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"""OpenAI embeddings file."""
from enum import Enum
from typing import List, Optional
import openai
from tenacity import retry, stop_after_attempt, wait_random_exponential
from gpt_index.embeddings.base import BaseEmbedding
class OpenAIEmbeddingMode(str, Enum):
"""OpenAI embedding mode."""
SIMILARITY_MODE = "similarity"
TEXT_SEARCH_MODE = "text_search"
class OpenAIEmbeddingModelType(str, Enum):
"""OpenAI embedding model type."""
DAVINCI = "davinci"
CURIE = "curie"
BABBAGE = "babbage"
ADA = "ada"
TEXT_EMBED_ADA_002 = "text-embedding-ada-002"
class OpenAIEmbeddingModeModel(str, Enum):
"""OpenAI embedding mode model."""
# davinci
TEXT_SIMILARITY_DAVINCI = "text-similarity-davinci-001"
TEXT_SEARCH_DAVINCI_QUERY = "text-search-davinci-query-001"
TEXT_SEARCH_DAVINCI_DOC = "text-search-davinci-doc-001"
# curie
TEXT_SIMILARITY_CURIE = "text-similarity-curie-001"
TEXT_SEARCH_CURIE_QUERY = "text-search-curie-query-001"
TEXT_SEARCH_CURIE_DOC = "text-search-curie-doc-001"
# babbage
TEXT_SIMILARITY_BABBAGE = "text-similarity-babbage-001"
TEXT_SEARCH_BABBAGE_QUERY = "text-search-babbage-query-001"
TEXT_SEARCH_BABBAGE_DOC = "text-search-babbage-doc-001"
# ada
TEXT_SIMILARITY_ADA = "text-similarity-ada-001"
TEXT_SEARCH_ADA_QUERY = "text-search-ada-query-001"
TEXT_SEARCH_ADA_DOC = "text-search-ada-doc-001"
# text-embedding-ada-002
TEXT_EMBED_ADA_002 = "text-embedding-ada-002"
# convenient shorthand
OAEM = OpenAIEmbeddingMode
OAEMT = OpenAIEmbeddingModelType
OAEMM = OpenAIEmbeddingModeModel
EMBED_MAX_TOKEN_LIMIT = 2048
_QUERY_MODE_MODEL_DICT = {
(OAEM.SIMILARITY_MODE, "davinci"): OAEMM.TEXT_SIMILARITY_DAVINCI,
(OAEM.SIMILARITY_MODE, "curie"): OAEMM.TEXT_SIMILARITY_CURIE,
(OAEM.SIMILARITY_MODE, "babbage"): OAEMM.TEXT_SIMILARITY_BABBAGE,
(OAEM.SIMILARITY_MODE, "ada"): OAEMM.TEXT_SIMILARITY_ADA,
(OAEM.SIMILARITY_MODE, "text-embedding-ada-002"): OAEMM.TEXT_EMBED_ADA_002,
(OAEM.TEXT_SEARCH_MODE, "davinci"): OAEMM.TEXT_SEARCH_DAVINCI_QUERY,
(OAEM.TEXT_SEARCH_MODE, "curie"): OAEMM.TEXT_SEARCH_CURIE_QUERY,
(OAEM.TEXT_SEARCH_MODE, "babbage"): OAEMM.TEXT_SEARCH_BABBAGE_QUERY,
(OAEM.TEXT_SEARCH_MODE, "ada"): OAEMM.TEXT_SEARCH_ADA_QUERY,
(OAEM.TEXT_SEARCH_MODE, "text-embedding-ada-002"): OAEMM.TEXT_EMBED_ADA_002,
}
_TEXT_MODE_MODEL_DICT = {
(OAEM.SIMILARITY_MODE, "davinci"): OAEMM.TEXT_SIMILARITY_DAVINCI,
(OAEM.SIMILARITY_MODE, "curie"): OAEMM.TEXT_SIMILARITY_CURIE,
(OAEM.SIMILARITY_MODE, "babbage"): OAEMM.TEXT_SIMILARITY_BABBAGE,
(OAEM.SIMILARITY_MODE, "ada"): OAEMM.TEXT_SIMILARITY_ADA,
(OAEM.SIMILARITY_MODE, "text-embedding-ada-002"): OAEMM.TEXT_EMBED_ADA_002,
(OAEM.TEXT_SEARCH_MODE, "davinci"): OAEMM.TEXT_SEARCH_DAVINCI_DOC,
(OAEM.TEXT_SEARCH_MODE, "curie"): OAEMM.TEXT_SEARCH_CURIE_DOC,
(OAEM.TEXT_SEARCH_MODE, "babbage"): OAEMM.TEXT_SEARCH_BABBAGE_DOC,
(OAEM.TEXT_SEARCH_MODE, "ada"): OAEMM.TEXT_SEARCH_ADA_DOC,
(OAEM.TEXT_SEARCH_MODE, "text-embedding-ada-002"): OAEMM.TEXT_EMBED_ADA_002,
}
@retry(wait=wait_random_exponential(min=20, max=60), stop=stop_after_attempt(100))
def get_embedding(
text: str,
engine: Optional[str] = None,
) -> List[float]:
"""Get embedding.
NOTE: Copied from OpenAI's embedding utils:
https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
Copied here to avoid importing unnecessary dependencies
like matplotlib, plotly, scipy, sklearn.
"""
text = text.replace("\n", " ")
return openai.Embedding.create(input=[text], engine=engine)["data"][0]["embedding"]
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
async def aget_embedding(text: str, engine: Optional[str] = None) -> List[float]:
"""Asynchronously get embedding.
NOTE: Copied from OpenAI's embedding utils:
https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
Copied here to avoid importing unnecessary dependencies
like matplotlib, plotly, scipy, sklearn.
"""
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
return (await openai.Embedding.acreate(input=[text], engine=engine))["data"][0][
"embedding"
]
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
def get_embeddings(
list_of_text: List[str],
engine: Optional[str] = None,
) -> List[List[float]]:
"""Get embeddings.
NOTE: Copied from OpenAI's embedding utils:
https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
Copied here to avoid importing unnecessary dependencies
like matplotlib, plotly, scipy, sklearn.
"""
assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
# replace newlines, which can negatively affect performance.
list_of_text = [text.replace("\n", " ") for text in list_of_text]
data = openai.Embedding.create(input=list_of_text, engine=engine).data
data = sorted(data, key=lambda x: x["index"]) # maintain the same order as input.
return [d["embedding"] for d in data]
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
async def aget_embeddings(
list_of_text: List[str], engine: Optional[str] = None
) -> List[List[float]]:
"""Asynchronously get embeddings.
NOTE: Copied from OpenAI's embedding utils:
https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
Copied here to avoid importing unnecessary dependencies
like matplotlib, plotly, scipy, sklearn.
"""
assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
# replace newlines, which can negatively affect performance.
list_of_text = [text.replace("\n", " ") for text in list_of_text]
data = (await openai.Embedding.acreate(input=list_of_text, engine=engine)).data
data = sorted(data, key=lambda x: x["index"]) # maintain the same order as input.
return [d["embedding"] for d in data]
class OpenAIEmbedding(BaseEmbedding):
"""OpenAI class for embeddings.
Args:
mode (str): Mode for embedding.
Defaults to OpenAIEmbeddingMode.TEXT_SEARCH_MODE.
Options are:
- OpenAIEmbeddingMode.SIMILARITY_MODE
- OpenAIEmbeddingMode.TEXT_SEARCH_MODE
model (str): Model for embedding.
Defaults to OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002.
Options are:
- OpenAIEmbeddingModelType.DAVINCI
- OpenAIEmbeddingModelType.CURIE
- OpenAIEmbeddingModelType.BABBAGE
- OpenAIEmbeddingModelType.ADA
- OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002
deployment_name (Optional[str]): Optional deployment of model. Defaults to None.
If this value is not None, mode and model will be ignored.
Only available for using AzureOpenAI.
"""
def __init__(
self,
mode: str = OpenAIEmbeddingMode.TEXT_SEARCH_MODE,
model: str = OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002,
deployment_name: Optional[str] = None,
) -> None:
"""Init params."""
super().__init__()
self.mode = OpenAIEmbeddingMode(mode)
self.model = OpenAIEmbeddingModelType(model)
self.deployment_name = deployment_name
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
if self.deployment_name is not None:
engine = self.deployment_name
else:
key = (self.mode, self.model)
if key not in _QUERY_MODE_MODEL_DICT:
raise ValueError(f"Invalid mode, model combination: {key}")
engine = _QUERY_MODE_MODEL_DICT[key]
return get_embedding(query, engine=engine)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
if self.deployment_name is not None:
engine = self.deployment_name
else:
key = (self.mode, self.model)
if key not in _TEXT_MODE_MODEL_DICT:
raise ValueError(f"Invalid mode, model combination: {key}")
engine = _TEXT_MODE_MODEL_DICT[key]
return get_embedding(text, engine=engine)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
if self.deployment_name is not None:
engine = self.deployment_name
else:
key = (self.mode, self.model)
if key not in _TEXT_MODE_MODEL_DICT:
raise ValueError(f"Invalid mode, model combination: {key}")
engine = _TEXT_MODE_MODEL_DICT[key]
return await aget_embedding(text, engine=engine)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings.
By default, this is a wrapper around _get_text_embedding.
Can be overriden for batch queries.
"""
if self.deployment_name is not None:
engine = self.deployment_name
else:
key = (self.mode, self.model)
if key not in _TEXT_MODE_MODEL_DICT:
raise ValueError(f"Invalid mode, model combination: {key}")
engine = _TEXT_MODE_MODEL_DICT[key]
embeddings = get_embeddings(texts, engine=engine)
return embeddings
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
if self.deployment_name is not None:
engine = self.deployment_name
else:
key = (self.mode, self.model)
if key not in _TEXT_MODE_MODEL_DICT:
raise ValueError(f"Invalid mode, model combination: {key}")
engine = _TEXT_MODE_MODEL_DICT[key]
embeddings = await aget_embeddings(texts, engine=engine)
return embeddings
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