8e0779a22d
* enforce Document as IO * Separate rerankers, splitters and extractors (#85) * partially refractor importing * add text to embedding outputs --------- Co-authored-by: Nguyen Trung Duc (john) <trungduc1992@gmail.com>
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
import json
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from pathlib import Path
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from unittest.mock import patch
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from kotaemon.base import Document
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from kotaemon.embeddings.cohere import CohereEmbdeddings
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from kotaemon.embeddings.huggingface import HuggingFaceEmbeddings
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from kotaemon.embeddings.openai import AzureOpenAIEmbeddings
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with open(Path(__file__).parent / "resources" / "embedding_openai_batch.json") as f:
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openai_embedding_batch = json.load(f)
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with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
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openai_embedding = json.load(f)
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@patch(
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"openai.resources.embeddings.Embeddings.create",
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side_effect=lambda *args, **kwargs: openai_embedding,
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)
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def test_azureopenai_embeddings_raw(openai_embedding_call):
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model = AzureOpenAIEmbeddings(
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model="text-embedding-ada-002",
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deployment="embedding-deployment",
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azure_endpoint="https://test.openai.azure.com/",
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openai_api_key="some-key",
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)
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output = model("Hello world")
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assert isinstance(output, list)
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assert isinstance(output[0], Document)
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assert isinstance(output[0].embedding, list)
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assert isinstance(output[0].embedding[0], float)
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openai_embedding_call.assert_called()
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@patch(
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"openai.resources.embeddings.Embeddings.create",
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side_effect=lambda *args, **kwargs: openai_embedding_batch,
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)
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def test_azureopenai_embeddings_batch_raw(openai_embedding_call):
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model = AzureOpenAIEmbeddings(
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model="text-embedding-ada-002",
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deployment="embedding-deployment",
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azure_endpoint="https://test.openai.azure.com/",
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openai_api_key="some-key",
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)
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output = model(["Hello world", "Goodbye world"])
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assert isinstance(output, list)
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assert isinstance(output[0], Document)
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assert isinstance(output[0].embedding, list)
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assert isinstance(output[0].embedding[0], float)
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openai_embedding_call.assert_called()
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@patch(
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"sentence_transformers.SentenceTransformer",
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side_effect=lambda *args, **kwargs: None,
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)
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@patch(
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"langchain.embeddings.huggingface.HuggingFaceBgeEmbeddings.embed_documents",
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side_effect=lambda *args, **kwargs: [[1.0, 2.1, 3.2]],
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)
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def test_huggingface_embddings(
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langchain_huggingface_embedding_call, sentence_transformers_init
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):
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model = HuggingFaceEmbeddings(
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model_name="intfloat/multilingual-e5-large",
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model_kwargs={"device": "cpu"},
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encode_kwargs={"normalize_embeddings": False},
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)
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output = model("Hello World")
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assert isinstance(output, list)
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assert isinstance(output[0], Document)
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assert isinstance(output[0].embedding, list)
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assert isinstance(output[0].embedding[0], float)
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sentence_transformers_init.assert_called()
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langchain_huggingface_embedding_call.assert_called()
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@patch(
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"langchain.embeddings.cohere.CohereEmbeddings.embed_documents",
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side_effect=lambda *args, **kwargs: [[1.0, 2.1, 3.2]],
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)
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def test_cohere_embeddings(langchain_cohere_embedding_call):
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model = CohereEmbdeddings(
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model="embed-english-light-v2.0", cohere_api_key="my-api-key"
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)
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output = model("Hello World")
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assert isinstance(output, list)
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assert isinstance(output[0], Document)
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assert isinstance(output[0].embedding, list)
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assert isinstance(output[0].embedding[0], float)
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langchain_cohere_embedding_call.assert_called()
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