* fix(studio): forward OpenAI tools/tool_choice to llama-server (#4999)
Studio's /v1/chat/completions silently stripped standard OpenAI `tools`
and `tool_choice` fields, so clients using standard function calling
(opencode, Claude Code, Cursor, Continue, ...) never got structured
tool_calls back. Adds a client-side pass-through path mirroring the
existing Anthropic /v1/messages flow: when `tools` is present without
Studio's `enable_tools` shorthand, the request is forwarded to
llama-server verbatim so the client sees native id, finish_reason
("tool_calls"), delta.tool_calls, and accurate usage tokens.
Also wires Anthropic tool_choice forwarding: /v1/messages previously
accepted tool_choice on the request model but silently dropped it with
a warning. Translate the four Anthropic shapes to OpenAI format and
forward them so agentic clients can actually enforce tool use.
- ChatCompletionRequest: add tools, tool_choice, stop; extra="allow"
- ChatMessage: accept role="tool", optional tool_call_id / tool_calls /
name; content is now optional (assistant with only tool_calls)
- routes/inference.py: _openai_passthrough_stream /
_openai_passthrough_non_streaming helpers, routing branch in
openai_chat_completions, vision+tools via content-parts injection
- _build_passthrough_payload: tool_choice parameter (default "auto")
- anthropic_compat: anthropic_tool_choice_to_openai() translator
- tests/test_openai_tool_passthrough.py: Pydantic + translator unit tests
- tests/test_studio_api.py: 5 new E2E tests (non-stream, stream,
multi-turn, OpenAI SDK, Anthropic tool_choice=any regression)
* fix(studio): surface httpx transport errors from OpenAI passthrough
When the managed llama-server subprocess crashes mid-request, the
async pass-through helpers in routes/inference.py used to return a
bare 500 (non-streaming) or an "An internal error occurred" SSE chunk
(streaming) because _friendly_error only recognized the sync path's
"Lost connection to llama-server" substring -- httpx transport
failures (ConnectError / ReadError / RemoteProtocolError /
ReadTimeout) stringify differently and fell through to the generic
case.
- _friendly_error: map any httpx.RequestError subclass to the same
"Lost connection to the model server" message the sync chat path
emits. Placed before the substring heuristics so the streaming path
automatically picks it up via its existing except Exception catch.
- _openai_passthrough_non_streaming: wrap the httpx.AsyncClient.post
in a try/except httpx.RequestError and re-raise as HTTPException
502 with the friendly detail.
- tests/test_openai_tool_passthrough.py: new TestFriendlyErrorHttpx
class pinning the mapping for ConnectError, ReadError,
RemoteProtocolError, ReadTimeout, and confirming non-httpx paths
(context-size heuristic, generic fallback) are unchanged.
* fix(studio): close aiter_bytes/aiter_lines explicitly in passthroughs
The httpcore asyncgen cleanup fix in 5cedd9a5 is incomplete on Python
3.13 + httpcore 1.0.x: it switched to manual client/response lifecycle
but still used anonymous `async for raw_line in resp.aiter_lines():`
patterns in all three streaming paths. Python's async for does NOT
auto-close the iterator on break/return, so the aiter_lines /
aiter_bytes async generator remains alive, reachable only from the
surrounding coroutine frame. Once `_stream()` returns the frame is
GC'd and the orphaned asyncgen is finalized on a LATER GC pass in a
DIFFERENT asyncio task, where httpcore's
HTTP11ConnectionByteStream.aclose() enters anyio.CancelScope.__exit__
with a mismatched task and prints "Exception ignored in: <async
generator>" / "async generator ignored GeneratorExit" / "Attempted
to exit cancel scope in a different task" to the server log.
User observed this on /v1/messages after successful (status 200)
requests, with the traceback pointing at HTTP11ConnectionByteStream
.__aiter__ / .aclose inside httpcore.
Fix: save resp.aiter_lines() / resp.aiter_bytes() as a variable and
explicitly `await iter.aclose()` in the finally block BEFORE
resp.aclose() / client.aclose(). This closes the asyncgen inside the
current task's event loop, so the internal httpcore byte stream is
cleaned up before Python's asyncgen GC hook has anything orphaned to
finalize. Each aclose is wrapped in try/except Exception so nested
anyio cleanup noise can't bubble out.
Applied to all three streaming passthrough paths:
- _anthropic_passthrough_stream (/v1/messages client-side tool path)
- _openai_passthrough_stream (/v1/chat/completions client-side tool
path, new in this PR)
- openai_completions (/v1/completions bytes proxy from PR #4956)
* fix(studio): default ChatCompletionRequest.stream to false per OpenAI spec
OpenAI's /v1/chat/completions spec defaults `stream` to false, so
clients that omit the field (naive curl, minimal integrations) expect
a single JSON response back. Studio was defaulting to true, silently
switching those clients into SSE and breaking any parser that didn't
also handle streaming. ResponsesRequest and AnthropicMessagesRequest
already default to false correctly; only ChatCompletionRequest was
wrong.
Studio's own frontend always sets `stream` explicitly on every
chat-adapter / chat-api / runtime-provider call site, so the flip has
no UI impact. SDK users (OpenAI Python/JS SDK, opencode, Claude Code,
Cursor, Continue) also always pass `stream` explicitly, so they're
unaffected. The only clients feeling the change are raw-curl users
who were relying on the wrong default -- those get the correct OpenAI
behavior now.
Added a regression test pinning the default so it can't silently
flip back.
* fix(studio): reject images in OpenAI tool passthrough for text-only GGUFs
The new tool passthrough branch runs before _extract_content_parts,
skipping the existing not is_vision guard. Requests combining tools
with an image on a text-only tool-capable GGUF were forwarded to
llama-server, producing opaque upstream errors instead of the
pre-existing clear 400. Restore the guard inline at the dispatch
point, checking both legacy image_base64 and inline image_url parts.
* fix(studio): require tool_call_id on role=tool chat messages
Enforce the OpenAI spec rule that role="tool" messages must carry a
tool_call_id. Without it, upstream backends cannot associate a tool
result with the assistant's prior tool_calls entry and the request
fails in non-obvious ways through the passthrough path. Reject at the
request boundary with a 422 instead.
* fix(studio): harden OpenAI tool passthrough validation and error surfacing
Three related fixes called out by the PR review:
1. Preserve upstream status codes in the streaming passthrough. The
httpx request is now dispatched before the StreamingResponse is
constructed. Non-200 upstream responses and httpx RequestError
transport failures raise HTTPException with the real status
instead of being buried inside a 200 SSE error frame, so OpenAI
SDK clients see APIError/BadRequestError/... as expected.
2. Require non-empty content on user/system/tool messages. Per the
OpenAI spec, content may only be omitted on assistant messages
that carry tool_calls; enforce that at the request boundary so
malformed messages never reach the passthrough path.
3. Role-constrain tool-call metadata. tool_calls is only valid on
role=assistant, tool_call_id and name only on role=tool. Without
this, a user/system message with tool_calls would flip the
passthrough branch on and be forwarded to llama-server, surfacing
as an opaque upstream error.
* fix(studio): normalize image mode and passthrough JSON verbatim
Two Gemini-code-assist review findings on PR #5099:
1. Unconditionally convert decoded images to RGB before PNG encoding.
The prior code only handled RGBA, letting CMYK/I/F images crash
at img.save(format="PNG") and surface as opaque 400s. Applied to
both the passthrough helper and the non-passthrough GGUF path
that originally carried this pattern, keeping the two sites in
sync.
2. Return the upstream JSON body as raw bytes via Response rather
than parse-then-re-serialize with JSONResponse. Matches the
passthrough helper's "verbatim" contract and drops a redundant
round-trip.
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
465 lines
17 KiB
Python
465 lines
17 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved.
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"""
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Tests for the OpenAI /v1/chat/completions client-side tool pass-through.
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Covers:
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- ChatCompletionRequest accepts standard OpenAI `tools` / `tool_choice` / `stop`.
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- ChatMessage accepts role="tool" with `tool_call_id` and role="assistant"
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with `content: None` + `tool_calls`.
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- ChatCompletionRequest carries unknown fields via `extra="allow"`.
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- anthropic_tool_choice_to_openai() covers all four Anthropic shapes.
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- _build_passthrough_payload() honors a caller-supplied tool_choice and
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defaults to "auto" when unset.
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- _friendly_error() maps httpx transport errors to a "Lost connection"
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message so passthrough failures are legible instead of bare 500s.
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No running server or GPU required.
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"""
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import os
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import sys
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_backend = os.path.join(os.path.dirname(__file__), "..")
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sys.path.insert(0, _backend)
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import httpx
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import pytest
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from pydantic import ValidationError
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from models.inference import (
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ChatCompletionRequest,
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ChatMessage,
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)
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from core.inference.anthropic_compat import (
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anthropic_tool_choice_to_openai,
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)
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from routes.inference import _build_passthrough_payload, _friendly_error
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# =====================================================================
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# ChatMessage — tool role, tool_calls, optional content
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# =====================================================================
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class TestChatMessageToolRoles:
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def test_tool_role_with_tool_call_id(self):
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msg = ChatMessage(
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role = "tool",
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tool_call_id = "call_abc123",
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content = '{"temperature": 72}',
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)
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assert msg.role == "tool"
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assert msg.tool_call_id == "call_abc123"
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assert msg.content == '{"temperature": 72}'
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def test_tool_role_with_name(self):
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msg = ChatMessage(
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role = "tool",
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tool_call_id = "call_abc123",
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name = "get_weather",
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content = '{"temperature": 72}',
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)
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assert msg.name == "get_weather"
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def test_assistant_with_tool_calls_no_content(self):
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msg = ChatMessage(
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role = "assistant",
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content = None,
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tool_calls = [
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{
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"id": "call_1",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"city": "Paris"}',
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},
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}
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],
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)
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assert msg.role == "assistant"
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assert msg.content is None
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assert msg.tool_calls is not None
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assert len(msg.tool_calls) == 1
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assert msg.tool_calls[0]["function"]["name"] == "get_weather"
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def test_assistant_with_content_and_tool_calls(self):
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msg = ChatMessage(
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role = "assistant",
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content = "Let me check the weather.",
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tool_calls = [
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{
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"id": "call_1",
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"type": "function",
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"function": {"name": "get_weather", "arguments": "{}"},
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}
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],
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)
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assert msg.content == "Let me check the weather."
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assert msg.tool_calls[0]["id"] == "call_1"
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def test_plain_user_message_still_works(self):
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msg = ChatMessage(role = "user", content = "Hello")
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assert msg.role == "user"
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assert msg.tool_call_id is None
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assert msg.tool_calls is None
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assert msg.name is None
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def test_invalid_role_rejected(self):
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with pytest.raises(ValidationError):
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ChatMessage(role = "function", content = "x")
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def test_content_absent_on_assistant_tool_call_defaults_to_none(self):
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# Assistant messages that carry only tool_calls are the one
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# documented case where `content=None` is permitted.
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msg = ChatMessage(
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role = "assistant",
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tool_calls = [
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{
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"id": "call_1",
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"type": "function",
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"function": {"name": "f", "arguments": "{}"},
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}
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],
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)
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assert msg.content is None
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def test_tool_role_missing_tool_call_id_rejected(self):
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# Per OpenAI spec, role="tool" messages must carry tool_call_id so
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# upstream backends can associate the result with its prior call.
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# Pin the boundary-level rejection so a malformed tool-result
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# message never reaches the passthrough path.
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with pytest.raises(ValidationError) as exc_info:
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ChatMessage(role = "tool", content = '{"temperature": 72}')
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assert "tool_call_id" in str(exc_info.value)
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def test_tool_role_empty_tool_call_id_rejected(self):
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with pytest.raises(ValidationError):
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ChatMessage(
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role = "tool",
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tool_call_id = "",
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content = '{"temperature": 72}',
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)
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# ── Role-aware content requirements ────────────────────────────
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def test_user_empty_content_rejected(self):
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with pytest.raises(ValidationError):
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ChatMessage(role = "user", content = "")
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def test_system_empty_content_rejected(self):
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with pytest.raises(ValidationError):
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ChatMessage(role = "system", content = "")
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def test_user_empty_list_content_rejected(self):
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with pytest.raises(ValidationError):
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ChatMessage(role = "user", content = [])
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def test_tool_empty_content_rejected(self):
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with pytest.raises(ValidationError) as exc_info:
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ChatMessage(role = "tool", tool_call_id = "call_1", content = "")
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assert "content" in str(exc_info.value)
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def test_assistant_without_content_or_tool_calls_rejected(self):
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with pytest.raises(ValidationError) as exc_info:
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ChatMessage(role = "assistant")
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assert "content" in str(exc_info.value) or "tool_calls" in str(exc_info.value)
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# ── Role-constrained tool-call metadata ────────────────────────
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def test_tool_calls_on_user_rejected(self):
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with pytest.raises(ValidationError) as exc_info:
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ChatMessage(
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role = "user",
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content = "Hi",
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tool_calls = [
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{
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"id": "c1",
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"type": "function",
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"function": {"name": "f", "arguments": "{}"},
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}
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],
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)
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assert "tool_calls" in str(exc_info.value)
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def test_tool_call_id_on_user_rejected(self):
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with pytest.raises(ValidationError) as exc_info:
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ChatMessage(role = "user", content = "Hi", tool_call_id = "call_1")
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assert "tool_call_id" in str(exc_info.value)
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def test_name_on_user_rejected(self):
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with pytest.raises(ValidationError) as exc_info:
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ChatMessage(role = "user", content = "Hi", name = "get_weather")
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assert "name" in str(exc_info.value)
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# =====================================================================
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# ChatCompletionRequest — standard OpenAI tool fields
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# =====================================================================
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class TestChatCompletionRequestToolFields:
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def _make(self, **kwargs):
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base = {"messages": [{"role": "user", "content": "Hi"}]}
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base.update(kwargs)
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return ChatCompletionRequest(**base)
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def test_tools_parses(self):
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req = self._make(
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Return the weather in a city",
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"parameters": {
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"type": "object",
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"properties": {"city": {"type": "string"}},
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"required": ["city"],
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},
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},
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}
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],
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)
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assert req.tools is not None
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assert len(req.tools) == 1
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assert req.tools[0]["function"]["name"] == "get_weather"
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def test_tool_choice_string_auto(self):
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assert self._make(tool_choice = "auto").tool_choice == "auto"
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def test_tool_choice_string_required(self):
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assert self._make(tool_choice = "required").tool_choice == "required"
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def test_tool_choice_string_none(self):
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assert self._make(tool_choice = "none").tool_choice == "none"
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def test_tool_choice_named_function(self):
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tc = {"type": "function", "function": {"name": "get_weather"}}
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assert self._make(tool_choice = tc).tool_choice == tc
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def test_stop_string(self):
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assert self._make(stop = "\nUser:").stop == "\nUser:"
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def test_stop_list(self):
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assert self._make(stop = ["\nUser:", "\nAssistant:"]).stop == [
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"\nUser:",
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"\nAssistant:",
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]
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def test_tools_default_none(self):
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req = self._make()
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assert req.tools is None
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assert req.tool_choice is None
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assert req.stop is None
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def test_extra_fields_accepted(self):
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# `frequency_penalty`, `seed`, `response_format` are not yet
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# explicitly declared but must survive Pydantic parsing now that
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# extra="allow" is set.
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req = self._make(
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frequency_penalty = 0.5,
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seed = 42,
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response_format = {"type": "json_object"},
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)
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# Extras land in model_extra
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assert req.model_extra is not None
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assert req.model_extra.get("frequency_penalty") == 0.5
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assert req.model_extra.get("seed") == 42
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assert req.model_extra.get("response_format") == {"type": "json_object"}
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def test_unsloth_extensions_still_work(self):
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req = self._make(
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enable_tools = True,
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enabled_tools = ["web_search", "python"],
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session_id = "abc",
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)
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assert req.enable_tools is True
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assert req.enabled_tools == ["web_search", "python"]
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assert req.session_id == "abc"
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def test_stream_defaults_false_matching_openai_spec(self):
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# OpenAI's /v1/chat/completions spec defaults `stream` to false.
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# Studio previously defaulted to true, which broke naive curl
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# clients that omit `stream` (they expect a JSON blob, got SSE).
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# Pin the corrected default so it can't silently regress.
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req = self._make()
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assert req.stream is False
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def test_multiturn_tool_loop_messages(self):
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req = ChatCompletionRequest(
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messages = [
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{"role": "user", "content": "What's the weather in Paris?"},
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{
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"role": "assistant",
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"content": None,
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"tool_calls": [
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{
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"id": "call_1",
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"type": "function",
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"function": {
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"name": "get_weather",
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"arguments": '{"city": "Paris"}',
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},
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}
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],
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},
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{
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"role": "tool",
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"tool_call_id": "call_1",
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"content": '{"temperature": 14, "unit": "celsius"}',
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},
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],
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"parameters": {"type": "object"},
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},
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}
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],
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)
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assert len(req.messages) == 3
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assert req.messages[1].role == "assistant"
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assert req.messages[1].content is None
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assert req.messages[1].tool_calls[0]["id"] == "call_1"
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assert req.messages[2].role == "tool"
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assert req.messages[2].tool_call_id == "call_1"
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# =====================================================================
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# anthropic_tool_choice_to_openai — pure translation helper
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# =====================================================================
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class TestAnthropicToolChoiceToOpenAI:
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def test_auto(self):
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assert anthropic_tool_choice_to_openai({"type": "auto"}) == "auto"
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def test_any_becomes_required(self):
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assert anthropic_tool_choice_to_openai({"type": "any"}) == "required"
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def test_none(self):
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assert anthropic_tool_choice_to_openai({"type": "none"}) == "none"
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def test_tool_named(self):
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result = anthropic_tool_choice_to_openai(
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{"type": "tool", "name": "get_weather"}
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)
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assert result == {
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"type": "function",
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"function": {"name": "get_weather"},
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}
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def test_tool_missing_name_returns_none(self):
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assert anthropic_tool_choice_to_openai({"type": "tool"}) is None
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def test_none_input_returns_none(self):
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assert anthropic_tool_choice_to_openai(None) is None
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def test_unrecognized_shape_returns_none(self):
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assert anthropic_tool_choice_to_openai({"type": "wibble"}) is None
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assert anthropic_tool_choice_to_openai("auto") is None
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assert anthropic_tool_choice_to_openai(42) is None
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# =====================================================================
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# _build_passthrough_payload — tool_choice propagation
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# =====================================================================
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class TestBuildPassthroughPayloadToolChoice:
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def _args(self):
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return dict(
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openai_messages = [{"role": "user", "content": "Hi"}],
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openai_tools = [
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{
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"type": "function",
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"function": {"name": "f", "parameters": {"type": "object"}},
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}
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],
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temperature = 0.6,
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top_p = 0.95,
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top_k = 20,
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max_tokens = 128,
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stream = False,
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)
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def test_default_tool_choice_is_auto(self):
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body = _build_passthrough_payload(**self._args())
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assert body["tool_choice"] == "auto"
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def test_override_tool_choice_required(self):
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body = _build_passthrough_payload(**self._args(), tool_choice = "required")
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assert body["tool_choice"] == "required"
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def test_override_tool_choice_none(self):
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body = _build_passthrough_payload(**self._args(), tool_choice = "none")
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assert body["tool_choice"] == "none"
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def test_override_tool_choice_named_function(self):
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tc = {"type": "function", "function": {"name": "f"}}
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body = _build_passthrough_payload(**self._args(), tool_choice = tc)
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assert body["tool_choice"] == tc
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def test_stream_adds_include_usage(self):
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args = self._args()
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args["stream"] = True
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body = _build_passthrough_payload(**args)
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assert body.get("stream_options") == {"include_usage": True}
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def test_repetition_penalty_renamed(self):
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body = _build_passthrough_payload(**self._args(), repetition_penalty = 1.1)
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assert body.get("repeat_penalty") == 1.1
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assert "repetition_penalty" not in body
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# =====================================================================
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# _friendly_error — httpx transport failures
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# =====================================================================
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class TestFriendlyErrorHttpx:
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"""The async pass-through helpers talk to llama-server via httpx.
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When the subprocess is down, httpx raises RequestError subclasses
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whose string form (``"All connection attempts failed"``, ``"[Errno 111]
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Connection refused"``, ...) does NOT contain the substring
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``"Lost connection to llama-server"`` the sync path uses, so the
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previous substring-only `_friendly_error` returned a useless generic
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message. These tests pin the new isinstance-based mapping.
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"""
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def _req(self):
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return httpx.Request("POST", "http://127.0.0.1:65535/v1/chat/completions")
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def test_connect_error_mapped(self):
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exc = httpx.ConnectError("All connection attempts failed", request = self._req())
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assert "Lost connection" in _friendly_error(exc)
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def test_read_error_mapped(self):
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exc = httpx.ReadError("EOF", request = self._req())
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assert "Lost connection" in _friendly_error(exc)
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def test_remote_protocol_error_mapped(self):
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exc = httpx.RemoteProtocolError("peer closed", request = self._req())
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assert "Lost connection" in _friendly_error(exc)
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def test_read_timeout_mapped(self):
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exc = httpx.ReadTimeout("timed out", request = self._req())
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assert "Lost connection" in _friendly_error(exc)
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def test_non_httpx_unchanged(self):
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# Non-httpx exceptions still fall through to the existing substring
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# heuristics — a context-size message must still produce the
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# "Message too long" path.
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ctx_msg = (
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"request (4096 tokens) exceeds the available context size (2048 tokens)"
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)
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assert "Message too long" in _friendly_error(ValueError(ctx_msg))
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def test_generic_exception_returns_generic_message(self):
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assert (
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_friendly_error(RuntimeError("unrelated")) == "An internal error occurred"
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)
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