from typing import List, Union, Generator, Iterator from schemas import OpenAIChatMessage from pydantic import BaseModel class Pipeline: class Valves(BaseModel): pass def __init__(self): # Optionally, you can set the id and name of the pipeline. # Best practice is to not specify the id so that it can be automatically inferred from the filename, so that users can install multiple versions of the same pipeline. # The identifier must be unique across all pipelines. # The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes. # self.id = "pipeline_example" # The name of the pipeline. self.name = "Pipeline Example" pass async def on_startup(self): # This function is called when the server is started. print(f"on_startup:{__name__}") pass async def on_shutdown(self): # This function is called when the server is stopped. print(f"on_shutdown:{__name__}") pass async def on_valves_updated(self): # This function is called when the valves are updated. pass async def inlet(self, body: dict, user: dict) -> dict: # This function is called before the OpenAI API request is made. You can modify the form data before it is sent to the OpenAI API. print(f"inlet:{__name__}") print(body) print(user) return body async def outlet(self, body: dict, user: dict) -> dict: # This function is called after the OpenAI API response is completed. You can modify the messages after they are received from the OpenAI API. print(f"outlet:{__name__}") print(body) print(user) return body def pipe( self, user_message: str, model_id: str, messages: List[dict], body: dict ) -> Union[str, Generator, Iterator]: # This is where you can add your custom pipelines like RAG. print(f"pipe:{__name__}") # If you'd like to check for title generation, you can add the following check if body.get("title", False): print("Title Generation Request") print(messages) print(user_message) print(body) return f"{__name__} response to: {user_message}"