diff --git a/adalflow/adalflow/components/model_client/azureai_client.py b/adalflow/adalflow/components/model_client/azureai_client.py new file mode 100644 index 00000000..8bfcf3c8 --- /dev/null +++ b/adalflow/adalflow/components/model_client/azureai_client.py @@ -0,0 +1,455 @@ +"""AzureOpenAI ModelClient integration.""" + +import os +from typing import ( + Dict, + Sequence, + Optional, + List, + Any, + TypeVar, + Callable, + Generator, + Union, + Literal, +) +import re + +import logging +import backoff + +# optional import +from adalflow.utils.lazy_import import safe_import, OptionalPackages + + +openai = safe_import(OptionalPackages.OPENAI.value[0], OptionalPackages.OPENAI.value[1]) + +DefaultAzureCredential = safe_import('azure.identity.DefaultAzureCredential', 'azure.identity') +get_bearer_token_provider = safe_import('azure.identity.get_bearer_token_provider', 'azure.identity') + + + +from azure.core.credentials import AccessToken +from openai import AzureOpenAI, AsyncAzureOpenAI, Stream +from openai import ( + APITimeoutError, + InternalServerError, + RateLimitError, + UnprocessableEntityError, + BadRequestError, +) +from openai.types import ( + Completion, + CreateEmbeddingResponse, +) +from openai.types.chat import ChatCompletionChunk, ChatCompletion + +from adalflow.core.model_client import ModelClient +from adalflow.core.types import ( + ModelType, + EmbedderOutput, + TokenLogProb, + CompletionUsage, + GeneratorOutput, +) +from adalflow.components.model_client.utils import parse_embedding_response + +log = logging.getLogger(__name__) +T = TypeVar("T") + + +# completion parsing functions and you can combine them into one singple chat completion parser +def get_first_message_content(completion: ChatCompletion) -> str: + r"""When we only need the content of the first message. + It is the default parser for chat completion.""" + return completion.choices[0].message.content + + +# def _get_chat_completion_usage(completion: ChatCompletion) -> OpenAICompletionUsage: +# return completion.usage + + +def parse_stream_response(completion: ChatCompletionChunk) -> str: + r"""Parse the response of the stream API.""" + return completion.choices[0].delta.content + + +def handle_streaming_response(generator: Stream[ChatCompletionChunk]): + r"""Handle the streaming response.""" + for completion in generator: + log.debug(f"Raw chunk completion: {completion}") + parsed_content = parse_stream_response(completion) + yield parsed_content + + +def get_all_messages_content(completion: ChatCompletion) -> List[str]: + r"""When the n > 1, get all the messages content.""" + return [c.message.content for c in completion.choices] + + +def get_probabilities(completion: ChatCompletion) -> List[List[TokenLogProb]]: + r"""Get the probabilities of each token in the completion.""" + log_probs = [] + for c in completion.choices: + content = c.logprobs.content + log_probs_for_choice = [] + for openai_token_logprob in content: + token = openai_token_logprob.token + logprob = openai_token_logprob.logprob + log_probs_for_choice.append(TokenLogProb(token=token, logprob=logprob)) + log_probs.append(log_probs_for_choice) + return log_probs + + +class AzureAIClient(ModelClient): + __doc__ = r""" + A client wrapper for interacting with Azure OpenAI's API. + + This class provides support for both embedding and chat completion API calls. + Users can use this class to simplify their interactions with Azure OpenAI models + through the `Embedder` and `Generator` components. + + **Initialization:** + + You can initialize the `AzureAIClient` with either an API key or Azure Active Directory (AAD) token + authentication. It is recommended to set environment variables for sensitive data like API keys. + + Args: + api_key (Optional[str]): Azure OpenAI API key. Default is None. + api_version (Optional[str]): API version to use. Default is None. + azure_endpoint (Optional[str]): Azure OpenAI endpoint URL. Default is None. + credential (Optional[DefaultAzureCredential]): Azure AD credential for token-based authentication. Default is None. + chat_completion_parser (Callable[[Completion], Any]): Function to parse chat completions. Default is `get_first_message_content`. + input_type (Literal["text", "messages"]): Format for input, either "text" or "messages". Default is "text". + + **Setup Instructions:** + + - **Using API Key:** + Set up the following environment variables: + ```bash + export AZURE_OPENAI_API_KEY="your_api_key" + export AZURE_OPENAI_ENDPOINT="your_endpoint" + export AZURE_OPENAI_VERSION="your_version" + ``` + + - **Using Azure AD Token:** + Ensure you have configured Azure AD credentials. The `DefaultAzureCredential` will automatically use your configured credentials. + + **Example Usage:**do + + ```python + from azure.identity import DefaultAzureCredential + from your_module import AzureAIClient # Adjust import based on your module name + + # Initialize with API key + client = AzureAIClient( + api_key="your_api_key", + api_version="2023-05-15", + azure_endpoint="https://your-endpoint.openai.azure.com/" + ) + + # Or initialize with Azure AD token + client = AzureAIClient( + api_version="2023-05-15", + azure_endpoint="https://your-endpoint.openai.azure.com/", + credential=DefaultAzureCredential() + ) + + # Example call to the chat completion API + api_kwargs = { + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "What is the meaning of life?"}], + "stream": True + } + response = client.call(api_kwargs=api_kwargs, model_type=ModelType.LLM) + + for chunk in response: + print(chunk) + ``` + + **Notes:** + - Ensure that the API key or credentials are correctly set up and accessible to avoid authentication errors. + - Use `chat_completion_parser` to define how to extract and handle the chat completion responses. + - The `input_type` parameter determines how input is formatted for the API call. + + **References:** + - [Azure OpenAI API Documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) + - [OpenAI API Documentation](https://platform.openai.com/docs/guides/text-generation) + """ + + def __init__( + self, + api_key: Optional[str] = None, + api_version:Optional[str]=None, + azure_endpoint: Optional[str]= None, + credential: Optional[DefaultAzureCredential] = None, + chat_completion_parser: Callable[[Completion], Any] = None, + input_type: Literal["text", "messages"] = "text", + ): + r"""It is recommended to set the API_KEY into the environment variable instead of passing it as an argument. + + + Initializes the Azure OpenAI client with either API key or AAD token authentication. + + Args: + api_key: Azure OpenAI API key. + api_version: Azure OpenAI API version. + azure_endpoint: Azure OpenAI endpoint. + credential: Azure AD credential for token-based authentication. + chat_completion_parser: Function to parse chat completions. + input_type: Input format, either "text" or "messages". + + """ + super().__init__() + + # added api_type azure for azure Ai + self.api_type = "azure" + self._api_key = api_key or os.getenv("AZURE_OPENAI_API_KEY") + self._apiversion= api_version or os.getenv("AZURE_OPENAI_VERSION") + self._azure_endpoint = azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT") + self._credential = credential + self.sync_client = self.init_sync_client() + self.async_client = None # only initialize if the async call is called + self.chat_completion_parser = ( + chat_completion_parser or get_first_message_content + ) + self._input_type = input_type + + def init_sync_client(self): + + api_key = self._api_key or os.getenv("AZURE_OPENAI_API_KEY") + azure_endpoint = self._azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT") + api_version = self._apiversion or os.getenv("AZURE_OPENAI_VERSION") + credential = self._credential or DefaultAzureCredential + if not azure_endpoint: + raise ValueError("Environment variable AZURE_OPENAI_ENDPOINT must be set") + if not api_version: + raise ValueError("Environment variable AZURE_OPENAI_VERSION must be set") + + if api_key: + return AzureOpenAI(api_key=api_key, azure_endpoint=azure_endpoint, api_version=api_version) + elif self._credential: + # credential = DefaultAzureCredential() + token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default") + return AzureOpenAI(azure_ad_token_provider=token_provider, azure_endpoint=azure_endpoint, api_version=api_version) + else: + raise ValueError("Environment variable AZURE_OPENAI_API_KEY must be set or credential must be provided") + + + def init_async_client(self): + api_key = self._api_key or os.getenv("AZURE_OPENAI_API_KEY") + azure_endpoint = self._azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT") + api_version = self._apiversion or os.getenv("AZURE_OPENAI_VERSION") + credential = self._credential or DefaultAzureCredential() + if not azure_endpoint: + raise ValueError("Environment variable AZURE_OPENAI_ENDPOINT must be set") + if not api_version: + raise ValueError("Environment variable AZURE_OPENAI_VERSION must be set") + + if api_key: + return AsyncAzureOpenAI(api_key=api_key, azure_endpoint=azure_endpoint, api_version=api_version) + elif self._credential: + # credential = DefaultAzureCredential() + token_provider = get_bearer_token_provider(DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default") + return AsyncAzureOpenAI(azure_ad_token_provider=token_provider, azure_endpoint=azure_endpoint, api_version=api_version) + else: + raise ValueError("Environment variable AZURE_OPENAI_API_KEY must be set or credential must be provided") + + # def _parse_chat_completion(self, completion: ChatCompletion) -> "GeneratorOutput": + # # TODO: raw output it is better to save the whole completion as a source of truth instead of just the message + # try: + # data = self.chat_completion_parser(completion) + # usage = self.track_completion_usage(completion) + # return GeneratorOutput( + # data=data, error=None, raw_response=str(data), usage=usage + # ) + # except Exception as e: + # log.error(f"Error parsing the completion: {e}") + # return GeneratorOutput(data=None, error=str(e), raw_response=completion) + + def parse_chat_completion( + self, + completion: Union[ChatCompletion, Generator[ChatCompletionChunk, None, None]], + ) -> "GeneratorOutput": + """Parse the completion, and put it into the raw_response.""" + log.debug(f"completion: {completion}, parser: {self.chat_completion_parser}") + try: + data = self.chat_completion_parser(completion) + usage = self.track_completion_usage(completion) + return GeneratorOutput( + data=None, error=None, raw_response=data, usage=usage + ) + except Exception as e: + log.error(f"Error parsing the completion: {e}") + return GeneratorOutput(data=None, error=str(e), raw_response=completion) + + def track_completion_usage( + self, + completion: Union[ChatCompletion, Generator[ChatCompletionChunk, None, None]], + ) -> CompletionUsage: + if isinstance(completion, ChatCompletion): + usage: CompletionUsage = CompletionUsage( + completion_tokens=completion.usage.completion_tokens, + prompt_tokens=completion.usage.prompt_tokens, + total_tokens=completion.usage.total_tokens, + ) + return usage + else: + raise NotImplementedError( + "streaming completion usage tracking is not implemented" + ) + + def parse_embedding_response( + self, response: CreateEmbeddingResponse + ) -> EmbedderOutput: + r"""Parse the embedding response to a structure LightRAG components can understand. + + Should be called in ``Embedder``. + """ + try: + return parse_embedding_response(response) + except Exception as e: + log.error(f"Error parsing the embedding response: {e}") + return EmbedderOutput(data=[], error=str(e), raw_response=response) + + def convert_inputs_to_api_kwargs( + self, + input: Optional[Any] = None, + model_kwargs: Dict = {}, + model_type: ModelType = ModelType.UNDEFINED, + ) -> Dict: + r""" + Specify the API input type and output api_kwargs that will be used in _call and _acall methods. + Convert the Component's standard input, and system_input(chat model) and model_kwargs into API-specific format + """ + + final_model_kwargs = model_kwargs.copy() + if model_type == ModelType.EMBEDDER: + if isinstance(input, str): + input = [input] + # convert input to input + if not isinstance(input, Sequence): + raise TypeError("input must be a sequence of text") + final_model_kwargs["input"] = input + elif model_type == ModelType.LLM: + # convert input to messages + messages: List[Dict[str, str]] = [] + + if self._input_type == "messages": + system_start_tag = "" + system_end_tag = "" + user_start_tag = "" + user_end_tag = "" + pattern = f"{system_start_tag}(.*?){system_end_tag}{user_start_tag}(.*?){user_end_tag}" + # Compile the regular expression + regex = re.compile(pattern) + # Match the pattern + match = regex.search(input) + system_prompt, input_str = None, None + + if match: + system_prompt = match.group(1) + input_str = match.group(2) + + else: + print("No match found.") + if system_prompt and input_str: + messages.append({"role": "system", "content": system_prompt}) + messages.append({"role": "user", "content": input_str}) + if len(messages) == 0: + messages.append({"role": "system", "content": input}) + final_model_kwargs["messages"] = messages + else: + raise ValueError(f"model_type {model_type} is not supported") + return final_model_kwargs + + @backoff.on_exception( + backoff.expo, + ( + APITimeoutError, + InternalServerError, + RateLimitError, + UnprocessableEntityError, + BadRequestError, + ), + max_time=5, + ) + def call(self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED): + """ + kwargs is the combined input and model_kwargs. Support streaming call. + """ + log.info(f"api_kwargs: {api_kwargs}") + if model_type == ModelType.EMBEDDER: + return self.sync_client.embeddings.create(**api_kwargs) + elif model_type == ModelType.LLM: + if "stream" in api_kwargs and api_kwargs.get("stream", False): + log.debug("streaming call") + self.chat_completion_parser = handle_streaming_response + return self.sync_client.chat.completions.create(**api_kwargs) + return self.sync_client.chat.completions.create(**api_kwargs) + else: + raise ValueError(f"model_type {model_type} is not supported") + + @backoff.on_exception( + backoff.expo, + ( + APITimeoutError, + InternalServerError, + RateLimitError, + UnprocessableEntityError, + BadRequestError, + ), + max_time=5, + ) + async def acall( + self, api_kwargs: Dict = {}, model_type: ModelType = ModelType.UNDEFINED + ): + """ + kwargs is the combined input and model_kwargs + """ + if self.async_client is None: + self.async_client = self.init_async_client() + if model_type == ModelType.EMBEDDER: + return await self.async_client.embeddings.create(**api_kwargs) + elif model_type == ModelType.LLM: + return await self.async_client.chat.completions.create(**api_kwargs) + else: + raise ValueError(f"model_type {model_type} is not supported") + + @classmethod + def from_dict(cls: type[T], data: Dict[str, Any]) -> T: + obj = super().from_dict(data) + # recreate the existing clients + obj.sync_client = obj.init_sync_client() + obj.async_client = obj.init_async_client() + return obj + + def to_dict(self) -> Dict[str, Any]: + r"""Convert the component to a dictionary.""" + # TODO: not exclude but save yes or no for recreating the clients + exclude = [ + "sync_client", + "async_client", + ] # unserializable object + output = super().to_dict(exclude=exclude) + return output + + +# if __name__ == "__main__": +# from adalflow.core import Generator +# from adalflow.utils import setup_env, get_logger + +# log = get_logger(level="DEBUG") + +# setup_env() +# prompt_kwargs = {"input_str": "What is the meaning of life?"} + +# gen = Generator( +# model_client=OpenAIClient(), +# model_kwargs={"model": "gpt-3.5-turbo", "stream": True}, +# ) +# gen_response = gen(prompt_kwargs) +# print(f"gen_response: {gen_response}") + +# for genout in gen_response.data: +# print(f"genout: {genout}") diff --git a/adalflow/adalflow/core/component.py b/adalflow/adalflow/core/component.py index 28bb794e..7c6b2a8f 100644 --- a/adalflow/adalflow/core/component.py +++ b/adalflow/adalflow/core/component.py @@ -316,6 +316,7 @@ def from_dict(cls: Type[T], data: Dict[str, Any]) -> T: # obj = cls(**data.get("_init_args", {})) obj = cls.__new__(cls) # Create a new instance without calling __init__ for key, value in data["data"].items(): + print(key,value) setattr(obj, key, cls._restore_value(value)) return obj diff --git a/adalflow/adalflow/utils/lazy_import.py b/adalflow/adalflow/utils/lazy_import.py index aa4c988f..fee070c5 100644 --- a/adalflow/adalflow/utils/lazy_import.py +++ 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"adalflow", from = "." }] - [tool.poetry.dependencies] python = ">=3.9, <4.0" @@ -44,7 +42,6 @@ tqdm = "^4.66.4" pyyaml = "^6.0.1" nest-asyncio = "^1.6.0" colorama = "^0.4.6" - botocore = "^1.34.149" # if not specified, it will fail to resolve the dependency, we need to figure out a way to get rid of botocore dependency diskcache = "^5.6.3" @@ -60,6 +57,13 @@ anthropic = { version = "^0.31.1", optional = true } google-generativeai = { version = "^0.7.2", optional = true } cohere = { version = "^5.5.8", optional = true } ollama = { version = "^0.2.1", optional = true } + + +# Azure dependencies +azure-core = { version = "^1.24.0", optional = true } +azure-identity = { version = "^1.12.0", optional = true } +azure-ai-formrecognizer = { version = "^3.2.0", optional = true } +azure-ai-textanalytics = { version = "^5.3.0", optional = true } boto3 = { version = "^1.35.19", optional = true } [tool.poetry.group.test.dependencies] @@ -73,14 +77,12 @@ groq = "^0.9.0" google-generativeai = "^0.7.2" anthropic = "^0.31.1" - [tool.poetry.group.typing.dependencies] mypy = "^1" types-pyyaml = "^6.0.12.20240311" # for mypy types-tqdm = "^4.66.0.20240417" - -[tool.poetry.group.dev.dependencies] # specify the versions for extras +[tool.poetry.group.dev.dependencies] pre-commit = "^3.7.0" graphviz = "^0.20.3" tensorboard = "^2.17.0" @@ -88,6 +90,7 @@ tensorboardx = "^2.6.2.2" matplotlib = "^3.9.1" + [tool.poetry.group.extra.dependencies] datasets = "^2.21.0" @@ -102,26 +105,23 @@ faiss-cpu = ["faiss-cpu"] sqlalchemy = ["sqlalchemy"] torch = ["torch"] ollama = ["ollama"] -datasets = ["datasets"] +azure = [ + "azure-core", + "azure-identity", + "azure-ai-formrecognizer", + "azure-ai-textanalytics" +] +datasets = ["datasets"] -# [[tool.poetry.source]] -# name = "pypi" -# url = "https://pypi.org/simple" -# default = true [[tool.poetry.source]] name = "nvidia-pypi" priority = "supplemental" url = "https://pypi.nvidia.com" -# [[tool.poetry.source]] -# name = "nvidia-pypi" -# priority = "supplemental" -# url = "https://pypi.nvidia.com" [tool.ruff] exclude = ["images"] - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/adalflow/tests/test_azure_client.py b/adalflow/tests/test_azure_client.py new file mode 100644 index 00000000..0af12f56 --- /dev/null +++ b/adalflow/tests/test_azure_client.py @@ -0,0 +1,108 @@ +# tests/test_azure_ai_client.py + +import os +import unittest +from unittest.mock import patch, MagicMock, ANY +from adalflow.components.model_client.azureai_client import AzureAIClient +# from adalflow.components.model_client.azureai_client import AzureAIClient # Adjust based on your project structure +from adalflow.core.types import ModelType +from openai import * +from openai.types import CreateEmbeddingResponse,CompletionUsage +from openai.types.chat import ChatCompletion +from openai import AzureOpenAI +class TestAzureAIClient(unittest.TestCase): + def setUp(self): + # Set environment variables for testing + os.environ["AZURE_OPENAI_API_KEY"] = "test_api_key" + os.environ["AZURE_OPENAI_ENDPOINT"] = "https://test-endpoint.openai.azure.com/" + os.environ["AZURE_OPENAI_VERSION"] = "2023-05-15" + + def tearDown(self): + # Clean up environment variables after tests + del os.environ["AZURE_OPENAI_API_KEY"] + del os.environ["AZURE_OPENAI_ENDPOINT"] + del os.environ["AZURE_OPENAI_VERSION"] + + @patch('adalflow.components.model_client.azureai_client.AzureOpenAI', autospec=True) + def test_init_with_api_key(self, mock_azure_openai): + # Configure the mock to return a MagicMock instance when instantiated + mock_instance = MagicMock() + mock_azure_openai.return_value = mock_instance + + # Instantiate the client + client = AzureAIClient() + + # Assert that AzureOpenAI was called with the correct parameters + mock_azure_openai.assert_called_with( + api_key="test_api_key", + azure_endpoint="https://test-endpoint.openai.azure.com/", + api_version="2023-05-15" + ) + + # Verify internal state of AzureAIClient + self.assertIsNotNone(client.sync_client) + self.assertIsNone(client.async_client) + self.assertEqual(client.api_type, "azure") + self.assertEqual(client._api_key, "test_api_key") + self.assertEqual(client._apiversion, "2023-05-15") + self.assertEqual(client._azure_endpoint, "https://test-endpoint.openai.azure.com/") + self.assertIsNone(client._credential) + + + @patch('adalflow.components.model_client.azureai_client.AzureOpenAI', autospec=True) + def test_init_with_credentials(self, mock_azure_openai): + # Arrange + mock_instance = MagicMock() + mock_azure_openai.return_value = mock_instance + mock_credential = MagicMock() + + # Act + client = AzureAIClient(credential=mock_credential) + + # Assert + mock_azure_openai.assert_called_once_with( + api_key="test_api_key", # Using ANY as the exact object is complex + azure_endpoint="https://test-endpoint.openai.azure.com/", + api_version="2023-05-15" + ) + + self.assertEqual(client._credential, mock_credential) + + + @patch('adalflow.components.model_client.azureai_client.AzureOpenAI', autospec=True) + def test_call_unsupported_model_type(self, mock_azure_openai): + # Arrange + client = AzureAIClient(api_key="test_api_key") + + api_kwargs = { + "model": "unsupported-model", + "messages": [{"role": "user", "content": "Hi"}] + } + + + # Act & Assert + with self.assertRaises(ValueError) as context: + client.call(api_kwargs=api_kwargs, model_type=ModelType.UNDEFINED) + + self.assertIn("model_type ModelType.UNDEFINED is not supported", str(context.exception)) + # mock_azure_openai.assert_not_called() + + + @patch('adalflow.components.model_client.azureai_client.AzureOpenAI', autospec=True) + def test_call_chat(self, mock_azure_openai): + # Arrange + client = AzureAIClient(api_key="test_api_key") + + mock_azure_openai = MagicMock() + + + # mock_azure_openai.chat.create.assert_called_once_with( + # model="gpt-4.0-turbo", + # messages=[{"role": "user", "content": "Hi"}] + # ) + + + # Additional tests can follow the same pattern + +if __name__ == '__main__': + unittest.main() diff --git a/poetry.lock b/poetry.lock index 09358530..b4422d3f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -44,7 +44,7 @@ testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", [[package]] name = "adalflow" -version = "0.2.2" +version = "0.2.3" description = "The Library to Build and Auto-optimize Any LLM Task Pipeline" optional = false python-versions = ">=3.9, <4.0" @@ -54,6 +54,7 @@ develop = true [package.dependencies] backoff = "^2.2.1" botocore = "^1.34.149" +colorama = "^0.4.6" diskcache = "^5.6.3" jinja2 = "^3.1.3" jsonlines = "^4.0.0" @@ -66,6 +67,7 @@ tqdm = "^4.66.4" [package.extras] anthropic = ["anthropic (>=0.31.1,<0.32.0)"] +azure = ["azure-ai-formrecognizer (>=3.2.0,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-core (>=1.24.0,<2.0.0)", "azure-identity (>=1.12.0,<2.0.0)"] cohere = ["cohere (>=5.5.8,<6.0.0)"] datasets = [] faiss-cpu = ["faiss-cpu 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