diff --git a/adalflow/adalflow/components/model_client/__init__.py b/adalflow/adalflow/components/model_client/__init__.py index 64db136f..ae508ece 100644 --- a/adalflow/adalflow/components/model_client/__init__.py +++ b/adalflow/adalflow/components/model_client/__init__.py @@ -34,7 +34,7 @@ ) BedrockAPIClient = LazyImport( "adalflow.components.model_client.bedrock_client.BedrockAPIClient", - OptionalPackages.BEDROCK, + OptionalPackages.BOTO3, ) GroqAPIClient = LazyImport( "adalflow.components.model_client.groq_client.GroqAPIClient", 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..28ef0e05 --- /dev/null +++ b/adalflow/adalflow/components/model_client/azureai_client.py @@ -0,0 +1,488 @@ +"""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 + +import sys + +openai = safe_import(OptionalPackages.OPENAI.value[0], OptionalPackages.OPENAI.value[1]) +# Importing all Azure packages together +azure_modules = safe_import( + OptionalPackages.AZURE.value[0], # List of package names + OptionalPackages.AZURE.value[1], # Error message +) +# Manually add each module to sys.modules to make them available globally as if imported normally +azure_module_names = OptionalPackages.AZURE.value[0] +for name, module in zip(azure_module_names, azure_modules): + sys.modules[name] = module + +# Use the modules as if they were imported normally +from azure.identity import DefaultAzureCredential, get_bearer_token_provider + +# 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") + + +__all__ = ["AzureAIClient"] + +# TODO: this overlaps with openai client largely, might need to refactor to subclass openai client to simplify the code + + +# 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 + print(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:** + + .. code-block:: 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 + self._apiversion = api_version + self._azure_endpoint = azure_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/components/model_client/bedrock_client.py b/adalflow/adalflow/components/model_client/bedrock_client.py index df100ac3..d25b48bc 100644 --- a/adalflow/adalflow/components/model_client/bedrock_client.py +++ b/adalflow/adalflow/components/model_client/bedrock_client.py @@ -8,7 +8,10 @@ from adalflow.core.model_client import ModelClient from adalflow.core.types import ModelType, CompletionUsage, GeneratorOutput -import boto3 +from adalflow.utils.lazy_import import safe_import, OptionalPackages + +boto3 = safe_import(OptionalPackages.BOTO3.value[0], OptionalPackages.BOTO3.value[1]) + from botocore.config import Config log = logging.getLogger(__name__) @@ -23,6 +26,7 @@ def get_first_message_content(completion: Dict) -> str: r"""When we only need the content of the first message. It is the default parser for chat completion.""" return completion["output"]["message"]["content"][0]["text"] + return completion["output"]["message"]["content"][0]["text"] __all__ = [ @@ -35,20 +39,58 @@ def get_first_message_content(completion: Dict) -> str: class BedrockAPIClient(ModelClient): __doc__ = r"""A component wrapper for the Bedrock API client. - Note: + Support: + 1. AWS Titan + 2. Claude + 3. Cohere + 4. LLama + 5. Mistral + 6. Jamba - This client needs a lot more work to be fully functional. - (1) Setup the AWS credentials. - (2) Access to the modelId. - (3) Convert the modelId to standard model. + Setup: + 1. Install boto3: `pip install boto3` + 2. Ensure you have the AWS credentials set up. There are four variables you can optionally set: + - AWS_PROFILE_NAME: The name of the AWS profile to use. + - AWS_REGION_NAME: The name of the AWS region to use. + - AWS_ACCESS_KEY_ID: The AWS access key ID. + - AWS_SECRET_ACCESS_KEY: The AWS secret access key. - To setup the AWS credentials, follow the instructions here: - https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html - Additionally, this medium article is a good reference: - https://medium.com/@harangpeter/setting-up-aws-bedrock-for-api-based-text-inference-dc25ab2b216b - Visit https://docs.aws.amazon.com/bedrock/latest/APIReference/welcome.html for more api details. + + Example: + + .. code-block:: python + + from adalflow.components.model_client import BedrockAPIClient + + template = " + You are a helpful assistant. + + User: {{input_str}} + You: + " + + # use AWS_PROFILE_NAME and AWS_REGION_NAME from the environment variables in this case + # ensure you request the modelId from the AWS team + self.generator = Generator( + model_client=BedrockAPIClient(), + model_kwargs={ + "modelId": "anthropic.claude-3-sonnet-20240229-v1:0", + "inferenceConfig": { + "temperature": 0.8 + } + }, template=template + ) + + Relevant API docs: + 1. https://docs.aws.amazon.com/bedrock/latest/APIReference/welcome.html + 2. https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started-api-ex-python.html + 3. https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html#API_runtime_Converse_RequestParameters + 4. To setup the AWS credentials, follow the instructions here: + https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html + 5. Additionally, this medium article is a good reference: + https://medium.com/@harangpeter/setting-up-aws-bedrock-for-api-based-text-inference-dc25ab2b216b """ def __init__( diff --git a/adalflow/adalflow/utils/lazy_import.py b/adalflow/adalflow/utils/lazy_import.py index ccf79332..3451714b 100644 --- a/adalflow/adalflow/utils/lazy_import.py +++ b/adalflow/adalflow/utils/lazy_import.py @@ -1,5 +1,6 @@ """Lazy import a module and class.""" +from typing import List, Union import importlib import logging from types import ModuleType @@ -13,12 +14,35 @@ class OptionalPackages(Enum): __doc__ = r"""Enum for optional packages that can be used in the library. The package name and error message are defined for each optional package as a tuple. + + The value of the tuple: + - The package name (str): The package name to import. Follows the right syntax: such as import azure.identity but the package itself is azure-identity. + Support a list of package names for related packages. This will be importing a list of packages while safe_import is used. + - The error message (str): The message to display if the package is not found. + + Example of using multiple related packages: + + .. code-block:: python + + from adalflow.utils.lazy_import import safe_import, OptionalPackages + import sys + + azure_modules = safe_import( + OptionalPackages.AZURE.value[0], # List of package names + OptionalPackages.AZURE.value[1], # Error message + ) + # Manually add each module to sys.modules to make them available globally as if imported normally + azure_module_names = OptionalPackages.AZURE.value[0] + for name, module in zip(azure_module_names, azure_modules): + sys.modules[name] = module + + # Use the modules as if they were imported normally + from azure.identity import DefaultAzureCredential, get_bearer_token_provider """ # model sdk GROQ = ("groq", "Please install groq with: pip install groq") OPENAI = ("openai", "Please install openai with: pip install openai") ANTHROPIC = ("anthropic", "Please install anthropic with: pip install anthropic") - BEDROCK = ("bedrock", "Please install boto3 with: pip install boto3") GOOGLE_GENERATIVEAI = ( "google.generativeai", "Please install google-generativeai with: pip install google-generativeai", @@ -29,9 +53,21 @@ class OptionalPackages(Enum): ) COHERE = ("cohere", "Please install cohere with: pip install cohere") OLLAMA = ("ollama", "Please install ollama with: pip install ollama") + # AWS + BOTO3 = ("boto3", "Please install boto3 with: pip install boto3") # modeling library TORCH = ("torch", "Please install torch with: pip install torch") + # Grouping all Azure-related packages under one entry + AZURE = ( + [ + "azure.identity", + "azure.core", + # "azure.ai-formrecognizer", + # "azure.ai-textanalytics", + ], + "Please install Azure packages with: pip install azure-identity azure-core azure-ai-formrecognizer azure-ai-textanalytics", + ) # search library FAISS = ( "faiss", @@ -121,10 +157,23 @@ def __call__(self, *args, **kwargs): return self.class_(*args, **kwargs) -def safe_import(module_name: str, install_message: str) -> ModuleType: +def safe_import( + module_names: Union[str, List[str]], install_message: str +) -> Union[ModuleType, List[ModuleType]]: """Safely import a module and raise an ImportError with the install message if the module is not found. - Mainly used internally to import optional packages only when needed. + Handles importing of multiple related packages. + + Args: + module_names (list or str): The package name(s) to import. + install_message (str): The message to display if import fails. + + Returns: + ModuleType: The imported module. + + Raises: + ImportError: If any of the packages are not found. + Example: @@ -150,7 +199,14 @@ def safe_import(module_name: str, install_message: str) -> ModuleType: numpy = safe_import(OptionalPackages.NUMPY.value[0], OptionalPackages.NUMPY.value[1]) """ - try: - return importlib.import_module(module_name) - except ImportError: - raise ImportError(f"{install_message}") + if isinstance(module_names, str): + module_names = [module_names] + + return_modules = [] + for module_name in module_names: + try: + return_modules.append(importlib.import_module(module_name)) + except ImportError: + raise ImportError(f"{install_message}") + + return return_modules[0] if len(return_modules) == 1 else return_modules diff --git a/adalflow/poetry.lock b/adalflow/poetry.lock index f50304a7..d2ee1af5 100644 --- a/adalflow/poetry.lock +++ b/adalflow/poetry.lock @@ -13,113 +13,113 @@ files = [ [[package]] name = "aiohappyeyeballs" -version = "2.4.0" +version = "2.4.3" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"}, - {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"}, + {file = 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"azure-identity"] +bedrock = ["boto3"] cohere = ["cohere"] datasets = [] faiss-cpu = ["faiss-cpu"] @@ -4062,4 +4376,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9, <4.0" -content-hash = "9c7a0522920a4f485ebce56c7f5b7da0674523933e1ca9f167fa26650423da2a" +content-hash = "0a58b82476e4794adbc5768d38911e1935a9c3941cc40499001199383fc8c8ec" diff --git a/adalflow/pyproject.toml b/adalflow/pyproject.toml index 98240617..8b1a68f6 100644 --- a/adalflow/pyproject.toml +++ b/adalflow/pyproject.toml @@ -27,10 +27,8 @@ include = [ "adalflow/optim/*", ] - packages = [{ include = "adalflow", from = "." }] - [tool.poetry.dependencies] python = ">=3.9, <4.0" @@ -44,8 +42,7 @@ 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 +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 +57,15 @@ 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 } + +# amazon bedrock boto3 = { version = "^1.35.19", optional = true } [tool.poetry.group.test.dependencies] @@ -73,19 +79,19 @@ 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" tensorboardx = "^2.6.2.2" matplotlib = "^3.9.1" +azure-identity = "^1.18.0" +azure-core = "^1.31.0" [tool.poetry.group.extra.dependencies] @@ -102,21 +108,20 @@ faiss-cpu = ["faiss-cpu"] sqlalchemy = ["sqlalchemy"] torch = ["torch"] ollama = ["ollama"] -datasets = ["datasets"] +azure = [ + "azure-core", + "azure-identity", + # "azure-ai-formrecognizer", + # "azure-ai-textanalytics", # will reevaluate the need for these two +] +bedrock = ["boto3"] +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" [build-system] diff --git a/adalflow/tests/test_AzureClient.py b/adalflow/tests/test_AzureClient.py new file mode 100644 index 00000000..14b7cf4a --- /dev/null +++ b/adalflow/tests/test_AzureClient.py @@ -0,0 +1,125 @@ +import unittest +from unittest.mock import patch +from openai import AzureOpenAI +from adalflow.components.model_client.azureai_client import AzureAIClient + + +class TestAzureAIClient(unittest.TestCase): + + @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + @patch("adalflow.components.model_client.azureai_client.DefaultAzureCredential") + def setUp(self, MockDefaultAzureCredential, MockAzureOpenAI): + self.mock_credential = MockDefaultAzureCredential() + self.mock_sync_client = MockAzureOpenAI.return_value + self.client = AzureAIClient( + api_key="test_api_key", + api_version="v1", + azure_endpoint="https://test.endpoint", + credential=self.mock_credential, + ) + self.client.sync_client = self.mock_sync_client + + def test_init_sync_client_with_api_key(self): + client = AzureAIClient( + api_key="test_key", api_version="v1", azure_endpoint="https://test.endpoint" + ) + self.assertIsInstance(client.sync_client, AzureOpenAI) + + def test_init_sync_client_with_credential(self): + client = AzureAIClient( + api_version="v1", + azure_endpoint="https://test.endpoint", + credential=self.mock_credential, + ) + self.assertIsInstance(client.sync_client, AzureOpenAI) + + # @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + # def test_call_embeddings(self, MockAzureOpenAI): + # mock_embeddings = MagicMock() + # MockAzureOpenAI.return_value.embeddings.create = mock_embeddings + # api_kwargs = {"input": ["test"]} + # model_type = ModelType.EMBEDDER + # self.client.call(api_kwargs=api_kwargs, model_type=model_type) + # MockAzureOpenAI.return_value.embeddings.create.assert_called_once_with( + # **api_kwargs + # ) + + # @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + # def test_call_chat_completions(self, MockAzureOpenAI): + # mock_chat_completions = MagicMock() + # MockAzureOpenAI.return_value.chat.completions.create = mock_chat_completions + # api_kwargs = {"input": "test"} + # model_type = ModelType.LLM + # self.client.call(api_kwargs=api_kwargs, model_type=model_type) + # MockAzureOpenAI.return_value.chat.completions.create.assert_called_once_with( + # **api_kwargs + # ) + + # @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + # def test_parse_chat_completion(self, MockAzureOpenAI): + # mock_chat_completion = MagicMock(spec=ChatCompletion) + # mock_chat_completion.choices = [ + # MagicMock(message=MagicMock(content="test_content")) + # ] + # self.client.chat_completion_parser = lambda completion: completion.choices[ + # 0 + # ].message.content + # result = self.client.parse_chat_completion(mock_chat_completion) + # self.assertEqual(result, "test_content") + + # @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + # def test_track_completion_usage(self, MockAzureOpenAI): + # mock_chat_completion = MagicMock(spec=ChatCompletion) + # mock_chat_completion.usage = MagicMock( + # completion_tokens=10, prompt_tokens=5, total_tokens=15 + # ) + # result = self.client.track_completion_usage(mock_chat_completion) + # self.assertEqual(result.completion_tokens, 10) + # self.assertEqual(result.prompt_tokens, 5) + # self.assertEqual(result.total_tokens, 15) + + # @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + # def test_parse_embedding_response(self, MockAzureOpenAI): + # mock_embedding_response = MagicMock(spec=CreateEmbeddingResponse) + # self.client.parse_embedding_response = lambda response: EmbedderOutput( + # data=["test_embedding"], error=None, raw_response=response + # ) + # result = self.client.parse_embedding_response(mock_embedding_response) + # self.assertEqual(result.data, ["test_embedding"]) + + # @patch("adalflow.components.model_client.azureai_client.AzureOpenAI") + # def test_convert_inputs_to_api_kwargs(self, MockAzureOpenAI): + # input_data = "test input" + # model_kwargs = {"param": "value"} + # result = self.client.convert_inputs_to_api_kwargs( + # input=input_data, model_kwargs=model_kwargs, model_type=ModelType.LLM + # ) + # expected = {"input": input_data, "param": "value"} + # self.assertEqual(result, expected) + + # def test_from_dict(self): + # data = { + # "api_key": "test_key", + # "api_version": "v1", + # "azure_endpoint": "https://test.endpoint", + # "credential": self.mock_credential, + # } + # client = AzureAIClient.from_dict(data) + # self.assertEqual(client._api_key, "test_key") + # self.assertEqual(client._apiversion, "v1") + # self.assertEqual(client._azure_endpoint, "https://test.endpoint") + + # def test_to_dict(self): + # expected = { + # "api_key": "test_api_key", + # "api_version": "v1", + # "azure_endpoint": "https://test.endpoint", + # "credential": self.mock_credential, + # } + # result = self.client.to_dict() + # for key, value in expected.items(): + # self.assertEqual(result.get(key), value) + + +if __name__ == "__main__": + unittest.main() diff --git a/adalflow/tests/test_lazy_import.py b/adalflow/tests/test_lazy_import.py index 1731308a..25a3d634 100644 --- a/adalflow/tests/test_lazy_import.py +++ b/adalflow/tests/test_lazy_import.py @@ -1,7 +1,8 @@ from types import ModuleType import pytest import unittest -from adalflow.utils.lazy_import import safe_import +from unittest.mock import MagicMock, patch +from adalflow.utils.lazy_import import safe_import, OptionalPackages class TestSafeImport(unittest.TestCase): @@ -27,6 +28,112 @@ def test_import_nonexistent_package(self): (f"Expected error message not found in {str(cm.exception)}"), ) + @patch("importlib.import_module") + def test_import_with_multiple_packages_success(self, mock_import): + """Test that safe_import successfully imports a module if all modules exist""" + # Set up the mock to return a MagicMock to simulate successful imports + mock_import.side_effect = [MagicMock(name="successful_import")] + + packages = ["math"] + imported_module = safe_import(packages, "Please install the required packages.") + + self.assertTrue( + hasattr(imported_module, "sqrt"), "math module did not load correctly." + ) + + @patch("importlib.import_module") + def test_import_with_multiple_packages_failure(self, mock_import): + """Test that safe_import raises ImportError when any of the modules in the list fail to import""" + # Set up the mock to raise ImportError for the first package + mock_import.side_effect = ImportError + + packages = ["non_existent_module_1", "math"] + with self.assertRaises(ImportError) as context: + safe_import(packages, "Please install the required packages.") + + self.assertIn( + "Please install the required packages", + str(context.exception), + "Expected ImportError message does not match.", + ) + + ############################################################################################################ + # For AWS bedrock_client + ############################################################################################################ + @patch("importlib.import_module") + def test_successful_import_boto3(self, mock_import): + """Test that safe_import successfully imports boto3 if installed""" + # Simulate a successful import of boto3 by returning a MagicMock object + mock_import.side_effect = lambda name: ( + MagicMock(name="boto3") if name == "boto3" else ImportError + ) + + module_name = OptionalPackages.BOTO3.value[0] + imported_module = safe_import(module_name, OptionalPackages.BOTO3.value[1]) + + # Assert that the mock was called with 'boto3' and the imported module is a MagicMock instance + mock_import.assert_called_with("boto3") + self.assertIsInstance( + imported_module, MagicMock, "boto3 module did not load correctly." + ) + + @patch("importlib.import_module", side_effect=ImportError) + def test_failed_import_boto3(self, mock_import): + """Test that safe_import raises an ImportError when boto3 is not installed""" + module_name = OptionalPackages.BOTO3.value[0] + with self.assertRaises(ImportError) as context: + safe_import(module_name, OptionalPackages.BOTO3.value[1]) + + self.assertIn( + "Please install boto3 with: pip install boto3", + str(context.exception), + "Expected ImportError message for boto3 does not match.", + ) + + ############################################################################################################ + # For Azure ai model client + ############################################################################################################ + @patch("importlib.import_module") + def test_successful_import_azure(self, mock_import): + """Test that safe_import successfully imports all Azure packages""" + # Simulate successful imports for each Azure package by returning a MagicMock for each + mock_import.side_effect = lambda name: MagicMock(name=name) + + module_names = OptionalPackages.AZURE.value[0] + imported_modules = safe_import(module_names, OptionalPackages.AZURE.value[1]) + + # Ensure that all azure modules were attempted to be imported + for module_name in module_names: + mock_import.assert_any_call(module_name) + + # Verify that the imported_modules is a list and contains each module as a MagicMock + self.assertIsInstance(imported_modules, list, "Expected a list of modules.") + self.assertEqual( + len(imported_modules), + len(module_names), + "Not all Azure modules were imported.", + ) + for imported_module, module_name in zip(imported_modules, module_names): + self.assertIsInstance( + imported_module, MagicMock, f"{module_name} did not load correctly." + ) + + @patch("importlib.import_module") + def test_failed_import_azure(self, mock_import): + """Test that safe_import raises ImportError when any Azure package is not installed""" + # Set up the mock to raise ImportError for all Azure packages to simulate a missing package + mock_import.side_effect = ImportError + + module_names = OptionalPackages.AZURE.value[0] + with self.assertRaises(ImportError) as context: + safe_import(module_names, OptionalPackages.AZURE.value[1]) + + self.assertIn( + "Please install Azure packages with: pip install azure-identity azure-core azure-ai-formrecognizer azure-ai-textanalytics", + str(context.exception), + "Expected ImportError message for Azure packages does not match.", + ) + if __name__ == "__main__": pytest.main() diff --git a/docs/poetry.lock b/docs/poetry.lock index dfa2d273..9c079273 100644 --- a/docs/poetry.lock +++ b/docs/poetry.lock @@ -20,7 +20,7 @@ tests = ["hypothesis", "pytest"] [[package]] name = "adalflow" -version = "0.2.0.beta.3" +version = "0.2.3" description = "The Library to Build and Auto-optimize Any LLM Task Pipeline" optional = false python-versions = ">=3.9, <4.0" @@ -30,6 +30,7 @@ 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" @@ -42,7 +43,10 @@ tqdm = "^4.66.4" [package.extras] anthropic = ["anthropic (>=0.31.1,<0.32.0)"] +azure = ["azure-core (>=1.24.0,<2.0.0)", "azure-identity (>=1.12.0,<2.0.0)"] +bedrock = ["boto3 (>=1.35.19,<2.0.0)"] cohere = ["cohere (>=5.5.8,<6.0.0)"] +datasets = [] faiss-cpu = ["faiss-cpu (>=1.8.0,<2.0.0)"] google-generativeai = ["google-generativeai (>=0.7.2,<0.8.0)"] groq = ["groq (>=0.9.0,<0.10.0)"] @@ -277,6 +281,43 @@ tests = ["attrs[tests-no-zope]", "zope-interface"] tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] +[[package]] +name = "azure-core" +version = "1.31.0" +description = "Microsoft Azure Core Library for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "azure_core-1.31.0-py3-none-any.whl", hash = "sha256:22954de3777e0250029360ef31d80448ef1be13b80a459bff80ba7073379e2cd"}, + {file = "azure_core-1.31.0.tar.gz", hash = "sha256:656a0dd61e1869b1506b7c6a3b31d62f15984b1a573d6326f6aa2f3e4123284b"}, +] + +[package.dependencies] +requests = ">=2.21.0" +six = ">=1.11.0" +typing-extensions = ">=4.6.0" + +[package.extras] +aio = ["aiohttp (>=3.0)"] + +[[package]] +name = "azure-identity" +version = "1.18.0" +description = "Microsoft Azure Identity Library for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "azure_identity-1.18.0-py3-none-any.whl", hash = "sha256:bccf6106245b49ff41d0c4cd7b72851c5a2ba3a32cef7589da246f5727f26f02"}, + {file = "azure_identity-1.18.0.tar.gz", hash = "sha256:f567579a65d8932fa913c76eddf3305101a15e5727a5e4aa5df649a0f553d4c3"}, +] + +[package.dependencies] +azure-core = ">=1.31.0" +cryptography = ">=2.5" +msal = ">=1.30.0" +msal-extensions = ">=1.2.0" +typing-extensions = ">=4.0.0" + [[package]] name = "babel" version = "2.15.0" @@ -601,6 +642,55 @@ files = [ {file = "colorama-0.4.6.tar.gz", hash = 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"pytest-benchmark", "pytest-cov", "pytest-xdist"] +test-randomorder = ["pytest-randomly"] + [[package]] name = "datasets" version = "2.20.0" @@ -1822,6 +1912,40 @@ docs = ["sphinx"] gmpy = ["gmpy2 (>=2.1.0a4)"] tests = ["pytest (>=4.6)"] +[[package]] +name = "msal" +version = "1.31.0" +description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect." +optional = false +python-versions = ">=3.7" +files = [ + {file = "msal-1.31.0-py3-none-any.whl", hash = "sha256:96bc37cff82ebe4b160d5fc0f1196f6ca8b50e274ecd0ec5bf69c438514086e7"}, + {file = "msal-1.31.0.tar.gz", hash = "sha256:2c4f189cf9cc8f00c80045f66d39b7c0f3ed45873fd3d1f2af9f22db2e12ff4b"}, +] + +[package.dependencies] +cryptography = ">=2.5,<46" +PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]} +requests = ">=2.0.0,<3" + +[package.extras] +broker = ["pymsalruntime (>=0.14,<0.18)", "pymsalruntime (>=0.17,<0.18)"] + +[[package]] +name = "msal-extensions" +version = "1.2.0" +description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism." +optional = false +python-versions = ">=3.7" +files = [ + {file = "msal_extensions-1.2.0-py3-none-any.whl", hash = "sha256:cf5ba83a2113fa6dc011a254a72f1c223c88d7dfad74cc30617c4679a417704d"}, + {file = "msal_extensions-1.2.0.tar.gz", hash = "sha256:6f41b320bfd2933d631a215c91ca0dd3e67d84bd1a2f50ce917d5874ec646bef"}, +] + +[package.dependencies] +msal = ">=1.29,<2" +portalocker = ">=1.4,<3" + [[package]] name = "multidict" version = "6.0.5" @@ -2801,6 +2925,26 @@ files = [ [package.extras] windows-terminal = ["colorama (>=0.4.6)"] +[[package]] +name = "pyjwt" +version = "2.9.0" +description = "JSON Web Token implementation in Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "PyJWT-2.9.0-py3-none-any.whl", hash = "sha256:3b02fb0f44517787776cf48f2ae25d8e14f300e6d7545a4315cee571a415e850"}, + {file = "pyjwt-2.9.0.tar.gz", hash = "sha256:7e1e5b56cc735432a7369cbfa0efe50fa113ebecdc04ae6922deba8b84582d0c"}, +] + +[package.dependencies] +cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""} + +[package.extras] +crypto = ["cryptography (>=3.4.0)"] +dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx", "sphinx-rtd-theme", "zope.interface"] +docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"] +tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] + [[package]] name = "pyparsing" version = "3.1.2" @@ -4551,4 +4695,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = ">=3.9, <4.0" -content-hash = "9558d0212fb3ace6733d867de454171304261bbd582561b0e09950faf19e2e4f" +content-hash = "850edd56328727c4ff8ebf9b6642b37ff645c866d2406beb21cbaf6d64332ebf" diff --git a/docs/pyproject.toml b/docs/pyproject.toml index 30b5a5a4..3c69682b 100644 --- a/docs/pyproject.toml +++ b/docs/pyproject.toml @@ -10,6 +10,7 @@ packages = [{ from = "_dummy", include = "dummy" }] # empty packages python = ">=3.9, <4.0" adalflow = { path = "../adalflow", develop = true } # document always follow the latest version of the library + [tool.poetry.group.doc.dependencies] pydata-sphinx-theme = "^0.15.3" sphinx-design = "^0.6.0" @@ -20,6 +21,7 @@ nbconvert = "^7.16.4" pandoc = "^2.3" readthedocs-sphinx-search = "^0.3.2" # add optional packages to compile the documents correctly +datasets = "^2.20.0" sqlalchemy = "^2.0.31" google-generativeai = "^0.7.1" anthropic = "^0.30.1" @@ -32,9 +34,12 @@ faiss-cpu = "^1.8.0.post1" ollama = "^0.3.0" qdrant-client = "^1.10.1" diskcache = "^5.6.3" -datasets = "^2.20.0" torch = "^2.4.0" +# azure model_client +azure-identity = "^1.18.0" +azure-core = "^1.31.0" + [build-system] requires = ["poetry-core>=1.0.0"]