Regular Prompts
Note
Additional details and the list of valid values for task_id
can be found in the Task
class.
Sample formats for model_parameters can be downloaded from here txt
- create_prompt(self, input_mode: str, name: str, task_id: str, model_id: str, prompt_details: PromptTemplate, description: str = None, container_type: str = None, container_id: str = None) AIGovAssetUtilities
Create a Regular Prompt Template Asset.
- Parameters:
input_mode (str) – The mode in which the prompt is being created. Currently supports “structured” and “freeflow” modes.
name (str) – The name of prompt being created.
model_id (str) – The identifier of the model associated with the prompt
task_id (str) – Describes possible Task for the prompt template creation
prompt_details (PromptTemplate) – Holds information about model version details, prompt variables, instructions, input/output prefixes, and example data
description (str) – (Optional) description of the extrnal prompt to be created
container_id (str) – (Optional) used to save the detached prompt
- Return Type:
AIGovAssetUtilities
Example-1 (Creating a Structured prompt template assest),:
prompt_template = PromptTemplate(model_version={"number": "2.0.0-rc.7", "tag": "tag", "description": "Description of the model version"}, input="Input text to be given", prompt_variables= {"text": "value"} prompt_instruction="Your prompt instruction", input_prefix="Your input prefix, output_prefix="Your output prefix", examples={"What is the capidddtal of France{text}?": "The capital of France is Paris{text}.", "Who wrote '1984{text}'?": "George Orwell wrote '1984'{text}."}, model_parameters={"decoding_method":"greedy" "max_new_tokens":2034, "min_new_tokens":0, "random_seed":0, "top_k":0, "top_p":0 } structured_prompt = facts.client.asests.create_prompt(input_mode="structured", name=" structured prompt sample", task_id="summarization", model_id="ibm/granite-13b-chat-v2", description="My First structured prompt", prompt_details=prompt_template, )
Example-2 (Creating a Freeflow prompt template assest),:
prompt_template = PromptTemplate( input="Input text to be given", prompt_variables= {"text": "value"} model_parameters={"decoding_method":"greedy" "max_new_tokens":2034, "min_new_tokens":0, "random_seed":0, "top_k":0, "top_p":0 } ) freeflow_prompt = facts.client.asests.create_prompt(input_mode="freeflow", name="Freeflow prompt sample", task_id="summarization", model_id="ibm/granite-13b-chat-v2", description="My First Freeflow prompt", prompt_details=prompt_template, )