mirror of
https://github.com/qdrant/qdrant-client.git
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fix fastembed for pydantic 1.x (#285)
* fix fastembed for pydantic 1.x * add extras setup in readme * move fastembed from group to extras * lock
This commit is contained in:
Andrey Vasnetsov
committed by
George Panchuk
George Panchuk
parent
c1e640ff25
commit
27d4e2197b
3
.github/workflows/integration-tests.yml
vendored
3
.github/workflows/integration-tests.yml
vendored
@@ -35,7 +35,7 @@ jobs:
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run: |
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python -m pip install poetry
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poetry config virtualenvs.create false
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poetry install --no-interaction --no-ansi
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poetry install --no-interaction --no-ansi --all-extras
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- name: Run integration tests
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run: |
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./tests/integration-tests.sh
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@@ -50,4 +50,3 @@ jobs:
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export RUNNER_OS=${{ runner.os }}
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./tests/coverage-test.sh
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shell: bash
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@@ -70,7 +70,7 @@ Local mode is useful for development, prototyping and testing.
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## Fast Embeddings + Simpler API
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```
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pip install fastembed qdrant-client
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pip install qdrant-client[fastembed]
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```
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FastEmbed is a library for creating fast vector embeddings on CPU. It is based on ONNX Runtime and allows to run inference on CPU with GPU-like performance.
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273
poetry.lock
generated
273
poetry.lock
generated
@@ -170,7 +170,7 @@ files = [
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name = "coloredlogs"
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version = "15.0.1"
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description = "Colored terminal output for Python's logging module"
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optional = false
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optional = true
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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files = [
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{file = "coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934"},
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@@ -272,13 +272,13 @@ test = ["pytest (>=6)"]
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[[package]]
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name = "fastembed"
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version = "0.0.2"
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version = "0.0.4"
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description = "Fast, State of the Art Quantized Embedding Models"
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optional = false
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optional = true
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python-versions = ">=3.8.0,<3.12"
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files = [
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{file = "fastembed-0.0.2-py3-none-any.whl", hash = "sha256:7460266828d8ce59f598fc854917489b6339e8240fbe5da6652a5b799b7dba2a"},
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{file = "fastembed-0.0.2.tar.gz", hash = "sha256:3a1dec12df1dfb1724f46a5a90a0a55bca242195f1d76b122e087dfe6b8ac144"},
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{file = "fastembed-0.0.4-py3-none-any.whl", hash = "sha256:3ea83bf164f4e2875e53b2795ce79986d99f8ecbc62bfb26d82592fbe87e0858"},
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{file = "fastembed-0.0.4.tar.gz", hash = "sha256:dab2f0f0370e7cbd2965fe4206957a5dffffd0c8c07fa90bdfd50d7743062856"},
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]
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[package.dependencies]
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@@ -292,7 +292,7 @@ tqdm = ">=4.65.0,<5.0.0"
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name = "flatbuffers"
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version = "23.5.26"
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description = "The FlatBuffers serialization format for Python"
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optional = false
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optional = true
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python-versions = "*"
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files = [
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{file = "flatbuffers-23.5.26-py2.py3-none-any.whl", hash = "sha256:c0ff356da363087b915fde4b8b45bdda73432fc17cddb3c8157472eab1422ad1"},
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@@ -301,117 +301,117 @@ files = [
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[[package]]
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name = "grpcio"
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version = "1.58.0rc1"
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version = "1.58.0"
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description = "HTTP/2-based RPC framework"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "grpcio-1.58.0rc1-cp310-cp310-linux_armv7l.whl", hash = "sha256:6789a5d601342687fca47bf6c0630008e5c1cc1f0ada211c1ac36301b7029b57"},
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{file = "grpcio-1.58.0rc1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:543324cb8b7c5a3a5aae983aace7ec50478719d2a0c29debbffa44010acc34be"},
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{file = "grpcio-1.58.0rc1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:6e45b19d985ebe83adb653e50ae1a0c4f0b2ababab6d90a593c9fccacf6554d2"},
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{file = "grpcio-1.58.0rc1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f03bc913cee9d316d92424105b260d69e52abf393e4b55561cddf74317d97ee0"},
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{file = "grpcio-1.58.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:112d44ab7d0c75b3e4f0ee917e0b6eab4b28433bf9848182f4de6e78448e3511"},
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{file = "grpcio-1.58.0rc1-cp310-cp310-win32.whl", hash = "sha256:4d2647ccab7449ef5b58611cc9ac3d80dafbc374948b1d0a930b6d847b9545b1"},
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setuptools = "*"
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@@ -454,13 +454,13 @@ files = [
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[[package]]
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|
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version = "0.18.0"
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python-versions = ">=3.7"
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python-versions = ">=3.8"
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[package.dependencies]
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@@ -475,19 +475,19 @@ socks = ["socksio (==1.*)"]
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[[package]]
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[package.dependencies]
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certifi = "*"
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||||
h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
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httpcore = ">=0.15.0,<0.18.0"
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httpcore = ">=0.18.0,<0.19.0"
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idna = "*"
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sniffio = "*"
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@@ -501,7 +501,7 @@ socks = ["socksio (==1.*)"]
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description = "Human friendly output for text interfaces using Python"
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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@@ -654,7 +654,7 @@ files = [
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@@ -779,7 +779,7 @@ files = [
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python-versions = "*"
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@@ -820,7 +820,7 @@ sympy = "*"
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name = "onnxruntime-silicon"
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description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
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optional = true
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@@ -884,24 +884,24 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
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[[package]]
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@@ -1059,7 +1059,7 @@ plugins = ["importlib-metadata"]
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||||
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||||
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@@ -1086,13 +1086,13 @@ dev = ["twine (>=3.4.1)"]
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||||
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[[package]]
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||||
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||||
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||||
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||||
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||||
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||||
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[package.dependencies]
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@@ -1377,7 +1377,7 @@ test = ["pytest"]
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||||
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||||
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||||
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||||
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@@ -1391,7 +1391,7 @@ mpmath = ">=0.19"
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||||
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||||
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@@ -1456,7 +1456,7 @@ files = [
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@@ -1525,7 +1525,10 @@ files = [
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||||
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
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||||
|
||||
[extras]
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||||
fastembed = ["fastembed"]
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||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
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||||
python-versions = ">=3.8,<3.12"
|
||||
content-hash = "a9d224b227708dc583aacf2515ab82a05a479157c52d0fa68374c57c81360024"
|
||||
content-hash = "a082769e610f3f5269b9fff70b67cf42f1cd5d84de6209effc432f12b1b6a232"
|
||||
|
||||
@@ -25,6 +25,7 @@ grpcio = { version = ">=1.41.0", allow-prereleases = true }
|
||||
grpcio-tools = ">=1.41.0"
|
||||
urllib3 = "^1.26.14"
|
||||
portalocker = "^2.7.0"
|
||||
fastembed = { version = "0.0.4", optional = true }
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^7.1"
|
||||
@@ -40,11 +41,8 @@ pyright = "^1.1.293"
|
||||
mypy = "^1.0.0"
|
||||
types-protobuf = "^4.21.0.5"
|
||||
|
||||
[tool.poetry.group.fastembed]
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.fastembed.dependencies]
|
||||
fastembed = "0.0.2"
|
||||
[tool.poetry.extras]
|
||||
fastembed = ["fastembed"]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core>=1.0.0"]
|
||||
|
||||
@@ -11,7 +11,7 @@ from qdrant_client.uploader.uploader import iter_batch
|
||||
try:
|
||||
from fastembed.embedding import DefaultEmbedding
|
||||
except ImportError:
|
||||
pass
|
||||
DefaultEmbedding = None
|
||||
|
||||
SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = {
|
||||
"BAAI/bge-base-en": (768, models.Distance.COSINE),
|
||||
@@ -102,13 +102,15 @@ class QdrantFastembedMixin(QdrantBase):
|
||||
if embed_type == "passage":
|
||||
vectors_batches = embedding_model.passage_embed(batch_docs, batch_size=batch_size)
|
||||
elif embed_type == "query":
|
||||
vectors_batches = embedding_model.query_embed(batch_docs, batch_size=batch_size)
|
||||
vectors_batches = (
|
||||
list(embedding_model.query_embed(query=query))[0] for query in batch_docs
|
||||
)
|
||||
elif embed_type == "default":
|
||||
vectors_batches = embedding_model.embed(batch_docs, batch_size=batch_size)
|
||||
else:
|
||||
raise ValueError(f"Unknown embed type: {embed_type}")
|
||||
for vector in vectors_batches:
|
||||
yield vector
|
||||
yield vector.tolist()
|
||||
|
||||
def _get_vector_field_name(self) -> str:
|
||||
model_name = self.embedding_model_name.split("/")[-1].lower()
|
||||
@@ -317,7 +319,8 @@ class QdrantFastembedMixin(QdrantBase):
|
||||
"""
|
||||
embedding_model_inst = self._get_or_init_model(model_name=self.embedding_model_name)
|
||||
query_vectors = [
|
||||
embedding_model_inst.query_embed(query=[query_text])[0] for query_text in query_texts
|
||||
list(embedding_model_inst.query_embed(query=query_text))[0]
|
||||
for query_text in query_texts
|
||||
]
|
||||
|
||||
requests = []
|
||||
|
||||
@@ -8,18 +8,18 @@ from qdrant_client import QdrantClient
|
||||
def test_add_without_query(
|
||||
local_client: QdrantClient = QdrantClient(":memory:"),
|
||||
collection_name: str = "demo_collection",
|
||||
docs: List[str] = [
|
||||
"Qdrant has Langchain integrations",
|
||||
"Qdrant also has Llama Index integrations",
|
||||
],
|
||||
docs: List[str] = None,
|
||||
):
|
||||
if docs is None:
|
||||
docs = [
|
||||
"Qdrant has Langchain integrations",
|
||||
"Qdrant also has Llama Index integrations",
|
||||
]
|
||||
|
||||
if not local_client._is_fastembed_installed:
|
||||
pytest.skip("FastEmbed is not installed, skipping test")
|
||||
|
||||
local_client.add(
|
||||
collection_name=collection_name,
|
||||
documents=docs
|
||||
)
|
||||
local_client.add(collection_name=collection_name, documents=docs)
|
||||
assert local_client.count(collection_name).count == 2
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user