mirror of
https://github.com/hwchase17/langchain.git
synced 2026-03-18 11:07:36 +00:00
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""Test PGVector functionality."""
|
|
import os
|
|
from typing import List
|
|
|
|
from langchain.docstore.document import Document
|
|
from langchain.vectorstores.pgvector import PGVector
|
|
from tests.integration_tests.vectorstores.fake_embeddings import (
|
|
FakeEmbeddings,
|
|
)
|
|
|
|
CONNECTION_STRING = PGVector.connection_string_from_db_params(
|
|
driver=os.environ.get("TEST_PGVECTOR_DRIVER", "psycopg2"),
|
|
host=os.environ.get("TEST_PGVECTOR_HOST", "localhost"),
|
|
port=int(os.environ.get("TEST_PGVECTOR_PORT", "5432")),
|
|
database=os.environ.get("TEST_PGVECTOR_DATABASE", "postgres"),
|
|
user=os.environ.get("TEST_PGVECTOR_USER", "postgres"),
|
|
password=os.environ.get("TEST_PGVECTOR_PASSWORD", "postgres"),
|
|
)
|
|
|
|
|
|
ADA_TOKEN_COUNT = 1536
|
|
|
|
|
|
class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
|
|
"""Fake embeddings functionality for testing."""
|
|
|
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
|
"""Return simple embeddings."""
|
|
return [
|
|
[float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
|
|
]
|
|
|
|
def embed_query(self, text: str) -> List[float]:
|
|
"""Return simple embeddings."""
|
|
return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
|
|
|
|
|
|
def test_pgvector() -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
docsearch = PGVector.from_texts(
|
|
texts=texts,
|
|
collection_name="test_collection",
|
|
embedding=FakeEmbeddingsWithAdaDimension(),
|
|
connection_string=CONNECTION_STRING,
|
|
pre_delete_collection=True,
|
|
)
|
|
output = docsearch.similarity_search("foo", k=1)
|
|
assert output == [Document(page_content="foo")]
|
|
|
|
|
|
def test_pgvector_with_metadatas() -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
|
docsearch = PGVector.from_texts(
|
|
texts=texts,
|
|
collection_name="test_collection",
|
|
embedding=FakeEmbeddingsWithAdaDimension(),
|
|
metadatas=metadatas,
|
|
connection_string=CONNECTION_STRING,
|
|
pre_delete_collection=True,
|
|
)
|
|
output = docsearch.similarity_search("foo", k=1)
|
|
assert output == [Document(page_content="foo", metadata={"page": "0"})]
|
|
|
|
|
|
def test_pgvector_with_metadatas_with_scores() -> None:
|
|
"""Test end to end construction and search."""
|
|
texts = ["foo", "bar", "baz"]
|
|
metadatas = [{"page": str(i)} for i in range(len(texts))]
|
|
docsearch = PGVector.from_texts(
|
|
texts=texts,
|
|
collection_name="test_collection",
|
|
embedding=FakeEmbeddingsWithAdaDimension(),
|
|
metadatas=metadatas,
|
|
connection_string=CONNECTION_STRING,
|
|
pre_delete_collection=True,
|
|
)
|
|
output = docsearch.similarity_search_with_score("foo", k=1)
|
|
assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
|