mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 03:04:13 +00:00
(Bug Fix) Using LiteLLM Python SDK with model=litellm_proxy/ for embedding, image_generation, transcription, speech, rerank (#8815)
* test_litellm_gateway_from_sdk * fix embedding check for openai * test litellm proxy provider * fix image generation openai compatible models * fix litellm.transcription * test_litellm_gateway_from_sdk_rerank * docs litellm python sdk * docs litellm python sdk with proxy * test_litellm_gateway_from_sdk_rerank * ci/cd run again * test_litellm_gateway_from_sdk_image_generation * test_litellm_gateway_from_sdk_embedding * test_litellm_gateway_from_sdk_embedding
This commit is contained in:
Ishaan Jaff
GitHub
parent
ef22209a15
commit
f9cee4c46b
6 changed files with 466 additions and 83 deletions
376
tests/llm_translation/test_litellm_proxy_provider.py
Normal file
376
tests/llm_translation/test_litellm_proxy_provider.py
Normal file
|
|
@ -0,0 +1,376 @@
|
|||
import json
|
||||
import os
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from unittest.mock import AsyncMock
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system-path
|
||||
|
||||
import litellm
|
||||
from litellm import completion, embedding
|
||||
import pytest
|
||||
from unittest.mock import MagicMock, patch
|
||||
from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
|
||||
import pytest_asyncio
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk():
|
||||
litellm.set_verbose = True
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Hello world",
|
||||
}
|
||||
]
|
||||
from openai import OpenAI
|
||||
|
||||
openai_client = OpenAI(api_key="fake-key")
|
||||
|
||||
with patch.object(
|
||||
openai_client.chat.completions, "create", new=MagicMock()
|
||||
) as mock_call:
|
||||
try:
|
||||
completion(
|
||||
model="litellm_proxy/my-vllm-model",
|
||||
messages=messages,
|
||||
response_format={"type": "json_object"},
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
hello="world",
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
mock_call.assert_called_once()
|
||||
|
||||
print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
|
||||
|
||||
assert "hello" in mock_call.call_args.kwargs["extra_body"]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk_structured_output():
|
||||
from pydantic import BaseModel
|
||||
|
||||
class Result(BaseModel):
|
||||
answer: str
|
||||
|
||||
litellm.set_verbose = True
|
||||
from openai import OpenAI
|
||||
|
||||
openai_client = OpenAI(api_key="fake-key")
|
||||
|
||||
with patch.object(
|
||||
openai_client.chat.completions, "create", new=MagicMock()
|
||||
) as mock_call:
|
||||
try:
|
||||
litellm.completion(
|
||||
model="litellm_proxy/openai/gpt-4o",
|
||||
messages=[
|
||||
{"role": "user", "content": "What is the capital of France?"}
|
||||
],
|
||||
api_key="my-test-api-key",
|
||||
user="test",
|
||||
response_format=Result,
|
||||
base_url="https://litellm.ml-serving-internal.scale.com",
|
||||
client=openai_client,
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
mock_call.assert_called_once()
|
||||
|
||||
print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
|
||||
json_schema = mock_call.call_args.kwargs["response_format"]
|
||||
assert "json_schema" in json_schema
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_async", [False, True])
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk_embedding(is_async):
|
||||
litellm.set_verbose = True
|
||||
litellm._turn_on_debug()
|
||||
|
||||
if is_async:
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
openai_client = AsyncOpenAI(api_key="fake-key")
|
||||
mock_method = AsyncMock()
|
||||
patch_target = openai_client.embeddings.create
|
||||
else:
|
||||
from openai import OpenAI
|
||||
|
||||
openai_client = OpenAI(api_key="fake-key")
|
||||
mock_method = MagicMock()
|
||||
patch_target = openai_client.embeddings.create
|
||||
|
||||
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
|
||||
try:
|
||||
if is_async:
|
||||
await litellm.aembedding(
|
||||
model="litellm_proxy/my-vllm-model",
|
||||
input="Hello world",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
else:
|
||||
litellm.embedding(
|
||||
model="litellm_proxy/my-vllm-model",
|
||||
input="Hello world",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
mock_method.assert_called_once()
|
||||
|
||||
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
|
||||
|
||||
assert "Hello world" == mock_method.call_args.kwargs["input"]
|
||||
assert "my-vllm-model" == mock_method.call_args.kwargs["model"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_async", [False, True])
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk_image_generation(is_async):
|
||||
litellm._turn_on_debug()
|
||||
|
||||
if is_async:
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
openai_client = AsyncOpenAI(api_key="fake-key")
|
||||
mock_method = AsyncMock()
|
||||
patch_target = openai_client.images.generate
|
||||
else:
|
||||
from openai import OpenAI
|
||||
|
||||
openai_client = OpenAI(api_key="fake-key")
|
||||
mock_method = MagicMock()
|
||||
patch_target = openai_client.images.generate
|
||||
|
||||
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
|
||||
try:
|
||||
if is_async:
|
||||
response = await litellm.aimage_generation(
|
||||
model="litellm_proxy/dall-e-3",
|
||||
prompt="A beautiful sunset over mountains",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
else:
|
||||
response = litellm.image_generation(
|
||||
model="litellm_proxy/dall-e-3",
|
||||
prompt="A beautiful sunset over mountains",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
print("response=", response)
|
||||
except Exception as e:
|
||||
print("got error", e)
|
||||
|
||||
mock_method.assert_called_once()
|
||||
|
||||
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
|
||||
|
||||
assert (
|
||||
"A beautiful sunset over mountains"
|
||||
== mock_method.call_args.kwargs["prompt"]
|
||||
)
|
||||
assert "dall-e-3" == mock_method.call_args.kwargs["model"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_async", [False, True])
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk_transcription(is_async):
|
||||
litellm.set_verbose = True
|
||||
litellm._turn_on_debug()
|
||||
|
||||
if is_async:
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
openai_client = AsyncOpenAI(api_key="fake-key")
|
||||
mock_method = AsyncMock()
|
||||
patch_target = openai_client.audio.transcriptions.create
|
||||
else:
|
||||
from openai import OpenAI
|
||||
|
||||
openai_client = OpenAI(api_key="fake-key")
|
||||
mock_method = MagicMock()
|
||||
patch_target = openai_client.audio.transcriptions.create
|
||||
|
||||
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
|
||||
try:
|
||||
if is_async:
|
||||
await litellm.atranscription(
|
||||
model="litellm_proxy/whisper-1",
|
||||
file=b"sample_audio",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
else:
|
||||
litellm.transcription(
|
||||
model="litellm_proxy/whisper-1",
|
||||
file=b"sample_audio",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
mock_method.assert_called_once()
|
||||
|
||||
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
|
||||
|
||||
assert "whisper-1" == mock_method.call_args.kwargs["model"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_async", [False, True])
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk_speech(is_async):
|
||||
litellm.set_verbose = True
|
||||
|
||||
if is_async:
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
openai_client = AsyncOpenAI(api_key="fake-key")
|
||||
mock_method = AsyncMock()
|
||||
patch_target = openai_client.audio.speech.create
|
||||
else:
|
||||
from openai import OpenAI
|
||||
|
||||
openai_client = OpenAI(api_key="fake-key")
|
||||
mock_method = MagicMock()
|
||||
patch_target = openai_client.audio.speech.create
|
||||
|
||||
with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
|
||||
try:
|
||||
if is_async:
|
||||
await litellm.aspeech(
|
||||
model="litellm_proxy/tts-1",
|
||||
input="Hello, this is a test of text to speech",
|
||||
voice="alloy",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
else:
|
||||
litellm.speech(
|
||||
model="litellm_proxy/tts-1",
|
||||
input="Hello, this is a test of text to speech",
|
||||
voice="alloy",
|
||||
client=openai_client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
mock_method.assert_called_once()
|
||||
|
||||
print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
|
||||
|
||||
assert (
|
||||
"Hello, this is a test of text to speech"
|
||||
== mock_method.call_args.kwargs["input"]
|
||||
)
|
||||
assert "tts-1" == mock_method.call_args.kwargs["model"]
|
||||
assert "alloy" == mock_method.call_args.kwargs["voice"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_async", [False, True])
|
||||
@pytest.mark.asyncio
|
||||
async def test_litellm_gateway_from_sdk_rerank(is_async):
|
||||
litellm.set_verbose = True
|
||||
litellm._turn_on_debug()
|
||||
|
||||
if is_async:
|
||||
client = AsyncHTTPHandler()
|
||||
mock_method = AsyncMock()
|
||||
patch_target = client.post
|
||||
else:
|
||||
client = HTTPHandler()
|
||||
mock_method = MagicMock()
|
||||
patch_target = client.post
|
||||
|
||||
with patch.object(client, "post", new=mock_method):
|
||||
mock_response = MagicMock()
|
||||
|
||||
# Create a mock response similar to OpenAI's rerank response
|
||||
mock_response.text = json.dumps(
|
||||
{
|
||||
"id": "rerank-123456",
|
||||
"object": "reranking",
|
||||
"results": [
|
||||
{
|
||||
"index": 0,
|
||||
"relevance_score": 0.9,
|
||||
"document": {
|
||||
"id": "0",
|
||||
"text": "Machine learning is a field of study in artificial intelligence",
|
||||
},
|
||||
},
|
||||
{
|
||||
"index": 1,
|
||||
"relevance_score": 0.2,
|
||||
"document": {
|
||||
"id": "1",
|
||||
"text": "Biology is the study of living organisms",
|
||||
},
|
||||
},
|
||||
],
|
||||
"model": "rerank-english-v2.0",
|
||||
"usage": {"prompt_tokens": 10, "total_tokens": 10},
|
||||
}
|
||||
)
|
||||
|
||||
mock_response.status_code = 200
|
||||
mock_response.headers = {"Content-Type": "application/json"}
|
||||
mock_response.json = lambda: json.loads(mock_response.text)
|
||||
|
||||
if is_async:
|
||||
mock_method.return_value = mock_response
|
||||
else:
|
||||
mock_method.return_value = mock_response
|
||||
|
||||
try:
|
||||
if is_async:
|
||||
response = await litellm.arerank(
|
||||
model="litellm_proxy/rerank-english-v2.0",
|
||||
query="What is machine learning?",
|
||||
documents=[
|
||||
"Machine learning is a field of study in artificial intelligence",
|
||||
"Biology is the study of living organisms",
|
||||
],
|
||||
client=client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
else:
|
||||
response = litellm.rerank(
|
||||
model="litellm_proxy/rerank-english-v2.0",
|
||||
query="What is machine learning?",
|
||||
documents=[
|
||||
"Machine learning is a field of study in artificial intelligence",
|
||||
"Biology is the study of living organisms",
|
||||
],
|
||||
client=client,
|
||||
api_base="my-custom-api-base",
|
||||
)
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
# Verify the request
|
||||
mock_method.assert_called_once()
|
||||
call_args = mock_method.call_args
|
||||
print("call_args=", call_args)
|
||||
|
||||
# Check that the URL is correct
|
||||
assert "my-custom-api-base/v1/rerank" == call_args.kwargs["url"]
|
||||
|
||||
# Check that the request body contains the expected data
|
||||
request_body = json.loads(call_args.kwargs["data"])
|
||||
assert request_body["query"] == "What is machine learning?"
|
||||
assert request_body["model"] == "rerank-english-v2.0"
|
||||
assert len(request_body["documents"]) == 2
|
||||
Loading…
Add table
Add a link
Reference in a new issue