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