A chat message.

Author of the 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( 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.

A language model that generates code.

.. rubric:: Examples

Getting answers:

generation_model = CodeGenerationModel.from_pretrained("code-bison@001") print(generation_model.predict( prefix="Write a function that checks if a year is a leap year.", ))

completion_model = CodeGenerationModel.from_pretrained("code-gecko@001") print(completion_model.predict( prefix="def reverse_string(s):", ))

InputOutputTextPair represents a pair of input and output texts.

Contains text embedding vector.

TextEmbeddingModel converts text into a vector of floating-point numbers.

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. Use LanguageModel.from_pretrained(model_name=...) instead.

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

The generated text

Scores for safety attributes. Learn more about the safety attributes here: https://cloud.google.com/vertex-ai/docs/generative-ai/learn/responsible-ai#safety_attribute_descriptions