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chore: Set up cross encoder client (#201)

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* chore: Set up cross encoder client

* fix: deps

* chore: move voyage to dev deps
This commit is contained in:
Pavlo Paliychuk 2024-10-24 11:36:10 -04:00 committed by GitHub
parent 47ba11e08d
commit 544f9e3fba
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GPG Key ID: B5690EEEBB952194
7 changed files with 924 additions and 29 deletions
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0
graphiti_core/cross_encoder/__init__.py Normal file
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45
graphiti_core/cross_encoder/bge_reranker_client.py Normal file
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@ -0,0 +1,45 @@
"""
Copyright 2024, Zep Software, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import asyncio
from typing import List, Tuple
from sentence_transformers import CrossEncoder
from graphiti_core.cross_encoder.client import CrossEncoderClient
class BGERerankerClient(CrossEncoderClient):
def __init__(self):
self.model = CrossEncoder('BAAI/bge-reranker-v2-m3')
async def rank(self, query: str, passages: List[str]) -> List[Tuple[str, float]]:
if not passages:
return []
input_pairs = [[query, passage] for passage in passages]
# Run the synchronous predict method in an executor
loop = asyncio.get_running_loop()
scores = await loop.run_in_executor(None, self.model.predict, input_pairs)
ranked_passages = sorted(
[(passage, float(score)) for passage, score in zip(passages, scores)],
key=lambda x: x[1],
reverse=True,
)
return ranked_passages

41
graphiti_core/cross_encoder/client.py Normal file
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@ -0,0 +1,41 @@
"""
Copyright 2024, Zep Software, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from abc import ABC, abstractmethod
from typing import List, Tuple
class CrossEncoderClient(ABC):
"""
CrossEncoderClient is an abstract base class that defines the interface
for cross-encoder models used for ranking passages based on their relevance to a query.
It allows for different implementations of cross-encoder models to be used interchangeably.
"""
@abstractmethod
async def rank(self, query: str, passages: List[str]) -> List[Tuple[str, float]]:
"""
Rank the given passages based on their relevance to the query.
Args:
query (str): The query string.
passages (List[str]): A list of passages to rank.
Returns:
List[Tuple[str, float]]: A list of tuples containing the passage and its score,
sorted in descending order of relevance.
"""
pass

52
graphiti_core/search/search.py
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@ -53,12 +53,12 @@ logger = logging.getLogger(__name__)
async def search(
driver: AsyncDriver,
embedder: EmbedderClient,
query: str,
group_ids: list[str] | None,
config: SearchConfig,
center_node_uuid: str | None = None,
driver: AsyncDriver,
embedder: EmbedderClient,
query: str,
group_ids: list[str] | None,
config: SearchConfig,
center_node_uuid: str | None = None,
) -> SearchResults:
start = time()
query_vector = await embedder.create(input=[query.replace('\n', ' ')])
@ -108,13 +108,13 @@ async def search(
async def edge_search(
driver: AsyncDriver,
query: str,
query_vector: list[float],
group_ids: list[str] | None,
config: EdgeSearchConfig | None,
center_node_uuid: str | None = None,
limit=DEFAULT_SEARCH_LIMIT,
driver: AsyncDriver,
query: str,
query_vector: list[float],
group_ids: list[str] | None,
config: EdgeSearchConfig | None,
center_node_uuid: str | None = None,
limit=DEFAULT_SEARCH_LIMIT,
) -> list[EntityEdge]:
if config is None:
return []
@ -175,13 +175,13 @@ async def edge_search(
async def node_search(
driver: AsyncDriver,
query: str,
query_vector: list[float],
group_ids: list[str] | None,
config: NodeSearchConfig | None,
center_node_uuid: str | None = None,
limit=DEFAULT_SEARCH_LIMIT,
driver: AsyncDriver,
query: str,
query_vector: list[float],
group_ids: list[str] | None,
config: NodeSearchConfig | None,
center_node_uuid: str | None = None,
limit=DEFAULT_SEARCH_LIMIT,
) -> list[EntityNode]:
if config is None:
return []
@ -227,12 +227,12 @@ async def node_search(
async def community_search(
driver: AsyncDriver,
query: str,
query_vector: list[float],
group_ids: list[str] | None,
config: CommunitySearchConfig | None,
limit=DEFAULT_SEARCH_LIMIT,
driver: AsyncDriver,
query: str,
query_vector: list[float],
group_ids: list[str] | None,
config: CommunitySearchConfig | None,
limit=DEFAULT_SEARCH_LIMIT,
) -> list[CommunityNode]:
if config is None:
return []

728
poetry.lock generated
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@ -1,4 +1,4 @@
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dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
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filelock = "*"
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build = ["cmake (>=3.20)", "lit"]
tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "types-python-dateutil"
version = "2.9.0.20241003"
@ -3828,4 +4552,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "33c2e2b582a9fc0f6800032d46e95217cf4201f162cce48aa8309e722ba4c2b9"
content-hash = "42066e1789af188d0a10e11ff2d54eb83d6ea7628d4069cfd58ff6529c9aa03f"

4
pyproject.toml
View File

@ -20,7 +20,6 @@ diskcache = "^5.6.3"
openai = "^1.50.2"
tenacity = "<9.0.0"
numpy = ">=1.0.0"
voyageai = "^0.2.3"
[tool.poetry.dev-dependencies]
pytest = "^8.3.3"
@ -41,6 +40,9 @@ langgraph = "^0.2.15"
langchain-anthropic = "^0.1.23"
langsmith = "^0.1.108"
langchain-openai = "^0.1.23"
sentence-transformers = "^3.2.1"
transformers = "^4.45.2"
voyageai = "^0.2.3"
[build-system]
requires = ["poetry-core"]

83
tests/cross_encoder/test_bge_reranker_client.py Normal file
View File

@ -0,0 +1,83 @@
"""
Copyright 2024, Zep Software, Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import pytest
from graphiti_core.cross_encoder.bge_reranker_client import BGERerankerClient
pytestmark = pytest.mark.integration
@pytest.fixture
def client():
return BGERerankerClient()
@pytest.mark.asyncio
@pytest.mark.integration
async def test_rank_basic_functionality(client):
query = 'What is the capital of France?'
passages = [
'Paris is the capital and most populous city of France.',
'London is the capital city of England and the United Kingdom.',
'Berlin is the capital and largest city of Germany.',
]
ranked_passages = await client.rank(query, passages)
# Check if the output is a list of tuples
assert isinstance(ranked_passages, list)
assert all(isinstance(item, tuple) for item in ranked_passages)
# Check if the output has the correct length
assert len(ranked_passages) == len(passages)
# Check if the scores are floats and passages are strings
for passage, score in ranked_passages:
assert isinstance(passage, str)
assert isinstance(score, float)
# Check if the results are sorted in descending order
scores = [score for _, score in ranked_passages]
assert scores == sorted(scores, reverse=True)
@pytest.mark.asyncio
@pytest.mark.integration
async def test_rank_empty_input(client):
query = 'Empty test'
passages = []
ranked_passages = await client.rank(query, passages)
# Check if the output is an empty list
assert ranked_passages == []
@pytest.mark.asyncio
@pytest.mark.integration
async def test_rank_single_passage(client):
query = 'Test query'
passages = ['Single test passage']
ranked_passages = await client.rank(query, passages)
# Check if the output has one item
assert len(ranked_passages) == 1
# Check if the passage is correct and the score is a float
assert ranked_passages[0][0] == passages[0]
assert isinstance(ranked_passages[0][1], float)