70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
from dataclasses import dataclass, field
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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, SecretStr
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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 openai import OpenAI
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class OctoAiEmbeddingConfig(EmbeddingConfig):
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api_key: SecretStr
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model_name: str = Field(default="thenlper/gte-large")
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base_url: str = Field(default="https://text.octoai.run/v1")
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@requires_dependencies(
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["openai", "tiktoken"],
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extras="embed-octoai",
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)
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def get_client(self) -> "OpenAI":
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"""Creates an OpenAI python client to embed elements. Uses the OpenAI SDK."""
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from openai import OpenAI
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return OpenAI(api_key=self.api_key.get_secret_value(), base_url=self.base_url)
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@dataclass
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class OctoAIEmbeddingEncoder(BaseEmbeddingEncoder):
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config: OctoAiEmbeddingConfig
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# Uses the OpenAI SDK
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_exemplary_embedding: Optional[List[float]] = field(init=False, default=None)
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def get_exemplary_embedding(self) -> List[float]:
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return self.embed_query("Q")
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def initialize(self):
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pass
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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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response = client.embeddings.create(input=str(query), model=self.config.model_name)
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return response.data[0].embedding
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def embed_documents(self, elements: List[Element]) -> List[Element]:
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embeddings = [self.embed_query(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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