import logging
import re
import string
import threading
import warnings
from concurrent.futures import ThreadPoolExecutor, wait
from enum import Enum, auto
from typing import Any, Dict, List, Literal, Optional, Tuple, Type
from google.api_core.exceptions import (
Aborted,
DeadlineExceeded,
InternalServerError,
InvalidArgument,
ResourceExhausted,
ServiceUnavailable,
)
from google.cloud.aiplatform import telemetry
from langchain_core._api.deprecation import deprecated
from langchain_core.embeddings import Embeddings
from langchain_core.language_models.llms import create_base_retry_decorator
from pydantic import ConfigDict, model_validator
from typing_extensions import Self
from vertexai.generative_models._generative_models import ( # type: ignore[import-untyped]
SafetySettingsType as SafetySettingsType,
)
from vertexai.language_models import ( # type: ignore
TextEmbeddingInput,
TextEmbeddingModel,
)
from vertexai.vision_models import ( # type: ignore
Image,
MultiModalEmbeddingModel,
MultiModalEmbeddingResponse,
)
from langchain_google_vertexai._base import _VertexAICommon
from langchain_google_vertexai._image_utils import ImageBytesLoader
from langchain_google_vertexai._utils import get_user_agent
logger = logging.getLogger(__name__)
_MAX_TOKENS_PER_BATCH = 20000
_MAX_BATCH_SIZE = 250
_MIN_BATCH_SIZE = 5
[docs]
class GoogleEmbeddingModelType(str, Enum):
TEXT = auto()
MULTIMODAL = auto()
@classmethod
def _missing_(cls, value: Any) -> Optional["GoogleEmbeddingModelType"]:
if value.lower().startswith("text"):
return GoogleEmbeddingModelType.TEXT
if "multimodalembedding" in value.lower():
return GoogleEmbeddingModelType.MULTIMODAL
return None
[docs]
class GoogleEmbeddingModelVersion(str, Enum):
EMBEDDINGS_JUNE_2023 = auto()
EMBEDDINGS_NOV_2023 = auto()
EMBEDDINGS_DEC_2023 = auto()
EMBEDDINGS_MAY_2024 = auto()
@classmethod
def _missing_(cls, value: Any) -> "GoogleEmbeddingModelVersion":
if "textembedding-gecko@001" in value.lower():
return GoogleEmbeddingModelVersion.EMBEDDINGS_JUNE_2023
if (
"textembedding-gecko@002" in value.lower()
or "textembedding-gecko-multilingual@001" in value.lower()
):
return GoogleEmbeddingModelVersion.EMBEDDINGS_NOV_2023
if "textembedding-gecko@003" in value.lower():
return GoogleEmbeddingModelVersion.EMBEDDINGS_DEC_2023
if (
"text-embedding-004" in value.lower()
or "text-multilingual-embedding-002" in value.lower()
or "text-embedding-preview-0409" in value.lower()
or "text-multilingual-embedding-preview-0409" in value.lower()
):
return GoogleEmbeddingModelVersion.EMBEDDINGS_MAY_2024
return GoogleEmbeddingModelVersion.EMBEDDINGS_JUNE_2023
@property
def task_type_supported(self) -> bool:
"""
Checks if the model generation supports task type.
"""
return self != GoogleEmbeddingModelVersion.EMBEDDINGS_JUNE_2023
@property
def output_dimensionality_supported(self) -> bool:
"""
Checks if the model generation supports output dimensionality.
"""
return self == GoogleEmbeddingModelVersion.EMBEDDINGS_MAY_2024
[docs]
class VertexAIEmbeddings(_VertexAICommon, Embeddings):
"""Google Cloud VertexAI embedding models."""
# Instance context
instance: Dict[str, Any] = {} #: :meta private:
model_config = ConfigDict(
extra="forbid",
protected_namespaces=(),
)
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validates that the python package exists in environment."""
values = {
"project": self.project,
"location": self.location,
"credentials": self.credentials,
"api_transport": self.api_transport,
"api_endpoint": self.api_endpoint,
"default_metadata": self.default_metadata,
}
self._init_vertexai(values)
_, user_agent = get_user_agent(f"{self.__class__.__name__}_{self.model_name}")
with telemetry.tool_context_manager(user_agent):
if (
GoogleEmbeddingModelType(self.model_name)
== GoogleEmbeddingModelType.MULTIMODAL
):
self.client = MultiModalEmbeddingModel.from_pretrained(self.model_name)
else:
self.client = TextEmbeddingModel.from_pretrained(self.model_name)
return self
def __init__(
self,
model_name: Optional[str] = None,
project: Optional[str] = None,
location: str = "us-central1",
request_parallelism: int = 5,
max_retries: int = 6,
credentials: Optional[Any] = None,
**kwargs: Any,
):
"""Initialize the sentence_transformer."""
if model_name:
kwargs["model_name"] = model_name
super().__init__(
project=project,
location=location,
credentials=credentials,
request_parallelism=request_parallelism,
max_retries=max_retries,
**kwargs,
)
self.instance["max_batch_size"] = kwargs.get("max_batch_size", _MAX_BATCH_SIZE)
self.instance["batch_size"] = self.instance["max_batch_size"]
self.instance["min_batch_size"] = kwargs.get("min_batch_size", _MIN_BATCH_SIZE)
self.instance["min_good_batch_size"] = self.instance["min_batch_size"]
self.instance["lock"] = threading.Lock()
self.instance["batch_size_validated"] = False
self.instance["task_executor"] = ThreadPoolExecutor(
max_workers=request_parallelism
)
retry_errors: List[Type[BaseException]] = [
ResourceExhausted,
ServiceUnavailable,
Aborted,
DeadlineExceeded,
InternalServerError,
]
retry_decorator = create_base_retry_decorator(
error_types=retry_errors, max_retries=self.max_retries
)
self.instance["get_embeddings_with_retry"] = retry_decorator(
self.client.get_embeddings
)
@property
def model_type(self) -> str:
return GoogleEmbeddingModelType(self.model_name)
@property
def model_version(self) -> GoogleEmbeddingModelVersion:
return GoogleEmbeddingModelVersion(self.model_name)
@staticmethod
def _split_by_punctuation(text: str) -> List[str]:
"""Splits a string by punctuation and whitespace characters."""
split_by = string.punctuation + "\t\n "
pattern = f"([{split_by}])"
# Using re.split to split the text based on the pattern
return [segment for segment in re.split(pattern, text) if segment]
@staticmethod
def _prepare_batches(texts: List[str], batch_size: int) -> List[List[str]]:
"""Splits texts in batches based on current maximum batch size
and maximum tokens per request.
"""
text_index = 0
texts_len = len(texts)
batch_token_len = 0
batches: List[List[str]] = []
current_batch: List[str] = []
if texts_len == 0:
return []
while text_index < texts_len:
current_text = texts[text_index]
# Number of tokens per a text is conservatively estimated
# as 2 times number of words, punctuation and whitespace characters.
# Using `count_tokens` API will make batching too expensive.
# Utilizing a tokenizer, would add a dependency that would not
# necessarily be reused by the application using this class.
current_text_token_cnt = (
len(VertexAIEmbeddings._split_by_punctuation(current_text)) * 2
)
end_of_batch = False
if current_text_token_cnt > _MAX_TOKENS_PER_BATCH:
# Current text is too big even for a single batch.
# Such request will fail, but we still make a batch
# so that the app can get the error from the API.
if len(current_batch) > 0:
# Adding current batch if not empty.
batches.append(current_batch)
current_batch = [current_text]
text_index += 1
end_of_batch = True
elif (
batch_token_len + current_text_token_cnt > _MAX_TOKENS_PER_BATCH
or len(current_batch) == batch_size
):
end_of_batch = True
else:
if text_index == texts_len - 1:
# Last element - even though the batch may be not big,
# we still need to make it.
end_of_batch = True
batch_token_len += current_text_token_cnt
current_batch.append(current_text)
text_index += 1
if end_of_batch:
batches.append(current_batch)
current_batch = []
batch_token_len = 0
return batches
def _get_embeddings_with_retry(
self,
texts: List[str],
embeddings_type: Optional[str] = None,
dimensions: Optional[int] = None,
) -> List[List[float]]:
"""Makes a Vertex AI model request with retry logic."""
with telemetry.tool_context_manager(self._user_agent):
if self.model_type == GoogleEmbeddingModelType.MULTIMODAL:
return self._get_multimodal_embeddings_with_retry(texts, dimensions)
return self._get_text_embeddings_with_retry(
texts, embeddings_type=embeddings_type, output_dimensionality=dimensions
)
def _get_multimodal_embeddings_with_retry(
self, texts: List[str], dimensions: Optional[int] = None
) -> List[List[float]]:
tasks = []
for text in texts:
tasks.append(
self.instance["task_executor"].submit(
self.instance["get_embeddings_with_retry"],
contextual_text=text,
dimension=dimensions,
)
)
if len(tasks) > 0:
wait(tasks)
embeddings = [task.result().text_embedding for task in tasks]
return embeddings
def _get_text_embeddings_with_retry(
self,
texts: List[str],
embeddings_type: Optional[str] = None,
output_dimensionality: Optional[int] = None,
) -> List[List[float]]:
"""Makes a Vertex AI model request with retry logic."""
if embeddings_type and self.model_version.task_type_supported:
requests = [
TextEmbeddingInput(text=t, task_type=embeddings_type) for t in texts
]
else:
requests = texts
kwargs = {}
if output_dimensionality and self.model_version.output_dimensionality_supported:
kwargs["output_dimensionality"] = output_dimensionality
embeddings = self.instance["get_embeddings_with_retry"](requests, **kwargs)
return [embedding.values for embedding in embeddings]
def _prepare_and_validate_batches(
self, texts: List[str], embeddings_type: Optional[str] = None
) -> Tuple[List[List[float]], List[List[str]]]:
"""Prepares text batches with one-time validation of batch size.
Batch size varies between GCP regions and individual project quotas.
# Returns embeddings of the first text batch that went through,
# and text batches for the rest of the texts.
"""
batches = VertexAIEmbeddings._prepare_batches(
texts, self.instance["batch_size"]
)
# If batch size if less or equal to one that went through before,
# then keep batches as they are.
if len(batches[0]) <= self.instance["min_good_batch_size"]:
return [], batches
with self.instance["lock"]:
# If largest possible batch size was validated
# while waiting for the lock, then check for rebuilding
# our batches, and return.
if self.instance["batch_size_validated"]:
if len(batches[0]) <= self.instance["batch_size"]:
return [], batches
else:
return [], VertexAIEmbeddings._prepare_batches(
texts, self.instance["batch_size"]
)
# Figure out the largest possible batch size by trying to push
# batches and lowering their size in half after every failure.
first_batch = batches[0]
first_result = []
had_failure = False
while True:
try:
first_result = self._get_embeddings_with_retry(
first_batch, embeddings_type
)
break
except InvalidArgument:
had_failure = True
first_batch_len = len(first_batch)
if first_batch_len == self.instance["min_batch_size"]:
raise
first_batch_len = max(
self.instance["min_batch_size"], int(first_batch_len / 2)
)
first_batch = first_batch[:first_batch_len]
first_batch_len = len(first_batch)
self.instance["min_good_batch_size"] = max(
self.instance["min_good_batch_size"], first_batch_len
)
# If had a failure and recovered
# or went through with the max size, then it's a legit batch size.
if had_failure or first_batch_len == self.instance["max_batch_size"]:
self.instance["batch_size"] = first_batch_len
self.instance["batch_size_validated"] = True
# If batch size was updated,
# rebuild batches with the new batch size
# (texts that went through are excluded here).
if first_batch_len != self.instance["max_batch_size"]:
batches = VertexAIEmbeddings._prepare_batches(
texts[first_batch_len:], self.instance["batch_size"]
)
else:
batches = batches[1:]
else:
# Still figuring out max batch size.
batches = batches[1:]
# Returning embeddings of the first text batch that went through,
# and text batches for the rest of texts.
return first_result, batches
[docs]
def embed(
self,
texts: List[str],
batch_size: int = 0,
embeddings_task_type: Optional[
Literal[
"RETRIEVAL_QUERY",
"RETRIEVAL_DOCUMENT",
"SEMANTIC_SIMILARITY",
"CLASSIFICATION",
"CLUSTERING",
"QUESTION_ANSWERING",
"FACT_VERIFICATION",
]
] = None,
dimensions: Optional[int] = None,
) -> List[List[float]]:
"""Embed a list of strings.
Args:
texts: List[str] The list of strings to embed.
batch_size: [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
embeddings_task_type: [str] optional embeddings task type,
one of the following
RETRIEVAL_QUERY - Text is a query
in a search/retrieval setting.
RETRIEVAL_DOCUMENT - Text is a document
in a search/retrieval setting.
SEMANTIC_SIMILARITY - Embeddings will be used
for Semantic Textual Similarity (STS).
CLASSIFICATION - Embeddings will be used for classification.
CLUSTERING - Embeddings will be used for clustering.
The following are only supported on preview models:
QUESTION_ANSWERING
FACT_VERIFICATION
dimensions: [int] optional. Output embeddings dimensions.
Only supported on preview models.
Returns:
List of embeddings, one for each text.
"""
if len(texts) == 0:
return []
embeddings: List[List[float]] = []
first_batch_result: List[List[float]] = []
if batch_size > 0:
# Fixed batch size.
batches = VertexAIEmbeddings._prepare_batches(texts, batch_size)
else:
# Dynamic batch size, starting from 250 at the first call.
first_batch_result, batches = self._prepare_and_validate_batches(
texts, embeddings_task_type
)
# First batch result may have some embeddings already.
# In such case, batches have texts that were not processed yet.
embeddings.extend(first_batch_result)
tasks = []
for batch in batches:
tasks.append(
self.instance["task_executor"].submit(
self._get_embeddings_with_retry,
texts=batch,
embeddings_type=embeddings_task_type,
dimensions=dimensions,
)
)
if len(tasks) > 0:
wait(tasks)
for t in tasks:
embeddings.extend(t.result())
return embeddings
[docs]
def embed_documents(
self, texts: List[str], batch_size: int = 0
) -> List[List[float]]:
"""Embed a list of documents.
Args:
texts: List[str] The list of texts to embed.
batch_size: [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
Returns:
List of embeddings, one for each text.
"""
return self.embed(texts, batch_size, "RETRIEVAL_DOCUMENT")
[docs]
def embed_query(self, text: str) -> List[float]:
"""Embed a text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
return self.embed([text], 1, "RETRIEVAL_QUERY")[0]
[docs]
@deprecated(
since="2.0.1", removal="3.0.0", alternative="VertexAIEmbeddings.embed_images()"
)
def embed_image(
self,
image_path: str,
contextual_text: Optional[str] = None,
dimensions: Optional[int] = None,
) -> List[float]:
"""Embed an image.
Args:
image_path: Path to image (Google Cloud Storage or web) to generate
embeddings for.
contextual_text: Text to generate embeddings for.
Returns:
Embedding for the image.
"""
warnings.warn(
"The `embed_image()` API will be deprecated and replaced by \
`embed_images()`. Change your usage to \
`embed_images([image_path1, image_path2])` and note\
that the result returned will be a list of image embeddings."
)
if self.model_type != GoogleEmbeddingModelType.MULTIMODAL:
raise NotImplementedError("Only supported for multimodal models")
image_loader = ImageBytesLoader()
bytes_image = image_loader.load_bytes(image_path)
image = Image(bytes_image)
result: MultiModalEmbeddingResponse = self.instance[
"get_embeddings_with_retry"
](image=image, contextual_text=contextual_text, dimension=dimensions)
return result.image_embedding
[docs]
def embed_images(
self,
uris: List[str],
contextual_text: Optional[str] = None,
dimensions: Optional[int] = None,
) -> List[List[float]]:
"""Embed a list of images.
Args:
uris: Paths to image (local, Google Cloud Storage or web) to generate
embeddings for.
contextual_text: Text to generate embeddings for.
Returns:
Embedding for the image.
"""
if self.model_type != GoogleEmbeddingModelType.MULTIMODAL:
raise NotImplementedError("Only supported for multimodal models")
image_loader = ImageBytesLoader()
embeddings = []
for image_path in uris:
bytes_image = image_loader.load_bytes(image_path)
image = Image(bytes_image)
result: MultiModalEmbeddingResponse = self.instance[
"get_embeddings_with_retry"
](image=image, contextual_text=contextual_text, dimension=dimensions)
embeddings.append(result.image_embedding)
return embeddings
VertexAIEmbeddings.model_rebuild()
