mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
|
"""Test ElasticSearch functionality."""
|
||
|
|
from typing import List
|
||
|
|
|
||
|
|
from langchain.docstore.document import Document
|
||
|
|
from langchain.embeddings.base import Embeddings
|
||
|
|
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
|
||
|
|
|
||
|
|
|
||
|
|
class FakeEmbeddings(Embeddings):
|
||
|
|
"""Fake embeddings functionality for testing."""
|
||
|
|
|
||
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||
|
|
"""Return simple embeddings."""
|
||
|
|
return [[1.0] * 9 + [i] for i in range(len(texts))]
|
||
|
|
|
||
|
|
def embed_query(self, text: str) -> List[float]:
|
||
|
|
"""Return simple embeddings."""
|
||
|
|
return [1.0] * 9 + [0.0]
|
||
|
|
|
||
|
|
|
||
|
|
def test_elasticsearch() -> None:
|
||
|
|
"""Test end to end construction and search."""
|
||
|
|
texts = ["foo", "bar", "baz"]
|
||
|
|
docsearch = ElasticVectorSearch.from_texts(
|
||
|
|
texts, FakeEmbeddings(), elasticsearch_url="http://localhost:9200"
|
||
|
|
)
|
||
|
|
output = docsearch.similarity_search("foo", k=1)
|
||
|
|
assert output == [Document(page_content="foo")]
|
||
|
|
|
||
|
|
|
||
|
|
def test_elasticsearch_with_metadatas() -> None:
|
||
|
|
"""Test end to end construction and search."""
|
||
|
|
texts = ["foo", "bar", "baz"]
|
||
|
|
metadatas = [{"page": i} for i in range(len(texts))]
|
||
|
|
docsearch = ElasticVectorSearch.from_texts(
|
||
|
|
texts,
|
||
|
|
FakeEmbeddings(),
|
||
|
|
metadatas=metadatas,
|
||
|
|
elasticsearch_url="http://localhost:9200",
|
||
|
|
)
|
||
|
|
output = docsearch.similarity_search("foo", k=1)
|
||
|
|
assert output == [Document(page_content="foo", metadata={"page": 0})]
|