from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
from google.cloud.aiplatform import telemetry
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import BaseChatModel, BaseLLM
from langchain_core.messages import AIMessage, BaseMessage
from langchain_core.outputs import ChatResult, LLMResult
from langchain_core.outputs.chat_generation import ChatGeneration
from langchain_core.outputs.generation import Generation
from langchain_core.pydantic_v1 import BaseModel, Field
from vertexai.preview.vision_models import ( # type: ignore[import-untyped]
GeneratedImage,
ImageGenerationModel,
)
from vertexai.vision_models import Image, ImageTextModel # type: ignore[import-untyped]
from langchain_google_vertexai._image_utils import (
ImageBytesLoader,
create_image_content_part,
get_image_str_from_content_part,
get_text_str_from_content_part,
image_bytes_to_b64_string,
)
from langchain_google_vertexai._utils import get_user_agent
class _BaseImageTextModel(BaseModel):
"""Base class for all integrations that use ImageTextModel"""
cached_client: Any = Field(default=None)
model_name: str = Field(default="imagetext@001")
""" Name of the model to use"""
number_of_results: int = Field(default=1)
"""Number of results to return from one query"""
language: str = Field(default="en")
"""Language of the query"""
project: Union[str, None] = Field(default=None)
"""Google cloud project"""
@property
def client(self) -> ImageTextModel:
if self.cached_client is None:
self.cached_client = ImageTextModel.from_pretrained(
model_name=self.model_name,
)
return self.cached_client
def _get_image_from_message_part(self, message_part: str | Dict) -> Image | None:
"""Given a message part obtain a image if the part represents it.
Args:
message_part: Item of a message content.
Returns:
Image is successful otherwise None.
"""
image_str = get_image_str_from_content_part(message_part)
if isinstance(image_str, str):
loader = ImageBytesLoader(project=self.project)
image_bytes = loader.load_bytes(image_str)
return Image(image_bytes=image_bytes)
else:
return None
def _get_text_from_message_part(self, message_part: str | Dict) -> str | None:
"""Given a message part obtain a text if the part represents it.
Args:
message_part: Item of a message content.
Returns:
str is successful otherwise None.
"""
return get_text_str_from_content_part(message_part)
@property
def _llm_type(self) -> str:
"""Returns the type of LLM"""
return "vertexai-vision"
@property
def _user_agent(self) -> str:
"""Gets the User Agent."""
_, user_agent = get_user_agent(f"{type(self).__name__}_{self.model_name}")
return user_agent
@property
def _default_params(self) -> Dict[str, Any]:
return {"number_of_results": self.number_of_results, "language": self.language}
def _prepare_params(self, **kwargs: Any) -> Dict[str, Any]:
params = self._default_params
for key, value in kwargs.items():
if key in params and value is not None:
params[key] = value
return params
class _BaseVertexAIImageCaptioning(_BaseImageTextModel):
"""Base class for Image Captioning models."""
def _get_captions(
self,
image: Image,
number_of_results: Optional[int] = None,
language: Optional[str] = None,
) -> List[str]:
"""Uses the sdk methods to generate a list of captions.
Args:
image: Image to get the captions for.
number_of_results: Number of results to return from one query.
language: Language of the query.
Returns:
List of captions obtained from the image.
"""
with telemetry.tool_context_manager(self._user_agent):
params = self._prepare_params(
number_of_results=number_of_results, language=language
)
captions = self.client.get_captions(image=image, **params)
return captions
[docs]class VertexAIImageCaptioning(_BaseVertexAIImageCaptioning, BaseLLM):
"""Implementation of the Image Captioning model as an LLM."""
def _generate(
self,
prompts: List[str],
stop: List[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> LLMResult:
"""Generates the captions.
Args:
prompts: List of prompts to use. Each prompt must be a string
that represents an image. Currently supported are:
- Google Cloud Storage URI
- B64 encoded string
- Local file path
- Remote url
Returns:
Captions generated from every prompt.
"""
generations = [
self._generate_one(prompt=prompt, **kwargs) for prompt in prompts
]
return LLMResult(generations=generations)
def _generate_one(self, prompt: str, **kwargs) -> List[Generation]:
"""Generates the captions for a single prompt.
Args:
prompt: Image url for the generation.
Returns:
List of generations
"""
image_loader = ImageBytesLoader(project=self.project)
image_bytes = image_loader.load_bytes(prompt)
image = Image(image_bytes=image_bytes)
caption_list = self._get_captions(image=image, **kwargs)
return [Generation(text=caption) for caption in caption_list]
[docs]class VertexAIImageCaptioningChat(_BaseVertexAIImageCaptioning, BaseChatModel):
"""Implementation of the Image Captioning model as a chat."""
def _generate(
self,
messages: List[BaseMessage],
stop: List[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
"""Generates the results.
Args:
messages: List of messages. Currently only one message is supported.
The message content must be a list with only one element with
a dict with format:
{
'type': 'image_url',
'image_url': {
'url' <image_string>
}
}
Currently supported image strings are:
- Google Cloud Storage URI
- B64 encoded string
- Local file path
- Remote url
"""
image = None
is_valid = (
len(messages) == 1
and isinstance(messages[0].content, List)
and len(messages[0].content) == 1
)
if is_valid:
content = messages[0].content[0]
image = self._get_image_from_message_part(content)
if image is None:
raise ValueError(
f"{self.__class__.__name__} messages should be a list with "
"only one message. This message content must be a list with "
"one dictionary with the format: "
"{'type': 'image_url', 'image_url': {'image': <image_str>}}"
)
captions = self._get_captions(image, **messages[0].additional_kwargs)
generations = [
ChatGeneration(message=AIMessage(content=caption)) for caption in captions
]
return ChatResult(generations=generations)
[docs]class VertexAIVisualQnAChat(_BaseImageTextModel, BaseChatModel):
"""Chat implementation of a visual QnA model"""
@property
def _default_params(self) -> Dict[str, Any]:
return {"number_of_results": self.number_of_results}
def _generate(
self,
messages: List[BaseMessage],
stop: List[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
"""Generates the results.
Args:
messages: List of messages. The first message should contain a
string representation of the image.
Currently supported are:
- Google Cloud Storage URI
- B64 encoded string
- Local file path
- Remote url
There has to be at least other message with the first question.
"""
image = None
user_question = None
is_valid = (
len(messages) == 1
and isinstance(messages[0].content, List)
and len(messages[0].content) == 2
)
if is_valid:
image_part = messages[0].content[0]
user_question_part = messages[0].content[1]
image = self._get_image_from_message_part(image_part)
user_question = self._get_text_from_message_part(user_question_part)
if (image is None) or (user_question is None):
raise ValueError(
f"{self.__class__.__name__} messages should be a list with "
"only one message. The message content should be a list with "
"two elements. The first element should be the image, a dictionary "
"with format"
"{'type': 'image_url', 'image_url': {'image': <image_str>}}."
"The second one should be the user question. Either a simple string"
"or a dictionary with format {'type': 'text', 'text': <message>}"
)
answers = self._ask_questions(
image=image, query=user_question, **messages[0].additional_kwargs
)
generations = [
ChatGeneration(message=AIMessage(content=answer)) for answer in answers
]
return ChatResult(generations=generations)
def _ask_questions(
self, image: Image, query: str, number_of_results: Optional[int] = None
) -> List[str]:
"""Interfaces with the sdk to get the question.
Args:
image: Image to question about.
query: User query.
Returns:
List of responses to the query.
"""
with telemetry.tool_context_manager(self._user_agent):
params = self._prepare_params(number_of_results=number_of_results)
answers = self.client.ask_question(image=image, question=query, **params)
return answers
class _BaseVertexAIImageGenerator(BaseModel):
"""Base class form generation and edition of images."""
cached_client: Any = Field(default=None)
model_name: str = Field(default="imagegeneration@002")
"""Name of the base model"""
negative_prompt: Union[str, None] = Field(default=None)
"""A description of what you want to omit in
the generated images"""
number_of_results: int = Field(default=1)
"""Number of images to generate"""
guidance_scale: Union[float, None] = Field(default=None)
"""Controls the strength of the prompt"""
language: Union[str, None] = Field(default=None)
"""Language of the text prompt for the image Supported values are "en" for English,
"hi" for Hindi, "ja" for Japanese, "ko" for Korean, and "auto" for automatic
language detection"""
seed: Union[int, None] = Field(default=None)
"""Random seed for the image generation"""
project: Union[str, None] = Field(default=None)
"""Google cloud project id"""
@property
def client(self) -> ImageGenerationModel:
if not self.cached_client:
self.cached_client = ImageGenerationModel.from_pretrained(
model_name=self.model_name,
)
return self.cached_client
@property
def _default_params(self) -> Dict[str, Any]:
return {
"number_of_images": self.number_of_results,
"language": self.language,
"negative_prompt": self.negative_prompt,
"guidance_scale": self.guidance_scale,
"seed": self.seed,
}
def _prepare_params(self, **kwargs: Any) -> Dict[str, Any]:
params = self._default_params
mapping = {"number_of_results": "number_of_images"}
for key, value in kwargs.items():
key = mapping.get(key, key)
if key in params and value is not None:
params[key] = value
return {k: v for k, v in params.items() if v is not None}
def _generate_images(self, prompt: str, **kwargs: Any) -> List[str]:
"""Generates images given a prompt.
Args:
prompt: Description of what the image should look like.
Returns:
List of b64 encoded strings.
"""
with telemetry.tool_context_manager(self._user_agent):
generation_result = self.client.generate_images(
prompt=prompt, **self._prepare_params(**kwargs)
)
image_str_list = [
self._to_b64_string(image) for image in generation_result.images
]
return image_str_list
def _edit_images(self, image_str: str, prompt: str, **kwargs: Any) -> List[str]:
"""Edit an image given a image and a prompt.
Args:
image_str: String representation of the image.
prompt: Description of what the image should look like.
Returns:
List of b64 encoded strings.
"""
with telemetry.tool_context_manager(self._user_agent):
image_loader = ImageBytesLoader(project=self.project)
image_bytes = image_loader.load_bytes(image_str)
image = Image(image_bytes=image_bytes)
generation_result = self.client.edit_image(
prompt=prompt, base_image=image, **self._prepare_params(**kwargs)
)
image_str_list = [
self._to_b64_string(image) for image in generation_result.images
]
return image_str_list
def _to_b64_string(self, image: GeneratedImage) -> str:
"""Transforms a generated image into a b64 encoded string.
Args:
image: Image to convert.
Returns:
b64 encoded string of the image.
"""
# This is a hack because at the moment, GeneratedImage doesn't provide
# a way to get the bytes of the image (or anything else). There is
# only private methods that are not reliable.
from tempfile import NamedTemporaryFile
temp_file = NamedTemporaryFile()
image.save(temp_file.name, include_generation_parameters=False)
temp_file.seek(0)
image_bytes = temp_file.read()
temp_file.close()
return image_bytes_to_b64_string(image_bytes=image_bytes)
@property
def _llm_type(self) -> str:
"""Returns the type of LLM"""
return "vertexai-vision"
@property
def _user_agent(self) -> str:
"""Gets the User Agent."""
_, user_agent = get_user_agent(f"{type(self).__name__}_{self.model_name}")
return user_agent
[docs]class VertexAIImageGeneratorChat(_BaseVertexAIImageGenerator, BaseChatModel):
"""Generates an image from a prompt."""
def _generate(
self,
messages: List[BaseMessage],
stop: List[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
"""
Args:
messages: The message must be a list of only one element with one part:
The user prompt.
"""
# Only one message allowed with one text part.
user_query = None
if len(messages) == 1:
if isinstance(messages[0].content, str):
user_query = messages[0].content
elif len(messages[0].content) == 1:
user_query = get_text_str_from_content_part(messages[0].content[0])
if user_query is None:
raise ValueError(
"Only one message with one text part allowed for image generation"
" Must The prompt of the image"
)
image_str_list = self._generate_images(
prompt=user_query, **messages[0].additional_kwargs
)
image_content_part_list = [
create_image_content_part(image_str=image_str)
for image_str in image_str_list
]
generations = [
ChatGeneration(message=AIMessage(content=[content_part]))
for content_part in image_content_part_list
]
return ChatResult(generations=generations)
[docs]class VertexAIImageEditorChat(_BaseVertexAIImageGenerator, BaseChatModel):
"""Given an image and a prompt, edits the image.
Currently only supports mask free editing.
"""
def _generate(
self,
messages: List[BaseMessage],
stop: List[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
"""
Args:
messages: The message must be a list of only one element with two part:
- The image as a dict {
'type': 'image_url', 'image_url': {'url': <message_str>}
}
- The user prompt.
"""
# Only one message allowed with two parts: the image and the text.
user_query = None
is_valid = len(messages) == 1 and len(messages[0].content) == 2
if is_valid:
image_str = get_image_str_from_content_part(messages[0].content[0])
user_query = get_text_str_from_content_part(messages[0].content[1])
if (user_query is None) or (image_str is None):
raise ValueError(
"Only one message allowed for image edition. The message must have"
"two parts: First the image and then the user prompt."
)
image_str_list = self._edit_images(
image_str=image_str, prompt=user_query, **messages[0].additional_kwargs
)
image_content_part_list = [
create_image_content_part(image_str=image_str)
for image_str in image_str_list
]
generations = [
ChatGeneration(message=AIMessage(content=[content_part]))
for content_part in image_content_part_list
]
return ChatResult(generations=generations)
