forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
"""Integration test for embedding-based redundant doc filtering."""
|
|
from langchain.document_transformers import (
|
|
EmbeddingsRedundantFilter,
|
|
_DocumentWithState,
|
|
)
|
|
from langchain.embeddings import OpenAIEmbeddings
|
|
from langchain.schema import Document
|
|
|
|
|
|
def test_embeddings_redundant_filter() -> None:
|
|
texts = [
|
|
"What happened to all of my cookies?",
|
|
"Where did all of my cookies go?",
|
|
"I wish there were better Italian restaurants in my neighborhood.",
|
|
]
|
|
docs = [Document(page_content=t) for t in texts]
|
|
embeddings = OpenAIEmbeddings()
|
|
redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
|
|
actual = redundant_filter.transform_documents(docs)
|
|
assert len(actual) == 2
|
|
assert set(texts[:2]).intersection([d.page_content for d in actual])
|
|
|
|
|
|
def test_embeddings_redundant_filter_with_state() -> None:
|
|
texts = ["What happened to all of my cookies?", "foo bar baz"]
|
|
state = {"embedded_doc": [0.5] * 10}
|
|
docs = [_DocumentWithState(page_content=t, state=state) for t in texts]
|
|
embeddings = OpenAIEmbeddings()
|
|
redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
|
|
actual = redundant_filter.transform_documents(docs)
|
|
assert len(actual) == 1
|