Files
langchain/tests/integration_tests/embeddings/test_llamacpp.py
Harrison Chase 186172838f cr
2023-04-06 14:44:17 -07:00

56 lines
2.2 KiB
Python

# 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"
migrate_script = "https://github.com/ggerganov/llama.cpp/raw/master/migrate-ggml-2023-03-30-pr613.py"
local_filename = model_url.split("/")[-1]
local_filename_ggjt = (
local_filename.split(".")[0] + "-ggjt." + local_filename.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("migrate-ggml-2023-03-30-pr613.py"):
urlretrieve(migrate_script, "migrate-ggml-2023-03-30-pr613.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(f"python convert-unversioned-ggml-to-ggml.py . tokenizer.model")
os.system(
f"python migrate-ggml-2023-03-30-pr613.py {local_filename} {local_filename_ggjt}"
)
return local_filename_ggjt
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