mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
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"""Test self-hosted embeddings."""
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from typing import Any
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from langchain.embeddings import (
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SelfHostedEmbeddings,
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SelfHostedHuggingFaceEmbeddings,
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SelfHostedHuggingFaceInstructEmbeddings,
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)
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def get_remote_instance() -> Any:
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"""Get remote instance for testing."""
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import runhouse as rh
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gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
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gpu.install_packages(["pip:./"])
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return gpu
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def test_self_hosted_huggingface_embedding_documents() -> None:
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"""Test self-hosted huggingface embeddings."""
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documents = ["foo bar"]
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gpu = get_remote_instance()
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embedding = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 768
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def test_self_hosted_huggingface_embedding_query() -> None:
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"""Test self-hosted huggingface embeddings."""
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document = "foo bar"
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gpu = get_remote_instance()
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embedding = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
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output = embedding.embed_query(document)
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assert len(output) == 768
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def test_self_hosted_huggingface_instructor_embedding_documents() -> None:
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"""Test self-hosted huggingface instruct embeddings."""
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documents = ["foo bar"]
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gpu = get_remote_instance()
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embedding = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 768
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def test_self_hosted_huggingface_instructor_embedding_query() -> None:
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"""Test self-hosted huggingface instruct embeddings."""
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query = "foo bar"
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gpu = get_remote_instance()
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embedding = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
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output = embedding.embed_query(query)
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assert len(output) == 768
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def get_pipeline() -> Any:
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"""Get pipeline for testing."""
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model_id = "facebook/bart-base"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
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def inference_fn(pipeline: Any, prompt: str) -> Any:
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"""Inference function for testing."""
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# Return last hidden state of the model
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if isinstance(prompt, list):
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return [emb[0][-1] for emb in pipeline(prompt)]
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return pipeline(prompt)[0][-1]
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def test_self_hosted_embedding_documents() -> None:
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"""Test self-hosted huggingface instruct embeddings."""
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documents = ["foo bar"] * 2
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gpu = get_remote_instance()
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embedding = SelfHostedEmbeddings(
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model_load_fn=get_pipeline, hardware=gpu, inference_fn=inference_fn
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)
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output = embedding.embed_documents(documents)
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assert len(output) == 2
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assert len(output[0]) == 50265
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def test_self_hosted_embedding_query() -> None:
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"""Test self-hosted custom embeddings."""
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query = "foo bar"
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gpu = get_remote_instance()
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embedding = SelfHostedEmbeddings(
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model_load_fn=get_pipeline, hardware=gpu, inference_fn=inference_fn
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)
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output = embedding.embed_query(query)
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assert len(output) == 50265
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