mirror of
https://github.com/BerriAI/litellm.git
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805bb6888b
* test: make 38 legacy live base-class translation tests offline * test: tighten offline base-class translation test assertions * test: cover remaining nova invoke and xai base copies offline * test: assert outbound bodies, restore dropped providers and fix shared tool dict mutation in base chat translation tests * test: assert outbound thinking bodies and streamed thinking blocks across anthropic and bedrock converse * test: restore volcengine/voyage max_retries and openai timestamp_granularities coverage, assert rerank id/meta/cost and scope the interactions transport fixture * test: call the raw provider helper for thinking stream cases * test: flatten thinking blocks before collecting signatures --------- Co-authored-by: yuneng <yuneng@berri.ai>
64 lines
1.9 KiB
Python
64 lines
1.9 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 litellm
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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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)
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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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