From 310dfb81947a8102b3f1f106db6e27423368785a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Cl=C3=A9mence=20Lesn=C3=A9?= <clemence@lesne.pro>
Date: Tue, 17 Dec 2024 13:11:38 +0100
Subject: [PATCH] breaking: Use AI Factory SDK and LLM tracing

---
 README.md                            |  31 ----
 app/helpers/call_llm.py              |  15 +-
 app/helpers/config_models/llm.py     | 119 +++----------
 app/helpers/config_models/prompts.py |  30 +---
 app/helpers/llm_utils.py             |  10 +-
 app/helpers/llm_worker.py            | 229 +++++++++---------------
 app/helpers/monitoring.py            |  11 +-
 app/models/message.py                |  47 +++--
 cicd/bicep/app.bicep                 |  81 +++++++--
 config-local-example.yaml            |  20 +--
 pyproject.toml                       |   4 +-
 tests/conftest.py                    |   3 +-
 uv.lock                              | 249 ++++++++++++---------------
 13 files changed, 329 insertions(+), 520 deletions(-)

diff --git a/README.md b/README.md
index 8a6b75d3..d5d2fe46 100644
--- a/README.md
+++ b/README.md
@@ -484,37 +484,6 @@ Conversation options are represented as features. They can be configured from Ap
 | `vad_silence_timeout_ms` | Silence to trigger voice activity detection in milliseconds. | `int` | 500 |
 | `vad_threshold` | The threshold for voice activity detection. Between 0.1 and 1. | `float` | 0.5 |
 
-### Use an OpenAI compatible model for the LLM
-
-To use a model compatible with the OpenAI completion API, you need to create an account and get the following information:
-
-- API key
-- Context window size
-- Endpoint URL
-- Model name
-- Streaming capability
-
-Then, add the following in the `config.yaml` file:
-
-```yaml
-# config.yaml
-llm:
-  fast:
-    mode: openai
-    openai:
-      context: 128000
-      endpoint: https://api.openai.com
-      model: gpt-4o-mini
-      streaming: true
-  slow:
-    mode: openai
-    openai:
-      context: 128000
-      endpoint: https://api.openai.com
-      model: gpt-4o
-      streaming: true
-```
-
 ### Use Twilio for SMS
 
 To use Twilio for SMS, you need to create an account and get the following information:
diff --git a/app/helpers/call_llm.py b/app/helpers/call_llm.py
index 794fdb8a..3f806871 100644
--- a/app/helpers/call_llm.py
+++ b/app/helpers/call_llm.py
@@ -9,7 +9,6 @@
     SpeechSynthesizer,
 )
 from azure.communication.callautomation.aio import CallAutomationClient
-from openai import APIError
 
 from app.helpers.call_utils import (
     AECStream,
@@ -427,7 +426,7 @@ def _clear_tasks() -> None:
 
 # TODO: Refacto, this function is too long
 @tracer.start_as_current_span("call_generate_chat_completion")
-async def _generate_chat_completion(  # noqa: PLR0913, PLR0911, PLR0912, PLR0915
+async def _generate_chat_completion(  # noqa: PLR0913, PLR0912, PLR0915
     call: CallStateModel,
     client: CallAutomationClient,
     post_callback: Callable[[CallStateModel], Awaitable[None]],
@@ -495,7 +494,7 @@ async def _content_callback(buffer: str) -> None:
     # Execute LLM inference
     maximum_tokens_reached = False
     content_buffer_pointer = 0
-    tool_calls_buffer: dict[int, MessageToolModel] = {}
+    tool_calls_buffer: dict[str, MessageToolModel] = {}
     try:
         async for delta in completion_stream(
             max_tokens=160,  # Lowest possible value for 90% of the cases, if not sufficient, retry will be triggered, 100 tokens ~= 75 words, 20 words ~= 1 sentence, 6 sentences ~= 160 tokens
@@ -505,10 +504,10 @@ async def _content_callback(buffer: str) -> None:
         ):
             if not delta.content:
                 for piece in delta.tool_calls or []:
-                    tool_calls_buffer[piece.index] = tool_calls_buffer.get(
-                        piece.index, MessageToolModel()
+                    tool_calls_buffer[piece.id] = tool_calls_buffer.get(
+                        piece.id, MessageToolModel()
                     )
-                    tool_calls_buffer[piece.index] += piece
+                    tool_calls_buffer[piece.id] += piece
             else:
                 # Store whole content
                 content_full += delta.content
@@ -522,10 +521,6 @@ async def _content_callback(buffer: str) -> None:
     except MaximumTokensReachedError:
         logger.warning("Maximum tokens reached for this completion, retry asked")
         maximum_tokens_reached = True
-    # Retry on API error
-    except APIError as e:
-        logger.warning("OpenAI API call error: %s", e)
-        return True, True, call  # Error, retry
     # Last user message is trash, remove it
     except SafetyCheckError as e:
         logger.warning("Safety Check error: %s", e)
diff --git a/app/helpers/config_models/llm.py b/app/helpers/config_models/llm.py
index a7ba8ba9..7538fa73 100644
--- a/app/helpers/config_models/llm.py
+++ b/app/helpers/config_models/llm.py
@@ -1,117 +1,40 @@
-from abc import abstractmethod
-from enum import Enum
-from typing import Any
-
-from openai import AsyncAzureOpenAI, AsyncOpenAI
-from pydantic import BaseModel, Field, SecretStr, ValidationInfo, field_validator
+from azure.ai.inference.aio import ChatCompletionsClient
+from pydantic import BaseModel
 
 from app.helpers.cache import async_lru_cache
-from app.helpers.identity import token
-
-
-class ModeEnum(str, Enum):
-    AZURE_OPENAI = "azure_openai"
-    """Use Azure OpenAI."""
-    OPENAI = "openai"
-    """Use OpenAI."""
+from app.helpers.http import azure_transport
+from app.helpers.identity import credential
 
 
-class AbstractPlatformModel(BaseModel, frozen=True):
-    _client_kwargs: dict[str, Any] = {
-        # Reliability
-        "max_retries": 0,  # Retries are managed manually
-        "timeout": 60,
-    }
+class DeploymentModel(BaseModel, frozen=True):
+    api_version: str = "2024-10-21"  # See: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#api-specs
     context: int
     endpoint: str
     model: str
     seed: int = 42  # Reproducible results
-    streaming: bool
     temperature: float = 0.0  # Most focused and deterministic
 
-    @abstractmethod
-    async def instance(
-        self,
-    ) -> tuple[AsyncAzureOpenAI | AsyncOpenAI, "AbstractPlatformModel"]:
-        pass
-
-
-class AzureOpenaiPlatformModel(AbstractPlatformModel, frozen=True):
-    api_version: str = "2024-06-01"
-    deployment: str
-
     @async_lru_cache()
-    async def instance(self) -> tuple[AsyncAzureOpenAI, AbstractPlatformModel]:
-        return AsyncAzureOpenAI(
-            **self._client_kwargs,
+    async def instance(self) -> tuple[ChatCompletionsClient, "DeploymentModel"]:
+        return ChatCompletionsClient(
+            # Reliability
+            seed=self.seed,
+            temperature=self.temperature,
             # Deployment
             api_version=self.api_version,
-            azure_deployment=self.deployment,
-            azure_endpoint=self.endpoint,
+            endpoint=self.endpoint,
+            model=self.model,
+            # Performance
+            transport=await azure_transport(),
             # Authentication
-            azure_ad_token_provider=await token(
-                "https://cognitiveservices.azure.com/.default"
-            ),
+            credential_scopes=["https://cognitiveservices.azure.com/.default"],
+            credential=await credential(),
         ), self
 
 
-class OpenaiPlatformModel(AbstractPlatformModel, frozen=True):
-    api_key: SecretStr
-
-    @async_lru_cache()
-    async def instance(self) -> tuple[AsyncOpenAI, AbstractPlatformModel]:
-        return AsyncOpenAI(
-            **self._client_kwargs,
-            # API root URL
-            base_url=self.endpoint,
-            # Authentication
-            api_key=self.api_key.get_secret_value(),
-        ), self
-
-
-class SelectedPlatformModel(BaseModel):
-    azure_openai: AzureOpenaiPlatformModel | None = None
-    mode: ModeEnum
-    openai: OpenaiPlatformModel | None = None
-
-    @field_validator("azure_openai")
-    @classmethod
-    def _validate_azure_openai(
-        cls,
-        azure_openai: AzureOpenaiPlatformModel | None,
-        info: ValidationInfo,
-    ) -> AzureOpenaiPlatformModel | None:
-        if not azure_openai and info.data.get("mode", None) == ModeEnum.AZURE_OPENAI:
-            raise ValueError("Azure OpenAI config required")
-        return azure_openai
-
-    @field_validator("openai")
-    @classmethod
-    def _validate_openai(
-        cls,
-        openai: OpenaiPlatformModel | None,
-        info: ValidationInfo,
-    ) -> OpenaiPlatformModel | None:
-        if not openai and info.data.get("mode", None) == ModeEnum.OPENAI:
-            raise ValueError("OpenAI config required")
-        return openai
-
-    def selected(self) -> AzureOpenaiPlatformModel | OpenaiPlatformModel:
-        platform = (
-            self.azure_openai if self.mode == ModeEnum.AZURE_OPENAI else self.openai
-        )
-        assert platform
-        return platform
-
-
 class LlmModel(BaseModel):
-    fast: SelectedPlatformModel = Field(
-        serialization_alias="backup",  # Backwards compatibility with v6
-    )
-    slow: SelectedPlatformModel = Field(
-        serialization_alias="primary",  # Backwards compatibility with v6
-    )
+    fast: DeploymentModel
+    slow: DeploymentModel
 
-    def selected(self, is_fast: bool) -> AzureOpenaiPlatformModel | OpenaiPlatformModel:
-        platform = self.fast if is_fast else self.slow
-        return platform.selected()
+    def selected(self, is_fast: bool) -> DeploymentModel:
+        return self.fast if is_fast else self.slow
diff --git a/app/helpers/config_models/prompts.py b/app/helpers/config_models/prompts.py
index 10149609..e0f44e54 100644
--- a/app/helpers/config_models/prompts.py
+++ b/app/helpers/config_models/prompts.py
@@ -6,8 +6,8 @@
 from logging import Logger
 from textwrap import dedent
 
+from azure.ai.inference.models import SystemMessage
 from azure.core.exceptions import HttpResponseError
-from openai.types.chat import ChatCompletionSystemMessageParam
 from pydantic import BaseModel, TypeAdapter
 
 from app.models.call import CallStateModel
@@ -329,7 +329,7 @@ def default_system(self, call: CallStateModel) -> str:
 
     def chat_system(
         self, call: CallStateModel, trainings: list[TrainingModel]
-    ) -> list[ChatCompletionSystemMessageParam]:
+    ) -> list[SystemMessage]:
         from app.models.message import (
             ActionEnum as MessageActionEnum,
             StyleEnum as MessageStyleEnum,
@@ -352,9 +352,7 @@ def chat_system(
             call=call,
         )
 
-    def sms_summary_system(
-        self, call: CallStateModel
-    ) -> list[ChatCompletionSystemMessageParam]:
+    def sms_summary_system(self, call: CallStateModel) -> list[SystemMessage]:
         return self._messages(
             self._format(
                 self.sms_summary_system_tpl,
@@ -373,9 +371,7 @@ def sms_summary_system(
             call=call,
         )
 
-    def synthesis_system(
-        self, call: CallStateModel
-    ) -> list[ChatCompletionSystemMessageParam]:
+    def synthesis_system(self, call: CallStateModel) -> list[SystemMessage]:
         return self._messages(
             self._format(
                 self.synthesis_system_tpl,
@@ -392,9 +388,7 @@ def synthesis_system(
             call=call,
         )
 
-    def citations_system(
-        self, call: CallStateModel, text: str
-    ) -> list[ChatCompletionSystemMessageParam]:
+    def citations_system(self, call: CallStateModel, text: str) -> list[SystemMessage]:
         """
         Return the formatted prompt. Prompt is used to add citations to the text, without cluttering the content itself.
 
@@ -412,9 +406,7 @@ def citations_system(
             call=call,
         )
 
-    def next_system(
-        self, call: CallStateModel
-    ) -> list[ChatCompletionSystemMessageParam]:
+    def next_system(self, call: CallStateModel) -> list[SystemMessage]:
         return self._messages(
             self._format(
                 self.next_system_tpl,
@@ -461,17 +453,13 @@ def _format(
         # self.logger.debug("Formatted prompt: %s", formatted_prompt)
         return formatted_prompt
 
-    def _messages(
-        self, system: str, call: CallStateModel
-    ) -> list[ChatCompletionSystemMessageParam]:
+    def _messages(self, system: str, call: CallStateModel) -> list[SystemMessage]:
         messages = [
-            ChatCompletionSystemMessageParam(
+            SystemMessage(
                 content=self.default_system(call),
-                role="system",
             ),
-            ChatCompletionSystemMessageParam(
+            SystemMessage(
                 content=system,
-                role="system",
             ),
         ]
         # self.logger.debug("Messages: %s", messages)
diff --git a/app/helpers/llm_utils.py b/app/helpers/llm_utils.py
index 807c35b6..7ef7bf29 100644
--- a/app/helpers/llm_utils.py
+++ b/app/helpers/llm_utils.py
@@ -14,14 +14,13 @@
 from typing import Annotated, Any, ForwardRef, TypeVar
 
 from aiojobs import Scheduler
+from azure.ai.inference.models import ChatCompletionsToolDefinition, FunctionDefinition
 from azure.cognitiveservices.speech import (
     SpeechSynthesizer,
 )
 from azure.communication.callautomation.aio import CallAutomationClient
 from jinja2 import Environment
 from json_repair import repair_json
-from openai.types.chat import ChatCompletionToolParam
-from openai.types.shared_params.function_definition import FunctionDefinition
 from pydantic import BaseModel, TypeAdapter
 from pydantic._internal._typing_extra import eval_type_lenient
 from pydantic.json_schema import JsonSchemaValue
@@ -77,7 +76,7 @@ def __init__(  # noqa: PLR0913
     async def to_openai(
         self,
         blacklist: frozenset[str],
-    ) -> list[ChatCompletionToolParam]:
+    ) -> list[ChatCompletionsToolDefinition]:
         """
         Get the OpenAI SDK schema for all functions of the plugin, excluding the ones in the blacklist.
         """
@@ -257,7 +256,7 @@ async def wrapper(
 async def _function_schema(
     f: Callable[..., Any],
     **kwargs: Any,
-) -> ChatCompletionToolParam:
+) -> ChatCompletionsToolDefinition:
     """
     Take a function and return a JSON schema for it as defined by the OpenAI API.
 
@@ -303,8 +302,7 @@ async def _function_schema(
         )
     ).model_dump()
 
-    return ChatCompletionToolParam(
-        type="function",
+    return ChatCompletionsToolDefinition(
         function=FunctionDefinition(
             description=description,
             name=name,
diff --git a/app/helpers/llm_worker.py b/app/helpers/llm_worker.py
index 3bfe22e9..dec184f0 100644
--- a/app/helpers/llm_worker.py
+++ b/app/helpers/llm_worker.py
@@ -5,32 +5,21 @@
 from typing import TypeVar
 
 import tiktoken
-from json_repair import repair_json
-from openai import (
-    APIConnectionError,
-    APIResponseValidationError,
-    AsyncAzureOpenAI,
-    AsyncOpenAI,
-    AsyncStream,
-    BadRequestError,
-    InternalServerError,
-    RateLimitError,
-)
-from openai.types.chat import (
-    ChatCompletion,
-    ChatCompletionAssistantMessageParam,
-    ChatCompletionChunk,
-    ChatCompletionSystemMessageParam,
-    ChatCompletionToolMessageParam,
-    ChatCompletionToolParam,
-    ChatCompletionUserMessageParam,
+from azure.ai.inference._model_base import Model, SdkJSONEncoder
+from azure.ai.inference.aio import ChatCompletionsClient
+from azure.ai.inference.models import (
+    AssistantMessage,
+    ChatCompletionsResponseFormatJSON,
+    ChatCompletionsToolDefinition,
+    ChatRequestMessage,
+    StreamingChatResponseMessageUpdate,
+    SystemMessage,
+    UserMessage,
 )
-from openai.types.chat.chat_completion_chunk import (
-    ChoiceDelta,
-    ChoiceDeltaToolCall,
-    ChoiceDeltaToolCallFunction,
+from azure.core.exceptions import (
+    ServiceResponseError,
 )
-from opentelemetry.instrumentation.openai import OpenAIInstrumentor
+from json_repair import repair_json
 from pydantic import ValidationError
 from tenacity import (
     AsyncRetrying,
@@ -42,20 +31,13 @@
 )
 
 from app.helpers.config import CONFIG
-from app.helpers.config_models.llm import (
-    AbstractPlatformModel as LlmAbstractPlatformModel,
-)
+from app.helpers.config_models.llm import DeploymentModel as LlmDeploymentModel
 from app.helpers.features import slow_llm_for_chat
 from app.helpers.logging import logger
 from app.helpers.monitoring import tracer
 from app.helpers.resources import resources_dir
 from app.models.message import MessageModel
 
-environ["TRACELOOP_TRACE_CONTENT"] = str(
-    True
-)  # Instrumentation logs prompts, completions, and embeddings to span attributes, set to False to lower monitoring costs or to avoid logging PII
-OpenAIInstrumentor().instrument()  # Instrument OpenAI
-
 # tiktoken cache
 environ["TIKTOKEN_CACHE_DIR"] = resources_dir("tiktoken")
 
@@ -77,10 +59,7 @@ class MaximumTokensReachedError(Exception):
 
 
 _retried_exceptions = [
-    APIConnectionError,
-    APIResponseValidationError,
-    InternalServerError,
-    RateLimitError,
+    ServiceResponseError,
 ]
 
 
@@ -88,9 +67,9 @@ class MaximumTokensReachedError(Exception):
 async def completion_stream(
     max_tokens: int,
     messages: list[MessageModel],
-    system: list[ChatCompletionSystemMessageParam],
-    tools: list[ChatCompletionToolParam] | None = None,
-) -> AsyncGenerator[ChoiceDelta, None]:
+    system: list[SystemMessage],
+    tools: list[ChatCompletionsToolDefinition] = [],
+) -> AsyncGenerator[StreamingChatResponseMessageUpdate, None]:
     """
     Returns a stream of completions.
 
@@ -140,21 +119,20 @@ async def completion_stream(
 
 
 # TODO: Refacto, too long (and remove PLR0912 ignore)
-async def _completion_stream_worker(  # noqa: PLR0912
+async def _completion_stream_worker(
     is_fast: bool,
     max_tokens: int,
     messages: list[MessageModel],
-    system: list[ChatCompletionSystemMessageParam],
-    tools: list[ChatCompletionToolParam] | None = None,
-) -> AsyncGenerator[ChoiceDelta, None]:
+    system: list[SystemMessage],
+    tools: list[ChatCompletionsToolDefinition] = [],
+) -> AsyncGenerator[StreamingChatResponseMessageUpdate, None]:
     """
     Returns a stream of completions.
     """
+    # Init client
     client, platform = await _use_llm(is_fast)
-    extra = {}
-    if tools:
-        extra["tools"] = tools  # Add tools if any
 
+    # Build context and limit to 20 messages for quick response and avoid hallucinations
     prompt = _limit_messages(
         context_window=platform.context,
         max_messages=20,  # Quick response
@@ -163,83 +141,32 @@ async def _completion_stream_worker(  # noqa: PLR0912
         model=platform.model,
         system=system,
         tools=tools,
-    )  # Limit to 20 messages for quick response and avoid hallucinations
-    chat_kwargs = {
-        "max_tokens": max_tokens,
-        "messages": prompt,
-        "model": platform.model,
-        "seed": platform.seed,
-        "temperature": platform.temperature,
-        **extra,
-    }  # Shared kwargs for both streaming and non-streaming
-    maximum_tokens_reached = False
+    )
 
-    try:
-        # Streaming
-        if platform.streaming:
-            stream: AsyncStream[
-                ChatCompletionChunk
-            ] = await client.chat.completions.create(
-                **chat_kwargs,
-                stream=True,
-            )
-            async for chunck in stream:
-                choices = chunck.choices
-                # Skip empty choices, happens sometimes with GPT-4 Turbo
-                if not choices:
-                    continue
-                choice = choices[0]
-                delta = choice.delta
-                # Azure OpenAI content filter
-                if choice.finish_reason == "content_filter":
-                    raise SafetyCheckError(f"Issue detected in text: {delta.content}")
-                if choice.finish_reason == "length":
-                    logger.warning(
-                        "Maximum tokens reached %s, should be fixed", max_tokens
-                    )
-                    maximum_tokens_reached = True
-                if delta:
-                    yield delta
-
-        # Non-streaming, emulate streaming with a single completion
-        else:
-            completion: ChatCompletion = await client.chat.completions.create(
-                **chat_kwargs
-            )
-            choice = completion.choices[0]
-            # Azure OpenAI content filter
-            if choice.finish_reason == "content_filter":
-                raise SafetyCheckError(
-                    f"Issue detected in generation: {choice.message.content}"
-                )
-            if choice.finish_reason == "length":
-                logger.warning("Maximum tokens reached %s, should be fixed", max_tokens)
-                maximum_tokens_reached = True
-            message = choice.message
-            delta = ChoiceDelta(
-                content=message.content,
-                role=message.role,
-                tool_calls=[
-                    ChoiceDeltaToolCall(
-                        id=tool.id,
-                        index=0,
-                        type=tool.type,
-                        function=ChoiceDeltaToolCallFunction(
-                            arguments=tool.function.arguments,
-                            name=tool.function.name,
-                        ),
-                    )
-                    for tool in message.tool_calls or []
-                ],
-            )
-            yield delta
-    except BadRequestError as e:
-        if e.code == "content_filter":
-            raise SafetyCheckError("Issue detected in prompt") from e
-        raise e
+    # Start completion
+    stream = await client.complete(
+        max_tokens=max_tokens,
+        messages=prompt,
+        stream=True,
+        tools=tools,
+    )
 
-    if maximum_tokens_reached:
-        raise MaximumTokensReachedError(f"Maximum tokens reached {max_tokens}")
+    # Yield chuncks
+    async for chunck in stream:
+        choices = chunck.choices
+        # Skip empty choices, happens sometimes with GPT-4 Turbo
+        if not choices:
+            continue
+        choice = choices[0]
+        delta = choice.delta
+        # Azure OpenAI content filter
+        if choice.finish_reason == "content_filter":
+            raise SafetyCheckError(f"Issue detected in text: {delta.content}")
+        if choice.finish_reason == "length":
+            logger.warning("Maximum tokens reached %s, should be fixed", max_tokens)
+            raise MaximumTokensReachedError(f"Maximum tokens reached {max_tokens}")
+        if delta:
+            yield delta
 
 
 @retry(
@@ -251,7 +178,7 @@ async def _completion_stream_worker(  # noqa: PLR0912
 @tracer.start_as_current_span("llm_completion_sync")
 async def completion_sync(
     res_type: type[T],
-    system: list[ChatCompletionSystemMessageParam],
+    system: list[SystemMessage],
     validation_callback: Callable[[str | None], tuple[bool, str | None, T | None]],
     validate_json: bool = False,
     _previous_result: str | None = None,
@@ -262,13 +189,11 @@ async def completion_sync(
     messages = system
     if _validation_error:
         messages += [
-            ChatCompletionAssistantMessageParam(
+            AssistantMessage(
                 content=_previous_result or "",
-                role="assistant",
             ),
-            ChatCompletionUserMessageParam(
+            UserMessage(
                 content=f"A validation error occurred, please retry: {_validation_error}",
-                role="user",
             ),
         ]
 
@@ -310,18 +235,17 @@ async def completion_sync(
 
 async def _completion_sync_worker(
     is_fast: bool,
-    system: list[ChatCompletionSystemMessageParam],
+    system: list[SystemMessage],
     json_output: bool = False,
     max_tokens: int | None = None,
 ) -> str | None:
     """
     Returns a completion.
     """
+    # Init client
     client, platform = await _use_llm(is_fast)
-    extra = {}
-    if json_output:
-        extra["response_format"] = {"type": "json_object"}
 
+    # Build context
     prompt = _limit_messages(
         context_window=platform.context,
         max_tokens=max_tokens,
@@ -344,20 +268,19 @@ async def _completion_sync_worker(
     choice = None
     async for attempt in retryed:
         with attempt:
-            try:
-                res = await client.chat.completions.create(
+            # Start completion
+            choice = (
+                await client.complete(
                     max_tokens=max_tokens,
                     messages=prompt,
                     model=platform.model,
                     seed=platform.seed,
                     temperature=platform.temperature,
-                    **extra,
+                    response_format=ChatCompletionsResponseFormatJSON()
+                    if json_output
+                    else None,
                 )
-            except BadRequestError as e:
-                if e.code == "content_filter":
-                    raise SafetyCheckError("Issue detected in prompt") from e
-                raise e
-            choice = res.choices[0]
+            ).choices[0]
             # Azure OpenAI content filter
             if choice.finish_reason == "content_filter":
                 raise SafetyCheckError(
@@ -374,15 +297,10 @@ def _limit_messages(  # noqa: PLR0913
     max_tokens: int | None,
     messages: list[MessageModel],
     model: str,
-    system: list[ChatCompletionSystemMessageParam],
+    system: list[SystemMessage],
     max_messages: int = 1000,
-    tools: list[ChatCompletionToolParam] | None = None,
-) -> list[
-    ChatCompletionAssistantMessageParam
-    | ChatCompletionSystemMessageParam
-    | ChatCompletionToolMessageParam
-    | ChatCompletionUserMessageParam
-]:
+    tools: list[ChatCompletionsToolDefinition] | None = None,
+) -> list[ChatRequestMessage]:
     """
     Returns a list of messages limited by the context size.
 
@@ -398,18 +316,18 @@ def _limit_messages(  # noqa: PLR0913
 
     # Add system messages
     for message in system:
-        tokens += _count_tokens(json.dumps(message), model)
+        tokens += _count_tokens(_dump_sdk_model(message), model)
         counter += 1
 
     # Add tools
     for tool in tools or []:
-        tokens += _count_tokens(json.dumps(tool), model)
+        tokens += _count_tokens(_dump_sdk_model(tool), model)
 
     # Add user messages until the available context is reached, from the newest to the oldest
     for message in messages[::-1]:
         openai_message = message.to_openai()
         new_tokens = _count_tokens(
-            "".join([json.dumps(x) for x in openai_message]),
+            "".join([_dump_sdk_model(x) for x in openai_message]),
             model,
         )
         if tokens + new_tokens >= max_context:
@@ -442,9 +360,20 @@ def _count_tokens(content: str, model: str) -> int:
     return len(tiktoken.get_encoding(encoding_name).encode(content))
 
 
+def _dump_sdk_model(message: Model) -> str:
+    """
+    Returns a JSON representation of the AI Inference SDK data model.
+    """
+    return json.dumps(
+        cls=SdkJSONEncoder,
+        exclude_readonly=True,
+        obj=message,
+    )
+
+
 async def _use_llm(
     is_fast: bool,
-) -> tuple[AsyncAzureOpenAI | AsyncOpenAI, LlmAbstractPlatformModel]:
+) -> tuple[ChatCompletionsClient, LlmDeploymentModel]:
     """
     Returns an LLM client and platform model.
 
diff --git a/app/helpers/monitoring.py b/app/helpers/monitoring.py
index 291ffa61..510b4c31 100644
--- a/app/helpers/monitoring.py
+++ b/app/helpers/monitoring.py
@@ -4,7 +4,6 @@
 from azure.monitor.opentelemetry import configure_azure_monitor
 from opentelemetry import metrics, trace
 from opentelemetry.instrumentation.aiohttp_client import AioHttpClientInstrumentor
-from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
 from opentelemetry.metrics._internal.instrument import Counter, Gauge
 from opentelemetry.semconv.attributes import service_attributes
 from opentelemetry.trace.span import INVALID_SPAN
@@ -98,9 +97,13 @@ def gauge(
 
 
 try:
-    configure_azure_monitor()  # Configure Azure Application Insights exporter
-    AioHttpClientInstrumentor().instrument()  # Instrument aiohttp
-    HTTPXClientInstrumentor().instrument()  # Instrument httpx
+    # Capture LLM prompt and completion contents
+    # See: https://learn.microsoft.com/en-us/azure/ai-studio/how-to/develop/trace-local-sdk?tabs=python#configuration
+    environ["AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED"] = "true"
+    # Configure Azure Application Insights exporter
+    configure_azure_monitor()
+    # Instrument aiohttp
+    AioHttpClientInstrumentor().instrument()
 except ValueError as e:
     print(  # noqa: T201
         "Azure Application Insights instrumentation failed, likely due to a missing APPLICATIONINSIGHTS_CONNECTION_STRING environment variable.",
diff --git a/app/models/message.py b/app/models/message.py
index 842a0c87..04c7a96c 100644
--- a/app/models/message.py
+++ b/app/models/message.py
@@ -2,13 +2,15 @@
 from datetime import UTC, datetime
 from enum import Enum
 
-from openai.types.chat import (
-    ChatCompletionAssistantMessageParam,
-    ChatCompletionMessageToolCallParam,
-    ChatCompletionToolMessageParam,
-    ChatCompletionUserMessageParam,
+from azure.ai.inference.models import (
+    AssistantMessage,
+    ChatCompletionsToolCall,
+    ChatRequestMessage,
+    FunctionCall,
+    StreamingChatResponseToolCallUpdate,
+    ToolMessage,
+    UserMessage,
 )
-from openai.types.chat.chat_completion_chunk import ChoiceDeltaToolCall
 from pydantic import BaseModel, Field, field_validator
 
 _FUNC_NAME_SANITIZER_R = r"[^a-zA-Z0-9_-]"
@@ -58,7 +60,7 @@ class ToolModel(BaseModel):
     tool_id: str = ""
 
     def __add__(self, other: object) -> "ToolModel":
-        if not isinstance(other, ChoiceDeltaToolCall):
+        if not isinstance(other, StreamingChatResponseToolCallUpdate):
             return NotImplemented
         if other.id:
             self.tool_id = other.id
@@ -77,20 +79,19 @@ def __eq__(self, other: object) -> bool:
             return False
         return self.tool_id == other.tool_id
 
-    def to_openai(self) -> ChatCompletionMessageToolCallParam:
-        return ChatCompletionMessageToolCallParam(
+    def to_openai(self) -> ChatCompletionsToolCall:
+        return ChatCompletionsToolCall(
             id=self.tool_id,
-            type="function",
-            function={
-                "arguments": self.function_arguments,
-                "name": "-".join(
+            function=FunctionCall(
+                arguments=self.function_arguments,
+                name="-".join(
                     re.sub(
                         _FUNC_NAME_SANITIZER_R,
                         "-",
                         self.function_name,
                     ).split("-")
                 ),  # Sanitize with dashes then deduplicate dashes, backward compatibility with old models
-            },
+            ),
         )
 
 
@@ -118,43 +119,35 @@ def _validate_created_at(cls, created_at: datetime) -> datetime:
 
     def to_openai(
         self,
-    ) -> list[
-        ChatCompletionAssistantMessageParam
-        | ChatCompletionToolMessageParam
-        | ChatCompletionUserMessageParam
-    ]:
+    ) -> list[ChatRequestMessage]:
         # Removing newlines from the content to avoid hallucinations issues with GPT-4 Turbo
         content = " ".join([line.strip() for line in self.content.splitlines()])
 
         if self.persona == PersonaEnum.HUMAN:
             return [
-                ChatCompletionUserMessageParam(
+                UserMessage(
                     content=f"action={self.action.value} {content}",
-                    role="user",
                 )
             ]
 
         if self.persona == PersonaEnum.ASSISTANT:
             if not self.tool_calls:
                 return [
-                    ChatCompletionAssistantMessageParam(
+                    AssistantMessage(
                         content=f"action={self.action.value} style={self.style.value} {content}",
-                        role="assistant",
                     )
                 ]
 
         res = []
         res.append(
-            ChatCompletionAssistantMessageParam(
+            AssistantMessage(
                 content=f"action={self.action.value} style={self.style.value} {content}",
-                role="assistant",
                 tool_calls=[tool_call.to_openai() for tool_call in self.tool_calls],
             )
         )
         res.extend(
-            ChatCompletionToolMessageParam(
+            ToolMessage(
                 content=tool_call.content,
-                role="tool",
                 tool_call_id=tool_call.tool_id,
             )
             for tool_call in self.tool_calls
diff --git a/cicd/bicep/app.bicep b/cicd/bicep/app.bicep
index 60d42066..e867abc1 100644
--- a/cicd/bicep/app.bicep
+++ b/cicd/bicep/app.bicep
@@ -72,24 +72,14 @@ var config = {
   }
   llm: {
     fast: {
-      mode: 'azure_openai'
-      azure_openai: {
-        context: llmFastContext
-        deployment: llmFast.name
-        endpoint: cognitiveOpenai.properties.endpoint
-        model: llmFastModel
-        streaming: true
-      }
+      context: llmFastContext
+      endpoint: '${cognitiveOpenai.properties.endpoint}/openai/deployments/${llmFast.name}'
+      model: llmFastModel
     }
     slow: {
-      mode: 'azure_openai'
-      azure_openai: {
-        context: llmSlowContext
-        deployment: llmSlow.name
-        endpoint: cognitiveOpenai.properties.endpoint
-        model: llmSlowModel
-        streaming: true
-      }
+      context: llmSlowContext
+      endpoint: '${cognitiveOpenai.properties.endpoint}/openai/deployments/${llmSlow.name}'
+      model: llmSlowModel
     }
   }
   ai_search: {
@@ -504,6 +494,65 @@ resource cognitiveCommunication 'Microsoft.CognitiveServices/accounts@2024-06-01
   }
 }
 
+var aiFoundryName = '${prefix}-${openaiLocation}-foundry'
+
+resource aiFoundry 'Microsoft.MachineLearningServices/workspaces@2024-10-01' = {
+  name: aiFoundryName
+  location: location
+  tags: tags
+  identity: {
+    type: 'SystemAssigned'
+  }
+  kind: 'hub'
+  properties: {
+    applicationInsights: applicationInsights.id
+    friendlyName: appName
+  }
+
+  resource aiServicesConnection 'connections@2024-10-01' = {
+    name: '${aiFoundryName}-connection-openai'
+    properties: {
+      authType: 'AAD'
+      category: 'AzureOpenAI'
+      isSharedToAll: true
+      target: cognitiveOpenai.properties.endpoint
+      metadata: {
+        ApiType: 'Azure'
+        ResourceId: cognitiveOpenai.id
+      }
+    }
+  }
+}
+
+// Log Analytics Contributor
+resource roleLogAnalyticsContributor 'Microsoft.Authorization/roleDefinitions@2022-04-01' existing = {
+  name: '92aaf0da-9dab-42b6-94a3-d43ce8d16293'
+}
+
+resource assignmentsAiProjectLogAnalyticsContributor 'Microsoft.Authorization/roleAssignments@2022-04-01' = {
+  name: guid(subscription().id, prefix, aiProject.name, 'assignmentsAiProjectLogAnalyticsContributor')
+  scope: logAnalytics
+  properties: {
+    principalId: aiProject.identity.principalId
+    principalType: 'ServicePrincipal'
+    roleDefinitionId: roleLogAnalyticsContributor.id
+  }
+}
+
+resource aiProject 'Microsoft.MachineLearningServices/workspaces@2024-10-01' = {
+  name: appName
+  location: location
+  tags: tags
+  identity: {
+    type: 'SystemAssigned'
+  }
+  kind: 'project'
+  properties: {
+    friendlyName: appName
+    hubResourceId: aiFoundry.id
+  }
+}
+
 // Cognitive Services OpenAI Contributor
 resource roleOpenaiContributor 'Microsoft.Authorization/roleDefinitions@2022-04-01' existing = {
   name: 'a001fd3d-188f-4b5d-821b-7da978bf7442'
diff --git a/config-local-example.yaml b/config-local-example.yaml
index b5f1ab8d..ee2a52e4 100644
--- a/config-local-example.yaml
+++ b/config-local-example.yaml
@@ -24,21 +24,13 @@ cognitive_service:
 
 llm:
   fast:
-    mode: azure_openai
-    azure_openai:
-      context: 16385
-      deployment: gpt-4o-mini-2024-07-18
-      endpoint: https://xxx.openai.azure.com
-      model: gpt-4o-mini
-      streaming: true
+    context: 16385
+    endpoint: https://xxx.openai.azure.com/openai/deployments/gpt-4o-mini-2024-07-18
+    model: gpt-4o-mini
   slow:
-    mode: azure_openai
-    azure_openai:
-      context: 128000
-      deployment: gpt-4o-2024-08-06
-      endpoint: https://xxx.openai.azure.com
-      model: gpt-4o
-      streaming: true
+    context: 128000
+    endpoint: https://xxx.openai.azure.com/openai/deployments/gpt-4o-2024-08-06
+    model: gpt-4o
 
 ai_search:
   embedding_deployment: text-embedding-3-large-1
diff --git a/pyproject.toml b/pyproject.toml
index 7fd75e1b..23b275c0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ dependencies = [
   "aiohttp-retry~=2.9",  # Retry middleware for aiohttp, used with Twilio SDK
   "aiohttp[speedups]~=3.10",  # Async HTTP client for Azure and Twilio SDKs, plus async DNS resolver and async Brotli compression
   "aiojobs~=1.3",  # Async job scheduler
+  "azure-ai-inference[opentelemetry]~=1.0.0a0",  # Azure AI Foundry LLM inference
   "azure-ai-translation-text~=1.0",  # Azure Cognitive Services Text Translation
   "azure-appconfiguration~=1.7",  # Outsourced configuration for live updates
   "azure-cognitiveservices-speech~=1.41",  # Azure AI Speech
@@ -27,10 +28,7 @@ dependencies = [
   "json-repair~=0.30",  # Repair JSON files from LLM
   "mistune~=3.0",  # Markdown parser for web views
   "noisereduce~=3.0", # Noise reduction
-  "openai~=1.52",  # OpenAI client
   "opentelemetry-instrumentation-aiohttp-client~=0.0a0",  # OpenTelemetry instrumentation for aiohttp client
-  "opentelemetry-instrumentation-httpx~=0.0a0",  # OpenTelemetry instrumentation for HTTPX
-  "opentelemetry-instrumentation-openai~=0.0a0",  # OpenTelemetry instrumentation for OpenAI
   "opentelemetry-instrumentation-redis~=0.0a0",  # OpenTelemetry instrumentation for Redis
   "opentelemetry-semantic-conventions~=0.0a0",  # OpenTelemetry conventions, to standardize telemetry data
   "phonenumbers~=8.13",  # Phone number parsing and formatting, used with Pydantic
diff --git a/tests/conftest.py b/tests/conftest.py
index c8e54d86..8b23f981 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -192,7 +192,7 @@ def __init__(
         cache: pytest.Cache,
         **kwargs,
     ):
-        platform = CONFIG.llm.fast.azure_openai
+        platform = CONFIG.llm.fast
         assert platform
 
         _langchain_kwargs = {
@@ -203,7 +203,6 @@ def __init__(
             "temperature": platform.temperature,
             # Deployment
             "api_version": platform.api_version,
-            "azure_deployment": platform.deployment,
             "azure_endpoint": platform.endpoint,
             "model": platform.model,
             # Authentication, either RBAC or API
diff --git a/uv.lock b/uv.lock
index 75352b2f..f1f2dda6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -103,14 +103,14 @@ wheels = [
 
 [[package]]
 name = "aiosignal"
-version = "1.3.1"
+version = "1.3.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "frozenlist" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ae/67/0952ed97a9793b4958e5736f6d2b346b414a2cd63e82d05940032f45b32f/aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc", size = 19422 }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/b5/6d55e80f6d8a08ce22b982eafa278d823b541c925f11ee774b0b9c43473d/aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54", size = 19424 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17", size = 7617 },
+    { url = "https://files.pythonhosted.org/packages/ec/6a/bc7e17a3e87a2985d3e8f4da4cd0f481060eb78fb08596c42be62c90a4d9/aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5", size = 7597 },
 ]
 
 [[package]]
@@ -156,11 +156,30 @@ wheels = [
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/48/c8/6260f8ccc11f0917360fc0da435c5c9c7504e3db174d5a12a1494887b045/attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff", size = 805984 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/89/aa/ab0f7891a01eeb2d2e338ae8fecbe57fcebea1a24dbb64d45801bfab481d/attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308", size = 63397 },
+]
+
+[[package]]
+name = "azure-ai-inference"
+version = "1.0.0b6"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/fc/0f/aafca9af9315aee06a89ffde799a10a582fe8de76c563ee80bbcdc08b3fb/attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346", size = 792678 }
+dependencies = [
+    { name = "azure-core" },
+    { name = "isodate" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2a/c9/264ae0ef0460dbd7c7efe1d3a093ad6a00fb2823d341ac457459396df2d6/azure_ai_inference-1.0.0b6.tar.gz", hash = "sha256:b8ac941de1e69151bad464191e18856d4e74f962ae03235da137a9a326143676", size = 145414 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6a/21/5b6702a7f963e95456c0de2d495f67bf5fd62840ac655dc451586d23d39a/attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2", size = 63001 },
+    { url = "https://files.pythonhosted.org/packages/5a/aa/47459ab2e67c55ff98dbb9694c47cf98e484ce1ae1acb244d28b25a8c1c1/azure_ai_inference-1.0.0b6-py3-none-any.whl", hash = "sha256:5699ad78d70ec2d227a5eff2c1bafc845018f6624edc5b03589dfff861c54958", size = 115312 },
+]
+
+[package.optional-dependencies]
+opentelemetry = [
+    { name = "azure-core-tracing-opentelemetry" },
 ]
 
 [[package]]
@@ -454,6 +473,7 @@ dependencies = [
     { name = "aiohttp", extra = ["speedups"] },
     { name = "aiohttp-retry" },
     { name = "aiojobs" },
+    { name = "azure-ai-inference", extra = ["opentelemetry"] },
     { name = "azure-ai-translation-text" },
     { name = "azure-appconfiguration" },
     { name = "azure-cognitiveservices-speech" },
@@ -472,10 +492,7 @@ dependencies = [
     { name = "json-repair" },
     { name = "mistune" },
     { name = "noisereduce" },
-    { name = "openai" },
     { name = "opentelemetry-instrumentation-aiohttp-client" },
-    { name = "opentelemetry-instrumentation-httpx" },
-    { name = "opentelemetry-instrumentation-openai" },
     { name = "opentelemetry-instrumentation-redis" },
     { name = "opentelemetry-semantic-conventions" },
     { name = "phonenumbers" },
@@ -514,6 +531,7 @@ requires-dist = [
     { name = "aiohttp", extras = ["speedups"], specifier = "~=3.10" },
     { name = "aiohttp-retry", specifier = "~=2.9" },
     { name = "aiojobs", specifier = "~=1.3" },
+    { name = "azure-ai-inference", extras = ["opentelemetry"], specifier = "~=1.0.0a0" },
     { name = "azure-ai-translation-text", specifier = "~=1.0" },
     { name = "azure-appconfiguration", specifier = "~=1.7" },
     { name = "azure-cognitiveservices-speech", specifier = "~=1.41" },
@@ -534,10 +552,7 @@ requires-dist = [
     { name = "json-repair", specifier = "~=0.30" },
     { name = "mistune", specifier = "~=3.0" },
     { name = "noisereduce", specifier = "~=3.0" },
-    { name = "openai", specifier = "~=1.52" },
     { name = "opentelemetry-instrumentation-aiohttp-client", specifier = "~=0.0a0" },
-    { name = "opentelemetry-instrumentation-httpx", specifier = "~=0.0a0" },
-    { name = "opentelemetry-instrumentation-openai", specifier = "~=0.0a0" },
     { name = "opentelemetry-instrumentation-redis", specifier = "~=0.0a0" },
     { name = "opentelemetry-semantic-conventions", specifier = "~=0.0a0" },
     { name = "phonenumbers", specifier = "~=8.13" },
@@ -567,11 +582,11 @@ requires-dist = [
 
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b0/ee/9b19140fe824b367c04c5e1b369942dd754c4c5462d5674002f75c4dedc1/certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9", size = 168507 }
+sdist = { url = "https://files.pythonhosted.org/packages/0f/bd/1d41ee578ce09523c81a15426705dd20969f5abf006d1afe8aeff0dd776a/certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db", size = 166010 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/12/90/3c9ff0512038035f59d279fddeb79f5f1eccd8859f06d6163c58798b9487/certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8", size = 167321 },
+    { url = "https://files.pythonhosted.org/packages/a5/32/8f6669fc4798494966bf446c8c4a162e0b5d893dff088afddf76414f70e1/certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56", size = 164927 },
 ]
 
 [[package]]
@@ -723,7 +738,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/98/65/13d9e76ca19b0ba5603d71ac8424b5694415b348e719db277b5edc985ff5/cryptography-44.0.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:761817a3377ef15ac23cd7834715081791d4ec77f9297ee694ca1ee9c2c7e5eb", size = 3915420 },
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