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LiteLLM Minor Fixes & Improvements (11/19/2024) (#6820)

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* fix(anthropic/chat/transformation.py): add json schema as values: json_schema

fixes passing pydantic obj to anthropic

Fixes https://github.com/BerriAI/litellm/issues/6766

* (feat): Add timestamp_granularities parameter to transcription API (#6457)

* Add timestamp_granularities parameter to transcription API

* add param to the local test

* fix(databricks/chat.py): handle max_retries optional param handling for openai-like calls

Fixes issue with calling finetuned vertex ai models via databricks route

* build(ui/): add team admins via proxy ui

* fix: fix linting error

* test: fix test

* docs(vertex.md): refactor docs

* test: handle overloaded anthropic model error

* test: remove duplicate test

* test: fix test

* test: update test to handle model overloaded error

---------

Co-authored-by: Show <35062952+BrunooShow@users.noreply.github.com>
This commit is contained in:
Krish Dholakia 2024-11-21 00:57:58 +05:30 • committed by GitHub
parent 7d0e1f05ac
commit b0be5bf3a1
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15 changed files with 200 additions and 193 deletions
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43
tests/llm_translation/base_llm_unit_tests.py
View file

@ -42,11 +42,14 @@ class BaseLLMChatTest(ABC):
"content": [{"type": "text", "text": "Hello, how are you?"}],
}
]
response = litellm.completion(
**base_completion_call_args,
messages=messages,
)
assert response is not None
try:
response = litellm.completion(
**base_completion_call_args,
messages=messages,
)
assert response is not None
except litellm.InternalServerError:
pass
# for OpenAI the content contains the JSON schema, so we need to assert that the content is not None
assert response.choices[0].message.content is not None
@ -89,6 +92,36 @@ class BaseLLMChatTest(ABC):
# relevant issue: https://github.com/BerriAI/litellm/issues/6741
assert response.choices[0].message.content is not None
def test_json_response_pydantic_obj(self):
from pydantic import BaseModel
from litellm.utils import supports_response_schema
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
class TestModel(BaseModel):
first_response: str
base_completion_call_args = self.get_base_completion_call_args()
if not supports_response_schema(base_completion_call_args["model"], None):
pytest.skip("Model does not support response schema")
try:
res = litellm.completion(
**base_completion_call_args,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": "What is the capital of France?",
},
],
response_format=TestModel,
)
assert res is not None
except litellm.InternalServerError:
pytest.skip("Model is overloaded")
def test_json_response_format_stream(self):
"""
Test that the JSON response format with streaming is supported by the LLM API

2
tests/llm_translation/test_anthropic_completion.py
View file

@ -657,7 +657,7 @@ def test_create_json_tool_call_for_response_format():
_input_schema = tool.get("input_schema")
assert _input_schema is not None
assert _input_schema.get("type") == "object"
assert _input_schema.get("properties") == custom_schema
assert _input_schema.get("properties") == {"values": custom_schema}
assert "additionalProperties" not in _input_schema

3
tests/llm_translation/test_optional_params.py
View file

@ -923,7 +923,6 @@ def test_watsonx_text_top_k():
assert optional_params["top_k"] == 10
def test_together_ai_model_params():
optional_params = get_optional_params(
model="together_ai", custom_llm_provider="together_ai", logprobs=1
@ -931,6 +930,7 @@ def test_together_ai_model_params():
print(optional_params)
assert optional_params["logprobs"] == 1
def test_forward_user_param():
from litellm.utils import get_supported_openai_params, get_optional_params
@ -943,6 +943,7 @@ def test_forward_user_param():
assert optional_params["metadata"]["user_id"] == "test_user"
def test_lm_studio_embedding_params():
optional_params = get_optional_params_embeddings(
model="lm_studio/gemma2-9b-it",

6
tests/local_testing/test_amazing_vertex_completion.py
View file

@ -3129,9 +3129,12 @@ async def test_vertexai_embedding_finetuned(respx_mock: MockRouter):
assert all(isinstance(x, float) for x in embedding["embedding"])
@pytest.mark.parametrize("max_retries", [None, 3])
@pytest.mark.asyncio
@pytest.mark.respx
async def test_vertexai_model_garden_model_completion(respx_mock: MockRouter):
async def test_vertexai_model_garden_model_completion(
respx_mock: MockRouter, max_retries
):
"""
Relevant issue: https://github.com/BerriAI/litellm/issues/6480
@ -3189,6 +3192,7 @@ async def test_vertexai_model_garden_model_completion(respx_mock: MockRouter):
messages=messages,
vertex_project="633608382793",
vertex_location="us-central1",
max_retries=max_retries,
)
# Assert request was made correctly

57
tests/local_testing/test_completion.py
View file

@ -1222,32 +1222,6 @@ def test_completion_mistral_api_modified_input():
pytest.fail(f"Error occurred: {e}")
def test_completion_claude2_1():
try:
litellm.set_verbose = True
print("claude2.1 test request")
messages = [
{
"role": "system",
"content": "Your goal is generate a joke on the topic user gives.",
},
{"role": "user", "content": "Generate a 3 liner joke for me"},
]
# test without max tokens
response = completion(model="claude-2.1", messages=messages)
# Add any assertions here to check the response
print(response)
print(response.usage)
print(response.usage.completion_tokens)
print(response["usage"]["completion_tokens"])
# print("new cost tracking")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_claude2_1()
@pytest.mark.asyncio
async def test_acompletion_claude2_1():
try:
@ -1268,6 +1242,8 @@ async def test_acompletion_claude2_1():
print(response.usage.completion_tokens)
print(response["usage"]["completion_tokens"])
# print("new cost tracking")
except litellm.InternalServerError:
pytest.skip("model is overloaded.")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
@ -4514,19 +4490,22 @@ async def test_dynamic_azure_params(stream, sync_mode):
@pytest.mark.flaky(retries=3, delay=1)
async def test_completion_ai21_chat():
litellm.set_verbose = True
response = await litellm.acompletion(
model="jamba-1.5-large",
user="ishaan",
tool_choice="auto",
seed=123,
messages=[{"role": "user", "content": "what does the document say"}],
documents=[
{
"content": "hello world",
"metadata": {"source": "google", "author": "ishaan"},
}
],
)
try:
response = await litellm.acompletion(
model="jamba-1.5-large",
user="ishaan",
tool_choice="auto",
seed=123,
messages=[{"role": "user", "content": "what does the document say"}],
documents=[
{
"content": "hello world",
"metadata": {"source": "google", "author": "ishaan"},
}
],
)
except litellm.InternalServerError:
pytest.skip("Model is overloaded")
@pytest.mark.parametrize(

10
tests/local_testing/test_whisper.py
View file

@ -51,10 +51,15 @@ from litellm import Router
),
],
)
@pytest.mark.parametrize("response_format", ["json", "vtt"])
@pytest.mark.parametrize(
"response_format, timestamp_granularities",
[("json", None), ("vtt", None), ("verbose_json", ["word"])],
)
@pytest.mark.parametrize("sync_mode", [True, False])
@pytest.mark.asyncio
async def test_transcription(model, api_key, api_base, response_format, sync_mode):
async def test_transcription(
model, api_key, api_base, response_format, sync_mode, timestamp_granularities
):
if sync_mode:
transcript = litellm.transcription(
model=model,
@ -62,6 +67,7 @@ async def test_transcription(model, api_key, api_base, response_format, sync_mod
api_key=api_key,
api_base=api_base,
response_format=response_format,
timestamp_granularities=timestamp_granularities,
drop_params=True,
)
else: