Source code for

import logging
from typing import Any, Dict, Optional

from steamship.base.client import Client
from steamship.base.error import SteamshipError
from import CreatePluginInstanceRequest, PluginInstance
from import TagKind, TagValueKey

[docs] class PromptGenerationPluginInstance(PluginInstance): """An instance of a configured prompt completion service such as GPT-3. The `generate` method synchronously invokes the prompt against a set of variables that parameterize it. The return value is a single string. Example Usage: llm = Steamship.use('prompt-generation-default', config={ "temperature": 0.9 }) PROMPT = "Greet {name} as if he were a {relation}." greeting = llm.generate(PROMPT, {"name": "Ted", "relation": "old friend"}) """
[docs] def generate( self, prompt: str, variables: Optional[Dict] = None, clean_output: bool = True ) -> str: """Complete the provided prompt, interpolating any variables.""" # Interpolate the prompt with Python formatting semantics. If no variables provided, supply an empty dict. try: prompt_text = prompt.format(**(variables or {})) except KeyError as e: raise SteamshipError( message="Some variables in the prompt template were not provided.", error=e ) # This requests generation from the parameterized prompt. Tagging with our prompt generator # plugin will result in a new tag that contains the generated output. tag_task = self.tag(doc=prompt_text) # We `wait()` because generation of text is done asynchronously and may take a few moments # (somewhat depending on the complexity of your prompt). tag_task.wait() # Here, we iterate through the content blocks associated with a file # as well as any tags on that content to find the generated text. # # The Steamship data model provides flexible content organization, # storage, and lookup. Read more about the data model via: # try: for text_block in tag_task.output.file.blocks: for block_tag in text_block.tags: if block_tag.kind == TagKind.GENERATION: generation = block_tag.value[TagValueKey.STRING_VALUE] if clean_output: return self._clean_output(generation) else: return generation except Exception as e: logging.error( "generate() got unexpected response shape back. This suggests an error rather an merely an empty response." ) logging.exception(e) raise e return ""
[docs] @staticmethod def create( client: Client, plugin_id: str = None, plugin_handle: str = None, plugin_version_id: str = None, plugin_version_handle: str = None, handle: str = None, fetch_if_exists: bool = True, config: Dict[str, Any] = None, ) -> "PromptGenerationPluginInstance": """Create a plugin instance When handle is empty the engine will automatically assign one fetch_if_exists controls whether we want to re-use an existing plugin instance or not.""" req = CreatePluginInstanceRequest( handle=handle, plugin_id=plugin_id, plugin_handle=plugin_handle, plugin_version_id=plugin_version_id, plugin_version_handle=plugin_version_handle, fetch_if_exists=fetch_if_exists, config=config, ) return "plugin/instance/create", payload=req, expect=PromptGenerationPluginInstance )
def _clean_output(self, text: str): """Remove any leading/trailing whitespace and partial sentences. This assumes that your generated output will include consistent punctuation. You may want to alter this method to better fit the format of your generated text. """ last_punc = -1 for i, c in enumerate(reversed(text)): if c in '.!?"': last_punc = len(text) - i break if last_punc != -1: result = text[: last_punc + 1] else: result = text return result.strip()