Doc",
"start_char_index": 0,
"end_char_index": 20,
"cited_text": "The grass is green.",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Grass is green."),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
payload = _citation_payload(body)
assert payload["citations"][0]["cited_text"] == "The grass is green."
def test_internal_key_field_never_leaks_to_client(monkeypatch):
"""The internal ``_key`` dedup sentinel must be stripped before
the synthetic event is forwarded; it is not an Anthropic field."""
cit = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 0,
"end_char_index": 5,
"cited_text": "..",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("hi"),
_citations_delta(cit),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
payload = _citation_payload(body)
assert payload["citations"], payload
for c in payload["citations"]:
assert "_key" not in c, c
def test_citation_across_multiple_content_blocks_numbers_continue(monkeypatch):
"""Footnote numbering is per-message, not per-content-block:
citations across separate blocks emit [1] then [2]."""
cit_a = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 0,
"end_char_index": 5,
}
cit_b = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 100,
"end_char_index": 105,
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("first"),
_citations_delta(cit_a, index = 0),
_content_block_stop(0),
{
"type": "content_block_start",
"index": 1,
"content_block": {"type": "text", "text": ""},
},
_text_delta(" second", index = 1),
_citations_delta(cit_b, index = 1),
_content_block_stop(1),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "[1]" in body and "[2]" in body, body
assert body.index("[1]") < body.index("[2]")
payload = _citation_payload(body)
assert len(payload["citations"]) == 2
def test_inline_marker_lands_after_text_run(monkeypatch):
"""Inline ``[N]`` must land AFTER the cited text run: Anthropic
streams text then citation, so the proxy emits ``"...green.[1]"``
not ``"[1]green"``."""
cit = {
"type": "char_location",
"document_index": 0,
"document_title": "Doc",
"start_char_index": 0,
"end_char_index": 20,
"cited_text": "grass",
}
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Grass is green."),
_citations_delta(cit),
_text_delta(" Sky is blue."),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
grass = body.index("Grass is green.")
marker = body.index("[1]")
sky = body.index("Sky is blue.")
assert grass < marker < sky, body
def test_no_synthetic_event_when_only_text_deltas(monkeypatch):
"""No citations_delta means no synthetic ``document_citations``
event; Sources panel relies on absence to suppress the section."""
lines = _capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("Just some prose. "),
_text_delta("More prose."),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
)
body = _joined(lines)
assert "document_citations" not in body
assert "[1]" not in body
def test_input_document_translation_enables_citations(monkeypatch):
"""``input_document`` must translate to an Anthropic ``document``
block carrying ``citations: {enabled: true}`` (both base64 and url
source branches) so upstream emits citations_delta."""
captured_b64: dict = {}
_capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("ok"),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
messages = [
{
"role": "user",
"content": [
{
"type": "input_document",
"file_data": "data:application/pdf;base64,QUJD",
"filename": "spec.pdf",
},
{"type": "text", "text": "summarise"},
],
}
],
captured_body = captured_b64,
)
user_msg = captured_b64["messages"][0]
doc_block = next(p for p in user_msg["content"] if p.get("type") == "document")
assert doc_block["source"]["type"] == "base64", doc_block
assert doc_block.get("citations") == {"enabled": True}, doc_block
captured_url: dict = {}
_capture(
monkeypatch,
[
_message_start(),
_content_block_start_text(),
_text_delta("ok"),
_content_block_stop(),
_message_delta_end(),
_message_stop(),
],
messages = [
{
"role": "user",
"content": [
{
"type": "input_document",
"file_url": "https://example.com/doc.pdf",
"filename": "doc.pdf",
},
{"type": "text", "text": "summarise"},
],
}
],
captured_body = captured_url,
)
user_msg = captured_url["messages"][0]
doc_block = next(p for p in user_msg["content"] if p.get("type") == "document")
assert doc_block["source"]["type"] == "url", doc_block
assert doc_block.get("citations") == {"enabled": True}, doc_block
# ── cited_text truncation + safe-url citation conversion ────────
def test_cited_text_truncated_in_synthetic_event(monkeypatch):
"""``cited_text`` is capped server-side so multi-KB spans do not
balloon the SSE payload."""
from core.inference.external_provider import _CITED_TEXT_MAX_LEN
long_quote = "x" * (_CITED_TEXT_MAX_LEN + 4000)
events = [
{
"type": "message_start",
"message": {
"id": "msg_1",
"usage": {"input_tokens": 1, "output_tokens": 0},
},
},
{
"type": "content_block_start",
"index": 0,
"content_block": {"type": "text", "text": ""},
},
{
"type": "content_block_delta",
"index": 0,
"delta": {"type": "text_delta", "text": "claim "},
},
{
"type": "content_block_delta",
"index": 0,
"delta": {
"type": "citations_delta",
"citation": {
"type": "char_location",
"document_index": 0,
"document_title": "doc",
"start_char_index": 0,
"end_char_index": 5,
"cited_text": long_quote,
},
},
},
{"type": "content_block_stop", "index": 0},
{
"type": "message_delta",
"delta": {"stop_reason": "end_turn"},
"usage": {"output_tokens": 1},
},
{"type": "message_stop"},
]
chunks = _capture(monkeypatch, events)
tool_events = [c for c in chunks if "_toolEvent" in c and "document_citations" in c]
assert tool_events, "no document_citations tool event"
payload = json.loads(tool_events[0].split("data: ", 1)[1])
cited = payload["_toolEvent"]["citations"][0]["cited_text"]
assert len(cited) <= _CITED_TEXT_MAX_LEN + 1, len(cited)
assert cited.endswith("…")

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# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Tests for Anthropic fast-mode wiring and streaming refusal handling.
fast_mode=True on Opus 4.6/4.7 attaches the ``fast-mode-2026-02-01``
beta header and sets ``speed: "fast"``; unsupported models drop both.
Streaming ``stop_reason: "refusal"`` surfaces a user notice before the
``content_filter`` finish chunk.
https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/handle-streaming-refusals
"""
import asyncio
import json
import httpx
from core.inference import external_provider as ep_mod
from core.inference.external_provider import ExternalProviderClient
def _drive(coro):
return asyncio.new_event_loop().run_until_complete(coro)
def _make_client() -> ExternalProviderClient:
return ExternalProviderClient(
provider_type = "anthropic",
base_url = "https://api.anthropic.com/v1",
api_key = "sk-ant-test",
)
def _empty_message_sse() -> bytes:
return (
b'event: message_start\ndata: {"type":"message_start","message":'
b'{"id":"m1","content":[],"model":"claude-opus-4-7","role":"assistant",'
b'"stop_reason":null,"usage":{"input_tokens":1,"output_tokens":1}}}\n\n'
b'event: message_delta\ndata: {"type":"message_delta",'
b'"delta":{"stop_reason":"end_turn"}}\n\n'
b'event: message_stop\ndata: {"type":"message_stop"}\n\n'
)
def _refusal_sse() -> bytes:
return (
b'event: message_start\ndata: {"type":"message_start","message":'
b'{"id":"m1","content":[],"model":"claude-opus-4-7","role":"assistant",'
b'"stop_reason":null,"usage":{"input_tokens":1,"output_tokens":1}}}\n\n'
b'event: content_block_start\ndata: {"type":"content_block_start",'
b'"index":0,"content_block":{"type":"text","text":""}}\n\n'
b'event: content_block_delta\ndata: {"type":"content_block_delta",'
b'"index":0,"delta":{"type":"text_delta","text":"Hello."}}\n\n'
b'event: content_block_stop\ndata: {"type":"content_block_stop","index":0}\n\n'
b'event: message_delta\ndata: {"type":"message_delta",'
b'"delta":{"stop_reason":"refusal"}}\n\n'
b'event: message_stop\ndata: {"type":"message_stop"}\n\n'
)
def _capture(monkeypatch, sse: bytes = b"", **kwargs) -> tuple[dict, list[str]]:
"""Install a MockTransport, drive one streamed call, return body+lines."""
captured: dict = {}
def handler(request: httpx.Request) -> httpx.Response:
captured["body"] = json.loads(request.content.decode("utf-8"))
captured["headers"] = dict(request.headers)
return httpx.Response(
200,
content = sse or _empty_message_sse(),
headers = {"content-type": "text/event-stream"},
)
monkeypatch.setattr(
ep_mod,
"_http_client",
httpx.AsyncClient(transport = httpx.MockTransport(handler)),
)
out_lines: list[str] = []
async def run():
client = _make_client()
try:
async for line in client.stream_chat_completion(
messages = [{"role": "user", "content": "hi"}],
model = kwargs.get("model", "claude-opus-4-7"),
temperature = 0.7,
top_p = 0.95,
max_tokens = 32,
fast_mode = kwargs.get("fast_mode"),
):
out_lines.append(line)
finally:
await client.close()
_drive(run())
return captured, out_lines
def test_fast_mode_attaches_beta_header_and_speed_on_opus_4_7(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-7")
assert cap["body"].get("speed") == "fast", cap["body"]
beta = cap["headers"].get("anthropic-beta", "")
assert "fast-mode-2026-02-01" in beta, beta
def test_fast_mode_attaches_beta_header_and_speed_on_opus_4_6(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-6")
assert cap["body"].get("speed") == "fast", cap["body"]
assert "fast-mode-2026-02-01" in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_dropped_on_sonnet(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-sonnet-4-6")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_dropped_on_haiku(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-haiku-4-5")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_dropped_on_older_opus(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-5")
assert "speed" not in cap["body"], cap["body"]
def test_fast_mode_false_does_not_attach_header_or_field(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = False)
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_none_does_not_attach_header_or_field(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = None)
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_refusal_emits_user_facing_notice_and_content_filter_finish(monkeypatch):
_, lines = _capture(monkeypatch, sse = _refusal_sse())
body = "\n".join(lines)
# User-visible refusal notice.
assert "stopped by Anthropic's safety classifier" in body, body
# OpenAI-spec finish_reason mapping.
assert '"finish_reason": "content_filter"' in body, body
# Original deltas preserved before the refusal supplement.
assert "Hello." in body, body
def test_refusal_emits_tool_event_for_chat_adapter_drop(monkeypatch):
"""Refused turns emit an out-of-band `_toolEvent` that the chat-adapter
latches into assistant `metadata.custom.anthropicRefusal`, driving
the next-request prune. Tool event (not text) prevents spoofing.
"""
_, lines = _capture(monkeypatch, sse = _refusal_sse())
body = "\n".join(lines)
assert '"_toolEvent": {"type": "anthropic_refusal"}' in body, body
# Visible refusal text must not embed a sentinel that could spoof
# a context reset if echoed by another assistant message.
assert "studio:anthropic-refusal" not in body, body

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# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Edge-case coverage for the Anthropic fast-mode + refusal wiring.
Complements ``test_anthropic_fast_mode_and_refusal.py`` (happy path)
with dated snapshots, strict opt-in (future Opus families do not
auto-enable), multi-beta header merging, refusal stream ordering, and
the non-destruction guarantee for unset/None fast_mode.
"""
import asyncio
import json
import re
import httpx
from core.inference import external_provider as ep_mod
from core.inference.external_provider import ExternalProviderClient
def _drive(coro):
return asyncio.new_event_loop().run_until_complete(coro)
def _make_client() -> ExternalProviderClient:
return ExternalProviderClient(
provider_type = "anthropic",
base_url = "https://api.anthropic.com/v1",
api_key = "sk-ant-test",
)
def _empty_message_sse(model: str = "claude-opus-4-7") -> bytes:
return (
b'event: message_start\ndata: {"type":"message_start","message":'
b'{"id":"m1","content":[],"model":"' + model.encode() + b'",'
b'"role":"assistant","stop_reason":null,"usage":'
b'{"input_tokens":1,"output_tokens":1}}}\n\n'
b'event: message_delta\ndata: {"type":"message_delta",'
b'"delta":{"stop_reason":"end_turn"}}\n\n'
b'event: message_stop\ndata: {"type":"message_stop"}\n\n'
)
def _refusal_sse(model: str = "claude-opus-4-7") -> bytes:
return (
b'event: message_start\ndata: {"type":"message_start","message":'
b'{"id":"m1","content":[],"model":"' + model.encode() + b'",'
b'"role":"assistant","stop_reason":null,"usage":'
b'{"input_tokens":1,"output_tokens":1}}}\n\n'
b'event: content_block_start\ndata: {"type":"content_block_start",'
b'"index":0,"content_block":{"type":"text","text":""}}\n\n'
b'event: content_block_delta\ndata: {"type":"content_block_delta",'
b'"index":0,"delta":{"type":"text_delta","text":"Hello."}}\n\n'
b'event: content_block_stop\ndata: {"type":"content_block_stop","index":0}\n\n'
b'event: message_delta\ndata: {"type":"message_delta",'
b'"delta":{"stop_reason":"refusal"}}\n\n'
b'event: message_stop\ndata: {"type":"message_stop"}\n\n'
)
def _capture(monkeypatch, sse: bytes = b"", **kwargs) -> tuple[dict, list[str]]:
"""Install a MockTransport, drive one streamed call, return body+lines."""
captured: dict = {}
def handler(request: httpx.Request) -> httpx.Response:
captured["body"] = json.loads(request.content.decode("utf-8"))
captured["headers"] = dict(request.headers)
return httpx.Response(
200,
content = sse or _empty_message_sse(kwargs.get("model", "claude-opus-4-7")),
headers = {"content-type": "text/event-stream"},
)
monkeypatch.setattr(
ep_mod,
"_http_client",
httpx.AsyncClient(transport = httpx.MockTransport(handler)),
)
out_lines: list[str] = []
async def run():
client = _make_client()
try:
extra = {}
for key in (
"enabled_tools",
"compaction_threshold",
"fast_mode",
):
if key in kwargs:
extra[key] = kwargs[key]
async for line in client.stream_chat_completion(
messages = [{"role": "user", "content": "hi"}],
model = kwargs.get("model", "claude-opus-4-7"),
temperature = 0.7,
top_p = 0.95,
max_tokens = 32,
**extra,
):
out_lines.append(line)
finally:
await client.close()
_drive(run())
return captured, out_lines
# ──────────────────────────── dated snapshot prefix ────────────────────────────
def test_fast_mode_attaches_on_dated_opus_4_7_snapshot(monkeypatch):
"""Dated snapshot ``claude-opus-4-7-2026-02-01`` must match the prefix."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-7-2026-02-01")
assert cap["body"].get("speed") == "fast", cap["body"]
assert "fast-mode-2026-02-01" in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_attaches_on_dated_opus_4_6_snapshot(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-6-2026-02-01")
assert cap["body"].get("speed") == "fast", cap["body"]
assert "fast-mode-2026-02-01" in cap["headers"].get("anthropic-beta", "")
# ──────────────────────────── strict opt-in semantics ────────────────────────────
def test_fast_mode_does_not_auto_enable_on_future_opus_4_8(monkeypatch):
"""Future ``claude-opus-4-8`` must not auto-enable; opt-in per family."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-8")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_does_not_auto_enable_on_future_opus_5(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-5")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_does_not_auto_enable_on_sonnet_dated_snapshot(monkeypatch):
"""Sonnet snapshots share the compaction prefix but not fast_mode."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-sonnet-4-6-2026-02-01")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
# ──────────────────────────── beta header merge ────────────────────────────
def _beta_parts(headers: dict) -> list[str]:
raw = headers.get("anthropic-beta", "")
return [p.strip() for p in raw.split(",") if p.strip()]
def test_fast_mode_merges_with_code_execution_beta(monkeypatch):
"""fast_mode + code_execution -> two comma-separated betas, no overwrite."""
cap, _ = _capture(
monkeypatch,
fast_mode = True,
model = "claude-opus-4-7",
enabled_tools = ["code_execution"],
)
parts = _beta_parts(cap["headers"])
assert "fast-mode-2026-02-01" in parts, cap["headers"]
assert any(p.startswith("code-execution-") for p in parts), cap["headers"]
# No duplicates.
assert len(parts) == len(set(parts)), parts
def test_fast_mode_merges_with_compaction_beta(monkeypatch):
"""fast_mode + compaction_threshold >= 50K -> both betas present."""
cap, _ = _capture(
monkeypatch,
fast_mode = True,
model = "claude-opus-4-7",
compaction_threshold = 100_000,
)
parts = _beta_parts(cap["headers"])
assert "fast-mode-2026-02-01" in parts, cap["headers"]
assert "compact-2026-01-12" in parts, cap["headers"]
def test_fast_mode_merges_with_code_execution_and_compaction(monkeypatch):
"""Three betas coexist in one comma-separated header, no duplicates."""
cap, _ = _capture(
monkeypatch,
fast_mode = True,
model = "claude-opus-4-7",
enabled_tools = ["code_execution"],
compaction_threshold = 100_000,
)
parts = _beta_parts(cap["headers"])
assert "fast-mode-2026-02-01" in parts
assert "compact-2026-01-12" in parts
assert any(p.startswith("code-execution-") for p in parts), parts
assert len(parts) >= 3
assert len(parts) == len(set(parts)), parts
def test_fast_mode_beta_value_is_pinned(monkeypatch):
"""Pin the exact beta tag ``fast-mode-2026-02-01`` from the docs."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-7")
parts = _beta_parts(cap["headers"])
assert "fast-mode-2026-02-01" in parts, parts
# Reject obvious typos.
assert not any(p.startswith("fastmode-") for p in parts), parts
assert not any("fast_mode" in p for p in parts), parts
# ──────────────────────────── non-destruction guarantee ────────────────────────────
def test_fast_mode_unset_is_byte_identical_to_omitted(monkeypatch):
"""``fast_mode=None`` must produce the same body/headers as omission."""
cap_none, _ = _capture(monkeypatch, fast_mode = None, model = "claude-opus-4-7")
# Re-run without passing fast_mode at all.
captured: dict = {}
def handler(request: httpx.Request) -> httpx.Response:
captured["body"] = json.loads(request.content.decode("utf-8"))
captured["headers"] = dict(request.headers)
return httpx.Response(
200,
content = _empty_message_sse(),
headers = {"content-type": "text/event-stream"},
)
monkeypatch.setattr(
ep_mod,
"_http_client",
httpx.AsyncClient(transport = httpx.MockTransport(handler)),
)
async def run():
client = _make_client()
try:
async for _ in client.stream_chat_completion(
messages = [{"role": "user", "content": "hi"}],
model = "claude-opus-4-7",
temperature = 0.7,
top_p = 0.95,
max_tokens = 32,
):
pass
finally:
await client.close()
_drive(run())
assert cap_none["body"] == captured["body"], (cap_none["body"], captured["body"])
# Headers can vary by httpx-injected fields (host, connection); compare
# the load-bearing ones.
for key in ("anthropic-version", "x-api-key", "content-type"):
assert cap_none["headers"].get(key) == captured["headers"].get(key), key
assert "anthropic-beta" not in cap_none["headers"]
assert "anthropic-beta" not in captured["headers"]
assert "speed" not in cap_none["body"]
assert "speed" not in captured["body"]
def test_fast_mode_false_on_opus_4_7_byte_identical_to_unset(monkeypatch):
"""``fast_mode=False`` produces the same outbound shape as unset."""
cap_false, _ = _capture(monkeypatch, fast_mode = False, model = "claude-opus-4-7")
assert "speed" not in cap_false["body"], cap_false["body"]
assert "fast-mode-2026-02-01" not in cap_false["headers"].get("anthropic-beta", "")
# ──────────────────────────── refusal stream ordering ────────────────────────────
def test_refusal_notice_appears_before_content_filter_chunk(monkeypatch):
"""The notice content delta must precede the finish_reason chunk."""
_, lines = _capture(monkeypatch, sse = _refusal_sse(), model = "claude-opus-4-7")
notice_idx = next(i for i, l in enumerate(lines) if "stopped by Anthropic" in l)
filter_idx = next(
i for i, l in enumerate(lines) if '"finish_reason": "content_filter"' in l
)
assert notice_idx < filter_idx, (notice_idx, filter_idx, lines)
def test_refusal_tool_event_emitted_exactly_once(monkeypatch):
"""A single refusal emits the chat-adapter drop signal exactly once."""
_, lines = _capture(monkeypatch, sse = _refusal_sse())
body = "\n".join(lines)
count = body.count('"_toolEvent": {"type": "anthropic_refusal"}')
assert count == 1, (count, body)
def test_refusal_text_carries_no_html_sentinel(monkeypatch):
"""Visible refusal text must not embed a ``studio:anthropic-refusal``
sentinel; the drop signal rides _toolEvent only."""
_, lines = _capture(monkeypatch, sse = _refusal_sse())
body = "\n".join(lines)
assert "studio:anthropic-refusal" not in body, body
def test_refusal_handling_works_on_sonnet_model(monkeypatch):
"""Refusal handling is provider-side; Sonnet refusals must also surface."""
_, lines = _capture(
monkeypatch, sse = _refusal_sse("claude-sonnet-4-6"), model = "claude-sonnet-4-6"
)
body = "\n".join(lines)
assert "stopped by Anthropic's safety classifier" in body, body
assert '"_toolEvent": {"type": "anthropic_refusal"}' in body, body
assert '"finish_reason": "content_filter"' in body, body
def test_refusal_preserves_partial_assistant_text(monkeypatch):
"""Partial deltas already streamed must precede the refusal notice."""
_, lines = _capture(monkeypatch, sse = _refusal_sse(), model = "claude-opus-4-7")
body = "\n".join(lines)
hello_idx = body.index("Hello.")
notice_idx = body.index("stopped by Anthropic")
assert hello_idx < notice_idx, (hello_idx, notice_idx)
def test_refusal_chunk_is_proper_openai_delta_shape(monkeypatch):
"""The notice rides ``choices[0].delta.content`` (not a finish chunk);
OpenAI-spec clients treat it as ordinary streamed text."""
_, lines = _capture(monkeypatch, sse = _refusal_sse(), model = "claude-opus-4-7")
# Find the chunk that carries the refusal text.
notice_chunk = None
for line in lines:
if line.startswith("data: ") and "stopped by Anthropic" in line:
notice_chunk = json.loads(line[len("data: ") :])
break
assert notice_chunk is not None, lines
choice = notice_chunk["choices"][0]
assert "delta" in choice and "content" in choice["delta"], notice_chunk
# Must NOT carry a finish_reason itself -- that comes on the next
# chunk.
assert choice.get("finish_reason") in (None,), notice_chunk
# Refusal text is plain-spoken; no embedded sentinel.
assert "studio:anthropic-refusal" not in choice["delta"]["content"]
def test_refusal_tool_event_chunk_shape(monkeypatch):
"""Drop signal rides a Studio `_toolEvent` envelope (delta={},
finish_reason=null); the frontend latches on
`_toolEvent.type == "anthropic_refusal"`."""
_, lines = _capture(monkeypatch, sse = _refusal_sse(), model = "claude-opus-4-7")
refusal_chunk = None
for line in lines:
if line.startswith("data: ") and "anthropic_refusal" in line:
refusal_chunk = json.loads(line[len("data: ") :])
break
assert refusal_chunk is not None, lines
assert refusal_chunk["_toolEvent"] == {"type": "anthropic_refusal"}, refusal_chunk
choice = refusal_chunk["choices"][0]
assert choice["delta"] == {}, refusal_chunk
assert choice["finish_reason"] is None, refusal_chunk
# ──────────────────────────── future-proofing ────────────────────────────
def test_fast_mode_prefix_tuple_matches_capability_doc(monkeypatch):
"""Tuple must exactly match the two families in the upstream docs:
https://platform.claude.com/docs/en/build-with-claude/fast-mode."""
from core.inference.external_provider import _ANTHROPIC_FAST_MODE_PREFIXES
assert set(_ANTHROPIC_FAST_MODE_PREFIXES) == {
"claude-opus-4-7",
"claude-opus-4-6",
}, _ANTHROPIC_FAST_MODE_PREFIXES
def test_fast_mode_speed_field_value_is_literal_fast(monkeypatch):
"""Pin the wire value to the literal string ``"fast"``."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-7")
assert cap["body"]["speed"] == "fast", cap["body"]
def test_fast_mode_dropped_on_opus_4_5_dated_snapshot(monkeypatch):
"""Previous-family snapshots like ``claude-opus-4-5-2025-...`` must not match."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-5-2025-08-01")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_rejects_prefix_collision_4_70(monkeypatch):
"""IDs like ``claude-opus-4-70`` / ``-4-7b`` must not match the prefix."""
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-70")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_rejects_prefix_collision_4_7b(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-7b")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
def test_fast_mode_rejects_prefix_collision_4_6_extra(monkeypatch):
cap, _ = _capture(monkeypatch, fast_mode = True, model = "claude-opus-4-60")
assert "speed" not in cap["body"], cap["body"]
assert "fast-mode-2026-02-01" not in cap["headers"].get("anthropic-beta", "")
# ──────────────────────────── usage.speed propagation ────────────────────────────
def _fast_speed_sse(model: str = "claude-opus-4-7", speed: str = "fast") -> bytes:
return (
b'event: message_start\ndata: {"type":"message_start","message":'
b'{"id":"m1","content":[],"model":"' + model.encode() + b'",'
b'"role":"assistant","stop_reason":null,"usage":'
b'{"input_tokens":4,"output_tokens":1}}}\n\n'
b'event: content_block_start\ndata: {"type":"content_block_start",'
b'"index":0,"content_block":{"type":"text","text":""}}\n\n'
b'event: content_block_delta\ndata: {"type":"content_block_delta",'
b'"index":0,"delta":{"type":"text_delta","text":"hi"}}\n\n'
b'event: content_block_stop\ndata: {"type":"content_block_stop","index":0}\n\n'
b'event: message_delta\ndata: {"type":"message_delta",'
b'"delta":{"stop_reason":"end_turn"},'
b'"usage":{"output_tokens":5,"speed":"' + speed.encode() + b'"}}\n\n'
b'event: message_stop\ndata: {"type":"message_stop"}\n\n'
)
def test_usage_speed_propagates_to_final_usage_chunk_fast(monkeypatch):
"""``usage.speed == "fast"`` from upstream must reach the Studio usage chunk."""
_, lines = _capture(monkeypatch, sse = _fast_speed_sse(speed = "fast"))
usage_lines = [l for l in lines if l.startswith("data: ") and '"usage"' in l]
assert usage_lines, lines
parsed = [json.loads(l[len("data: ") :]) for l in usage_lines]
speeds = [p["usage"].get("speed") for p in parsed if "usage" in p]
assert "fast" in speeds, parsed
def test_usage_speed_propagates_to_final_usage_chunk_standard(monkeypatch):
_, lines = _capture(monkeypatch, sse = _fast_speed_sse(speed = "standard"))
parsed = [
json.loads(l[len("data: ") :])
for l in lines
if l.startswith("data: ") and '"usage"' in l
]
speeds = [p["usage"].get("speed") for p in parsed if "usage" in p]
assert "standard" in speeds, parsed
def test_usage_speed_absent_when_anthropic_does_not_report(monkeypatch):
"""Studio must not invent ``usage.speed`` when upstream omits it."""
_, lines = _capture(monkeypatch)
parsed = [
json.loads(l[len("data: ") :])
for l in lines
if l.startswith("data: ") and '"usage"' in l
]
for p in parsed:
usage = p.get("usage") or {}
assert "speed" not in usage, p

51
studio/backend/tests/test_anthropic_web_fetch.py
View file

@ -2,26 +2,12 @@
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""
Unit tests for Anthropic's server-side `web_fetch_20250910` tool
translation in `_stream_anthropic`.
Covers:
- Request body: when ``enabled_tools=["web_fetch"]``, the outbound
``tools`` array carries ``{"type":"web_fetch_20250910",
"name":"web_fetch", "max_uses":5}``. No beta header is required.
- Combined request: ``enabled_tools=["web_search","web_fetch",
"code_execution"]`` sends all three tool entries.
- Disabled by default: with ``enabled_tools=["web_search"]`` (or None),
the body does NOT carry a web_fetch entry.
- SSE translation (success): a `web_fetch` server_tool_use streaming
``{"url": "..."}`` followed by a `web_fetch_tool_result` block with
a document source emits one ``tool_start`` and one ``tool_end``
`_toolEvent`. The ``tool_start.arguments.url`` matches the fetched
URL and the ``tool_end.result`` carries the Title / URL / snippet
prefix the source-pill renderer expects.
- SSE translation (error): a `web_fetch_tool_error` with
``error_code="url_not_accessible"`` renders as ``"Error:
url_not_accessible"`` in the tool_end result.
Unit tests for Anthropic's `web_fetch_20250910` / `web_fetch_20260209`
translation in ``_stream_anthropic``. Covers request body emission
(version picked by ``_anthropic_web_fetch_version``: ``_20260209`` for
Opus 4.6/4.7 + Sonnet 4.6, ``_20250910`` otherwise), combined tool
requests, off-by-default behavior, and SSE translation of success and
``url_not_accessible`` error paths into ``tool_start`` / ``tool_end``.
"""
import asyncio
@ -117,8 +103,9 @@ def test_web_fetch_tool_appended_to_request_body(monkeypatch):
body = captured["body"]
tools = body.get("tools") or []
# claude-opus-4-7 routes web_fetch to _20260209 (dynamic filtering).
assert {
"type": "web_fetch_20250910",
"type": "web_fetch_20260209",
"name": "web_fetch",
"max_uses": 5,
} in tools
@ -157,13 +144,10 @@ def test_web_fetch_combined_with_web_search_and_code_execution(monkeypatch):
tools = captured["body"].get("tools") or []
tool_types = [t.get("type") for t in tools]
# After PR 5679's per-model tool version dispatch landed,
# claude-opus-4-7 routes web_search to the _20260209 variant and
# code_execution to _20260120. web_fetch still hardcodes
# _20250910 today; see follow-up to thread it through
# _anthropic_web_fetch_version.
# claude-opus-4-7 routes web_search and web_fetch to _20260209
# and code_execution to _20260120 (per PR 5679 dispatch).
assert "web_search_20260209" in tool_types, tool_types
assert "web_fetch_20250910" in tool_types, tool_types
assert "web_fetch_20260209" in tool_types, tool_types
assert "code_execution_20260120" in tool_types, tool_types
# Code-execution still adds its beta flag; web_fetch must not
# have accidentally stripped it.
@ -199,7 +183,9 @@ def test_no_web_fetch_tool_when_pill_off(monkeypatch):
_drive(run())
tools = captured["body"].get("tools") or []
assert all(t.get("type") != "web_fetch_20250910" for t in tools)
assert all(
t.get("type") not in ("web_fetch_20250910", "web_fetch_20260209") for t in tools
)
# ── SSE translation ─────────────────────────────────────────────────
@ -365,7 +351,9 @@ def test_web_fetch_error_renders_error_code(monkeypatch):
def _finish_reasons(lines: list[str]) -> list:
"""Return the finish_reason fields from every chat.completion.chunk."""
"""Return non-null finish_reason fields from each chat.completion.chunk.
Mid-stream content deltas carry ``finish_reason: None`` and are skipped
(the refusal path emits a notice delta before the content_filter chunk)."""
out: list = []
for line in lines:
if not line.startswith("data:"):
@ -380,8 +368,9 @@ def _finish_reasons(lines: list[str]) -> list:
if parsed.get("object") != "chat.completion.chunk":
continue
for choice in parsed.get("choices") or []:
if "finish_reason" in choice:
out.append(choice["finish_reason"])
reason = choice.get("finish_reason")
if reason is not None:
out.append(reason)
return out

248
studio/backend/tests/test_frontend_resolution.py Normal file
View file

@ -0,0 +1,248 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Tests for the frontend-dist resolver in studio/backend/run.py.
Loads only the relevant helpers via importlib so the test does not pull in
uvicorn / FastAPI / unsloth's full dependency tree. Pairs with the AST-style
test_host_defaults.py.
"""
import ast
import importlib.util
import os
import sys
from pathlib import Path
_RUN_PY = Path(__file__).resolve().parent.parent / "run.py"
_REPO_STUDIO_DIR = _RUN_PY.parent.parent # studio/
def _load_helpers_only():
"""Import just the resolver helpers from run.py without executing the
server-side imports (uvicorn, structlog, etc.)."""
source = _RUN_PY.read_text(encoding = "utf-8")
tree = ast.parse(source)
keep = []
wanted = {
"_DEFAULT_FRONTEND_PATH",
"_iter_frontend_fallback_candidates",
"_resolve_frontend_path",
}
for node in tree.body:
if isinstance(node, (ast.Import, ast.ImportFrom)):
keep.append(node)
elif isinstance(node, ast.Assign):
names = {t.id for t in node.targets if isinstance(t, ast.Name)}
if names & wanted:
keep.append(node)
elif isinstance(node, ast.FunctionDef) and node.name in wanted:
keep.append(node)
module = ast.Module(body = keep, type_ignores = [])
code = compile(module, str(_RUN_PY), "exec")
ns: dict = {"__file__": str(_RUN_PY), "__name__": "_run_helpers_test"}
exec(code, ns)
return ns
def test_resolver_returns_none_when_nothing_exists(tmp_path, monkeypatch):
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(tmp_path / "no_studio"))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, attempted = helpers["_resolve_frontend_path"](tmp_path / "missing")
assert chosen is None
assert attempted == [tmp_path / "missing"]
def test_resolver_picks_first_existing_candidate(tmp_path, monkeypatch):
dist = tmp_path / "good" / "frontend" / "dist"
dist.mkdir(parents = True)
(dist / "index.html").write_text("<!doctype html>", encoding = "utf-8")
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(tmp_path / "no_studio"))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, attempted = helpers["_resolve_frontend_path"](dist)
assert chosen == dist
assert attempted[-1] == dist
def test_resolver_falls_back_to_studio_home_site_packages(tmp_path, monkeypatch):
studio_home = tmp_path / "studio_home"
sp_dist = (
studio_home
/ "unsloth_studio"
/ "lib"
/ "python3.13"
/ "site-packages"
/ "studio"
/ "frontend"
/ "dist"
)
sp_dist.mkdir(parents = True)
(sp_dist / "index.html").write_text("<!doctype html>", encoding = "utf-8")
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, attempted = helpers["_resolve_frontend_path"](tmp_path / "bogus")
assert chosen is not None
assert chosen.resolve() == sp_dist.resolve()
assert (tmp_path / "bogus") in attempted
def test_resolver_falls_back_via_editable_pth(tmp_path, monkeypatch):
"""Simulates a `--local` install: dedicated venv with an editable .pth
pointing at a cloned repo that owns the built dist."""
studio_home = tmp_path / "studio_home"
sp = studio_home / "unsloth_studio" / "lib" / "python3.13" / "site-packages"
sp.mkdir(parents = True)
repo_root = tmp_path / "clone"
repo_studio = repo_root / "studio"
repo_dist = repo_studio / "frontend" / "dist"
repo_dist.mkdir(parents = True)
(repo_dist / "index.html").write_text("<!doctype html>", encoding = "utf-8")
# Minimal `__editable___pkg_finder.py` carrying a MAPPING dict that
# setuptools' editable install generator writes.
finder = sp / "__editable___unsloth_0_0_0_finder.py"
finder.write_text(
"MAPPING: dict[str, str] = "
f"{{'studio': {str(repo_studio)!r}, 'unsloth': '/x', 'unsloth_cli': '/y'}}\n",
encoding = "utf-8",
)
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, attempted = helpers["_resolve_frontend_path"](tmp_path / "bogus")
assert chosen is not None
assert chosen.resolve() == repo_dist.resolve()
def test_iter_candidates_handles_missing_studio_home(tmp_path, monkeypatch):
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(tmp_path / "nonexistent"))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
# Glob over a non-existent dir is empty; must not raise.
candidates = helpers["_iter_frontend_fallback_candidates"]()
assert candidates == []
def test_resolver_falls_back_to_windows_layout_site_packages(tmp_path, monkeypatch):
"""Pins the `Lib/site-packages` (capital L) Windows venv layout
alongside the POSIX `lib/python*/site-packages` path."""
studio_home = tmp_path / "studio_home"
sp_dist = (
studio_home
/ "unsloth_studio"
/ "Lib"
/ "site-packages"
/ "studio"
/ "frontend"
/ "dist"
)
sp_dist.mkdir(parents = True)
(sp_dist / "index.html").write_text("<!doctype html>", encoding = "utf-8")
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, _ = helpers["_resolve_frontend_path"](tmp_path / "bogus")
assert chosen is not None
assert chosen.resolve() == sp_dist.resolve()
def test_resolver_does_not_crash_on_non_dict_mapping_literal(tmp_path, monkeypatch):
"""A finder file whose MAPPING value is a set / list / non-dict literal
(theoretically possible if the regex matched a brace-delimited literal
that ast.literal_eval can parse) must not AttributeError. The resolver
should skip that finder and keep probing."""
studio_home = tmp_path / "studio_home"
sp = studio_home / "unsloth_studio" / "lib" / "python3.13" / "site-packages"
sp.mkdir(parents = True)
# Bad finder: set literal, not a dict. ast.literal_eval parses it as set;
# any .get() call on it would raise AttributeError.
(sp / "__editable___bad_0_0_0_finder.py").write_text(
"MAPPING: dict[str, str] = {'studio', 'unsloth', 'unsloth_cli'}\n",
encoding = "utf-8",
)
# Good finder that should still be discovered after the bad one is skipped.
repo_root = tmp_path / "clone"
repo_dist = repo_root / "studio" / "frontend" / "dist"
repo_dist.mkdir(parents = True)
(repo_dist / "index.html").write_text("<!doctype html>", encoding = "utf-8")
(sp / "__editable___good_0_0_0_finder.py").write_text(
f"MAPPING: dict[str, str] = {{'studio': {str(repo_root / 'studio')!r}}}\n",
encoding = "utf-8",
)
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, _ = helpers["_resolve_frontend_path"](tmp_path / "bogus")
assert chosen is not None
assert chosen.resolve() == repo_dist.resolve()
def test_resolver_handles_multiline_mapping_dict(tmp_path, monkeypatch):
"""A future setuptools / black reformat that wraps the MAPPING dict
across multiple lines must still parse and resolve. Locks in the
`[^}]*` + re.DOTALL behaviour."""
studio_home = tmp_path / "studio_home"
sp = studio_home / "unsloth_studio" / "lib" / "python3.13" / "site-packages"
sp.mkdir(parents = True)
repo_root = tmp_path / "clone"
repo_studio = repo_root / "studio"
repo_dist = repo_studio / "frontend" / "dist"
repo_dist.mkdir(parents = True)
(repo_dist / "index.html").write_text("<!doctype html>", encoding = "utf-8")
finder = sp / "__editable___unsloth_0_0_0_finder.py"
finder.write_text(
"MAPPING: dict[str, str] = {\n"
f" 'studio': {str(repo_studio)!r},\n"
" 'unsloth': '/x',\n"
" 'unsloth_cli': '/y',\n"
"}\n",
encoding = "utf-8",
)
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(studio_home))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
chosen, _ = helpers["_resolve_frontend_path"](tmp_path / "bogus")
assert chosen is not None
assert chosen.resolve() == repo_dist.resolve()
def test_systemexit_message_contains_actionable_fixes(tmp_path, monkeypatch):
"""The user-facing recovery message is a contract: it must surface the
attempted paths and every concrete fix. Pin its structure so a future
refactor doesn't drop one."""
import os
import sys
monkeypatch.setenv("UNSLOTH_STUDIO_HOME", str(tmp_path / "no_studio"))
monkeypatch.delenv("STUDIO_HOME", raising = False)
helpers = _load_helpers_only()
bogus = tmp_path / "no_such_dist"
_, attempted = helpers["_resolve_frontend_path"](bogus)
home = Path(os.environ["UNSLOTH_STUDIO_HOME"]).expanduser()
if sys.platform == "win32":
installer_bin = home / "bin" / "unsloth.exe"
else:
installer_bin = home / "unsloth_studio" / "bin" / "unsloth"
tried_lines = "\n".join(f" - {p}" for p in attempted)
message = (
"[ERROR] Studio frontend build not found.\n"
f"Tried:\n{tried_lines}\n"
"\n"
"Likely cause: another 'unsloth' on PATH is shadowing the "
"installer's binary and points at a site-packages tree with "
"no built dist.\n"
"\n"
"Fix one of:\n"
f" - run the installer's binary directly: {installer_bin} studio\n"
" - pass --frontend <path/to/studio/frontend/dist>\n"
" - pass --api-only to skip serving the web UI\n"
" - reinstall: curl -fsSL https://unsloth.ai/install.sh | sh"
)
assert str(bogus) in message
assert "--frontend" in message
assert "--api-only" in message
assert "reinstall" in message
assert "installer's binary directly" in message
assert str(installer_bin) in message

144
studio/backend/tests/test_index_bootstrap_origin.py Normal file
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@ -0,0 +1,144 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Regression coverage for the bootstrap-pw cross-origin leak (PR 5739).
``_is_same_origin_request`` gates ``_inject_bootstrap`` so the seeded
admin password only ships to same-origin callers.
"""
import os
from types import SimpleNamespace
from unittest.mock import MagicMock
import pytest
def _build_request(host: str, origin: str | None, scheme: str = "http") -> MagicMock:
request = MagicMock()
request.url.scheme = scheme
request.url.netloc = host
request.headers = {"origin": origin} if origin is not None else {}
return request
def test_is_same_origin_request_missing_origin_is_same_origin(monkeypatch):
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8888", origin = None)
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_matching_origin_is_same_origin():
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8888", origin = "http://127.0.0.1:8888")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_evil_origin_is_cross_origin():
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8888", origin = "https://evil.example")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_scheme_mismatch_is_cross_origin():
# https origin against an http listener is not same-origin.
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8888", origin = "https://127.0.0.1:8888")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_port_mismatch_is_cross_origin():
# Same host different port is not same-origin per the web platform.
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8888", origin = "http://127.0.0.1:5173")
assert _is_same_origin_request(req) is False
# ── Canonicalisation: default-port stripping + case folding ─────────
def test_is_same_origin_request_https_default_port_stripped_on_origin():
"""RFC 6454 strips default ports on Origin; Starlette's netloc may still
carry ``:443``. Canonicalise both sides so this stays same-origin.
"""
from main import _is_same_origin_request
req = _build_request(
"example.com:443", origin = "https://example.com", scheme = "https"
)
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_http_default_port_stripped_on_origin():
from main import _is_same_origin_request
req = _build_request("example.com:80", origin = "http://example.com")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_default_port_present_on_origin():
"""Mirror case: Origin carries the default port, netloc doesn't. Same-origin."""
from main import _is_same_origin_request
req = _build_request(
"example.com", origin = "https://example.com:443", scheme = "https"
)
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_host_case_insensitive():
"""Host portion is case-insensitive per RFC 3986."""
from main import _is_same_origin_request
req = _build_request("example.com", origin = "http://EXAMPLE.com")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_scheme_case_insensitive():
"""Scheme portion is case-insensitive per RFC 3986."""
from main import _is_same_origin_request
req = _build_request("example.com", origin = "HTTP://example.com")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_null_origin_is_cross_origin():
"""Sandboxed iframes / file:// pages send ``Origin: null``; cross-origin."""
from main import _is_same_origin_request
req = _build_request("example.com", origin = "null")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_unparseable_origin_is_cross_origin():
"""Garbage values without a host fall to cross-origin; a malformed header
must not leak the bootstrap.
"""
from main import _is_same_origin_request
req = _build_request("example.com", origin = "not-a-url")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_userinfo_in_netloc_ignored():
"""``user:pass@host:port`` netlocs (RFC 3986) must compare equal to the
credentials-less Origin.
"""
from main import _is_same_origin_request
req = _build_request("user:pass@example.com:80", origin = "http://example.com")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_explicit_non_default_port_still_mismatch():
"""Canonicalisation does NOT collapse non-default ports to default."""
from main import _is_same_origin_request
req = _build_request(
"example.com", origin = "https://example.com:9999", scheme = "https"
)
assert _is_same_origin_request(req) is False

196
studio/backend/tests/test_index_bootstrap_origin_extra.py Normal file
View file

@ -0,0 +1,196 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Extra edge-case coverage for the bootstrap-pw cross-origin gate.
Companion to ``test_index_bootstrap_origin.py``: IPv6 netlocs, opaque
origins (``data:``, ``blob:``), comma-joined multi-Origin headers, and
the ``localhost`` vs ``127.0.0.1`` distinct-origin rule.
"""
from unittest.mock import MagicMock
def _build_request(host: str, origin, scheme: str = "http") -> MagicMock:
request = MagicMock()
request.url.scheme = scheme
request.url.netloc = host
request.headers = {"origin": origin} if origin is not None else {}
return request
# ── IPv6 ────────────────────────────────────────────────────────────
def test_is_same_origin_request_ipv6_loopback_same_origin():
"""Studio supports ``-H ::1`` binds; netloc is ``[::1]:8902``. Bare
``partition(":")`` mis-parses the bracketed form and would refuse the
bootstrap on legitimate same-origin nav.
"""
from main import _is_same_origin_request
req = _build_request("[::1]:8902", origin = "http://[::1]:8902")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_ipv6_full_address_same_origin():
from main import _is_same_origin_request
req = _build_request(
"[2001:db8::1]:8443",
origin = "https://[2001:db8::1]:8443",
scheme = "https",
)
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_ipv6_default_port_stripped():
"""Browser drops :80 on ``http://[::1]``."""
from main import _is_same_origin_request
req = _build_request("[::1]:80", origin = "http://[::1]")
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_ipv6_case_insensitive():
"""Hex digits in IPv6 are case-insensitive per RFC 5952."""
from main import _is_same_origin_request
req = _build_request(
"[2001:DB8::1]:8443",
origin = "https://[2001:db8::1]:8443",
scheme = "https",
)
assert _is_same_origin_request(req) is True
def test_is_same_origin_request_ipv6_different_host_cross_origin():
from main import _is_same_origin_request
req = _build_request("[::1]:8902", origin = "http://[2001:db8::1]:8902")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_ipv6_port_mismatch_cross_origin():
from main import _is_same_origin_request
req = _build_request("[::1]:8902", origin = "http://[::1]:9999")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_ipv6_userinfo_stripped():
from main import _is_same_origin_request
req = _build_request("user:pass@[::1]:8902", origin = "http://[::1]:8902")
assert _is_same_origin_request(req) is True
# ── Opaque origins (data:, blob:) ───────────────────────────────────
def test_is_same_origin_request_data_url_origin_is_cross_origin():
"""``data:`` URLs are opaque origins (HTML living standard); no host,
never same-origin.
"""
from main import _is_same_origin_request
req = _build_request(
"127.0.0.1:8902", origin = "data:text/html,<script>alert(1)</script>"
)
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_blob_url_origin_is_cross_origin():
"""``blob:`` URLs carry the inner origin only in non-canonical form; the
canonical comparison rejects them.
"""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "blob:http://127.0.0.1:8902/uuid")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_file_url_origin_is_cross_origin():
"""``file://`` pages usually send ``Origin: null``; historical engines
sent ``Origin: file://``. Neither is same-origin vs an http listener.
"""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "file://")
assert _is_same_origin_request(req) is False
# ── Multi-Origin header (comma-joined by Starlette) ────────────────
def test_is_same_origin_request_comma_joined_origins_cross_origin():
"""Starlette concatenates repeated headers with ``, ``; the canonical
parser can't safely split this, so it falls to cross-origin.
"""
from main import _is_same_origin_request
req = _build_request(
"127.0.0.1:8902",
origin = "http://127.0.0.1:8902, http://evil.example",
)
assert _is_same_origin_request(req) is False
# ── localhost vs 127.0.0.1 (distinct origins per web platform) ──────
def test_is_same_origin_request_localhost_vs_127_is_cross_origin():
"""Browsers treat ``localhost`` and ``127.0.0.1`` as distinct origins;
the canonical comparison must not DNS-collapse them.
"""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "http://localhost:8902")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_127_vs_localhost_is_cross_origin():
from main import _is_same_origin_request
req = _build_request("localhost:8902", origin = "http://127.0.0.1:8902")
assert _is_same_origin_request(req) is False
# ── urlparse ValueError robustness ─────────────────────────────────
def test_is_same_origin_request_malformed_ipv6_bracket_is_cross_origin():
"""``urlparse`` raises ``ValueError('Invalid IPv6 URL')`` on unclosed
brackets (CVE-2024-11168 hardening). The gate must swallow and fall to
cross-origin rather than 500 the SPA handler.
"""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "http://[malformed")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_invalid_ipv6_address_is_cross_origin():
"""Bracketed but invalid IPv6 (e.g. ``[::g]``) also raises
``ValueError`` inside ``urlparse``."""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "http://[::g]:8902")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_bracket_with_trailing_garbage_is_cross_origin():
"""Text after the closing bracket also raises inside ``urlparse``."""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "http://[2001:db8::1]extra:8902")
assert _is_same_origin_request(req) is False
def test_is_same_origin_request_empty_origin_header_is_cross_origin():
"""Explicit empty ``Origin:`` is not a valid serialised origin and must
not be conflated with a missing header; cross-origin, bootstrap withheld.
"""
from main import _is_same_origin_request
req = _build_request("127.0.0.1:8902", origin = "")
assert _is_same_origin_request(req) is False

163
studio/backend/tests/test_llama_server_args.py
View file

@ -3,21 +3,35 @@
"""Unit tests for the llama-server pass-through args validator.
The validator is the security boundary between user-supplied CLI / HTTP
input and the llama-server subprocess command. These tests pin the
denylist behavior so the boundary doesn't quietly regress when new
managed flags are added.
The validator is the boundary between user CLI/HTTP input and the
llama-server subprocess. These tests pin denylist behaviour so it
doesn't quietly regress when new managed flags are added.
"""
from __future__ import annotations
import importlib.util
import re
from pathlib import Path
import pytest
from core.inference.llama_server_args import (
is_managed_flag,
strip_shadowing_flags,
validate_extra_args,
# Load llama_server_args.py directly so this test doesn't drag in the
# full backend chain (fastapi / structlog / loggers / utils.hardware)
# via core/inference/__init__.py. The validator is intentionally
# dependency-free and unit-tests should reflect that.
_LSA_PATH = (
Path(__file__).resolve().parent.parent
/ "core"
/ "inference"
/ "llama_server_args.py"
)
_spec = importlib.util.spec_from_file_location("_lsa_test_only", _LSA_PATH)
_lsa = importlib.util.module_from_spec(_spec)
_spec.loader.exec_module(_lsa)
is_managed_flag = _lsa.is_managed_flag
strip_shadowing_flags = _lsa.strip_shadowing_flags
validate_extra_args = _lsa.validate_extra_args
# ── Pass-through (allowed) ───────────────────────────────────────────
@ -60,13 +74,12 @@ from core.inference.llama_server_args import (
# Reasoning controls
["--reasoning-format", "deepseek"],
["-rea", "auto"],
# Soft-managed flags the user may want to override on the CLI;
# llama.cpp's last-wins parsing means these win over Studio's
# auto-set version.
# Soft-managed: user-supplied flags last-wins-override Studio's
# auto-set version. --parallel / -np / --n-parallel are NOT
# here -- they're hard-denied (KV-cache + slot count would
# desync). Use `unsloth studio run --parallel N` instead.
["-c", "131072"],
["--ctx-size", "8192"],
["--parallel", "1"],
["-np", "8"],
["--flash-attn", "off"],
["-fa", "on"],
["--no-context-shift"],
@ -99,8 +112,7 @@ def test_value_with_equals_form_passes_through():
def test_non_flag_token_passes_through():
# A bare positional value (not preceded by a flag) is preserved
# verbatim. llama-server may reject it, but that's not our job.
# Bare positionals are passed through; llama-server can reject them.
assert validate_extra_args(["foo"]) == ["foo"]
@ -110,18 +122,33 @@ def test_non_flag_token_passes_through():
@pytest.mark.parametrize(
"denied",
[
# Model identity
# Parallel slots -- owned by the typer --parallel flag.
"-np",
"--parallel",
"--n-parallel",
# Model identity (every alias; bumping llama.cpp must keep
# every form rejected, not just the long).
"-m",
"--model",
"-mu",
"--model-url",
"-dr",
"--docker-repo",
"-hf",
"-hfr",
"--hf-repo",
"-hff",
"--hf-file",
"-hfv",
"-hfrv",
"--hf-repo-v",
"-hffv",
"--hf-file-v",
"-hft",
"--hf-token",
"-mm",
"--mmproj",
"-mmu",
"--mmproj-url",
# Networking (Studio binds + proxies)
"--host",
@ -134,11 +161,28 @@ def test_non_flag_token_passes_through():
"--api-key-file",
"--ssl-key-file",
"--ssl-cert-file",
# Single-model server
# Single-model server (legacy --webui + current --ui group)
"--webui",
"--no-webui",
"--ui",
"--no-ui",
"--ui-config",
"--ui-config-file",
"--ui-mcp-proxy",
"--no-ui-mcp-proxy",
"--models-dir",
"--models-preset",
"--models-max",
"--models-autoload",
"--no-models-autoload",
# Server-mode flips: --embedding / --rerank would restrict
# llama-server to those endpoints and break Studio's chat hop.
"--embedding",
"--embeddings",
"--rerank",
"--reranking",
# llama-server's own --tools clashes with Studio's tool policy.
"--tools",
],
)
def test_denylist_rejects_all_aliases(denied):
@ -146,14 +190,65 @@ def test_denylist_rejects_all_aliases(denied):
validate_extra_args([denied, "value"])
@pytest.mark.parametrize(
"args,offending",
[
# Pass-through --parallel would last-wins-override the real
# slot count while Studio's KV-cache fit + llama_parallel_slots
# stay at the typer value -- plan vs. process disagree.
(["--parallel", "8"], "--parallel"),
(["--parallel=8"], "--parallel"),
(["--n-parallel", "16"], "--n-parallel"),
(["--n-parallel=16"], "--n-parallel"),
(["-np", "32"], "-np"),
# Attached short form: Click clusters it CLI-side; HTTP /load
# with `["-np8"]` must still resolve to managed.
(["-np8"], "-np"),
(["-np64"], "-np"),
# Out-of-range values that would bypass the typer 1..64 guard.
(["--parallel", "999"], "--parallel"),
(["-np", "0"], "-np"),
(["-np999"], "-np"),
# Signed attached forms; `-np-1` must not slip past.
(["-np-1"], "-np"),
(["-np+1"], "-np"),
],
)
def test_parallel_flags_are_managed(args, offending):
with pytest.raises(ValueError, match = re.escape(offending)):
validate_extra_args(args)
def test_denylist_rejects_equals_form():
with pytest.raises(ValueError, match = "--port"):
validate_extra_args(["--port=9000"])
@pytest.mark.parametrize(
"padded",
[" --parallel", "--parallel ", "\t--parallel", " -np", "-np \n", "-np\t"],
)
def test_denylist_rejects_whitespace_padded_forms(padded):
# `_flag_name` trims whitespace before lookup; otherwise a trailing
# space could slip a managed flag past the boundary.
with pytest.raises(ValueError, match = "parallel|np"):
validate_extra_args([padded, "8"])
@pytest.mark.parametrize(
"attached",
["-np8x", "-np-1foo", "-np+1bar", "-np9zzz"],
)
def test_denylist_rejects_np_with_digit_prefix_and_junk(attached):
# Backend `_flag_name` must classify the same forms the CLI
# rewriter expands, else HTTP /load could smuggle `-np8x` through.
with pytest.raises(ValueError, match = "np"):
validate_extra_args([attached])
def test_denylist_rejects_short_form_when_long_is_denied():
# -m is the short form of the hard-denied --model; rejecting only
# the long form would leave a trivial bypass.
# `-m` is the short form of --model; rejecting only the long
# form would leave a trivial bypass.
with pytest.raises(ValueError, match = "-m"):
validate_extra_args(["-m", "/some/other/path.gguf"])
@ -165,9 +260,7 @@ def test_denylist_message_names_offending_flag():
def test_first_denied_flag_short_circuits():
# Validation stops at the first denied flag; later denied flags
# in the same call don't matter for behaviour, but the message
# should name the first one we hit.
# Validation stops at the first denied flag; the message names it.
with pytest.raises(ValueError, match = "--port"):
validate_extra_args(["--port", "1", "--host", "x"])
@ -177,8 +270,7 @@ def test_first_denied_flag_short_circuits():
@pytest.mark.parametrize("value", ["-1", "-0.5", "-42", "-.5"])
def test_negative_number_value_is_not_flag(value):
# ``--seed -1`` is a value, not a flag. Validator must not try
# to look up "-1" in the denylist.
# `--seed -1`: the -1 is a value, not a flag.
assert validate_extra_args(["--seed", value]) == ["--seed", value]
@ -190,6 +282,15 @@ def test_is_managed_flag_true_for_denied():
assert is_managed_flag("--api-key") is True
assert is_managed_flag("-m") is True
assert is_managed_flag("--model") is True
# Parallel slots owned by the typer --parallel flag.
assert is_managed_flag("--parallel") is True
assert is_managed_flag("--n-parallel") is True
assert is_managed_flag("-np") is True
# Normalised forms must classify like the canonical token so
# is_managed_flag filtering stays in sync with validate_extra_args.
assert is_managed_flag("-np8") is True
assert is_managed_flag("--parallel=8") is True
assert is_managed_flag("--port=9000") is True
def test_is_managed_flag_false_for_pass_through():
@ -199,7 +300,6 @@ def test_is_managed_flag_false_for_pass_through():
# Soft-managed flags pass through (last-wins override)
assert is_managed_flag("-c") is False
assert is_managed_flag("--ctx-size") is False
assert is_managed_flag("--parallel") is False
assert is_managed_flag("--flash-attn") is False
assert is_managed_flag("-ngl") is False
assert is_managed_flag("--threads") is False
@ -231,8 +331,8 @@ def test_strip_shadowing_flags_keeps_context_when_not_requested():
def test_strip_shadowing_flags_keeps_chat_template_when_template_disabled():
# Caller did not supply chat_template_override; the inherited
# --chat-template-file must survive the strip.
# No chat_template_override supplied; inherited
# --chat-template-file must survive.
out = strip_shadowing_flags(
["--chat-template-file", "/tmp/custom.jinja", "--top-k", "20"],
strip_context = True,
@ -282,7 +382,7 @@ def test_strip_shadowing_flags_keeps_spec_when_spec_disabled():
def test_strip_shadowing_flags_drops_mtp_flags_when_requested():
# MTP / draft-mtp flags must be stripped when speculative_type is re-applied.
# MTP / draft-mtp flags must drop when speculative_type re-applies.
out = strip_shadowing_flags(
[
"--spec-type",
@ -311,8 +411,7 @@ def test_is_managed_flag_false_for_mtp_pass_through():
def test_strip_shadowing_flags_boolean_does_not_consume_next_token():
# --spec-default is a boolean shadowing flag; the value-skipping
# heuristic must skip just the flag, not the following positional.
# `--spec-default` is boolean; drop just the flag, keep the next token.
out = strip_shadowing_flags(["--spec-default", "ngram-mod"], strip_spec = True)
assert out == ["ngram-mod"]
@ -343,8 +442,8 @@ def test_strip_shadowing_flags_handles_empty_input():
def test_strip_shadowing_flags_defaults_strip_everything():
# The route's already-loaded comparator calls strip_shadowing_flags
# with no kwargs to detect ANY shadowing flag in stored extras.
# The route's already-loaded comparator calls with no kwargs to
# detect ANY shadowing flag in stored extras.
out = strip_shadowing_flags(
["-c", "4096", "--cache-type-k", "q8_0", "--spec-default", "--jinja"]
)

6
studio/backend/tests/test_multimodal_document.py
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@ -117,6 +117,8 @@ def test_anthropic_base64_pdf_becomes_document_block(monkeypatch):
types = [p.get("type") for p in parts]
assert "document" in types, parts
doc = _strip_cache(next(p for p in parts if p.get("type") == "document"))
# citations: {enabled: true} opts into Anthropic's natural-citation
# pipeline; without it the citations_delta handler is a no-op.
assert doc == {
"type": "document",
"source": {
@ -124,6 +126,7 @@ def test_anthropic_base64_pdf_becomes_document_block(monkeypatch):
"media_type": "application/pdf",
"data": _TINY_PDF_B64,
},
"citations": {"enabled": True},
"title": "paper.pdf",
}
@ -151,6 +154,7 @@ def test_anthropic_url_pdf_becomes_document_block(monkeypatch):
assert doc == {
"type": "document",
"source": {"type": "url", "url": "https://example.com/doc.pdf"},
"citations": {"enabled": True},
}
@ -255,6 +259,7 @@ def test_anthropic_empty_data_uri_falls_back_to_file_url(monkeypatch):
assert doc == {
"type": "document",
"source": {"type": "url", "url": "https://example.com/doc.pdf"},
"citations": {"enabled": True},
"title": "doc.pdf",
}
@ -283,6 +288,7 @@ def test_anthropic_whitespace_only_data_uri_falls_back_to_file_url(monkeypatch):
assert doc == {
"type": "document",
"source": {"type": "url", "url": "https://example.com/doc.pdf"},
"citations": {"enabled": True},
}

251
studio/backend/tests/test_openai_citation_markers.py Normal file
View file

@ -0,0 +1,251 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Tests for the OpenAI Responses-API citation marker rewriter.
The stream interleaves text deltas with ``\\ue200cite\\ue202SOURCE_ID\\ue201``
markers. The rewriter resolves each to `[N](URL)` when the annotation has
arrived and drops it otherwise; the URL list still flows to Sources via
`_record_url_citation`.
Reference: https://developers.openai.com/api/docs/guides/citation-formatting
"""
import pytest
from core.inference.external_provider import (
_replace_openai_citation_markers,
_rewrite_citation_markers_partial,
)
# Citation marker control codepoints (private-use area):
CITE_START = ""
CITE_STOP = ""
CITE_DELIM = ""
def _marker(source_id: str, locator: str | None = None) -> str:
payload = f"{CITE_START}cite{CITE_DELIM}{source_id}"
if locator:
payload = f"{payload}{CITE_DELIM}{locator}"
return f"{payload}{CITE_STOP}"
def _has_marker_codepoints(text: str) -> bool:
return any(c in text for c in (CITE_START, CITE_STOP, CITE_DELIM))
def test_passthrough_when_no_marker_present():
text = "Plain text with no citation markers."
assert _replace_openai_citation_markers(text, []) == text
def test_marker_rewritten_to_link_when_annotation_known():
text = f"The capital is Paris {_marker('turn0view0')}."
citations = [
{
"source_id": "turn0view0",
"url": "https://example.com/paris",
"title": "Paris",
},
]
out = _replace_openai_citation_markers(text, citations)
assert not _has_marker_codepoints(out)
assert "[[1]](https://example.com/paris)" in out
def test_unknown_source_marker_dropped_silently():
text = f"Foo {_marker('turn9view9')} bar."
out = _replace_openai_citation_markers(text, [])
# Marker stripped, no garbled "E202" glyph leaks through, and the
# surrounding text stays intact.
assert not _has_marker_codepoints(out)
assert "E202" not in out
assert "turn9view9" not in out
assert "Foo" in out and "bar" in out
def test_multiple_concatenated_markers_resolved_in_order():
"""Real-world wire shape: a string of markers butted up against each other
after a sentence, as in the user-reported bug."""
markers = "".join(_marker(f"turn{i}view{j}") for i, j in [(1, 0), (1, 1), (3, 0)])
text = f"All animals ranked. {markers}"
citations = [
{"source_id": "turn1view0", "url": "https://a.example/dog", "title": "Dog"},
{"source_id": "turn1view1", "url": "https://a.example/cat", "title": "Cat"},
{"source_id": "turn3view0", "url": "https://a.example/tiger", "title": "Tiger"},
]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://a.example/dog)" in out
assert "[[2]](https://a.example/cat)" in out
assert "[[3]](https://a.example/tiger)" in out
assert not _has_marker_codepoints(out)
def test_marker_with_locator_resolves():
text = f"See {_marker('turn2file0', 'L8-L13')}."
citations = [
{"source_id": "turn2file0", "url": "https://example.com/doc.txt"},
]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://example.com/doc.txt)" in out
assert "L8-L13" not in out # locator detail dropped; we just link.
assert not _has_marker_codepoints(out)
def test_mixed_known_and_unknown_markers():
known = _marker("turn0view0")
unknown = _marker("turn0view99")
text = f"Known {known} and unknown {unknown}."
citations = [
{"source_id": "turn0view0", "url": "https://example.com/known"},
]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://example.com/known)" in out
# Unknown markers leave no trace, but surrounding prose stays.
assert "Known" in out and "unknown" in out
assert not _has_marker_codepoints(out)
assert "E202" not in out
def test_empty_text_returns_verbatim():
assert _replace_openai_citation_markers("", []) == ""
def test_idempotent_on_pre_stripped_text():
"""Pre-stripped text (no private-use codepoints) returns verbatim."""
text = "citeturn1view0 plain"
assert _replace_openai_citation_markers(text, []) == text
@pytest.mark.parametrize(
"citation",
[
{"url": "https://example.com/a"}, # no source_id at all
{"source_id": None, "url": "https://example.com/b"},
{"source_id": "", "url": "https://example.com/c"},
],
)
def test_citation_without_source_id_does_not_crash(citation):
text = f"X {_marker('turnXviewY')} Y"
out = _replace_openai_citation_markers(text, [citation])
# No mapping, marker stripped. Crash-free is the contract.
assert not _has_marker_codepoints(out)
assert "turnXviewY" not in out
def test_multiple_source_id_aliases_resolve_to_same_url():
"""Every alias for the same URL must resolve, not just the first.
Regression for the Codex P1 on the original PR."""
a = _marker("turn0view0")
b = _marker("turn0view0_span_1")
c = _marker("turn0view0_span_2")
text = f"Triple {a}{b}{c} cite."
citations = [
{
"source_ids": ["turn0view0", "turn0view0_span_1", "turn0view0_span_2"],
"url": "https://example.com/paris",
"title": "Paris",
},
]
out = _replace_openai_citation_markers(text, citations)
# All three aliases collapse onto citation [1] -- the URL is the
# same so it would be misleading to show three different numbers.
assert out.count("[[1]](https://example.com/paris)") == 3
assert not _has_marker_codepoints(out)
def test_source_ids_list_and_legacy_source_id_both_resolve():
"""Mixed-shape citation: legacy ``source_id`` plus newer
``source_ids`` aliases both resolve."""
legacy = _marker("legacy_id")
alias = _marker("alias_id")
text = f"Both {legacy} and {alias} work."
citations = [
{
"source_id": "legacy_id",
"source_ids": ["alias_id"],
"url": "https://example.com/doc",
},
]
out = _replace_openai_citation_markers(text, citations)
assert out.count("[[1]](https://example.com/doc)") == 2
assert not _has_marker_codepoints(out)
# ---------------------------------------------------------------------------
# _rewrite_citation_markers_partial: deferred-annotation tests. OpenAI emits
# url_citation annotations on a subsequent SSE event; this helper reports
# `has_unresolved` so the stream loop defers emission. See PR #5713 audit.
# ---------------------------------------------------------------------------
def test_partial_known_marker_resolves_and_clears_unresolved():
text = f"Foo {_marker('s1')} bar."
out, unresolved = _rewrite_citation_markers_partial(
text,
[{"source_id": "s1", "url": "https://example.com/a"}],
)
assert "[[1]](https://example.com/a)" in out
assert unresolved is False
assert not _has_marker_codepoints(out)
def test_partial_unknown_marker_preserves_verbatim_and_flags():
text = f"Foo {_marker('s1')} bar."
out, unresolved = _rewrite_citation_markers_partial(text, [])
assert unresolved is True
# Codepoints must remain so a follow-up pass can re-parse.
assert _has_marker_codepoints(out)
assert "Foo" in out and "bar." in out
def test_partial_resolves_after_late_annotation():
"""Two-pass: first call sees no citations, second resolves after annotation."""
text = f"See {_marker('s1')} for details."
out1, unresolved1 = _rewrite_citation_markers_partial(text, [])
assert unresolved1 is True
citations = [{"source_id": "s1", "url": "https://example.com/x"}]
out2, unresolved2 = _rewrite_citation_markers_partial(out1, citations)
assert unresolved2 is False
assert "[[1]](https://example.com/x)" in out2
assert not _has_marker_codepoints(out2)
def test_partial_multi_source_partial_resolution_keeps_marker_pending():
"""Any unresolved token in a multi-source marker leaves the whole marker
verbatim with ``unresolved`` True; defer until every id resolves or
end-of-stream forces a flush (dropping unresolved tokens then)."""
cite = f"{CITE_START}cite{CITE_DELIM}known{CITE_DELIM}locator{CITE_STOP}"
text = f"Pre {cite} post."
citations = [{"source_id": "known", "url": "https://example.com/y"}]
out, unresolved = _rewrite_citation_markers_partial(text, citations)
assert unresolved is True
assert cite in out
# End-of-stream force flush: drop the unresolved token, keep the
# resolved link. The streamer routes pending segments through
# `_replace_openai_citation_markers` at force=True for this.
forced = _replace_openai_citation_markers(out, citations)
assert "[[1]](https://example.com/y)" in forced
assert "locator" not in forced
assert not _has_marker_codepoints(forced)
def test_partial_idempotent_on_marker_free_text():
text = "Plain text."
out, unresolved = _rewrite_citation_markers_partial(text, [])
assert out == text
assert unresolved is False
def test_partial_mixed_known_and_pending_markers_flags_unresolved():
known = _marker("known")
pending = _marker("pending")
text = f"{known} {pending}"
citations = [{"source_id": "known", "url": "https://example.com/k"}]
out, unresolved = _rewrite_citation_markers_partial(text, citations)
assert unresolved is True # the pending marker drives the flag
assert "[[1]](https://example.com/k)" in out
# The pending marker stays verbatim for the next pass.
assert CITE_START in out and "pending" in out

413
studio/backend/tests/test_openai_citation_markers_edge.py Normal file
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@ -0,0 +1,413 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Edge-case tests for the OpenAI Responses citation marker rewriter.
Covers multi-source markers, source+locator, marker SPLIT across SSE deltas,
unterminated tails at end-of-stream, multiple markers per delta, late
annotation ordering, and idempotency.
Reference: https://developers.openai.com/api/docs/guides/citation-formatting
"""
import importlib
# Streaming integration is exercised by ``_simulate_delta_stream`` further
# down, mirroring the head/buffer/flush dance from ``_stream_openai_responses``.
_module = importlib.import_module("core.inference.external_provider")
_replace_openai_citation_markers = _module._replace_openai_citation_markers
_split_pending_citation_tail = _module._split_pending_citation_tail
CITE_START = ""
CITE_STOP = ""
CITE_DELIM = ""
def _marker(*source_ids: str, locator: str | None = None) -> str:
"""Build a ``\\ue200cite\\ue202<sid>[\\ue202<sid>...][\\ue202<loc>]\\ue201``
marker. Accepts one or many ``source_ids`` plus an optional ``locator``."""
payload = f"{CITE_START}cite{CITE_DELIM}" + CITE_DELIM.join(source_ids)
if locator:
payload = f"{payload}{CITE_DELIM}{locator}"
return f"{payload}{CITE_STOP}"
def _no_private_use(text: str) -> bool:
return all(c not in text for c in (CITE_START, CITE_STOP, CITE_DELIM))
# Harness mirroring the head/pending-tail/flush dance in
# `_stream_openai_responses`, so streaming tests skip the httpx mock.
def _simulate_delta_stream(
deltas: list[str],
citations: list[dict],
*,
flush: bool = True,
) -> str:
pending = ""
emitted: list[str] = []
for delta in deltas:
combined = pending + delta
head, pending = _split_pending_citation_tail(combined)
if head:
head = _replace_openai_citation_markers(head, citations)
if head:
emitted.append(head)
if flush and pending:
# Mirror `_flush_pending_marker_tail`: drop the tail entirely if no
# closing stop byte arrived; the literal ``cite<sid>`` would leak otherwise.
if CITE_STOP not in pending:
rendered = ""
else:
rendered = _replace_openai_citation_markers(pending, citations)
for ch in (CITE_START, CITE_STOP, CITE_DELIM):
rendered = rendered.replace(ch, "")
import re as _re
rendered = _re.sub(r"^cite\S*", "", rendered)
if rendered:
emitted.append(rendered)
return "".join(emitted)
# ---------------------------------------------------------------------------
# 1. Multi-source markers per the OpenAI docs.
# ---------------------------------------------------------------------------
def test_multi_source_marker_all_resolve():
"""\\ue200cite\\ue202id1\\ue202id2\\ue202id3\\ue201 expands to three links
when every id is known. Earlier regex captured only id1 and dropped id2/id3."""
text = f"All three: {_marker('id1', 'id2', 'id3')}"
citations = [
{"source_id": "id1", "url": "https://example.com/1"},
{"source_id": "id2", "url": "https://example.com/2"},
{"source_id": "id3", "url": "https://example.com/3"},
]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://example.com/1)" in out
assert "[[2]](https://example.com/2)" in out
assert "[[3]](https://example.com/3)" in out
assert _no_private_use(out)
def test_multi_source_marker_partial_resolution():
"""Known ids render, unknown ids drop silently, no glyph leaks."""
text = f"Mixed: {_marker('known', 'unknown', 'also_known')}"
citations = [
{"source_id": "known", "url": "https://k.example"},
{"source_id": "also_known", "url": "https://ak.example"},
]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://k.example)" in out
assert "[[2]](https://ak.example)" in out
assert "unknown" not in out
assert _no_private_use(out)
# ---------------------------------------------------------------------------
# 2. Source + locator: locator is dropped, link still resolves.
# ---------------------------------------------------------------------------
def test_marker_with_numeric_locator():
text = f"See {_marker('tu0', locator = '42')}."
citations = [{"source_id": "tu0", "url": "https://example.com/doc"}]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://example.com/doc)" in out
assert "42" not in out
assert _no_private_use(out)
def test_marker_with_range_locator():
text = f"See {_marker('tu0', locator = 'L8-L13')}."
citations = [{"source_id": "tu0", "url": "https://example.com/code"}]
out = _replace_openai_citation_markers(text, citations)
assert "[[1]](https://example.com/code)" in out
assert "L8-L13" not in out
assert _no_private_use(out)
# ---------------------------------------------------------------------------
# 3. Marker SPLIT across two SSE deltas -- the codex-flagged P1.
# ---------------------------------------------------------------------------
def test_marker_split_in_source_id():
"""Delta-1 ends mid-source-id (``\\ue200cite\\ue202tu``), delta-2 starts
with the rest (``rn0view0\\ue201``). The buffer stitches the halves
back together so they resolve to one link instead of leaking."""
full = f"See {_marker('turn0view0')} now."
# Cut right after the second delim + "tu" inside the source id.
cut = full.index("tu", full.index(CITE_START)) + len("tu")
d1, d2 = full[:cut], full[cut:]
# Sanity check: delta-1 actually contains a partial marker.
assert CITE_START in d1 and CITE_STOP not in d1
assert CITE_STOP in d2
citations = [{"source_id": "turn0view0", "url": "https://x"}]
out = _simulate_delta_stream([d1, d2], citations)
assert out == "See [[1]](https://x) now."
assert _no_private_use(out)
def test_marker_split_at_start_byte():
"""Split exactly after the opening ``\\ue200`` byte; the buffer must
hold the lone open byte until the rest arrives."""
full = f"Text {_marker('sid')} done"
cut = full.index(CITE_START) + 1 # right AFTER the open byte
d1, d2 = full[:cut], full[cut:]
citations = [{"source_id": "sid", "url": "https://y"}]
out = _simulate_delta_stream([d1, d2], citations)
assert out == "Text [[1]](https://y) done"
assert _no_private_use(out)
def test_marker_split_across_three_deltas():
"""Worst case: marker chopped into three pieces across three deltas."""
full = f"A {_marker('threesplit')} B"
# cut at two points inside the marker
open_pos = full.index(CITE_START)
stop_pos = full.index(CITE_STOP)
cut1 = open_pos + 4
cut2 = stop_pos - 2
parts = [full[:cut1], full[cut1:cut2], full[cut2:]]
citations = [{"source_id": "threesplit", "url": "https://z"}]
out = _simulate_delta_stream(parts, citations)
assert out == "A [[1]](https://z) B"
assert _no_private_use(out)
def test_marker_split_with_trailing_text_after_close():
"""Delta-2 closes the marker AND carries trailing prose; both emit cleanly."""
full = f"X {_marker('sid')} after"
cut = full.index("cite") + len("ci")
d1, d2 = full[:cut], full[cut:]
citations = [{"source_id": "sid", "url": "https://a"}]
out = _simulate_delta_stream([d1, d2], citations)
assert out == "X [[1]](https://a) after"
assert _no_private_use(out)
def test_split_marker_unknown_source_is_dropped_cleanly():
"""Split marker for an unknown source drops silently on flush."""
full = f"Pre {_marker('never_seen')} post"
cut = full.index(CITE_START) + 3
d1, d2 = full[:cut], full[cut:]
out = _simulate_delta_stream([d1, d2], [])
assert out == "Pre post"
assert _no_private_use(out)
# ---------------------------------------------------------------------------
# 4. Unterminated marker at end-of-stream -- truncation safety.
# ---------------------------------------------------------------------------
def test_unterminated_marker_at_stream_end_dropped_on_flush():
"""Stream ends mid-marker (e.g. response.incomplete); the tail is
flushed with private-use bytes stripped, no `E202` text leaks."""
deltas = ["Some text ", f"{CITE_START}citetu", "rn0view0"] # no STOP ever
out = _simulate_delta_stream(deltas, [], flush = True)
assert _no_private_use(out)
assert "E200" not in out and "E202" not in out
# Surrounding prose stays; we don't assert exact marker remainder.
assert "Some text " in out
def test_flush_resolves_marker_when_late_annotation_arrives():
"""Marker in a delta, matching annotation arrives later (on
response.output_text.annotation.added after the final delta). The
rewriter reads ``all_url_citations`` LIVE at flush, so the buffered
marker still resolves."""
deltas = ["Look ", f"{CITE_START}cite{CITE_DELIM}late_sid"]
pending = ""
citations: list[dict] = []
emitted: list[str] = []
for d in deltas:
combined = pending + d
head, pending = _split_pending_citation_tail(combined)
if head:
emitted.append(_replace_openai_citation_markers(head, citations))
# Annotation arrives AFTER all deltas but BEFORE flush.
citations.append({"source_id": "late_sid", "url": "https://late.example"})
# Append the STOP byte that closed the marker in a later delta.
pending = pending + CITE_STOP
flushed = _replace_openai_citation_markers(pending, citations)
for ch in (CITE_START, CITE_STOP, CITE_DELIM):
flushed = flushed.replace(ch, "")
emitted.append(flushed)
out = "".join(emitted)
assert "[[1]](https://late.example)" in out
assert _no_private_use(out)
# ---------------------------------------------------------------------------
# 5. Multiple unrelated markers in a single delta.
# ---------------------------------------------------------------------------
def test_three_markers_in_one_delta_resolve_independently():
text = f"alpha {_marker('a')} beta {_marker('b')} gamma {_marker('c')} end"
citations = [
{"source_id": "a", "url": "https://example.com/a"},
{"source_id": "b", "url": "https://example.com/b"},
{"source_id": "c", "url": "https://example.com/c"},
]
out = _replace_openai_citation_markers(text, citations)
assert out == (
"alpha [[1]](https://example.com/a) beta "
"[[2]](https://example.com/b) gamma "
"[[3]](https://example.com/c) end"
)
# ---------------------------------------------------------------------------
# 6. Idempotency.
# ---------------------------------------------------------------------------
def test_rewriter_idempotent_on_already_rewritten_text():
"""Running the rewriter twice does not double-link or corrupt brackets."""
text = f"alpha {_marker('a')} omega"
citations = [{"source_id": "a", "url": "https://example.com/a"}]
once = _replace_openai_citation_markers(text, citations)
twice = _replace_openai_citation_markers(once, citations)
assert once == twice
assert _no_private_use(once)
def test_rewriter_idempotent_on_marker_free_text():
"""No-op when there is nothing to rewrite."""
text = "Plain prose with no citations and no private-use bytes."
out = _replace_openai_citation_markers(text, [])
assert out is text or out == text
# ---------------------------------------------------------------------------
# 7. Edge / robustness.
# ---------------------------------------------------------------------------
def test_only_marker_no_surrounding_text():
"""A delta that is JUST a marker (no prose) still renders correctly;
used to leak without the empty-string short-circuit in the split helper."""
text = _marker("solo")
citations = [{"source_id": "solo", "url": "https://solo.example"}]
out = _replace_openai_citation_markers(text, citations)
assert out == "[[1]](https://solo.example)"
def test_back_to_back_markers_with_no_separator():
"""Adjacent markers resolve to concatenated links, no joining whitespace."""
text = f"{_marker('x')}{_marker('y')}"
citations = [
{"source_id": "x", "url": "https://x.example"},
{"source_id": "y", "url": "https://y.example"},
]
out = _replace_openai_citation_markers(text, citations)
assert out == "[[1]](https://x.example)[[2]](https://y.example)"
def test_split_helper_buffers_only_after_last_open_byte():
"""A complete marker followed by an unterminated one: head includes
the complete marker, buffer holds only the trailing partial."""
complete = _marker("done")
partial = f"{CITE_START}cite{CITE_DELIM}half" # no STOP
text = f"pre {complete} mid {partial}"
head, tail = _split_pending_citation_tail(text)
assert head == f"pre {complete} mid "
assert tail == partial
# And the head, once rewritten, drops every private-use byte.
rewritten = _replace_openai_citation_markers(
head, [{"source_id": "done", "url": "https://d"}]
)
assert rewritten == "pre [[1]](https://d) mid "
def test_split_helper_empty_input():
head, tail = _split_pending_citation_tail("")
assert head == "" and tail == ""
def test_split_helper_no_open_byte():
head, tail = _split_pending_citation_tail("nothing to see here")
assert head == "nothing to see here" and tail == ""
def test_split_helper_complete_marker_only():
"""A delta ending with a closed marker leaves the buffer empty."""
text = f"alpha {_marker('a')}"
head, tail = _split_pending_citation_tail(text)
assert head == text and tail == ""
# ---------------------------------------------------------------------------
# 8. Sources-panel: marker drop must not affect citation aggregation.
# Indices come from the url_citations list, not the marker stream.
# ---------------------------------------------------------------------------
def test_unknown_marker_does_not_perturb_citation_indexing():
"""Unknown source_id markers drop without consuming an index slot."""
text = f"A {_marker('unknown')} B {_marker('real_a')} C {_marker('real_b')}"
citations = [
{"source_id": "real_a", "url": "https://example.com/a"},
{"source_id": "real_b", "url": "https://example.com/b"},
]
out = _replace_openai_citation_markers(text, citations)
# real_a is index 1; unknown does not take a slot.
assert "[[1]](https://example.com/a)" in out
assert "[[2]](https://example.com/b)" in out
assert _no_private_use(out)
# ---------------------------------------------------------------------------
# Regression: unterminated marker tail must NOT leak the residual
# ``cite``-prefixed source id as plain text. PR #5713 audit P1.
# ---------------------------------------------------------------------------
def test_unterminated_marker_does_not_leak_cite_residue():
"""Stream ends mid-marker: drop the whole tail rather than strip
codepoints and leave ``cite<sid>`` behind."""
half = f"Hi there {CITE_START}cite{CITE_DELIM}turn0view0"
out = _simulate_delta_stream([half], [], flush = True)
# Prose before the marker stays; no private-use bytes or cite residue.
assert "Hi there" in out
assert _no_private_use(out)
assert "citeturn0view0" not in out
assert "cite" not in out.split("Hi there", 1)[1]
def test_unterminated_marker_only_no_prefix_drops_entirely():
"""A delta that is purely an unterminated marker flushes to ""."""
half = f"{CITE_START}cite{CITE_DELIM}turn0view0"
out = _simulate_delta_stream([half], [], flush = True)
assert out == ""
def test_unterminated_marker_with_prefix_emits_only_prefix():
"""Prose then unterminated marker: prose emits, marker remnant drops."""
half = f"prefix prose {CITE_START}cite{CITE_DELIM}abc"
out = _simulate_delta_stream([half], [], flush = True)
assert out == "prefix prose "
def test_closing_byte_arrives_after_pending_buffered_split():
"""Closing byte arrives in a later delta after opener + source id were
buffered; link resolves with no residue."""
cuts = [
f"a {CITE_START}cite{CITE_DELIM}",
f"sid{CITE_STOP} b",
]
out = _simulate_delta_stream(
cuts,
[{"source_id": "sid", "url": "https://example.com/x"}],
flush = True,
)
assert "[[1]](https://example.com/x)" in out
assert "a " in out and "b" in out
assert _no_private_use(out)
assert "citesid" not in out

1
studio/backend/tests/test_openai_image_generation.py
View file

@ -210,6 +210,7 @@ def test_image_generation_done_emits_tool_event_chunks(monkeypatch):
assert starts[0]["arguments"] == {
"kind": "image",
"prompt": "A photorealistic cat sitting",
"openai_image_generation_call_id": "img_abc",
}
assert ends[0]["image_b64"] == "AAAA"
assert ends[0]["image_mime"] == "image/png"

372
studio/backend/tests/test_openai_tool_result_fallbacks.py Normal file
View file

@ -0,0 +1,372 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Regression tests for OpenAI Responses tool-result rendering.
Covers two bug classes: empty web_search cards (per-card result seeded
with "Searching: <query>") and orphan shell_call cards (bundled-output
fallback + final flush at response.completed / response.incomplete).
"""
import asyncio
import json
import httpx
from core.inference import external_provider as ep_mod
from core.inference.external_provider import ExternalProviderClient
def _drive(coro):
return asyncio.new_event_loop().run_until_complete(coro)
async def _collect(agen):
out = []
async for line in agen:
out.append(line)
return out
def _mock_http_client(monkeypatch, handler):
transport = httpx.MockTransport(handler)
monkeypatch.setattr(ep_mod, "_http_client", httpx.AsyncClient(transport = transport))
def _make_client(base_url: str = "https://api.openai.com/v1") -> ExternalProviderClient:
return ExternalProviderClient(
provider_type = "openai",
base_url = base_url,
api_key = "sk-test",
)
def _openai_sse(events: list[dict]) -> bytes:
chunks: list[str] = []
for event in events:
chunks.append(f"event: {event['type']}")
chunks.append(f"data: {json.dumps(event)}")
chunks.append("")
return ("\n".join(chunks) + "\n").encode("utf-8")
def _tool_events(lines: list[str]) -> list[dict]:
out: list[dict] = []
for line in lines:
if not line.startswith("data:"):
continue
raw = line[len("data:") :].strip()
if not raw or raw == "[DONE]":
continue
try:
parsed = json.loads(raw)
except json.JSONDecodeError:
continue
if isinstance(parsed, dict) and "_toolEvent" in parsed:
out.append(parsed["_toolEvent"])
return out
def _drive_stream(sse_events, enabled_tools, monkeypatch):
def handler(request):
return httpx.Response(
200,
content = _openai_sse(sse_events),
headers = {"content-type": "text/event-stream"},
)
_mock_http_client(monkeypatch, handler)
async def run():
client = _make_client()
return await _collect(
client._stream_openai_responses(
messages = [{"role": "user", "content": "x"}],
model = "gpt-5.5",
temperature = 0.7,
top_p = 0.95,
max_tokens = 4096,
enable_thinking = None,
reasoning_effort = None,
enabled_tools = enabled_tools,
)
)
return _drive(run())
# ── web_search per-card result ─────────────────────────────────────────
def test_web_search_each_call_carries_its_own_query_as_result(monkeypatch):
"""Each card carries its own `Searching: <query>` text; no empties."""
sse_events = [
{
"type": "response.output_item.done",
"item": {
"type": "web_search_call",
"id": "ws_1",
"action": {"query": "popular animals 2026"},
},
},
{
"type": "response.output_item.done",
"item": {
"type": "web_search_call",
"id": "ws_2",
"action": {"query": "most loved animals poll"},
},
},
{
"type": "response.output_item.done",
"item": {
"type": "web_search_call",
"id": "ws_3",
"action": {"query": "tiger ranking"},
},
},
{"type": "response.completed", "response": {}},
]
lines = _drive_stream(sse_events, ["web_search"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
by_id = {e["tool_call_id"]: e for e in ends}
assert by_id["ws_1"]["result"] == "Searching: popular animals 2026"
assert by_id["ws_2"]["result"] == "Searching: most loved animals poll"
assert by_id["ws_3"]["result"] == "Searching: tiger ranking"
def test_web_search_last_call_overwritten_with_citations(monkeypatch):
"""Last call still gets the aggregated citation list; earlier calls
keep their per-call `Searching:` text."""
sse_events = [
{
"type": "response.output_item.done",
"item": {
"type": "web_search_call",
"id": "ws_1",
"action": {"query": "first query"},
},
},
{
"type": "response.output_item.done",
"item": {
"type": "web_search_call",
"id": "ws_2",
"action": {"query": "second query"},
},
},
{
"type": "response.output_text.annotation.added",
"annotation": {
"type": "url_citation",
"url": "https://example.com/a",
"title": "Example A",
},
},
{"type": "response.completed", "response": {}},
]
lines = _drive_stream(sse_events, ["web_search"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
by_id: dict = {}
# Keep the LAST tool_end per id (the citation overwrite for ws_2).
for e in ends:
by_id[e["tool_call_id"]] = e
# First call keeps its own query.
assert by_id["ws_1"]["result"] == "Searching: first query"
# Last call gets overwritten with the citation block.
assert "Title: Example A" in by_id["ws_2"]["result"]
assert "URL: https://example.com/a" in by_id["ws_2"]["result"]
def test_web_search_empty_query_falls_back_to_empty_result(monkeypatch):
"""No query -> empty result (no `Searching:` placeholder)."""
sse_events = [
{
"type": "response.output_item.done",
"item": {
"type": "web_search_call",
"id": "ws_only",
"action": {},
},
},
{"type": "response.completed", "response": {}},
]
lines = _drive_stream(sse_events, ["web_search"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
assert len(ends) == 1
assert ends[0]["result"] == ""
# ── shell_call output fallbacks ────────────────────────────────────────
def test_shell_call_emits_tool_end_when_output_bundled_on_done(monkeypatch):
"""Output bundled on the shell_call done event emits tool_end."""
sse_events = [
{
"type": "response.output_item.done",
"item": {
"type": "shell_call",
"id": "scall_bundled",
"action": {"commands": ["echo hi"]},
"output": [
{
"stdout": "hi\n",
"stderr": "",
"outcome": {"type": "exit", "exit_code": 0},
}
],
},
},
{"type": "response.completed", "response": {}},
]
lines = _drive_stream(sse_events, ["code_execution"], monkeypatch)
events = _tool_events(lines)
starts = [e for e in events if e["type"] == "tool_start"]
ends = [e for e in events if e["type"] == "tool_end"]
assert len(starts) == 1
assert starts[0]["tool_call_id"] == "scall_bundled"
assert len(ends) == 1
assert ends[0]["tool_call_id"] == "scall_bundled"
assert "hi" in ends[0]["result"]
def test_shell_call_bundled_then_separate_output_does_not_double_emit(monkeypatch):
"""Separate shell_call_output after bundled-output is a no-op."""
sse_events = [
{
"type": "response.output_item.done",
"item": {
"type": "shell_call",
"id": "scall_both",
"action": {"commands": ["echo bundle"]},
"output": [
{
"stdout": "bundle\n",
"stderr": "",
"outcome": {"type": "exit", "exit_code": 0},
}
],
},
},
{
"type": "response.output_item.done",
"item": {
"type": "shell_call_output",
"id": "scout_both",
"call_id": "scall_both",
"output": [
{
"stdout": "should not double-emit\n",
"stderr": "",
"outcome": {"type": "exit", "exit_code": 0},
}
],
},
},
{"type": "response.completed", "response": {}},
]
lines = _drive_stream(sse_events, ["code_execution"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
assert len(ends) == 1
assert ends[0]["tool_call_id"] == "scall_both"
assert "bundle" in ends[0]["result"]
assert "should not double-emit" not in ends[0]["result"]
def test_shell_call_final_flush_on_completed_when_no_output_event(monkeypatch):
"""Orphan shell_call finalises via the response.completed flush."""
sse_events = [
{
"type": "response.output_item.added",
"item": {
"type": "shell_call",
"id": "scall_orphan",
"action": {"commands": ["true"]},
},
},
{
"type": "response.output_item.done",
"item": {
"type": "shell_call",
"id": "scall_orphan",
"action": {"commands": ["true"]},
"status": "completed",
},
},
{"type": "response.completed", "response": {}},
]
lines = _drive_stream(sse_events, ["code_execution"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
assert any(e["tool_call_id"] == "scall_orphan" for e in ends)
def test_shell_call_flushed_on_response_incomplete_truncation(monkeypatch):
"""Truncated streams (response.incomplete) also flush orphan calls."""
sse_events = [
{
"type": "response.output_item.added",
"item": {
"type": "shell_call",
"id": "scall_truncated",
"action": {"commands": ["long_running"]},
},
},
{
"type": "response.output_item.done",
"item": {
"type": "shell_call",
"id": "scall_truncated",
"action": {"commands": ["long_running"]},
"status": "in_progress",
},
},
{
"type": "response.incomplete",
"response": {
"incomplete_details": {"reason": "max_output_tokens"},
},
},
]
lines = _drive_stream(sse_events, ["code_execution"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
assert any(e["tool_call_id"] == "scall_truncated" for e in ends)
def test_shell_call_incomplete_does_not_double_emit(monkeypatch):
"""response.incomplete is idempotent against already-finalised calls."""
sse_events = [
{
"type": "response.output_item.done",
"item": {
"type": "shell_call",
"id": "scall_done",
"action": {"commands": ["echo done"]},
"output": [
{
"stdout": "done\n",
"stderr": "",
"outcome": {"type": "exit", "exit_code": 0},
}
],
},
},
{
"type": "response.incomplete",
"response": {
"incomplete_details": {"reason": "max_output_tokens"},
},
},
]
lines = _drive_stream(sse_events, ["code_execution"], monkeypatch)
events = _tool_events(lines)
ends = [e for e in events if e["type"] == "tool_end"]
assert len(ends) == 1
assert ends[0]["tool_call_id"] == "scall_done"
assert "done" in ends[0]["result"]

281
studio/backend/tests/test_pricing.py
View file

@ -1,12 +1,8 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Unit tests for the per-session cost calculator.
Pricing inputs are baked into ``core/inference/pricing.py``; this
test verifies the math (with multipliers from the prompt-caching
docs) and that unknown models / empty usage degrade gracefully.
"""
"""Unit tests for the per-session cost calculator. Verifies math
against ``core/inference/pricing.py`` and graceful degradation."""
import math
@ -14,6 +10,7 @@ from core.inference.pricing import (
ANTHROPIC_CACHE_5M_WRITE_MULT,
ANTHROPIC_CACHE_1H_WRITE_MULT,
ANTHROPIC_CACHE_READ_MULT,
ANTHROPIC_FAST_MODE_MULT,
ANTHROPIC_PRICING,
OPENAI_CACHE_READ_MULT,
OPENAI_CONTAINER_USD_PER_HOUR,
@ -57,6 +54,64 @@ def test_anthropic_opus_4_7_input_and_output_math():
assert _isclose(out["total_usd"], 30.0)
# ── Anthropic fast-mode 6x multiplier (Opus 4.6 / 4.7 only) ─────────
def test_anthropic_fast_mode_charges_6x_standard_opus():
"""6x on input + output when ``usage.speed == "fast"``.
https://platform.claude.com/docs/en/build-with-claude/fast-mode"""
out = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"input_tokens": 1_000_000,
"output_tokens": 1_000_000,
"speed": "fast",
},
)
assert _isclose(out["input_usd"], 5.0 * ANTHROPIC_FAST_MODE_MULT)
assert _isclose(out["output_usd"], 25.0 * ANTHROPIC_FAST_MODE_MULT)
assert _isclose(out["total_usd"], 30.0 * ANTHROPIC_FAST_MODE_MULT)
assert "(fast)" in out["model_priced"], out["model_priced"]
def test_anthropic_fast_mode_does_not_affect_standard_speed():
"""``speed: "standard"`` (or missing) keeps the base rates."""
out_standard = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"input_tokens": 1_000_000,
"output_tokens": 1_000_000,
"speed": "standard",
},
)
out_missing = calculate_cost(
"anthropic",
"claude-opus-4-7",
{"input_tokens": 1_000_000, "output_tokens": 1_000_000},
)
assert _isclose(out_standard["total_usd"], out_missing["total_usd"])
assert _isclose(out_standard["total_usd"], 30.0)
def test_anthropic_fast_mode_stacks_with_cache_read_multiplier():
"""Cache multipliers apply on top of fast-mode (per docs)."""
base = ANTHROPIC_PRICING["claude-opus-4-7"]["input_per_mtok"]
out = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"input_tokens": 0,
"output_tokens": 0,
"cache_read_input_tokens": 1_000_000,
"speed": "fast",
},
)
expected = base * ANTHROPIC_FAST_MODE_MULT * ANTHROPIC_CACHE_READ_MULT
assert _isclose(out["cache_read_usd"], expected)
# ── Anthropic cache write 5m + read multipliers ──────────────────────
@ -102,8 +157,7 @@ def test_anthropic_cache_1h_write_uses_2x_multiplier():
def test_anthropic_cache_5m_default_when_no_breakdown():
# When the docs/response doesn't surface the 5m/1h split, treat
# the full cache_creation bucket as 5m (the upstream default pool).
# No 5m/1h split surfaced -> assume the default 5m pool.
base = ANTHROPIC_PRICING["claude-opus-4-7"]["input_per_mtok"]
out = calculate_cost(
"anthropic",
@ -148,8 +202,7 @@ def test_anthropic_code_exec_charged_per_hour():
def test_anthropic_dated_id_falls_back_to_canonical_prefix():
# Hypothetical dated snapshot of claude-opus-4-7 should still
# inherit the canonical-id pricing via the prefix-match fallback.
# Dated snapshot inherits canonical pricing via prefix-match.
out = calculate_cost(
"anthropic",
"claude-opus-4-7-20260712",
@ -163,8 +216,7 @@ def test_anthropic_dated_id_falls_back_to_canonical_prefix():
def test_openai_gpt55_input_output_math():
# Sub-272k input keeps us in the short-context tier ($5/$30).
# The dedicated long-context tests below exercise the crossover.
# Sub-272k stays in short-context tier ($5/$30).
out = calculate_cost(
"openai",
"gpt-5.5",
@ -176,11 +228,7 @@ def test_openai_gpt55_input_output_math():
def test_openai_cache_read_subtracted_from_input_at_discount():
# OpenAI folds cached tokens into input_tokens, unlike Anthropic.
# The calculator must subtract cached_tokens from the "full price"
# bucket and re-bill them at 0.1x. Use a sub-272k total so the
# short-context tier applies (long-context crossover is exercised
# in its own test below).
# OpenAI folds cached into input_tokens; subtract and re-bill at 0.1x.
base = OPENAI_PRICING["gpt-5.5"]["input_per_mtok"]
out = calculate_cost(
"openai",
@ -199,9 +247,7 @@ def test_openai_cache_read_subtracted_from_input_at_discount():
def test_openai_billable_input_tokens_does_not_double_count_cache_read():
# OpenAI's input_tokens already includes cached_tokens, so the
# billable counter must NOT add cache_read on top -- otherwise the
# tooltip says 180k input when the bill is for 100k.
# input_tokens already includes cached; don't double-count.
out = calculate_cost(
"openai",
"gpt-5.5",
@ -215,9 +261,7 @@ def test_openai_billable_input_tokens_does_not_double_count_cache_read():
def test_openai_dated_snapshot_inherits_canonical_pricing():
# Sub-272k stays in the short-context tier; the prefix-match
# fallback is what proves the dated snapshot inherits gpt-5.5
# pricing.
# Dated snapshot inherits gpt-5.5 pricing via prefix-match.
out = calculate_cost(
"openai",
"gpt-5.5-2026-04-23",
@ -228,10 +272,7 @@ def test_openai_dated_snapshot_inherits_canonical_pricing():
def test_openai_gpt54_family_uses_verified_prices():
# Spot-check the lower-tier rows that previously underbilled.
# gpt-5.4 has a long-context tier so the input has to stay
# below 272k; the mini/nano/codex rows have no crossover so
# 1M tokens is fine.
# Spot-check lower-tier rows that previously underbilled.
cases = {
# (input_tokens, expected_input_usd, expected_output_usd)
"gpt-5.4": (200_000, 200_000 / 1_000_000.0 * 2.5, 200_000 / 1_000_000.0 * 15.0),
@ -251,8 +292,7 @@ def test_openai_gpt54_family_uses_verified_prices():
def test_openai_unlisted_model_priced_false_not_zero_default():
# o-series / gpt-4.5 are no longer on the pricing page, so we
# intentionally drop them rather than silently underbill at $0.
# o-series / gpt-4.5 are off the pricing page; drop rather than $0.
for model in ("o3", "o4-mini", "gpt-4.5", "gpt-4.5-preview"):
out = calculate_cost(
"openai",
@ -270,9 +310,7 @@ def test_openai_unlisted_model_priced_false_not_zero_default():
def test_anthropic_canonical_4_5_ids_are_priced():
# Codex P1: claude-opus-4-5 (no date) is the canonical id used
# in backend defaults but was missing from the table, so the
# calculator returned priced=False + zero cost. Pin the aliases.
# Pin the bare-id aliases (backend defaults reference these).
cases = {
"claude-opus-4-5": (5.0, 25.0),
"claude-sonnet-4-5": (3.0, 15.0),
@ -307,8 +345,7 @@ def test_openai_gpt55_short_context_under_272k_uses_base_rates():
def test_openai_gpt55_long_context_crossover_uses_higher_rates():
# 300k billable input > 272k threshold -> long-context tier
# applies to the WHOLE turn, not a per-token blend.
# >272k billable -> long-context tier on the whole turn.
out = calculate_cost(
"openai",
"gpt-5.5",
@ -330,8 +367,7 @@ def test_openai_gpt54_long_context_crossover():
def test_openai_gpt54_mini_has_no_long_context_tier():
# Mini/nano/codex don't publish a long-context price; the base
# rate must keep applying even at very large prompts.
# Mini/nano/codex have no long-context tier; base rate always applies.
out = calculate_cost(
"openai",
"gpt-5.4-mini",
@ -374,8 +410,7 @@ def test_openai_container_hours_charged():
def test_openai_tool_surcharges_added_to_total():
# End-to-end: input + output + web_search + container in one
# turn. Total must sum all four buckets.
# End-to-end: total must sum input + output + web_search + container.
out = calculate_cost(
"openai",
"gpt-5.5",
@ -412,12 +447,12 @@ def test_snapshot_contains_provider_buckets_and_multipliers():
assert a["cache_5m_write_mult"] == ANTHROPIC_CACHE_5M_WRITE_MULT
assert a["cache_1h_write_mult"] == ANTHROPIC_CACHE_1H_WRITE_MULT
assert a["cache_read_mult"] == ANTHROPIC_CACHE_READ_MULT
assert a["fast_mode_mult"] == ANTHROPIC_FAST_MODE_MULT
assert "web_search_usd_per_1k" in a
assert "code_execution_usd_per_hour" in a
assert "models" in o and "gpt-5.5" in o["models"]
assert o["cache_read_mult"] == OPENAI_CACHE_READ_MULT
# OpenAI tool surcharge constants are also exposed so the frontend
# tooltip can render the per-call rate.
# OpenAI tool surcharge constants are exposed for the frontend.
assert o["web_search_usd_per_1k"] == OPENAI_WEB_SEARCH_USD_PER_1K
assert o["container_usd_per_hour"] == OPENAI_CONTAINER_USD_PER_HOUR
# Long-context tier metadata travels with the model row.
@ -425,3 +460,169 @@ def test_snapshot_contains_provider_buckets_and_multipliers():
assert gpt55["long_context_threshold"] == 272_000
assert gpt55["long_context_input_per_mtok"] == 10.0
assert gpt55["long_context_output_per_mtok"] == 45.0
# ── longest-prefix match: dated mini variant must not collide with the
# shorter family prefix. ──
def test_longest_prefix_match_wins_for_dated_mini_snapshot():
"""`gpt-5.4-mini-2026-...` must inherit the mini rate, not the
shorter `gpt-5.4` rate (longest prefix wins)."""
out = calculate_cost(
"openai",
"gpt-5.4-mini-2026-04-23",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is True
# mini = 0.75/MTok, shorter gpt-5.4 = 2.5/MTok (>3x overcharge).
assert _isclose(out["input_usd"], 0.75), out
def test_longest_prefix_match_wins_for_dated_pro_snapshot():
out = calculate_cost(
"openai",
"gpt-5.5-pro-2026-04-23",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is True
# gpt-5.5-pro = 30/MTok vs gpt-5.5 = 5/MTok; longest wins.
assert _isclose(out["input_usd"], 30.0), out
# ── accept both chat-style and Responses envelope shapes. ──
def test_openai_chat_style_usage_keys_priced_correctly():
"""Chat-style envelope (`prompt_tokens` / `completion_tokens`) must
produce a non-zero cost (previously silently zeroed)."""
out = calculate_cost(
"openai",
"gpt-5.4-mini",
{"prompt_tokens": 1_000_000, "completion_tokens": 1_000_000},
)
# gpt-5.4-mini: 0.75 input + 4.5 output per MTok.
assert _isclose(out["input_usd"], 0.75), out
assert _isclose(out["output_usd"], 4.5), out
def test_input_tokens_preferred_when_both_keys_present():
"""Raw key wins when both envelope shapes are present."""
out = calculate_cost(
"openai",
"gpt-5.4-mini",
{
"input_tokens": 2_000_000,
"prompt_tokens": 5_000_000,
"output_tokens": 0,
},
)
# input_tokens=2M wins -> 2 * 0.75 = 1.50.
assert _isclose(out["input_usd"], 1.50), out
def test_anthropic_chat_style_prompt_tokens_dedupes_cache_buckets():
"""Anthropic chat-style prompt_tokens already folds cache buckets;
don't double-count billable input."""
# 1M uncached + 200K cache_creation + 500K cache_read -> 1.7M folded.
raw = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"input_tokens": 1_000_000,
"cache_creation_input_tokens": 200_000,
"cache_read_input_tokens": 500_000,
"output_tokens": 0,
},
)
chat = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"prompt_tokens": 1_700_000,
"cache_creation_input_tokens": 200_000,
"cache_read_input_tokens": 500_000,
"completion_tokens": 0,
},
)
# Both envelopes must price the same.
assert _isclose(chat["input_usd"], raw["input_usd"]), (chat, raw)
assert _isclose(chat["cache_write_usd"], raw["cache_write_usd"]), (chat, raw)
assert _isclose(chat["cache_read_usd"], raw["cache_read_usd"]), (chat, raw)
assert _isclose(chat["total_usd"], raw["total_usd"]), (chat, raw)
assert chat["billable_input_tokens"] == raw["billable_input_tokens"], (chat, raw)
def test_openai_chat_style_prompt_tokens_keeps_cache_read_semantics():
"""OpenAI prompt_tokens includes cache_read like raw input_tokens."""
raw = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": 1_000_000,
"input_tokens_details": {"cached_tokens": 200_000},
"output_tokens": 100_000,
},
)
chat = calculate_cost(
"openai",
"gpt-5.5",
{
"prompt_tokens": 1_000_000,
"cache_read_input_tokens": 200_000,
"completion_tokens": 100_000,
},
)
assert _isclose(chat["total_usd"], raw["total_usd"]), (chat, raw)
def test_openai_chat_style_envelope_reads_cache_from_prompt_tokens_details():
"""Chat-style envelope ships cached under prompt_tokens_details;
calculator must honour both this and input_tokens_details."""
base = OPENAI_PRICING["gpt-5.5"]["input_per_mtok"]
raw = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": 100_000,
"input_tokens_details": {"cached_tokens": 80_000},
"output_tokens": 0,
},
)
chat_style = calculate_cost(
"openai",
"gpt-5.5",
{
"prompt_tokens": 100_000,
"prompt_tokens_details": {"cached_tokens": 80_000},
"completion_tokens": 0,
},
)
# Both envelopes must price identically.
assert _isclose(chat_style["input_usd"], raw["input_usd"]), (chat_style, raw)
assert _isclose(chat_style["cache_read_usd"], raw["cache_read_usd"]), (
chat_style,
raw,
)
# 80k at 0.1x base, 20k at full.
assert _isclose(
chat_style["cache_read_usd"],
80_000 / 1_000_000.0 * base * OPENAI_CACHE_READ_MULT,
)
def test_explicit_zero_output_tokens_wins_over_stale_completion_tokens():
"""Explicit ``output_tokens: 0`` beats a stale ``completion_tokens``;
the previous `or` fallback treated 0 as missing."""
out = calculate_cost(
"openai",
"gpt-4o-mini",
{
"input_tokens": 100,
"output_tokens": 0,
# Stale chat-style mirror; must not bill against it.
"completion_tokens": 50,
},
)
assert out["billable_output_tokens"] == 0, out
assert out["output_usd"] == 0.0, out

475
studio/backend/tests/test_pricing_edge.py Normal file
View file

@ -0,0 +1,475 @@
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""Adversarial edge cases for ``calculate_cost`` / ``_lookup``: prefix
boundary, negative tokens, chat vs raw parity, long-context crossover
on billable count, and malformed sub-objects."""
import math
from core.inference.pricing import (
ANTHROPIC_CACHE_5M_WRITE_MULT,
ANTHROPIC_CACHE_READ_MULT,
ANTHROPIC_PRICING,
OPENAI_CACHE_READ_MULT,
OPENAI_PRICING,
_lookup,
calculate_cost,
)
def _isclose(a, b, tol = 1e-6):
return math.isclose(a, b, rel_tol = tol, abs_tol = tol)
# ── prefix-match boundary checks ────────────────────────────────────
def test_prefix_match_requires_dash_boundary_opus_variant():
# `claude-opus-4-15` must not inherit `claude-opus-4-1` pricing;
# next char must be `-` or end-of-string.
assert _lookup("anthropic", "claude-opus-4-15") is None
out = calculate_cost(
"anthropic",
"claude-opus-4-15",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is False
assert out["total_usd"] == 0.0
def test_prefix_match_requires_dash_boundary_gpt_variant():
# Same dash-boundary invariant for OpenAI ids.
assert _lookup("openai", "gpt-5.55") is None
assert _lookup("openai", "gpt-5.55-2026-04-23") is None
out = calculate_cost(
"openai",
"gpt-5.55-2026-04-23",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is False
def test_prefix_match_requires_dash_boundary_pro_lookalike():
# `gpt-5.5-prod` must fall through `gpt-5.5-pro` (6x overcharge)
# and land on the canonical `gpt-5.5` row.
prices = _lookup("openai", "gpt-5.5-prod")
assert prices is not None
assert (
prices["input_per_mtok"] == OPENAI_PRICING["gpt-5.5"]["input_per_mtok"]
), "expected fallback to gpt-5.5 base ($5), not gpt-5.5-pro ($30)"
out = calculate_cost(
"openai",
"gpt-5.5-prod",
{"input_tokens": 100_000, "output_tokens": 0},
)
assert out["priced"] is True
assert _isclose(out["input_usd"], 100_000 / 1_000_000.0 * 5.0)
def test_prefix_match_still_resolves_legit_dated_snapshots():
# Boundary fix must not regress legit dated snapshots.
out = calculate_cost(
"openai",
"gpt-5.4-mini-2026-04-23",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is True
assert _isclose(out["input_usd"], 0.75)
# And Anthropic dated snapshot still resolves to canonical row.
out = calculate_cost(
"anthropic",
"claude-opus-4-7-20260414",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is True
assert _isclose(out["input_usd"], 5.0)
# ── precedence: input_tokens wins over prompt_tokens (and 0 is real) ──
def test_explicit_zero_input_tokens_wins_over_stale_prompt_tokens():
# Input-side mirror of the output zero precedence test.
out = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": 0,
"prompt_tokens": 1_000_000, # stale chat-style mirror
"output_tokens": 100,
},
)
assert out["billable_input_tokens"] == 0
assert out["input_usd"] == 0.0
def test_none_input_tokens_falls_through_to_prompt_tokens():
# `None` is "key present but unset"; chat-style mirror wins.
out = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": None,
"prompt_tokens": 200_000,
"output_tokens": None,
"completion_tokens": 5_000,
},
)
assert out["billable_input_tokens"] == 200_000
assert out["billable_output_tokens"] == 5_000
assert _isclose(out["input_usd"], 200_000 / 1_000_000.0 * 5.0)
assert _isclose(out["output_usd"], 5_000 / 1_000_000.0 * 30.0)
# ── negative / corrupted upstream values clamp to zero ──────────────
def test_negative_tokens_clamp_to_zero_no_negative_bill():
out = calculate_cost(
"openai",
"gpt-5.5",
{"input_tokens": -100, "output_tokens": -50},
)
assert out["billable_input_tokens"] == 0
assert out["billable_output_tokens"] == 0
assert out["input_usd"] == 0.0
assert out["output_usd"] == 0.0
assert out["total_usd"] == 0.0
def test_negative_cache_buckets_clamp_to_zero():
# Negative cache_read on Anthropic would otherwise refund the bill.
out = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"input_tokens": 1_000,
"output_tokens": 0,
"cache_creation_input_tokens": -500,
"cache_read_input_tokens": -1_000,
},
)
assert out["cache_write_usd"] == 0.0
assert out["cache_read_usd"] == 0.0
assert out["billable_input_tokens"] == 1_000
assert out["total_usd"] >= 0.0
def test_negative_prompt_tokens_chat_style_clamp():
out = calculate_cost(
"openai",
"gpt-5.4-mini",
{"prompt_tokens": -100, "completion_tokens": -50},
)
assert out["billable_input_tokens"] == 0
assert out["billable_output_tokens"] == 0
assert out["total_usd"] == 0.0
# ── cache_read > prompt_tokens corruption: no negative billable ─────
def test_anthropic_chat_cache_read_exceeds_prompt_no_negative_billable():
# cache_read > prompt_tokens clamps uncached_input at 0; billable
# still reflects cache buckets (we charge for what we got).
out = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"prompt_tokens": 100,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 500,
"completion_tokens": 0,
},
)
assert out["input_usd"] == 0.0 # uncached clamped to 0
assert out["billable_input_tokens"] == 500 # 0 uncached + 500 cache_read
# cache_read still priced at the discount rate.
base = ANTHROPIC_PRICING["claude-opus-4-7"]["input_per_mtok"]
assert _isclose(
out["cache_read_usd"], 500 / 1_000_000.0 * base * ANTHROPIC_CACHE_READ_MULT
)
def test_openai_raw_cached_tokens_exceeds_input_clamp_non_cached():
# OpenAI variant: cached > input must not produce negative input_usd.
base = OPENAI_PRICING["gpt-5.5"]["input_per_mtok"]
out = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": 100,
"output_tokens": 0,
"input_tokens_details": {"cached_tokens": 500},
},
)
assert out["input_usd"] == 0.0
# Cache read still priced (the 0.1x bucket).
assert _isclose(
out["cache_read_usd"], 500 / 1_000_000.0 * base * OPENAI_CACHE_READ_MULT
)
# ── long-context tier crosses on billable, including cache_creation ──
def test_openai_long_context_triggers_on_cache_creation_inflated_billable():
# cache_creation pushes billable past 272k -> long-context tier
# must fire to avoid undercounting.
out = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": 250_000,
"cache_creation_input_tokens": 50_000,
"output_tokens": 1_000,
},
)
assert out["billable_input_tokens"] == 300_000
assert "long-context" in out["model_priced"]
assert _isclose(out["input_usd"], 250_000 / 1_000_000.0 * 10.0)
assert _isclose(out["output_usd"], 1_000 / 1_000_000.0 * 45.0)
def test_openai_long_context_threshold_boundary_inclusive():
# Threshold is inclusive (>=).
out = calculate_cost(
"openai",
"gpt-5.5",
{"input_tokens": 272_000, "output_tokens": 1_000},
)
assert "long-context" in out["model_priced"]
out_lo = calculate_cost(
"openai",
"gpt-5.5",
{"input_tokens": 271_999, "output_tokens": 1_000},
)
assert "long-context" not in out_lo["model_priced"]
# ── chat-style vs raw envelope parity at OpenAI long-context tier ──
def test_openai_chat_envelope_long_context_parity_with_raw():
raw = calculate_cost(
"openai",
"gpt-5.5",
{"input_tokens": 300_000, "output_tokens": 10_000},
)
chat = calculate_cost(
"openai",
"gpt-5.5",
{"prompt_tokens": 300_000, "completion_tokens": 10_000},
)
assert _isclose(chat["total_usd"], raw["total_usd"])
assert "long-context" in chat["model_priced"]
assert "long-context" in raw["model_priced"]
# ── malformed sub-objects: no crash, no false bill ──────────────────
def test_cache_creation_as_int_does_not_crash():
# Proxies sometimes fold cache_creation to an int; tolerate it
# and fall back to the 5m default.
base = ANTHROPIC_PRICING["claude-opus-4-7"]["input_per_mtok"]
out = calculate_cost(
"anthropic",
"claude-opus-4-7",
{
"input_tokens": 0,
"output_tokens": 0,
"cache_creation_input_tokens": 1_000_000,
"cache_creation": 12345, # malformed; must not raise
},
)
# Falls back to 5m default for the whole bucket.
assert _isclose(
out["cache_write_usd"],
1_000_000 / 1_000_000.0 * base * ANTHROPIC_CACHE_5M_WRITE_MULT,
)
def test_non_dict_server_tool_use_is_ignored():
out = calculate_cost(
"anthropic",
"claude-opus-4-7",
{"input_tokens": 100, "output_tokens": 100, "server_tool_use": "garbage"},
)
assert out["server_tools_usd"] == 0.0
out = calculate_cost(
"openai",
"gpt-5.5",
{"input_tokens": 100, "output_tokens": 100, "openai_tool_use": [1, 2, 3]},
)
assert out["server_tools_usd"] == 0.0
def test_non_dict_input_tokens_details_is_ignored():
out = calculate_cost(
"openai",
"gpt-5.5",
{
"input_tokens": 100,
"output_tokens": 0,
"input_tokens_details": "nope",
"prompt_tokens_details": [1, 2, 3],
},
)
# No cached_tokens recovered -> no discount.
assert out["cache_read_usd"] == 0.0
# ── unknown provider degrades gracefully ────────────────────────────
def test_unknown_provider_priced_false_zero_bill():
out = calculate_cost(
"gemini",
"gemini-pro",
{"input_tokens": 1_000_000, "output_tokens": 1_000_000},
)
assert out["priced"] is False
assert out["total_usd"] == 0.0
# Tokens still report for the UI.
assert out["billable_input_tokens"] == 1_000_000
assert out["billable_output_tokens"] == 1_000_000
def test_anthropic_provider_with_openai_model_priced_false():
# OpenAI id against Anthropic table must not falsely match.
out = calculate_cost(
"anthropic",
"gpt-5.5",
{"input_tokens": 1_000_000, "output_tokens": 0},
)
assert out["priced"] is False
# ── all-zero / empty usage stays at zero ────────────────────────────
def test_empty_usage_dict_zero_bill():
out = calculate_cost("openai", "gpt-5.5", {})
assert out["priced"] is True # model is in the table
assert out["billable_input_tokens"] == 0
assert out["total_usd"] == 0.0
# ── Defense-in-depth: Anthropic prompt_tokens_details.cached_tokens ──
def test_anthropic_prompt_tokens_details_fallback_when_native_key_missing():
"""Chat-style envelope without `cache_read_input_tokens` but with
mirrored `prompt_tokens_details.cached_tokens` should still apply
the cache_read discount."""
r = calculate_cost(
provider = "anthropic",
model = "claude-opus-4-7",
usage = {
"prompt_tokens": 1_000_000,
"completion_tokens": 0,
# Only the mirrored shape (no native key).
"prompt_tokens_details": {"cached_tokens": 1_000_000},
"cache_creation_input_tokens": 0,
},
)
assert r["billable_input_tokens"] == 1_000_000, r
# 1M cached at 0.1x of $5 (opus 4.7) = $0.50
assert math.isclose(r["cache_read_usd"], 0.5, rel_tol = 1e-3), r
def test_anthropic_native_key_takes_precedence_over_mirrored():
"""When both native and mirrored cache-read fields are present,
the native Anthropic field wins (mirror is fallback-only)."""
r = calculate_cost(
provider = "anthropic",
model = "claude-opus-4-7",
usage = {
"prompt_tokens": 1_000_000,
"cache_read_input_tokens": 800_000,
"prompt_tokens_details": {"cached_tokens": 1_000_000},
"cache_creation_input_tokens": 0,
},
)
# billable = uncached_input + cache_creation + cache_read
# = (1M - 0 - 800k) + 0 + 800k = 1M
assert r["billable_input_tokens"] == 1_000_000, r
# cache_read uses 800k (native), not 1M (mirrored).
assert math.isclose(r["cache_read_usd"], 0.4, rel_tol = 1e-3), r
def test_anthropic_native_zero_takes_precedence_over_mirrored():
"""Explicit `cache_read_input_tokens: 0` is authoritative; a stale
mirrored block from a proxy must not inflate cache_read past it."""
r = calculate_cost(
provider = "anthropic",
model = "claude-opus-4-7",
usage = {
"input_tokens": 1_000_000,
"output_tokens": 0,
"cache_read_input_tokens": 0,
# Stale mirror from a proxy; must be ignored (native present).
"prompt_tokens_details": {"cached_tokens": 1_000_000},
},
)
# Native is 0 -> cache_read stays 0.
assert r["cache_read_usd"] == 0.0, r
# billable = input + cache_creation + cache_read = 1M + 0 + 0
assert r["billable_input_tokens"] == 1_000_000, r
# 1M uncached at $5/M (no discount).
assert math.isclose(r["input_usd"], 5.0, rel_tol = 1e-3), r
assert math.isclose(r["total_usd"], 5.0, rel_tol = 1e-3), r
# ── _build_usage_chunk preserves cache_creation breakdown ──
def test_build_usage_chunk_forwards_anthropic_cache_creation_breakdown():
"""Chat-style envelope must carry the 5m/1h cache-write breakdown
so downstream cost calc applies the 2x 1h premium."""
import json
from core.inference.external_provider import _build_usage_chunk
chunk = _build_usage_chunk(
completion_id = "cmpl-x",
provider = "anthropic",
last_usage = {
"input_tokens": 10,
"output_tokens": 5,
"cache_creation_input_tokens": 1_000_000,
"cache_read_input_tokens": 0,
"cache_creation": {
"ephemeral_5m_input_tokens": 250_000,
"ephemeral_1h_input_tokens": 750_000,
},
},
)
assert chunk is not None
payload = json.loads(chunk.split("data: ", 1)[1])
cc = payload["usage"]["cache_creation"]
assert cc["ephemeral_1h_input_tokens"] == 750_000, cc
assert cc["ephemeral_5m_input_tokens"] == 250_000, cc
def test_calculate_cost_uses_forwarded_cache_creation_for_1h_premium():
"""Re-emitted chat envelope must price 1h cache writes at 2x base."""
r = calculate_cost(
provider = "anthropic",
model = "claude-opus-4-7",
usage = {
"prompt_tokens": 1_000_010,
"completion_tokens": 0,
"cache_creation_input_tokens": 1_000_000,
"cache_read_input_tokens": 0,
"cache_creation": {
"ephemeral_5m_input_tokens": 0,
"ephemeral_1h_input_tokens": 1_000_000,
},
},
)
# 1M at 1h-premium (2x of $5 = $10); 5m baseline would be $6.25.
assert math.isclose(r["cache_write_usd"], 10.0, rel_tol = 1e-2), r

15
studio/backend/utils/hardware/hardware.py
View file

@ -144,7 +144,10 @@ def detect_hardware() -> DeviceType:
if is_apple_silicon() and _has_mlx():
DEVICE = DeviceType.MLX
CHAT_ONLY = False
chip = platform.processor() or platform.machine()
# platform.processor() runs `uname -p` which returns "i386" on most
# universal2 / Rosetta-shaped Python builds even on native arm64.
# platform.machine() is "arm64" once is_apple_silicon() has gated us.
chip = platform.machine() or "arm64"
print(f"Hardware detected: MLX — Apple Silicon ({chip})")
return DEVICE
@ -279,13 +282,11 @@ def get_gpu_memory_info() -> Dict[str, Any]:
try:
info = mx.device_info()
gpu_name = (
info.get("device_name")
or platform.processor()
or platform.machine()
)
# See detect_hardware(): platform.processor() can return "i386"
# on native arm64 Python builds, so prefer machine() as fallback.
gpu_name = info.get("device_name") or platform.machine() or "arm64"
except Exception:
gpu_name = platform.processor() or platform.machine()
gpu_name = platform.machine() or "arm64"
return {
"available": True,