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https://github.com/hwchase17/langchain
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36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
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"""Test SparkLLM Text Embedding."""
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from langchain_community.embeddings.sparkllm import SparkLLMTextEmbeddings
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def test_baichuan_embedding_documents() -> None:
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"""Test SparkLLM Text Embedding for documents."""
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documents = [
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"iFLYTEK is a well-known intelligent speech and artificial intelligence "
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"publicly listed company in the Asia-Pacific Region. Since its establishment,"
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"the company is devoted to cornerstone technological research "
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"in speech and languages, natural language understanding, machine learning,"
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"machine reasoning, adaptive learning, "
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"and has maintained the world-leading position in those "
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"domains. The company actively promotes the development of A.I. "
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"products and their sector-based "
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"applications, with visions of enabling machines to listen and speak, "
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"understand and think, "
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"creating a better world with artificial intelligence."
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]
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embedding = SparkLLMTextEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1 # type: ignore[arg-type]
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assert len(output[0]) == 2560 # type: ignore[index]
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def test_baichuan_embedding_query() -> None:
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"""Test SparkLLM Text Embedding for query."""
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document = (
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"iFLYTEK Open Platform was launched in 2010 by iFLYTEK as China’s "
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"first Artificial Intelligence open platform for Mobile Internet "
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"and intelligent hardware developers"
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)
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embedding = SparkLLMTextEmbeddings()
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output = embedding.embed_query(document)
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assert len(output) == 2560 # type: ignore[arg-type]
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