forked from Archives/langchain
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
# flake8: noqa
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"""Test llamacpp embeddings."""
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import os
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from urllib.request import urlretrieve
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from langchain.embeddings.llamacpp import LlamaCppEmbeddings
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def get_model() -> str:
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"""Download model.
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From https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/,
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convert to new ggml format and return model path.
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"""
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model_url = "https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/resolve/main/ggml-alpaca-7b-q4.bin"
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tokenizer_url = "https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer.model"
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conversion_script = "https://github.com/ggerganov/llama.cpp/raw/master/convert-unversioned-ggml-to-ggml.py"
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local_filename = model_url.split("/")[-1]
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if not os.path.exists("convert-unversioned-ggml-to-ggml.py"):
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urlretrieve(conversion_script, "convert-unversioned-ggml-to-ggml.py")
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if not os.path.exists("tokenizer.model"):
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urlretrieve(tokenizer_url, "tokenizer.model")
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if not os.path.exists(local_filename):
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urlretrieve(model_url, local_filename)
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os.system("python convert-unversioned-ggml-to-ggml.py . tokenizer.model")
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return local_filename
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def test_llamacpp_embedding_documents() -> None:
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"""Test llamacpp embeddings."""
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documents = ["foo bar"]
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model_path = get_model()
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embedding = LlamaCppEmbeddings(model_path=model_path)
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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]) == 512
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def test_llamacpp_embedding_query() -> None:
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"""Test llamacpp embeddings."""
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document = "foo bar"
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model_path = get_model()
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embedding = LlamaCppEmbeddings(model_path=model_path)
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output = embedding.embed_query(document)
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assert len(output) == 512
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