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* VoyageEmbeddingConfig * fix voyage logic to get params * add voyage embedding transformation * add get_provider_embedding_config * use BaseEmbeddingConfig * voyage clean up * use llm http handler for embedding transformations * test_voyage_ai_embedding_extra_params * add voyage async * test_voyage_ai_embedding_extra_params * add async for llm http handler * update BaseLLMEmbeddingTest * test_voyage_ai_embedding_extra_params * fix linting * fix get_provider_embedding_config * fix anthropic text test * update location of base/chat/transformation * fix import path * fix IBMWatsonXAIConfig
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
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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import pytest
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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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from base_embedding_unit_tests import BaseLLMEmbeddingTest
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import litellm
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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from unittest.mock import patch, MagicMock
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class TestVoyageAI(BaseLLMEmbeddingTest):
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def get_custom_llm_provider(self) -> litellm.LlmProviders:
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return litellm.LlmProviders.VOYAGE
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def get_base_embedding_call_args(self) -> dict:
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return {
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"model": "voyage/voyage-3-lite",
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}
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def test_voyage_ai_embedding_extra_params():
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try:
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client = HTTPHandler()
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litellm.set_verbose = True
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with patch.object(client, "post") as mock_client:
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response = litellm.embedding(
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model="voyage/voyage-3-lite",
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input=["a"],
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dimensions=512,
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input_type="document",
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client=client,
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)
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mock_client.assert_called_once()
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json_data = json.loads(mock_client.call_args.kwargs["data"])
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print("request data to voyage ai", json.dumps(json_data, indent=4))
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# Assert the request parameters
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assert json_data["input"] == ["a"]
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assert json_data["model"] == "voyage-3-lite"
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assert json_data["output_dimension"] == 512
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assert json_data["input_type"] == "document"
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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