68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
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
|