"""
title: Azure AI Foundry Pipeline
author: owndev
author_url: https://github.com/owndev/
project_url: https://github.com/owndev/Open-WebUI-Functions
funding_url: https://github.com/sponsors/owndev
version: 2.3.1
license: Apache License 2.0
description: A pipeline for interacting with Azure AI services, enabling seamless communication with various AI models via configurable headers and robust error handling. This includes support for Azure OpenAI models as well as other Azure AI models by dynamically managing headers and request configurations.
features:
- Supports dynamic model specification via headers.
- Filters valid parameters to ensure clean requests.
- Handles streaming and non-streaming responses.
- Provides flexible timeout and error handling mechanisms.
- Compatible with Azure OpenAI and other Azure AI models.
- Predefined models for easy access.
- Encrypted storage of sensitive API keys
"""
from typing import List, Union, Generator, Iterator, Optional, Dict, Any, AsyncIterator
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field, GetCoreSchemaHandler
from starlette.background import BackgroundTask
from open_webui.env import AIOHTTP_CLIENT_TIMEOUT, SRC_LOG_LEVELS
from cryptography.fernet import Fernet, InvalidToken
import aiohttp
import json
import os
import logging
import base64
import hashlib
from pydantic_core import core_schema
# Simplified encryption implementation with automatic handling
class EncryptedStr(str):
"""A string type that automatically handles encryption/decryption"""
@classmethod
def _get_encryption_key(cls) -> Optional[bytes]:
"""
Generate encryption key from WEBUI_SECRET_KEY if available
Returns None if no key is configured
"""
secret = os.getenv("WEBUI_SECRET_KEY")
if not secret:
return None
hashed_key = hashlib.sha256(secret.encode()).digest()
return base64.urlsafe_b64encode(hashed_key)
@classmethod
def encrypt(cls, value: str) -> str:
"""
Encrypt a string value if a key is available
Returns the original value if no key is available
"""
if not value or value.startswith("encrypted:"):
return value
key = cls._get_encryption_key()
if not key: # No encryption if no key
return value
f = Fernet(key)
encrypted = f.encrypt(value.encode())
return f"encrypted:{encrypted.decode()}"
@classmethod
def decrypt(cls, value: str) -> str:
"""
Decrypt an encrypted string value if a key is available
Returns the original value if no key is available or decryption fails
"""
if not value or not value.startswith("encrypted:"):
return value
key = cls._get_encryption_key()
if not key: # No decryption if no key
return value[len("encrypted:") :] # Return without prefix
try:
encrypted_part = value[len("encrypted:") :]
f = Fernet(key)
decrypted = f.decrypt(encrypted_part.encode())
return decrypted.decode()
except (InvalidToken, Exception):
return value
# Pydantic integration
@classmethod
def __get_pydantic_core_schema__(
cls, _source_type: Any, _handler: GetCoreSchemaHandler
) -> core_schema.CoreSchema:
return core_schema.union_schema(
[
core_schema.is_instance_schema(cls),
core_schema.chain_schema(
[
core_schema.str_schema(),
core_schema.no_info_plain_validator_function(
lambda value: cls(cls.encrypt(value) if value else value)
),
]
),
],
serialization=core_schema.plain_serializer_function_ser_schema(
lambda instance: str(instance)
),
)
def get_decrypted(self) -> str:
"""Get the decrypted value"""
return self.decrypt(self)
# Helper functions
async def cleanup_response(
response: Optional[aiohttp.ClientResponse],
session: Optional[aiohttp.ClientSession],
) -> None:
"""
Clean up the response and session objects.
Args:
response: The ClientResponse object to close
session: The ClientSession object to close
"""
if response:
response.close()
if session:
await session.close()
class Pipe:
# Environment variables for API key, endpoint, and optional model
class Valves(BaseModel):
# API key for Azure AI
AZURE_AI_API_KEY: EncryptedStr = Field(
default=os.getenv("AZURE_AI_API_KEY", "API_KEY"),
description="API key for Azure AI",
)
# Endpoint for Azure AI (e.g. "https://<your-endpoint>/chat/completions?api-version=2024-05-01-preview" or "https://<your-endpoint>/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview")
AZURE_AI_ENDPOINT: str = Field(
default=os.getenv(
"AZURE_AI_ENDPOINT",
"https://<your-endpoint>/chat/completions?api-version=2024-05-01-preview",
),
description="Endpoint for Azure AI",
)
# Optional model name, only necessary if not Azure OpenAI or if model name not in URL (e.g. "https://<your-endpoint>/openai/deployments/<model-name>/chat/completions")
# Multiple models can be specified as a semicolon-separated list (e.g. "gpt-4o;gpt-4o-mini")
# or a comma-separated list (e.g. "gpt-4o,gpt-4o-mini").
AZURE_AI_MODEL: str = Field(
default=os.getenv("AZURE_AI_MODEL", ""),
description="Optional model names for Azure AI (e.g. gpt-4o, gpt-4o-mini)",
)
# Switch for sending model name in request body
AZURE_AI_MODEL_IN_BODY: bool = Field(
default=os.getenv("AZURE_AI_MODEL_IN_BODY", False),
description="If True, include the model name in the request body instead of as a header.",
)
# Flag to indicate if predefined Azure AI models should be used
USE_PREDEFINED_AZURE_AI_MODELS: bool = Field(
default=os.getenv("USE_PREDEFINED_AZURE_AI_MODELS", False),
description="Flag to indicate if predefined Azure AI models should be used.",
)
# If True, use Authorization header with Bearer token instead of api-key header.
USE_AUTHORIZATION_HEADER: bool = Field(
default=bool(os.getenv("AZURE_AI_USE_AUTHORIZATION_HEADER", False)),
description="Set to True to use Authorization header with Bearer token instead of api-key header.",
)
def __init__(self):
self.valves = self.Valves()
self.name: str = "Azure AI"
def validate_environment(self) -> None:
"""
Validates that required environment variables are set.
Raises:
ValueError: If required environment variables are not set.
"""
# Access the decrypted API key
api_key = self.valves.AZURE_AI_API_KEY.get_decrypted()
if not api_key:
raise ValueError("AZURE_AI_API_KEY is not set!")
if not self.valves.AZURE_AI_ENDPOINT:
raise ValueError("AZURE_AI_ENDPOINT is not set!")
def get_headers(self, model_name: str = None) -> Dict[str, str]:
"""
Constructs the headers for the API request, including the model name if defined.
Args:
model_name: Optional model name to use instead of the default one
Returns:
Dictionary containing the required headers for the API request.
"""
# Access the decrypted API key
api_key = self.valves.AZURE_AI_API_KEY.get_decrypted()
if self.valves.USE_AUTHORIZATION_HEADER:
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
else:
headers = {"api-key": api_key, "Content-Type": "application/json"}
# If we have a model name and it shouldn't be in the body, add it to headers
if not self.valves.AZURE_AI_MODEL_IN_BODY:
# If specific model name provided, use it
if model_name:
headers["x-ms-model-mesh-model-name"] = model_name
# Otherwise, if AZURE_AI_MODEL has a single value, use that
elif (
self.valves.AZURE_AI_MODEL
and ";" not in self.valves.AZURE_AI_MODEL
and "," not in self.valves.AZURE_AI_MODEL
and " " not in self.valves.AZURE_AI_MODEL
):
headers["x-ms-model-mesh-model-name"] = self.valves.AZURE_AI_MODEL
return headers
def validate_body(self, body: Dict[str, Any]) -> None:
"""
Validates the request body to ensure required fields are present.
Args:
body: The request body to validate
Raises:
ValueError: If required fields are missing or invalid.
"""
if "messages" not in body or not isinstance(body["messages"], list):
raise ValueError("The 'messages' field is required and must be a list.")
def parse_models(self, models_str: str) -> List[str]:
"""
Parses a string of models separated by commas, semicolons, or spaces.
Args:
models_str: String containing model names separated by commas, semicolons, or spaces
Returns:
List of individual model names
"""
if not models_str:
return []
# Replace semicolons and commas with spaces, then split by spaces and filter empty strings
models = []
for model in models_str.replace(";", " ").replace(",", " ").split():
if model.strip():
models.append(model.strip())
return models
def get_azure_models(self) -> List[Dict[str, str]]:
"""
Returns a list of predefined Azure AI models.
Returns:
List of dictionaries containing model id and name.
"""
return [
{"id": "AI21-Jamba-1.5-Large", "name": "AI21 Jamba 1.5 Large"},
{"id": "AI21-Jamba-1.5-Mini", "name": "AI21 Jamba 1.5 Mini"},
{"id": "Codestral-2501", "name": "Codestral 25.01"},
{"id": "Cohere-command-r", "name": "Cohere Command R"},
{"id": "Cohere-command-r-08-2024", "name": "Cohere Command R 08-2024"},
{"id": "Cohere-command-r-plus", "name": "Cohere Command R+"},
{
"id": "Cohere-command-r-plus-08-2024",
"name": "Cohere Command R+ 08-2024",
},
{"id": "cohere-command-a", "name": "Cohere Command A"},
{"id": "DeepSeek-R1", "name": "DeepSeek-R1"},
{"id": "DeepSeek-V3", "name": "DeepSeek-V3"},
{"id": "DeepSeek-V3-0324", "name": "DeepSeek-V3-0324"},
{"id": "jais-30b-chat", "name": "JAIS 30b Chat"},
{
"id": "Llama-3.2-11B-Vision-Instruct",
"name": "Llama-3.2-11B-Vision-Instruct",
},
{
"id": "Llama-3.2-90B-Vision-Instruct",
"name": "Llama-3.2-90B-Vision-Instruct",
},
{"id": "Llama-3.3-70B-Instruct", "name": "Llama-3.3-70B-Instruct"},
{"id": "Meta-Llama-3-70B-Instruct", "name": "Meta-Llama-3-70B-Instruct"},
{"id": "Meta-Llama-3-8B-Instruct", "name": "Meta-Llama-3-8B-Instruct"},
{
"id": "Meta-Llama-3.1-405B-Instruct",
"name": "Meta-Llama-3.1-405B-Instruct",
},
{
"id": "Meta-Llama-3.1-70B-Instruct",
"name": "Meta-Llama-3.1-70B-Instruct",
},
{"id": "Meta-Llama-3.1-8B-Instruct", "name": "Meta-Llama-3.1-8B-Instruct"},
{"id": "Ministral-3B", "name": "Ministral 3B"},
{"id": "Mistral-large", "name": "Mistral Large"},
{"id": "Mistral-large-2407", "name": "Mistral Large (2407)"},
{"id": "Mistral-Large-2411", "name": "Mistral Large 24.11"},
{"id": "Mistral-Nemo", "name": "Mistral Nemo"},
{"id": "Mistral-small", "name": "Mistral Small"},
{"id": "mistral-small-2503", "name": "Mistral Small 3.1"},
{"id": "mistral-medium-2505", "name": "Mistral Medium 3 (25.05)"},
{"id": "grok-3", "name": "Grok 3"},
{"id": "gpt-4o", "name": "OpenAI GPT-4o"},
{"id": "gpt-4o-mini", "name": "OpenAI GPT-4o mini"},
{"id": "gpt-4.1", "name": "OpenAI GPT-4.1"},
{"id": "gpt-4.1-mini", "name": "OpenAI GPT-4.1 Mini"},
{"id": "gpt-4.1-nano", "name": "OpenAI GPT-4.1 Nano"},
{"id": "o1", "name": "OpenAI o1"},
{"id": "o1-mini", "name": "OpenAI o1-mini"},
{"id": "o1-preview", "name": "OpenAI o1-preview"},
{"id": "o3", "name": "OpenAI o3"},
{"id": "o3-mini", "name": "OpenAI o3-mini"},
{"id": "o4-mini", "name": "OpenAI o4-mini"},
{
"id": "Phi-3-medium-128k-instruct",
"name": "Phi-3-medium instruct (128k)",
},
{"id": "Phi-3-medium-4k-instruct", "name": "Phi-3-medium instruct (4k)"},
{"id": "Phi-3-mini-128k-instruct", "name": "Phi-3-mini instruct (128k)"},
{"id": "Phi-3-mini-4k-instruct", "name": "Phi-3-mini instruct (4k)"},
{"id": "Phi-3-small-128k-instruct", "name": "Phi-3-small instruct (128k)"},
{"id": "Phi-3-small-8k-instruct", "name": "Phi-3-small instruct (8k)"},
{"id": "Phi-3.5-mini-instruct", "name": "Phi-3.5-mini instruct (128k)"},
{"id": "Phi-3.5-MoE-instruct", "name": "Phi-3.5-MoE instruct (128k)"},
{"id": "Phi-3.5-vision-instruct", "name": "Phi-3.5-vision instruct (128k)"},
{"id": "Phi-4", "name": "Phi-4"},
{"id": "Phi-4-mini-instruct", "name": "Phi-4 mini instruct"},
{"id": "Phi-4-multimodal-instruct", "name": "Phi-4 multimodal instruct"},
{"id": "Phi-4-reasoning", "name": "Phi-4 Reasoning"},
{"id": "Phi-4-mini-reasoning", "name": "Phi-4 Mini Reasoning"},
{"id": "MAI-DS-R1", "name": "Microsoft Deepseek R1"},
{"id": "model-router", "name": "Model Router"},
]
def pipes(self) -> List[Dict[str, str]]:
"""
Returns a list of available pipes based on configuration.
Returns:
List of dictionaries containing pipe id and name.
"""
self.validate_environment()
# If custom models are provided, parse them and return as pipes
if self.valves.AZURE_AI_MODEL:
self.name = "Azure AI: "
models = self.parse_models(self.valves.AZURE_AI_MODEL)
if models:
return [{"id": model, "name": model} for model in models]
else:
# Fallback for backward compatibility
return [
{
"id": self.valves.AZURE_AI_MODEL,
"name": self.valves.AZURE_AI_MODEL,
}
]
# If custom model is not provided but predefined models are enabled, return those.
if self.valves.USE_PREDEFINED_AZURE_AI_MODELS:
self.name = "Azure AI: "
return self.get_azure_models()
# Otherwise, use a default name.
return [{"id": "Azure AI", "name": "Azure AI"}]
async def stream_processor(
self, content: aiohttp.StreamReader, __event_emitter__=None
) -> AsyncIterator[bytes]:
"""
Process streaming content and properly handle completion status updates.
Args:
content: The streaming content from the response
__event_emitter__: Optional event emitter for status updates
Yields:
Bytes from the streaming content
"""
try:
async for chunk in content:
yield chunk
# Send completion status update when streaming is done
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {"description": "Streaming completed", "done": True},
}
)
except Exception as e:
log = logging.getLogger("azure_ai.stream_processor")
log.error(f"Error processing stream: {e}")
# Send error status update
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {"description": f"Error: {str(e)}", "done": True},
}
)
async def pipe(
self, body: Dict[str, Any], __event_emitter__=None
) -> Union[str, Generator, Iterator, Dict[str, Any], StreamingResponse]:
"""
Main method for sending requests to the Azure AI endpoint.
The model name is passed as a header if defined.
Args:
body: The request body containing messages and other parameters
__event_emitter__: Optional event emitter function for status updates
Returns:
Response from Azure AI API, which could be a string, dictionary or streaming response
"""
log = logging.getLogger("azure_ai.pipe")
log.setLevel(SRC_LOG_LEVELS["OPENAI"])
# Validate the request body
self.validate_body(body)
selected_model = None
if "model" in body and body["model"]:
selected_model = body["model"]
# Safer model extraction with split
selected_model = (
selected_model.split(".", 1)[1]
if "." in selected_model
else selected_model
)
# Construct headers with selected model
headers = self.get_headers(selected_model)
# Filter allowed parameters
allowed_params = {
"model",
"messages",
"frequency_penalty",
"max_tokens",
"presence_penalty",
"reasoning_effort",
"response_format",
"seed",
"stop",
"stream",
"temperature",
"tool_choice",
"tools",
"top_p",
}
filtered_body = {k: v for k, v in body.items() if k in allowed_params}
if self.valves.AZURE_AI_MODEL and self.valves.AZURE_AI_MODEL_IN_BODY:
# If a model was explicitly selected in the request, use that
if selected_model:
filtered_body["model"] = selected_model
else:
# Otherwise, if AZURE_AI_MODEL contains multiple models, only use the first one to avoid errors
models = self.parse_models(self.valves.AZURE_AI_MODEL)
if models and len(models) > 0:
filtered_body["model"] = models[0]
else:
# Fallback to the original value
filtered_body["model"] = self.valves.AZURE_AI_MODEL
elif "model" in filtered_body and filtered_body["model"]:
# Safer model extraction with split
filtered_body["model"] = (
filtered_body["model"].split(".", 1)[1]
if "." in filtered_body["model"]
else filtered_body["model"]
)
# Convert the modified body back to JSON
payload = json.dumps(filtered_body)
# Send status update via event emitter if available
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "Sending request to Azure AI...",
"done": False,
},
}
)
request = None
session = None
streaming = False
response = None
try:
session = aiohttp.ClientSession(
trust_env=True,
timeout=aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT),
)
request = await session.request(
method="POST",
url=self.valves.AZURE_AI_ENDPOINT,
data=payload,
headers=headers,
)
# Check if response is SSE
if "text/event-stream" in request.headers.get("Content-Type", ""):
streaming = True
# Send status update for successful streaming connection
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {
"description": "Streaming response from Azure AI...",
"done": False,
},
}
)
return StreamingResponse(
self.stream_processor(request.content, __event_emitter__),
status_code=request.status,
headers=dict(request.headers),
background=BackgroundTask(
cleanup_response, response=request, session=session
),
)
else:
try:
response = await request.json()
except Exception as e:
log.error(f"Error parsing JSON response: {e}")
response = await request.text()
request.raise_for_status()
# Send completion status update
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {"description": "Request completed", "done": True},
}
)
return response
except Exception as e:
log.exception(f"Error in Azure AI request: {e}")
detail = f"Exception: {str(e)}"
if isinstance(response, dict):
if "error" in response:
detail = f"{response['error']['message'] if 'message' in response['error'] else response['error']}"
elif isinstance(response, str):
detail = response
# Send error status update
if __event_emitter__:
await __event_emitter__(
{
"type": "status",
"data": {"description": f"Error: {detail}", "done": True},
}
)
return f"Error: {detail}"
finally:
if not streaming and session:
if request:
request.close()
await session.close()
