""" title: Azure OpenAI Pipe author: open-webui, adapted by nomppy author_url: https://github.com/open-webui funding_url: https://github.com/open-webui version: 0.1.0 license: MIT """ from typing import List, Union, Generator, Iterator from pydantic import BaseModel import requests import os class Pipe: class Valves(BaseModel): # You can add your custom valves here. AZURE_OPENAI_API_KEY: str = os.getenv("AZURE_OPENAI_API_KEY_GPT", "API_KEY") AZURE_OPENAI_ENDPOINT: str = os.getenv( "AZURE_OPENAI_ENDPOINT_GPT", "API_ENDPOINT" ) AZURE_OPENAI_DEPLOYMENT_NAME: str = os.getenv( "AZURE_OPENAI_DEPLOYMENT_NAME_GPT", "DEPLOYMENT_NAME" ) AZURE_OPENAI_API_VERSION: str = os.getenv( "AZURE_OPENAI_API_VERSION_GPT", "API_VERSION" ) 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 = "azure_openai_pipeline" self.name = "Azure OpenAI Pipe" self.valves = self.Valves() 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 def pipe(self, body: dict) -> Union[str, Generator, Iterator]: # This is where you can add your custom pipelines like RAG. print(f"pipe:{__name__}") headers = { "api-key": self.valves.AZURE_OPENAI_API_KEY, "Content-Type": "application/json", "x-ms-model-mesh-model-name": self.valves.AZURE_OPENAI_DEPLOYMENT_NAME, } url = f"{self.valves.AZURE_OPENAI_ENDPOINT}/openai/deployments/{self.valves.AZURE_OPENAI_DEPLOYMENT_NAME}/chat/completions?api-version={self.valves.AZURE_OPENAI_API_VERSION}" allowed_params = { "messages", "temperature", "role", "content", "contentPart", "contentPartImage", "enhancements", "dataSources", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "function_call", "funcions", "tools", "tool_choice", "top_p", "log_probs", "top_logprobs", "response_format", "seed", } # remap user field if "user" in body and not isinstance(body["user"], str): body["user"] = ( body["user"]["id"] if "id" in body["user"] else str(body["user"]) ) filtered_body = {k: v for k, v in body.items() if k in allowed_params} # log fields that were filtered out as a single line if len(body) != len(filtered_body): print( f"Dropped params: {', '.join(set(body.keys()) - set(filtered_body.keys()))}" ) try: r = requests.post( url=url, json=filtered_body, headers=headers, stream=True, ) r.raise_for_status() if body["stream"]: return r.iter_lines() else: return r.json() except Exception as e: print("Requests error in Azure pipeline") if r: text = r.text return f"Error: {e} ({text})" else: return f"Error: {e}"