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567 | class AzureAICompletionsModel(FunctionCallingLLM):
"""Azure AI model inference for LLM.
Examples:
```python
from llama_index.core import Settings
from llama_index.core.llms import ChatMessage
from llama_index.llms.azure_inference import AzureAICompletionsModel
llm = AzureAICompletionsModel(
endpoint="https://[your-endpoint].inference.ai.azure.com",
credential="your-api-key",
temperature=0
)
# If using Microsoft Entra ID authentication, you can create the
# client as follows:
#
# from azure.identity import DefaultAzureCredential
#
# llm = AzureAICompletionsModel(
# endpoint="https://[your-endpoint].inference.ai.azure.com",
# credential=DefaultAzureCredential()
# )
#
# # If you plan to use asynchronous calling, make sure to use the async
# # credentials as follows:
#
# from azure.identity.aio import DefaultAzureCredential as DefaultAzureCredentialAsync
#
# llm = AzureAICompletionsModel(
# endpoint="https://[your-endpoint].inference.ai.azure.com",
# credential=DefaultAzureCredentialAsync()
# )
resp = llm.chat(
messages=ChatMessage(role="user", content="Who is Paul Graham?")
)
print(resp)
# Once the client is instantiated, you can set the context to use the model
Settings.llm = llm
```
"""
model_config = ConfigDict(protected_namespaces=())
model_name: Optional[str] = Field(
default=None,
description="The model id to use. Optional for endpoints running a single model.",
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use for sampling.",
ge=0.0,
le=1.0,
)
max_tokens: Optional[int] = Field(
default=None,
description="The maximum number of tokens to generate.",
gt=0,
)
seed: str = Field(default=None, description="The random seed to use for sampling.")
model_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs model parameters.",
)
_client: ChatCompletionsClient = PrivateAttr()
_async_client: ChatCompletionsClientAsync = PrivateAttr()
_model_name: str = PrivateAttr(None)
_model_type: str = PrivateAttr(None)
_model_provider: str = PrivateAttr(None)
def __init__(
self,
endpoint: str = None,
credential: Union[str, AzureKeyCredential, "TokenCredential"] = None,
temperature: float = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = None,
model_name: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
client_kwargs: Dict[str, Any] = None,
**kwargs: Dict[str, Any],
) -> None:
client_kwargs = client_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
endpoint = get_from_param_or_env(
"endpoint", endpoint, "AZURE_INFERENCE_ENDPOINT", None
)
credential = get_from_param_or_env(
"credential", credential, "AZURE_INFERENCE_CREDENTIAL", None
)
credential = (
AzureKeyCredential(credential)
if isinstance(credential, str)
else credential
)
if not endpoint:
raise ValueError(
"You must provide an endpoint to use the Azure AI model inference LLM."
"Pass the endpoint as a parameter or set the AZURE_INFERENCE_ENDPOINT"
"environment variable."
)
if not credential:
raise ValueError(
"You must provide an credential to use the Azure AI model inference LLM."
"Pass the credential as a parameter or set the AZURE_INFERENCE_CREDENTIAL"
)
super().__init__(
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
**kwargs,
)
self._client = ChatCompletionsClient(
endpoint=endpoint,
credential=credential,
user_agent="llamaindex",
**client_kwargs,
)
self._async_client = ChatCompletionsClientAsync(
endpoint=endpoint,
credential=credential,
user_agent="llamaindex",
**client_kwargs,
)
@classmethod
def class_name(cls) -> str:
return "AzureAICompletionsModel"
@property
def metadata(self) -> LLMMetadata:
if not self._model_name:
model_info = None
try:
# Get model info from the endpoint. This method may not be supported by all
# endpoints.
model_info = self._client.get_model_info()
except HttpResponseError:
logger.warning(
f"Endpoint '{self._client._config.endpoint}' does not support model metadata retrieval. "
"Failed to get model info for method `metadata()`."
)
finally:
if model_info:
self._model_name = model_info.get("model_name", None)
self._model_type = model_info.get("model_type", None)
self._model_provider = model_info.get("model_provider_name", None)
else:
self._model_name = self.model_name or "unknown"
self._model_type = "unknown"
self._model_provider = "unknown"
return LLMMetadata(
is_chat_model=self._model_type == "chat-completions",
model_name=self._model_name,
model_type=self._model_type,
model_provider=self._model_provider,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"max_tokens": self.max_tokens,
}
if self.model_name:
base_kwargs["model"] = self.model_name
return {
**base_kwargs,
**self.model_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
messages = to_inference_message(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.complete(messages=messages, **all_kwargs)
response_message = from_inference_message(response.choices[0].message)
return ChatResponse(
message=response_message,
raw=response.as_dict(),
)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
complete_fn = chat_to_completion_decorator(self.chat)
return complete_fn(prompt, **kwargs)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
messages = to_inference_message(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._client.complete(messages=messages, stream=True, **all_kwargs)
def gen() -> ChatResponseGen:
content = ""
role = MessageRole.ASSISTANT
for chunk in response:
content_delta = (
chunk.choices[0].delta.content if len(chunk.choices) > 0 else None
)
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return gen()
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
stream_complete_fn = stream_chat_to_completion_decorator(self.stream_chat)
return stream_complete_fn(prompt, **kwargs)
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
messages = to_inference_message(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = await self._async_client.complete(messages=messages, **all_kwargs)
response_message = from_inference_message(response.choices[0].message)
return ChatResponse(
message=response_message,
raw=response.as_dict(),
)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
acomplete_fn = achat_to_completion_decorator(self.achat)
return await acomplete_fn(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
messages = to_inference_message(messages)
all_kwargs = self._get_all_kwargs(**kwargs)
response = self._async_client.complete(
messages=messages, stream=True, **all_kwargs
)
async def gen() -> ChatResponseAsyncGen:
content = ""
role = MessageRole.ASSISTANT
async for chunk in response:
content_delta = (
chunk.choices[0].delta.content if chunk.choices else None
)
if content_delta is None:
continue
content += content_delta
yield ChatResponse(
message=ChatMessage(role=role, content=content),
delta=content_delta,
raw=chunk,
)
return gen()
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
astream_complete_fn = astream_chat_to_completion_decorator(self.astream_chat)
return await astream_complete_fn(prompt, stream=True, **kwargs)
def chat_with_tools(
self,
tools: List["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
**kwargs: Any,
) -> ChatResponse:
"""Predict and call the tool."""
# Azure AI model inference uses the same openai tool format
tool_specs = [
tool.metadata.to_openai_tool(skip_length_check=True) for tool in tools
]
if isinstance(user_msg, str):
user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
messages = chat_history or []
if user_msg:
messages.append(user_msg)
response = self.chat(
messages,
tools=tool_specs,
**kwargs,
)
if not allow_parallel_tool_calls:
force_single_tool_call(response)
return response
async def achat_with_tools(
self,
tools: List["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
**kwargs: Any,
) -> ChatResponse:
"""Predict and call the tool."""
# Azure AI model inference uses the same openai tool format
tool_specs = [
tool.metadata.to_openai_tool(skip_length_check=True) for tool in tools
]
if isinstance(user_msg, str):
user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
messages = chat_history or []
if user_msg:
messages.append(user_msg)
response = await self.achat(
messages,
tools=tool_specs,
**kwargs,
)
if not allow_parallel_tool_calls:
force_single_tool_call(response)
return response
def get_tool_calls_from_response(
self,
response: "AgentChatResponse",
error_on_no_tool_call: bool = True,
) -> List[ToolSelection]:
"""Predict and call the tool."""
tool_calls = response.message.additional_kwargs.get("tool_calls", [])
if len(tool_calls) < 1:
if error_on_no_tool_call:
raise ValueError(
f"Expected at least one tool call, but got {len(tool_calls)} tool calls."
)
else:
return []
tool_selections = []
for tool_call in tool_calls:
if not isinstance(tool_call, ChatCompletionsToolCall):
raise ValueError("Invalid tool_call object")
if tool_call.type != "function":
raise ValueError(
"Invalid tool type. Only `function` is supported but `{tool_call.type}` was received."
)
argument_dict = json.loads(tool_call.function.arguments)
tool_selections.append(
ToolSelection(
tool_id=tool_call.id,
tool_name=tool_call.function.name,
tool_kwargs=argument_dict,
)
)
return tool_selections
def _prepare_chat_with_tools(
self,
tools: List["BaseTool"],
user_msg: Optional[Union[str, ChatMessage]] = None,
chat_history: Optional[List[ChatMessage]] = None,
verbose: bool = False,
allow_parallel_tool_calls: bool = False,
**kwargs: Any,
) -> Dict[str, Any]:
"""Prepare the arguments needed to let the LLM chat with tools."""
chat_history = chat_history or []
if isinstance(user_msg, str):
user_msg = ChatMessage(role=MessageRole.USER, content=user_msg)
chat_history.append(user_msg)
tool_dicts = [to_inference_tool(tool.metadata) for tool in tools]
return {
"messages": chat_history,
"tools": tool_dicts or None,
**kwargs,
}
|