Spaces:
Sleeping
Sleeping
File size: 1,730 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
# flake8: noqa
"""Test llamacpp embeddings."""
import os
from urllib.request import urlretrieve
from langchain.embeddings.llamacpp import LlamaCppEmbeddings
def get_model() -> str:
"""Download model.
From https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/,
convert to new ggml format and return model path.
"""
model_url = "https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/resolve/main/ggml-alpaca-7b-q4.bin"
tokenizer_url = "https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer.model"
conversion_script = "https://github.com/ggerganov/llama.cpp/raw/master/convert-unversioned-ggml-to-ggml.py"
local_filename = model_url.split("/")[-1]
if not os.path.exists("convert-unversioned-ggml-to-ggml.py"):
urlretrieve(conversion_script, "convert-unversioned-ggml-to-ggml.py")
if not os.path.exists("tokenizer.model"):
urlretrieve(tokenizer_url, "tokenizer.model")
if not os.path.exists(local_filename):
urlretrieve(model_url, local_filename)
os.system("python convert-unversioned-ggml-to-ggml.py . tokenizer.model")
return local_filename
def test_llamacpp_embedding_documents() -> None:
"""Test llamacpp embeddings."""
documents = ["foo bar"]
model_path = get_model()
embedding = LlamaCppEmbeddings(model_path=model_path)
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 512
def test_llamacpp_embedding_query() -> None:
"""Test llamacpp embeddings."""
document = "foo bar"
model_path = get_model()
embedding = LlamaCppEmbeddings(model_path=model_path)
output = embedding.embed_query(document)
assert len(output) == 512
|