修改为东南天坐标系

backend_service/venv/lib/python3.13/site-packages/unstructured/embed/octoai.py

@@ -0,0 +1,69 @@
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, List, Optional
+
+import numpy as np
+from pydantic import Field, SecretStr
+
+from unstructured.documents.elements import (
+    Element,
+)
+from unstructured.embed.interfaces import BaseEmbeddingEncoder, EmbeddingConfig
+from unstructured.utils import requires_dependencies
+
+if TYPE_CHECKING:
+    from openai import OpenAI
+
+
+class OctoAiEmbeddingConfig(EmbeddingConfig):
+    api_key: SecretStr
+    model_name: str = Field(default="thenlper/gte-large")
+    base_url: str = Field(default="https://text.octoai.run/v1")
+
+    @requires_dependencies(
+        ["openai", "tiktoken"],
+        extras="embed-octoai",
+    )
+    def get_client(self) -> "OpenAI":
+        """Creates an OpenAI python client to embed elements. Uses the OpenAI SDK."""
+        from openai import OpenAI
+
+        return OpenAI(api_key=self.api_key.get_secret_value(), base_url=self.base_url)
+
+
+@dataclass
+class OctoAIEmbeddingEncoder(BaseEmbeddingEncoder):
+    config: OctoAiEmbeddingConfig
+    # Uses the OpenAI SDK
+    _exemplary_embedding: Optional[List[float]] = field(init=False, default=None)
+
+    def get_exemplary_embedding(self) -> List[float]:
+        return self.embed_query("Q")
+
+    def initialize(self):
+        pass
+
+    def num_of_dimensions(self):
+        exemplary_embedding = self.get_exemplary_embedding()
+        return np.shape(exemplary_embedding)
+
+    def is_unit_vector(self):
+        exemplary_embedding = self.get_exemplary_embedding()
+        return np.isclose(np.linalg.norm(exemplary_embedding), 1.0)
+
+    def embed_query(self, query):
+        client = self.config.get_client()
+        response = client.embeddings.create(input=str(query), model=self.config.model_name)
+        return response.data[0].embedding
+
+    def embed_documents(self, elements: List[Element]) -> List[Element]:
+        embeddings = [self.embed_query(e) for e in elements]
+        elements_with_embeddings = self._add_embeddings_to_elements(elements, embeddings)
+        return elements_with_embeddings
+
+    def _add_embeddings_to_elements(self, elements, embeddings) -> List[Element]:
+        assert len(elements) == len(embeddings)
+        elements_w_embedding = []
+        for i, element in enumerate(elements):
+            element.embeddings = embeddings[i]
+            elements_w_embedding.append(element)
+        return elements