mirror of
https://github.com/open-webui/pipelines
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68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
from typing import List, Union, Generator, Iterator
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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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class Pipeline:
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class Valves(BaseModel):
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# You can add your custom valves here.
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AZURE_OPENAI_API_KEY: str = "your-azure-openai-api-key-here"
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AZURE_OPENAI_ENDPOINT: str = "your-azure-openai-endpoint-here"
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DEPLOYMENT_NAME: str = "your-deployment-name-here"
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API_VERSION: str = "2023-10-01-preview"
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MODEL: str = "gpt-3.5-turbo"
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pass
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def __init__(self):
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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 = "azure_openai_pipeline"
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self.name = "Azure OpenAI Pipeline"
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self.valves = self.Valves()
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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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def pipe(
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self, user_message: str, model_id: str, messages: List[dict], body: dict
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) -> Union[str, Generator, Iterator]:
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# This is where you can add your custom pipelines like RAG.
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print(f"pipe:{__name__}")
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print(messages)
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print(user_message)
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headers = {
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"api-key": self.valves.AZURE_OPENAI_API_KEY,
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"Content-Type": "application/json",
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}
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url = f"{self.valves.AZURE_OPENAI_ENDPOINT}/openai/deployments/{self.valves.DEPLOYMENT_NAME}/chat/completions?api-version={self.valves.API_VERSION}"
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try:
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r = requests.post(
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url=url,
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json={**body, "model": self.valves.MODEL},
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headers=headers,
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stream=True,
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)
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r.raise_for_status()
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if body["stream"]:
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return r.iter_lines()
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else:
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return r.json()
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except Exception as e:
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return f"Error: {e}"
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