68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
from dataclasses import dataclass
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from typing import TYPE_CHECKING, List, Optional
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import numpy as np
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from pydantic import Field
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from unstructured.documents.elements import (
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Element,
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)
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from unstructured.embed.interfaces import BaseEmbeddingEncoder, EmbeddingConfig
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from unstructured.utils import requires_dependencies
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if TYPE_CHECKING:
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from langchain_huggingface.embeddings import HuggingFaceEmbeddings
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class HuggingFaceEmbeddingConfig(EmbeddingConfig):
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model_name: Optional[str] = Field(default="sentence-transformers/all-MiniLM-L6-v2")
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model_kwargs: Optional[dict] = Field(default_factory=lambda: {"device": "cpu"})
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encode_kwargs: Optional[dict] = Field(default_factory=lambda: {"normalize_embeddings": False})
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cache_folder: Optional[dict] = Field(default=None)
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@requires_dependencies(
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["langchain_huggingface"],
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extras="embed-huggingface",
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)
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def get_client(self) -> "HuggingFaceEmbeddings":
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"""Creates a langchain Huggingface python client to embed elements."""
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from langchain_huggingface.embeddings import HuggingFaceEmbeddings
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client = HuggingFaceEmbeddings(**self.dict())
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return client
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@dataclass
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class HuggingFaceEmbeddingEncoder(BaseEmbeddingEncoder):
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config: HuggingFaceEmbeddingConfig
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def get_exemplary_embedding(self) -> List[float]:
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return self.embed_query(query="Q")
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def num_of_dimensions(self):
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exemplary_embedding = self.get_exemplary_embedding()
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return np.shape(exemplary_embedding)
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def is_unit_vector(self):
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exemplary_embedding = self.get_exemplary_embedding()
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return np.isclose(np.linalg.norm(exemplary_embedding), 1.0)
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def embed_query(self, query):
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client = self.config.get_client()
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return client.embed_query(str(query))
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def embed_documents(self, elements: List[Element]) -> List[Element]:
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client = self.config.get_client()
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embeddings = client.embed_documents([str(e) for e in elements])
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elements_with_embeddings = self._add_embeddings_to_elements(elements, embeddings)
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return elements_with_embeddings
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def _add_embeddings_to_elements(self, elements, embeddings) -> List[Element]:
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assert len(elements) == len(embeddings)
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elements_w_embedding = []
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for i, element in enumerate(elements):
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element.embeddings = embeddings[i]
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elements_w_embedding.append(element)
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return elements
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