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Format code with ruff

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This commit is contained in:
Andy Lee
2025-12-24 00:23:25 +00:00
parent b754474c44
commit a0e53ef8f1
4 changed files with 68 additions and 8 deletions
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65
tests/test_prompt_template_persistence.py
View File

@@ -581,7 +581,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
# Create a concrete implementation for testing
class TestSearcher(BaseSearcher):
def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
def search(
self,
query,
top_k,
complexity=64,
beam_width=1,
prune_ratio=0.0,
recompute_embeddings=False,
pruning_strategy="global",
zmq_port=None,
**kwargs,
):
return {"labels": [], "distances": []}
searcher = object.__new__(TestSearcher)
@@ -625,7 +636,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
# Create a concrete implementation for testing
class TestSearcher(BaseSearcher):
def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
def search(
self,
query,
top_k,
complexity=64,
beam_width=1,
prune_ratio=0.0,
recompute_embeddings=False,
pruning_strategy="global",
zmq_port=None,
**kwargs,
):
return {"labels": [], "distances": []}
searcher = object.__new__(TestSearcher)
@@ -671,7 +693,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
from leann.searcher_base import BaseSearcher
class TestSearcher(BaseSearcher):
def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
def search(
self,
query,
top_k,
complexity=64,
beam_width=1,
prune_ratio=0.0,
recompute_embeddings=False,
pruning_strategy="global",
zmq_port=None,
**kwargs,
):
return {"labels": [], "distances": []}
searcher = object.__new__(TestSearcher)
@@ -710,7 +743,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
from leann.searcher_base import BaseSearcher
class TestSearcher(BaseSearcher):
def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
def search(
self,
query,
top_k,
complexity=64,
beam_width=1,
prune_ratio=0.0,
recompute_embeddings=False,
pruning_strategy="global",
zmq_port=None,
**kwargs,
):
return {"labels": [], "distances": []}
searcher = object.__new__(TestSearcher)
@@ -774,7 +818,18 @@ class TestQueryTemplateApplicationInComputeEmbedding:
from leann.searcher_base import BaseSearcher
class TestSearcher(BaseSearcher):
def search(self, query, top_k, complexity=64, beam_width=1, prune_ratio=0.0, recompute_embeddings=False, pruning_strategy="global", zmq_port=None, **kwargs):
def search(
self,
query,
top_k,
complexity=64,
beam_width=1,
prune_ratio=0.0,
recompute_embeddings=False,
pruning_strategy="global",
zmq_port=None,
**kwargs,
):
return {"labels": [], "distances": []}
searcher = object.__new__(TestSearcher)

4
tests/test_readme_examples.py
View File

@@ -98,7 +98,9 @@ def test_backend_options():
with tempfile.TemporaryDirectory() as temp_dir:
# Use smaller model in CI to avoid memory issues
is_ci = os.environ.get("CI") == "true"
embedding_model = "sentence-transformers/all-MiniLM-L6-v2" if is_ci else "facebook/contriever"
embedding_model = (
"sentence-transformers/all-MiniLM-L6-v2" if is_ci else "facebook/contriever"
)
dimensions = 384 if is_ci else None
# Test HNSW backend (as shown in README)