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API documentation for language_models
package.
Classes
ChatMessage
A chat 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
Creates a LanguageModel.
This constructor should not be called directly.
Use LanguageModel.from_pretrained(model_name=...)
instead.
GroundingSource
GroundingSource()
InputOutputTextPair
InputOutputTextPair represents a pair of input and output texts.
TextEmbedding
Text embedding vector and statistics.
TextEmbeddingInput
Structural text embedding input.
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
:type: str
Modules
_language_models
Classes for working with language models.