Kye
1 year ago
parent
69401042fe
commit
191bce15bd
@ -0,0 +1,512 @@
|
|||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import warnings
|
||||||
|
from typing import (
|
||||||
|
Any,
|
||||||
|
Callable,
|
||||||
|
Dict,
|
||||||
|
List,
|
||||||
|
Literal,
|
||||||
|
Optional,
|
||||||
|
Sequence,
|
||||||
|
Set,
|
||||||
|
Tuple,
|
||||||
|
Union,
|
||||||
|
)
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from pydantic import BaseModel, Extra, Field, root_validator
|
||||||
|
from tenacity import (
|
||||||
|
AsyncRetrying,
|
||||||
|
before_sleep_log,
|
||||||
|
retry,
|
||||||
|
retry_if_exception_type,
|
||||||
|
stop_after_attempt,
|
||||||
|
wait_exponential,
|
||||||
|
)
|
||||||
|
|
||||||
|
from langchain.embeddings.base import Embeddings
|
||||||
|
from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
def _create_retry_decorator(embeddings: OpenAIEmbeddings) -> Callable[[Any], 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
|
||||||
|
return retry(
|
||||||
|
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 _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
|
||||||
|
|
||||||
|
|
||||||
|
# https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
|
||||||
|
def _check_response(response: dict) -> dict:
|
||||||
|
if any(len(d["embedding"]) == 1 for d in response["data"]):
|
||||||
|
import openai
|
||||||
|
|
||||||
|
raise openai.error.APIError("OpenAI API returned an empty embedding")
|
||||||
|
return response
|
||||||
|
|
||||||
|
|
||||||
|
def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any:
|
||||||
|
"""Use tenacity to retry the embedding call."""
|
||||||
|
retry_decorator = _create_retry_decorator(embeddings)
|
||||||
|
|
||||||
|
@retry_decorator
|
||||||
|
def _embed_with_retry(**kwargs: Any) -> Any:
|
||||||
|
response = embeddings.client.create(**kwargs)
|
||||||
|
return _check_response(response)
|
||||||
|
|
||||||
|
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:
|
||||||
|
response = await embeddings.client.acreate(**kwargs)
|
||||||
|
return _check_response(response)
|
||||||
|
|
||||||
|
return await _async_embed_with_retry(**kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
class OpenAIEmbeddings(BaseModel, Embeddings):
|
||||||
|
"""OpenAI embedding models.
|
||||||
|
|
||||||
|
To use, you should have the ``openai`` python package installed, and the
|
||||||
|
environment variable ``OPENAI_API_KEY`` set with your API key or pass it
|
||||||
|
as a named parameter to the constructor.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
from langchain.embeddings import OpenAIEmbeddings
|
||||||
|
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
|
||||||
|
|
||||||
|
In order to use the library with Microsoft Azure endpoints, you need to set
|
||||||
|
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
|
||||||
|
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
|
||||||
|
the properties of your endpoint.
|
||||||
|
In addition, the deployment name must be passed as the model parameter.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
.. code-block:: python
|
||||||
|
|
||||||
|
import os
|
||||||
|
os.environ["OPENAI_API_TYPE"] = "azure"
|
||||||
|
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
|
||||||
|
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
|
||||||
|
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
|
||||||
|
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
|
||||||
|
|
||||||
|
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||||
|
embeddings = OpenAIEmbeddings(
|
||||||
|
deployment="your-embeddings-deployment-name",
|
||||||
|
model="your-embeddings-model-name",
|
||||||
|
openai_api_base="https://your-endpoint.openai.azure.com/",
|
||||||
|
openai_api_type="azure",
|
||||||
|
)
|
||||||
|
text = "This is a test query."
|
||||||
|
query_result = embeddings.embed_query(text)
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
client: Any #: :meta private:
|
||||||
|
model: str = "text-embedding-ada-002"
|
||||||
|
deployment: str = model # to support Azure OpenAI Service custom deployment names
|
||||||
|
openai_api_version: Optional[str] = None
|
||||||
|
# to support Azure OpenAI Service custom endpoints
|
||||||
|
openai_api_base: Optional[str] = None
|
||||||
|
# to support Azure OpenAI Service custom endpoints
|
||||||
|
openai_api_type: Optional[str] = None
|
||||||
|
# to support explicit proxy for OpenAI
|
||||||
|
openai_proxy: Optional[str] = None
|
||||||
|
embedding_ctx_length: int = 8191
|
||||||
|
"""The maximum number of tokens to embed at once."""
|
||||||
|
openai_api_key: Optional[str] = None
|
||||||
|
openai_organization: Optional[str] = None
|
||||||
|
allowed_special: Union[Literal["all"], Set[str]] = set()
|
||||||
|
disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
|
||||||
|
chunk_size: int = 1000
|
||||||
|
"""Maximum number of texts to embed in each batch"""
|
||||||
|
max_retries: int = 6
|
||||||
|
"""Maximum number of retries to make when generating."""
|
||||||
|
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
|
||||||
|
"""Timeout in seconds for the OpenAPI request."""
|
||||||
|
headers: Any = None
|
||||||
|
tiktoken_model_name: Optional[str] = None
|
||||||
|
"""The model name to pass to tiktoken when using this class.
|
||||||
|
Tiktoken is used to count the number of tokens in documents to constrain
|
||||||
|
them to be under a certain limit. By default, when set to None, this will
|
||||||
|
be the same as the embedding model name. However, there are some cases
|
||||||
|
where you may want to use this Embedding class with a model name not
|
||||||
|
supported by tiktoken. This can include when using Azure embeddings or
|
||||||
|
when using one of the many model providers that expose an OpenAI-like
|
||||||
|
API but with different models. In those cases, in order to avoid erroring
|
||||||
|
when tiktoken is called, you can specify a model name to use here."""
|
||||||
|
show_progress_bar: bool = False
|
||||||
|
"""Whether to show a progress bar when embedding."""
|
||||||
|
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
|
||||||
|
"""Holds any model parameters valid for `create` call not explicitly specified."""
|
||||||
|
|
||||||
|
class Config:
|
||||||
|
"""Configuration for this pydantic object."""
|
||||||
|
|
||||||
|
extra = Extra.forbid
|
||||||
|
|
||||||
|
@root_validator(pre=True)
|
||||||
|
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
|
||||||
|
"""Build extra kwargs from additional params that were passed in."""
|
||||||
|
all_required_field_names = get_pydantic_field_names(cls)
|
||||||
|
extra = values.get("model_kwargs", {})
|
||||||
|
for field_name in list(values):
|
||||||
|
if field_name in extra:
|
||||||
|
raise ValueError(f"Found {field_name} supplied twice.")
|
||||||
|
if field_name not in all_required_field_names:
|
||||||
|
warnings.warn(
|
||||||
|
f"""WARNING! {field_name} is not default parameter.
|
||||||
|
{field_name} was transferred to model_kwargs.
|
||||||
|
Please confirm that {field_name} is what you intended."""
|
||||||
|
)
|
||||||
|
extra[field_name] = values.pop(field_name)
|
||||||
|
|
||||||
|
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
|
||||||
|
if invalid_model_kwargs:
|
||||||
|
raise ValueError(
|
||||||
|
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
|
||||||
|
f"Instead they were passed in as part of `model_kwargs` parameter."
|
||||||
|
)
|
||||||
|
|
||||||
|
values["model_kwargs"] = extra
|
||||||
|
return values
|
||||||
|
|
||||||
|
@root_validator()
|
||||||
|
def validate_environment(cls, values: Dict) -> Dict:
|
||||||
|
"""Validate that api key and python package exists in environment."""
|
||||||
|
values["openai_api_key"] = get_from_dict_or_env(
|
||||||
|
values, "openai_api_key", "OPENAI_API_KEY"
|
||||||
|
)
|
||||||
|
values["openai_api_base"] = get_from_dict_or_env(
|
||||||
|
values,
|
||||||
|
"openai_api_base",
|
||||||
|
"OPENAI_API_BASE",
|
||||||
|
default="",
|
||||||
|
)
|
||||||
|
values["openai_api_type"] = get_from_dict_or_env(
|
||||||
|
values,
|
||||||
|
"openai_api_type",
|
||||||
|
"OPENAI_API_TYPE",
|
||||||
|
default="",
|
||||||
|
)
|
||||||
|
values["openai_proxy"] = get_from_dict_or_env(
|
||||||
|
values,
|
||||||
|
"openai_proxy",
|
||||||
|
"OPENAI_PROXY",
|
||||||
|
default="",
|
||||||
|
)
|
||||||
|
if values["openai_api_type"] in ("azure", "azure_ad", "azuread"):
|
||||||
|
default_api_version = "2022-12-01"
|
||||||
|
else:
|
||||||
|
default_api_version = ""
|
||||||
|
values["openai_api_version"] = get_from_dict_or_env(
|
||||||
|
values,
|
||||||
|
"openai_api_version",
|
||||||
|
"OPENAI_API_VERSION",
|
||||||
|
default=default_api_version,
|
||||||
|
)
|
||||||
|
values["openai_organization"] = get_from_dict_or_env(
|
||||||
|
values,
|
||||||
|
"openai_organization",
|
||||||
|
"OPENAI_ORGANIZATION",
|
||||||
|
default="",
|
||||||
|
)
|
||||||
|
try:
|
||||||
|
import openai
|
||||||
|
|
||||||
|
values["client"] = openai.Embedding
|
||||||
|
except ImportError:
|
||||||
|
raise ImportError(
|
||||||
|
"Could not import openai python package. "
|
||||||
|
"Please install it with `pip install openai`."
|
||||||
|
)
|
||||||
|
return values
|
||||||
|
|
||||||
|
@property
|
||||||
|
def _invocation_params(self) -> Dict:
|
||||||
|
openai_args = {
|
||||||
|
"model": self.model,
|
||||||
|
"request_timeout": self.request_timeout,
|
||||||
|
"headers": self.headers,
|
||||||
|
"api_key": self.openai_api_key,
|
||||||
|
"organization": self.openai_organization,
|
||||||
|
"api_base": self.openai_api_base,
|
||||||
|
"api_type": self.openai_api_type,
|
||||||
|
"api_version": self.openai_api_version,
|
||||||
|
**self.model_kwargs,
|
||||||
|
}
|
||||||
|
if self.openai_api_type in ("azure", "azure_ad", "azuread"):
|
||||||
|
openai_args["engine"] = self.deployment
|
||||||
|
if self.openai_proxy:
|
||||||
|
import openai
|
||||||
|
|
||||||
|
openai.proxy = {
|
||||||
|
"http": self.openai_proxy,
|
||||||
|
"https": self.openai_proxy,
|
||||||
|
} # type: ignore[assignment] # noqa: E501
|
||||||
|
return openai_args
|
||||||
|
|
||||||
|
# please refer to
|
||||||
|
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
|
||||||
|
def _get_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 = []
|
||||||
|
model_name = self.tiktoken_model_name or self.model
|
||||||
|
try:
|
||||||
|
encoding = tiktoken.encoding_for_model(model_name)
|
||||||
|
except KeyError:
|
||||||
|
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||||
|
model = "cl100k_base"
|
||||||
|
encoding = tiktoken.get_encoding(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.append(token[j : j + self.embedding_ctx_length])
|
||||||
|
indices.append(i)
|
||||||
|
|
||||||
|
batched_embeddings: List[List[float]] = []
|
||||||
|
_chunk_size = chunk_size or self.chunk_size
|
||||||
|
|
||||||
|
if self.show_progress_bar:
|
||||||
|
try:
|
||||||
|
import tqdm
|
||||||
|
|
||||||
|
_iter = tqdm.tqdm(range(0, len(tokens), _chunk_size))
|
||||||
|
except ImportError:
|
||||||
|
_iter = range(0, len(tokens), _chunk_size)
|
||||||
|
else:
|
||||||
|
_iter = range(0, len(tokens), _chunk_size)
|
||||||
|
|
||||||
|
for i in _iter:
|
||||||
|
response = embed_with_retry(
|
||||||
|
self,
|
||||||
|
input=tokens[i : i + _chunk_size],
|
||||||
|
**self._invocation_params,
|
||||||
|
)
|
||||||
|
batched_embeddings.extend(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 = 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
|
||||||
|
|
||||||
|
# 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 = []
|
||||||
|
model_name = self.tiktoken_model_name or self.model
|
||||||
|
try:
|
||||||
|
encoding = tiktoken.encoding_for_model(model_name)
|
||||||
|
except KeyError:
|
||||||
|
logger.warning("Warning: model not found. Using cl100k_base encoding.")
|
||||||
|
model = "cl100k_base"
|
||||||
|
encoding = tiktoken.get_encoding(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.append(token[j : j + self.embedding_ctx_length])
|
||||||
|
indices.append(i)
|
||||||
|
|
||||||
|
batched_embeddings: List[List[float]] = []
|
||||||
|
_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.extend(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 embed_documents(
|
||||||
|
self, texts: List[str], chunk_size: Optional[int] = 0
|
||||||
|
) -> List[List[float]]:
|
||||||
|
"""Call out to OpenAI's embedding endpoint 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 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.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
text: The text to embed.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
Embedding for the text.
|
||||||
|
"""
|
||||||
|
return self.embed_documents([text])[0]
|
||||||
|
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
embeddings = await self.aembed_documents([text])
|
||||||
|
return embeddings[0]
|
||||||
Loading…
Reference in new issue