A chat message.

ChatModel represents a language model that is capable of chat.

Examples::

chat_model = ChatModel.from_pretrained("chat-bison@001")chat = chat_model.start_chat(    context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",    examples=[        InputOutputTextPair(            input_text="Who do you work for?",            output_text="I work for Ned.",        ),        InputOutputTextPair(            input_text="What do I like?",            output_text="Ned likes watching movies.",        ),    ],    temperature=0.3,)chat.send_message("Do you know any cool events this weekend?")

ChatSession represents a chat session with a language model.

Within a chat session, the model keeps context and remembers the previous conversation.

CodeChatModel represents a model that is capable of completing code.

.. rubric:: Examples

code_chat_model = CodeChatModel.from_pretrained("codechat-bison@001")

code_chat = code_chat_model.start_chat( context="I'm writing a large-scale enterprise application.", max_output_tokens=128, temperature=0.2,)

code_chat.send_message("Please help write a function to calculate the min of two numbers")

CodeChatSession represents a chat session with code chat language model.

Within a code chat session, the model keeps context and remembers the previous converstion.

Creates a LanguageModel.

This constructor should not be called directly.UseLanguageModel.from_pretrained(model_name=...) instead.

GroundingSource()

InputOutputTextPair represents a pair of input and output texts.

Text embedding vector and statistics.

Structural text embedding input.

TextEmbeddingModel class calculates embeddings for the given texts.

Examples::

# Getting embedding:model = TextEmbeddingModel.from_pretrained("textembedding-gecko@001")embeddings = model.get_embeddings(["What is life?"])for embedding in embeddings:    vector = embedding.values    print(len(vector))

Creates a LanguageModel.

This constructor should not be called directly.UseLanguageModel.from_pretrained(model_name=...) instead.

TextGenerationResponse represents a response of a language model... attribute:: text

The generated text

:type: str

Classes for working with language models.