Update AzureLLMService to use OpenAILLMService

CHANGELOG.md

@@ -11,10 +11,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - `GroqLLMService` and `GrokLLMService` for Groq and Grok API integration, with
   OpenAI-compatible interface
-- New examples demonstrating function calling with Groq and Grok
+- New examples demonstrating function calling with Groq, Grok, and Azure OpenAI
 
   - `14f-function-calling-groq.py`
   - `14g-function-calling-grok.py`
+  - `14h-function-calling-azure.py`
 
 - In order to obtain the audio stored by the `AudioBufferProcessor` you can now
   also register an `on_audio_data` event handler. The `on_audio_data` handler
@@ -43,6 +44,9 @@ async def on_audio_data(processor, audio, sample_rate, num_channels):
 - Updated STT and TTS services with language options that match the supported
   languages for each service.
 
+- Updated the `AzureLLMService` to use the `OpenAILLMService`. Updated the
+  `api_version` to `2024-09-01-preview`.
+
 ### Removed
 
 - Removed `AppFrame`. This was used as a special user custom frame, but there's

examples/foundational/14h-function-calling-azure.py

@@ -0,0 +1,141 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.azure import AzureLLMService
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMContext
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def start_fetch_weather(function_name, llm, context):
+    # note: we can't push a frame to the LLM here. the bot
+    # can interrupt itself and/or cause audio overlapping glitches.
+    # possible question for Aleix and Chad about what the right way
+    # to trigger speech is, now, with the new queues/async/sync refactors.
+    # await llm.push_frame(TextFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

pyproject.toml

@@ -39,7 +39,7 @@ Website = "https://pipecat.ai"
 anthropic = [ "anthropic~=0.34.0" ]
 assemblyai = [ "assemblyai~=0.34.0" ]
 aws = [ "boto3~=1.35.27" ]
-azure = [ "azure-cognitiveservices-speech~=1.40.0" ]
+azure = [ "azure-cognitiveservices-speech~=1.40.0", "openai~=1.50.2" ]
 canonical = [ "aiofiles~=24.1.0" ]
 cartesia = [ "cartesia~=1.0.13", "websockets~=13.1" ]
 daily = [ "daily-python~=0.13.0" ]

src/pipecat/services/azure.py

@@ -25,13 +25,9 @@ from pipecat.frames.frames import (
     TTSStoppedFrame,
     URLImageRawFrame,
 )
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.ai_services import ImageGenService, STTService, TTSService
 from pipecat.services.openai import (
-    BaseOpenAILLMService,
+    OpenAILLMService,
-    OpenAIAssistantContextAggregator,
-    OpenAIContextAggregatorPair,
-    OpenAIUserContextAggregator,
 )
 from pipecat.transcriptions.language import Language
 from pipecat.utils.time import time_now_iso8601
@@ -398,33 +394,44 @@ def sample_rate_to_output_format(sample_rate: int) -> SpeechSynthesisOutputForma
     return sample_rate_map.get(sample_rate, SpeechSynthesisOutputFormat.Raw24Khz16BitMonoPcm)
 
 
-class AzureLLMService(BaseOpenAILLMService):
+class AzureLLMService(OpenAILLMService):
+    """A service for interacting with Azure OpenAI using the OpenAI-compatible interface.
+
+    This service extends OpenAILLMService to connect to Azure's OpenAI endpoint while
+    maintaining full compatibility with OpenAI's interface and functionality.
+
+    Args:
+        api_key (str): The API key for accessing Azure OpenAI
+        endpoint (str): The Azure endpoint URL
+        model (str): The model identifier to use
+        api_version (str, optional): Azure API version. Defaults to "2024-09-01-preview"
+        **kwargs: Additional keyword arguments passed to OpenAILLMService
+    """
+
     def __init__(
-        self, *, api_key: str, endpoint: str, model: str, api_version: str = "2023-12-01-preview"
+        self,
+        *,
+        api_key: str,
+        endpoint: str,
+        model: str,
+        api_version: str = "2024-09-01-preview",
+        **kwargs,
     ):
         # Initialize variables before calling parent __init__() because that
         # will call create_client() and we need those values there.
         self._endpoint = endpoint
         self._api_version = api_version
-        super().__init__(api_key=api_key, model=model)
+        super().__init__(api_key=api_key, model=model, **kwargs)
 
     def create_client(self, api_key=None, base_url=None, **kwargs):
+        """Create OpenAI-compatible client for Azure OpenAI endpoint."""
+        logger.debug(f"Creating Azure OpenAI client with endpoint {self._endpoint}")
         return AsyncAzureOpenAI(
             api_key=api_key,
             azure_endpoint=self._endpoint,
             api_version=self._api_version,
         )
 
-    @staticmethod
-    def create_context_aggregator(
-        context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True
-    ) -> OpenAIContextAggregatorPair:
-        user = OpenAIUserContextAggregator(context)
-        assistant = OpenAIAssistantContextAggregator(
-            user, expect_stripped_words=assistant_expect_stripped_words
-        )
-        return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
-
 
 class AzureBaseTTSService(TTSService):
     class InputParams(BaseModel):