mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-04 02:31:27 +00:00
a84f68b6e3
Each route now has litellm/rust_bridge/<route>/{entrypoints,callbacks}.py and a
public dispatch module (litellm/chat_completions/dispatch.py,
litellm/responses/dispatch.py, litellm/messages/dispatch.py) that binds the
public call to the legacy Python signature, builds a frozen request, and asks
the runtime to pick Rust or Python from the catalog. The legacy implementations
stay in litellm/main.py, litellm/responses/main.py and the anthropic messages
handler, and litellm/__init__.py re-exports the dispatch names over them the
same way it already does for ocr
The per-handler shims in rust_bridge/chat_completions/native.py and
rust_bridge/messages/native.py are removed along with their call sites in the
anthropic and bedrock chat handlers and the http handler. The exception
mapping that every callbacks module repeated moves to rust_bridge/failures.py
and the signature binding helpers to rust_bridge/public_call.py
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
from types import MappingProxyType
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from typing import Final
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import pytest
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from pydantic import ValidationError
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from litellm.rust_bridge.responses.callbacks import arguments, response
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from litellm.rust_bridge.responses.entrypoints import LiteLLMResponsesRequest
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from litellm.types.llms.openai import ResponsesAPIResponse
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def test_response_validates_into_the_public_responses_model() -> None:
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built: Final = response(
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MappingProxyType(
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{
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"id": "resp_native",
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"object": "response",
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"created_at": 1,
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"model": "gpt-4o",
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"status": "completed",
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"output": [
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{
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"type": "message",
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"id": "msg_native",
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"role": "assistant",
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"status": "completed",
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"content": [{"type": "output_text", "text": "native", "annotations": []}],
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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 isinstance(built, ResponsesAPIResponse)
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assert built.id == "resp_native"
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assert built.output[0].content[0].text == "native"
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def test_response_rejects_a_payload_missing_required_fields() -> None:
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with pytest.raises(ValidationError):
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response(MappingProxyType({"object": "response"}))
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def test_arguments_are_the_public_kwargs_view() -> None:
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kwargs: Final = MappingProxyType({"litellm_metadata": {"user_id": "u"}})
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request: Final = LiteLLMResponsesRequest(
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model="gpt-4o",
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input="hi",
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stream=None,
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api_key=None,
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api_base=None,
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custom_llm_provider="openai",
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extra_headers=None,
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kwargs=kwargs,
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)
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assert arguments(request) is kwargs
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