langchain-google-genai: 2.1.7#

LangChain Google Generative AI Integration

This module integrates Google’s Generative AI models, specifically the Gemini series, with the LangChain framework. It provides classes for interacting with chat models and generating embeddings, leveraging Google’s advanced AI capabilities.

Chat Models

The ChatGoogleGenerativeAI class is the primary interface for interacting with Google’s Gemini chat models. It allows users to send and receive messages using a specified Gemini model, suitable for various conversational AI applications.

LLMs

The GoogleGenerativeAI class is the primary interface for interacting with Google’s Gemini LLMs. It allows users to generate text using a specified Gemini model.

Embeddings

The GoogleGenerativeAIEmbeddings class provides functionalities to generate embeddings using Google’s models. These embeddings can be used for a range of NLP tasks, including semantic analysis, similarity comparisons, and more.

Installation

To install the package, use pip:

## Using Chat Models

After setting up your environment with the required API key, you can interact with the Google Gemini models.

## Using LLMs

The package also supports generating text with Google’s models.

## Embedding Generation

The package also supports creating embeddings with Google’s models, useful for textual similarity and other NLP applications.

chat_models#

Classes

chat_models.ChatGoogleGenerativeAI

Google AI chat models integration.

chat_models.ChatGoogleGenerativeAIError

Custom exception class for errors associated with the Google GenAI API.

embeddings#

Classes

embeddings.GoogleGenerativeAIEmbeddings

Google Generative AI Embeddings.

genai_aqa#

Classes

genai_aqa.AqaInput

Input to GenAIAqa.invoke.

genai_aqa.AqaOutput

Output from GenAIAqa.invoke.

genai_aqa.GenAIAqa

Google's Attributed Question and Answering service.

google_vector_store#

Classes

google_vector_store.DoesNotExistsException(*, ...)

google_vector_store.GoogleVectorStore(*, ...)

Google GenerativeAI Vector Store.

google_vector_store.ServerSideEmbedding()

Do nothing embedding model where the embedding is done by the server.

llms#

Classes

llms.GoogleGenerativeAI

Google GenerativeAI models.