langchain-google-vertexai: 2.0.11#

LangChain Google Generative AI Integration

This module contains the LangChain integrations for Vertex AI service - Google foundational models, third-party foundational modela available on Vertex Model Garden and.

Supported integrations

  1. Google’s founational models: Gemini family, Codey, embeddings - ChatVertexAI, VertexAI, VertexAIEmbeddings.

  2. Other Google’s foundational models: Imagen - VertexAIImageCaptioning, VertexAIImageCaptioningChat, VertexAIImageEditorChat, VertexAIImageGeneratorChat, VertexAIVisualQnAChat.

  3. Third-party foundational models available as a an API (mdel-as-a-service) on Vertex Model Garden (Mistral, Llama, Anthropic) - model_garden.ChatAnthropicVertex, model_garden_maas.VertexModelGardenLlama, model_garden_maas.VertexModelGardenMistral.

  4. Third-party foundational models deployed on Vertex AI endpoints from Vertex Model Garden or Hugginface - VertexAIModelGarden.

5. Gemma deployed on Vertex AI endpoints or locally - GemmaChatLocalHF, GemmaChatLocalKaggle, GemmaChatVertexAIModelGarden, GemmaLocalHF, GemmaLocalKaggle, GemmaVertexAIModelGarden. 5. Vector Search on Vertex AI - VectorSearchVectorStore, VectorSearchVectorStoreDatastore, VectorSearchVectorStoreGCS. 6. Vertex AI evaluators for generative AI - VertexPairWiseStringEvaluator, VertexStringEvaluator.

Take a look at detailed documentation for each class for further details.

Installation

`bash pip install -U langchain-google-vertexai `

You need to enable needed Google Cloud APIs (depending on the integration you’re using) and set up credentials by either:
  • Have credentials configured for your environment

    (gcloud, workload identity, etc…)

  • Store the path to a service account JSON file as the

    GOOGLE_APPLICATION_CREDENTIALS environment variable

This codebase uses the google.auth library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.

For more information, see: https://cloud.google.com/docs/authentication/application-default-credentials#GAC and https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth.

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.CacheUsageMetadata

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