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qdrant: New sparse embeddings provider interface - PART 1 (#24015)

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## Description

This PR introduces a new sparse embedding provider interface to work
with the new Qdrant implementation that will follow this PR.

Additionally, an implementation of this interface is provided with
https://github.com/qdrant/fastembed.

This PR will be followed by
https://github.com/Anush008/langchain/pull/3.
This commit is contained in:
Anush 2024-07-12 05:37:25 +05:30 committed by GitHub
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commit 7790d67f94
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8 changed files with 828 additions and 8 deletions
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1
libs/partners/qdrant/Makefile
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@ -53,6 +53,7 @@ help:
@echo 'check_imports - check imports' @echo 'check_imports - check imports'
@echo 'format - run code formatters' @echo 'format - run code formatters'
@echo 'lint - run linters' @echo 'lint - run linters'
@echo 'lint_tests - run linters on tests'
@echo 'test - run unit tests' @echo 'test - run unit tests'
@echo 'tests - run unit tests' @echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file' @echo 'test TEST_FILE=<test_file> - run all tests in file'

4
libs/partners/qdrant/langchain_qdrant/__init__.py
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@ -1,3 +1,5 @@
from langchain_qdrant.fastembed_sparse import FastEmbedSparse
from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector
from langchain_qdrant.vectorstores import Qdrant from langchain_qdrant.vectorstores import Qdrant
__all__ = ["Qdrant"] __all__ = ["Qdrant", "SparseEmbeddings", "SparseVector", "FastEmbedSparse"]

73
libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py Normal file
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@ -0,0 +1,73 @@
from typing import Any, List, Optional, Sequence
from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector
class FastEmbedSparse(SparseEmbeddings):
"""An interface for sparse embedding models to use with Qdrant."""
def __init__(
self,
model_name: str = "Qdrant/bm25",
batch_size: int = 256,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
providers: Optional[Sequence[Any]] = None,
parallel: Optional[int] = None,
**kwargs: Any,
) -> None:
"""
Sparse encoder implementation using FastEmbed - https://qdrant.github.io/fastembed/
For a list of available models, see https://qdrant.github.io/fastembed/examples/Supported_Models/
Args:
model_name (str): The name of the model to use. Defaults to `"Qdrant/bm25"`.
batch_size (int): Batch size for encoding. Defaults to 256.
cache_dir (str, optional): The path to the model cache directory.\
Can also be set using the\
`FASTEMBED_CACHE_PATH` env variable.
threads (int, optional): The number of threads onnxruntime session can use.
providers (Sequence[Any], optional): List of ONNX execution providers.\
parallel (int, optional): If `>1`, data-parallel encoding will be used, r\
Recommended for encoding of large datasets.\
If `0`, use all available cores.\
If `None`, don't use data-parallel processing,\
use default onnxruntime threading instead.\
Defaults to None.
**kwargs: Additional options to pass to fastembed.SparseTextEmbedding
Raises:
ValueError: If the model_name is not supported in SparseTextEmbedding.
"""
try:
from fastembed import SparseTextEmbedding # type: ignore
except ImportError:
raise ValueError(
"The 'fastembed' package is not installed. "
"Please install it with "
"`pip install fastembed` or `pip install fastembed-gpu`."
)
self._batch_size = batch_size
self._parallel = parallel
self._model = SparseTextEmbedding(
model_name=model_name,
cache_dir=cache_dir,
threads=threads,
providers=providers,
**kwargs,
)
def embed_documents(self, texts: List[str]) -> List[SparseVector]:
results = self._model.embed(
texts, batch_size=self._batch_size, parallel=self._parallel
)
return [
SparseVector(indices=result.indices.tolist(), values=result.values.tolist())
for result in results
]
def embed_query(self, text: str) -> SparseVector:
result = next(self._model.query_embed(text))
return SparseVector(
indices=result.indices.tolist(), values=result.values.tolist()
)

36
libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py Normal file
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@ -0,0 +1,36 @@
from abc import ABC, abstractmethod
from typing import List
from langchain_core.runnables.config import run_in_executor
from pydantic import BaseModel, Field
class SparseVector(BaseModel, extra="forbid"):
"""
Sparse vector structure
"""
indices: List[int] = Field(..., description="indices must be unique")
values: List[float] = Field(
..., description="values and indices must be the same length"
)
class SparseEmbeddings(ABC):
"""An interface for sparse embedding models to use with Qdrant."""
@abstractmethod
def embed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Embed search docs."""
@abstractmethod
def embed_query(self, text: str) -> SparseVector:
"""Embed query text."""
async def aembed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Asynchronous Embed search docs."""
return await run_in_executor(None, self.embed_documents, texts)
async def aembed_query(self, text: str) -> SparseVector:
"""Asynchronous Embed query text."""
return await run_in_executor(None, self.embed_query, text)

680
libs/partners/qdrant/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]] [[package]]
name = "annotated-types" name = "annotated-types"
@ -174,6 +174,23 @@ files = [
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] ]
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description = "Colored terminal output for Python's logging module"
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cron = ["capturer (>=2.4)"]
[[package]] [[package]]
name = "exceptiongroup" name = "exceptiongroup"
version = "1.2.1" version = "1.2.1"
@ -188,6 +205,61 @@ files = [
[package.extras] [package.extras]
test = ["pytest (>=6)"] test = ["pytest (>=6)"]
[[package]]
name = "fastembed"
version = "0.3.1"
description = "Fast, light, accurate library built for retrieval embedding generation"
optional = true
python-versions = "<3.13,>=3.8.0"
files = [
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{file = "fastembed-0.3.1.tar.gz", hash = "sha256:01a52c5c9cdfe2504dfa56b3c1b032d1ad88d621de144fc9a571371a79df6457"},
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loguru = ">=0.7.2,<0.8.0"
mmh3 = ">=4.0,<5.0"
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]
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onnxruntime = ">=1.17.0,<2.0.0"
pillow = ">=10.3.0,<11.0.0"
PyStemmer = ">=2.2.0,<3.0.0"
requests = ">=2.31,<3.0"
snowballstemmer = ">=2.2.0,<3.0.0"
tokenizers = ">=0.15,<1.0"
tqdm = ">=4.66,<5.0"
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testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
typing = ["typing-extensions (>=4.8)"]
[[package]]
name = "flatbuffers"
version = "24.3.25"
description = "The FlatBuffers serialization format for Python"
optional = true
python-versions = "*"
files = [
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[[package]] [[package]]
name = "freezegun" name = "freezegun"
version = "1.5.1" version = "1.5.1"
@ -202,6 +274,45 @@ files = [
[package.dependencies] [package.dependencies]
python-dateutil = ">=2.7" python-dateutil = ">=2.7"
[[package]]
name = "fsspec"
version = "2024.6.1"
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"]
dev = ["pre-commit", "ruff"]
doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
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"]
test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
tqdm = ["tqdm"]
[[package]] [[package]]
name = "grpcio" name = "grpcio"
version = "1.63.0" version = "1.63.0"
@ -403,6 +514,54 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"] http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"] socks = ["socksio (==1.*)"]
[[package]]
name = "huggingface-hub"
version = "0.23.4"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = true
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]
[package.dependencies]
huggingface-hub = ">=0.16.4,<1.0"
[package.extras]
dev = ["tokenizers[testing]"]
docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
[[package]] [[package]]
name = "tomli" name = "tomli"
version = "2.0.1" version = "2.0.1"
@ -1192,6 +1823,26 @@ files = [
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
] ]
[[package]]
name = "tqdm"
version = "4.66.4"
description = "Fast, Extensible Progress Meter"
optional = true
python-versions = ">=3.7"
files = [
{file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
{file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
]
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
[[package]] [[package]]
name = "typing-extensions" name = "typing-extensions"
version = "4.11.0" version = "4.11.0"
@ -1261,7 +1912,24 @@ files = [
[package.extras] [package.extras]
watchmedo = ["PyYAML (>=3.10)"] watchmedo = ["PyYAML (>=3.10)"]
[[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)"]
[extras]
fastembed = ["fastembed"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
content-hash = "c743f55157d3b1da71b393e40d291dc806f4f3113a519a8a6b888e9ab779295b" content-hash = "192e50ec7768ff659cfe7d2bbc75497dbeba3ee9f1e245d12f2eb3cdf7fe07d9"

5
libs/partners/qdrant/pyproject.toml
View File

@ -24,6 +24,11 @@ disallow_untyped_defs = true
python = ">=3.8.1,<4.0" python = ">=3.8.1,<4.0"
langchain-core = ">=0.1.52,<0.3" langchain-core = ">=0.1.52,<0.3"
qdrant-client = "^1.9.0" qdrant-client = "^1.9.0"
pydantic = "^2.7.4"
fastembed = { version = "^0.3.1", python = ">=3.8.1,<3.13", optional = true}
[tool.poetry.extras]
fastembed = [ "fastembed"]
[tool.coverage.run] [tool.coverage.run]
omit = [ "tests/*",] omit = [ "tests/*",]

35
libs/partners/qdrant/tests/integration_tests/fastembed/test_fastembed_sparse.py Normal file
View File

@ -0,0 +1,35 @@
import numpy as np
import pytest
from langchain_qdrant import FastEmbedSparse
@pytest.mark.parametrize(
"model_name", ["Qdrant/bm25", "Qdrant/bm42-all-minilm-l6-v2-attentions"]
)
def test_attention_embeddings(model_name: str) -> None:
model = FastEmbedSparse(model_name=model_name)
query_output = model.embed_query("Stay, steady and sprint.")
assert len(query_output.indices) == len(query_output.values)
assert np.allclose(query_output.values, np.ones(len(query_output.values)))
texts = [
"The journey of a thousand miles begins with a single step.",
"Be yourself in a world that is constantly trying to make you something else",
"In the end, we only regret the chances we didn't take.",
"Every moment is a fresh beginning.",
"Not all those who wander are lost.",
"Do not go where the path may lead, go elsewhere and leave a trail.",
"Life is what happens when you're busy making other plans.",
"The only limit to our realization of tomorrow is our doubts of today.",
]
output = model.embed_documents(texts)
assert len(output) == len(texts)
for result in output:
assert len(result.indices) == len(result.values)
assert len(result.indices) > 0

2
libs/partners/qdrant/tests/unit_tests/test_imports.py
View File

@ -1,6 +1,6 @@
from langchain_qdrant import __all__ from langchain_qdrant import __all__
EXPECTED_ALL = ["Qdrant"] EXPECTED_ALL = ["Qdrant", "SparseEmbeddings", "SparseVector", "FastEmbedSparse"]
def test_all_imports() -> None: def test_all_imports() -> None: