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(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
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6 changed files with 466 additions and 83 deletions
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@ -3,13 +3,15 @@ import TabItem from '@theme/TabItem';
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# LiteLLM Proxy (LLM Gateway)
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:::tip
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[LiteLLM Providers a **self hosted** proxy server (AI Gateway)](../simple_proxy) to call all the LLMs in the OpenAI format
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| Property | Details |
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|-------|-------|
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| Description | LiteLLM Proxy is an OpenAI-compatible gateway that allows you to interact with multiple LLM providers through a unified API. Simply use the `litellm_proxy/` prefix before the model name to route your requests through the proxy. |
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| Provider Route on LiteLLM | `litellm_proxy/` (add this prefix to the model name, to route any requests to litellm_proxy - e.g. `litellm_proxy/your-model-name`) |
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| Setup LiteLLM Gateway | [LiteLLM Gateway ↗](../simple_proxy) |
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| Supported Endpoints |`/chat/completions`, `/completions`, `/embeddings`, `/audio/speech`, `/audio/transcriptions`, `/images`, `/rerank` |
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:::
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**[LiteLLM Proxy](../simple_proxy) is OpenAI compatible**, you just need the `litellm_proxy/` prefix before the model
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## Required Variables
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@ -83,7 +85,76 @@ for chunk in response:
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print(chunk)
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```
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## Embeddings
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```python
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import litellm
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response = litellm.embedding(
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model="litellm_proxy/your-embedding-model",
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input="Hello world",
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api_base="your-litellm-proxy-url",
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api_key="your-litellm-proxy-api-key"
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)
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```
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## Image Generation
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```python
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import litellm
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response = litellm.image_generation(
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model="litellm_proxy/dall-e-3",
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prompt="A beautiful sunset over mountains",
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api_base="your-litellm-proxy-url",
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api_key="your-litellm-proxy-api-key"
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)
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```
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## Audio Transcription
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```python
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import litellm
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response = litellm.transcription(
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model="litellm_proxy/whisper-1",
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file="your-audio-file",
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api_base="your-litellm-proxy-url",
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api_key="your-litellm-proxy-api-key"
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)
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```
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## Text to Speech
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```python
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import litellm
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response = litellm.speech(
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model="litellm_proxy/tts-1",
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input="Hello world",
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api_base="your-litellm-proxy-url",
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api_key="your-litellm-proxy-api-key"
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)
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```
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## Rerank
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```python
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import litellm
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import litellm
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response = litellm.rerank(
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model="litellm_proxy/rerank-english-v2.0",
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query="What is machine learning?",
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documents=[
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"Machine learning is a field of study in artificial intelligence",
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"Biology is the study of living organisms"
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],
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api_base="your-litellm-proxy-url",
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api_key="your-litellm-proxy-api-key"
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)
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```
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## **Usage with Langchain, LLamaindex, OpenAI Js, Anthropic SDK, Instructor**
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#### [Follow this doc to see how to use litellm proxy with langchain, llamaindex, anthropic etc](../proxy/user_keys)
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@ -112,6 +112,7 @@ class OpenAIAudioTranscription(OpenAIChatCompletion):
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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client=client,
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)
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## LOGGING
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@ -3409,6 +3409,7 @@ def embedding( # noqa: PLR0915
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or custom_llm_provider == "openai"
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or custom_llm_provider == "together_ai"
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or custom_llm_provider == "nvidia_nim"
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or custom_llm_provider == "litellm_proxy"
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):
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api_base = (
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api_base
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@ -3485,7 +3486,8 @@ def embedding( # noqa: PLR0915
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# set API KEY
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if api_key is None:
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api_key = (
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litellm.api_key
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api_key
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or litellm.api_key
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or litellm.openai_like_key
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or get_secret_str("OPENAI_LIKE_API_KEY")
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)
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@ -4596,7 +4598,10 @@ def image_generation( # noqa: PLR0915
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client=client,
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headers=headers,
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)
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elif custom_llm_provider == "openai":
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elif (
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custom_llm_provider == "openai"
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or custom_llm_provider in litellm.openai_compatible_providers
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):
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model_response = openai_chat_completions.image_generation(
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model=model,
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prompt=prompt,
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@ -5042,8 +5047,7 @@ def transcription(
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)
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elif (
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custom_llm_provider == "openai"
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or custom_llm_provider == "groq"
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or custom_llm_provider == "fireworks_ai"
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or custom_llm_provider in litellm.openai_compatible_providers
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):
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api_base = (
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api_base
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@ -5201,7 +5205,10 @@ def speech(
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custom_llm_provider=custom_llm_provider,
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)
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response: Optional[HttpxBinaryResponseContent] = None
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if custom_llm_provider == "openai":
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if (
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custom_llm_provider == "openai"
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or custom_llm_provider in litellm.openai_compatible_providers
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):
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if voice is None or not (isinstance(voice, str)):
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raise litellm.BadRequestError(
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message="'voice' is required to be passed as a string for OpenAI TTS",
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@ -75,7 +75,7 @@ def rerank( # noqa: PLR0915
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query: str,
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documents: List[Union[str, Dict[str, Any]]],
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custom_llm_provider: Optional[
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Literal["cohere", "together_ai", "azure_ai", "infinity"]
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Literal["cohere", "together_ai", "azure_ai", "infinity", "litellm_proxy"]
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] = None,
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top_n: Optional[int] = None,
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rank_fields: Optional[List[str]] = None,
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@ -162,7 +162,7 @@ def rerank( # noqa: PLR0915
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)
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# Implement rerank logic here based on the custom_llm_provider
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if _custom_llm_provider == "cohere":
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if _custom_llm_provider == "cohere" or _custom_llm_provider == "litellm_proxy":
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# Implement Cohere rerank logic
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api_key: Optional[str] = (
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dynamic_api_key or optional_params.api_key or litellm.api_key
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376
tests/llm_translation/test_litellm_proxy_provider.py
Normal file
376
tests/llm_translation/test_litellm_proxy_provider.py
Normal file
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@ -0,0 +1,376 @@
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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 litellm
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from litellm import completion, embedding
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import pytest
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from unittest.mock import MagicMock, patch
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from litellm.llms.custom_httpx.http_handler import HTTPHandler, AsyncHTTPHandler
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import pytest_asyncio
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from openai import AsyncOpenAI
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk():
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litellm.set_verbose = True
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messages = [
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{
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"role": "user",
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"content": "Hello world",
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}
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]
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from openai import OpenAI
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openai_client = OpenAI(api_key="fake-key")
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with patch.object(
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openai_client.chat.completions, "create", new=MagicMock()
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) as mock_call:
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try:
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completion(
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model="litellm_proxy/my-vllm-model",
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messages=messages,
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response_format={"type": "json_object"},
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client=openai_client,
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api_base="my-custom-api-base",
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hello="world",
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)
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except Exception as e:
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print(e)
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mock_call.assert_called_once()
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print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
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assert "hello" in mock_call.call_args.kwargs["extra_body"]
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk_structured_output():
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from pydantic import BaseModel
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class Result(BaseModel):
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answer: str
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litellm.set_verbose = True
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from openai import OpenAI
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openai_client = OpenAI(api_key="fake-key")
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with patch.object(
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openai_client.chat.completions, "create", new=MagicMock()
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) as mock_call:
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try:
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litellm.completion(
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model="litellm_proxy/openai/gpt-4o",
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messages=[
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{"role": "user", "content": "What is the capital of France?"}
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],
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api_key="my-test-api-key",
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user="test",
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response_format=Result,
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base_url="https://litellm.ml-serving-internal.scale.com",
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client=openai_client,
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)
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except Exception as e:
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print(e)
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mock_call.assert_called_once()
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print("Call KWARGS - {}".format(mock_call.call_args.kwargs))
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json_schema = mock_call.call_args.kwargs["response_format"]
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assert "json_schema" in json_schema
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@pytest.mark.parametrize("is_async", [False, True])
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk_embedding(is_async):
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litellm.set_verbose = True
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litellm._turn_on_debug()
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if is_async:
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from openai import AsyncOpenAI
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openai_client = AsyncOpenAI(api_key="fake-key")
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mock_method = AsyncMock()
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patch_target = openai_client.embeddings.create
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else:
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from openai import OpenAI
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openai_client = OpenAI(api_key="fake-key")
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mock_method = MagicMock()
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patch_target = openai_client.embeddings.create
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with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
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try:
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if is_async:
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await litellm.aembedding(
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model="litellm_proxy/my-vllm-model",
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input="Hello world",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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else:
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litellm.embedding(
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model="litellm_proxy/my-vllm-model",
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input="Hello world",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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except Exception as e:
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print(e)
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mock_method.assert_called_once()
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print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
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assert "Hello world" == mock_method.call_args.kwargs["input"]
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assert "my-vllm-model" == mock_method.call_args.kwargs["model"]
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@pytest.mark.parametrize("is_async", [False, True])
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk_image_generation(is_async):
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litellm._turn_on_debug()
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if is_async:
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from openai import AsyncOpenAI
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openai_client = AsyncOpenAI(api_key="fake-key")
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mock_method = AsyncMock()
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patch_target = openai_client.images.generate
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else:
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from openai import OpenAI
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openai_client = OpenAI(api_key="fake-key")
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mock_method = MagicMock()
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patch_target = openai_client.images.generate
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with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
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try:
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if is_async:
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response = await litellm.aimage_generation(
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model="litellm_proxy/dall-e-3",
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prompt="A beautiful sunset over mountains",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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else:
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response = litellm.image_generation(
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model="litellm_proxy/dall-e-3",
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prompt="A beautiful sunset over mountains",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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print("response=", response)
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except Exception as e:
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print("got error", e)
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mock_method.assert_called_once()
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print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
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assert (
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"A beautiful sunset over mountains"
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== mock_method.call_args.kwargs["prompt"]
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)
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assert "dall-e-3" == mock_method.call_args.kwargs["model"]
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@pytest.mark.parametrize("is_async", [False, True])
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk_transcription(is_async):
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litellm.set_verbose = True
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litellm._turn_on_debug()
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if is_async:
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from openai import AsyncOpenAI
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openai_client = AsyncOpenAI(api_key="fake-key")
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mock_method = AsyncMock()
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patch_target = openai_client.audio.transcriptions.create
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else:
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from openai import OpenAI
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openai_client = OpenAI(api_key="fake-key")
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mock_method = MagicMock()
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patch_target = openai_client.audio.transcriptions.create
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with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
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try:
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if is_async:
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await litellm.atranscription(
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model="litellm_proxy/whisper-1",
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file=b"sample_audio",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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else:
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litellm.transcription(
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model="litellm_proxy/whisper-1",
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file=b"sample_audio",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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except Exception as e:
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print(e)
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mock_method.assert_called_once()
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print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
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assert "whisper-1" == mock_method.call_args.kwargs["model"]
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@pytest.mark.parametrize("is_async", [False, True])
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk_speech(is_async):
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litellm.set_verbose = True
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|
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if is_async:
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from openai import AsyncOpenAI
|
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|
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openai_client = AsyncOpenAI(api_key="fake-key")
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mock_method = AsyncMock()
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patch_target = openai_client.audio.speech.create
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else:
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from openai import OpenAI
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openai_client = OpenAI(api_key="fake-key")
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mock_method = MagicMock()
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patch_target = openai_client.audio.speech.create
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with patch.object(patch_target.__self__, patch_target.__name__, new=mock_method):
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try:
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if is_async:
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await litellm.aspeech(
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model="litellm_proxy/tts-1",
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input="Hello, this is a test of text to speech",
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voice="alloy",
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client=openai_client,
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api_base="my-custom-api-base",
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)
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else:
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litellm.speech(
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model="litellm_proxy/tts-1",
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input="Hello, this is a test of text to speech",
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voice="alloy",
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client=openai_client,
|
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api_base="my-custom-api-base",
|
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)
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except Exception as e:
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print(e)
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mock_method.assert_called_once()
|
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|
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print("Call KWARGS - {}".format(mock_method.call_args.kwargs))
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|
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assert (
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"Hello, this is a test of text to speech"
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== mock_method.call_args.kwargs["input"]
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)
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assert "tts-1" == mock_method.call_args.kwargs["model"]
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assert "alloy" == mock_method.call_args.kwargs["voice"]
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@pytest.mark.parametrize("is_async", [False, True])
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@pytest.mark.asyncio
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async def test_litellm_gateway_from_sdk_rerank(is_async):
|
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litellm.set_verbose = True
|
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litellm._turn_on_debug()
|
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|
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if is_async:
|
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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
|
|
@ -1819,78 +1819,6 @@ def test_lm_studio_completion(monkeypatch):
|
|||
print(e)
|
||||
|
||||
|
||||
@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
|
||||
|
||||
|
||||
# ################### Hugging Face Conversational models ########################
|
||||
# def hf_test_completion_conv():
|
||||
# try:
|
||||
|
|
Loading…
Add table
Add a link
Reference in a new issue