litellm-mirror/tests/llm_translation/base_embedding_unit_tests.py
Krish Dholakia 760328b6ad
Litellm dev 12 25 2025 p2 (#7420)
* test: add new test image embedding to base llm unit tests

Addresses https://github.com/BerriAI/litellm/issues/6515

* fix(bedrock/embed/multimodal-embeddings): strip data prefix from image urls for bedrock multimodal embeddings

Fix https://github.com/BerriAI/litellm/issues/6515

* feat: initial commit for fireworks ai audio transcription support

Relevant issue: https://github.com/BerriAI/litellm/issues/7134

* test: initial fireworks ai test

* feat(fireworks_ai/): implemented fireworks ai audio transcription config

* fix(utils.py): register fireworks ai audio transcription config, in config manager

* fix(utils.py): add fireworks ai param translation to 'get_optional_params_transcription'

* refactor(fireworks_ai/): define text completion route with model name handling

moves model name handling to specific fireworks routes, as required by their api

* refactor(fireworks_ai/chat): define transform_Request - allows fixing model if accounts/ is missing

* fix: fix linting errors

* fix: fix linting errors

* fix: fix linting errors

* fix: fix linting errors

* fix(handler.py): fix linting errors

* fix(main.py): fix tgai text completion route

* refactor(together_ai/completion): refactors together ai text completion route to just use provider transform request

* refactor: move test_fine_tuning_api out of local_testing

reduces local testing ci/cd time
2024-12-25 18:35:34 -08:00

95 lines
3 KiB
Python

import asyncio
import httpx
import json
import pytest
import sys
from typing import Any, Dict, List
from unittest.mock import MagicMock, Mock, patch
import os
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import litellm
from litellm import embedding
from litellm.exceptions import BadRequestError
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.utils import (
CustomStreamWrapper,
get_supported_openai_params,
get_optional_params,
get_optional_params_embeddings,
)
import requests
import base64
# test_example.py
from abc import ABC, abstractmethod
url = "https://dummyimage.com/100/100/fff&text=Test+image"
response = requests.get(url)
file_data = response.content
encoded_file = base64.b64encode(file_data).decode("utf-8")
base64_image = f"data:image/png;base64,{encoded_file}"
class BaseLLMEmbeddingTest(ABC):
"""
Abstract base test class that enforces a common test across all test classes.
"""
@abstractmethod
def get_base_embedding_call_args(self) -> dict:
"""Must return the base embedding call args"""
pass
@abstractmethod
def get_custom_llm_provider(self) -> litellm.LlmProviders:
"""Must return the custom llm provider"""
pass
@pytest.mark.asyncio()
@pytest.mark.parametrize("sync_mode", [True, False])
async def test_basic_embedding(self, sync_mode):
litellm.set_verbose = True
embedding_call_args = self.get_base_embedding_call_args()
if sync_mode is True:
response = litellm.embedding(
**embedding_call_args,
input=["hello", "world"],
)
print("embedding response: ", response)
else:
response = await litellm.aembedding(
**embedding_call_args,
input=["hello", "world"],
)
print("async embedding response: ", response)
from openai.types.create_embedding_response import CreateEmbeddingResponse
CreateEmbeddingResponse.model_validate(response.model_dump())
def test_embedding_optional_params_max_retries(self):
embedding_call_args = self.get_base_embedding_call_args()
optional_params = get_optional_params_embeddings(
**embedding_call_args, max_retries=20
)
assert optional_params["max_retries"] == 20
def test_image_embedding(self):
litellm.set_verbose = True
from litellm.utils import supports_embedding_image_input
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
base_embedding_call_args = self.get_base_embedding_call_args()
if not supports_embedding_image_input(base_embedding_call_args["model"], None):
print("Model does not support embedding image input")
pytest.skip("Model does not support embedding image input")
embedding(**base_embedding_call_args, input=[base64_image])