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https://github.com/open-webui/pipelines
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feat: filter outlet & libretranslate example
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parent
95ed193ef7
commit
2e934304cf
2
main.py
2
main.py
@ -526,7 +526,7 @@ async def filter_outlet(pipeline_id: str, form_data: FilterForm):
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@app.post("/v1/chat/completions")
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@app.post("/chat/completions")
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async def generate_openai_chat_completion(form_data: OpenAIChatCompletionForm):
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user_message = get_last_user_message(form_data.messages)
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user_message = get_last_user_message(form_data.messages.model_dump())
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messages = [message.model_dump() for message in form_data.messages]
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if (
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140
pipelines/examples/libretranlsate_filter_pipeline.py
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140
pipelines/examples/libretranlsate_filter_pipeline.py
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@ -0,0 +1,140 @@
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from typing import List, Optional
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from schemas import OpenAIChatMessage
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from pydantic import BaseModel
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import requests
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import os
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from utils.main import get_last_user_message, get_last_assistant_message
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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 = "libretranslate_filter_pipeline"
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self.name = "LibreTranslate 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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# e.g. ["llama3:latest", "gpt-3.5-turbo"]
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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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# Valves
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libretranslate_url: str
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# Source and target languages
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# User message will be translated from source_user to target_user
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source_user: Optional[str] = "auto"
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target_user: Optional[str] = "en"
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# Assistant languages
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# Assistant message will be translated from source_assistant to target_assistant
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source_assistant: Optional[str] = "en"
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target_assistant: Optional[str] = "es"
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# Initialize
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self.valves = Valves(
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**{
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"pipelines": ["*"], # Connect to all pipelines
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"libretranslate_url": os.getenv(
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"LIBRETRANSLATE_API_BASE_URL", "http://localhost:5000"
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),
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}
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)
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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 on_valves_updated(self):
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# This function is called when the valves are updated.
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pass
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def translate(self, text: str, source: str, target: str) -> str:
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payload = {
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"q": text,
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"source": source,
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"target": target,
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}
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try:
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r = requests.post(
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f"{self.valves.libretranslate_url}/translate", json=payload
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)
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r.raise_for_status()
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data = r.json()
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return data["translatedText"]
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except Exception as e:
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print(f"Error translating text: {e}")
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return text
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async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
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print(f"inlet:{__name__}")
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messages = body["messages"]
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user_message = get_last_user_message(messages)
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print(f"User message: {user_message}")
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# Translate user message
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translated_user_message = self.translate(
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user_message,
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self.valves.source_user,
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self.valves.target_user,
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)
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print(f"Translated user message: {translated_user_message}")
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for message in reversed(messages):
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if message["role"] == "user":
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message["content"] = translated_user_message
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break
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body = {**body, "messages": messages}
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return body
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async def outlet(self, body: dict, user: Optional[dict] = None) -> dict:
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print(f"outlet:{__name__}")
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messages = body["messages"]
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assistant_message = get_last_assistant_message(messages)
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print(f"Assistant message: {assistant_message}")
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# Translate assistant message
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translated_assistant_message = self.translate(
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assistant_message,
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self.valves.source_assistant,
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self.valves.target_assistant,
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)
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print(f"Translated assistant message: {translated_assistant_message}")
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for message in reversed(messages):
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if message["role"] == "assistant":
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message["content"] = translated_assistant_message
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break
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body = {**body, "messages": messages}
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return body
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@ -24,6 +24,21 @@ def stream_message_template(model: str, message: str):
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def get_last_user_message(messages: List[dict]) -> str:
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for message in reversed(messages):
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if message.role == "user":
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return message.content
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if message["role"] == "user":
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if isinstance(message["content"], list):
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for item in message["content"]:
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if item["type"] == "text":
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return item["text"]
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return message["content"]
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return None
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def get_last_assistant_message(messages: List[dict]) -> str:
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for message in reversed(messages):
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if message["role"] == "assistant":
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if isinstance(message["content"], list):
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for item in message["content"]:
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if item["type"] == "text":
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return item["text"]
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return message["content"]
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return None
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