513cfbf7b2
* Refactor imports * Fix: Remove duplicate sentence-transformers dependency from dev requirements * Refactor: Update optional import patterns across various modules for better type checking and error handling * Update CONTRIBUTING.md Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com> --------- Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com>
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
"""
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Copyright 2024, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from collections.abc import Iterable
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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import voyageai
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else:
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try:
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import voyageai
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except ImportError:
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raise ImportError(
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'voyageai is required for VoyageAIEmbedderClient. '
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'Install it with: pip install graphiti-core[voyageai]'
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) from None
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from pydantic import Field
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from .client import EmbedderClient, EmbedderConfig
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DEFAULT_EMBEDDING_MODEL = 'voyage-3'
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class VoyageAIEmbedderConfig(EmbedderConfig):
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embedding_model: str = Field(default=DEFAULT_EMBEDDING_MODEL)
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api_key: str | None = None
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class VoyageAIEmbedder(EmbedderClient):
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"""
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VoyageAI Embedder Client
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"""
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def __init__(self, config: VoyageAIEmbedderConfig | None = None):
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if config is None:
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config = VoyageAIEmbedderConfig()
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self.config = config
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self.client = voyageai.AsyncClient(api_key=config.api_key) # type: ignore[reportUnknownMemberType]
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async def create(
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self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
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) -> list[float]:
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if isinstance(input_data, str):
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input_list = [input_data]
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elif isinstance(input_data, list):
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input_list = [str(i) for i in input_data if i]
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else:
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input_list = [str(i) for i in input_data if i is not None]
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input_list = [i for i in input_list if i]
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if len(input_list) == 0:
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return []
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result = await self.client.embed(input_list, model=self.config.embedding_model)
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return [float(x) for x in result.embeddings[0][: self.config.embedding_dim]]
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async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
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result = await self.client.embed(input_data_list, model=self.config.embedding_model)
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return [
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[float(x) for x in embedding[: self.config.embedding_dim]]
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for embedding in result.embeddings
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]
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