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Upgrade FastEmbed Version (#493)

Browse Source 
* Update fastembed to v0.2.1

* chore(qdrant_fastembed.py): update DEFAULT_EMBEDDING_MODEL

* fix(fastembed integration): upgrade to latest version

* Prefer black over ruff

* Prefer black over ruff

* Remove hardcoded directory structure from Qdrant Client checks

* new: deprecate current default model, deprecate max token length, update fastembed

* fix: make embedding_model_name method sync

* fix: update poetry lock

* refactor: use list_supported_models() (#501)

* fix: fix fastembed check

* fix: fix fastembed class var assignment

* fix: remove fastembed deprecation from qdrant client (#524)

---------

Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>
Co-authored-by: Anush <anushshetty90@gmail.com>
This commit is contained in:
Nirant
2024-03-06 01:39:31 +05:30
committed by George Panchuk
parent 7365f83eb8
commit cb0aa80e8e
8 changed files with 416 additions and 165 deletions
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363
poetry.lock generated
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@@ -564,21 +564,27 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
[[package]]
name = "fastembed"
version = "0.1.1"
version = "0.2.2"
description = "Fast, light, accurate library built for retrieval embedding generation"
optional = true
python-versions = ">=3.8.0,<3.12"
python-versions = ">=3.8.0,<3.13"
files = [
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{file = "fastembed-0.2.2.tar.gz", hash = "sha256:2430bcd0022d50f858a9d5ff13d66a47c6799f48dae47a41cb8573c67d9388bd"},
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onnx = ">=1.11,<2.0"
onnxruntime = ">=1.15,<2.0"
huggingface-hub = ">=0.20,<0.21"
loguru = ">=0.7.2,<0.8.0"
numpy = [
{version = ">=1.21", markers = "python_version < \"3.12\""},
{version = ">=1.26", markers = "python_version >= \"3.12\""},
]
onnx = ">=1.15.0,<2.0.0"
onnxruntime = ">=1.17.0,<2.0.0"
requests = ">=2.31,<3.0"
tokenizers = ">=0.13,<0.14"
tqdm = ">=4.65,<5.0"
tokenizers = ">=0.15.1,<0.16.0"
tqdm = ">=4.66,<5.0"
[[package]]
name = "fastjsonschema"
@@ -594,6 +600,22 @@ files = [
[package.extras]
devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
[[package]]
name = "filelock"
version = "3.13.1"
description = "A platform independent file lock."
optional = true
python-versions = ">=3.8"
files = [
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testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
typing = ["typing-extensions (>=4.8)"]
[[package]]
name = "flatbuffers"
version = "23.5.26"
@@ -605,6 +627,41 @@ files = [
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[[package]]
name = "fsspec"
version = "2024.2.0"
description = "File-system specification"
optional = true
python-versions = ">=3.8"
files = [
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abfs = ["adlfs"]
adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
devel = ["pytest", "pytest-cov"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
fuse = ["fusepy"]
gcs = ["gcsfs"]
git = ["pygit2"]
github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
tqdm = ["tqdm"]
[[package]]
name = "grpcio"
version = "1.62.0"
@@ -882,6 +939,38 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
[[package]]
name = "huggingface-hub"
version = "0.20.3"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = true
python-versions = ">=3.8.0"
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packaging = ">=20.9"
pyyaml = ">=5.1"
requests = "*"
tqdm = ">=4.42.1"
typing-extensions = ">=3.7.4.3"
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fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
torch = ["torch"]
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
[[package]]
name = "humanfriendly"
version = "10.0"
@@ -1158,6 +1247,24 @@ files = [
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[[package]]
name = "markupsafe"
version = "2.1.5"
@@ -2060,13 +2167,13 @@ pytest = ">=5.0.0"
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@@ -2610,56 +2766,129 @@ test = ["flake8", "isort", "pytest"]
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description = "Fast and Customizable Tokenizers"
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{file = "tokenizers-0.15.2.tar.gz", hash = "sha256:e6e9c6e019dd5484be5beafc775ae6c925f4c69a3487040ed09b45e13df2cb91"},
]
[package.dependencies]
huggingface_hub = ">=0.16.4,<1.0"
[package.extras]
dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
dev = ["tokenizers[testing]"]
docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
[[package]]
@@ -2730,13 +2959,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "types-protobuf"
version = "4.24.0.20240129"
version = "4.24.0.20240302"
description = "Typing stubs for protobuf"
optional = false
python-versions = ">=3.8"
files = [
{file = "types-protobuf-4.24.0.20240129.tar.gz", hash = "sha256:8a83dd3b9b76a33e08d8636c5daa212ace1396418ed91837635fcd564a624891"},
{file = "types_protobuf-4.24.0.20240129-py3-none-any.whl", hash = "sha256:23be68cc29f3f5213b5c5878ac0151706182874040e220cfb11336f9ee642ead"},
{file = "types-protobuf-4.24.0.20240302.tar.gz", hash = "sha256:f22c00cc0cea9722e71e14d389bba429af9e35a74a949719c167203a5abbe2e4"},
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]
[[package]]
@@ -2789,6 +3018,20 @@ files = [
{file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
]
[[package]]
name = "win32-setctime"
version = "1.1.0"
description = "A small Python utility to set file creation time on Windows"
optional = true
python-versions = ">=3.5"
files = [
{file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"},
{file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"},
]
[package.extras]
dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
[[package]]
name = "zipp"
version = "3.17.0"
@@ -2810,4 +3053,4 @@ fastembed = ["fastembed"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8"
content-hash = "2184f7af40328e44ba99b94d0f88b42d1cab581fb77cea11c850e64c92d6300a"
content-hash = "2134572fdebcad549e86bf2fa23ad8a548462e3ea80c8606ad98a462debb03cb"

2
pyproject.toml
View File

@@ -27,7 +27,7 @@ grpcio-tools = ">=1.41.0"
urllib3 = ">=1.26.14,<3"
portalocker = "^2.7.0"
fastembed = [
{ version = "0.1.1", optional = true, python = "<3.12" }
{ version = "0.2.2", optional = true, python = "<3.13" }
]
[tool.poetry.group.dev.dependencies]

8
qdrant_client/async_qdrant_client.py
View File

@@ -119,14 +119,6 @@ class AsyncQdrantClient(AsyncQdrantFastembedMixin):
grpc_options=grpc_options,
**kwargs,
)
self._is_fastembed_installed: Optional[bool] = None
if self._is_fastembed_installed is None:
try:
from fastembed.embedding import DefaultEmbedding
self._is_fastembed_installed = True
except ImportError:
self._is_fastembed_installed = False
async def close(self, grpc_grace: Optional[float] = None, **kwargs: Any) -> None:
"""Closes the connection to Qdrant

89
qdrant_client/async_qdrant_fastembed.py
View File

@@ -10,6 +10,7 @@
# ****** WARNING: THIS FILE IS AUTOGENERATED ******
import uuid
import warnings
from itertools import tee
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
@@ -19,31 +20,44 @@ from qdrant_client.fastembed_common import QueryResponse
from qdrant_client.http import models
try:
from fastembed.embedding import DefaultEmbedding
from fastembed import TextEmbedding
except ImportError:
DefaultEmbedding = None
SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = {
"BAAI/bge-base-en": (768, models.Distance.COSINE),
"sentence-transformers/all-MiniLM-L6-v2": (384, models.Distance.COSINE),
"BAAI/bge-small-en": (384, models.Distance.COSINE),
"BAAI/bge-small-en-v1.5": (384, models.Distance.COSINE),
"BAAI/bge-base-en-v1.5": (768, models.Distance.COSINE),
"intfloat/multilingual-e5-large": (1024, models.Distance.COSINE),
}
TextEmbedding = None
SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = (
{
model["model"]: (model["dim"], models.Distance.COSINE)
for model in TextEmbedding.list_supported_models()
}
if TextEmbedding
else {}
)
class AsyncQdrantFastembedMixin(AsyncQdrantBase):
DEFAULT_EMBEDDING_MODEL = "BAAI/bge-small-en"
embedding_models: Dict[str, "DefaultEmbedding"] = {}
embedding_models: Dict[str, "TextEmbedding"] = {}
_FASTEMBED_INSTALLED: bool
def __init__(self, **kwargs: Any):
self.embedding_model_name = self.DEFAULT_EMBEDDING_MODEL
self._embedding_model_name: Optional[str] = None
try:
from fastembed import TextEmbedding
self.__class__._FASTEMBED_INSTALLED = True
except ImportError:
self.__class__._FASTEMBED_INSTALLED = False
super().__init__(**kwargs)
@property
def embedding_model_name(self) -> str:
if self._embedding_model_name is None:
self._embedding_model_name = self.DEFAULT_EMBEDDING_MODEL
return self._embedding_model_name
def set_model(
self,
embedding_model_name: str,
max_length: int = 512,
max_length: Optional[int] = None,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
**kwargs: Any,
@@ -52,7 +66,7 @@ class AsyncQdrantFastembedMixin(AsyncQdrantBase):
Set embedding model to use for encoding documents and queries.
Args:
embedding_model_name: One of the supported embedding models. See `SUPPORTED_EMBEDDING_MODELS` for details.
max_length (int, optional): The maximum number of tokens. Defaults to 512. Unknown behavior for values > 512.
max_length (int, optional): Deprecated. Defaults to None.
cache_dir (str, optional): The path to the cache directory.
Can be set using the `FASTEMBED_CACHE_PATH` env variable.
Defaults to `fastembed_cache` in the system's temp directory.
@@ -64,26 +78,28 @@ class AsyncQdrantFastembedMixin(AsyncQdrantBase):
Returns:
None
"""
self._get_or_init_model(
model_name=embedding_model_name,
max_length=max_length,
cache_dir=cache_dir,
threads=threads,
**kwargs,
)
self.embedding_model_name = embedding_model_name
@staticmethod
def _import_fastembed() -> None:
try:
from fastembed.embedding import DefaultEmbedding
except ImportError:
raise ImportError(
"fastembed is not installed. Please install it to enable fast vector indexing with `pip install fastembed`."
if max_length is not None:
warnings.warn(
"max_length parameter is deprecated and will be removed in the future. It's not used by fastembed models.",
DeprecationWarning,
stacklevel=2,
)
self._get_or_init_model(
model_name=embedding_model_name, cache_dir=cache_dir, threads=threads, **kwargs
)
self._embedding_model_name = embedding_model_name
@classmethod
def _import_fastembed(cls) -> None:
if cls._FASTEMBED_INSTALLED:
return
raise ImportError(
"fastembed is not installed. Please install it to enable fast vector indexing with `pip install fastembed`."
)
@classmethod
def _get_model_params(cls, model_name: str) -> Tuple[int, models.Distance]:
cls._import_fastembed()
if model_name not in SUPPORTED_EMBEDDING_MODELS:
raise ValueError(
f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
@@ -94,24 +110,19 @@ class AsyncQdrantFastembedMixin(AsyncQdrantBase):
def _get_or_init_model(
cls,
model_name: str,
max_length: int = 512,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
**kwargs: Any,
) -> "DefaultEmbedding":
) -> "TextEmbedding":
if model_name in cls.embedding_models:
return cls.embedding_models[model_name]
cls._import_fastembed()
if model_name not in SUPPORTED_EMBEDDING_MODELS:
raise ValueError(
f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
)
cls._import_fastembed()
cls.embedding_models[model_name] = DefaultEmbedding(
model_name=model_name,
max_length=max_length,
cache_dir=cache_dir,
threads=threads,
**kwargs,
cls.embedding_models[model_name] = TextEmbedding(
model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs
)
return cls.embedding_models[model_name]

9
qdrant_client/qdrant_client.py
View File

@@ -118,15 +118,6 @@ class QdrantClient(QdrantFastembedMixin):
grpc_options=grpc_options,
**kwargs,
)
self._is_fastembed_installed: Optional[bool] = None
# if fastembed is installed, set to true else False
if self._is_fastembed_installed is None:
try:
from fastembed.embedding import DefaultEmbedding # noqa: F401
self._is_fastembed_installed = True
except ImportError:
self._is_fastembed_installed = False
def __del__(self) -> None:
self.close()

89
qdrant_client/qdrant_fastembed.py
View File

@@ -1,4 +1,5 @@
import uuid
import warnings
from itertools import tee
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
@@ -8,33 +9,49 @@ from qdrant_client.fastembed_common import QueryResponse
from qdrant_client.http import models
try:
from fastembed.embedding import DefaultEmbedding
from fastembed import TextEmbedding
except ImportError:
DefaultEmbedding = None
TextEmbedding = None
SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = {
"BAAI/bge-base-en": (768, models.Distance.COSINE),
"sentence-transformers/all-MiniLM-L6-v2": (384, models.Distance.COSINE),
"BAAI/bge-small-en": (384, models.Distance.COSINE),
"BAAI/bge-small-en-v1.5": (384, models.Distance.COSINE),
"BAAI/bge-base-en-v1.5": (768, models.Distance.COSINE),
"intfloat/multilingual-e5-large": (1024, models.Distance.COSINE),
}
SUPPORTED_EMBEDDING_MODELS: Dict[str, Tuple[int, models.Distance]] = (
{
model["model"]: (model["dim"], models.Distance.COSINE)
for model in TextEmbedding.list_supported_models()
}
if TextEmbedding
else {}
)
class QdrantFastembedMixin(QdrantBase):
DEFAULT_EMBEDDING_MODEL = "BAAI/bge-small-en"
embedding_models: Dict[str, "DefaultEmbedding"] = {}
embedding_models: Dict[str, "TextEmbedding"] = {}
_FASTEMBED_INSTALLED: bool
def __init__(self, **kwargs: Any):
self.embedding_model_name = self.DEFAULT_EMBEDDING_MODEL
self._embedding_model_name: Optional[str] = None
try:
from fastembed import TextEmbedding # noqa: F401
self.__class__._FASTEMBED_INSTALLED = True
except ImportError:
self.__class__._FASTEMBED_INSTALLED = False
super().__init__(**kwargs)
@property
def embedding_model_name(self) -> str:
if self._embedding_model_name is None:
self._embedding_model_name = self.DEFAULT_EMBEDDING_MODEL
return self._embedding_model_name
def set_model(
self,
embedding_model_name: str,
max_length: int = 512,
max_length: Optional[int] = None,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
**kwargs: Any,
@@ -43,7 +60,7 @@ class QdrantFastembedMixin(QdrantBase):
Set embedding model to use for encoding documents and queries.
Args:
embedding_model_name: One of the supported embedding models. See `SUPPORTED_EMBEDDING_MODELS` for details.
max_length (int, optional): The maximum number of tokens. Defaults to 512. Unknown behavior for values > 512.
max_length (int, optional): Deprecated. Defaults to None.
cache_dir (str, optional): The path to the cache directory.
Can be set using the `FASTEMBED_CACHE_PATH` env variable.
Defaults to `fastembed_cache` in the system's temp directory.
@@ -55,28 +72,38 @@ class QdrantFastembedMixin(QdrantBase):
Returns:
None
"""
if max_length is not None:
warnings.warn(
"max_length parameter is deprecated and will be removed in the future. "
"It's not used by fastembed models.",
DeprecationWarning,
stacklevel=2,
)
self._get_or_init_model(
model_name=embedding_model_name,
max_length=max_length,
cache_dir=cache_dir,
threads=threads,
**kwargs,
)
self.embedding_model_name = embedding_model_name
self._embedding_model_name = embedding_model_name
@staticmethod
def _import_fastembed() -> None:
try:
from fastembed.embedding import DefaultEmbedding
except ImportError:
# If it's not, ask the user to install it
raise ImportError(
"fastembed is not installed."
" Please install it to enable fast vector indexing with `pip install fastembed`."
)
@classmethod
def _import_fastembed(cls) -> None:
if cls._FASTEMBED_INSTALLED:
return
# If it's not, ask the user to install it
raise ImportError(
"fastembed is not installed."
" Please install it to enable fast vector indexing with `pip install fastembed`."
)
@classmethod
def _get_model_params(cls, model_name: str) -> Tuple[int, models.Distance]:
cls._import_fastembed()
if model_name not in SUPPORTED_EMBEDDING_MODELS:
raise ValueError(
f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
@@ -88,24 +115,22 @@ class QdrantFastembedMixin(QdrantBase):
def _get_or_init_model(
cls,
model_name: str,
max_length: int = 512,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
**kwargs: Any,
) -> "DefaultEmbedding": # -> Embedding: # noqa: F821
) -> "TextEmbedding":
if model_name in cls.embedding_models:
return cls.embedding_models[model_name]
cls._import_fastembed()
if model_name not in SUPPORTED_EMBEDDING_MODELS:
raise ValueError(
f"Unsupported embedding model: {model_name}. Supported models: {SUPPORTED_EMBEDDING_MODELS}"
)
cls._import_fastembed()
cls.embedding_models[model_name] = DefaultEmbedding(
cls.embedding_models[model_name] = TextEmbedding(
model_name=model_name,
max_length=max_length,
cache_dir=cache_dir,
threads=threads,
**kwargs,

20
tests/test_fastembed.py
View File

@@ -16,7 +16,7 @@ def test_add_without_query(
"Qdrant also has Llama Index integrations",
]
if not local_client._is_fastembed_installed:
if not local_client._FASTEMBED_INSTALLED:
pytest.skip("FastEmbed is not installed, skipping test")
local_client.add(collection_name=collection_name, documents=docs)
@@ -42,7 +42,7 @@ def test_no_install(
}
# When FastEmbed is not installed, the add method should raise an ImportError
if local_client._is_fastembed_installed:
if local_client._FASTEMBED_INSTALLED:
pytest.skip("FastEmbed is installed, skipping test")
else:
with pytest.raises(ImportError):
@@ -67,7 +67,7 @@ def test_query(
"ids": [42, 2],
}
if not local_client._is_fastembed_installed:
if not local_client._FASTEMBED_INSTALLED:
pytest.skip("FastEmbed is not installed, skipping test")
local_client.add(
@@ -99,31 +99,19 @@ def test_set_model(
"You can just add documents and query them to do semantic search",
]
if not local_client._is_fastembed_installed:
if not local_client._FASTEMBED_INSTALLED:
pytest.skip("FastEmbed is not installed, skipping test")
embedding_model_name = "sentence-transformers/all-MiniLM-L6-v2"
max_length = 384
temp_cache_dir = tempfile.TemporaryDirectory()
local_client.set_model(
embedding_model_name=embedding_model_name,
max_length=max_length,
cache_dir=temp_cache_dir.name,
)
# Check if the model is initialized & cls.embeddings_models is set with expected values
dim, dist = local_client._get_model_params(embedding_model_name)
assert dim == max_length
# Assert that the cache directory has a directory which contains `model.onnx` file
import os
assert os.path.exists(
os.path.join(
temp_cache_dir.name,
f"fast-{embedding_model_name.split('/')[1]}",
"model.onnx",
)
)
# Use the initialized model to add documents with vector embeddings
local_client.add(collection_name=collection_name, documents=docs)
assert local_client.count(collection_name).count == 2

1
tools/async_client_generator/fastembed_generator.py
View File

@@ -56,6 +56,7 @@ if __name__ == "__main__":
"set_model",
"get_vector_field_name",
"get_fastembed_vector_params",
"embedding_model_name",
],
class_replace_map={
"QdrantBase": "AsyncQdrantBase",