feat: interfaces for async embeddings, implement async openai (#6563)

Since it seems like #6111 will be blocked for a bit, I've forked
@tyree731's fork and implemented the requested changes.

This change adds support to the base Embeddings class for two methods,
aembed_query and aembed_documents, those two methods supporting async
equivalents of embed_query and
embed_documents respectively. This ever so slightly rounds out async
support within langchain, with an initial implementation of this
functionality being implemented for openai.

Implements https://github.com/hwchase17/langchain/issues/6109

---------

Co-authored-by: Stephen Tyree <tyree731@gmail.com>
This commit is contained in:
Brendan Graham 2023-06-21 23:16:33 -07:00 committed by GitHub
parent ca24dc2d5f
commit d718f3b6d0
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3 changed files with 185 additions and 0 deletions

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@ -13,3 +13,11 @@ class Embeddings(ABC):
@abstractmethod
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
raise NotImplementedError
async def aembed_query(self, text: str) -> List[float]:
"""Embed query text."""
raise NotImplementedError

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@ -18,6 +18,7 @@ from typing import (
import numpy as np
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
AsyncRetrying,
before_sleep_log,
retry,
retry_if_exception_type,
@ -53,6 +54,38 @@ def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], Any
)
def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any:
import openai
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
async_retrying = AsyncRetrying(
reraise=True,
stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError)
| retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def wrap(func: Callable) -> Callable:
async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
async for _ in async_retrying:
return await func(*args, **kwargs)
raise AssertionError("this is unreachable")
return wrapped_f
return wrap
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@ -64,6 +97,16 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
return _embed_with_retry(**kwargs)
async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
"""Use tenacity to retry the embedding call."""
@_async_retry_decorator(embeddings)
async def _async_embed_with_retry(**kwargs: Any) -> Any:
return await embeddings.client.acreate(**kwargs)
return await _async_embed_with_retry(**kwargs)
class OpenAIEmbeddings(BaseModel, Embeddings):
"""Wrapper around OpenAI embedding models.
@ -269,6 +312,70 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
return embeddings
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
async def _aget_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please install it with `pip install tiktoken`."
)
tokens = []
indices = []
encoding = tiktoken.model.encoding_for_model(self.model)
for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
token = encoding.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
for j in range(0, len(token), self.embedding_ctx_length):
tokens += [token[j : j + self.embedding_ctx_length]]
indices += [i]
batched_embeddings = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = await async_embed_with_retry(
self,
input=tokens[i : i + _chunk_size],
**self._invocation_params,
)
batched_embeddings += [r["embedding"] for r in response["data"]]
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
average = (
await async_embed_with_retry(
self,
input="",
**self._invocation_params,
)
)["data"][0]["embedding"]
else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
def _embedding_func(self, text: str, *, engine: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint."""
# handle large input text
@ -287,6 +394,24 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
"data"
][0]["embedding"]
async def _aembedding_func(self, text: str, *, engine: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint."""
# handle large input text
if len(text) > self.embedding_ctx_length:
return (await self._aget_len_safe_embeddings([text], engine=engine))[0]
else:
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
return (
await async_embed_with_retry(
self,
input=[text],
**self._invocation_params,
)
)["data"][0]["embedding"]
def embed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
@ -304,6 +429,23 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
# than the maximum context and use length-safe embedding function.
return self._get_len_safe_embeddings(texts, engine=self.deployment)
async def aembed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
return await self._aget_len_safe_embeddings(texts, engine=self.deployment)
def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
@ -315,3 +457,15 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
"""
embedding = self._embedding_func(text, engine=self.deployment)
return embedding
async def aembed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint async for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embedding = await self._aembedding_func(text, engine=self.deployment)
return embedding

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@ -1,6 +1,7 @@
"""Test openai embeddings."""
import numpy as np
import openai
import pytest
from langchain.embeddings.openai import OpenAIEmbeddings
@ -26,6 +27,19 @@ def test_openai_embedding_documents_multiple() -> None:
assert len(output[2]) == 1536
@pytest.mark.asyncio
async def test_openai_embedding_documents_async_multiple() -> None:
"""Test openai embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = OpenAIEmbeddings(chunk_size=2)
embedding.embedding_ctx_length = 8191
output = await embedding.aembed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1536
assert len(output[1]) == 1536
assert len(output[2]) == 1536
def test_openai_embedding_query() -> None:
"""Test openai embeddings."""
document = "foo bar"
@ -34,6 +48,15 @@ def test_openai_embedding_query() -> None:
assert len(output) == 1536
@pytest.mark.asyncio
async def test_openai_embedding_async_query() -> None:
"""Test openai embeddings."""
document = "foo bar"
embedding = OpenAIEmbeddings()
output = await embedding.aembed_query(document)
assert len(output) == 1536
def test_openai_embedding_with_empty_string() -> None:
"""Test openai embeddings with empty string."""
document = ["", "abc"]