mirror of
https://github.com/tiennm99/litellm.git
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Mateo Wang
2c733c00f5
* test: modernize models used in CircleCI e2e test suites
Replaces obsolete models (gpt-4o, gpt-4o-mini, gpt-3.5-turbo,
claude-3-5-sonnet-20240620, claude-sonnet-4-20250514) with current
equivalents across the e2e_openai_endpoints and
proxy_e2e_anthropic_messages_tests CircleCI jobs.
- gpt-4o -> gpt-5.5 (responses API e2e tests)
- gpt-4o-mini -> gpt-5-mini (websocket responses, oai_misc_config)
- gpt-4o-mini-2024-07-18 -> gpt-4.1-mini-2025-04-14 (fine-tuning,
still actively fine-tunable)
- gpt-4 / gpt-3.5-turbo target_model_names example -> gpt-5.5 /
gpt-5-mini
- bedrock claude-3-5-sonnet-20240620 batch entry -> haiku-4-5-20251001
(also aligning oai_misc_config model_name with what
test_bedrock_batches_api.py actually requests)
- bedrock claude-sonnet-4-20250514 (deprecated, retires 2026-06-15)
-> claude-sonnet-4-5-20250929
* test: point bedrock-claude-sonnet-4 alias at Sonnet 4.6, not 4.5
Greptile/Cursor flagged that after the previous commit, the
bedrock-claude-sonnet-4 alias collided with bedrock-claude-sonnet-4.5
(both pointed to claude-sonnet-4-5-20250929). Rename to
bedrock-claude-sonnet-4.6 and point it at the Sonnet 4.6 Bedrock ID
(us.anthropic.claude-sonnet-4-6, already in the litellm model
registry) so the alias name matches the underlying model version.
* test: modernize models across remaining CI-mounted configs & tests
Expands the modernization sweep to all CircleCI-mounted proxy configs
and to test directories where the model literal is a fixture/route key
(not the test's subject).
Config changes:
- proxy_server_config.yaml: bump gpt-3.5-turbo / gpt-3.5-turbo-1106 /
gpt-4o / gemini-1.5-flash / dall-e-3 underlying models; rename
gpt-3.5-turbo-end-user-test alias to gpt-5-mini-end-user-test; bump
text-embedding-ada-002 underlying to text-embedding-3-small. User-
facing aliases (gpt-3.5-turbo, gpt-4, text-embedding-ada-002, etc.)
preserved for backward compatibility with tests.
- simple_config.yaml, otel_test_config.yaml, spend_tracking_config.yaml:
bump gpt-3.5-turbo underlying to gpt-5-mini.
- pass_through_config.yaml: claude-3-5-sonnet / claude-3-7-sonnet /
claude-3-haiku entries replaced with claude-sonnet-4-5 / claude-
haiku-4-5 / claude-opus-4-7.
- oai_misc_config.yaml: align alias name with the gpt-5-mini rename.
Test changes (proactive: claude-sonnet-4-20250514 / claude-opus-4-
20250514 retire 2026-06-15):
- tests/llm_translation/test_anthropic_completion.py: bump 3 references
+ paired Vertex AI ID to claude-sonnet-4-5.
- tests/llm_translation/test_optional_params.py: bump 2 references.
- tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py
and test_bedrock_anthropic_messages_test.py: bump router fixtures
using the deprecated model IDs.
- tests/pass_through_unit_tests/base_anthropic_messages_tool_search_test.py:
modernize docstring examples.
- tests/test_end_users.py: update references to renamed alias.
* test: modernize placeholder model literals in router_unit_tests
Mass replace_all on fixture/placeholder model literals across the
router_unit_tests/ suite (model name is a routing key / label, not the
test subject). Sub-agent sweep so far — additional commits will follow
for logging_callback_tests/, enterprise/, top-level tests/test_*.py,
and other CI-mounted dirs.
Mappings applied:
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 / claude-3-opus-20240229 /
claude-3-haiku-20240307 / claude-3-5-sonnet-20240620 ->
claude-sonnet-4-5-20250929 / claude-opus-4-7 /
claude-haiku-4-5-20251001 as appropriate
Explicitly preserved:
- gpt-4o-mini-* variants (transcribe, tts, etc.) where they're current
- gpt-4-turbo / gpt-4-vision-preview / gpt-4-0613 (subject literals)
- JSONL batch body literals
- Mock LLM response model fields (must match upstream)
- Fake/mock identifiers
* test: modernize placeholder model literals across remaining CI suites
Sub-agent sweep across logging_callback_tests/, guardrails_tests/,
enterprise/, pass_through_unit_tests/, otel_tests/,
llm_responses_api_testing/, batches_tests/, spend_tracking_tests/,
litellm_utils_tests/, unified_google_tests/, and a few top-level
tests/test_*.py files where the model literal is a fixture or
placeholder (router model_list, mock standard logging payload, mock
callback data) rather than the test's subject.
Mappings applied (see scope notes below):
- gpt-3.5-turbo -> gpt-5-mini
- gpt-4 (bare) -> gpt-5.5
- gpt-4o (bare) -> gpt-5.5 (corrected from initial gpt-5 — bare gpt-5
is not a valid OpenAI alias; only gpt-5.5 / gpt-5.4 / gpt-5.2-codex
/ gpt-5-mini exist)
- gpt-4o-mini (bare) -> gpt-5-mini
- text-embedding-ada-002 -> text-embedding-3-small
- claude-3-sonnet-20240229 -> claude-sonnet-4-5-20250929
- claude-3-opus-20240229 -> claude-opus-4-7
- claude-3-haiku-20240307 -> claude-haiku-4-5-20251001
- claude-3-5-sonnet-20240620/20241022 -> claude-sonnet-4-5-20250929
- claude-3-7-sonnet-20250219 -> claude-sonnet-4-6
- gemini-1.5-flash -> gemini-2.5-flash
- gemini-1.5-pro -> gemini-2.5-pro
Explicitly preserved (not modernized):
- llm_translation/ tests where model is the SUBJECT (provider-specific
translation/transformation logic). Only the deprecated 20250514
references were already bumped in a prior commit.
- Cost-calc / tokenizer subject tests in test_utils.py (skip-ranges
documented by the sub-agent).
- Bedrock model IDs in test_health_check.py path-stripping tests.
- JSONL batch request bodies and mock LLM response bodies (must match
upstream literal).
- Langfuse expected-request-body JSON fixtures (cost values are exact-
match-asserted; changing the model would shift response_cost).
- gpt-3.5-turbo-instruct (text-completion endpoint; no modern OpenAI
equivalent).
- Top-level tests calling the proxy through user-facing aliases
(gpt-3.5-turbo, gpt-4, text-embedding-ada-002, dall-e-3) — aliases
in proxy_server_config.yaml stay; only the underlying model was
bumped.
- tests/test_gpt5_azure_temperature_support.py (the test's whole point
is model-name handling).
- Fake / mock / openai/fake identifiers.
Notable side fixes:
- test_spend_accuracy_tests.py: UPSTREAM_MODEL now matches what
spend_tracking_config.yaml's proxy actually routes to (gpt-5-mini),
resolving a latent inconsistency.
- proxy_server_config.yaml: bare `gpt-5` alias renamed to `gpt-5.5`
(bare gpt-5 is not a valid OpenAI alias).
- test_batches_logging_unit_tests.py: explicit_models list entries
kept distinct (gpt-5-mini + gpt-5.5) after bulk rename.
* test: fix CI failures from model modernization sweep
CI surfaced 4 categories of regression from the bulk modernization:
1. Azure deployment names are customer-specific. Reverted:
- tests/litellm_utils_tests/test_health_check.py: azure/text-
embedding-3-small -> azure/text-embedding-ada-002 (the CI Azure
account does not have a text-embedding-3-small deployment).
- tests/logging_callback_tests/test_custom_callback_router.py:
same revert for two router fixtures driving aembedding.
2. gpt-5 family does not accept temperature != 1. Tests that pass a
custom temperature swapped from gpt-5-mini to gpt-4.1-mini (modern
non-reasoning OpenAI mini that still accepts temperature/logprobs):
- tests/logging_callback_tests/test_datadog.py
- tests/logging_callback_tests/test_langsmith_unit_test.py
- tests/logging_callback_tests/test_otel_logging.py
3. proxy_server_config.yaml's gpt-3.5-turbo-large alias was routing to
gpt-5.5 (a reasoning model that rejects logprobs). The proxy test
tests/test_openai_endpoints.py::test_chat_completion_streaming
exercises logprobs/top_logprobs through that alias. Bumped the
underlying model to gpt-4.1 (non-reasoning, still modern).
4. tests/logging_callback_tests/test_gcs_pub_sub.py asserts against a
pinned JSON fixture (gcs_pub_sub_body/spend_logs_payload.json) with
hardcoded model="gpt-4o" and a model-specific spend value. Reverted
the litellm.acompletion calls in the test to model="gpt-4o" so the
fixture's exact-match assertions still hold.
5. tests/pass_through_unit_tests/test_anthropic_messages_passthrough.py:
anthropic.messages.create routing to openai/gpt-5-mini returned an
empty content[0] with max_tokens=100 (reasoning-token consumption).
Swapped to openai/gpt-4.1-mini.
* test: fix Assistants API model + 2 cursor[bot] review nits
1. pass_through_unit_tests/test_custom_logger_passthrough.py: gpt-5.5
isn't accepted by the /v1/assistants endpoint
("unsupported_model"). Switch to gpt-4.1-mini (modern, Assistants-
API-supported, non-reasoning).
2. example_config_yaml/pass_through_config.yaml: the previous sweep
bumped the claude-3-7-sonnet alias to claude-opus-4-7, which is a
tier change (Sonnet -> Opus). Map to claude-sonnet-4-6 to keep the
Sonnet tier intact. (Cursor bugbot review.)
3. example_config_yaml/simple_config.yaml: model_name was left as
gpt-3.5-turbo while the underlying was bumped to gpt-5-mini, which
muddles the "simple" example. Make both sides gpt-5-mini so the
most basic example is a straight 1:1 mapping again. (Cursor bugbot
review.)
* fix: revert gpt-4/gpt-3.5-turbo alias underlying to non-reasoning models
tests/test_openai_endpoints.py::test_completion calls the proxy alias
"gpt-4" with temperature=0, and other tests call gpt-3.5-turbo with
custom temperature / logprobs / the legacy /v1/completions endpoint.
The earlier modernization mapped both aliases to gpt-5.5 / gpt-5-mini,
which are reasoning models that reject temperature != 1 and don't
expose /v1/completions. Map the aliases to gpt-4.1 / gpt-4.1-mini
(modern non-reasoning OpenAI models) instead — keeps user-facing
aliases preserved while picking a current underlying that still
supports the parameters/endpoints the tests exercise.
368 lines
12 KiB
Python
368 lines
12 KiB
Python
"""
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Testing for _assemble_complete_response_from_streaming_chunks
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- Test 1 - ModelResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
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- Test 2 - TextCompletionResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
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- Test 3 - Have multiple lists of streaming chunks, Assert that chunks are added to the correct list and that complete_streaming_response is None. After final chunk sent assert complete_streaming_response is not None
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- Test 4 - build a complete response when 1 chunk is poorly formatted
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"""
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import json
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import os
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import sys
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from datetime import datetime
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from unittest.mock import AsyncMock
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import httpx
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import pytest
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from respx import MockRouter
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import litellm
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from litellm import (
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Choices,
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Message,
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ModelResponse,
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ModelResponseStream,
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TextCompletionResponse,
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TextChoices,
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)
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from litellm.litellm_core_utils.logging_utils import (
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_assemble_complete_response_from_streaming_chunks,
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)
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@pytest.mark.parametrize("is_async", [True, False])
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def test_assemble_complete_response_from_streaming_chunks_1(is_async):
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"""
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Test 1 - ModelResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
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"""
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request_kwargs = {
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"model": "test_model",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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}
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list_streaming_chunks = []
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chunk = {
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"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
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"choices": [
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litellm.utils.StreamingChoices(
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delta=litellm.utils.Delta(
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content="hello in response",
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function_call=None,
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role=None,
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tool_calls=None,
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),
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index=0,
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logprobs=None,
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)
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],
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"created": 1721353246,
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"model": "gpt-5-mini",
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"object": "chat.completion.chunk",
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"system_fingerprint": None,
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"usage": None,
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}
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chunk = ModelResponseStream(**chunk)
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complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
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result=chunk,
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start_time=datetime.now(),
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end_time=datetime.now(),
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request_kwargs=request_kwargs,
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streaming_chunks=list_streaming_chunks,
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is_async=is_async,
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)
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# this is the 1st chunk - complete_streaming_response should be None
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print("list_streaming_chunks", list_streaming_chunks)
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print("complete_streaming_response", complete_streaming_response)
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assert complete_streaming_response is None
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assert len(list_streaming_chunks) == 1
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assert list_streaming_chunks[0] == chunk
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# Add final chunk
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chunk = {
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"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
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"choices": [
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litellm.utils.StreamingChoices(
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finish_reason="stop",
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delta=litellm.utils.Delta(
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content="end of response",
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function_call=None,
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role=None,
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tool_calls=None,
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),
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index=0,
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logprobs=None,
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)
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],
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"created": 1721353246,
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"model": "gpt-5-mini",
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"object": "chat.completion.chunk",
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"system_fingerprint": None,
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"usage": None,
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}
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chunk = ModelResponseStream(**chunk)
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complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
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result=chunk,
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start_time=datetime.now(),
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end_time=datetime.now(),
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request_kwargs=request_kwargs,
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streaming_chunks=list_streaming_chunks,
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is_async=is_async,
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)
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print("list_streaming_chunks", list_streaming_chunks)
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print("complete_streaming_response", complete_streaming_response)
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# this is the 2nd chunk - complete_streaming_response should not be None
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assert complete_streaming_response is not None
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assert len(list_streaming_chunks) == 2
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assert isinstance(complete_streaming_response, ModelResponse)
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assert isinstance(complete_streaming_response.choices[0], Choices)
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pass
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@pytest.mark.parametrize("is_async", [True, False])
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def test_assemble_complete_response_from_streaming_chunks_2(is_async):
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"""
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Test 2 - TextCompletionResponse with 1 list of streaming chunks. Assert chunks are added to the streaming_chunks, after final chunk sent assert complete_streaming_response is not None
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"""
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from litellm.utils import TextCompletionStreamWrapper
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_text_completion_stream_wrapper = TextCompletionStreamWrapper(
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completion_stream=None, model="test_model"
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)
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request_kwargs = {
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"model": "test_model",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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}
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list_streaming_chunks = []
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chunk = {
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"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
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"choices": [
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litellm.utils.StreamingChoices(
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delta=litellm.utils.Delta(
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content="hello in response",
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function_call=None,
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role=None,
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tool_calls=None,
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),
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index=0,
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logprobs=None,
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)
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],
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"created": 1721353246,
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"model": "gpt-5-mini",
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"object": "chat.completion.chunk",
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"system_fingerprint": None,
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"usage": None,
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}
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chunk = ModelResponseStream(**chunk)
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chunk = _text_completion_stream_wrapper.convert_to_text_completion_object(chunk)
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complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
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result=chunk,
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start_time=datetime.now(),
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end_time=datetime.now(),
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request_kwargs=request_kwargs,
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streaming_chunks=list_streaming_chunks,
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is_async=is_async,
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)
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# this is the 1st chunk - complete_streaming_response should be None
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print("list_streaming_chunks", list_streaming_chunks)
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print("complete_streaming_response", complete_streaming_response)
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assert complete_streaming_response is None
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assert len(list_streaming_chunks) == 1
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assert list_streaming_chunks[0] == chunk
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# Add final chunk
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chunk = {
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"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
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"choices": [
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litellm.utils.StreamingChoices(
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finish_reason="stop",
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delta=litellm.utils.Delta(
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content="end of response",
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function_call=None,
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role=None,
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tool_calls=None,
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),
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index=0,
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logprobs=None,
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)
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],
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"created": 1721353246,
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"model": "gpt-5-mini",
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"object": "chat.completion.chunk",
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"system_fingerprint": None,
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"usage": None,
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}
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chunk = ModelResponseStream(**chunk)
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chunk = _text_completion_stream_wrapper.convert_to_text_completion_object(chunk)
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complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
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result=chunk,
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start_time=datetime.now(),
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end_time=datetime.now(),
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request_kwargs=request_kwargs,
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streaming_chunks=list_streaming_chunks,
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is_async=is_async,
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)
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print("list_streaming_chunks", list_streaming_chunks)
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print("complete_streaming_response", complete_streaming_response)
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# this is the 2nd chunk - complete_streaming_response should not be None
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assert complete_streaming_response is not None
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assert len(list_streaming_chunks) == 2
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assert isinstance(complete_streaming_response, TextCompletionResponse)
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assert isinstance(complete_streaming_response.choices[0], TextChoices)
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pass
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@pytest.mark.parametrize("is_async", [True, False])
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def test_assemble_complete_response_from_streaming_chunks_3(is_async):
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request_kwargs = {
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"model": "test_model",
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"messages": [{"role": "user", "content": "Hello, world!"}],
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}
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list_streaming_chunks_1 = []
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list_streaming_chunks_2 = []
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chunk = {
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"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
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"choices": [
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litellm.utils.StreamingChoices(
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delta=litellm.utils.Delta(
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content="hello in response",
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function_call=None,
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role=None,
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tool_calls=None,
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),
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index=0,
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logprobs=None,
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)
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],
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"created": 1721353246,
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"model": "gpt-5-mini",
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"object": "chat.completion.chunk",
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"system_fingerprint": None,
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"usage": None,
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}
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chunk = ModelResponseStream(**chunk)
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complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
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result=chunk,
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start_time=datetime.now(),
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end_time=datetime.now(),
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request_kwargs=request_kwargs,
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streaming_chunks=list_streaming_chunks_1,
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is_async=is_async,
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)
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# this is the 1st chunk - complete_streaming_response should be None
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print("list_streaming_chunks_1", list_streaming_chunks_1)
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print("complete_streaming_response", complete_streaming_response)
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assert complete_streaming_response is None
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assert len(list_streaming_chunks_1) == 1
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assert list_streaming_chunks_1[0] == chunk
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assert len(list_streaming_chunks_2) == 0
|
|
|
|
# now add a chunk to the 2nd list
|
|
|
|
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
|
|
result=chunk,
|
|
start_time=datetime.now(),
|
|
end_time=datetime.now(),
|
|
request_kwargs=request_kwargs,
|
|
streaming_chunks=list_streaming_chunks_2,
|
|
is_async=is_async,
|
|
)
|
|
|
|
print("list_streaming_chunks_2", list_streaming_chunks_2)
|
|
print("complete_streaming_response", complete_streaming_response)
|
|
assert complete_streaming_response is None
|
|
assert len(list_streaming_chunks_2) == 1
|
|
assert list_streaming_chunks_2[0] == chunk
|
|
assert len(list_streaming_chunks_1) == 1
|
|
|
|
# now add a chunk to the 1st list
|
|
|
|
|
|
@pytest.mark.parametrize("is_async", [True, False])
|
|
def test_assemble_complete_response_from_streaming_chunks_4(is_async):
|
|
"""
|
|
Test 4 - build a complete response when 1 chunk is poorly formatted
|
|
|
|
- Assert complete_streaming_response is None
|
|
- Assert list_streaming_chunks is not empty
|
|
"""
|
|
|
|
request_kwargs = {
|
|
"model": "test_model",
|
|
"messages": [{"role": "user", "content": "Hello, world!"}],
|
|
}
|
|
|
|
list_streaming_chunks = []
|
|
|
|
chunk = {
|
|
"id": "chatcmpl-9mWtyDnikZZoB75DyfUzWUxiiE2Pi",
|
|
"choices": [
|
|
litellm.utils.StreamingChoices(
|
|
finish_reason="stop",
|
|
delta=litellm.utils.Delta(
|
|
content="end of response",
|
|
function_call=None,
|
|
role=None,
|
|
tool_calls=None,
|
|
),
|
|
index=0,
|
|
logprobs=None,
|
|
)
|
|
],
|
|
"created": 1721353246,
|
|
"model": "gpt-5-mini",
|
|
"object": "chat.completion.chunk",
|
|
"system_fingerprint": None,
|
|
"usage": None,
|
|
}
|
|
chunk = ModelResponseStream(**chunk)
|
|
|
|
# remove attribute id from chunk
|
|
del chunk.object
|
|
|
|
complete_streaming_response = _assemble_complete_response_from_streaming_chunks(
|
|
result=chunk,
|
|
start_time=datetime.now(),
|
|
end_time=datetime.now(),
|
|
request_kwargs=request_kwargs,
|
|
streaming_chunks=list_streaming_chunks,
|
|
is_async=is_async,
|
|
)
|
|
|
|
print("complete_streaming_response", complete_streaming_response)
|
|
assert complete_streaming_response is None
|
|
|
|
print("list_streaming_chunks", list_streaming_chunks)
|
|
|
|
assert len(list_streaming_chunks) == 1
|