Package language_models (1.33.0)

Version 1.33.0

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API documentation for language_models package.

Classes

ChatMessage

A chat message.

Author of the message.

ChatModel

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

ChatSession represents a chat session with a language model.

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

CodeChatModel

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

CodeChatSession represents a chat session with code chat language model.

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

CodeGenerationModel

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

InputOutputTextPair represents a pair of input and output texts.

TextEmbedding

Text embedding vector and statistics.

TextEmbeddingInput

Structural text embedding input.

The name of the downstream task the embeddings will be used for. Valid values: RETRIEVAL_QUERY Specifies the given text is a query in a search/retrieval setting. RETRIEVAL_DOCUMENT Specifies the given text is a document from the corpus being searched. SEMANTIC_SIMILARITY Specifies the given text will be used for STS. CLASSIFICATION Specifies that the given text will be classified. CLUSTERING Specifies that the embeddings will be used for clustering.

TextEmbeddingModel

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))

TextGenerationModel

Creates a LanguageModel.

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

TextGenerationResponse

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

Modules

_language_models

Classes for working with language models.