修改为东南天坐标系

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

@@ -0,0 +1,67 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List
+
+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 langchain_openai.embeddings import OpenAIEmbeddings
+
+
+class OpenAIEmbeddingConfig(EmbeddingConfig):
+    api_key: SecretStr
+    model_name: str = Field(default="text-embedding-ada-002")
+
+    @requires_dependencies(["langchain_openai"], extras="openai")
+    def get_client(self) -> "OpenAIEmbeddings":
+        """Creates a langchain OpenAI python client to embed elements."""
+        from langchain_openai import OpenAIEmbeddings
+
+        openai_client = OpenAIEmbeddings(
+            openai_api_key=self.api_key.get_secret_value(),
+            model=self.model_name,  # type:ignore
+        )
+        return openai_client
+
+
+@dataclass
+class OpenAIEmbeddingEncoder(BaseEmbeddingEncoder):
+    config: OpenAIEmbeddingConfig
+
+    def get_exemplary_embedding(self) -> List[float]:
+        return self.embed_query(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()
+        return client.embed_query(str(query))
+
+    def embed_documents(self, elements: List[Element]) -> List[Element]:
+        client = self.config.get_client()
+        embeddings = client.embed_documents([str(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