Azure AI function and charset

[SbiRomainLopez]

Hi, I am using your function for Azure Ai, it works pretty fine but when using french prompte it show an issue with character set :

Do you have an idea where is it from ?

Please note I have not this issue with other Pipe for like AzureOpenAi on the same configuration. First prompt is with your function and Llama, Second prompt is from bellow function that is pretty much the same :

"""
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",
        }

        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}"

[owndev]

I found the error. It should work now (tested with Llama-3.3-70B-Instruct). Thanks for the feedback 👍