mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-20 18:07:05 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
136 lines
4.6 KiB
Python
136 lines
4.6 KiB
Python
import uuid
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from typing import Optional
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import pytest # type: ignore[import-not-found]
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from langchain_core.documents import Document
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from langchain_qdrant import Qdrant
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from tests.integration_tests.common import (
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ConsistentFakeEmbeddings,
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assert_documents_equals,
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)
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@pytest.mark.parametrize("batch_size", [1, 64])
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_add_documents_extends_existing_collection(
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batch_size: int, vector_name: Optional[str]
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) -> None:
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"""Test end to end construction and search."""
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texts = ["foo", "bar", "baz"]
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docsearch: Qdrant = Qdrant.from_texts(
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texts,
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ConsistentFakeEmbeddings(),
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location=":memory:",
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batch_size=batch_size,
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vector_name=vector_name,
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)
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new_texts = ["foobar", "foobaz"]
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docsearch.add_documents(
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[Document(page_content=content) for content in new_texts], batch_size=batch_size
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)
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output = docsearch.similarity_search("foobar", k=1)
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# ConsistentFakeEmbeddings return the same query embedding as the first document
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# embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
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# same as "foobar" embedding
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assert_documents_equals(output, [Document(page_content="foobar")])
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
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"""Test end to end Qdrant.add_texts returns unique ids."""
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docsearch: Qdrant = Qdrant.from_texts(
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["foobar"],
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ConsistentFakeEmbeddings(),
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location=":memory:",
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batch_size=batch_size,
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)
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ids = docsearch.add_texts(["foo", "bar", "baz"])
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assert 3 == len(ids)
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assert 3 == len(set(ids))
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@pytest.mark.parametrize("vector_name", [None, "my-vector"])
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def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
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"""Test end to end Qdrant.add_texts stores duplicated texts separately."""
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as rest
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client = QdrantClient(":memory:")
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collection_name = uuid.uuid4().hex
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vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
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if vector_name is not None:
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vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
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client.recreate_collection(collection_name, vectors_config=vectors_config)
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vec_store = Qdrant(
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client,
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collection_name,
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embeddings=ConsistentFakeEmbeddings(),
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vector_name=vector_name,
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)
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ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
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assert 2 == len(set(ids))
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assert 2 == client.count(collection_name).count
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@pytest.mark.parametrize("batch_size", [1, 64])
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def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
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"""Test end to end Qdrant.add_texts stores provided ids."""
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as rest
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ids = [
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"fa38d572-4c31-4579-aedc-1960d79df6df",
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"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
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]
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client = QdrantClient(":memory:")
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collection_name = uuid.uuid4().hex
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client.recreate_collection(
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collection_name,
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vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
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)
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vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
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returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)
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assert all(first == second for first, second in zip(ids, returned_ids))
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assert 2 == client.count(collection_name).count
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stored_ids = [point.id for point in client.scroll(collection_name)[0]]
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assert set(ids) == set(stored_ids)
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@pytest.mark.parametrize("vector_name", ["custom-vector"])
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def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
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"""Test end to end Qdrant.add_texts stores named vectors if name is provided."""
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from qdrant_client import QdrantClient
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from qdrant_client.http import models as rest
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collection_name = uuid.uuid4().hex
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client = QdrantClient(":memory:")
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client.recreate_collection(
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collection_name,
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vectors_config={
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vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
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},
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)
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vec_store = Qdrant(
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client,
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collection_name,
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ConsistentFakeEmbeddings(),
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vector_name=vector_name,
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)
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vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
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assert 5 == client.count(collection_name).count
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assert all(
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vector_name in point.vector # type: ignore[operator]
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for point in client.scroll(collection_name, with_vectors=True)[0]
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)
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