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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}"
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