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* test: load the embedding base image from a committed 100x100 PNG instead of downloading it * test: move the volcengine embedding test into tests/unit * test: check gpt2 and r50k_base tokenizer parity against committed tiktoken reference files * test: check hub tokenizer selection against an in-memory Hugging Face hub * test: serve image URLs from respx in the gemini tool-result and format-param tests * ci: drop the emptied legacy core-utils test path * test: cover the cohere and anthropic tokenizer paths in the hub tokenizer test * test: fetch every format-param image through respx and check its bytes reach the request * test: drop the gpt2 and r50k_base parity tests, which no litellm path uses * test: drop comments that restate assertions in the format-param test
88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
import asyncio
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import httpx
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import json
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import pytest
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from typing import Any, Dict, List
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from unittest.mock import MagicMock, Mock, patch
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import os
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import litellm
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from litellm import embedding
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from litellm.exceptions import BadRequestError
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.utils import (
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CustomStreamWrapper,
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get_supported_openai_params,
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get_optional_params,
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get_optional_params_embeddings,
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)
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import base64
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from pathlib import Path
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from abc import ABC, abstractmethod
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file_data = (Path(__file__).parent.parent / "white_100x100.png").read_bytes()
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encoded_file = base64.b64encode(file_data).decode("utf-8")
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base64_image = f"data:image/png;base64,{encoded_file}"
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class BaseLLMEmbeddingTest(ABC):
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"""
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Abstract base test class that enforces a common test across all test classes.
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"""
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@abstractmethod
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def get_base_embedding_call_args(self) -> dict:
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"""Must return the base embedding call args"""
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pass
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@abstractmethod
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def get_custom_llm_provider(self) -> litellm.LlmProviders:
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"""Must return the custom llm provider"""
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pass
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [True, False])
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async def test_basic_embedding(self, sync_mode):
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litellm.set_verbose = True
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embedding_call_args = self.get_base_embedding_call_args()
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if sync_mode is True:
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response = litellm.embedding(
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**embedding_call_args,
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input=["hello", "world"],
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)
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print("embedding response: ", response)
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else:
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response = await litellm.aembedding(
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**embedding_call_args,
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input=["hello", "world"],
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)
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print("async embedding response: ", response)
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from openai.types.create_embedding_response import CreateEmbeddingResponse
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CreateEmbeddingResponse.model_validate(response.model_dump())
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def test_embedding_optional_params_max_retries(self):
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embedding_call_args = self.get_base_embedding_call_args()
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optional_params = get_optional_params_embeddings(
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**embedding_call_args, max_retries=20
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)
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assert optional_params["max_retries"] == 20
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def test_image_embedding(self):
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litellm.set_verbose = True
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from litellm.utils import supports_embedding_image_input
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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base_embedding_call_args = self.get_base_embedding_call_args()
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if not supports_embedding_image_input(base_embedding_call_args["model"], None):
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print("Model does not support embedding image input")
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pytest.skip("Model does not support embedding image input")
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embedding(**base_embedding_call_args, input=[base64_image])
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