langchain-google-vertexai: 2.0.10

callbacks

Classes

callbacks.VertexAICallbackHandler()

Callback Handler that tracks VertexAI info.

chains

Functions

chains.create_structured_runnable(function, ...)	Create a runnable sequence that uses OpenAI functions.
chains.get_output_parser(functions)	Get the appropriate function output parser given the user functions.

chat_models

Classes

chat_models.ChatVertexAI

Google Cloud Vertex AI chat model integration.

embeddings

Classes

embeddings.GoogleEmbeddingModelType(value[, ...])
embeddings.GoogleEmbeddingModelVersion(value)
embeddings.VertexAIEmbeddings	Google Cloud VertexAI embedding models.

evaluators

Classes

evaluators.evaluation.VertexPairWiseStringEvaluator(...)	Evaluate the perplexity of a predicted string.
evaluators.evaluation.VertexStringEvaluator(...)	Evaluate the perplexity of a predicted string.

functions_utils

Classes

functions_utils.PydanticFunctionsOutputParser

Parse an output as a pydantic object.

gemma

Classes

gemma.GemmaChatLocalHF
gemma.GemmaChatLocalKaggle	Needed for mypy typing to recognize model_name as a valid arg.
gemma.GemmaChatVertexAIModelGarden	Needed for mypy typing to recognize model_name as a valid arg.
gemma.GemmaLocalHF	Local gemma model loaded from HuggingFace.
gemma.GemmaLocalKaggle	Local gemma chat model loaded from Kaggle.
gemma.GemmaVertexAIModelGarden	Create a new model by parsing and validating input data from keyword arguments.

Functions

gemma.gemma_messages_to_prompt(history)

Converts a list of messages to a chat prompt for Gemma.

llms

Classes

llms.VertexAI

Google Vertex AI large language models.

model_garden

Classes

model_garden.ChatAnthropicVertex	Create a new model by parsing and validating input data from keyword arguments.
model_garden.VertexAIModelGarden	Large language models served from Vertex AI Model Garden.

model_garden_maas

Classes

model_garden_maas.llama.VertexModelGardenLlama	Integration for Llama 3.1 on Google Cloud Vertex AI Model-as-a-Service.
model_garden_maas.mistral.VertexModelGardenMistral	Create a new model by parsing and validating input data from keyword arguments.

utils

Functions

utils.create_context_cache(model, messages)

Creates a cache for content in some model.

vectorstores

Classes

vectorstores.document_storage.DataStoreDocumentStorage(...)	Stores documents in Google Cloud DataStore.
vectorstores.document_storage.DocumentStorage()	Abstract interface of a key, text storage for retrieving documents.
vectorstores.document_storage.GCSDocumentStorage(bucket)	Stores documents in Google Cloud Storage.
vectorstores.vectorstores.VectorSearchVectorStore(...)	VertexAI VectorStore that handles the search and indexing using Vector Search and stores the documents in Google Cloud Storage.
vectorstores.vectorstores.VectorSearchVectorStoreDatastore(...)	VectorSearch with DatasTore document storage.
vectorstores.vectorstores.VectorSearchVectorStoreGCS(...)	Alias of VectorSearchVectorStore for consistency with the rest of vector stores with different document storage backends.

vision_models

Classes

vision_models.VertexAIImageCaptioning	Implementation of the Image Captioning model as an LLM.
vision_models.VertexAIImageCaptioningChat	Implementation of the Image Captioning model as a chat.
vision_models.VertexAIImageEditorChat	Given an image and a prompt, edits the image.
vision_models.VertexAIImageGeneratorChat	Generates an image from a prompt.
vision_models.VertexAIVisualQnAChat	Chat implementation of a visual QnA model