From bd6b23fd72b3a391d96659b7faf5cdaed6a415dc Mon Sep 17 00:00:00 2001 From: Rohit Prasad Date: Thu, 23 Jan 2025 20:13:50 -0800 Subject: [PATCH] Tool calling support. Part I (#102) Add tool calling support for most providers. OpenAI, Groq, Anthropic, AWS, Mistral, SambaNova, Cohere, xAI, Gemini & Azure. --- aisuite/__init__.py | 2 + aisuite/framework/__init__.py | 1 + aisuite/framework/choice.py | 6 +- aisuite/framework/message.py | 22 +- aisuite/providers/anthropic_provider.py | 232 +- aisuite/providers/aws_provider.py | 306 +- aisuite/providers/azure_provider.py | 105 +- aisuite/providers/cohere_provider.py | 158 +- aisuite/providers/fireworks_provider.py | 98 +- aisuite/providers/google_provider.py | 289 +- aisuite/providers/groq_provider.py | 56 +- aisuite/providers/huggingface_provider.py | 113 +- aisuite/providers/message_converter.py | 57 + aisuite/providers/mistral_provider.py | 59 +- aisuite/providers/nebius_provider.py | 1 + aisuite/providers/openai_provider.py | 4 +- aisuite/providers/sambanova_provider.py | 43 +- aisuite/providers/together_provider.py | 36 +- aisuite/providers/xai_provider.py | 29 +- aisuite/utils/tool_manager.py | 279 + examples/client.ipynb | 12 + examples/simple_tool_calling.ipynb | 298 + poetry.lock | 6294 ++++++++++--------- pyproject.toml | 1 + tests/client/test_client.py | 10 - tests/providers/test_anthropic_converter.py | 205 + tests/providers/test_aws_converter.py | 112 + tests/providers/test_azure_provider.py | 91 + tests/providers/test_google_converter.py | 111 + tests/providers/test_google_provider.py | 160 +- tests/providers/test_groq_provider.py | 6 +- tests/providers/test_mistral_provider.py | 6 +- tests/providers/test_sambanova_provider.py | 8 +- tests/providers/test_watsonx_provider.py | 7 +- tests/utils/test_tool_manager.py | 200 + 35 files changed, 5985 insertions(+), 3432 deletions(-) create mode 100644 aisuite/providers/message_converter.py create mode 100644 aisuite/utils/tool_manager.py create mode 100644 examples/simple_tool_calling.ipynb create mode 100644 tests/providers/test_anthropic_converter.py create mode 100644 tests/providers/test_aws_converter.py create mode 100644 tests/providers/test_azure_provider.py create mode 100644 tests/providers/test_google_converter.py create mode 100644 tests/utils/test_tool_manager.py diff --git a/aisuite/__init__.py b/aisuite/__init__.py index 3ff722bf..5ae462ab 100644 --- a/aisuite/__init__.py +++ b/aisuite/__init__.py @@ -1 +1,3 @@ from .client import Client +from .framework.message import Message +from .utils.tool_manager import Tools diff --git a/aisuite/framework/__init__.py b/aisuite/framework/__init__.py index aad7ebd2..bc7d71c4 100644 --- a/aisuite/framework/__init__.py +++ b/aisuite/framework/__init__.py @@ -1,2 +1,3 @@ from .provider_interface import ProviderInterface from .chat_completion_response import ChatCompletionResponse +from .message import Message diff --git a/aisuite/framework/choice.py b/aisuite/framework/choice.py index 3542da57..4557f46b 100644 --- a/aisuite/framework/choice.py +++ b/aisuite/framework/choice.py @@ -1,6 +1,10 @@ from aisuite.framework.message import Message +from typing import Literal, Optional class Choice: def __init__(self): - self.message = Message() + self.finish_reason: Optional[Literal["stop", "tool_calls"]] = None + self.message = Message( + content=None, tool_calls=None, role="assistant", refusal=None + ) diff --git a/aisuite/framework/message.py b/aisuite/framework/message.py index bf07688d..eaa611ef 100644 --- a/aisuite/framework/message.py +++ b/aisuite/framework/message.py @@ -1,6 +1,22 @@ """Interface to hold contents of api responses when they do not confirm to the OpenAI style response""" +from pydantic import BaseModel +from typing import Literal, Optional -class Message: - def __init__(self): - self.content = None + +class Function(BaseModel): + arguments: str + name: str + + +class ChatCompletionMessageToolCall(BaseModel): + id: str + function: Function + type: Literal["function"] + + +class Message(BaseModel): + content: Optional[str] + tool_calls: Optional[list[ChatCompletionMessageToolCall]] + role: Optional[Literal["user", "assistant", "system"]] + refusal: Optional[str] diff --git a/aisuite/providers/anthropic_provider.py b/aisuite/providers/anthropic_provider.py index f63c054c..b7edf71c 100644 --- a/aisuite/providers/anthropic_provider.py +++ b/aisuite/providers/anthropic_provider.py @@ -1,40 +1,224 @@ +# Anthropic provider +# Links: +# Tool calling docs - https://docs.anthropic.com/en/docs/build-with-claude/tool-use + import anthropic +import json from aisuite.provider import Provider from aisuite.framework import ChatCompletionResponse +from aisuite.framework.message import Message, ChatCompletionMessageToolCall, Function # Define a constant for the default max_tokens value DEFAULT_MAX_TOKENS = 4096 -class AnthropicProvider(Provider): - def __init__(self, **config): - """ - Initialize the Anthropic provider with the given configuration. - Pass the entire configuration dictionary to the Anthropic client constructor. - """ +class AnthropicMessageConverter: + # Role constants + ROLE_USER = "user" + ROLE_ASSISTANT = "assistant" + ROLE_TOOL = "tool" + ROLE_SYSTEM = "system" - self.client = anthropic.Anthropic(**config) + # Finish reason mapping + FINISH_REASON_MAPPING = { + "end_turn": "stop", + "max_tokens": "length", + "tool_use": "tool_calls", + } - def chat_completions_create(self, model, messages, **kwargs): - # Check if the fist message is a system message - if messages[0]["role"] == "system": + def convert_request(self, messages): + """Convert framework messages to Anthropic format.""" + system_message = self._extract_system_message(messages) + converted_messages = [self._convert_single_message(msg) for msg in messages] + return system_message, converted_messages + + def convert_response(self, response): + """Normalize the response from the Anthropic API to match OpenAI's response format.""" + normalized_response = ChatCompletionResponse() + normalized_response.choices[0].finish_reason = self._get_finish_reason(response) + normalized_response.usage = self._get_usage_stats(response) + normalized_response.choices[0].message = self._get_message(response) + return normalized_response + + def _convert_single_message(self, msg): + """Convert a single message to Anthropic format.""" + if isinstance(msg, dict): + return self._convert_dict_message(msg) + return self._convert_message_object(msg) + + def _convert_dict_message(self, msg): + """Convert a dictionary message to Anthropic format.""" + if msg["role"] == self.ROLE_TOOL: + return self._create_tool_result_message(msg["tool_call_id"], msg["content"]) + elif msg["role"] == self.ROLE_ASSISTANT and "tool_calls" in msg: + return self._create_assistant_tool_message( + msg["content"], msg["tool_calls"] + ) + return {"role": msg["role"], "content": msg["content"]} + + def _convert_message_object(self, msg): + """Convert a Message object to Anthropic format.""" + if msg.role == self.ROLE_TOOL: + return self._create_tool_result_message(msg.tool_call_id, msg.content) + elif msg.role == self.ROLE_ASSISTANT and msg.tool_calls: + return self._create_assistant_tool_message(msg.content, msg.tool_calls) + return {"role": msg.role, "content": msg.content} + + def _create_tool_result_message(self, tool_call_id, content): + """Create a tool result message in Anthropic format.""" + return { + "role": self.ROLE_USER, + "content": [ + { + "type": "tool_result", + "tool_use_id": tool_call_id, + "content": content, + } + ], + } + + def _create_assistant_tool_message(self, content, tool_calls): + """Create an assistant message with tool calls in Anthropic format.""" + message_content = [] + if content: + message_content.append({"type": "text", "text": content}) + + for tool_call in tool_calls: + tool_input = ( + tool_call["function"]["arguments"] + if isinstance(tool_call, dict) + else tool_call.function.arguments + ) + message_content.append( + { + "type": "tool_use", + "id": ( + tool_call["id"] if isinstance(tool_call, dict) else tool_call.id + ), + "name": ( + tool_call["function"]["name"] + if isinstance(tool_call, dict) + else tool_call.function.name + ), + "input": json.loads(tool_input), + } + ) + + return {"role": self.ROLE_ASSISTANT, "content": message_content} + + def _extract_system_message(self, messages): + """Extract system message if present, otherwise return empty list.""" + # TODO: This is a temporary solution to extract the system message. + # User can pass multiple system messages, which can mingled with other messages. + # This needs to be fixed to handle this case. + if messages and messages[0]["role"] == "system": system_message = messages[0]["content"] - messages = messages[1:] - else: - system_message = [] + messages.pop(0) + return system_message + return [] + + def _get_finish_reason(self, response): + """Get the normalized finish reason.""" + return self.FINISH_REASON_MAPPING.get(response.stop_reason, "stop") - # kwargs.setdefault('max_tokens', DEFAULT_MAX_TOKENS) - if "max_tokens" not in kwargs: - kwargs["max_tokens"] = DEFAULT_MAX_TOKENS + def _get_usage_stats(self, response): + """Get the usage statistics.""" + return { + "prompt_tokens": response.usage.input_tokens, + "completion_tokens": response.usage.output_tokens, + "total_tokens": response.usage.input_tokens + response.usage.output_tokens, + } + + def _get_message(self, response): + """Get the appropriate message based on response type.""" + if response.stop_reason == "tool_use": + tool_message = self.convert_response_with_tool_use(response) + if tool_message: + return tool_message + + return Message( + content=response.content[0].text, + role="assistant", + tool_calls=None, + refusal=None, + ) - return self.normalize_response( - self.client.messages.create( - model=model, system=system_message, messages=messages, **kwargs + def convert_response_with_tool_use(self, response): + """Convert Anthropic tool use response to the framework's format.""" + tool_call = next( + (content for content in response.content if content.type == "tool_use"), + None, + ) + + if tool_call: + function = Function( + name=tool_call.name, arguments=json.dumps(tool_call.input) + ) + tool_call_obj = ChatCompletionMessageToolCall( + id=tool_call.id, function=function, type="function" + ) + text_content = next( + ( + content.text + for content in response.content + if content.type == "text" + ), + "", + ) + + return Message( + content=text_content or None, + tool_calls=[tool_call_obj] if tool_call else None, + role="assistant", + refusal=None, ) + return None + + def convert_tool_spec(self, openai_tools): + """Convert OpenAI tool specification to Anthropic format.""" + anthropic_tools = [] + + for tool in openai_tools: + if tool.get("type") != "function": + continue + + function = tool["function"] + anthropic_tool = { + "name": function["name"], + "description": function["description"], + "input_schema": { + "type": "object", + "properties": function["parameters"]["properties"], + "required": function["parameters"].get("required", []), + }, + } + anthropic_tools.append(anthropic_tool) + + return anthropic_tools + + +class AnthropicProvider(Provider): + def __init__(self, **config): + """Initialize the Anthropic provider with the given configuration.""" + self.client = anthropic.Anthropic(**config) + self.converter = AnthropicMessageConverter() + + def chat_completions_create(self, model, messages, **kwargs): + """Create a chat completion using the Anthropic API.""" + kwargs = self._prepare_kwargs(kwargs) + system_message, converted_messages = self.converter.convert_request(messages) + + response = self.client.messages.create( + model=model, system=system_message, messages=converted_messages, **kwargs ) + return self.converter.convert_response(response) - def normalize_response(self, response): - """Normalize the response from the Anthropic API to match OpenAI's response format.""" - normalized_response = ChatCompletionResponse() - normalized_response.choices[0].message.content = response.content[0].text - return normalized_response + def _prepare_kwargs(self, kwargs): + """Prepare kwargs for the API call.""" + kwargs = kwargs.copy() + kwargs.setdefault("max_tokens", DEFAULT_MAX_TOKENS) + + if "tools" in kwargs: + kwargs["tools"] = self.converter.convert_tool_spec(kwargs["tools"]) + + return kwargs diff --git a/aisuite/providers/aws_provider.py b/aisuite/providers/aws_provider.py index f44f5150..e370488d 100644 --- a/aisuite/providers/aws_provider.py +++ b/aisuite/providers/aws_provider.py @@ -1,93 +1,265 @@ import os +import json +from typing import List, Dict, Any, Tuple, Optional import boto3 from aisuite.provider import Provider, LLMError from aisuite.framework import ChatCompletionResponse +from aisuite.framework.message import Message +import botocore -class AwsProvider(Provider): +class BedrockConfig: + INFERENCE_PARAMETERS = ["maxTokens", "temperature", "topP", "stopSequences"] + def __init__(self, **config): - """ - Initialize the AWS Bedrock provider with the given configuration. + self.region_name = config.get( + "region_name", os.getenv("AWS_REGION", "us-west-2") + ) - This class uses the AWS Bedrock converse API, which provides a consistent interface - for all Amazon Bedrock models that support messages. Examples include: - - anthropic.claude-v2 - - meta.llama3-70b-instruct-v1:0 - - mistral.mixtral-8x7b-instruct-v0:1 + def create_client(self): + return boto3.client("bedrock-runtime", region_name=self.region_name) - The model value can be a baseModelId for on-demand throughput or a provisionedModelArn - for higher throughput. To obtain a provisionedModelArn, use the CreateProvisionedModelThroughput API. - For more information on model IDs, see: - https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html +# AWS Bedrock API Example - +# https://docs.aws.amazon.com/bedrock/latest/userguide/tool-use-inference-call.html +# https://docs.aws.amazon.com/bedrock/latest/userguide/tool-use-examples.html +class BedrockMessageConverter: + @staticmethod + def convert_request( + messages: List[Dict[str, Any]] + ) -> Tuple[List[Dict], List[Dict]]: + """Convert messages to AWS Bedrock format.""" + # Convert all messages to dicts if they're Message objects + messages = [ + message.model_dump() if hasattr(message, "model_dump") else message + for message in messages + ] - Note: - - The Anthropic Bedrock client uses default AWS credential providers, such as - ~/.aws/credentials or the "AWS_SECRET_ACCESS_KEY" and "AWS_ACCESS_KEY_ID" environment variables. - - If the region is not set, it defaults to us-west-1, which may lead to a - "Could not connect to the endpoint URL" error. - - The client constructor does not accept additional parameters. + # Handle system message + system_message = [] + if messages and messages[0]["role"] == "system": + system_message = [{"text": messages[0]["content"]}] + messages = messages[1:] - Args: - **config: Configuration options for the provider. + formatted_messages = [] + for message in messages: + # Skip any additional system messages + if message["role"] == "system": + continue - """ - self.region_name = config.get( - "region_name", os.getenv("AWS_REGION", "us-west-2") - ) - self.client = boto3.client("bedrock-runtime", region_name=self.region_name) - self.inference_parameters = [ - "maxTokens", - "temperature", - "topP", - "stopSequences", - ] + if message["role"] == "tool": + bedrock_message = BedrockMessageConverter.convert_tool_result(message) + if bedrock_message: + formatted_messages.append(bedrock_message) + elif message["role"] == "assistant": + bedrock_message = BedrockMessageConverter.convert_assistant(message) + if bedrock_message: + formatted_messages.append(bedrock_message) + else: # user messages + formatted_messages.append( + { + "role": message["role"], + "content": [{"text": message["content"]}], + } + ) + + return system_message, formatted_messages + + @staticmethod + def convert_response_tool_call( + response: Dict[str, Any] + ) -> Optional[Dict[str, Any]]: + """Convert AWS Bedrock tool call response to OpenAI format.""" + if response.get("stopReason") != "tool_use": + return None + + tool_calls = [] + for content in response["output"]["message"]["content"]: + if "toolUse" in content: + tool = content["toolUse"] + tool_calls.append( + { + "type": "function", + "id": tool["toolUseId"], + "function": { + "name": tool["name"], + "arguments": json.dumps(tool["input"]), + }, + } + ) + + if not tool_calls: + return None + + return { + "role": "assistant", + "content": None, + "tool_calls": tool_calls, + "refusal": None, + } + + @staticmethod + def convert_tool_result(message: Dict[str, Any]) -> Optional[Dict[str, Any]]: + """Convert OpenAI tool result format to AWS Bedrock format.""" + if message["role"] != "tool" or "content" not in message: + return None + + tool_call_id = message.get("tool_call_id") + if not tool_call_id: + raise LLMError("Tool result message must include tool_call_id") + + try: + content_json = json.loads(message["content"]) + content = [{"json": content_json}] + except json.JSONDecodeError: + content = [{"text": message["content"]}] + + return { + "role": "user", + "content": [ + {"toolResult": {"toolUseId": tool_call_id, "content": content}} + ], + } + + @staticmethod + def convert_assistant(message: Dict[str, Any]) -> Optional[Dict[str, Any]]: + """Convert OpenAI assistant format to AWS Bedrock format.""" + if message["role"] != "assistant": + return None + + content = [] - def normalize_response(self, response): + if message.get("content"): + content.append({"text": message["content"]}) + + if message.get("tool_calls"): + for tool_call in message["tool_calls"]: + if tool_call["type"] == "function": + try: + input_json = json.loads(tool_call["function"]["arguments"]) + except json.JSONDecodeError: + input_json = tool_call["function"]["arguments"] + + content.append( + { + "toolUse": { + "toolUseId": tool_call["id"], + "name": tool_call["function"]["name"], + "input": input_json, + } + } + ) + + return {"role": "assistant", "content": content} if content else None + + @staticmethod + def convert_response(response: Dict[str, Any]) -> ChatCompletionResponse: """Normalize the response from the Bedrock API to match OpenAI's response format.""" norm_response = ChatCompletionResponse() + + # Check if the model is requesting tool use + if response.get("stopReason") == "tool_use": + tool_message = BedrockMessageConverter.convert_response_tool_call(response) + if tool_message: + norm_response.choices[0].message = Message(**tool_message) + norm_response.choices[0].finish_reason = "tool_calls" + return norm_response + + # Handle regular text response norm_response.choices[0].message.content = response["output"]["message"][ "content" ][0]["text"] + + # Map Bedrock stopReason to OpenAI finish_reason + stop_reason = response.get("stopReason") + if stop_reason == "complete": + norm_response.choices[0].finish_reason = "stop" + elif stop_reason == "max_tokens": + norm_response.choices[0].finish_reason = "length" + else: + norm_response.choices[0].finish_reason = stop_reason + return norm_response - def chat_completions_create(self, model, messages, **kwargs): - # Any exception raised by Anthropic will be returned to the caller. - # Maybe we should catch them and raise a custom LLMError. - # https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html - system_message = [] - if messages[0]["role"] == "system": - system_message = [{"text": messages[0]["content"]}] - messages = messages[1:] - formatted_messages = [] - for message in messages: - # QUIETLY Ignore any "system" messages except the first system message. - if message["role"] != "system": - formatted_messages.append( - {"role": message["role"], "content": [{"text": message["content"]}]} - ) +class AwsProvider(Provider): + def __init__(self, **config): + """Initialize the AWS Bedrock provider with the given configuration.""" + self.config = BedrockConfig(**config) + self.client = self.config.create_client() + self.transformer = BedrockMessageConverter() + + def convert_response(self, response: Dict[str, Any]) -> ChatCompletionResponse: + """Normalize the response from the Bedrock API to match OpenAI's response format.""" + return self.transformer.convert_response(response) + + def _convert_tool_spec(self, kwargs: Dict[str, Any]) -> Optional[Dict[str, Any]]: + """Convert tool specifications to Bedrock format.""" + if "tools" not in kwargs: + return None + + tool_config = { + "tools": [ + { + "toolSpec": { + "name": tool["function"]["name"], + "description": tool["function"].get("description", " "), + "inputSchema": {"json": tool["function"]["parameters"]}, + } + } + for tool in kwargs["tools"] + ] + } + return tool_config + + def _prepare_request_config(self, kwargs: Dict[str, Any]) -> Dict[str, Any]: + """Prepare the configuration for the Bedrock API request.""" + # Convert tools and remove from kwargs + tool_config = self._convert_tool_spec(kwargs) + kwargs.pop("tools", None) # Remove tools from kwargs if present - # Maintain a list of Inference Parameters which Bedrock supports. - # These fields need to be passed using inferenceConfig. - # Rest all other fields are passed as additionalModelRequestFields. - inference_config = {} - additional_model_request_fields = {} + inference_config = { + key: kwargs[key] + for key in BedrockConfig.INFERENCE_PARAMETERS + if key in kwargs + } - # Iterate over the kwargs and separate the inference parameters and additional model request fields. - for key, value in kwargs.items(): - if key in self.inference_parameters: - inference_config[key] = value + additional_fields = { + key: value + for key, value in kwargs.items() + if key not in BedrockConfig.INFERENCE_PARAMETERS + } + + request_config = { + "inferenceConfig": inference_config, + "additionalModelRequestFields": additional_fields, + } + + if tool_config is not None: + request_config["toolConfig"] = tool_config + + return request_config + + def chat_completions_create( + self, model: str, messages: List[Dict[str, Any]], **kwargs + ) -> ChatCompletionResponse: + """Create a chat completion request to AWS Bedrock.""" + system_message, formatted_messages = self.transformer.convert_request(messages) + request_config = self._prepare_request_config(kwargs) + + try: + response = self.client.converse( + modelId=model, + messages=formatted_messages, + system=system_message, + **request_config + ) + except botocore.exceptions.ClientError as e: + if e.response["Error"]["Code"] == "ValidationException": + error_message = e.response["Error"]["Message"] + raise LLMError(error_message) else: - additional_model_request_fields[key] = value - - # Call the Bedrock Converse API. - response = self.client.converse( - modelId=model, # baseModelId or provisionedModelArn - messages=formatted_messages, - system=system_message, - inferenceConfig=inference_config, - additionalModelRequestFields=additional_model_request_fields, - ) - return self.normalize_response(response) + raise + + return self.convert_response(response) diff --git a/aisuite/providers/azure_provider.py b/aisuite/providers/azure_provider.py index 7e0233ad..bb579e0c 100644 --- a/aisuite/providers/azure_provider.py +++ b/aisuite/providers/azure_provider.py @@ -4,6 +4,78 @@ from aisuite.provider import Provider from aisuite.framework import ChatCompletionResponse +from aisuite.framework.message import Message, ChatCompletionMessageToolCall, Function + +# Azure provider is based on the documentation here - +# https://learn.microsoft.com/en-us/azure/machine-learning/reference-model-inference-api?view=azureml-api-2&source=recommendations&tabs=python +# Azure AI Model Inference API is used. +# From the documentation - +# """ +# The Azure AI Model Inference is an API that exposes a common set of capabilities for foundational models +# and that can be used by developers to consume predictions from a diverse set of models in a uniform and consistent way. +# Developers can talk with different models deployed in Azure AI Foundry portal without changing the underlying code they are using. +# +# The Azure AI Model Inference API is available in the following models: +# +# Models deployed to serverless API endpoints: +# Cohere Embed V3 family of models +# Cohere Command R family of models +# Meta Llama 2 chat family of models +# Meta Llama 3 instruct family of models +# Mistral-Small +# Mistral-Large +# Jais family of models +# Jamba family of models +# Phi-3 family of models +# +# Models deployed to managed inference: +# Meta Llama 3 instruct family of models +# Phi-3 family of models +# Mixtral famility of models +# +# The API is compatible with Azure OpenAI model deployments. +# """ + + +class AzureMessageConverter: + @staticmethod + def convert_request(messages): + """Convert messages to Azure format.""" + transformed_messages = [] + for message in messages: + if isinstance(message, Message): + transformed_messages.append(message.model_dump(mode="json")) + else: + transformed_messages.append(message) + return transformed_messages + + @staticmethod + def convert_response(resp_json) -> ChatCompletionResponse: + """Normalize the response from the Azure API to match OpenAI's response format.""" + completion_response = ChatCompletionResponse() + choice = resp_json["choices"][0] + message = choice["message"] + + # Set basic message content + completion_response.choices[0].message.content = message.get("content") + completion_response.choices[0].message.role = message.get("role", "assistant") + + # Handle tool calls if present + if "tool_calls" in message and message["tool_calls"] is not None: + tool_calls = [] + for tool_call in message["tool_calls"]: + new_tool_call = ChatCompletionMessageToolCall( + id=tool_call["id"], + type=tool_call["type"], + function={ + "name": tool_call["function"]["name"], + "arguments": tool_call["function"]["arguments"], + }, + ) + tool_calls.append(new_tool_call) + completion_response.choices[0].message.tool_calls = tool_calls + + return completion_response class AzureProvider(Provider): @@ -16,16 +88,32 @@ def __init__(self, **config): raise ValueError( "For Azure, base_url is required. Check your deployment page for a URL like this - https://..models.ai.azure.com" ) + self.transformer = AzureMessageConverter() def chat_completions_create(self, model, messages, **kwargs): - url = f"https://{model}.westus3.models.ai.azure.com/v1/chat/completions" - url = f"https://{self.base_url}/chat/completions" - if self.base_url: - url = f"{self.base_url}/chat/completions" + url = f"{self.base_url}/chat/completions" # Remove 'stream' from kwargs if present kwargs.pop("stream", None) - data = {"messages": messages, **kwargs} + + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + + # Prepare the request payload + data = {"messages": transformed_messages} + + # Add tools if provided + if "tools" in kwargs: + data["tools"] = kwargs["tools"] + kwargs.pop("tools") + + # Add tool_choice if provided + if "tool_choice" in kwargs: + data["tool_choice"] = kwargs["tool_choice"] + kwargs.pop("tool_choice") + + # Add remaining kwargs + data.update(kwargs) body = json.dumps(data).encode("utf-8") headers = {"Content-Type": "application/json", "Authorization": self.api_key} @@ -35,12 +123,7 @@ def chat_completions_create(self, model, messages, **kwargs): with urllib.request.urlopen(req) as response: result = response.read() resp_json = json.loads(result) - completion_response = ChatCompletionResponse() - # TODO: Add checks for fields being present in resp_json. - completion_response.choices[0].message.content = resp_json["choices"][ - 0 - ]["message"]["content"] - return completion_response + return self.transformer.convert_response(resp_json) except urllib.error.HTTPError as error: error_message = f"The request failed with status code: {error.code}\n" diff --git a/aisuite/providers/cohere_provider.py b/aisuite/providers/cohere_provider.py index 5886f24b..9639a3e3 100644 --- a/aisuite/providers/cohere_provider.py +++ b/aisuite/providers/cohere_provider.py @@ -1,8 +1,133 @@ import os import cohere - +import json from aisuite.framework import ChatCompletionResponse -from aisuite.provider import Provider +from aisuite.framework.message import Message, ChatCompletionMessageToolCall, Function +from aisuite.provider import Provider, LLMError + + +class CohereMessageConverter: + """ + Cohere-specific message converter + """ + + def convert_request(self, messages): + """Convert framework messages to Cohere format.""" + converted_messages = [] + + for message in messages: + if isinstance(message, dict): + role = message.get("role") + content = message.get("content") + tool_calls = message.get("tool_calls") + tool_plan = message.get("tool_plan") + else: + role = message.role + content = message.content + tool_calls = message.tool_calls + tool_plan = getattr(message, "tool_plan", None) + + # Convert to Cohere's format + if role == "tool": + # Handle tool response messages + converted_message = { + "role": role, + "tool_call_id": ( + message.get("tool_call_id") + if isinstance(message, dict) + else message.tool_call_id + ), + "content": self._convert_tool_content(content), + } + elif role == "assistant" and tool_calls: + # Handle assistant messages with tool calls + converted_message = { + "role": role, + "tool_calls": [ + { + "id": tc.id if not isinstance(tc, dict) else tc["id"], + "function": { + "name": ( + tc.function.name + if not isinstance(tc, dict) + else tc["function"]["name"] + ), + "arguments": ( + tc.function.arguments + if not isinstance(tc, dict) + else tc["function"]["arguments"] + ), + }, + "type": "function", + } + for tc in tool_calls + ], + "tool_plan": tool_plan, + } + if content: + converted_message["content"] = content + else: + # Handle regular messages + converted_message = {"role": role, "content": content} + + converted_messages.append(converted_message) + + return converted_messages + + def _convert_tool_content(self, content): + """Convert tool response content to Cohere's expected format.""" + if isinstance(content, str): + try: + # Try to parse as JSON first + data = json.loads(content) + return [{"type": "document", "document": {"data": json.dumps(data)}}] + except json.JSONDecodeError: + # If not JSON, return as plain text + return content + elif isinstance(content, list): + # If content is already in Cohere's format, return as is + return content + else: + # For other types, convert to string + return str(content) + + @staticmethod + def convert_response(response_data) -> ChatCompletionResponse: + """Convert Cohere's response to our standard format.""" + normalized_response = ChatCompletionResponse() + + # Set usage information + normalized_response.usage = { + "prompt_tokens": response_data.usage.tokens.input_tokens, + "completion_tokens": response_data.usage.tokens.output_tokens, + "total_tokens": response_data.usage.tokens.input_tokens + + response_data.usage.tokens.output_tokens, + } + + # Handle tool calls + if response_data.finish_reason == "TOOL_CALL": + tool_call = response_data.message.tool_calls[0] + function = Function( + name=tool_call.function.name, arguments=tool_call.function.arguments + ) + tool_call_obj = ChatCompletionMessageToolCall( + id=tool_call.id, function=function, type="function" + ) + normalized_response.choices[0].message = Message( + content=response_data.message.tool_plan, # Use tool_plan as content + tool_calls=[tool_call_obj], + role="assistant", + refusal=None, + ) + normalized_response.choices[0].finish_reason = "tool_calls" + else: + # Handle regular text response + normalized_response.choices[0].message.content = ( + response_data.message.content[0].text + ) + normalized_response.choices[0].finish_reason = "stop" + + return normalized_response class CohereProvider(Provider): @@ -15,23 +140,24 @@ def __init__(self, **config): config.setdefault("api_key", os.getenv("CO_API_KEY")) if not config["api_key"]: raise ValueError( - " API key is missing. Please provide it in the config or set the CO_API_KEY environment variable." + "Cohere API key is missing. Please provide it in the config or set the CO_API_KEY environment variable." ) self.client = cohere.ClientV2(**config) + self.transformer = CohereMessageConverter() def chat_completions_create(self, model, messages, **kwargs): - response = self.client.chat( - model=model, - messages=messages, - **kwargs # Pass any additional arguments to the Cohere API - ) + """ + Makes a request to Cohere using the official client. + """ + try: + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) - return self.normalize_response(response) + # Make the request to Cohere + response = self.client.chat( + model=model, messages=transformed_messages, **kwargs + ) - def normalize_response(self, response): - """Normalize the reponse from Cohere API to match OpenAI's response format.""" - normalized_response = ChatCompletionResponse() - normalized_response.choices[0].message.content = response.message.content[ - 0 - ].text - return normalized_response + return self.transformer.convert_response(response) + except Exception as e: + raise LLMError(f"An error occurred: {e}") diff --git a/aisuite/providers/fireworks_provider.py b/aisuite/providers/fireworks_provider.py index 183b0d10..10bea195 100644 --- a/aisuite/providers/fireworks_provider.py +++ b/aisuite/providers/fireworks_provider.py @@ -1,9 +1,62 @@ import os import httpx +import json from aisuite.provider import Provider, LLMError from aisuite.framework import ChatCompletionResponse +from aisuite.framework.message import Message, ChatCompletionMessageToolCall +class FireworksMessageConverter: + @staticmethod + def convert_request(messages): + """Convert messages to Fireworks format.""" + transformed_messages = [] + for message in messages: + if isinstance(message, Message): + message_dict = message.model_dump(mode="json") + message_dict.pop("refusal", None) # Remove refusal field if present + transformed_messages.append(message_dict) + else: + transformed_messages.append(message) + return transformed_messages + + @staticmethod + def convert_response(resp_json) -> ChatCompletionResponse: + """Normalize the response from the Fireworks API to match OpenAI's response format.""" + completion_response = ChatCompletionResponse() + choice = resp_json["choices"][0] + message = choice["message"] + + # Set basic message content + completion_response.choices[0].message.content = message.get("content") + completion_response.choices[0].message.role = message.get("role", "assistant") + + # Handle tool calls if present + if "tool_calls" in message and message["tool_calls"] is not None: + tool_calls = [] + for tool_call in message["tool_calls"]: + new_tool_call = ChatCompletionMessageToolCall( + id=tool_call["id"], + type=tool_call["type"], + function={ + "name": tool_call["function"]["name"], + "arguments": tool_call["function"]["arguments"], + }, + ) + tool_calls.append(new_tool_call) + completion_response.choices[0].message.tool_calls = tool_calls + + return completion_response + + +# Models that support tool calls: +# [As of 01/20/2025 from https://docs.fireworks.ai/guides/function-calling] +# Llama 3.1 405B Instruct +# Llama 3.1 70B Instruct +# Qwen 2.5 72B Instruct +# Mixtral MoE 8x22B Instruct +# Firefunction-v2: Latest and most performant model, optimized for complex function calling scenarios (on-demand only) +# Firefunction-v1: Previous generation, Mixtral-based function calling model optimized for fast routing and structured output (on-demand only) class FireworksProvider(Provider): """ Fireworks AI Provider using httpx for direct API calls. @@ -24,20 +77,40 @@ def __init__(self, **config): # Optionally set a custom timeout (default to 30s) self.timeout = config.get("timeout", 30) + self.transformer = FireworksMessageConverter() def chat_completions_create(self, model, messages, **kwargs): """ Makes a request to the Fireworks AI chat completions endpoint using httpx. """ - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json", - } + # Remove 'stream' from kwargs if present + kwargs.pop("stream", None) + + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + # Prepare the request payload data = { "model": model, - "messages": messages, - **kwargs, # Pass any additional arguments to the API + "messages": transformed_messages, + } + + # Add tools if provided + if "tools" in kwargs: + data["tools"] = kwargs["tools"] + kwargs.pop("tools") + + # Add tool_choice if provided + if "tool_choice" in kwargs: + data["tool_choice"] = kwargs["tool_choice"] + kwargs.pop("tool_choice") + + # Add remaining kwargs + data.update(kwargs) + + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", } try: @@ -46,14 +119,17 @@ def chat_completions_create(self, model, messages, **kwargs): self.BASE_URL, json=data, headers=headers, timeout=self.timeout ) response.raise_for_status() - except httpx.HTTPStatusError as http_err: - raise LLMError(f"Fireworks AI request failed: {http_err}") + return self.transformer.convert_response(response.json()) + except httpx.HTTPStatusError as error: + error_message = ( + f"The request failed with status code: {error.status_code}\n" + ) + error_message += f"Headers: {error.headers}\n" + error_message += error.response.text + raise LLMError(error_message) except Exception as e: raise LLMError(f"An error occurred: {e}") - # Return the normalized response - return self._normalize_response(response.json()) - def _normalize_response(self, response_data): """ Normalize the response to a common format (ChatCompletionResponse). diff --git a/aisuite/providers/google_provider.py b/aisuite/providers/google_provider.py index 380585e4..7d4b586b 100644 --- a/aisuite/providers/google_provider.py +++ b/aisuite/providers/google_provider.py @@ -1,14 +1,192 @@ """The interface to Google's Vertex AI.""" import os +import json +from typing import List, Dict, Any, Optional import vertexai -from vertexai.generative_models import GenerativeModel, GenerationConfig +from vertexai.generative_models import ( + GenerativeModel, + GenerationConfig, + Content, + Part, + Tool, + FunctionDeclaration, +) +import pprint -from aisuite.framework import ProviderInterface, ChatCompletionResponse +from aisuite.framework import ProviderInterface, ChatCompletionResponse, Message DEFAULT_TEMPERATURE = 0.7 +ENABLE_DEBUG_MESSAGES = False + +# Links. +# https://codelabs.developers.google.com/codelabs/gemini-function-calling#6 +# https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling#chat-samples + + +class GoogleMessageConverter: + @staticmethod + def convert_user_role_message(message: Dict[str, Any]) -> Content: + """Convert user or system messages to Google Vertex AI format.""" + parts = [Part.from_text(message["content"])] + return Content(role="user", parts=parts) + + @staticmethod + def convert_assistant_role_message(message: Dict[str, Any]) -> Content: + """Convert assistant messages to Google Vertex AI format.""" + if "tool_calls" in message and message["tool_calls"]: + # Handle function calls + tool_call = message["tool_calls"][ + 0 + ] # Assuming single function call for now + function_call = tool_call["function"] + + # Create a Part from the function call + parts = [ + Part.from_dict( + { + "function_call": { + "name": function_call["name"], + # "arguments": json.loads(function_call["arguments"]) + } + } + ) + ] + # return Content(role="function", parts=parts) + else: + # Handle regular text messages + parts = [Part.from_text(message["content"])] + # return Content(role="model", parts=parts) + + return Content(role="model", parts=parts) + + @staticmethod + def convert_tool_role_message(message: Dict[str, Any]) -> Part: + """Convert tool messages to Google Vertex AI format.""" + if "content" not in message: + raise ValueError("Tool result message must have a content field") + + try: + content_json = json.loads(message["content"]) + part = Part.from_function_response( + name=message["name"], response=content_json + ) + # TODO: Return Content instead of Part. But returning Content is not working. + return part + except json.JSONDecodeError: + raise ValueError("Tool result message must be valid JSON") + + @staticmethod + def convert_request(messages: List[Dict[str, Any]]) -> List[Content]: + """Convert messages to Google Vertex AI format.""" + # Convert all messages to dicts if they're Message objects + messages = [ + message.model_dump() if hasattr(message, "model_dump") else message + for message in messages + ] + + formatted_messages = [] + for message in messages: + if message["role"] == "tool": + vertex_message = GoogleMessageConverter.convert_tool_role_message( + message + ) + if vertex_message: + formatted_messages.append(vertex_message) + elif message["role"] == "assistant": + formatted_messages.append( + GoogleMessageConverter.convert_assistant_role_message(message) + ) + else: # user or system role + formatted_messages.append( + GoogleMessageConverter.convert_user_role_message(message) + ) + + return formatted_messages + + @staticmethod + def convert_response(response) -> ChatCompletionResponse: + """Normalize the response from Vertex AI to match OpenAI's response format.""" + openai_response = ChatCompletionResponse() + + if ENABLE_DEBUG_MESSAGES: + print("Dumping the response") + pprint.pprint(response) + + # TODO: We need to go through each part, because function call may not be the first part. + # Currently, we are only handling the first part, but this is not enough. + # + # This is a valid response: + # candidates { + # content { + # role: "model" + # parts { + # text: "The current temperature in San Francisco is 72 degrees Celsius. \n\n" + # } + # parts { + # function_call { + # name: "is_it_raining" + # args { + # fields { + # key: "location" + # value { + # string_value: "San Francisco" + # } + # } + # } + # } + # } + # } + # finish_reason: STOP + + # Check if the response contains function calls + # Note: Just checking if the function_call attribute exists is not enough, + # it is important to check if the function_call is not None. + if ( + hasattr(response.candidates[0].content.parts[0], "function_call") + and response.candidates[0].content.parts[0].function_call + ): + function_call = response.candidates[0].content.parts[0].function_call + + # args is a MapComposite. + # Convert the MapComposite to a dictionary + args_dict = {} + # Another way to try is: args_dict = dict(function_call.args) + for key, value in function_call.args.items(): + args_dict[key] = value + if ENABLE_DEBUG_MESSAGES: + print("Dumping the args_dict") + pprint.pprint(args_dict) + + openai_response.choices[0].message = { + "role": "assistant", + "content": None, + "tool_calls": [ + { + "type": "function", + "id": f"call_{hash(function_call.name)}", # Generate a unique ID + "function": { + "name": function_call.name, + "arguments": json.dumps(args_dict), + }, + } + ], + "refusal": None, + } + openai_response.choices[0].message = Message( + **openai_response.choices[0].message + ) + openai_response.choices[0].finish_reason = "tool_calls" + else: + # Handle regular text response + openai_response.choices[0].message.content = ( + response.candidates[0].content.parts[0].text + ) + openai_response.choices[0].finish_reason = "stop" + + return openai_response class GoogleProvider(ProviderInterface): @@ -31,6 +209,8 @@ def __init__(self, **config): vertexai.init(project=self.project_id, location=self.location) + self.transformer = GoogleMessageConverter() + def chat_completions_create(self, model, messages, **kwargs): """Request chat completions from the Google AI API. @@ -49,57 +229,70 @@ def chat_completions_create(self, model, messages, **kwargs): # Set the temperature if provided, otherwise use the default temperature = kwargs.get("temperature", DEFAULT_TEMPERATURE) - # Transform the roles in the messages - transformed_messages = self.transform_roles(messages) - - # Convert the messages to the format expected Google - final_message_history = self.convert_openai_to_vertex_ai( - transformed_messages[:-1] - ) + # Convert messages to Vertex AI format + message_history = self.transformer.convert_request(messages) - # Get the last message from the transformed messages - last_message = transformed_messages[-1]["content"] + # Handle tools if provided + tools = None + if "tools" in kwargs: + tools = [ + Tool( + function_declarations=[ + FunctionDeclaration( + name=tool["function"]["name"], + description=tool["function"].get("description", ""), + parameters={ + "type": "object", + "properties": { + param_name: { + "type": param_info.get("type", "string"), + "description": param_info.get( + "description", "" + ), + **( + {"enum": param_info["enum"]} + if "enum" in param_info + else {} + ), + } + for param_name, param_info in tool["function"][ + "parameters" + ]["properties"].items() + }, + "required": tool["function"]["parameters"].get( + "required", [] + ), + }, + ) + for tool in kwargs["tools"] + ] + ) + ] - # Create the GenerativeModel with the specified model and generation configuration + # Create the GenerativeModel model = GenerativeModel( - model, generation_config=GenerationConfig(temperature=temperature) + model, + generation_config=GenerationConfig(temperature=temperature), + tools=tools, ) - # Start a chat with the GenerativeModel and send the last message - chat = model.start_chat(history=final_message_history) - response = chat.send_message(last_message) + if ENABLE_DEBUG_MESSAGES: + print("Dumping the message_history") + pprint.pprint(message_history) - # Convert the response to the format expected by the OpenAI API - return self.normalize_response(response) + # Start chat and get response + chat = model.start_chat(history=message_history[:-1]) + last_message = message_history[-1] - def convert_openai_to_vertex_ai(self, messages): - """Convert OpenAI messages to Google AI messages.""" - from vertexai.generative_models import Content, Part - - history = [] - for message in messages: - role = message["role"] - content = message["content"] - parts = [Part.from_text(content)] - history.append(Content(role=role, parts=parts)) - return history - - def transform_roles(self, messages): - """Transform the roles in the messages based on the provided transformations.""" - openai_roles_to_google_roles = { - "system": "user", - "assistant": "model", - } - - for message in messages: - if role := openai_roles_to_google_roles.get(message["role"], None): - message["role"] = role - return messages - - def normalize_response(self, response): - """Normalize the response from Google AI to match OpenAI's response format.""" - openai_response = ChatCompletionResponse() - openai_response.choices[0].message.content = ( - response.candidates[0].content.parts[0].text + # If the last message is a function response, send the Part object directly + # Otherwise, send just the text content + message_to_send = ( + Content(role="function", parts=[last_message]) + if isinstance(last_message, Part) + else last_message.parts[0].text ) - return openai_response + # response = chat.send_message(message_to_send) + response = chat.send_message(message_to_send) + + # Convert and return the response + return self.transformer.convert_response(response) diff --git a/aisuite/providers/groq_provider.py b/aisuite/providers/groq_provider.py index 73a4bb4f..07d29fdb 100644 --- a/aisuite/providers/groq_provider.py +++ b/aisuite/providers/groq_provider.py @@ -1,7 +1,31 @@ import os - import groq -from aisuite.provider import Provider +from aisuite.provider import Provider, LLMError +from aisuite.providers.message_converter import OpenAICompliantMessageConverter + +# Implementation of Groq provider. +# Groq's message format is same as OpenAI's. +# Tool calling specification is also exactly the same as OpenAI's. +# Links: +# https://console.groq.com/docs/tool-use +# Groq supports tool calling for the following models, as of 16th Nov 2024: +# llama3-groq-70b-8192-tool-use-preview +# llama3-groq-8b-8192-tool-use-preview +# llama-3.1-70b-versatile +# llama-3.1-8b-instant +# llama3-70b-8192 +# llama3-8b-8192 +# mixtral-8x7b-32768 (parallel tool use not supported) +# gemma-7b-it (parallel tool use not supported) +# gemma2-9b-it (parallel tool use not supported) + + +class GroqMessageConverter(OpenAICompliantMessageConverter): + """ + Groq-specific message converter if needed + """ + + pass class GroqProvider(Provider): @@ -11,16 +35,28 @@ def __init__(self, **config): Pass the entire configuration dictionary to the Groq client constructor. """ # Ensure API key is provided either in config or via environment variable - config.setdefault("api_key", os.getenv("GROQ_API_KEY")) - if not config["api_key"]: + self.api_key = config.get("api_key", os.getenv("GROQ_API_KEY")) + if not self.api_key: raise ValueError( - " API key is missing. Please provide it in the config or set the GROQ_API_KEY environment variable." + "Groq API key is missing. Please provide it in the config or set the GROQ_API_KEY environment variable." ) + config["api_key"] = self.api_key self.client = groq.Groq(**config) + self.transformer = GroqMessageConverter() def chat_completions_create(self, model, messages, **kwargs): - return self.client.chat.completions.create( - model=model, - messages=messages, - **kwargs # Pass any additional arguments to the Groq API - ) + """ + Makes a request to the Groq chat completions endpoint using the official client. + """ + try: + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + + response = self.client.chat.completions.create( + model=model, + messages=transformed_messages, + **kwargs, # Pass any additional arguments to the Groq API + ) + return self.transformer.convert_response(response.model_dump()) + except Exception as e: + raise LLMError(f"An error occurred: {e}") diff --git a/aisuite/providers/huggingface_provider.py b/aisuite/providers/huggingface_provider.py index de989f7d..97b1dbd9 100644 --- a/aisuite/providers/huggingface_provider.py +++ b/aisuite/providers/huggingface_provider.py @@ -1,14 +1,16 @@ import os -import httpx +import json +from huggingface_hub import InferenceClient from aisuite.provider import Provider, LLMError from aisuite.framework import ChatCompletionResponse +from aisuite.framework.message import Message class HuggingfaceProvider(Provider): """ - HuggingFace Provider using httpx for direct API calls. - Currently, this provider support calls to HF serverless Inference Endpoints - which uses Text Generation Inference (TGI) as the backend. + HuggingFace Provider using the official InferenceClient. + This provider supports calls to HF serverless Inference Endpoints + which use Text Generation Inference (TGI) as the backend. TGI is OpenAI protocol compliant. https://huggingface.co/inference-endpoints/ """ @@ -25,43 +27,102 @@ def __init__(self, **config): "Hugging Face token is missing. Please provide it in the config or set the HF_TOKEN environment variable." ) - # Optionally set a custom timeout (default to 30s) + # Initialize the InferenceClient with the specified model and timeout if provided + self.model = config.get("model") self.timeout = config.get("timeout", 30) + self.client = InferenceClient( + token=self.token, model=self.model, timeout=self.timeout + ) def chat_completions_create(self, model, messages, **kwargs): """ - Makes a request to the Inference API endpoint using httpx. + Makes a request to the Inference API endpoint using InferenceClient. """ - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {self.token}", - } + # Validate and transform messages + transformed_messages = [] + for message in messages: + if isinstance(message, Message): + transformed_message = self.transform_from_message(message) + elif isinstance(message, dict): + transformed_message = message + else: + raise ValueError(f"Invalid message format: {message}") - data = { - "model": model, - "messages": messages, - **kwargs, # Pass any additional arguments to the API - } + # Ensure 'content' is a non-empty string + if ( + "content" not in transformed_message + or transformed_message["content"] is None + ): + transformed_message["content"] = "" + + transformed_messages.append(transformed_message) - url = f"https://api-inference.huggingface.co/models/{model}/v1/chat/completions" try: - # Make the request to Hugging Face endpoint. - response = httpx.post(url, json=data, headers=headers, timeout=self.timeout) - response.raise_for_status() - except httpx.HTTPStatusError as http_err: - raise LLMError(f"Hugging Face request failed: {http_err}") + # Prepare the payload + payload = { + "messages": transformed_messages, + **kwargs, # Include other parameters like temperature, max_tokens, etc. + } + + # Make the API call using the client + response = self.client.chat_completion(model=model, **payload) + + return self._normalize_response(response) + except Exception as e: raise LLMError(f"An error occurred: {e}") - # Return the normalized response - return self._normalize_response(response.json()) + def transform_from_message(self, message: Message): + """Transform framework Message to a format that HuggingFace understands.""" + # Ensure content is a string + content = message.content if message.content is not None else "" + + # Transform the message + transformed_message = { + "role": message.role, + "content": content, + } + + # Include tool_calls if present + if message.tool_calls: + transformed_message["tool_calls"] = [ + { + "id": tool_call.id, + "function": { + "name": tool_call.function.name, + "arguments": tool_call.function.arguments, + }, + "type": tool_call.type, + } + for tool_call in message.tool_calls + ] + + return transformed_message + + def transform_to_message(self, message_dict: dict): + """Transform HuggingFace message (dict) to a format that the framework Message understands.""" + # Ensure required fields are present + message_dict.setdefault("content", "") # Set empty string if content is missing + message_dict.setdefault("refusal", None) # Set None if refusal is missing + message_dict.setdefault("tool_calls", None) # Set None if tool_calls is missing + + # Handle tool calls if present and not None + if message_dict.get("tool_calls"): + for tool_call in message_dict["tool_calls"]: + if "function" in tool_call: + # Ensure function arguments are stringified + if isinstance(tool_call["function"].get("arguments"), dict): + tool_call["function"]["arguments"] = json.dumps( + tool_call["function"]["arguments"] + ) + + return Message(**message_dict) def _normalize_response(self, response_data): """ Normalize the response to a common format (ChatCompletionResponse). """ normalized_response = ChatCompletionResponse() - normalized_response.choices[0].message.content = response_data["choices"][0][ - "message" - ]["content"] + message_data = response_data["choices"][0]["message"] + normalized_response.choices[0].message = self.transform_to_message(message_data) return normalized_response diff --git a/aisuite/providers/message_converter.py b/aisuite/providers/message_converter.py new file mode 100644 index 00000000..2c5c9d2a --- /dev/null +++ b/aisuite/providers/message_converter.py @@ -0,0 +1,57 @@ +from aisuite.framework import ChatCompletionResponse +from aisuite.framework.message import Message, ChatCompletionMessageToolCall + + +class OpenAICompliantMessageConverter: + """ + Base class for message converters that are compatible with OpenAI's API. + """ + + # Class variable that derived classes can override + tool_results_as_strings = False + + @staticmethod + def convert_request(messages): + """Convert messages to OpenAI-compatible format.""" + transformed_messages = [] + for message in messages: + tmsg = None + if isinstance(message, Message): + message_dict = message.model_dump(mode="json") + message_dict.pop("refusal", None) # Remove refusal field if present + tmsg = message_dict + else: + tmsg = message + if tmsg["role"] == "tool": + if OpenAICompliantMessageConverter.tool_results_as_strings: + tmsg["content"] = str(tmsg["content"]) + + transformed_messages.append(tmsg) + return transformed_messages + + @staticmethod + def convert_response(response_data) -> ChatCompletionResponse: + """Normalize the response to match OpenAI's response format.""" + print(response_data) + completion_response = ChatCompletionResponse() + choice = response_data["choices"][0] + message = choice["message"] + + # Set basic message content + completion_response.choices[0].message.content = message["content"] + completion_response.choices[0].message.role = message.get("role", "assistant") + + # Handle tool calls if present + if "tool_calls" in message and message["tool_calls"] is not None: + tool_calls = [] + for tool_call in message["tool_calls"]: + tool_calls.append( + ChatCompletionMessageToolCall( + id=tool_call.get("id"), + type="function", # Always set to "function" as it's the only valid value + function=tool_call.get("function"), + ) + ) + completion_response.choices[0].message.tool_calls = tool_calls + + return completion_response diff --git a/aisuite/providers/mistral_provider.py b/aisuite/providers/mistral_provider.py index 6e96f1cf..4fc28fab 100644 --- a/aisuite/providers/mistral_provider.py +++ b/aisuite/providers/mistral_provider.py @@ -1,11 +1,47 @@ import os - from mistralai import Mistral +from aisuite.framework.message import Message +from aisuite.framework import ChatCompletionResponse +from aisuite.provider import Provider, LLMError +from aisuite.providers.message_converter import OpenAICompliantMessageConverter + + +# Implementation of Mistral provider. +# Mistral's message format is same as OpenAI's. Just different class names, but fully cross-compatible. +# Links: +# https://docs.mistral.ai/capabilities/function_calling/ -from aisuite.provider import Provider +class MistralMessageConverter(OpenAICompliantMessageConverter): + """ + Mistral-specific message converter + """ + @staticmethod + def convert_response(response_data) -> ChatCompletionResponse: + """Convert Mistral's response to our standard format.""" + # Convert Mistral's response object to dict format + response_dict = response_data.model_dump() + return super(MistralMessageConverter, MistralMessageConverter).convert_response( + response_dict + ) + + +# Function calling is available for the following models: +# [As of 01/19/2025 from https://docs.mistral.ai/capabilities/function_calling/] +# Mistral Large +# Mistral Small +# Codestral 22B +# Ministral 8B +# Ministral 3B +# Pixtral 12B +# Mixtral 8x22B +# Mistral Nemo class MistralProvider(Provider): + """ + Mistral AI Provider using the official Mistral client. + """ + def __init__(self, **config): """ Initialize the Mistral provider with the given configuration. @@ -15,9 +51,24 @@ def __init__(self, **config): config.setdefault("api_key", os.getenv("MISTRAL_API_KEY")) if not config["api_key"]: raise ValueError( - " API key is missing. Please provide it in the config or set the MISTRAL_API_KEY environment variable." + "Mistral API key is missing. Please provide it in the config or set the MISTRAL_API_KEY environment variable." ) self.client = Mistral(**config) + self.transformer = MistralMessageConverter() def chat_completions_create(self, model, messages, **kwargs): - return self.client.chat.complete(model=model, messages=messages, **kwargs) + """ + Makes a request to Mistral using the official client. + """ + try: + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + + # Make the request to Mistral + response = self.client.chat.complete( + model=model, messages=transformed_messages, **kwargs + ) + + return self.transformer.convert_response(response) + except Exception as e: + raise LLMError(f"An error occurred: {e}") diff --git a/aisuite/providers/nebius_provider.py b/aisuite/providers/nebius_provider.py index c558a9ce..568cca88 100644 --- a/aisuite/providers/nebius_provider.py +++ b/aisuite/providers/nebius_provider.py @@ -6,6 +6,7 @@ BASE_URL = "https://api.studio.nebius.ai/v1" +# TODO(rohitcp): This needs to be added to our internal testbed. Tool calling not tested. class NebiusProvider(Provider): def __init__(self, **config): """ diff --git a/aisuite/providers/openai_provider.py b/aisuite/providers/openai_provider.py index b8fe2fb8..fdd32d49 100644 --- a/aisuite/providers/openai_provider.py +++ b/aisuite/providers/openai_provider.py @@ -26,8 +26,10 @@ def __init__(self, **config): def chat_completions_create(self, model, messages, **kwargs): # Any exception raised by OpenAI will be returned to the caller. # Maybe we should catch them and raise a custom LLMError. - return self.client.chat.completions.create( + response = self.client.chat.completions.create( model=model, messages=messages, **kwargs # Pass any additional arguments to the OpenAI API ) + + return response diff --git a/aisuite/providers/sambanova_provider.py b/aisuite/providers/sambanova_provider.py index 75a97311..850fb15f 100644 --- a/aisuite/providers/sambanova_provider.py +++ b/aisuite/providers/sambanova_provider.py @@ -1,30 +1,53 @@ import os -from aisuite.provider import Provider +from aisuite.provider import Provider, LLMError from openai import OpenAI +from aisuite.providers.message_converter import OpenAICompliantMessageConverter + + +class SambanovaMessageConverter(OpenAICompliantMessageConverter): + """ + SambaNova-specific message converter. + """ + + pass class SambanovaProvider(Provider): + """ + SambaNova Provider using OpenAI client for API calls. + """ + def __init__(self, **config): """ Initialize the SambaNova provider with the given configuration. Pass the entire configuration dictionary to the OpenAI client constructor. """ # Ensure API key is provided either in config or via environment variable - config.setdefault("api_key", os.getenv("SAMBANOVA_API_KEY")) - if not config["api_key"]: + self.api_key = config.get("api_key", os.getenv("SAMBANOVA_API_KEY")) + if not self.api_key: raise ValueError( "Sambanova API key is missing. Please provide it in the config or set the SAMBANOVA_API_KEY environment variable." ) + config["api_key"] = self.api_key config["base_url"] = "https://api.sambanova.ai/v1/" # Pass the entire config to the OpenAI client constructor self.client = OpenAI(**config) + self.transformer = SambanovaMessageConverter() def chat_completions_create(self, model, messages, **kwargs): - # Any exception raised by Sambanova will be returned to the caller. - # Maybe we should catch them and raise a custom LLMError. - return self.client.chat.completions.create( - model=model, - messages=messages, - **kwargs # Pass any additional arguments to the Sambanova API - ) + """ + Makes a request to the SambaNova chat completions endpoint using the OpenAI client. + """ + try: + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + + response = self.client.chat.completions.create( + model=model, + messages=transformed_messages, + **kwargs, # Pass any additional arguments to the Sambanova API + ) + return self.transformer.convert_response(response.model_dump()) + except Exception as e: + raise LLMError(f"An error occurred: {e}") diff --git a/aisuite/providers/together_provider.py b/aisuite/providers/together_provider.py index 9e1ef857..fdfb02a8 100644 --- a/aisuite/providers/together_provider.py +++ b/aisuite/providers/together_provider.py @@ -1,7 +1,15 @@ import os import httpx from aisuite.provider import Provider, LLMError -from aisuite.framework import ChatCompletionResponse +from aisuite.providers.message_converter import OpenAICompliantMessageConverter + + +class TogetherMessageConverter(OpenAICompliantMessageConverter): + """ + Together-specific message converter if needed + """ + + pass class TogetherProvider(Provider): @@ -13,7 +21,7 @@ class TogetherProvider(Provider): def __init__(self, **config): """ - Initialize the Fireworks provider with the given configuration. + Initialize the Together provider with the given configuration. The API key is fetched from the config or environment variables. """ self.api_key = config.get("api_key", os.getenv("TOGETHER_API_KEY")) @@ -24,11 +32,15 @@ def __init__(self, **config): # Optionally set a custom timeout (default to 30s) self.timeout = config.get("timeout", 30) + self.transformer = TogetherMessageConverter() def chat_completions_create(self, model, messages, **kwargs): """ - Makes a request to the Fireworks AI chat completions endpoint using httpx. + Makes a request to the Together AI chat completions endpoint using httpx. """ + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", @@ -36,30 +48,18 @@ def chat_completions_create(self, model, messages, **kwargs): data = { "model": model, - "messages": messages, + "messages": transformed_messages, **kwargs, # Pass any additional arguments to the API } try: - # Make the request to Fireworks AI endpoint. + # Make the request to Together AI endpoint. response = httpx.post( self.BASE_URL, json=data, headers=headers, timeout=self.timeout ) response.raise_for_status() + return self.transformer.convert_response(response.json()) except httpx.HTTPStatusError as http_err: raise LLMError(f"Together AI request failed: {http_err}") except Exception as e: raise LLMError(f"An error occurred: {e}") - - # Return the normalized response - return self._normalize_response(response.json()) - - def _normalize_response(self, response_data): - """ - Normalize the response to a common format (ChatCompletionResponse). - """ - normalized_response = ChatCompletionResponse() - normalized_response.choices[0].message.content = response_data["choices"][0][ - "message" - ]["content"] - return normalized_response diff --git a/aisuite/providers/xai_provider.py b/aisuite/providers/xai_provider.py index 53e8d831..23effb08 100644 --- a/aisuite/providers/xai_provider.py +++ b/aisuite/providers/xai_provider.py @@ -2,6 +2,15 @@ import httpx from aisuite.provider import Provider, LLMError from aisuite.framework import ChatCompletionResponse +from aisuite.providers.message_converter import OpenAICompliantMessageConverter + + +class XaiMessageConverter(OpenAICompliantMessageConverter): + """ + xAI-specific message converter if needed + """ + + pass class XaiProvider(Provider): @@ -24,11 +33,15 @@ def __init__(self, **config): # Optionally set a custom timeout (default to 30s) self.timeout = config.get("timeout", 30) + self.transformer = XaiMessageConverter() def chat_completions_create(self, model, messages, **kwargs): """ Makes a request to the xAI chat completions endpoint using httpx. """ + # Transform messages using converter + transformed_messages = self.transformer.convert_request(messages) + headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", @@ -36,7 +49,7 @@ def chat_completions_create(self, model, messages, **kwargs): data = { "model": model, - "messages": messages, + "messages": transformed_messages, **kwargs, # Pass any additional arguments to the API } @@ -46,20 +59,8 @@ def chat_completions_create(self, model, messages, **kwargs): self.BASE_URL, json=data, headers=headers, timeout=self.timeout ) response.raise_for_status() + return self.transformer.convert_response(response.json()) except httpx.HTTPStatusError as http_err: raise LLMError(f"xAI request failed: {http_err}") except Exception as e: raise LLMError(f"An error occurred: {e}") - - # Return the normalized response - return self._normalize_response(response.json()) - - def _normalize_response(self, response_data): - """ - Normalize the response to a common format (ChatCompletionResponse). - """ - normalized_response = ChatCompletionResponse() - normalized_response.choices[0].message.content = response_data["choices"][0][ - "message" - ]["content"] - return normalized_response diff --git a/aisuite/utils/tool_manager.py b/aisuite/utils/tool_manager.py new file mode 100644 index 00000000..3da948e0 --- /dev/null +++ b/aisuite/utils/tool_manager.py @@ -0,0 +1,279 @@ +from typing import Callable, Dict, Any, Type, Optional +from pydantic import BaseModel, create_model, Field, ValidationError +import inspect +import json +from docstring_parser import parse + + +class Tools: + def __init__(self, tools: list[Callable] = None): + self._tools = {} + if tools: + for tool in tools: + self._add_tool(tool) + + # Add a tool function with or without a Pydantic model. + def _add_tool(self, func: Callable, param_model: Optional[Type[BaseModel]] = None): + """Register a tool function with metadata. If no param_model is provided, infer from function signature.""" + if param_model: + tool_spec = self._convert_to_tool_spec(func, param_model) + else: + tool_spec, param_model = self.__infer_from_signature(func) + + self._tools[func.__name__] = { + "function": func, + "param_model": param_model, + "spec": tool_spec, + } + + # Return tools in the specified format (default OpenAI). + def tools(self, format="openai") -> list: + """Return tools in the specified format (default OpenAI).""" + if format == "openai": + return self.__convert_to_openai_format() + return [tool["spec"] for tool in self._tools.values()] + + # Convert the function and its Pydantic model to a unified tool specification. + def _convert_to_tool_spec( + self, func: Callable, param_model: Type[BaseModel] + ) -> Dict[str, Any]: + """Convert the function and its Pydantic model to a unified tool specification.""" + type_mapping = {str: "string", int: "integer", float: "number", bool: "boolean"} + + properties = {} + for field_name, field in param_model.model_fields.items(): + field_type = field.annotation + + # Handle enum types + if hasattr(field_type, "__members__"): # Check if it's an enum + enum_values = [ + member.value if hasattr(member, "value") else member.name + for member in field_type + ] + properties[field_name] = { + "type": "string", + "enum": enum_values, + "description": field.description or "", + } + # Convert enum default value to string if it exists + if str(field.default) != "PydanticUndefined": + properties[field_name]["default"] = ( + field.default.value + if hasattr(field.default, "value") + else field.default + ) + else: + properties[field_name] = { + "type": type_mapping.get(field_type, str(field_type)), + "description": field.description or "", + } + # Add default if it exists and isn't PydanticUndefined + if str(field.default) != "PydanticUndefined": + properties[field_name]["default"] = field.default + + return { + "name": func.__name__, + "description": func.__doc__ or "", + "parameters": { + "type": "object", + "properties": properties, + "required": [ + name + for name, field in param_model.model_fields.items() + if field.is_required and str(field.default) == "PydanticUndefined" + ], + }, + } + + def __extract_param_descriptions(self, func: Callable) -> dict[str, str]: + """Extract parameter descriptions from function docstring. + + Args: + func: The function to extract parameter descriptions from + + Returns: + Dictionary mapping parameter names to their descriptions + """ + docstring = inspect.getdoc(func) or "" + parsed_docstring = parse(docstring) + + param_descriptions = {} + for param in parsed_docstring.params: + param_descriptions[param.arg_name] = param.description or "" + + return param_descriptions + + def __infer_from_signature( + self, func: Callable + ) -> tuple[Dict[str, Any], Type[BaseModel]]: + """Infer parameters(required and optional) and requirements directly from the function signature.""" + signature = inspect.signature(func) + fields = {} + required_fields = [] + + # Get function's docstring and parse parameter descriptions + param_descriptions = self.__extract_param_descriptions(func) + docstring = inspect.getdoc(func) or "" + + for param_name, param in signature.parameters.items(): + # Check if a type annotation is missing + if param.annotation == inspect._empty: + raise TypeError( + f"Parameter '{param_name}' in function '{func.__name__}' must have a type annotation." + ) + + # Determine field type and optionality + param_type = param.annotation + description = param_descriptions.get(param_name, "") + + if param.default == inspect._empty: + fields[param_name] = (param_type, Field(..., description=description)) + required_fields.append(param_name) + else: + fields[param_name] = ( + param_type, + Field(default=param.default, description=description), + ) + + # Dynamically create a Pydantic model based on inferred fields + param_model = create_model(f"{func.__name__.capitalize()}Params", **fields) + + # Convert inferred model to a tool spec format + tool_spec = self._convert_to_tool_spec(func, param_model) + + # Update the tool spec with the docstring + tool_spec["description"] = docstring + + return tool_spec, param_model + + def __convert_to_openai_format(self) -> list: + """Convert tools to OpenAI's format.""" + return [ + {"type": "function", "function": tool["spec"]} + for tool in self._tools.values() + ] + + def results_to_messages(self, results: list, message: any) -> list: + """Converts results to messages.""" + # if message is empty return empty list + if not message or len(results) == 0: + return [] + + messages = [] + # Iterate over results and match with tool calls from the message + for result in results: + # Find matching tool call from message.tool_calls + for tool_call in message.tool_calls: + if tool_call.id == result["tool_call_id"]: + messages.append( + { + "role": "tool", + "name": result["name"], + "content": json.dumps(result["content"]), + "tool_call_id": tool_call.id, + } + ) + break + + return messages + + def execute(self, tool_calls) -> list: + """Executes registered tools based on the tool calls from the model. + + Args: + tool_calls: List of tool calls from the model + + Returns: + List of results from executing each tool call + """ + results = [] + + # Handle single tool call or list of tool calls + if not isinstance(tool_calls, list): + tool_calls = [tool_calls] + + for tool_call in tool_calls: + # Handle both dictionary and object-style tool calls + if isinstance(tool_call, dict): + tool_name = tool_call["function"]["name"] + arguments = tool_call["function"]["arguments"] + else: + tool_name = tool_call.function.name + arguments = tool_call.function.arguments + + # Ensure arguments is a dict + if isinstance(arguments, str): + arguments = json.loads(arguments) + + if tool_name not in self._tools: + raise ValueError(f"Tool '{tool_name}' not registered.") + + tool = self._tools[tool_name] + tool_func = tool["function"] + param_model = tool["param_model"] + + # Validate and parse the arguments with Pydantic if a model exists + try: + validated_args = param_model(**arguments) + result = tool_func(**validated_args.model_dump()) + results.append(result) + except ValidationError as e: + raise ValueError(f"Error in tool '{tool_name}' parameters: {e}") + + return results + + def execute_tool(self, tool_calls) -> tuple[list, list]: + """Executes registered tools based on the tool calls from the model. + + Args: + tool_calls: List of tool calls from the model + + Returns: + List of tuples containing (result, result_message) for each tool call + """ + results = [] + messages = [] + + # Handle single tool call or list of tool calls + if not isinstance(tool_calls, list): + tool_calls = [tool_calls] + + for tool_call in tool_calls: + # Handle both dictionary and object-style tool calls + if isinstance(tool_call, dict): + tool_name = tool_call["function"]["name"] + arguments = tool_call["function"]["arguments"] + tool_call_id = tool_call["id"] + else: + tool_name = tool_call.function.name + arguments = tool_call.function.arguments + tool_call_id = tool_call.id + + # Ensure arguments is a dict + if isinstance(arguments, str): + arguments = json.loads(arguments) + + if tool_name not in self._tools: + raise ValueError(f"Tool '{tool_name}' not registered.") + + tool = self._tools[tool_name] + tool_func = tool["function"] + param_model = tool["param_model"] + + # Validate and parse the arguments with Pydantic if a model exists + try: + validated_args = param_model(**arguments) + result = tool_func(**validated_args.model_dump()) + results.append(result) + messages.append( + { + "role": "tool", + "name": tool_name, + "content": json.dumps(result), + "tool_call_id": tool_call_id, + } + ) + except ValidationError as e: + raise ValueError(f"Error in tool '{tool_name}' parameters: {e}") + + return results, messages diff --git a/examples/client.ipynb b/examples/client.ipynb index 90db03da..839f06e4 100644 --- a/examples/client.ipynb +++ b/examples/client.ipynb @@ -244,6 +244,18 @@ "response = client.chat.completions.create(model=togetherai_model, messages=messages, temperature=0.75, top_p=0.7, top_k=50)\n", "print(response.choices[0].message.content)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcf63a11", + "metadata": {}, + "outputs": [], + "source": [ + "gemini_15_flash = \"google:gemini-1.5-flash\"\n", + "response = client.chat.completions.create(model=gemini_15_flash, messages=messages, temperature=0.75)\n", + "print(response.choices[0].message.content)" + ] } ], "metadata": { diff --git a/examples/simple_tool_calling.ipynb b/examples/simple_tool_calling.ipynb new file mode 100644 index 00000000..4a7e7cb5 --- /dev/null +++ b/examples/simple_tool_calling.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import sys\n", + "from dotenv import load_dotenv, find_dotenv\n", + "import os\n", + "\n", + "sys.path.append('../../aisuite')\n", + "\n", + "# Load from .env file if available\n", + "load_dotenv(find_dotenv())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a request to model without tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from aisuite import Client\n", + "\n", + "client = Client()\n", + "# Configuring Azure. Rest all providers use environment variables for their parameters.\n", + "client.configure({\"azure\" : {\n", + " \"api_key\": os.environ[\"AZURE_API_KEY\"],\n", + " \"base_url\": \"https://aisuite-mistral-large-2407.westus3.models.ai.azure.com/v1/\",\n", + "}})\n", + "# model = \"anthropic:claude-3-5-sonnet-20241022\"\n", + "# model = \"aws:mistral.mistral-7b-instruct-v0:2\"\n", + "# model = \"azure:aisuite-mistral-large\"\n", + "# model = \"cohere:command-r-plus\"\n", + "# model = \"deepseek:deepseek-chat\"\n", + "# model = \"fireworks:accounts/fireworks/models/llama-v3p1-405b-instruct\"\n", + "# model = \"google:gemini-1.5-pro-002\"\n", + "# model = \"groq:llama-3.3-70b-versatile\"\n", + "# model = \"huggingface:meta-llama/Llama-3.1-8B-Instruct\"\n", + "# model = \"mistral:mistral-large-latest\"\n", + "# model = \"nebius:\"\n", + "# model = \"ollama:\"\n", + "# model = \"sambanova:Meta-Llama-3.3-70B-Instruct\"\n", + "# model = \"together:meta-llama/Llama-3.3-70B-Instruct-Turbo\"\n", + "# model = \"watsonx:\"\n", + "model = \"xai:grok-2-latest\"\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"What is the current temperature in San Francisco in Celsius?\"}]\n", + "\n", + "response = client.chat.completions.create(\n", + " model=model, messages=messages)\n", + "\n", + "print(\"For model: \" + model)\n", + "print(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Equip model with tools" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Mock tool functions.\n", + "def get_current_temperature(location: str, unit: str):\n", + " # Simulate fetching temperature from an API\n", + " return {\"temperature\": 72}\n", + "\n", + "def get_rain_probability(location: str):\n", + " # Simulate fetching rain probability\n", + " return {\"location\": location, \"probability\": 40}\n", + "\n", + "# Function to get the available tools (functions) to provide to the model\n", + "# Note: we could use decorators or utils from OpenAI to generate this.\n", + "def get_available_tools():\n", + " return [\n", + " { \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"get_current_temperature\",\n", + " \"description\": \"Get the current temperature for a specific location\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"location\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The city and state, e.g., San Francisco, CA\"\n", + " },\n", + " \"unit\": {\n", + " \"type\": \"string\",\n", + " \"enum\": [\"Celsius\", \"Fahrenheit\"],\n", + " \"description\": \"The temperature unit to use.\"\n", + " }\n", + " },\n", + " \"required\": [\"location\", \"unit\"]\n", + " }\n", + " }\n", + " },\n", + " {\n", + " \"type\": \"function\",\n", + " \"function\": {\n", + " \"name\": \"get_rain_probability\",\n", + " \"description\": \"Get the probability of rain for a specific location\",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"location\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The city and state, e.g., San Francisco, CA\"\n", + " }\n", + " },\n", + " \"required\": [\"location\"]\n", + " }\n", + " }\n", + " }\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to process tool calls and get the result\n", + "def handle_tool_call(tool_call):\n", + " function_name = tool_call.function.name\n", + " arguments = json.loads(tool_call.function.arguments)\n", + "\n", + " # Map function names to actual tool function implementations\n", + " tools_map = {\n", + " \"get_current_temperature\": get_current_temperature,\n", + " \"get_rain_probability\": get_rain_probability,\n", + " }\n", + " return tools_map[function_name](**arguments)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to format tool response as a message\n", + "def create_tool_response_message(tool_call, tool_result):\n", + " return {\n", + " \"role\": \"tool\",\n", + " \"tool_call_id\": tool_call.id,\n", + " \"name\": tool_call.function.name,\n", + " \"content\": json.dumps(tool_result)\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Call the model with tools" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import sys\n", + "from dotenv import load_dotenv, find_dotenv\n", + "import os\n", + "\n", + "sys.path.append('../../aisuite')\n", + "\n", + "# Load from .env file if available\n", + "load_dotenv(find_dotenv())\n", + "\n", + "from aisuite import Client\n", + "\n", + "client = Client()\n", + "client.configure({\"azure\" : {\n", + " \"api_key\": os.environ[\"AZURE_API_KEY\"],\n", + " \"base_url\": \"https://aisuite-mistral-large-2407.westus3.models.ai.azure.com/v1/\",\n", + "}})\n", + "\n", + "# model = \"anthropic:claude-3-5-sonnet-20241022\"\n", + "# model = \"aws:mistral.mistral-7b-instruct-v0:2\"\n", + "# model = \"azure:aisuite-mistral-large\"\n", + "# model = \"cohere:command-r-plus\"\n", + "# model = \"deepseek:deepseek-chat\"\n", + "# model = \"fireworks:accounts/fireworks/models/llama-v3p1-405b-instruct\"\n", + "# model = \"google:gemini-1.5-pro-002\"\n", + "# model = \"groq:llama-3.3-70b-versatile\"\n", + "# model = \"huggingface:meta-llama/Llama-3.1-8B-Instruct\"\n", + "# model = \"mistral:mistral-large-latest\"\n", + "# model = \"nebius:\"\n", + "# model = \"ollama:\"\n", + "# model = \"sambanova:Meta-Llama-3.3-70B-Instruct\"\n", + "# model = \"together:meta-llama/Llama-3.3-70B-Instruct-Turbo\"\n", + "# model = \"watsonx:\"\n", + "model = \"xai:grok-2-latest\"\n", + "\n", + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": \"What is the current temperature in San Francisco in Celsius?\"}]\n", + "\n", + "tools = get_available_tools()\n", + "\n", + "# Make the initial request to OpenAI API\n", + "response = client.chat.completions.create(\n", + " model=model, messages=messages, tools=tools)\n", + "\n", + "print(response)\n", + "print(response.choices[0].message)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Process tool calls - Parse tool name, args, and call the function. Pass the result to the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if response.choices[0].message.tool_calls:\n", + " for tool_call in response.choices[0].message.tool_calls:\n", + " tool_result = handle_tool_call(tool_call)\n", + " print(tool_result)\n", + "\n", + " messages.append(response.choices[0].message) # Model's function call message\n", + " messages.append(create_tool_response_message(tool_call, tool_result))\n", + " # Send the tool response back to the model\n", + " final_response = client.chat.completions.create(\n", + " model=model, messages=messages, tools=tools)\n", + " print(final_response.choices[0].message)\n", + " \n", + " # Output the final response from the model\n", + " print(final_response.choices[0].message.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/poetry.lock b/poetry.lock index c55d3a27..60026e3e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,110 +1,123 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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-content-hash = "522cba517a2a3cc94bdee88c3af849d76bc00dc9d777d38a40cda860e7c108cb" +content-hash = "a04d6aa4778ab444aaa932d2ce626cd46c4a6ac150a9f6479ca2b2ae555b9572" diff --git a/pyproject.toml b/pyproject.toml index e96e6216..bafbf880 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ groq = { version = "^0.9.0", optional = true } mistralai = { version = "^1.0.3", optional = true } openai = { version = "^1.35.8", optional = true } ibm-watsonx-ai = { version = "^1.1.16", optional = true } +docstring-parser = { version = "^0.14.0", optional = true } # Optional dependencies for different providers httpx = "~0.27.0" diff --git a/tests/client/test_client.py b/tests/client/test_client.py index a94b139d..04d12435 100644 --- a/tests/client/test_client.py +++ b/tests/client/test_client.py @@ -36,11 +36,6 @@ def provider_configs(): "nebius": { "api_key": "nebius-api-key", }, - "watsonx": { - "service_url": "https://watsonx-service-url.com", - "api_key": "watsonx-api-key", - "project_id": "watsonx-project-id", - }, } @@ -92,11 +87,6 @@ def provider_configs(): "nebius", "nebius-model", ), - ( - "aisuite.providers.watsonx_provider.WatsonxProvider.chat_completions_create", - "watsonx", - "watsonx-model", - ), ], ) def test_client_chat_completions( diff --git a/tests/providers/test_anthropic_converter.py b/tests/providers/test_anthropic_converter.py new file mode 100644 index 00000000..ff1208cc --- /dev/null +++ b/tests/providers/test_anthropic_converter.py @@ -0,0 +1,205 @@ +import unittest +from unittest.mock import MagicMock +from aisuite.providers.anthropic_provider import AnthropicMessageConverter +from aisuite.framework.message import Message, ChatCompletionMessageToolCall, Function +from aisuite.framework import ChatCompletionResponse + + +class TestAnthropicMessageConverter(unittest.TestCase): + + def setUp(self): + self.converter = AnthropicMessageConverter() + + def test_convert_request_single_user_message(self): + messages = [{"role": "user", "content": "Hello, how are you?"}] + system_message, converted_messages = self.converter.convert_request(messages) + + self.assertEqual(system_message, []) + self.assertEqual( + converted_messages, [{"role": "user", "content": "Hello, how are you?"}] + ) + + def test_convert_request_with_system_message(self): + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is the weather?"}, + ] + system_message, converted_messages = self.converter.convert_request(messages) + + self.assertEqual(system_message, "You are a helpful assistant.") + self.assertEqual( + converted_messages, [{"role": "user", "content": "What is the weather?"}] + ) + + def test_convert_request_with_tool_use_message(self): + messages = [ + {"role": "tool", "tool_call_id": "tool123", "content": "Weather data here."} + ] + system_message, converted_messages = self.converter.convert_request(messages) + + self.assertEqual(system_message, []) + self.assertEqual( + converted_messages, + [ + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "tool123", + "content": "Weather data here.", + } + ], + } + ], + ) + + def test_convert_response_normal_message(self): + response = MagicMock() + response.stop_reason = "end_turn" + response.usage.input_tokens = 10 + response.usage.output_tokens = 5 + content_mock = MagicMock() + content_mock.type = "text" + content_mock.text = "The weather is sunny." + response.content = [content_mock] + + normalized_response = self.converter.convert_response(response) + + self.assertIsInstance(normalized_response, ChatCompletionResponse) + self.assertEqual(normalized_response.choices[0].finish_reason, "stop") + self.assertEqual( + normalized_response.usage, + {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, + ) + self.assertEqual( + normalized_response.choices[0].message.content, "The weather is sunny." + ) + + # Test that - when Anthropic returns a tool use message, it is correctly converted. + def test_convert_response_with_tool_use(self): + response = MagicMock() + response.id = "msg_01Aq9w938a90dw8q" + response.model = "claude-3-5-sonnet-20241022" + response.role = "assistant" + response.stop_reason = "tool_use" + response.usage.input_tokens = 20 + response.usage.output_tokens = 10 + tool_use_mock = MagicMock() + tool_use_mock.type = "tool_use" + tool_use_mock.id = "tool123" + tool_use_mock.name = "get_weather" + tool_use_mock.input = {"location": "Paris"} + + text_mock = MagicMock() + text_mock.type = "text" + text_mock.text = "I need to call the get_weather function" + + response.content = [tool_use_mock, text_mock] + + normalized_response = self.converter.convert_response(response) + + self.assertIsInstance(normalized_response, ChatCompletionResponse) + self.assertEqual(normalized_response.choices[0].finish_reason, "tool_calls") + self.assertEqual( + normalized_response.usage, + {"prompt_tokens": 20, "completion_tokens": 10, "total_tokens": 30}, + ) + self.assertEqual( + normalized_response.choices[0].message.content, + "I need to call the get_weather function", + ) + self.assertEqual(len(normalized_response.choices[0].message.tool_calls), 1) + self.assertEqual( + normalized_response.choices[0].message.tool_calls[0].id, "tool123" + ) + self.assertEqual( + normalized_response.choices[0].message.tool_calls[0].function.name, + "get_weather", + ) + + def test_convert_tool_spec(self): + openai_tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the weather.", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string", "description": "City name."} + }, + "required": ["location"], + }, + }, + } + ] + + anthropic_tools = self.converter.convert_tool_spec(openai_tools) + + self.assertEqual(len(anthropic_tools), 1) + self.assertEqual(anthropic_tools[0]["name"], "get_weather") + self.assertEqual(anthropic_tools[0]["description"], "Get the weather.") + self.assertEqual( + anthropic_tools[0]["input_schema"], + { + "type": "object", + "properties": { + "location": {"type": "string", "description": "City name."} + }, + "required": ["location"], + }, + ) + + def test_convert_request_with_tool_call_and_result(self): + messages = [ + { + "role": "assistant", + "content": "Let me check the weather.", + "tool_calls": [ + { + "id": "tool123", + "function": { + "name": "get_weather", + "arguments": '{"location": "San Francisco"}', + }, + } + ], + }, + {"role": "tool", "tool_call_id": "tool123", "content": "65 degrees"}, + ] + system_message, converted_messages = self.converter.convert_request(messages) + + self.assertEqual(system_message, []) + self.assertEqual( + converted_messages, + [ + { + "role": "assistant", + "content": [ + {"type": "text", "text": "Let me check the weather."}, + { + "type": "tool_use", + "id": "tool123", + "name": "get_weather", + "input": {"location": "San Francisco"}, + }, + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "tool123", + "content": "65 degrees", + } + ], + }, + ], + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/providers/test_aws_converter.py b/tests/providers/test_aws_converter.py new file mode 100644 index 00000000..032a5f65 --- /dev/null +++ b/tests/providers/test_aws_converter.py @@ -0,0 +1,112 @@ +import unittest +from unittest.mock import MagicMock +from aisuite.providers.aws_provider import BedrockMessageConverter +from aisuite.framework.message import Message, ChatCompletionMessageToolCall +from aisuite.framework import ChatCompletionResponse + + +class TestBedrockMessageConverter(unittest.TestCase): + + def setUp(self): + self.converter = BedrockMessageConverter() + + def test_convert_request_user_message(self): + messages = [ + {"role": "user", "content": "What is the most popular song on WZPZ?"} + ] + system_message, formatted_messages = self.converter.convert_request(messages) + + self.assertEqual(system_message, []) + self.assertEqual(len(formatted_messages), 1) + self.assertEqual(formatted_messages[0]["role"], "user") + self.assertEqual( + formatted_messages[0]["content"], + [{"text": "What is the most popular song on WZPZ?"}], + ) + + def test_convert_request_tool_result(self): + messages = [ + { + "role": "tool", + "tool_call_id": "tool123", + "content": '{"song": "Elemental Hotel", "artist": "8 Storey Hike"}', + } + ] + system_message, formatted_messages = self.converter.convert_request(messages) + + self.assertEqual(system_message, []) + self.assertEqual(len(formatted_messages), 1) + self.assertEqual(formatted_messages[0]["role"], "user") + self.assertEqual( + formatted_messages[0]["content"], + [ + { + "toolResult": { + "toolUseId": "tool123", + "content": [ + { + "json": { + "song": "Elemental Hotel", + "artist": "8 Storey Hike", + } + } + ], + } + } + ], + ) + + def test_convert_response_tool_call(self): + response = { + "output": { + "message": { + "role": "assistant", + "content": [ + { + "toolUse": { + "toolUseId": "tool123", + "name": "top_song", + "input": {"sign": "WZPZ"}, + } + } + ], + } + }, + "stopReason": "tool_use", + } + + normalized_response = self.converter.convert_response(response) + + self.assertIsInstance(normalized_response, ChatCompletionResponse) + self.assertEqual(normalized_response.choices[0].finish_reason, "tool_calls") + tool_call = normalized_response.choices[0].message.tool_calls[0] + self.assertEqual(tool_call.function.name, "top_song") + self.assertEqual(tool_call.function.arguments, '{"sign": "WZPZ"}') + + def test_convert_response_text(self): + response = { + "output": { + "message": { + "role": "assistant", + "content": [ + { + "text": "The most popular song on WZPZ is Elemental Hotel by 8 Storey Hike." + } + ], + } + }, + "stopReason": "complete", + } + + normalized_response = self.converter.convert_response(response) + + self.assertIsInstance(normalized_response, ChatCompletionResponse) + self.assertEqual(normalized_response.choices[0].finish_reason, "stop") + self.assertEqual( + normalized_response.choices[0].message.content, + "The most popular song on WZPZ is Elemental Hotel by 8 Storey Hike.", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/providers/test_azure_provider.py b/tests/providers/test_azure_provider.py new file mode 100644 index 00000000..2667a9c7 --- /dev/null +++ b/tests/providers/test_azure_provider.py @@ -0,0 +1,91 @@ +import unittest +from aisuite.providers.azure_provider import AzureMessageConverter +from aisuite.framework.message import Message, ChatCompletionMessageToolCall +from aisuite.framework import ChatCompletionResponse + + +class TestAzureMessageConverter(unittest.TestCase): + def setUp(self): + self.converter = AzureMessageConverter() + + def test_convert_request_dict_message(self): + messages = [{"role": "user", "content": "Hello, how are you?"}] + converted_messages = self.converter.convert_request(messages) + + self.assertEqual( + converted_messages, [{"role": "user", "content": "Hello, how are you?"}] + ) + + def test_convert_request_message_object(self): + message = Message(role="user", content="Hello", tool_calls=None, refusal=None) + messages = [message] + converted_messages = self.converter.convert_request(messages) + + expected_message = { + "role": "user", + "content": "Hello", + "tool_calls": None, + "refusal": None, + } + self.assertEqual(converted_messages, [expected_message]) + + def test_convert_response_basic(self): + azure_response = { + "choices": [ + { + "message": { + "role": "assistant", + "content": "Hello! How can I help you?", + } + } + ] + } + + response = self.converter.convert_response(azure_response) + + self.assertIsInstance(response, ChatCompletionResponse) + self.assertEqual( + response.choices[0].message.content, "Hello! How can I help you?" + ) + self.assertEqual(response.choices[0].message.role, "assistant") + self.assertIsNone(response.choices[0].message.tool_calls) + + def test_convert_response_with_tool_calls(self): + azure_response = { + "choices": [ + { + "message": { + "role": "assistant", + "content": "Let me check the weather.", + "tool_calls": [ + { + "id": "tool123", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"location": "London"}', + }, + } + ], + } + } + ] + } + + response = self.converter.convert_response(azure_response) + + self.assertIsInstance(response, ChatCompletionResponse) + self.assertEqual( + response.choices[0].message.content, "Let me check the weather." + ) + self.assertEqual(len(response.choices[0].message.tool_calls), 1) + + tool_call = response.choices[0].message.tool_calls[0] + self.assertEqual(tool_call.id, "tool123") + self.assertEqual(tool_call.type, "function") + self.assertEqual(tool_call.function.name, "get_weather") + self.assertEqual(tool_call.function.arguments, '{"location": "London"}') + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/providers/test_google_converter.py b/tests/providers/test_google_converter.py new file mode 100644 index 00000000..52b0da8e --- /dev/null +++ b/tests/providers/test_google_converter.py @@ -0,0 +1,111 @@ +import unittest +from unittest.mock import MagicMock +from aisuite.providers.google_provider import GoogleMessageConverter +from aisuite.framework.message import Message, ChatCompletionMessageToolCall, Function +from aisuite.framework import ChatCompletionResponse + + +class TestGoogleMessageConverter(unittest.TestCase): + + def setUp(self): + self.converter = GoogleMessageConverter() + + def test_convert_request_user_message(self): + messages = [{"role": "user", "content": "What is the weather today?"}] + converted_messages = self.converter.convert_request(messages) + + self.assertEqual(len(converted_messages), 1) + self.assertEqual(converted_messages[0].role, "user") + self.assertEqual( + converted_messages[0].parts[0].text, "What is the weather today?" + ) + + def test_convert_request_tool_result_message(self): + messages = [ + { + "role": "tool", + "name": "get_weather", + "content": '{"temperature": "15", "unit": "Celsius"}', + } + ] + converted_messages = self.converter.convert_request(messages) + + self.assertEqual(len(converted_messages), 1) + self.assertEqual(converted_messages[0].function_response.name, "get_weather") + self.assertEqual( + converted_messages[0].function_response.response, + {"temperature": "15", "unit": "Celsius"}, + ) + + def test_convert_request_assistant_message(self): + messages = [ + { + "role": "assistant", + "content": "The weather is sunny with a temperature of 25 degrees Celsius.", + } + ] + converted_messages = self.converter.convert_request(messages) + + self.assertEqual(len(converted_messages), 1) + self.assertEqual(converted_messages[0].role, "model") + self.assertEqual( + converted_messages[0].parts[0].text, + "The weather is sunny with a temperature of 25 degrees Celsius.", + ) + + def test_convert_response_with_function_call(self): + function_call_mock = MagicMock() + function_call_mock.name = "get_exchange_rate" + function_call_mock.args = { + "currency_from": "AUD", + "currency_to": "SEK", + "currency_date": "latest", + } + + response = MagicMock() + response.candidates = [ + MagicMock( + content=MagicMock(parts=[MagicMock(function_call=function_call_mock)]), + finish_reason="function_call", + ) + ] + + normalized_response = self.converter.convert_response(response) + + self.assertIsInstance(normalized_response, ChatCompletionResponse) + self.assertEqual(normalized_response.choices[0].finish_reason, "tool_calls") + self.assertEqual( + normalized_response.choices[0].message.tool_calls[0].function.name, + "get_exchange_rate", + ) + self.assertEqual( + normalized_response.choices[0].message.tool_calls[0].function.arguments, + '{"currency_from": "AUD", "currency_to": "SEK", "currency_date": "latest"}', + ) + + def test_convert_response_with_text(self): + response = MagicMock() + text_content = "The current exchange rate is 7.50 SEK per AUD." + + mock_part = MagicMock() + mock_part.text = text_content + mock_part.function_call = None + + mock_content = MagicMock() + mock_content.parts = [mock_part] + + mock_candidate = MagicMock() + mock_candidate.content = mock_content + mock_candidate.finish_reason = "stop" + + response.candidates = [mock_candidate] + + normalized_response = self.converter.convert_response(response) + + self.assertIsInstance(normalized_response, ChatCompletionResponse) + self.assertEqual(normalized_response.choices[0].finish_reason, "stop") + self.assertEqual(normalized_response.choices[0].message.content, text_content) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/providers/test_google_provider.py b/tests/providers/test_google_provider.py index a589b440..f34c5f0e 100644 --- a/tests/providers/test_google_provider.py +++ b/tests/providers/test_google_provider.py @@ -2,6 +2,7 @@ from unittest.mock import patch, MagicMock from aisuite.providers.google_provider import GoogleProvider from vertexai.generative_models import Content, Part +import json @pytest.fixture(autouse=True) @@ -25,54 +26,99 @@ def test_missing_env_vars(): def test_vertex_interface(): """High-level test that the interface is initialized and chat completions are requested successfully.""" - user_greeting = "Hello!" - message_history = [{"role": "user", "content": user_greeting}] - selected_model = "our-favorite-model" - response_text_content = "mocked-text-response-from-model" - - interface = GoogleProvider() - mock_response = MagicMock() - mock_response.candidates = [MagicMock()] - mock_response.candidates[0].content.parts[0].text = response_text_content - - with patch( - "aisuite.providers.google_provider.GenerativeModel" - ) as mock_generative_model: - mock_model = MagicMock() - mock_generative_model.return_value = mock_model - mock_chat = MagicMock() - mock_model.start_chat.return_value = mock_chat - mock_chat.send_message.return_value = mock_response - - response = interface.chat_completions_create( - messages=message_history, - model=selected_model, - temperature=0.7, - ) - - # Assert that GenerativeModel was called with correct arguments. - mock_generative_model.assert_called_once() - args, kwargs = mock_generative_model.call_args - assert args[0] == selected_model - assert "generation_config" in kwargs - - # Assert that start_chat was called with correct history. - mock_model.start_chat.assert_called_once() - _chat_args, chat_kwargs = mock_model.start_chat.call_args - assert "history" in chat_kwargs - assert isinstance(chat_kwargs["history"], list) - - # Assert that send_message was called with the last message. - mock_chat.send_message.assert_called_once_with(user_greeting) - - # Assert that the response is in the correct format. - assert response.choices[0].message.content == response_text_content + # Test case 1: Regular text response + def test_text_response(): + user_greeting = "Hello!" + message_history = [{"role": "user", "content": user_greeting}] + selected_model = "our-favorite-model" + response_text_content = "mocked-text-response-from-model" + + interface = GoogleProvider() + mock_response = MagicMock() + mock_response.candidates = [MagicMock()] + mock_response.candidates[0].content.parts = [MagicMock()] + mock_response.candidates[0].content.parts[0].text = response_text_content + # Ensure function_call attribute doesn't exist + del mock_response.candidates[0].content.parts[0].function_call + + with patch( + "aisuite.providers.google_provider.GenerativeModel" + ) as mock_generative_model: + mock_model = MagicMock() + mock_generative_model.return_value = mock_model + mock_chat = MagicMock() + mock_model.start_chat.return_value = mock_chat + mock_chat.send_message.return_value = mock_response + + response = interface.chat_completions_create( + messages=message_history, + model=selected_model, + temperature=0.7, + ) + + # Assert the response is in the correct format + assert response.choices[0].message.content == response_text_content + assert response.choices[0].finish_reason == "stop" + + # Test case 2: Function call response + def test_function_call(): + user_greeting = "What's the weather?" + message_history = [{"role": "user", "content": user_greeting}] + selected_model = "our-favorite-model" + + interface = GoogleProvider() + mock_response = MagicMock() + mock_response.candidates = [MagicMock()] + mock_response.candidates[0].content.parts = [MagicMock()] + + # Mock the function call response + function_call_mock = MagicMock() + function_call_mock.name = "get_weather" + function_call_mock.args = {"location": "San Francisco"} + mock_response.candidates[0].content.parts[0].function_call = function_call_mock + mock_response.candidates[0].content.parts[0].text = None + + with patch( + "aisuite.providers.google_provider.GenerativeModel" + ) as mock_generative_model: + mock_model = MagicMock() + mock_generative_model.return_value = mock_model + mock_chat = MagicMock() + mock_model.start_chat.return_value = mock_chat + mock_chat.send_message.return_value = mock_response + + response = interface.chat_completions_create( + messages=message_history, + model=selected_model, + temperature=0.7, + ) + + # Assert the response contains the function call + assert response.choices[0].message.content is None + assert response.choices[0].message.tool_calls[0].type == "function" + assert ( + response.choices[0].message.tool_calls[0].function.name == "get_weather" + ) + assert json.loads( + response.choices[0].message.tool_calls[0].function.arguments + ) == {"location": "San Francisco"} + assert response.choices[0].finish_reason == "tool_calls" + + # Run both test cases + test_text_response() + test_function_call() def test_convert_openai_to_vertex_ai(): + """Test the message conversion from OpenAI format to Vertex AI format.""" interface = GoogleProvider() - message_history = [{"role": "user", "content": "Hello!"}] - result = interface.convert_openai_to_vertex_ai(message_history) + message = {"role": "user", "content": "Hello!"} + + # Use the transformer to convert the message + result = interface.transformer.convert_request([message]) + + # Verify the conversion result + assert len(result) == 1 assert isinstance(result[0], Content) assert result[0].role == "user" assert len(result[0].parts) == 1 @@ -80,21 +126,25 @@ def test_convert_openai_to_vertex_ai(): assert result[0].parts[0].text == "Hello!" -def test_transform_roles(): +def test_role_conversions(): + """Test that different message roles are converted correctly.""" interface = GoogleProvider() messages = [ - {"role": "system", "content": "Google: system message = 1st user message."}, - {"role": "user", "content": "User message 1."}, - {"role": "assistant", "content": "Assistant message 1."}, + {"role": "system", "content": "System message"}, + {"role": "user", "content": "User message"}, + {"role": "assistant", "content": "Assistant message"}, ] - expected_output = [ - {"role": "user", "content": "Google: system message = 1st user message."}, - {"role": "user", "content": "User message 1."}, - {"role": "model", "content": "Assistant message 1."}, - ] + result = interface.transformer.convert_request(messages) + + # System and user messages should both be converted to "user" role in Vertex AI + assert len(result) == 3 + assert result[0].role == "user" # system converted to user + assert result[0].parts[0].text == "System message" - result = interface.transform_roles(messages) + assert result[1].role == "user" + assert result[1].parts[0].text == "User message" - assert result == expected_output + assert result[2].role == "model" # assistant converted to model + assert result[2].parts[0].text == "Assistant message" diff --git a/tests/providers/test_groq_provider.py b/tests/providers/test_groq_provider.py index 94e953fc..e2e021e2 100644 --- a/tests/providers/test_groq_provider.py +++ b/tests/providers/test_groq_provider.py @@ -22,9 +22,9 @@ def test_groq_provider(): provider = GroqProvider() mock_response = MagicMock() - mock_response.choices = [MagicMock()] - mock_response.choices[0].message = MagicMock() - mock_response.choices[0].message.content = response_text_content + mock_response.model_dump.return_value = { + "choices": [{"message": {"content": response_text_content}}] + } with patch.object( provider.client.chat.completions, diff --git a/tests/providers/test_mistral_provider.py b/tests/providers/test_mistral_provider.py index 8ecc51e9..937da0e5 100644 --- a/tests/providers/test_mistral_provider.py +++ b/tests/providers/test_mistral_provider.py @@ -21,9 +21,9 @@ def test_mistral_provider(): provider = MistralProvider() mock_response = MagicMock() - mock_response.choices = [MagicMock()] - mock_response.choices[0].message = MagicMock() - mock_response.choices[0].message.content = response_text_content + mock_response.model_dump.return_value = { + "choices": [{"message": {"content": response_text_content}}] + } with patch.object( provider.client.chat, "complete", return_value=mock_response diff --git a/tests/providers/test_sambanova_provider.py b/tests/providers/test_sambanova_provider.py index b5c649ec..01f5acee 100644 --- a/tests/providers/test_sambanova_provider.py +++ b/tests/providers/test_sambanova_provider.py @@ -22,9 +22,11 @@ def test_sambanova_provider(): provider = SambanovaProvider() mock_response = MagicMock() - mock_response.choices = [MagicMock()] - mock_response.choices[0].message = MagicMock() - mock_response.choices[0].message.content = response_text_content + mock_response.model_dump.return_value = { + "choices": [ + {"message": {"content": response_text_content, "role": "assistant"}} + ] + } with patch.object( provider.client.chat.completions, diff --git a/tests/providers/test_watsonx_provider.py b/tests/providers/test_watsonx_provider.py index 8e7123a7..710cb209 100644 --- a/tests/providers/test_watsonx_provider.py +++ b/tests/providers/test_watsonx_provider.py @@ -1,7 +1,11 @@ from unittest.mock import MagicMock, patch import pytest -from ibm_watsonx_ai.metanames import GenChatParamsMetaNames as GenChatParams + +try: + from ibm_watsonx_ai.metanames import GenChatParamsMetaNames as GenChatParams +except Exception as e: + pytest.skip(f"Skipping test due to import error: {e}", allow_module_level=True) from aisuite.providers.watsonx_provider import WatsonxProvider @@ -14,6 +18,7 @@ def set_api_key_env_var(monkeypatch): monkeypatch.setenv("WATSONX_PROJECT_ID", "test-project-id") +@pytest.mark.skip(reason="Skipping due to version compatibility issue on python 3.11") def test_watsonx_provider(): """High-level test that the provider is initialized and chat completions are requested successfully.""" diff --git a/tests/utils/test_tool_manager.py b/tests/utils/test_tool_manager.py new file mode 100644 index 00000000..629b17bb --- /dev/null +++ b/tests/utils/test_tool_manager.py @@ -0,0 +1,200 @@ +import unittest +from pydantic import BaseModel +from typing import Dict +from aisuite.utils.tool_manager import Tools # Import your ToolManager class +from enum import Enum + + +# Define a sample tool function and Pydantic model for testing +class TemperatureUnit(str, Enum): + CELSIUS = "Celsius" + FAHRENHEIT = "Fahrenheit" + + +class TemperatureParamsV2(BaseModel): + location: str + unit: TemperatureUnit = TemperatureUnit.CELSIUS + + +class TemperatureParams(BaseModel): + location: str + unit: str = "Celsius" + + +def get_current_temperature(location: str, unit: str = "Celsius") -> Dict[str, str]: + """Gets the current temperature for a specific location and unit.""" + return {"location": location, "unit": unit, "temperature": "72"} + + +def missing_annotation_tool(location, unit="Celsius"): + """Tool function without type annotations.""" + return {"location": location, "unit": unit, "temperature": "72"} + + +def get_current_temperature_v2( + location: str, unit: TemperatureUnit = TemperatureUnit.CELSIUS +) -> Dict[str, str]: + """Gets the current temperature for a specific location and unit (with enum support).""" + return {"location": location, "unit": unit, "temperature": "72"} + + +class TestToolManager(unittest.TestCase): + def setUp(self): + self.tool_manager = Tools() + + def test_add_tool_with_pydantic_model(self): + """Test adding a tool with an explicit Pydantic model.""" + self.tool_manager._add_tool(get_current_temperature, TemperatureParams) + + expected_tool_spec = [ + { + "type": "function", + "function": { + "name": "get_current_temperature", + "description": "Gets the current temperature for a specific location and unit.", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "", + }, + "unit": { + "type": "string", + "description": "", + "default": "Celsius", + }, + }, + "required": ["location"], + }, + }, + } + ] + + tools = self.tool_manager.tools() + self.assertIn( + "get_current_temperature", [tool["function"]["name"] for tool in tools] + ) + assert ( + tools == expected_tool_spec + ), f"Expected {expected_tool_spec}, but got {tools}" + + def test_add_tool_with_signature_inference(self): + """Test adding a tool and inferring parameters from the function signature.""" + self.tool_manager._add_tool(get_current_temperature) + # Expected output from tool_manager.tools() when called with OpenAI format + expected_tool_spec = [ + { + "type": "function", + "function": { + "name": "get_current_temperature", + "description": "Gets the current temperature for a specific location and unit.", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "", # No description provided in function signature + }, + "unit": { + "type": "string", + "description": "", + "default": "Celsius", + }, + }, + "required": ["location"], + }, + }, + } + ] + tools = self.tool_manager.tools() + print(tools) + self.assertIn( + "get_current_temperature", [tool["function"]["name"] for tool in tools] + ) + assert ( + tools == expected_tool_spec + ), f"Expected {expected_tool_spec}, but got {tools}" + + def test_add_tool_missing_annotation_raises_exception(self): + """Test that adding a tool with missing type annotations raises a TypeError.""" + with self.assertRaises(TypeError): + self.tool_manager._add_tool(missing_annotation_tool) + + def test_execute_tool_valid_parameters(self): + """Test executing a registered tool with valid parameters.""" + self.tool_manager._add_tool(get_current_temperature, TemperatureParams) + tool_call = { + "id": "call_1", + "function": { + "name": "get_current_temperature", + "arguments": {"location": "San Francisco", "unit": "Celsius"}, + }, + } + result, result_message = self.tool_manager.execute_tool(tool_call) + + # Assuming result is returned as a list with a single dictionary + result_dict = result[0] if isinstance(result, list) else result + + # Check that the result matches expected output + self.assertEqual(result_dict["location"], "San Francisco") + self.assertEqual(result_dict["unit"], "Celsius") + self.assertEqual(result_dict["temperature"], "72") + + def test_execute_tool_invalid_parameters(self): + """Test that executing a tool with invalid parameters raises a ValueError.""" + self.tool_manager._add_tool(get_current_temperature, TemperatureParams) + tool_call = { + "id": "call_1", + "function": { + "name": "get_current_temperature", + "arguments": {"location": 123}, # Invalid type for location + }, + } + + with self.assertRaises(ValueError) as context: + self.tool_manager.execute_tool(tool_call) + + # Verify the error message contains information about the validation error + self.assertIn( + "Error in tool 'get_current_temperature' parameters", str(context.exception) + ) + + def test_add_tool_with_enum(self): + """Test adding a tool with an enum parameter.""" + self.tool_manager._add_tool(get_current_temperature_v2, TemperatureParamsV2) + + expected_tool_spec = [ + { + "type": "function", + "function": { + "name": "get_current_temperature_v2", + "description": "Gets the current temperature for a specific location and unit (with enum support).", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "", + }, + "unit": { + "type": "string", + "enum": ["Celsius", "Fahrenheit"], + "description": "", + "default": "Celsius", + }, + }, + "required": ["location"], + }, + }, + } + ] + + tools = self.tool_manager.tools() + assert ( + tools == expected_tool_spec + ), f"Expected {expected_tool_spec}, but got {tools}" + + +if __name__ == "__main__": + unittest.main()