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Classes for working with language models.
Classes
ChatModel
ChatModel(model_id: str, endpoint_name: Optional[str] = None)
ChatModel represents a language model that is capable of chat.
.. rubric:: 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(
model: vertexai.language_models._language_models.ChatModel,
context: Optional[str] = None,
examples: Optional[
List[vertexai.language_models._language_models.InputOutputTextPair]
] = None,
max_output_tokens: int = 128,
temperature: float = 0.0,
top_k: int = 40,
top_p: float = 0.95,
)
ChatSession represents a chat session with a language model.
Within a chat session, the model keeps context and remembers the previous conversation.
InputOutputTextPair
InputOutputTextPair(input_text: str, output_text: str)
InputOutputTextPair represents a pair of input and output texts.
TextEmbedding
TextEmbedding(values: List[float], _prediction_response: Optional[Any] = None)
Contains text embedding vector.
TextEmbeddingModel
TextEmbeddingModel(model_id: str, endpoint_name: Optional[str] = None)
TextEmbeddingModel converts text into a vector of floating-point numbers.
.. rubric:: Examples
Getting embedding:
model = TextEmbeddingModel.from_pretrained("embedding-gecko@001") embeddings = model.get_embeddings(["What is life?"]) for embedding in embeddings: vector = embedding.values print(len(vector))
TextGenerationModel
TextGenerationModel(model_id: str, endpoint_name: Optional[str] = None)
TextGenerationModel represents a general language model.
.. rubric:: Examples
Getting answers:
model = TextGenerationModel.from_pretrained("text-bison@001") model.predict("What is life?")
TextGenerationResponse
TextGenerationResponse(text: str, _prediction_response: Any)
TextGenerationResponse represents a response of a language model.