pipelines/examples/filter_pipeline.py
2024-05-30 22:24:29 -07:00

64 lines
2.2 KiB
Python

"""
title: Filter Pipeline
author: open-webui
date: 2024-05-30
version: 1.1
license: MIT
description: Example of a filter pipeline that can be used to edit the form data before it is sent to the OpenAI API.
requirements: requests
"""
from typing import List, Optional
from pydantic import BaseModel
from schemas import OpenAIChatMessage
class Pipeline:
def __init__(self):
# Pipeline filters are only compatible with Open WebUI
# You can think of filter pipeline as a middleware that can be used to edit the form data before it is sent to the OpenAI API.
self.type = "filter"
# Optionally, you can set the id and name of the pipeline.
# Assign a unique identifier to the 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 = "filter_pipeline"
self.name = "Filter"
class Valves(BaseModel):
# List target pipeline ids (models) that this filter will be connected to.
# If you want to connect this filter to all pipelines, you can set pipelines to ["*"]
pipelines: List[str] = []
# Assign a priority level to the filter pipeline.
# The priority level determines the order in which the filter pipelines are executed.
# The lower the number, the higher the priority.
priority: int = 0
# Add your custom parameters here
pass
self.valves = Valves(**{"pipelines": ["llama3:latest"]})
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 inlet(self, body: dict, user: Optional[dict] = None) -> dict:
# This filter is applied to the form data before it is sent to the OpenAI API.
print(f"inlet:{__name__}")
print(body)
print(user)
return body