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64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
"""
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title: Filter Pipeline
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author: open-webui
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date: 2024-05-30
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version: 1.1
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license: MIT
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description: Example of a filter pipeline that can be used to edit the form data before it is sent to the OpenAI API.
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requirements: requests
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"""
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from typing import List, Optional
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from pydantic import BaseModel
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from schemas import OpenAIChatMessage
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class Pipeline:
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def __init__(self):
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# Pipeline filters are only compatible with Open WebUI
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# 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.
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self.type = "filter"
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# Optionally, you can set the id and name of the pipeline.
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# Assign a unique identifier to the pipeline.
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# The identifier must be unique across all pipelines.
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# The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
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self.id = "filter_pipeline"
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self.name = "Filter"
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class Valves(BaseModel):
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# List target pipeline ids (models) that this filter will be connected to.
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# If you want to connect this filter to all pipelines, you can set pipelines to ["*"]
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pipelines: List[str] = []
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# Assign a priority level to the filter pipeline.
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# The priority level determines the order in which the filter pipelines are executed.
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# The lower the number, the higher the priority.
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priority: int = 0
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# Add your custom parameters here
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pass
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self.valves = Valves(**{"pipelines": ["llama3:latest"]})
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pass
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async def on_startup(self):
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# This function is called when the server is started.
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print(f"on_startup:{__name__}")
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pass
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async def on_shutdown(self):
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# This function is called when the server is stopped.
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print(f"on_shutdown:{__name__}")
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pass
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async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
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# This filter is applied to the form data before it is sent to the OpenAI API.
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print(f"inlet:{__name__}")
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print(body)
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print(user)
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return body
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