"""
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: 2.6.0
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. Azure AI Search (RAG) integration is only supported with Azure OpenAI endpoints.
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
- Azure AI Search / RAG integration with native OpenWebUI citations (Azure OpenAI only)
- Automatic [docX] to markdown link conversion for clickable citations
- Relevance scores from Azure AI Search displayed in citation cards
"""
from typing import (
List,
Union,
Generator,
Iterator,
Optional,
Dict,
Any,
AsyncIterator,
Set,
Callable,
)
from urllib.parse import urlparse
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field, GetCoreSchemaHandler
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
import re
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)
),
)
# 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:
# Regex pattern for matching [docX] citation references
DOC_REF_PATTERN = re.compile(r"\[doc(\d+)\]")
# Environment variables for API key, endpoint, and optional model
class Valves(BaseModel):
# Custom prefix for pipeline display name
AZURE_AI_PIPELINE_PREFIX: str = Field(
default=os.getenv("AZURE_AI_PIPELINE_PREFIX", "Azure AI"),
description="Custom prefix for the pipeline display name (e.g., 'Azure AI', 'My Azure', 'Company AI'). The final display will be: '<prefix>: <model_name>'",
)
# 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=bool(
os.getenv("AZURE_AI_MODEL_IN_BODY", "false").lower() == "true"
),
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=bool(
os.getenv("USE_PREDEFINED_AZURE_AI_MODELS", "false").lower() == "true"
),
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").lower()
== "true"
),
description="Set to True to use Authorization header with Bearer token instead of api-key header.",
)
# Azure AI Data Sources Configuration (for Azure AI Search / RAG)
# Only works with Azure OpenAI endpoints: https://<deployment>.openai.azure.com/openai/deployments/<model>/chat/completions?api-version=2025-01-01-preview
AZURE_AI_DATA_SOURCES: str = Field(
default=os.getenv("AZURE_AI_DATA_SOURCES", ""),
description='JSON configuration for data_sources field (for Azure AI Search / RAG). Example: \'[{"type":"azure_search","parameters":{"endpoint":"https://xxx.search.windows.net","index_name":"your-index","authentication":{"type":"api_key","key":"your-key"}}}]\'',
)
# Enable relevance scores from Azure AI Search
AZURE_AI_INCLUDE_SEARCH_SCORES: bool = Field(
default=bool(
os.getenv("AZURE_AI_INCLUDE_SEARCH_SCORES", "true").lower() == "true"
),
description="If True, automatically add 'include_contexts' with 'all_retrieved_documents' to Azure AI Search requests to get relevance scores (original_search_score and rerank_score). This enables relevance percentage display in citation cards.",
)
# BM25 score normalization factor for relevance percentage display
# BM25 scores are unbounded and vary by collection. This value is used to normalize
# scores to 0-1 range: normalized = min(score / BM25_SCORE_MAX, 1.0)
# See: https://learn.microsoft.com/en-us/azure/search/index-ranking-similarity
BM25_SCORE_MAX: float = Field(
default=float(os.getenv("AZURE_AI_BM25_SCORE_MAX", "100.0")),
description="Normalization divisor for BM25 search scores (0-1 range). Adjust based on your index characteristics. Default 100.0 is suitable for typical collections; higher values (e.g., 200.0) reduce saturation for large documents.",
)
# Rerank score normalization factor for relevance percentage display
# Cohere rerankers via Azure return 0-4, most others 0-1. This value normalizes
# scores above 1.0 to 0-1 range: normalized = min(score / RERANK_SCORE_MAX, 1.0)
# See: https://learn.microsoft.com/en-us/azure/search/semantic-ranking
RERANK_SCORE_MAX: float = Field(
default=float(os.getenv("AZURE_AI_RERANK_SCORE_MAX", "4.0")),
description="Normalization divisor for rerank scores (0-1 range). Use 4.0 for Cohere rerankers, 1.0 for standard semantic rerankers.",
)
def __init__(self):
self.valves = self.Valves()
self.name: str = f"{self.valves.AZURE_AI_PIPELINE_PREFIX}:"
# Extract model name from Azure OpenAI URL if available
self._extracted_model_name = self._extract_model_from_url()
def _extract_model_from_url(self) -> Optional[str]:
"""
Extract model name from Azure OpenAI URL format.
Expected format: https://<deployment>.openai.azure.com/openai/deployments/<model>/chat/completions
Returns:
Model name if found in URL, None otherwise
"""
if not self.valves.AZURE_AI_ENDPOINT:
return None
try:
endpoint_host = urlparse(self.valves.AZURE_AI_ENDPOINT).hostname or ""
if (
endpoint_host == "openai.azure.com"
or endpoint_host.endswith(".openai.azure.com")
) and "/deployments/" in self.valves.AZURE_AI_ENDPOINT:
# Extract model name from URL pattern
# Pattern: .../deployments/{model}/chat/completions...
parts = self.valves.AZURE_AI_ENDPOINT.split("/deployments/")
if len(parts) > 1:
model_part = parts[1].split("/")[
0
] # Get first segment after deployments/
if model_part:
# Log for debugging
log = logging.getLogger("azure_ai._extract_model_from_url")
log.debug(
f"Extracted model name '{model_part}' from URL: {self.valves.AZURE_AI_ENDPOINT}"
)
return model_part
except Exception as e:
# Log parsing errors
log = logging.getLogger("azure_ai._extract_model_from_url")
log.warning(
f"Error extracting model from URL {self.valves.AZURE_AI_ENDPOINT}: {e}"
)
return None
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 = EncryptedStr.decrypt(self.valves.AZURE_AI_API_KEY)
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 = EncryptedStr.decrypt(self.valves.AZURE_AI_API_KEY)
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 get_azure_ai_data_sources(self) -> Optional[List[Dict[str, Any]]]:
"""
Builds Azure AI data sources configuration from the AZURE_AI_DATA_SOURCES environment variable.
Only works with Azure OpenAI endpoints: https://<deployment>.openai.azure.com/openai/deployments/<model>/chat/completions?api-version=2025-01-01-preview
If AZURE_AI_INCLUDE_SEARCH_SCORES is enabled, automatically adds 'include_contexts'
with 'all_retrieved_documents' to get relevance scores from Azure AI Search.
Returns:
List containing Azure AI data source configuration, or None if not configured.
"""
if not self.valves.AZURE_AI_DATA_SOURCES:
return None
log = logging.getLogger("azure_ai.get_azure_ai_data_sources")
try:
data_sources = json.loads(self.valves.AZURE_AI_DATA_SOURCES)
if not isinstance(data_sources, list):
# If it's a single object, wrap it in a list
data_sources = [data_sources]
# If AZURE_AI_INCLUDE_SEARCH_SCORES is enabled, add include_contexts
if self.valves.AZURE_AI_INCLUDE_SEARCH_SCORES:
for source in data_sources:
if (
isinstance(source, dict)
and source.get("type") == "azure_search"
and "parameters" in source
):
params = source["parameters"]
# Get or create include_contexts list
include_contexts = params.get("include_contexts", [])
if not isinstance(include_contexts, list):
include_contexts = [include_contexts]
# Add 'citations' and 'all_retrieved_documents' if not present
if "citations" not in include_contexts:
include_contexts.append("citations")
if "all_retrieved_documents" not in include_contexts:
include_contexts.append("all_retrieved_documents")
params["include_contexts"] = include_contexts
log.debug(
f"Added include_contexts to Azure Search: {include_contexts}"
)
return data_sources
except json.JSONDecodeError as e:
# Log error and return None if JSON parsing fails
log.error(f"Error parsing AZURE_AI_DATA_SOURCES: {e}")
return None
def _extract_citations_from_response(
self, response_data: Dict[str, Any]
) -> Optional[List[Dict[str, Any]]]:
"""
Extract citations from an Azure AI response (streaming or non-streaming).
Supports both 'citations' and 'all_retrieved_documents' response structures.
When include_contexts includes 'all_retrieved_documents', the response contains
additional score fields like 'original_search_score' and 'rerank_score'.
Args:
response_data: Response data from Azure AI (can be a delta or full message)
Returns:
List of citation objects, or None if no citations found
"""
log = logging.getLogger("azure_ai._extract_citations_from_response")
if not isinstance(response_data, dict):
log.debug(f"Response data is not a dict: {type(response_data)}")
return None
# Try multiple possible locations for citations
citations = None
# Check in choices[0].delta.context or choices[0].message.context
if "choices" in response_data and response_data["choices"]:
choice = response_data["choices"][0]
context = None
# Get context from delta (streaming) or message (non-streaming)
if "delta" in choice and isinstance(choice["delta"], dict):
context = choice["delta"].get("context")
elif "message" in choice and isinstance(choice["message"], dict):
context = choice["message"].get("context")
if context and isinstance(context, dict):
# Try citations first
if "citations" in context:
citations = context["citations"]
log.info(
f"Found {len(citations) if citations else 0} citations in context.citations"
)
# If all_retrieved_documents is present, merge score data into citations
if "all_retrieved_documents" in context:
all_docs = context["all_retrieved_documents"]
log.debug(
f"Found {len(all_docs) if all_docs else 0} all_retrieved_documents"
)
# If we have both citations and all_retrieved_documents,
# try to merge score data from all_retrieved_documents into citations
if citations and all_docs:
self._merge_score_data(citations, all_docs, log)
elif all_docs and not citations:
# Use all_retrieved_documents as citations if no citations found
citations = all_docs
log.info(
f"Using {len(citations)} all_retrieved_documents as citations"
)
else:
log.debug(
f"No context found in response. Choice keys: {choice.keys() if isinstance(choice, dict) else 'not a dict'}"
)
else:
log.debug(f"No choices in response. Response keys: {response_data.keys()}")
if citations and isinstance(citations, list):
log.info(f"Extracted {len(citations)} citations from response")
# Log first citation structure for debugging (only if INFO logging is enabled)
if citations and log.isEnabledFor(logging.INFO):
log.info(
f"First citation structure: {json.dumps(citations[0], default=str)[:500]}"
)
return citations
log.debug("No valid citations found in response")
return None
def _merge_score_data(
self,
citations: List[Dict[str, Any]],
all_docs: List[Dict[str, Any]],
log: logging.Logger,
) -> None:
"""
Merge score data from all_retrieved_documents into citations.
When include_contexts includes 'all_retrieved_documents', Azure returns
additional documents with score fields. This method attempts to match
them with citations and copy over the score data.
Copies:
- original_search_score: BM25/keyword search score
- rerank_score: Semantic reranker score (if enabled)
- filter_reason: Indicates which score is relevant ("score" or "rerank")
Args:
citations: List of citation objects to update (modified in place)
all_docs: List of all_retrieved_documents with score data
log: Logger instance
"""
# Build multiple lookup maps to maximize matching chances
# all_retrieved_documents may have different keys than citations
doc_data_by_title = {}
doc_data_by_filepath = {}
doc_data_by_content = {}
doc_data_by_chunk_id = {}
for doc in all_docs:
doc_data = {
"original_search_score": doc.get("original_search_score"),
"rerank_score": doc.get("rerank_score"),
"filter_reason": doc.get("filter_reason"),
}
log.debug(
f"Processing all_retrieved_document: title='{doc.get('title')}', "
f"chunk_id='{doc.get('chunk_id')}', "
f"original_search_score={doc_data['original_search_score']}, "
f"rerank_score={doc_data['rerank_score']}, "
f"filter_reason={doc_data['filter_reason']}"
)
# Only store if we have at least one score
if (
doc_data["original_search_score"] is None
and doc_data["rerank_score"] is None
):
log.debug(f"Skipping doc with no scores: {doc.get('title')}")
continue
# Index by title
if doc.get("title"):
doc_data_by_title[doc["title"]] = doc_data
# Index by filepath
if doc.get("filepath"):
doc_data_by_filepath[doc["filepath"]] = doc_data
# Index by chunk_id (may include title as prefix for uniqueness)
if doc.get("chunk_id") is not None:
# Store by plain chunk_id
doc_data_by_chunk_id[str(doc["chunk_id"])] = doc_data
# Also store by title-prefixed chunk_id for uniqueness
if doc.get("title"):
chunk_key_with_title = f"{doc['title']}_{doc['chunk_id']}"
doc_data_by_chunk_id[chunk_key_with_title] = doc_data
# Index by content prefix (first 100 chars)
if doc.get("content"):
content_key = (
doc["content"][:100]
if len(doc.get("content", "")) > 100
else doc.get("content")
)
doc_data_by_content[content_key] = doc_data
log.debug(
f"Built score lookup: by_title={len(doc_data_by_title)}, "
f"by_filepath={len(doc_data_by_filepath)}, "
f"by_chunk_id={len(doc_data_by_chunk_id)}, "
f"by_content={len(doc_data_by_content)}"
)
# Match citations with score data using multiple strategies
matched = 0
for citation in citations:
doc_data = None
# Try matching by title first (most reliable)
if not doc_data and citation.get("title"):
doc_data = doc_data_by_title.get(citation["title"])
if doc_data:
log.debug(f"Matched citation by title: {citation['title']}")
# Try matching by filepath
if not doc_data and citation.get("filepath"):
doc_data = doc_data_by_filepath.get(citation["filepath"])
if doc_data:
log.debug(f"Matched citation by filepath: {citation['filepath']}")
# Try matching by chunk_id with title prefix
if not doc_data and citation.get("chunk_id") is not None:
chunk_key = str(citation["chunk_id"])
if citation.get("title"):
chunk_key_with_title = f"{citation['title']}_{citation['chunk_id']}"
doc_data = doc_data_by_chunk_id.get(chunk_key_with_title)
if not doc_data:
doc_data = doc_data_by_chunk_id.get(chunk_key)
if doc_data:
log.debug(f"Matched citation by chunk_id: {citation['chunk_id']}")
# Try matching by content prefix
if not doc_data and citation.get("content"):
content_key = (
citation["content"][:100]
if len(citation.get("content", "")) > 100
else citation.get("content")
)
doc_data = doc_data_by_content.get(content_key)
if doc_data:
log.debug("Matched citation by content prefix")
if doc_data:
if doc_data.get("original_search_score") is not None:
citation["original_search_score"] = doc_data[
"original_search_score"
]
if doc_data.get("rerank_score") is not None:
citation["rerank_score"] = doc_data["rerank_score"]
if doc_data.get("filter_reason") is not None:
citation["filter_reason"] = doc_data["filter_reason"]
matched += 1
log.debug(
f"Citation scores: original={doc_data.get('original_search_score')}, "
f"rerank={doc_data.get('rerank_score')}, "
f"filter_reason={doc_data.get('filter_reason')}"
)
log.info(f"Merged score data for {matched}/{len(citations)} citations")
def _normalize_citation_for_openwebui(
self, citation: Dict[str, Any], index: int
) -> Dict[str, Any]:
"""
Normalize an Azure citation object to OpenWebUI citation event format.
The format follows OpenWebUI's official citation event structure:
https://docs.openwebui.com/features/plugin/development/events#source-or-citation-and-code-execution
Args:
citation: Azure citation object
index: Citation index (1-based)
Returns:
Complete citation event object with type and data fields
"""
log = logging.getLogger("azure_ai._normalize_citation_for_openwebui")
# Get title with fallback chain: title → filepath → url → "Unknown Document"
# Handle None values explicitly since dict.get() returns None if key exists but value is None
title_raw = citation.get("title") or ""
filepath_raw = citation.get("filepath") or ""
url_raw = citation.get("url") or ""
base_title = (
title_raw.strip()
or filepath_raw.strip()
or url_raw.strip()
or "Unknown Document"
)
# Include [docX] prefix in OpenWebUI citation card titles for document identification
title = f"[doc{index}] - {base_title}"
# Build source URL for metadata
source_url = url_raw or filepath_raw
# Build metadata with source information
# Use title with [docX] prefix as metadata source for OpenWebUI display
# The UI may extract display name from metadata.source rather than source.name
metadata_entry = {"source": title, "url": source_url}
if citation.get("metadata"):
metadata_entry.update(citation.get("metadata", {}))
# Get document content (handle None values)
content = citation.get("content") or ""
# Build normalized citation data structure matching OpenWebUI format exactly
citation_data = {
"document": [content],
"metadata": [metadata_entry],
"source": {"name": title},
}
# Add URL to source if available
if source_url:
citation_data["source"]["url"] = source_url
# Add distances array for relevance score (OpenWebUI uses this for percentage display)
# Azure AI Search returns filter_reason to indicate which score type is relevant:
# - filter_reason not present or "score": use original_search_score (BM25/keyword)
# - filter_reason "rerank": use rerank_score (semantic reranker)
# Reference: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/references/on-your-data
filter_reason = citation.get("filter_reason")
rerank_score = citation.get("rerank_score")
original_search_score = citation.get("original_search_score")
legacy_score = citation.get("score")
normalized_score = 0.0
# Select score based on filter_reason as per Azure documentation:
# - filter_reason="rerank": Document filtered by rerank score threshold, use rerank_score
# - filter_reason="score" or not present: Document filtered by/passed original search score, use original_search_score
if filter_reason == "rerank" and rerank_score is not None:
# Document filtered by rerank score - use rerank_score
# Cohere rerankers via Azure AI return scores in 0-4 range (source: Azure AI Search documentation)
# Most semantic rerankers return 0-1, so we normalize 0-4 range down to 0-1 for consistency.
# Reference: https://learn.microsoft.com/en-us/azure/search/semantic-ranking
score_val = float(rerank_score)
if score_val > 1.0:
normalized_score = min(score_val / self.valves.RERANK_SCORE_MAX, 1.0)
else:
normalized_score = score_val
log.debug(
f"Using rerank_score (filter_reason=rerank): {rerank_score} -> {normalized_score} "
f"(normalized via {self.valves.RERANK_SCORE_MAX})"
)
elif (
filter_reason is None or filter_reason == "score"
) and original_search_score is not None:
# filter_reason is "score" or not present - use original_search_score
# BM25 scores are unbounded and vary by collection size and term distribution.
# We normalize by dividing by BM25_SCORE_MAX to produce a value in 0-1 range.
# This preserves relative ranking without hard-capping high-relevance documents.
# Reference: https://learn.microsoft.com/en-us/azure/search/index-ranking-similarity
score_val = float(original_search_score)
if score_val > 1.0:
normalized_score = min(score_val / self.valves.BM25_SCORE_MAX, 1.0)
else:
normalized_score = score_val
log.debug(
f"Using original_search_score (filter_reason={filter_reason}): {original_search_score} -> {normalized_score} "
f"(normalized via {self.valves.BM25_SCORE_MAX})"
)
elif original_search_score is not None:
# Fallback for unknown filter_reason values - use original_search_score
score_val = float(original_search_score)
if score_val > 1.0:
normalized_score = min(score_val / self.valves.BM25_SCORE_MAX, 1.0)
else:
normalized_score = score_val
log.debug(
f"Using original_search_score (fallback, filter_reason={filter_reason}): {original_search_score} -> {normalized_score} "
f"(normalized via {self.valves.BM25_SCORE_MAX})"
)
elif rerank_score is not None:
# Fallback to rerank_score if available but filter_reason doesn't match
score_val = float(rerank_score)
if score_val > 1.0:
normalized_score = min(score_val / self.valves.RERANK_SCORE_MAX, 1.0)
else:
normalized_score = score_val
log.debug(
f"Using rerank_score (fallback): {rerank_score} -> {normalized_score} "
f"(normalized via {self.valves.RERANK_SCORE_MAX})"
)
elif legacy_score is not None:
normalized_score = float(legacy_score)
log.debug(f"Using legacy score: {legacy_score}")
else:
log.debug("No score available, using default 0.0")
citation_data["distances"] = [normalized_score]
# Build complete citation event structure
citation_event = {
"type": "citation",
"data": citation_data,
}
# Log the normalized citation for debugging (only if INFO logging is enabled)
if log.isEnabledFor(logging.INFO):
log.info(
f"Normalized citation {index}: title='{title}', "
f"content_length={len(content)}, "
f"url='{source_url}', "
f"filter_reason={filter_reason}, "
f"rerank_score={rerank_score}, original_search_score={original_search_score}, "
f"distances={citation_data['distances']}, "
f"event={json.dumps(citation_event, default=str)[:500]}"
)
return citation_event
def _build_citation_urls_map(
self, citations: Optional[List[Dict[str, Any]]]
) -> Dict[int, Optional[str]]:
"""
Build a mapping of citation indices to document URLs.
Args:
citations: List of citation objects with title, filepath, url, etc.
Returns:
Dict mapping 1-based citation index to URL (or None if no URL available)
"""
citation_urls: Dict[int, Optional[str]] = {}
if not citations:
return citation_urls
for i, citation in enumerate(citations, 1):
if isinstance(citation, dict):
# Get URL with fallback to filepath
url = citation.get("url") or ""
filepath = citation.get("filepath") or ""
citation_url = url.strip() or filepath.strip() or None
citation_urls[i] = citation_url
return citation_urls
def _format_citation_link(self, doc_num: int, url: Optional[str] = None) -> str:
"""
Format a markdown link for a [docX] reference.
If a URL is available, creates a clickable markdown link.
Otherwise, returns the original [docX] reference.
Args:
doc_num: The document number (1-based)
url: Optional URL for the document
Returns:
Formatted markdown link string or original [docX] reference
"""
if url:
# Create markdown link: [[doc1]](url)
return f"[[doc{doc_num}]]({url})"
else:
# No URL available, keep original reference
return f"[doc{doc_num}]"
def _convert_doc_refs_to_links(
self, content: str, citations: List[Dict[str, Any]]
) -> str:
"""
Convert [docX] references in content to markdown links with document URLs.
If a citation has a URL, [doc1] becomes [[doc1]](url). This creates clickable
links to the source documents in the response.
Args:
content: The response content containing [docX] references
citations: List of citation objects with title, url, etc.
Returns:
Content with [docX] references converted to markdown links
"""
if not content or not citations:
return content
log = logging.getLogger("azure_ai._convert_doc_refs_to_links")
# Build a mapping of citation index to URL
citation_urls = self._build_citation_urls_map(citations)
def replace_doc_ref(match):
"""Replace [docX] with [[docX]](url) if URL available"""
doc_num = int(match.group(1))
url = citation_urls.get(doc_num)
return self._format_citation_link(doc_num, url)
# Replace all [docX] references
converted = re.sub(self.DOC_REF_PATTERN, replace_doc_ref, content)
# Count conversions for logging
original_count = len(re.findall(self.DOC_REF_PATTERN, content))
linked_count = sum(
1 for i in range(1, len(citations) + 1) if citation_urls.get(i)
)
if original_count > 0:
log.info(
f"Converted {original_count} [docX] references to markdown links ({linked_count} with URLs)"
)
return converted
async def _emit_openwebui_citation_events(
self,
citations: List[Dict[str, Any]],
__event_emitter__: Optional[Callable[..., Any]],
content: str = "",
) -> None:
"""
Emit OpenWebUI citation events for citations.
Emits one citation event per source document, following the OpenWebUI
citation event format. Each citation is emitted separately to ensure
all sources appear in the UI.
Only emits citations that are actually referenced in the content (e.g., [doc1], [doc2]).
Args:
citations: List of Azure citation objects
__event_emitter__: Event emitter callable for sending citation events
content: The response content (used to filter only referenced citations)
"""
log = logging.getLogger("azure_ai._emit_openwebui_citation_events")
if not __event_emitter__:
log.warning("No __event_emitter__ provided, cannot emit citation events")
return
if not citations:
log.info("No citations to emit")
return
# Extract which citations are actually referenced in the content
referenced_indices = self._extract_referenced_citations(content)
# If we couldn't find any references, include all citations (backward compatibility)
if not referenced_indices:
referenced_indices = set(range(1, len(citations) + 1))
log.debug(
f"No [docX] references found in content, including all {len(citations)} citations"
)
else:
log.info(
f"Found {len(referenced_indices)} referenced citations: {sorted(referenced_indices)}"
)
log.info(
f"Emitting citation events for {len(referenced_indices)} referenced citations via __event_emitter__"
)
emitted_count = 0
for i, citation in enumerate(citations, 1):
# Skip citations that are not referenced in the content
if i not in referenced_indices:
log.debug(f"Skipping citation {i} - not referenced in content")
continue
if not isinstance(citation, dict):
log.warning(f"Citation {i} is not a dict, skipping: {type(citation)}")
continue
try:
normalized = self._normalize_citation_for_openwebui(citation, i)
# Log the full citation JSON for debugging
# log.debug(
# f"Full citation event JSON for doc{i}: {json.dumps(normalized, default=str)}"
# )
# Emit citation event for this individual source
source_name = (
normalized.get("data", {}).get("source", {}).get("name", "unknown")
)
log.info(
f"Emitting citation event {i}/{len(citations)} with source.name='{source_name}'"
)
await __event_emitter__(normalized)
emitted_count += 1
log.info(f"Successfully emitted citation event for doc{i}")
except Exception as e:
log.exception(f"Failed to emit citation event for citation {i}: {e}")
log.info(
f"Finished emitting {emitted_count}/{len(referenced_indices)} citation events"
)
def enhance_azure_search_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
"""
Enhance Azure AI Search responses by converting [docX] references to markdown links.
Modifies the response in-place and returns it.
Args:
response: The original response from Azure AI (modified in-place)
Returns:
The enhanced response with markdown links for citations
"""
if not isinstance(response, dict):
return response
# Check if this is an Azure AI Search response with citations
if (
"choices" not in response
or not response["choices"]
or "message" not in response["choices"][0]
or "context" not in response["choices"][0]["message"]
or "citations" not in response["choices"][0]["message"]["context"]
):
return response
try:
choice = response["choices"][0]
message = choice["message"]
context = message["context"]
citations = context["citations"]
content = message["content"]
# Convert [docX] references to markdown links
enhanced_content = self._convert_doc_refs_to_links(content, citations)
# Update the message content
message["content"] = enhanced_content
return response
except Exception as e:
log = logging.getLogger("azure_ai.enhance_azure_search_response")
log.warning(f"Failed to enhance Azure Search response: {e}")
return response
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": "grok-3-mini", "name": "Grok 3 Mini"},
{"id": "grok-4", "name": "Grok 4"},
{"id": "grok-4-fast-reasoning", "name": "Grok 4 Fast Reasoning"},
{"id": "grok-4-fast-non-reasoning", "name": "Grok 4 Fast Non-Reasoning"},
{"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": "gpt-4.5-preview", "name": "OpenAI GPT-4.5 Preview"},
{"id": "gpt-5", "name": "OpenAI GPT-5"},
{"id": "gpt‑5‑codex", "name": "OpenAI GPT-5 Codex"},
{"id": "gpt-5-mini", "name": "OpenAI GPT-5 Mini"},
{"id": "gpt-5-nano", "name": "OpenAI GPT-5 Nano"},
{"id": "gpt-5-chat", "name": "OpenAI GPT-5 Chat"},
{"id": "gpt-oss-20b", "name": "OpenAI GPT-OSS 20B"},
{"id": "gpt-oss-120b", "name": "OpenAI GPT-OSS 120B"},
{"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-pro", "name": "OpenAI o3 Pro"},
{"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()
# Re-extract model name in case valves were updated
self._extracted_model_name = self._extract_model_from_url()
# If custom models are provided, parse them and return as pipes
if self.valves.AZURE_AI_MODEL:
self.name = f"{self.valves.AZURE_AI_PIPELINE_PREFIX}: "
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 = f"{self.valves.AZURE_AI_PIPELINE_PREFIX}: "
return self.get_azure_models()
# Check if we can extract model name from Azure OpenAI URL
if self._extracted_model_name:
self.name = f"{self.valves.AZURE_AI_PIPELINE_PREFIX}: "
return [
{"id": self._extracted_model_name, "name": self._extracted_model_name}
]
# Otherwise, use a default name.
self.name = f"{self.valves.AZURE_AI_PIPELINE_PREFIX}: "
return [{"id": "azure_ai", "name": self.valves.AZURE_AI_PIPELINE_PREFIX}]
async def stream_processor_with_citations(
self,
content: aiohttp.StreamReader,
__event_emitter__=None,
response: Optional[aiohttp.ClientResponse] = None,
session: Optional[aiohttp.ClientSession] = None,
) -> AsyncIterator[bytes]:
"""
Enhanced stream processor that can handle Azure AI Search citations in streaming responses.
Args:
content: The streaming content from the response
__event_emitter__: Optional event emitter for status updates
Yields:
Bytes from the streaming content with enhanced citations
"""
log = logging.getLogger("azure_ai.stream_processor_with_citations")
try:
full_response_buffer = ""
response_content = "" # Track the actual response content
citations_data = None
citation_urls = {} # Pre-allocate citation URLs map
# Pre-define the replacement function outside the loop to avoid repeated creation
def replace_ref(m, urls_map):
doc_num = int(m.group(1))
url = urls_map.get(doc_num)
return self._format_citation_link(doc_num, url)
async for chunk in content:
chunk_str = chunk.decode("utf-8", errors="ignore")
full_response_buffer += chunk_str
# Log chunk for debugging (only first 200 chars to avoid spam)
# log.debug(f"Processing chunk: {chunk_str[:200]}...")
# Extract content from delta messages to build the full response content
try:
lines = chunk_str.split("\n")
for line in lines:
if line.startswith("data: ") and line.strip() != "data: [DONE]":
json_str = line[6:].strip()
if json_str and json_str != "[DONE]":
try:
response_data = json.loads(json_str)
if (
isinstance(response_data, dict)
and "choices" in response_data
):
for choice in response_data["choices"]:
if (
"delta" in choice
and "content" in choice["delta"]
):
response_content += choice["delta"][
"content"
]
except json.JSONDecodeError:
# Malformed or incomplete JSON is expected in streamed chunks; safely skip.
pass
except Exception as e:
log.debug(f"Exception while processing chunk: {e}")
# Look for citations or all_retrieved_documents in any part of the response
if (
"citations" in chunk_str.lower()
or "all_retrieved_documents" in chunk_str.lower()
) and not citations_data:
log.debug(
"Found 'citations' or 'all_retrieved_documents' in chunk, attempting to parse..."
)
# Try to extract citation data from the current buffer
try:
# Look for SSE data lines
lines = full_response_buffer.split("\n")
for line in lines:
if (
line.startswith("data: ")
and line.strip() != "data: [DONE]"
):
json_str = line[6:].strip() # Remove 'data: ' prefix
if json_str and json_str != "[DONE]":
try:
response_data = json.loads(json_str)
# Check multiple possible locations for citations
citations_found = None
all_docs_found = None
if (
isinstance(response_data, dict)
and "choices" in response_data
):
for choice in response_data["choices"]:
context = None
# Get context from delta or message
if "delta" in choice and isinstance(
choice["delta"], dict
):
context = choice["delta"].get(
"context"
)
elif "message" in choice and isinstance(
choice["message"], dict
):
context = choice["message"].get(
"context"
)
if context and isinstance(
context, dict
):
# Check for citations
if "citations" in context:
citations_found = context[
"citations"
]
log.debug(
f"Found citations in context: {len(citations_found)} citations"
)
# Check for all_retrieved_documents
if (
"all_retrieved_documents"
in context
):
all_docs_found = context[
"all_retrieved_documents"
]
log.debug(
f"Found all_retrieved_documents in context: {len(all_docs_found)} docs"
)
break
# Merge score data if we have both
if citations_found and all_docs_found:
self._merge_score_data(
citations_found, all_docs_found, log
)
# Use citations if found, otherwise use all_retrieved_documents
if citations_found and not citations_data:
citations_data = citations_found
# Build citation URLs map once when citations are found
citation_urls = (
self._build_citation_urls_map(
citations_data
)
)
log.info(
f"Successfully extracted {len(citations_data)} citations from stream"
)
elif all_docs_found and not citations_data:
citations_data = all_docs_found
# Build citation URLs map once when citations are found
citation_urls = (
self._build_citation_urls_map(
citations_data
)
)
log.info(
f"Using {len(citations_data)} all_retrieved_documents as citations"
)
# Note: OpenWebUI citation events are emitted after the stream ends
# to filter only citations referenced in the response content
except json.JSONDecodeError:
# Skip invalid JSON
continue
except Exception as parse_error:
log.debug(f"Error parsing citations from chunk: {parse_error}")
# Convert [docX] references to markdown links in the chunk content
# This creates clickable links to source documents in streaming responses
chunk_modified = False
if "[doc" in chunk_str and citation_urls:
try:
# Parse and modify each SSE data line
modified_lines = []
chunk_lines = chunk_str.split("\n")
for line in chunk_lines:
# Early exit: skip lines without [doc references
if "[doc" not in line:
modified_lines.append(line)
continue
# Process only SSE data lines
if (
line.startswith("data: ")
and line.strip() != "data: [DONE]"
):
json_str = line[6:].strip()
if json_str and json_str != "[DONE]":
try:
data = json.loads(json_str)
if (
isinstance(data, dict)
and "choices" in data
and data["choices"]
):
line_modified = False
# Process choices until we find and modify content
for choice in data["choices"]:
if (
"delta" in choice
and "content" in choice["delta"]
):
content_val = choice["delta"][
"content"
]
if "[doc" in content_val:
# Convert [docX] to markdown link using pre-compiled pattern
# Use lambda to pass citation_urls to pre-defined function
choice["delta"]["content"] = (
self.DOC_REF_PATTERN.sub(
lambda m: replace_ref(
m, citation_urls
),
content_val,
)
)
line_modified = True
# Early exit: content found and modified
break
if line_modified:
modified_lines.append(
f"data: {json.dumps(data)}"
)
chunk_modified = True
else:
modified_lines.append(line)
else:
modified_lines.append(line)
except json.JSONDecodeError:
modified_lines.append(line)
else:
modified_lines.append(line)
else:
modified_lines.append(line)
# Reconstruct the chunk only if something was modified
if chunk_modified:
modified_chunk_str = "\n".join(modified_lines)
log.debug(
"Converted [docX] references to markdown links in streaming chunk"
)
chunk = modified_chunk_str.encode("utf-8")
except Exception as convert_err:
log.debug(
f"Error converting [docX] to markdown links: {convert_err}"
)
# Fall through to yield original chunk
# Yield the (possibly modified) chunk
yield chunk
# Check if this is the end of the stream
if "data: [DONE]" in chunk_str:
log.debug("End of stream detected")
break
# After the stream ends, emit OpenWebUI citation events
if citations_data and __event_emitter__:
log.info("Emitting OpenWebUI citation events at end of stream...")
# Filter to only citations referenced in the response content
await self._emit_openwebui_citation_events(
citations_data, __event_emitter__, response_content
)
# 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.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},
}
)
finally:
# Always attempt to close response and session to avoid resource leaks
try:
if response:
response.close()
except Exception:
pass
try:
if session:
await session.close()
except Exception:
# Suppress close-time errors (e.g., SSL shutdown timeouts)
pass
def _extract_referenced_citations(self, content: str) -> Set[int]:
"""
Extract citation references (e.g., [doc1], [doc2]) from the content.
Args:
content: The response content containing citation references
Returns:
Set of citation indices that are referenced (e.g., {1, 2, 7, 8, 9})
"""
# Find all [docN] references in the content using class constant
matches = re.findall(self.DOC_REF_PATTERN, content)
# Convert to integers and return as a set
return {int(match) for match in matches}
async def stream_processor(
self,
content: aiohttp.StreamReader,
__event_emitter__=None,
response: Optional[aiohttp.ClientResponse] = None,
session: Optional[aiohttp.ClientSession] = 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},
}
)
finally:
# Always attempt to close response and session to avoid resource leaks
try:
if response:
response.close()
except Exception:
pass
try:
if session:
await session.close()
except Exception:
# Suppress close-time errors (e.g., SSL shutdown timeouts)
pass
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",
"deployment",
"frequency_penalty",
"max_tokens",
"max_citations",
"presence_penalty",
"reasoning_effort",
"response_format",
"seed",
"stop",
"stream",
"temperature",
"tool_choice",
"tools",
"top_p",
"data_sources",
}
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"]
)
# Add Azure AI data sources if configured and not already present in request
if "data_sources" not in filtered_body:
azure_ai_data_sources = self.get_azure_ai_data_sources()
if azure_ai_data_sources:
filtered_body["data_sources"] = azure_ai_data_sources
# 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,
)
# If the server returned an error status, parse and raise before streaming logic
if request.status >= 400:
err_ct = (request.headers.get("Content-Type") or "").lower()
if "json" in err_ct:
try:
response = await request.json()
except Exception as e:
# In error status, provider may mislabel content-type; keep log at debug to avoid noise
log.debug(
f"Failed to parse JSON error body despite JSON content-type: {e}"
)
response = await request.text()
else:
response = await request.text()
request.raise_for_status()
# Auto-detect streaming: either requested via body or indicated by response headers
content_type_header = (request.headers.get("Content-Type") or "").lower()
wants_stream = bool(filtered_body.get("stream", False))
is_sse_header = "text/event-stream" in content_type_header
if wants_stream or is_sse_header:
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,
},
}
)
# Ensure correct SSE headers are set for downstream consumers
sse_headers = dict(request.headers)
sse_headers["Content-Type"] = "text/event-stream"
sse_headers.pop("Content-Length", None)
# Use enhanced stream processor if Azure AI Search is configured
if self.valves.AZURE_AI_DATA_SOURCES:
stream_processor = self.stream_processor_with_citations
else:
stream_processor = self.stream_processor
return StreamingResponse(
stream_processor(
request.content,
__event_emitter__=__event_emitter__,
response=request,
session=session,
),
status_code=request.status,
headers=sse_headers,
)
else:
# Parse non-stream response based on content-type without noisy error logs
if "json" in content_type_header:
try:
response = await request.json()
except Exception as e:
log.debug(
f"Failed to parse JSON response despite JSON content-type: {e}"
)
response = await request.text()
else:
response = await request.text()
request.raise_for_status()
# Enhance Azure Search responses with citation linking and emit citation events
if isinstance(response, dict) and self.valves.AZURE_AI_DATA_SOURCES:
response = self.enhance_azure_search_response(response)
# Emit OpenWebUI citation events for non-streaming responses
if __event_emitter__:
citations = self._extract_citations_from_response(response)
if citations:
# Get response content for filtering
response_content = ""
if (
isinstance(response, dict)
and "choices" in response
and response["choices"]
):
message = response["choices"][0].get("message", {})
response_content = message.get("content", "")
await self._emit_openwebui_citation_events(
citations, __event_emitter__, response_content
)
# 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()
