From 057e4fb17c4e9ef46e1f7f248b970caf1affb865 Mon Sep 17 00:00:00 2001 From: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> Date: Wed, 18 Dec 2024 10:42:10 +0000 Subject: [PATCH 01/38] chore: Provider Unit Tests (#173) * chore: added unit tests for core provider. small bugfix on calculate_metrics of provider * added unit tests and docstring for join chunks * added unit tests and docstrings for calculate_cost on provider * added unit tests and docstrings for input_to_string on provider * added unit tests and docstrings for chat and achat * added unit tests and docstrings for chat and achat * chore: cleaned provider unit tests * chore: separated provider tests into different files. fixed some of its tests * chore: linted code * chore: deleted some comments * chore: linted * chore: Added Azure Provider Unit Tests (#176) * chore: added unit tests for azure provider * chore: added more unit tests and docstrings on azure, removed redundant comments * chore: added unit tests for generate client on Azure Provider * chore: separated azure unit tests into separate files. fixed some of its tests. * chore: linted code * chore: new line Signed-off-by: Diogo Goncalves --------- Signed-off-by: Diogo Goncalves Co-authored-by: Diogo Goncalves --- .gitignore | 4 +- Makefile | 3 + libs/core/llmstudio_core/providers/azure.py | 137 +++++++-- .../core/llmstudio_core/providers/provider.py | 245 ++++++++++++++- libs/core/tests/unit_tests/conftest.py | 51 +++- libs/core/tests/unit_tests/test_azure.py | 232 +++++++++++++++ .../core/tests/unit_tests/test_azure_build.py | 279 +++++++++++++++++ libs/core/tests/unit_tests/test_provider.py | 271 ++++++++++++++--- .../test_provider_costs_and_metrics.py | 128 ++++++++ .../test_provider_handle_response.py | 281 ++++++++++++++++++ 10 files changed, 1555 insertions(+), 76 deletions(-) create mode 100644 libs/core/tests/unit_tests/test_azure.py create mode 100644 libs/core/tests/unit_tests/test_azure_build.py create mode 100644 libs/core/tests/unit_tests/test_provider_costs_and_metrics.py create mode 100644 libs/core/tests/unit_tests/test_provider_handle_response.py diff --git a/.gitignore b/.gitignore index 19015866..d80305ef 100644 --- a/.gitignore +++ b/.gitignore @@ -56,6 +56,7 @@ env3 .env* .env*.local .venv* +*venv* env*/ venv*/ ENV/ @@ -66,6 +67,7 @@ venv.bak/ config.yaml bun.lockb + # Jupyter Notebook .ipynb_checkpoints @@ -76,4 +78,4 @@ bun.lockb llmstudio/llm_engine/logs/execution_logs.jsonl *.db .prettierignore -db \ No newline at end of file +db diff --git a/Makefile b/Makefile index 5a43a3e6..6c957607 100644 --- a/Makefile +++ b/Makefile @@ -1,2 +1,5 @@ format: pre-commit run --all-files + +unit-tests: + pytest libs/core/tests/unit_tests diff --git a/libs/core/llmstudio_core/providers/azure.py b/libs/core/llmstudio_core/providers/azure.py index 2dbd7307..f558f9d6 100644 --- a/libs/core/llmstudio_core/providers/azure.py +++ b/libs/core/llmstudio_core/providers/azure.py @@ -62,7 +62,25 @@ async def agenerate_client(self, request: ChatRequest) -> Any: return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Any: - """Generate an AzureOpenAI client""" + """ + Generates an AzureOpenAI client for processing a chat request. + + This method prepares and configures the arguments required to create a client + request to AzureOpenAI's chat completions API. It determines model-specific + configurations (e.g., whether tools or functions are enabled) and combines + these with the base arguments for the API call. + + Args: + request (ChatRequest): The chat request object containing the model, + parameters, and other necessary details. + + Returns: + Any: The result of the chat completions API call. + + Raises: + ProviderError: If there is an issue with the API connection or an error + returned from the API. + """ self.is_llama = "llama" in request.model.lower() self.is_openai = "gpt" in request.model.lower() @@ -72,7 +90,6 @@ def generate_client(self, request: ChatRequest) -> Any: try: messages = self.prepare_messages(request) - # Prepare the optional tool-related arguments tool_args = {} if not self.is_llama and self.has_tools and self.is_openai: tool_args = { @@ -80,7 +97,6 @@ def generate_client(self, request: ChatRequest) -> Any: "tool_choice": "auto" if request.parameters.get("tools") else None, } - # Prepare the optional function-related arguments function_args = {} if not self.is_llama and self.has_functions and self.is_openai: function_args = { @@ -90,14 +106,12 @@ def generate_client(self, request: ChatRequest) -> Any: else None, } - # Prepare the base arguments base_args = { "model": request.model, "messages": messages, "stream": True, } - # Combine all arguments combined_args = { **base_args, **tool_args, @@ -116,13 +130,13 @@ def prepare_messages(self, request: ChatRequest): if self.is_llama and (self.has_tools or self.has_functions): user_message = self.convert_to_openai_format(request.chat_input) content = "<|begin_of_text|>" - content = self.add_system_message( + content = self.build_llama_system_message( user_message, content, request.parameters.get("tools"), request.parameters.get("functions"), ) - content = self.add_conversation(user_message, content) + content = self.build_llama_conversation(user_message, content) return [{"role": "user", "content": content}] else: return ( @@ -139,6 +153,20 @@ async def aparse_response( yield chunk def parse_response(self, response: AsyncGenerator, **kwargs) -> Any: + """ + Processes a generator response and yields processed chunks. + + If `is_llama` is True and tools or functions are enabled, it processes the response + using `handle_tool_response`. Otherwise, it processes each chunk and yields only those + containing "choices". + + Args: + response (Generator): The response generator to process. + **kwargs: Additional arguments for tool handling. + + Yields: + Any: Processed response chunks. + """ if self.is_llama and (self.has_tools or self.has_functions): for chunk in self.handle_tool_response(response, **kwargs): if chunk: @@ -388,9 +416,25 @@ def convert_to_openai_format(self, message: Union[str, list]) -> list: return [{"role": "user", "content": message}] return message - def add_system_message( + def build_llama_system_message( self, openai_message: list, llama_message: str, tools: list, functions: list ) -> str: + """ + Builds a complete system message for Llama based on OpenAI's message, tools, and functions. + + If a system message is present in the OpenAI message, it is included in the result. + Otherwise, a default system message is used. Additional tool and function instructions + are appended if provided. + + Args: + openai_message (list): List of OpenAI messages. + llama_message (str): The message to prepend to the system message. + tools (list): List of tools to include in the system message. + functions (list): List of functions to include in the system message. + + Returns: + str: The formatted system message combined with Llama message. + """ system_message = "" system_message_found = False for message in openai_message: @@ -407,15 +451,31 @@ def add_system_message( """ if tools: - system_message = system_message + self.add_tool_instructions(tools) + system_message = system_message + self.build_tool_instructions(tools) if functions: - system_message = system_message + self.add_function_instructions(functions) + system_message = system_message + self.build_function_instructions( + functions + ) end_tag = "\n<|eot_id|>" return llama_message + system_message + end_tag - def add_tool_instructions(self, tools: list) -> str: + def build_tool_instructions(self, tools: list) -> str: + """ + Builds a detailed instructional prompt for tools available to the assistant. + + This function generates a message describing the available tools, focusing on tools + of type "function." It explains to the LLM how to use each tool and provides an example of the + correct response format for function calls. + + Args: + tools (list): A list of tool dictionaries, where each dictionary contains tool + details such as type, function name, description, and parameters. + + Returns: + str: A formatted string detailing the tool instructions and usage examples. + """ tool_prompt = """ You have access to the following tools: """ @@ -449,7 +509,21 @@ def add_tool_instructions(self, tools: list) -> str: return tool_prompt - def add_function_instructions(self, functions: list) -> str: + def build_function_instructions(self, functions: list) -> str: + """ + Builds a detailed instructional prompt for available functions. + + This method creates a message describing the functions accessible to the assistant. + It includes the function name, description, and required parameters, along with + specific guidelines for calling functions. + + Args: + functions (list): A list of function dictionaries, each containing details such as + name, description, and parameters. + + Returns: + str: A formatted string with instructions on using the provided functions. + """ function_prompt = """ You have access to the following functions: """ @@ -479,35 +553,60 @@ def add_function_instructions(self, functions: list) -> str: """ return function_prompt - def add_conversation(self, openai_message: list, llama_message: str) -> str: + def build_llama_conversation(self, openai_message: list, llama_message: str) -> str: + """ + Appends the OpenAI message to the Llama message while formatting OpenAI messages. + + This function iterates through a list of OpenAI messages and formats them for inclusion + in a Llama message. It handles user messages that might include nested content (lists of + messages) by safely evaluating the content. System messages are skipped. + + Args: + openai_message (list): A list of dictionaries representing the OpenAI messages. Each + dictionary should have "role" and "content" keys. + llama_message (str): The initial Llama message to which the conversation is appended. + + Returns: + str: The Llama message with the conversation appended. + """ conversation_parts = [] for message in openai_message: if message["role"] == "system": continue elif message["role"] == "user" and isinstance(message["content"], str): try: - # Attempt to safely evaluate the string to a Python object content_as_list = ast.literal_eval(message["content"]) if isinstance(content_as_list, list): - # If the content is a list, process each nested message for nested_message in content_as_list: conversation_parts.append( self.format_message(nested_message) ) else: - # If the content is not a list, append it directly conversation_parts.append(self.format_message(message)) except (ValueError, SyntaxError): - # If evaluation fails or content is not a list/dict string, append the message directly conversation_parts.append(self.format_message(message)) else: - # For all other messages, use the existing formatting logic conversation_parts.append(self.format_message(message)) return llama_message + "".join(conversation_parts) def format_message(self, message: dict) -> str: - """Format a single message for the conversation.""" + """ + Formats a single message dictionary into a structured string for a conversation. + + The formatting depends on the content of the message, such as tool calls, + function calls, or simple user/assistant messages. Each type of message + is formatted with specific headers and tags. + + Args: + message (dict): A dictionary containing message details. Expected keys + include "role", "content", and optionally "tool_calls", + "tool_call_id", or "function_call". + + Returns: + str: A formatted string representing the message. Returns an empty + string if the message cannot be formatted. + """ if "tool_calls" in message: for tool_call in message["tool_calls"]: function_name = tool_call["function"]["name"] diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 07e03835..69a673dd 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -152,8 +152,38 @@ async def achat( parameters: Optional[dict] = {}, **kwargs, ): - - """Makes a chat connection with the provider's API""" + """ + Asynchronously establishes a chat connection with the provider’s API, handling retries, + request validation, and streaming response options. + + Parameters + ---------- + chat_input : Any + The input data for the chat request, such as a string or dictionary, to be sent to the API. + model : str + The identifier of the model to be used for the chat request. + is_stream : Optional[bool], default=False + Flag to indicate if the response should be streamed. If True, returns an async generator + for streaming content; otherwise, returns the complete response. + retries : Optional[int], default=0 + Number of retry attempts on error. Retries will be attempted for specific HTTP errors like rate limits. + parameters : Optional[dict], default={} + Additional configuration parameters for the request, such as temperature or max tokens. + **kwargs + Additional keyword arguments to customize the request. + + Returns + ------- + Union[AsyncGenerator, Any] + - If `is_stream` is True, returns an async generator yielding response chunks. + - If `is_stream` is False, returns the first complete response chunk. + + Raises + ------ + ProviderError + - Raised if the request validation fails or if all retry attempts are exhausted. + - Also raised for unexpected exceptions during request handling. + """ try: request = self.validate_request( dict( @@ -198,8 +228,38 @@ def chat( parameters: Optional[dict] = {}, **kwargs, ): - - """Makes a chat connection with the provider's API""" + """ + Establishes a chat connection with the provider’s API, handling retries, request validation, + and streaming response options. + + Parameters + ---------- + chat_input : Any + The input data for the chat request, often a string or dictionary, to be sent to the API. + model : str + The model identifier for selecting the model used in the chat request. + is_stream : Optional[bool], default=False + Flag to indicate if the response should be streamed. If True, the function returns a generator + for streaming content. Otherwise, it returns the complete response. + retries : Optional[int], default=0 + Number of retry attempts on error. Retries will be attempted on specific HTTP errors like rate limits. + parameters : Optional[dict], default={} + Additional configuration parameters for the request, such as temperature or max tokens. + **kwargs + Additional keyword arguments that can be passed to customize the request. + + Returns + ------- + Union[Generator, Any] + - If `is_stream` is True, returns a generator that yields chunks of the response. + - If `is_stream` is False, returns the first complete response chunk. + + Raises + ------ + ProviderError + - Raised if the request validation fails or if the request fails after the specified number of retries. + - Also raised on other unexpected exceptions during request handling. + """ try: request = self.validate_request( dict( @@ -238,7 +298,28 @@ def chat( async def ahandle_response( self, request: ChatRequest, response: AsyncGenerator, start_time: float ) -> AsyncGenerator[str, None]: - """Handles the response from an API""" + """ + Asynchronously handles the response from an API, processing response chunks for either + streaming or non-streaming responses. + + Buffers response chunks for non-streaming responses to output one single message. For streaming responses sends incremental chunks. + + Parameters + ---------- + request : ChatRequest + The chat request object, which includes input data, model name, and streaming options. + response : AsyncGenerator + The async generator yielding response chunks from the API. + start_time : float + The timestamp when the response handling started, used for latency calculations. + + Yields + ------ + Union[ChatCompletionChunk, ChatCompletion] + - If `request.is_stream` is True, yields `ChatCompletionChunk` objects with incremental + response chunks for streaming. + - If `request.is_stream` is False, yields a final `ChatCompletion` object after processing all chunks. + """ first_token_time = None previous_token_time = None token_times = [] @@ -294,7 +375,7 @@ async def ahandle_response( chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks, request) + response, output_string = self.join_chunks(chunks) metrics = self.calculate_metrics( request.chat_input, @@ -346,7 +427,29 @@ async def ahandle_response( def handle_response( self, request: ChatRequest, response: Generator, start_time: float ) -> Generator: - """Handles the response from an API""" + """ + Processes API response chunks to build a structured, complete response, yielding + each chunk if streaming is enabled. + + If streaming, each chunk is yielded as soon as it’s processed. Otherwise, all chunks + are combined and yielded as a single response at the end. + + Parameters + ---------- + request : ChatRequest + The original request details, including model, input, and streaming preference. + response : Generator + A generator yielding partial response chunks from the API. + start_time : float + The start time for measuring response timing. + + Yields + ------ + Union[ChatCompletionChunk, ChatCompletion] + If streaming (`is_stream=True`), yields each `ChatCompletionChunk` as it’s processed. + Otherwise, yields a single `ChatCompletion` with the full response data. + + """ first_token_time = None previous_token_time = None token_times = [] @@ -402,7 +505,7 @@ def handle_response( chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks, request) + response, output_string = self.join_chunks(chunks) metrics = self.calculate_metrics( request.chat_input, @@ -451,7 +554,29 @@ def handle_response( else: yield ChatCompletion(**response) - def join_chunks(self, chunks, request): + def join_chunks(self, chunks): + """ + Combine multiple response chunks from the model into a single, structured response. + Handles tool calls, function calls, and standard text completion based on the + purpose indicated by the final chunk. + + Parameters + ---------- + chunks : List[Dict] + A list of partial responses (chunks) from the model. + + Returns + ------- + Tuple[ChatCompletion, str] + - `ChatCompletion`: The structured response based on the type of completion + (tool calls, function call, or text). + - `str`: The concatenated content or arguments, depending on the completion type. + + Raises + ------ + Exception + If there is an issue constructing the response, an exception is raised. + """ finish_reason = chunks[-1].get("choices")[0].get("finish_reason") if finish_reason == "tool_calls": @@ -612,7 +737,42 @@ def calculate_metrics( token_times: Tuple[float, ...], token_count: int, ) -> Dict[str, Any]: - """Calculates metrics based on token times and output""" + """ + Calculates performance and cost metrics for a model response based on timing + information, token counts, and model-specific costs. + + Parameters + ---------- + input : Any + The input provided to the model, used to determine input token count. + output : Any + The output generated by the model, used to determine output token count. + model : str + The model identifier, used to retrieve model-specific configuration and costs. + start_time : float + The timestamp marking the start of the model response. + end_time : float + The timestamp marking the end of the model response. + first_token_time : float + The timestamp when the first token was received, used for latency calculations. + token_times : Tuple[float, ...] + A tuple of time intervals between received tokens, used for inter-token latency. + token_count : int + The total number of tokens processed in the response. + + Returns + ------- + Dict[str, Any] + A dictionary containing calculated metrics, including: + - `input_tokens`: Number of tokens in the input. + - `output_tokens`: Number of tokens in the output. + - `total_tokens`: Total token count (input + output). + - `cost_usd`: Total cost of the response in USD. + - `latency_s`: Total time taken for the response, in seconds. + - `time_to_first_token_s`: Time to receive the first token, in seconds. + - `inter_token_latency_s`: Average time between tokens, in seconds. If `token_times` is empty sets it to 0. + - `tokens_per_second`: Processing rate of tokens per second. + """ model_config = self.config.models[model] input_tokens = len(self.tokenizer.encode(self.input_to_string(input))) output_tokens = len(self.tokenizer.encode(self.output_to_string(output))) @@ -628,17 +788,42 @@ def calculate_metrics( "cost_usd": input_cost + output_cost, "latency_s": total_time, "time_to_first_token_s": first_token_time - start_time, - "inter_token_latency_s": sum(token_times) / len(token_times), - "tokens_per_second": token_count / total_time, + "inter_token_latency_s": sum(token_times) / len(token_times) + if token_times + else 0, + "tokens_per_second": token_count / total_time + if token_times + else 1 / total_time, } def calculate_cost( self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] ) -> float: + """ + Calculates the cost for a given number of tokens based on a fixed cost per token + or a variable rate structure. + + If `token_cost` is a fixed float, the total cost is `token_count * token_cost`. + If `token_cost` is a list, it checks each range and calculates cost based on the applicable range's rate. + + Parameters + ---------- + token_count : int + The total number of tokens for which the cost is being calculated. + token_cost : Union[float, List[Dict[str, Any]]] + Either a fixed cost per token (as a float) or a list of dictionaries defining + variable cost ranges. Each dictionary in the list represents a range with + 'range' (a tuple of minimum and maximum token counts) and 'cost' (cost per token) keys. + + Returns + ------- + float + The calculated cost based on the token count and cost structure. + """ if isinstance(token_cost, list): for cost_range in token_cost: if token_count >= cost_range.range[0] and ( - token_count <= cost_range.range[1] or cost_range.range[1] is None + cost_range.range[1] is None or token_count <= cost_range.range[1] ): return cost_range.cost * token_count else: @@ -646,6 +831,23 @@ def calculate_cost( return 0 def input_to_string(self, input): + """ + Converts an input, which can be a string or a structured list of messages, into a single concatenated string. + + Parameters + ---------- + input : Any + The input data to be converted. This can be: + - A simple string, which is returned as-is. + - A list of message dictionaries, where each dictionary may contain `content`, `role`, + and nested items like `text` or `image_url`. + + Returns + ------- + str + A concatenated string representing the text content of all messages, + including text and URLs from image content if present. + """ if isinstance(input, str): return input else: @@ -667,6 +869,23 @@ def input_to_string(self, input): return "".join(result) def output_to_string(self, output): + """ + Extracts and returns the content or arguments from the output based on + the `finish_reason` of the first choice in `output`. + + Parameters + ---------- + output : Any + The model output object, expected to have a `choices` attribute that should contain a `finish_reason` indicating the type of output + ("stop", "tool_calls", or "function_call") and corresponding content or arguments. + + Returns + ------- + str + - If `finish_reason` is "stop": Returns the message content. + - If `finish_reason` is "tool_calls": Returns the arguments for the first tool call. + - If `finish_reason` is "function_call": Returns the arguments for the function call. + """ if output.choices[0].finish_reason == "stop": return output.choices[0].message.content elif output.choices[0].finish_reason == "tool_calls": diff --git a/libs/core/tests/unit_tests/conftest.py b/libs/core/tests/unit_tests/conftest.py index 23f070e3..5eeccc83 100644 --- a/libs/core/tests/unit_tests/conftest.py +++ b/libs/core/tests/unit_tests/conftest.py @@ -1,6 +1,7 @@ from unittest.mock import MagicMock import pytest +from llmstudio_core.providers.azure import AzureProvider from llmstudio_core.providers.provider import ProviderCore @@ -11,14 +12,6 @@ async def aparse_response(self, response, **kwargs): def parse_response(self, response, **kwargs): return response - def chat(self, chat_input, model, **kwargs): - # Mock the response to match expected structure - return MagicMock(choices=[MagicMock(finish_reason="stop")]) - - async def achat(self, chat_input, model, **kwargs): - # Mock the response to match expected structure - return MagicMock(choices=[MagicMock(finish_reason="stop")]) - def output_to_string(self, output): # Handle string inputs if isinstance(output, str): @@ -27,6 +20,24 @@ def output_to_string(self, output): return output.choices[0].message.content return "" + def validate_request(self, request): + # For testing, simply return the request + return request + + async def agenerate_client(self, request): + # For testing, return an async generator + async def async_gen(): + yield {} + + return async_gen() + + def generate_client(self, request): + # For testing, return a generator + def gen(): + yield {} + + return gen() + @staticmethod def _provider_config_name(): return "mock_provider" @@ -42,3 +53,27 @@ def mock_provider(): tokenizer = MagicMock() tokenizer.encode = lambda x: x.split() # Simple tokenizer mock return MockProvider(config=config, tokenizer=tokenizer) + + +class MockAzureProvider(AzureProvider): + async def aparse_response(self, response, **kwargs): + return response + + async def agenerate_client(self, request): + # For testing, return an async generator + async def async_gen(): + yield {} + + return async_gen() + + @staticmethod + def _provider_config_name(): + return "mock_azure_provider" + + +@pytest.fixture +def mock_azure_provider(): + config = MagicMock() + config.id = "mock_azure_provider" + base_url = "mock_url.com" + return MockAzureProvider(config=config, base_url=base_url) diff --git a/libs/core/tests/unit_tests/test_azure.py b/libs/core/tests/unit_tests/test_azure.py new file mode 100644 index 00000000..28e5f684 --- /dev/null +++ b/libs/core/tests/unit_tests/test_azure.py @@ -0,0 +1,232 @@ +from unittest.mock import MagicMock + + +class TestParseResponse: + def test_tool_response_handling(self, mock_azure_provider): + + mock_azure_provider.is_llama = True + mock_azure_provider.has_tools = True + mock_azure_provider.has_functions = False + + mock_azure_provider.handle_tool_response = MagicMock( + return_value=iter(["chunk1", None, "chunk2", None, "chunk3"]) + ) + + response = iter(["irrelevant"]) + + results = list(mock_azure_provider.parse_response(response)) + + assert results == ["chunk1", "chunk2", "chunk3"] + mock_azure_provider.handle_tool_response.assert_called_once_with(response) + + def test_direct_response_handling_with_choices(self, mock_azure_provider): + mock_azure_provider.is_llama = False + + chunk1 = MagicMock() + chunk1.model_dump.return_value = {"choices": ["choice1", "choice2"]} + chunk2 = MagicMock() + chunk2.model_dump.return_value = {"choices": ["choice2"]} + response = iter([chunk1, chunk2]) + + results = list(mock_azure_provider.parse_response(response)) + + assert results == [ + {"choices": ["choice1", "choice2"]}, + {"choices": ["choice2"]}, + ] + chunk1.model_dump.assert_called_once() + chunk2.model_dump.assert_called_once() + + def test_direct_response_handling_without_choices(self, mock_azure_provider): + mock_azure_provider.is_llama = False + + chunk1 = MagicMock() + chunk1.model_dump.return_value = {"key": "value"} + chunk2 = MagicMock() + chunk2.model_dump.return_value = {"another_key": "another_value"} + response = iter([chunk1, chunk2]) + + results = list(mock_azure_provider.parse_response(response)) + + assert results == [] + chunk1.model_dump.assert_called_once() + chunk2.model_dump.assert_called_once() + + +class TestFormatMessage: + def test_format_message_tool_calls(self, mock_azure_provider): + message = { + "tool_calls": [ + { + "function": { + "name": "example_tool", + "arguments": '{"arg1": "value1"}', + } + } + ] + } + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>assistant<|end_header_id|> + {"arg1": "value1"} + <|eom_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_tool_call_id(self, mock_azure_provider): + message = {"tool_call_id": "123", "content": "This is the tool response."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>ipython<|end_header_id|> + This is the tool response. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_function_call(self, mock_azure_provider): + message = { + "function_call": { + "name": "example_function", + "arguments": '{"arg1": "value1"}', + } + } + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>assistant<|end_header_id|> + {"arg1": "value1"} + <|eom_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_user_message(self, mock_azure_provider): + message = {"role": "user", "content": "This is a user message."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>user<|end_header_id|> + This is a user message. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_assistant_message(self, mock_azure_provider): + message = {"role": "assistant", "content": "This is an assistant message."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>assistant<|end_header_id|> + This is an assistant message. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_function_response(self, mock_azure_provider): + message = {"role": "function", "content": "This is the function response."} + result = mock_azure_provider.format_message(message) + expected = """ + <|start_header_id|>ipython<|end_header_id|> + This is the function response. + <|eot_id|> + """ + assert result.strip() == expected.strip() + + def test_format_message_empty_message(self, mock_azure_provider): + message = {"role": "user", "content": None} + result = mock_azure_provider.format_message(message) + expected = "" + assert result == expected + + +class TestGenerateClient: + def test_generate_client_with_tools_and_functions(self, mock_azure_provider): + mock_azure_provider.prepare_messages = MagicMock( + return_value="prepared_messages" + ) + mock_azure_provider._client.chat.completions.create = MagicMock( + return_value="mock_response" + ) + + request = MagicMock() + request.model = "gpt-4" + request.parameters = { + "tools": ["tool1", "tool2"], + "functions": ["function1", "function2"], + "other_param": "value", + } + + result = mock_azure_provider.generate_client(request) + + expected_args = { + "model": "gpt-4", + "messages": "prepared_messages", + "stream": True, + "tools": ["tool1", "tool2"], + "tool_choice": "auto", + "functions": ["function1", "function2"], + "function_call": "auto", + "other_param": "value", + } + + assert result == "mock_response" + mock_azure_provider.prepare_messages.assert_called_once_with(request) + mock_azure_provider._client.chat.completions.create.assert_called_once_with( + **expected_args + ) + + def test_generate_client_without_tools_or_functions(self, mock_azure_provider): + mock_azure_provider.prepare_messages = MagicMock( + return_value="prepared_messages" + ) + mock_azure_provider._client.chat.completions.create = MagicMock( + return_value="mock_response" + ) + + request = MagicMock() + request.model = "gpt-4" + request.parameters = {"other_param": "value"} + + result = mock_azure_provider.generate_client(request) + + expected_args = { + "model": "gpt-4", + "messages": "prepared_messages", + "stream": True, + "other_param": "value", + } + + assert result == "mock_response" + mock_azure_provider.prepare_messages.assert_called_once_with(request) + mock_azure_provider._client.chat.completions.create.assert_called_once_with( + **expected_args + ) + + def test_generate_client_with_llama_model(self, mock_azure_provider): + mock_azure_provider.prepare_messages = MagicMock( + return_value="prepared_messages" + ) + mock_azure_provider._client.chat.completions.create = MagicMock( + return_value="mock_response" + ) + + request = MagicMock() + request.model = "llama-2" + request.parameters = { + "tools": ["tool1"], + "functions": ["function1"], + "other_param": "value", + } + + result = mock_azure_provider.generate_client(request) + + expected_args = { + "model": "llama-2", + "messages": "prepared_messages", + "stream": True, + "tools": ["tool1"], + "functions": ["function1"], + "other_param": "value", + } + + assert result == "mock_response" + mock_azure_provider.prepare_messages.assert_called_once_with(request) + mock_azure_provider._client.chat.completions.create.assert_called_once_with( + **expected_args + ) diff --git a/libs/core/tests/unit_tests/test_azure_build.py b/libs/core/tests/unit_tests/test_azure_build.py new file mode 100644 index 00000000..17d73f99 --- /dev/null +++ b/libs/core/tests/unit_tests/test_azure_build.py @@ -0,0 +1,279 @@ +from unittest.mock import MagicMock, patch + + +class TestBuildLlamaSystemMessage: + def test_build_llama_system_message_with_existing_sm(self, mock_azure_provider): + mock_azure_provider.build_tool_instructions = MagicMock( + return_value="Tool Instructions" + ) + mock_azure_provider.build_function_instructions = MagicMock( + return_value="\nFunction Instructions" + ) + + openai_message = [ + {"role": "user", "content": "Hello"}, + {"role": "system", "content": "Custom system message"}, + ] + llama_message = "Initial message" + tools = ["Tool1", "Tool2"] + functions = ["Function1"] + + result = mock_azure_provider.build_llama_system_message( + openai_message, llama_message, tools, functions + ) + + expected = ( + "Initial message\n" + " <|start_header_id|>system<|end_header_id|>\n" + " Custom system message\n" + " Tool Instructions\nFunction Instructions\n<|eot_id|>" # identation here exists because in Python when adding a newline to a triple quote string it keeps identation + ) + assert result == expected + mock_azure_provider.build_tool_instructions.assert_called_once_with(tools) + mock_azure_provider.build_function_instructions.assert_called_once_with( + functions + ) + + def test_build_llama_system_message_with_default_sm(self, mock_azure_provider): + mock_azure_provider.build_tool_instructions = MagicMock( + return_value="Tool Instructions" + ) + mock_azure_provider.build_function_instructions = MagicMock( + return_value="\nFunction Instructions" + ) + + openai_message = [{"role": "user", "content": "Hello"}] + llama_message = "Initial message" + tools = ["Tool1"] + functions = [] + + result = mock_azure_provider.build_llama_system_message( + openai_message, llama_message, tools, functions + ) + + expected = ( + "Initial message\n" + " <|start_header_id|>system<|end_header_id|>\n" + " You are a helpful AI assistant.\n" + " Tool Instructions\n<|eot_id|>" + ) + assert result == expected + mock_azure_provider.build_tool_instructions.assert_called_once_with(tools) + mock_azure_provider.build_function_instructions.assert_not_called() + + def test_build_llama_system_message_without_tools_or_functions( + self, mock_azure_provider + ): + mock_azure_provider.build_tool_instructions = MagicMock() + mock_azure_provider.build_function_instructions = MagicMock() + + openai_message = [{"role": "system", "content": "Minimal system message"}] + llama_message = "Initial message" + tools = [] + functions = [] + + result = mock_azure_provider.build_llama_system_message( + openai_message, llama_message, tools, functions + ) + + expected = ( + "Initial message\n" + " <|start_header_id|>system<|end_header_id|>\n" + " Minimal system message\n \n<|eot_id|>" + ) + assert result == expected + mock_azure_provider.build_tool_instructions.assert_not_called() + mock_azure_provider.build_function_instructions.assert_not_called() + + +class TestBuildInstructions: + def test_build_tool_instructions(self, mock_azure_provider): + tools = [ + { + "type": "function", + "function": { + "name": "python_repl_ast", + "description": "execute Python code", + "parameters": {"query": "string"}, + }, + }, + { + "type": "function", + "function": { + "name": "data_lookup", + "description": "retrieve data from a database", + "parameters": {"database": "string", "query": "string"}, + }, + }, + ] + + result = mock_azure_provider.build_tool_instructions(tools) + + expected = """ + You have access to the following tools: + Use the function 'python_repl_ast' to 'execute Python code': +Parameters format: +{ + "query": "string" +} + +Use the function 'data_lookup' to 'retrieve data from a database': +Parameters format: +{ + "database": "string", + "query": "string" +} + + +If you choose to use a function to produce this response, ONLY reply in the following format with no prefix or suffix: +§{"type": "function", "name": "FUNCTION_NAME", "parameters": {"PARAMETER_NAME": PARAMETER_VALUE}} +IMPORTANT: IT IS VITAL THAT YOU NEVER ADD A PREFIX OR A SUFFIX TO THE FUNCTION CALL. + +Here is an example of the output I desiere when performing function call: +§{"type": "function", "name": "python_repl_ast", "parameters": {"query": "print(df.shape)"}} +NOTE: There is no prefix before the symbol '§' and nothing comes after the call is done. + + Reminder: + - Function calls MUST follow the specified format. + - Only call one function at a time. + - Required parameters MUST be specified. + - Put the entire function call reply on one line. + - If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls. + - If you have already called a tool and got the response for the users question please reply with the response. + """ + assert result.strip() == expected.strip() + + def test_build_function_instructions(self, mock_azure_provider): + functions = [ + { + "name": "python_repl_ast", + "description": "execute Python code", + "parameters": {"query": "string"}, + }, + { + "name": "data_lookup", + "description": "retrieve data from a database", + "parameters": {"database": "string", "query": "string"}, + }, + ] + + result = mock_azure_provider.build_function_instructions(functions) + + expected = """ +You have access to the following functions: +Use the function 'python_repl_ast' to: 'execute Python code' +{ + "query": "string" +} + +Use the function 'data_lookup' to: 'retrieve data from a database' +{ + "database": "string", + "query": "string" +} + + +If you choose to use a function to produce this response, ONLY reply in the following format with no prefix or suffix: +§{"type": "function", "name": "FUNCTION_NAME", "parameters": {"PARAMETER_NAME": PARAMETER_VALUE}} + +Here is an example of the output I desiere when performing function call: +§{"type": "function", "name": "python_repl_ast", "parameters": {"query": "print(df.shape)"}} + +Reminder: +- Function calls MUST follow the specified format. +- Only call one function at a time. +- NEVER call more than one function at a time. +- Required parameters MUST be specified. +- Put the entire function call reply on one line. +- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls. +- If you have already called a function and got the response for the user's question, please reply with the response. +""" + + assert result.strip() == expected.strip() + + +class TestBuildLlamaConversation: + def test_build_llama_conversation_with_nested_messages(self, mock_azure_provider): + mock_azure_provider.format_message = MagicMock( + side_effect=lambda msg: f"[formatted:{msg['content']}]" + ) + + openai_message = [ + { + "role": "user", + "content": "[{'content': 'nested message 1'}, {'content': 'nested message 2'}]", + }, + {"role": "assistant", "content": "assistant reply"}, + ] + llama_message = "Initial message: " + + result = mock_azure_provider.build_llama_conversation( + openai_message, llama_message + ) + + expected = "Initial message: [formatted:nested message 1][formatted:nested message 2][formatted:assistant reply]" + + assert result == expected + mock_azure_provider.format_message.assert_any_call( + {"content": "nested message 1"} + ) + mock_azure_provider.format_message.assert_any_call( + {"content": "nested message 2"} + ) + mock_azure_provider.format_message.assert_any_call( + {"role": "assistant", "content": "assistant reply"} + ) + + def test_build_llama_conversation_with_invalid_nested_content( + self, mock_azure_provider + ): + mock_azure_provider.format_message = MagicMock( + side_effect=lambda msg: f"[formatted:{msg['content']}]" + ) + + openai_message = [ + {"role": "user", "content": "[invalid json/dict]"}, + {"role": "assistant", "content": "assistant reply"}, + ] + llama_message = "Initial message: " + + with patch("ast.literal_eval", side_effect=ValueError) as mock_literal_eval: + result = mock_azure_provider.build_llama_conversation( + openai_message, llama_message + ) + + expected = "Initial message: [formatted:[invalid json/dict]][formatted:assistant reply]" + + assert result == expected + mock_azure_provider.format_message.assert_any_call( + {"role": "user", "content": "[invalid json/dict]"} + ) + mock_azure_provider.format_message.assert_any_call( + {"role": "assistant", "content": "assistant reply"} + ) + + mock_literal_eval.assert_called_once_with("[invalid json/dict]") + + def test_build_llama_conversation_skipping_system_messages( + self, mock_azure_provider + ): + mock_azure_provider.format_message = MagicMock( + side_effect=lambda msg: f"[formatted:{msg['content']}]" + ) + + openai_message = [ + {"role": "system", "content": "system message"}, + {"role": "user", "content": "user message"}, + ] + llama_message = "Initial message: " + + result = mock_azure_provider.build_llama_conversation( + openai_message, llama_message + ) + + expected = "Initial message: [formatted:user message]" + + assert result == expected + mock_azure_provider.format_message.assert_any_call( + {"role": "user", "content": "user message"} + ) diff --git a/libs/core/tests/unit_tests/test_provider.py b/libs/core/tests/unit_tests/test_provider.py index 118367a3..42396c99 100644 --- a/libs/core/tests/unit_tests/test_provider.py +++ b/libs/core/tests/unit_tests/test_provider.py @@ -1,30 +1,39 @@ -from unittest.mock import AsyncMock, MagicMock +from unittest.mock import MagicMock import pytest -from llmstudio_core.providers.provider import ChatRequest, ProviderError +from llmstudio_core.providers.provider import ChatRequest, ProviderError, time -request = ChatRequest(chat_input="Hello", model="test_model") +request = ChatRequest(chat_input="Hello World", model="test_model") -def test_chat(mock_provider): - mock_provider.generate_client = MagicMock(return_value=MagicMock()) - mock_provider.handle_response = MagicMock(return_value=iter(["response"])) +def test_chat_response_non_stream(mock_provider): + mock_provider.validate_request = MagicMock() + mock_provider.validate_model = MagicMock() + mock_provider.generate_client = MagicMock(return_value="mock_response") + mock_provider.handle_response = MagicMock(return_value="final_response") - print(request.model_dump()) - response = mock_provider.chat(request.chat_input, request.model) + response = mock_provider.chat(chat_input="Hello", model="test_model") - assert response is not None + assert response == "final_response" + mock_provider.validate_request.assert_called_once() + mock_provider.validate_model.assert_called_once() -@pytest.mark.asyncio -async def test_achat(mock_provider): - mock_provider.agenerate_client = AsyncMock(return_value=AsyncMock()) - mock_provider.ahandle_response = AsyncMock(return_value=AsyncMock()) - - print(request.model_dump()) - response = await mock_provider.achat(request.chat_input, request.model) +def test_chat_streaming_response(mock_provider): + mock_provider.validate_request = MagicMock() + mock_provider.validate_model = MagicMock() + mock_provider.generate_client = MagicMock(return_value="mock_response_stream") + mock_provider.handle_response = MagicMock( + return_value=iter(["streamed_response_1", "streamed_response_2"]) + ) - assert response is not None + response_stream = mock_provider.chat( + chat_input="Hello", model="test_model", is_stream=True + ) + assert next(response_stream) == "streamed_response_1" + assert next(response_stream) == "streamed_response_2" + mock_provider.validate_request.assert_called_once() + mock_provider.validate_model.assert_called_once() def test_validate_model(mock_provider): @@ -36,22 +45,214 @@ def test_validate_model(mock_provider): mock_provider.validate_model(request_invalid) -def test_calculate_metrics(mock_provider): - metrics = mock_provider.calculate_metrics( - input="Hello", - output="World", - model="test_model", - start_time=0, - end_time=1, - first_token_time=0.5, - token_times=(0.1, 0.2), - token_count=2, - ) +def test_join_chunks_finish_reason_stop(mock_provider): + current_time = int(time.time()) + chunks = [ + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"content": "Hello, "}, + "finish_reason": None, + "index": 0, + } + ], + }, + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"content": "world!"}, + "finish_reason": "stop", + "index": 0, + } + ], + }, + ] + response, output_string = mock_provider.join_chunks(chunks) + + assert output_string == "Hello, world!" + assert response.choices[0].message.content == "Hello, world!" + + +def test_join_chunks_finish_reason_function_call(mock_provider): + current_time = int(time.time()) + chunks = [ + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": { + "function_call": {"name": "my_function", "arguments": "arg1"} + }, + "finish_reason": None, + "index": 0, + } + ], + }, + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"function_call": {"arguments": "arg2"}}, + "finish_reason": "function_call", + "index": 0, + } + ], + }, + { + "id": "test_id", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": {"function_call": {"arguments": "}"}}, + "finish_reason": "function_call", + "index": 0, + } + ], + }, + ] + response, output_string = mock_provider.join_chunks(chunks) + + assert output_string == "arg1arg2" + assert response.choices[0].message.function_call.arguments == "arg1arg2" + assert response.choices[0].message.function_call.name == "my_function" + + +def test_join_chunks_tool_calls(mock_provider): + current_time = int(time.time()) + + chunks = [ + { + "id": "test_id_1", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": { + "tool_calls": [ + { + "id": "tool_1", + "index": 0, + "function": { + "name": "search_tool", + "arguments": '{"query": "weather', + }, + "type": "function", + } + ] + }, + "finish_reason": None, + "index": 0, + } + ], + }, + { + "id": "test_id_2", + "model": "test_model", + "created": current_time, + "choices": [ + { + "delta": { + "tool_calls": [ + { + "id": "tool_1", + "index": 0, + "function": { + "name": "search_tool", + "arguments": ' details"}', + }, + } + ] + }, + "finish_reason": "tool_calls", + "index": 0, + } + ], + }, + ] + + response, output_string = mock_provider.join_chunks(chunks) + + assert output_string == "['search_tool', '{\"query\": \"weather details\"}']" + + assert response.object == "chat.completion" + assert response.choices[0].finish_reason == "tool_calls" + tool_call = response.choices[0].message.tool_calls[0] + + assert tool_call.id == "tool_1" + assert tool_call.function.name == "search_tool" + assert tool_call.function.arguments == '{"query": "weather details"}' + assert tool_call.type == "function" + + +def test_input_to_string_with_string(mock_provider): + input_data = "Hello, world!" + assert mock_provider.input_to_string(input_data) == "Hello, world!" + + +def test_input_to_string_with_list_of_text_messages(mock_provider): + input_data = [ + {"content": "Hello"}, + {"content": " world!"}, + ] + assert mock_provider.input_to_string(input_data) == "Hello world!" + + +def test_input_to_string_with_list_of_text_and_url(mock_provider): + input_data = [ + {"role": "user", "content": [{"type": "text", "text": "Hello "}]}, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "http://example.com/image.jpg"}, + } + ], + }, + {"role": "user", "content": [{"type": "text", "text": " world!"}]}, + ] + expected_output = "Hello http://example.com/image.jpg world!" + assert mock_provider.input_to_string(input_data) == expected_output + + +def test_input_to_string_with_mixed_roles_and_missing_content(mock_provider): + input_data = [ + {"role": "assistant", "content": "Admin text;"}, + {"role": "user", "content": [{"type": "text", "text": "User text"}]}, + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": {"url": "http://example.com/another.jpg"}, + } + ], + }, + ] + expected_output = "Admin text;User texthttp://example.com/another.jpg" + assert mock_provider.input_to_string(input_data) == expected_output + + +def test_input_to_string_with_missing_content_key(mock_provider): + input_data = [ + {"role": "user"}, + {"role": "user", "content": [{"type": "text", "text": "Hello again"}]}, + ] + expected_output = "Hello again" + assert mock_provider.input_to_string(input_data) == expected_output + - assert metrics["input_tokens"] == pytest.approx(1) - assert metrics["output_tokens"] == pytest.approx(1) - assert metrics["cost_usd"] == pytest.approx(0.03) - assert metrics["latency_s"] == pytest.approx(1) - assert metrics["time_to_first_token_s"] == pytest.approx(0.5) - assert metrics["inter_token_latency_s"] == pytest.approx(0.15) - assert metrics["tokens_per_second"] == pytest.approx(2) +def test_input_to_string_with_empty_list(mock_provider): + input_data = [] + assert mock_provider.input_to_string(input_data) == "" diff --git a/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py b/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py new file mode 100644 index 00000000..fb54d602 --- /dev/null +++ b/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py @@ -0,0 +1,128 @@ +from unittest.mock import MagicMock + + +def test_calculate_metrics(mock_provider): + + metrics = mock_provider.calculate_metrics( + input="Hello", + output="Hello World", + model="test_model", + start_time=0.0, + end_time=1.0, + first_token_time=0.5, + token_times=(0.1,), + token_count=2, + ) + + assert metrics["input_tokens"] == 1 + assert metrics["output_tokens"] == 2 + assert metrics["total_tokens"] == 3 + assert metrics["cost_usd"] == 0.01 * 1 + 0.02 * 2 # input_cost + output_cost + assert metrics["latency_s"] == 1.0 # end_time - start_time + assert ( + metrics["time_to_first_token_s"] == 0.5 - 0.0 + ) # first_token_time - start_time + assert metrics["inter_token_latency_s"] == 0.1 # Average of token_times + assert metrics["tokens_per_second"] == 2 / 1.0 # token_count / total_time + + +def test_calculate_metrics_single_token(mock_provider): + + metrics = mock_provider.calculate_metrics( + input="Hello", + output="World", + model="test_model", + start_time=0.0, + end_time=1.0, + first_token_time=0.5, + token_times=(), + token_count=1, + ) + + assert metrics["input_tokens"] == 1 + assert metrics["output_tokens"] == 1 + assert metrics["total_tokens"] == 2 + assert metrics["cost_usd"] == 0.01 * 1 + 0.02 * 1 + assert metrics["latency_s"] == 1.0 + assert metrics["time_to_first_token_s"] == 0.5 - 0.0 + assert metrics["inter_token_latency_s"] == 0 + assert metrics["tokens_per_second"] == 1 / 1.0 + + +def test_calculate_cost_fixed_cost(mock_provider): + fixed_cost = 0.02 + token_count = 100 + expected_cost = token_count * fixed_cost + assert mock_provider.calculate_cost(token_count, fixed_cost) == expected_cost + + +def test_calculate_cost_variable_cost(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (0, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + variable_cost = [cost_range_1, cost_range_2] + token_count = 75 + expected_cost = token_count * 0.02 + assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + + +def test_calculate_cost_variable_cost_higher_range(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (0, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + cost_range_3 = MagicMock() + cost_range_3.range = (101, None) + cost_range_3.cost = 0.03 + + variable_cost = [cost_range_1, cost_range_2, cost_range_3] + token_count = 150 + expected_cost = token_count * 0.03 + assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + + +def test_calculate_cost_variable_cost_no_matching_range(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (0, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + cost_range_3 = MagicMock() + cost_range_3.range = (101, 150) + cost_range_3.cost = 0.03 + + variable_cost = [cost_range_1, cost_range_2, cost_range_3] + token_count = 200 + expected_cost = 0 + assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + + +def test_calculate_cost_variable_cost_no_matching_range_inferior(mock_provider): + cost_range_1 = MagicMock() + cost_range_1.range = (10, 50) + cost_range_1.cost = 0.01 + + cost_range_2 = MagicMock() + cost_range_2.range = (51, 100) + cost_range_2.cost = 0.02 + + cost_range_3 = MagicMock() + cost_range_3.range = (101, 150) + cost_range_3.cost = 0.03 + + variable_cost = [cost_range_1, cost_range_2, cost_range_3] + token_count = 5 + expected_cost = 0 + assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost diff --git a/libs/core/tests/unit_tests/test_provider_handle_response.py b/libs/core/tests/unit_tests/test_provider_handle_response.py new file mode 100644 index 00000000..ac04e32b --- /dev/null +++ b/libs/core/tests/unit_tests/test_provider_handle_response.py @@ -0,0 +1,281 @@ +from unittest.mock import MagicMock + +import pytest +from llmstudio_core.providers.provider import ChatCompletion, ChatCompletionChunk, time + + +@pytest.mark.asyncio +async def test_ahandle_response_non_streaming(mock_provider): + request = MagicMock( + is_stream=False, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Non-streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + } + start_time = time.time() + + async def mock_aparse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_aparse_response + mock_provider.join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Non-streamed response", + ) + ) + mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + + response = [] + async for chunk in mock_provider.ahandle_response( + request, mock_aparse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletion) + assert response[0].choices == [] + assert response[0].chat_output == "Non-streamed response" + + +@pytest.mark.asyncio +async def test_ahandle_response_streaming_length(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "length", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + async def mock_aparse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_aparse_response + mock_provider.join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + + response = [] + async for chunk in mock_provider.ahandle_response( + request, mock_aparse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunk) + assert response[0].chat_output_stream == "Streamed response" + + +@pytest.mark.asyncio +async def test_ahandle_response_streaming_stop(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + async def mock_aparse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_aparse_response + mock_provider.join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + + response = [] + async for chunk in mock_provider.ahandle_response( + request, mock_aparse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunk) + assert response[0].chat_output == "Streamed response" + + +def test_handle_response_non_streaming(mock_provider): + request = MagicMock( + is_stream=False, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Non-streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + } + start_time = time.time() + + def mock_parse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_parse_response + mock_provider.join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Non-streamed response", + ) + ) + mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + + response = [] + for chunk in mock_provider.handle_response( + request, mock_parse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletion) + assert response[0].choices == [] + assert response[0].chat_output == "Non-streamed response" + + +def test_handle_response_streaming_length(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "length", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + def mock_parse_response(*args, **kwargs): + yield response_chunk + + mock_provider.aparse_response = mock_parse_response + mock_provider.join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + + response = [] + for chunk in mock_provider.handle_response( + request, mock_parse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunk) + assert response[0].chat_output_stream == "Streamed response" + + +def test_handle_response_streaming_stop(mock_provider): + request = MagicMock( + is_stream=True, chat_input="Hello", model="test_model", parameters={} + ) + response_chunk = { + "choices": [ + { + "delta": {"content": "Streamed response"}, + "finish_reason": "stop", + "index": 0, + } + ], + "model": "test_model", + "object": "chat.completion.chunk", + "created": 0, + } + start_time = time.time() + + def mock_parse_response(*args, **kwargs): + yield response_chunk + + mock_provider.parse_response = mock_parse_response + mock_provider.join_chunks = MagicMock( + return_value=( + ChatCompletion( + id="id", + choices=[], + created=0, + model="test_model", + object="chat.completion", + ), + "Streamed response", + ) + ) + mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + + response = [] + for chunk in mock_provider.handle_response( + request, mock_parse_response(), start_time + ): + response.append(chunk) + + assert isinstance(response[0], ChatCompletionChunk) + assert response[0].chat_output == "Streamed response" From 2cd3f94a78a51eb16cce9d5abb2c693e180b3745 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 18 Dec 2024 10:46:28 +0000 Subject: [PATCH 02/38] [fix] bump prerelease version in pyproject.toml --- libs/core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index de5a22c2..0847b86b 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-core" -version = "1.0.2" +version = "1.0.3a0" description = "LLMStudio core capabilities for routing llm calls for any vendor. No proxy server required. For that use llmstudio[proxy]" authors = ["Cláudio Lemos "] license = "MIT" From cdeb84f0e4057c28f0107d09382b70496e5de3b5 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 18 Dec 2024 10:49:30 +0000 Subject: [PATCH 03/38] chore: rename action --- .github/workflows/upload-pypi-dev.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/upload-pypi-dev.yml b/.github/workflows/upload-pypi-dev.yml index b13bf403..85cf886c 100644 --- a/.github/workflows/upload-pypi-dev.yml +++ b/.github/workflows/upload-pypi-dev.yml @@ -1,4 +1,4 @@ -name: PyPI prerelease and build/push Docker image. +name: PyPI prerelease any module. on: workflow_dispatch: From f9feb73b0f60037b08e02b9ecea6d296d568abe4 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 18 Dec 2024 11:05:25 +0000 Subject: [PATCH 04/38] feat: added action to run tests on PR --- .github/workflows/tests.yml | 38 +++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 .github/workflows/tests.yml diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 00000000..3be26272 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,38 @@ +name: Tests + +on: + pull_request: + branches: + - main + types: + - opened + - synchronize + - reopened + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + # Set up Python environment + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: "3.x" + + # Install Poetry + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + # Build and publish package to PyPI + - name: Install llmstudio-core + working-directory: ./libs/core + run: | + poetry install + + # Build and publish package to PyPI + - name: Run unit-tests + run: | + make unit-tests From 95c17239f7df1bd946073b1c5b92bde663033012 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 18 Dec 2024 11:06:30 +0000 Subject: [PATCH 05/38] chore: comments --- .github/workflows/tests.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 3be26272..cf2a3002 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -26,13 +26,12 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - - # Build and publish package to PyPI + # Install lib and dev dependencies - name: Install llmstudio-core working-directory: ./libs/core run: | poetry install - # Build and publish package to PyPI - name: Run unit-tests run: | make unit-tests From 8ea43c6a62db6d42ed0efc8415901613e9363cba Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 18 Dec 2024 12:38:58 +0000 Subject: [PATCH 06/38] fix: fix azure config tests --- libs/core/tests/unit_tests/conftest.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/libs/core/tests/unit_tests/conftest.py b/libs/core/tests/unit_tests/conftest.py index 5eeccc83..73ef66e5 100644 --- a/libs/core/tests/unit_tests/conftest.py +++ b/libs/core/tests/unit_tests/conftest.py @@ -75,5 +75,10 @@ def _provider_config_name(): def mock_azure_provider(): config = MagicMock() config.id = "mock_azure_provider" - base_url = "mock_url.com" - return MockAzureProvider(config=config, base_url=base_url) + api_key="key" + api_endpoint="http://azureopenai.com" + api_version="2025-01-01-preview" + return MockAzureProvider(config=config, + api_endpoint=api_endpoint, + api_key=api_key, + api_version=api_version) From 9f42faa534e0aa761a51c9836ee511601bf37082 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 18 Dec 2024 12:42:12 +0000 Subject: [PATCH 07/38] chore: style format --- libs/core/tests/unit_tests/conftest.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/libs/core/tests/unit_tests/conftest.py b/libs/core/tests/unit_tests/conftest.py index 73ef66e5..1d1939a0 100644 --- a/libs/core/tests/unit_tests/conftest.py +++ b/libs/core/tests/unit_tests/conftest.py @@ -75,10 +75,12 @@ def _provider_config_name(): def mock_azure_provider(): config = MagicMock() config.id = "mock_azure_provider" - api_key="key" - api_endpoint="http://azureopenai.com" - api_version="2025-01-01-preview" - return MockAzureProvider(config=config, - api_endpoint=api_endpoint, - api_key=api_key, - api_version=api_version) + api_key = "key" + api_endpoint = "http://azureopenai.com" + api_version = "2025-01-01-preview" + return MockAzureProvider( + config=config, + api_endpoint=api_endpoint, + api_key=api_key, + api_version=api_version, + ) From 91f232e89656b9b938adcb0fd4337f225efc1724 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 18 Dec 2024 12:43:51 +0000 Subject: [PATCH 08/38] fix: tests workflow --- .github/workflows/tests.yml | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index cf2a3002..c59971b6 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -10,7 +10,7 @@ on: - reopened jobs: - lint: + tests: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 @@ -25,13 +25,17 @@ jobs: - name: Install Poetry run: | curl -sSL https://install.python-poetry.org | python3 - - # Install lib and dev dependencies - name: Install llmstudio-core working-directory: ./libs/core run: | poetry install - - - name: Run unit-tests + POETRY_ENV=$(poetry env info --path) + echo $POETRY_ENV + echo "POETRY_ENV=$POETRY_ENV" >> $GITHUB_ENV + + - name: Run unit tests run: | - make unit-tests + echo ${{ env.POETRY_ENV }} + source ${{ env.POETRY_ENV }}/bin/activate + poetry run pytest libs/core From ee7c373a48fe909adca68c854d4ba3391204e378 Mon Sep 17 00:00:00 2001 From: brunoalho99 <132477278+brunoalho99@users.noreply.github.com> Date: Thu, 23 Jan 2025 14:33:22 +0000 Subject: [PATCH 09/38] Feature/prompt management (#200) * [feat] prompt management * [feat] testing * [feat] only one active prompt --- .../prompt_manager/__init__.py | 0 .../llmstudio_tracker/prompt_manager/crud.py | 91 ++++++++++++++++ .../prompt_manager/endpoints.py | 56 ++++++++++ .../prompt_manager/manager.py | 61 +++++++++++ .../prompt_manager/models.py | 102 ++++++++++++++++++ .../prompt_manager/schemas.py | 25 +++++ libs/tracker/llmstudio_tracker/server.py | 2 + 7 files changed, 337 insertions(+) create mode 100644 libs/tracker/llmstudio_tracker/prompt_manager/__init__.py create mode 100644 libs/tracker/llmstudio_tracker/prompt_manager/crud.py create mode 100644 libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py create mode 100644 libs/tracker/llmstudio_tracker/prompt_manager/manager.py create mode 100644 libs/tracker/llmstudio_tracker/prompt_manager/models.py create mode 100644 libs/tracker/llmstudio_tracker/prompt_manager/schemas.py diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/__init__.py b/libs/tracker/llmstudio_tracker/prompt_manager/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/crud.py b/libs/tracker/llmstudio_tracker/prompt_manager/crud.py new file mode 100644 index 00000000..f4341592 --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/crud.py @@ -0,0 +1,91 @@ +from llmstudio_tracker.prompt_manager import models, schemas +from sqlalchemy.orm import Session + + +def get_prompt_by_name_model_provider( + db: Session, name: str, model: str, provider: str +): + return ( + db.query(models.PromptDefault) + .filter( + models.PromptDefault.name == name, + models.PromptDefault.model == model, + models.PromptDefault.provider == provider, + models.PromptDefault.is_active == True, + ) + .order_by(models.PromptDefault.version.desc()) + .first() + ) + + +def get_prompt_by_id(db: Session, prompt_id: str): + return ( + db.query(models.PromptDefault) + .filter(models.PromptDefault.prompt_id == prompt_id) + .first() + ) + + +def get_prompt( + db: Session, + prompt_id: str = None, + name: str = None, + model: str = None, + provider: str = None, +): + if prompt_id: + return get_prompt_by_id(db, prompt_id) + else: + return get_prompt_by_name_model_provider(db, name, model, provider) + + +def add_prompt(db: Session, prompt: schemas.PromptDefault): + + prompt_created = models.PromptDefault.create_with_incremental_version( + db, + config=prompt.config, + prompt=prompt.prompt, + is_active=prompt.is_active, + name=prompt.name, + label=prompt.label, + model=prompt.model, + provider=prompt.provider, + ) + db.add(prompt_created) + db.commit() + db.refresh(prompt_created) + return prompt_created + + +def update_prompt(db: Session, prompt: schemas.PromptDefault): + if prompt.prompt_id: + existing_prompt = get_prompt_by_id(db, prompt.prompt_id) + else: + existing_prompt = get_prompt_by_name_model_provider( + db, prompt.name, prompt.model, prompt.provider + ) + + existing_prompt.config = prompt.config + existing_prompt.prompt = prompt.prompt + existing_prompt.is_active = prompt.is_active + existing_prompt.name = prompt.name + existing_prompt.model = prompt.model + existing_prompt.provider = prompt.provider + existing_prompt.version = prompt.version + existing_prompt.label = prompt.label + + db.commit() + db.refresh(existing_prompt) + return existing_prompt + + +def delete_prompt(db: Session, prompt: schemas.PromptDefault): + if prompt.prompt_id: + existing_prompt = get_prompt_by_id(db, prompt.prompt_id) + else: + existing_prompt = get_prompt_by_name_model_provider( + db, prompt.name, prompt.model, prompt.provider + ) + + db.delete(existing_prompt) + db.commit() diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py b/libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py new file mode 100644 index 00000000..bcffc07b --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/endpoints.py @@ -0,0 +1,56 @@ +from fastapi import APIRouter, Depends +from llmstudio_tracker.database import engine, get_db +from llmstudio_tracker.prompt_manager import crud, models, schemas +from sqlalchemy.orm import Session + +models.Base.metadata.create_all(bind=engine) + + +class PromptsRoutes: + def __init__(self, router: APIRouter): + self.router = router + self.define_routes() + + def define_routes(self): + self.router.post( + "/add/prompt", + response_model=schemas.PromptDefault, + )(self.add_prompt) + + self.router.get("/get/prompt", response_model=schemas.PromptDefault)( + self.get_prompt + ) + + self.router.patch("/update/prompt", response_model=schemas.PromptDefault)( + self.update_prompt + ) + + self.router.delete("/delete/prompt")(self.delete_prompt) + + async def add_prompt( + self, prompt: schemas.PromptDefault, db: Session = Depends(get_db) + ): + return crud.add_prompt(db=db, prompt=prompt) + + async def update_prompt( + self, prompt: schemas.PromptDefault, db: Session = Depends(get_db) + ): + return crud.update_prompt(db, prompt) + + async def get_prompt( + self, + prompt_info: schemas.PromptInfo, + db: Session = Depends(get_db), + ): + return crud.get_prompt( + db, + prompt_id=prompt_info.prompt_id, + name=prompt_info.name, + model=prompt_info.model, + provider=prompt_info.provider, + ) + + async def delete_prompt( + self, prompt: schemas.PromptDefault, db: Session = Depends(get_db) + ): + return crud.delete_prompt(db, prompt) diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/manager.py b/libs/tracker/llmstudio_tracker/prompt_manager/manager.py new file mode 100644 index 00000000..7f47ec09 --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/manager.py @@ -0,0 +1,61 @@ +import json + +import requests +from llmstudio_tracker.prompt_manager.schemas import PromptDefault +from llmstudio_tracker.tracker import TrackingConfig + + +class PromptManager: + def __init__(self, tracking_config: TrackingConfig): + self.tracking_url = tracking_config.url + self._session = requests.Session() + + def add_prompt(self, prompt: PromptDefault): + req = self._session.post( + f"{self.tracking_url}/api/tracking/add/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + data=prompt.model_dump_json(), + timeout=100, + ) + return req + + def delete_prompt(self, prompt: PromptDefault): + req = self._session.delete( + f"{self.tracking_url}/api/tracking/delete/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + data=prompt.model_dump_json(), + timeout=100, + ) + return req + + def update_prompt(self, prompt: PromptDefault): + req = self._session.patch( + f"{self.tracking_url}/api/tracking/update/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + data=prompt.model_dump_json(), + timeout=100, + ) + return req + + def get_prompt( + self, + prompt_id: str = None, + name: str = None, + model: str = None, + provider: str = None, + ): + + data = { + "prompt_id": prompt_id, + "name": name, + "model": model, + "provider": provider, + } + + req = self._session.get( + f"{self.tracking_url}/api/tracking/get/prompt", + headers={"accept": "application/json", "Content-Type": "application/json"}, + timeout=100, + data=json.dumps(data), + ) + return req diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/models.py b/libs/tracker/llmstudio_tracker/prompt_manager/models.py new file mode 100644 index 00000000..2d96e35b --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/models.py @@ -0,0 +1,102 @@ +import uuid +from datetime import datetime, timezone + +from llmstudio_tracker.config import DB_TYPE +from llmstudio_tracker.database import Base +from llmstudio_tracker.db_utils import JSONEncodedDict +from sqlalchemy import ( + JSON, + Boolean, + Column, + DateTime, + Integer, + String, + UniqueConstraint, + event, + func, +) +from sqlalchemy.orm import Session + + +class PromptDefault(Base): + __tablename__ = "prompts" + + if DB_TYPE == "bigquery": + prompt_id = Column( + String, + primary_key=True, + default=lambda: str(uuid.uuid4()), + ) + config = Column(JSONEncodedDict, nullable=True) + else: + prompt_id = Column( + String, primary_key=True, default=lambda: str(uuid.uuid4()) + ) # Generate UUID as a string + config = Column(JSON, nullable=True) + + prompt = Column(String) + is_active = Column(Boolean, default=False) + name = Column(String, nullable=False) + model = Column(String, nullable=False) + provider = Column(String, nullable=False) + version = Column(Integer, nullable=False) + label = Column(String) + updated_at = Column( + DateTime(timezone=True), + onupdate=lambda: datetime.now(timezone.utc), + default=lambda: datetime.now(timezone.utc), + ) + created_at = Column( + DateTime(timezone=True), default=lambda: datetime.now(timezone.utc) + ) + + __table_args__ = ( + UniqueConstraint( + "name", "provider", "model", "version", name="uq_prompt_version" + ), + ) + + @staticmethod + def get_next_version(session, name, model, provider): + """ + Get the next version number for a combination of name, model, and provider. + """ + max_version = ( + session.query(func.max(PromptDefault.version)) + .filter_by(name=name, model=model, provider=provider) + .scalar() + ) + return (max_version or 0) + 1 + + @classmethod + def create_with_incremental_version(cls, session, **kwargs): + """ + Create a new PromptDefault entry with an incremental version. + """ + name = kwargs.get("name") + model = kwargs.get("model") + provider = kwargs.get("provider") + if not all([name, model, provider]): + raise ValueError("name, model, and provider must be provided") + + kwargs["version"] = cls.get_next_version(session, name, model, provider) + + instance = cls(**kwargs) + session.add(instance) + return instance + + @event.listens_for(Session, "before_flush") + def ensure_single_active_prompt(session, flush_context, instances): + """ + Ensures only one PromptDefault entry per (name, model, provider) can have is_active=True. + If a new entry is set as is_active=True, deactivate others in the same group. + """ + for instance in session.new.union(session.dirty): + if isinstance(instance, PromptDefault) and instance.is_active: + session.query(PromptDefault).filter( + PromptDefault.name == instance.name, + PromptDefault.model == instance.model, + PromptDefault.provider == instance.provider, + PromptDefault.is_active == True, + PromptDefault.prompt_id != instance.prompt_id, + ).update({"is_active": False}, synchronize_session="fetch") diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py b/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py new file mode 100644 index 00000000..3209f8ee --- /dev/null +++ b/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py @@ -0,0 +1,25 @@ +from datetime import datetime +from typing import Dict, Optional + +from pydantic import BaseModel + + +class PromptInfo(BaseModel): + prompt_id: Optional[str] = None + name: Optional[str] = None + model: Optional[str] = None + provider: Optional[str] = None + + +class PromptDefault(BaseModel): + prompt_id: Optional[str] = None + config: Optional[Dict] = {} + prompt: str + is_active: Optional[bool] = None + name: str + version: Optional[int] = None + label: Optional[str] = "production" + model: str + provider: str + updated_at: Optional[datetime] = None + created_at: Optional[datetime] = None diff --git a/libs/tracker/llmstudio_tracker/server.py b/libs/tracker/llmstudio_tracker/server.py index ed22c94c..8caff406 100644 --- a/libs/tracker/llmstudio_tracker/server.py +++ b/libs/tracker/llmstudio_tracker/server.py @@ -6,6 +6,7 @@ from fastapi.middleware.cors import CORSMiddleware from llmstudio_tracker.config import TRACKING_HOST, TRACKING_PORT from llmstudio_tracker.logs.endpoints import LogsRoutes +from llmstudio_tracker.prompt_manager.endpoints import PromptsRoutes from llmstudio_tracker.session.endpoints import SessionsRoutes from llmstudio_tracker.utils import get_current_version @@ -42,6 +43,7 @@ def health_check(): tracking_router = APIRouter(prefix=TRACKING_BASE_ENDPOINT) LogsRoutes(tracking_router) SessionsRoutes(tracking_router) + PromptsRoutes(tracking_router) app.include_router(tracking_router) From 549e3885711be581faaef95e9167afe7c5adc419 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Thu, 23 Jan 2025 14:37:01 +0000 Subject: [PATCH 10/38] [fix] bump prerelease version in pyproject.toml --- libs/tracker/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/tracker/pyproject.toml b/libs/tracker/pyproject.toml index ada25d21..c5bb8400 100644 --- a/libs/tracker/pyproject.toml +++ b/libs/tracker/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-tracker" -version = "1.0.5" +version = "1.1.0a0" description = "LLMstudio Tracker is the module of LLMstudio that allows monitoring and logging your LLM calls. By leveraging LLMstudio Tracker, users can gain insights on model performance and streamline development workflows with actionable analytics." authors = ["Claudio Lemos "] readme = "README.md" From c6cdad0488ac054a92a39370eae8de66f94ba5fd Mon Sep 17 00:00:00 2001 From: brunoalho Date: Wed, 29 Jan 2025 11:47:02 +0000 Subject: [PATCH 11/38] [bugfix] return empty prompt --- .../llmstudio_tracker/prompt_manager/crud.py | 14 ++++++++++---- .../llmstudio_tracker/prompt_manager/schemas.py | 8 ++++---- 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/crud.py b/libs/tracker/llmstudio_tracker/prompt_manager/crud.py index f4341592..7cff7362 100644 --- a/libs/tracker/llmstudio_tracker/prompt_manager/crud.py +++ b/libs/tracker/llmstudio_tracker/prompt_manager/crud.py @@ -33,10 +33,16 @@ def get_prompt( model: str = None, provider: str = None, ): - if prompt_id: - return get_prompt_by_id(db, prompt_id) - else: - return get_prompt_by_name_model_provider(db, name, model, provider) + prompt = ( + get_prompt_by_id(db, prompt_id) + if prompt_id + else get_prompt_by_name_model_provider(db, name, model, provider) + ) + + if not prompt: + return schemas.PromptDefault() + + return prompt def add_prompt(db: Session, prompt: schemas.PromptDefault): diff --git a/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py b/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py index 3209f8ee..7ddbf992 100644 --- a/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py +++ b/libs/tracker/llmstudio_tracker/prompt_manager/schemas.py @@ -14,12 +14,12 @@ class PromptInfo(BaseModel): class PromptDefault(BaseModel): prompt_id: Optional[str] = None config: Optional[Dict] = {} - prompt: str + prompt: Optional[str] = None is_active: Optional[bool] = None - name: str + name: Optional[str] = None version: Optional[int] = None label: Optional[str] = "production" - model: str - provider: str + model: Optional[str] = None + provider: Optional[str] = None updated_at: Optional[datetime] = None created_at: Optional[datetime] = None From 2a5b4fb2c6147f4c03ac1cfc6740e89e46bb4301 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 29 Jan 2025 12:22:15 +0000 Subject: [PATCH 12/38] [fix] bump prerelease version in pyproject.toml --- libs/tracker/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/tracker/pyproject.toml b/libs/tracker/pyproject.toml index c5bb8400..395220df 100644 --- a/libs/tracker/pyproject.toml +++ b/libs/tracker/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-tracker" -version = "1.1.0a0" +version = "1.1.0a1" description = "LLMstudio Tracker is the module of LLMstudio that allows monitoring and logging your LLM calls. By leveraging LLMstudio Tracker, users can gain insights on model performance and streamline development workflows with actionable analytics." authors = ["Claudio Lemos "] readme = "README.md" From d3303036d85ba85e07a20adcd490aba277b6e2e2 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Wed, 29 Jan 2025 12:37:23 +0000 Subject: [PATCH 13/38] Update CONTRIBUTING.md Signed-off-by: Diogo Goncalves --- CONTRIBUTING.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6ee1c7ad..a637756c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,9 +9,30 @@ Thank you for expressing your interest in contributing to LLMstudio. To ensure t 3. Follow our repo guidelines - Ensure that you update any relevant docstrings and comments within your code - Run `pre-commit run --all-files` to lint your code +4. Sign your commits. Without signed commits, your changes will not be accepted for main. ## Branches - All development happens in per-feature branches prefixed by contributor's initials. For example `feat/feature_name`. - Approved PRs are merged to the `main` branch. + +## Alpha releases: +You need to have your changes in the `develop` branch in order to push a new alpha version of any library `(llmstudio, llmstudio-proxy, llmstudio-tracker)`. Therefore, first guarantee that you feature branch is reviewed and working before merging to develop. + +Process: +- Ensure the `feature/**` you worked is passing the tests and has the approvals necessary. +- Merge to `develop` +- Ensure the changes are in the develop branch +- Use GitHub Actions to initiate the pre-release process: [PyPI pre-release any module](https://github.com/TensorOpsAI/LLMstudio/actions/workflows/upload-pypi-dev.yml) +- Select the target library `(llmstudio, llmstudio-proxy, llmstudio-tracker)` and the target version for the final release (e.g., 1.1.0). Consult main branch and PyPI for current versions. +- Run the workflow. +- The workflow will automatically bump the version and create an alpha release of the library/module specified +- The workflow will automatically push changes back (bump version) to the develop branch + +Repeat the process in case your `development` branch contains changes in multiple libraries. + +## Final releases: +Once you're happy with the versions, create the Release notes on the PR between `develop` and `main` and merge to main branch when ready for full release. The workflow will automatically remove any `alpha` tag in your libraries and push the versions for every library/module that suffered changes. + + From 21e9352b9ebad4096ca02f446a647634e3384a4b Mon Sep 17 00:00:00 2001 From: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> Date: Thu, 30 Jan 2025 15:07:59 +0000 Subject: [PATCH 14/38] Feat/ Use Openai Usage to calculate Cache and Reasoning Costs (#199) * feat: collects usage from stream and non stream openai calls * chore: refactored to provider to have a Metrics obj * feat: calculate_metrics now takes into account cached & reasoning tokens. Prices of openai models updated * fix: added caching tokens to model config obj * chore: added integration test for cache and reasoning * chore: added integration test for usage retrieval when max tokens reached * chore: uncommented runs from examples/core.py * fix: bugfix regarding usage on function calling. added a test for this * chore: merged with develop * chore: extracted provider data structures to another file * chore: renamed to private methods some within provider. splitted integration tests into 2 files * chore: deletion of a todo comment --- examples/_config.yaml | 17 +- examples/core.py | 131 ++++---- examples/langchain_integration.py | 20 +- libs/core/llmstudio_core/config.yaml | 15 +- .../providers/data_structures.py | 148 +++++++++ libs/core/llmstudio_core/providers/openai.py | 1 + .../core/llmstudio_core/providers/provider.py | 304 +++++++++++------- libs/core/llmstudio_core/utils.py | 1 + .../test_cache_and_reasoning_costs.py | 193 +++++++++++ .../test_langchain_agents.py | 93 ++++++ libs/core/tests/unit_tests/conftest.py | 2 +- libs/core/tests/unit_tests/test_provider.py | 22 +- .../test_provider_costs_and_metrics.py | 16 +- .../test_provider_handle_response.py | 60 ++-- libs/llmstudio/llmstudio/langchain.py | 2 +- libs/proxy/llmstudio_proxy/server.py | 12 +- 16 files changed, 797 insertions(+), 240 deletions(-) create mode 100644 libs/core/llmstudio_core/providers/data_structures.py create mode 100644 libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py create mode 100644 libs/core/tests/integration_tests/test_langchain_agents.py diff --git a/examples/_config.yaml b/examples/_config.yaml index ae82394b..12f84a16 100644 --- a/examples/_config.yaml +++ b/examples/_config.yaml @@ -115,16 +115,29 @@ providers: keys: - OPENAI_API_KEY models: + o1-preview: + mode: chat + max_completion_tokens: 128000 + input_token_cost: 0.000015 + output_token_cost: 0.000060 + o1-mini: + mode: chat + max_completion_tokens: 128000 + input_token_cost: 0.000003 + cached_token_cost: 0.0000015 + output_token_cost: 0.000012 gpt-4o-mini: mode: chat max_tokens: 128000 input_token_cost: 0.00000015 + cached_token_cost: 0.000000075 output_token_cost: 0.00000060 gpt-4o: mode: chat max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 + input_token_cost: 0.0000025 + cached_token_cost: 0.00000125 + output_token_cost: 0.00001 gpt-4-turbo: mode: chat max_tokens: 128000 diff --git a/examples/core.py b/examples/core.py index 6cae128a..255bcae4 100644 --- a/examples/core.py +++ b/examples/core.py @@ -4,22 +4,59 @@ from pprint import pprint import os +import asyncio from dotenv import load_dotenv load_dotenv() def run_provider(provider, model, api_key, **kwargs): + print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") llm = LLMCore(provider=provider, api_key=api_key, **kwargs) latencies = {} + + print("\nAsync Non-Stream") chat_request = build_chat_request(model, chat_input="Hello, my name is Jason Json", is_stream=False) + string = """ +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + + """ + #chat_request = build_chat_request(model, chat_input=string, is_stream=False) + - import asyncio response_async = asyncio.run(llm.achat(**chat_request)) pprint(response_async) latencies["async (ms)"]= response_async.metrics["latency_s"]*1000 - - # stream - print("\nasync stream") + + + print("\nAsync Stream") async def async_stream(): chat_request = build_chat_request(model, chat_input="Hello, my name is Tom Json", is_stream=True) @@ -36,14 +73,15 @@ async def async_stream(): asyncio.run(async_stream()) - print("# Now sync calls") + print("\nSync Non-Stream") chat_request = build_chat_request(model, chat_input="Hello, my name is Alice Json", is_stream=False) response_sync = llm.chat(**chat_request) pprint(response_sync) latencies["sync (ms)"]= response_sync.metrics["latency_s"]*1000 + - print("# Now sync calls streaming") + print("\nSync Stream") chat_request = build_chat_request(model, chat_input="Hello, my name is Mary Json", is_stream=True) response_sync_stream = llm.chat(**chat_request) @@ -85,72 +123,33 @@ def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: return chat_request +def multiple_provider_runs(provider:str, model:str, num_runs:int, api_key:str, **kwargs): + for _ in range(num_runs): + latencies = run_provider(provider=provider, model=model, api_key=api_key, **kwargs) + pprint(latencies) + + +# OpenAI +multiple_provider_runs(provider="openai", model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) +multiple_provider_runs(provider="openai", model="o1-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) +#multiple_provider_runs(provider="openai", model="o1-preview", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) -provider = "openai" -model = "gpt-4o-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) +# Azure +multiple_provider_runs(provider="azure", model="gpt-4o-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="gpt-4o", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - -provider = "openai" -model = "o1-preview" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) - -provider = "openai" -model = "o1-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"]) - pprint(latencies) - -# provider = "anthropic" -# model = "claude-3-opus-20240229" -# for _ in range(1): -# latencies = run_provider(provider=provider, model=model, api_key=os.environ["ANTHROPIC_API_KEY"]) -# pprint(latencies) -# # we need credits - -provider = "azure" -model = "gpt-4o-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) - -provider = "azure" -model = "o1-preview" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) - -provider = "azure" -model = "o1-mini" -for _ in range(1): - latencies = run_provider(provider=provider, model=model, - api_key=os.environ["AZURE_API_KEY"], - api_version=os.environ["AZURE_API_VERSION"], - api_endpoint=os.environ["AZURE_API_ENDPOINT"]) - pprint(latencies) - -# provider = "azure" -# model = "gpt-4o" -# for _ in range(1): -# latencies = run_provider(provider=provider, model=model, -# api_key=os.environ["AZURE_API_KEY_llama"], -# base_url=os.environ["AZURE_BASE_URL"] -# ) -# pprint(latencies) + +#multiple_provider_runs(provider="anthropic", model="claude-3-opus-20240229", num_runs=1, api_key=os.environ["ANTHROPIC_API_KEY"]) + +#multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +#multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) +multiple_provider_runs(provider="vertexai", model="gemini-1.5-flash", num_runs=1, api_key=os.environ["GOOGLE_API_KEY"]) # provider = "vertexai" # model = "gemini-1.5-pro-latest" # for _ in range(1): diff --git a/examples/langchain_integration.py b/examples/langchain_integration.py index 4d408535..51940624 100644 --- a/examples/langchain_integration.py +++ b/examples/langchain_integration.py @@ -18,7 +18,12 @@ # %% from langchain.tools import tool -from langchain.agents import AgentType, initialize_agent +from langchain.agents import AgentType, initialize_agent, AgentExecutor + +from langchain.agents.openai_functions_agent.base import ( + create_openai_functions_agent, +) +from langchain import hub # # %% # print("\n", chat_llm.invoke('Hello')) @@ -192,9 +197,16 @@ def assistant(question: str)->str: tools = [power_disco_ball, start_music, dim_lights] print(tools) - #rebuild agent with new tools - agent_executor = initialize_agent( - tools, chat_llm, agent=AgentType.OPENAI_FUNCTIONS, verbose = True, debug = True + #rebuild agent with new tools - This is the old outdated way of using agents in langchain + #agent_executor = initialize_agent( + # tools, chat_llm, agent=AgentType.OPENAI_FUNCTIONS, verbose = True, debug = True + #) + prompt = hub.pull("hwchase17/openai-functions-agent") + + agent = create_openai_functions_agent(llm=chat_llm, tools=tools, prompt=prompt) + + agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, return_intermediate_steps=True ) response = agent_executor.invoke( diff --git a/libs/core/llmstudio_core/config.yaml b/libs/core/llmstudio_core/config.yaml index 5f0920b2..86b84c96 100644 --- a/libs/core/llmstudio_core/config.yaml +++ b/libs/core/llmstudio_core/config.yaml @@ -208,22 +208,26 @@ providers: mode: chat max_completion_tokens: 128000 input_token_cost: 0.000015 + cached_token_cost: 0.0000075 output_token_cost: 0.000060 o1-mini: mode: chat max_completion_tokens: 128000 input_token_cost: 0.000003 + cached_token_cost: 0.0000015 output_token_cost: 0.000012 gpt-4o-mini: mode: chat max_tokens: 128000 input_token_cost: 0.00000015 + cached_token_cost: 0.000000075 output_token_cost: 0.00000060 gpt-4o: mode: chat max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 + input_token_cost: 0.0000025 + cached_token_cost: 0.00000125 + output_token_cost: 0.00001 gpt-4-turbo: mode: chat max_tokens: 128000 @@ -294,11 +298,13 @@ providers: mode: chat max_completion_tokens: 128000 input_token_cost: 0.0000165 + cached_token_cost: 0.00000825 output_token_cost: 0.000066 o1-mini: mode: chat max_completion_tokens: 128000 input_token_cost: 0.0000033 + cached_token_cost: 0.00000165 output_token_cost: 0.0000132 gpt-4o-mini: mode: chat @@ -308,8 +314,9 @@ providers: gpt-4o: mode: chat max_tokens: 128000 - input_token_cost: 0.000005 - output_token_cost: 0.000015 + input_token_cost: 0.0000025 + cached_token_cost: 0.00000125 + output_token_cost: 0.00001 gpt-4-turbo: mode: chat max_tokens: 128000 diff --git a/libs/core/llmstudio_core/providers/data_structures.py b/libs/core/llmstudio_core/providers/data_structures.py new file mode 100644 index 00000000..374ad6eb --- /dev/null +++ b/libs/core/llmstudio_core/providers/data_structures.py @@ -0,0 +1,148 @@ +from typing import Any, List, Optional + +from openai.types.chat import ChatCompletion, ChatCompletionChunk +from pydantic import BaseModel + + +class Metrics(BaseModel): + input_tokens: int + """Number of tokens in the input.""" + + output_tokens: int + """Number of tokens in the output.""" + + reasoning_tokens: int + """Number of reasoning tokens used by the model.""" + + total_tokens: int + """Total token count (input + output + reasoning).""" + + cached_tokens: int + """Number of cached tokens which will lower the price of input.""" + + cost_usd: float + """Total cost of the response in USD (input + output + reasoning - cached).""" + + latency_s: float + """Total time taken for the response, in seconds.""" + + time_to_first_token_s: Optional[float] = None + """Time to receive the first token, in seconds.""" + + inter_token_latency_s: Optional[float] = None + """Average time between tokens, in seconds. Defaults to None if not provided.""" + + tokens_per_second: Optional[float] = None + """Processing rate of tokens per second. Defaults to None if not provided.""" + + def __getitem__(self, key: str) -> Any: + """ + Allows subscriptable access to class fields. + + Parameters + ---------- + key : str + The name of the field to retrieve. + + Returns + ------- + Any + The value of the specified field. + + Raises + ------ + KeyError + If the key does not exist. + """ + try: + return getattr(self, key) + except AttributeError: + raise KeyError(f"'{key}' not found in MetricsStream.") + + def __iter__(self): + """ + Allows iteration over the class fields as key-value pairs. + """ + return iter(self.model_dump().items()) + + def __len__(self): + """ + Returns the number of fields in the class. + """ + return len(self.model_fields) + + def keys(self): + """ + Returns the keys of the fields. + """ + return self.model_fields.keys() + + def values(self): + """ + Returns the values of the fields. + """ + return self.model_dump().values() + + def items(self): + """ + Returns the key-value pairs of the fields. + """ + return self.model_dump().items() + + +class ChatCompletionLLMstudio(ChatCompletion): + chat_input: str + """The input prompt for the chat completion.""" + + chat_output: str + """The final response generated by the model.""" + + chat_output_stream: str + """Incremental chunks of the response for streaming.""" + + context: List[dict] + """The conversation history or context provided to the model.""" + + provider: str + """Identifier for the backend service provider.""" + + deployment: Optional[str] + """Information about the deployment configuration used.""" + + timestamp: float + """The timestamp when the response was generated.""" + + parameters: dict + """The parameters used in the request.""" + + metrics: Optional[Metrics] + """Performance and usage metrics calculated for the response.""" + + +class ChatCompletionChunkLLMstudio(ChatCompletionChunk): + chat_input: str + """The input prompt for the chat completion.""" + + chat_output: Optional[str] + """The final response generated by the model.""" + + chat_output_stream: Optional[str] + """Incremental chunks of the response for streaming.""" + + context: List[dict] + """The conversation history or context provided to the model.""" + + provider: str + """Identifier for the backend service provider.""" + + deployment: Optional[str] + """Information about the deployment configuration used.""" + + timestamp: float + """The timestamp when the chunk was generated.""" + + parameters: dict + """The parameters used in the request.""" + + metrics: Optional[Metrics] + """Performance and usage metrics calculated for the response chunk.""" diff --git a/libs/core/llmstudio_core/providers/openai.py b/libs/core/llmstudio_core/providers/openai.py index e75fcad4..d4c86536 100644 --- a/libs/core/llmstudio_core/providers/openai.py +++ b/libs/core/llmstudio_core/providers/openai.py @@ -39,6 +39,7 @@ def generate_client(self, request: ChatRequest) -> Generator: else request.chat_input ), stream=True, + stream_options={"include_usage": True}, **request.parameters, ) except openai._exceptions.APIError as e: diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 69a673dd..3cb9ad57 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -15,6 +15,11 @@ import tiktoken from llmstudio_core.exceptions import ProviderError +from llmstudio_core.providers.data_structures import ( + ChatCompletionChunkLLMstudio, + ChatCompletionLLMstudio, + Metrics, +) from openai.types.chat import ( ChatCompletion, ChatCompletionChunk, @@ -137,6 +142,10 @@ async def aparse_response(self, response: AsyncGenerator, **kwargs) -> Any: def parse_response(self, response: AsyncGenerator, **kwargs) -> Any: raise NotImplementedError("ProviderCore needs a parse_response method.") + @abstractmethod + def get_usage(self, response: AsyncGenerator, **kwargs) -> Any: + raise NotImplementedError("ProviderCore needs a get_usage method.") + def validate_model(self, request: ChatRequest): if request.model not in self.config.models: raise ProviderError( @@ -204,7 +213,7 @@ async def achat( try: start_time = time.time() response = await self.agenerate_client(request) - response_handler = self.ahandle_response(request, response, start_time) + response_handler = self._ahandle_response(request, response, start_time) if request.is_stream: return response_handler @@ -280,7 +289,7 @@ def chat( try: start_time = time.time() response = self.generate_client(request) - response_handler = self.handle_response(request, response, start_time) + response_handler = self._handle_response(request, response, start_time) if request.is_stream: return response_handler @@ -295,7 +304,7 @@ def chat( raise ProviderError(str(e)) raise ProviderError("Too many requests") - async def ahandle_response( + async def _ahandle_response( self, request: ChatRequest, response: AsyncGenerator, start_time: float ) -> AsyncGenerator[str, None]: """ @@ -325,6 +334,8 @@ async def ahandle_response( token_times = [] token_count = 0 chunks = [] + is_next_usage = False + usage = {} async for chunk in self.aparse_response(response, request=request): token_count += 1 @@ -334,50 +345,57 @@ async def ahandle_response( token_times.append(current_time - previous_token_time) previous_token_time = current_time + if is_next_usage: + usage = self.get_usage(chunk) + break + chunks.append(chunk) + finish_reason = chunk.get("choices")[0].get("finish_reason") + if finish_reason: + is_next_usage = True + if request.is_stream: chunk = chunk[0] if isinstance(chunk, tuple) else chunk model = chunk.get("model") - if chunk.get("choices")[0].get("finish_reason") != "stop": - chat_output = chunk.get("choices")[0].get("delta").get("content") - chunk = { - **chunk, - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": None, - "chat_output_stream": chat_output if chat_output else "", - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": None, - } - yield ChatCompletionChunk(**chunk) + chat_output = chunk.get("choices")[0].get("delta").get("content") + chunk = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": None, + "chat_output_stream": chat_output if chat_output else "", + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunkLLMstudio(**chunk) chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks) + response, output_string = self._join_chunks(chunks) - metrics = self.calculate_metrics( + metrics = self._calculate_metrics( request.chat_input, response, request.model, @@ -386,6 +404,8 @@ async def ahandle_response( first_token_time, token_times, token_count, + is_stream=request.is_stream, + usage=usage, ) response = { @@ -420,11 +440,11 @@ async def ahandle_response( } if request.is_stream: - yield ChatCompletionChunk(**response) + yield ChatCompletionChunkLLMstudio(**response) else: - yield ChatCompletion(**response) + yield ChatCompletionLLMstudio(**response) - def handle_response( + def _handle_response( self, request: ChatRequest, response: Generator, start_time: float ) -> Generator: """ @@ -455,6 +475,8 @@ def handle_response( token_times = [] token_count = 0 chunks = [] + is_next_usage = False + usage = {} for chunk in self.parse_response(response, request=request): token_count += 1 @@ -464,50 +486,58 @@ def handle_response( token_times.append(current_time - previous_token_time) previous_token_time = current_time + if is_next_usage: + usage = self.get_usage(chunk) + break + chunks.append(chunk) + finish_reason = chunk.get("choices")[0].get("finish_reason") + if finish_reason: + is_next_usage = True + if request.is_stream: chunk = chunk[0] if isinstance(chunk, tuple) else chunk model = chunk.get("model") - if chunk.get("choices")[0].get("finish_reason") != "stop": - chat_output = chunk.get("choices")[0].get("delta").get("content") - chunk = { - **chunk, - "id": str(uuid.uuid4()), - "chat_input": ( - request.chat_input - if isinstance(request.chat_input, str) - else request.chat_input[-1]["content"] - ), - "chat_output": None, - "chat_output_stream": chat_output if chat_output else "", - "context": ( - [{"role": "user", "content": request.chat_input}] - if isinstance(request.chat_input, str) - else request.chat_input - ), - "provider": self.config.id, - "model": ( - request.model - if model and model.startswith(request.model) - else (model or request.model) - ), - "deployment": ( - model - if model and model.startswith(request.model) - else (request.model if model != request.model else None) - ), - "timestamp": time.time(), - "parameters": request.parameters, - "metrics": None, - } - yield ChatCompletionChunk(**chunk) + + chat_output = chunk.get("choices")[0].get("delta").get("content") + chunk = { + **chunk, + "id": str(uuid.uuid4()), + "chat_input": ( + request.chat_input + if isinstance(request.chat_input, str) + else request.chat_input[-1]["content"] + ), + "chat_output": None, + "chat_output_stream": chat_output if chat_output else "", + "context": ( + [{"role": "user", "content": request.chat_input}] + if isinstance(request.chat_input, str) + else request.chat_input + ), + "provider": self.config.id, + "model": ( + request.model + if model and model.startswith(request.model) + else (model or request.model) + ), + "deployment": ( + model + if model and model.startswith(request.model) + else (request.model if model != request.model else None) + ), + "timestamp": time.time(), + "parameters": request.parameters, + "metrics": None, + } + yield ChatCompletionChunkLLMstudio(**chunk) chunks = [chunk[0] if isinstance(chunk, tuple) else chunk for chunk in chunks] model = next(chunk["model"] for chunk in chunks if chunk.get("model")) - response, output_string = self.join_chunks(chunks) + response, output_string = self._join_chunks(chunks) - metrics = self.calculate_metrics( + metrics = self._calculate_metrics( request.chat_input, response, request.model, @@ -516,6 +546,8 @@ def handle_response( first_token_time, token_times, token_count, + is_stream=request.is_stream, + usage=usage, ) response = { @@ -550,11 +582,11 @@ def handle_response( } if request.is_stream: - yield ChatCompletionChunk(**response) + yield ChatCompletionChunkLLMstudio(**response) else: - yield ChatCompletion(**response) + yield ChatCompletionLLMstudio(**response) - def join_chunks(self, chunks): + def _join_chunks(self, chunks): """ Combine multiple response chunks from the model into a single, structured response. Handles tool calls, function calls, and standard text completion based on the @@ -726,7 +758,7 @@ def join_chunks(self, chunks): stop_content, ) - def calculate_metrics( + def _calculate_metrics( self, input: Any, output: Any, @@ -736,7 +768,9 @@ def calculate_metrics( first_token_time: float, token_times: Tuple[float, ...], token_count: int, - ) -> Dict[str, Any]: + is_stream: bool, + usage: Dict = {}, + ) -> Metrics: """ Calculates performance and cost metrics for a model response based on timing information, token counts, and model-specific costs. @@ -762,41 +796,78 @@ def calculate_metrics( Returns ------- - Dict[str, Any] - A dictionary containing calculated metrics, including: - - `input_tokens`: Number of tokens in the input. - - `output_tokens`: Number of tokens in the output. - - `total_tokens`: Total token count (input + output). - - `cost_usd`: Total cost of the response in USD. - - `latency_s`: Total time taken for the response, in seconds. - - `time_to_first_token_s`: Time to receive the first token, in seconds. - - `inter_token_latency_s`: Average time between tokens, in seconds. If `token_times` is empty sets it to 0. - - `tokens_per_second`: Processing rate of tokens per second. + Metrics """ + model_config = self.config.models[model] - input_tokens = len(self.tokenizer.encode(self.input_to_string(input))) - output_tokens = len(self.tokenizer.encode(self.output_to_string(output))) - input_cost = self.calculate_cost(input_tokens, model_config.input_token_cost) - output_cost = self.calculate_cost(output_tokens, model_config.output_token_cost) + # Token counts + cached_tokens = 0 + reasoning_tokens = 0 + input_tokens = len(self.tokenizer.encode(self._input_to_string(input))) + output_tokens = len(self.tokenizer.encode(self._output_to_string(output))) + total_tokens = input_tokens + output_tokens + + # Cost calculations + input_cost = self._calculate_cost(input_tokens, model_config.input_token_cost) + output_cost = self._calculate_cost( + output_tokens, model_config.output_token_cost + ) + total_cost_usd = input_cost + output_cost + + if usage: + if getattr(model_config, "cached_token_cost", None): + cached_tokens = usage["prompt_tokens_details"]["cached_tokens"] + cached_savings = self._calculate_cost( + cached_tokens, model_config.cached_token_cost + ) + total_cost_usd -= cached_savings + reasoning_tokens = usage.get("completion_tokens_details", {}).get( + "reasoning_tokens", None + ) + if reasoning_tokens: + total_tokens += reasoning_tokens + reasoning_cost = self._calculate_cost( + reasoning_tokens, model_config.output_token_cost + ) # billed as output tokens + print(f"Reasoning Cost: {reasoning_cost}") + total_cost_usd += reasoning_cost + + # Latency calculations total_time = end_time - start_time - return { - "input_tokens": input_tokens, - "output_tokens": output_tokens, - "total_tokens": input_tokens + output_tokens, - "cost_usd": input_cost + output_cost, - "latency_s": total_time, - "time_to_first_token_s": first_token_time - start_time, - "inter_token_latency_s": sum(token_times) / len(token_times) - if token_times - else 0, - "tokens_per_second": token_count / total_time - if token_times - else 1 / total_time, - } - def calculate_cost( + if is_stream: + time_to_first_token = first_token_time - start_time + inter_token_latency = ( + sum(token_times) / len(token_times) if token_times else 0.0 + ) + tokens_per_second = token_count / total_time if total_time > 0 else 0.0 + + return Metrics( + input_tokens=input_tokens, + output_tokens=output_tokens, + reasoning_tokens=reasoning_tokens, + total_tokens=total_tokens, + cached_tokens=cached_tokens, + cost_usd=total_cost_usd, + latency_s=total_time, + time_to_first_token_s=time_to_first_token, + inter_token_latency_s=inter_token_latency, + tokens_per_second=tokens_per_second, + ) + else: + return Metrics( + input_tokens=input_tokens, + output_tokens=output_tokens, + reasoning_tokens=reasoning_tokens, + total_tokens=total_tokens, + cached_tokens=cached_tokens, + cost_usd=total_cost_usd, + latency_s=total_time, + ) + + def _calculate_cost( self, token_count: int, token_cost: Union[float, List[Dict[str, Any]]] ) -> float: """ @@ -830,7 +901,16 @@ def calculate_cost( return token_cost * token_count return 0 - def input_to_string(self, input): + def get_usage(self, chunk) -> Dict: + """ + Gets Usage Object from chunk - usually the last one. + Returns an empty dictionary if usage does not exist or is None. + """ + if not chunk or "usage" not in chunk or chunk["usage"] is None: + return {} + return dict(chunk["usage"]) + + def _input_to_string(self, input): """ Converts an input, which can be a string or a structured list of messages, into a single concatenated string. @@ -868,7 +948,7 @@ def input_to_string(self, input): result.append(url) return "".join(result) - def output_to_string(self, output): + def _output_to_string(self, output): """ Extracts and returns the content or arguments from the output based on the `finish_reason` of the first choice in `output`. diff --git a/libs/core/llmstudio_core/utils.py b/libs/core/llmstudio_core/utils.py index bf8a2a2b..70c090f4 100644 --- a/libs/core/llmstudio_core/utils.py +++ b/libs/core/llmstudio_core/utils.py @@ -33,6 +33,7 @@ class ModelConfig(BaseModel): max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens") max_completion_tokens: Optional[int] = None input_token_cost: Union[float, List["CostRange"]] + cached_token_cost: Optional[Union[float, List["CostRange"]]] = None output_token_cost: Union[float, List["CostRange"]] diff --git a/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py b/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py new file mode 100644 index 00000000..4a910814 --- /dev/null +++ b/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py @@ -0,0 +1,193 @@ +import asyncio +import os +from pprint import pprint + +import pytest +from dotenv import load_dotenv +from llmstudio_core.providers import LLMCore + +load_dotenv() + +# input prompt has to be >1024 tokens to auto cache on OpenAI +input_prompt = """ +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +After reading this answer just in one line, saying hello how are you in latin. +""" + + +def run_provider(provider, model, api_key, **kwargs): + print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") + llm = LLMCore(provider=provider, api_key=api_key, **kwargs) + + metrics = {} + + print("\nAsync Non-Stream") + + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) + + response_async = asyncio.run(llm.achat(**chat_request)) + pprint(response_async) + metrics["async non-stream"] = response_async.metrics + + print("\nAsync Stream") + + async def async_stream(): + chat_request = build_chat_request( + model, chat_input=input_prompt, is_stream=True + ) + + response_async = await llm.achat(**chat_request) + async for p in response_async: + if not p.metrics: + print("that: ", p.chat_output_stream) + if p.metrics: + pprint(p) + metrics["async stream"] = p.metrics + + asyncio.run(async_stream()) + + print("\nSync Non-Stream") + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) + + response_sync = llm.chat(**chat_request) + pprint(response_sync) + metrics["sync non-stream"] = response_sync.metrics + + print("\nSync Stream") + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=True) + + response_sync_stream = llm.chat(**chat_request) + for p in response_sync_stream: + if p.metrics: + pprint(p) + metrics["sync stream"] = p.metrics + + print(f"\n\n###REPORT for <{provider}>, <{model}> ###") + return metrics + + +def build_chat_request( + model: str, chat_input: str, is_stream: bool, max_tokens: int = 1000 +): + if model == "o1-preview" or model == "o1-mini": + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": {"max_completion_tokens": max_tokens}, + } + else: + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": max_tokens, + "response_format": {"type": "json_object"}, + "functions": None, + }, + } + return chat_request + + +# Fixture for provider-model pairs +@pytest.fixture( + scope="module", params=[("openai", "gpt-4o-mini"), ("openai", "o1-mini")] +) +def provider_model(request): + return request.param + + +# Fixture for metrics, computes them once per provider-model pair +@pytest.fixture(scope="module") +def metrics(provider_model): + provider, model = provider_model + print(f"Running provider {provider} with model {model}") + return run_provider( + provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"] + ) + + +# Test cache metrics (runs for all models) +def test_metrics_cache(provider_model, metrics): + provider, model = provider_model + at_least_one_cached = False + for current_metrics in metrics.values(): + print(current_metrics) + assert current_metrics["input_tokens"] > current_metrics["cached_tokens"] + if current_metrics["cached_tokens"] > 0: + at_least_one_cached = True + assert at_least_one_cached == True + print(f"All Cache Tests Passed for {provider} - {model}") + + +# Test reasoning metrics (only runs for o1-mini and o1-preview) +def test_metrics_reasoning(provider_model, metrics): + provider, model = provider_model + + # Skip test if model is not o1-mini or o1-preview + if model not in ["o1-mini", "o1-preview"]: + pytest.skip(f"Reasoning metrics test not applicable for model {model}") + + for current_metrics in metrics.values(): + assert current_metrics["reasoning_tokens"] > 0 + assert current_metrics["reasoning_tokens"] < current_metrics["total_tokens"] + assert ( + current_metrics["input_tokens"] + + current_metrics["output_tokens"] + + current_metrics["reasoning_tokens"] + == current_metrics["total_tokens"] + ) + print(f"All Reasoning Tests Passed for {provider} - {model}") + + +def usage_when_max_tokens_reached(): + """ + Usefull to test handling of Usage in other finish_reason scenarios + """ + provider, model = ("openai", "o1-mini") + api_key = os.environ["OPENAI_API_KEY"] + + llm = LLMCore(provider=provider, api_key=api_key) + chat_request = build_chat_request( + model, chat_input=input_prompt, is_stream=False, max_tokens=7 + ) + response = asyncio.run(llm.achat(**chat_request)) + + assert response.metrics["async non-stream"]["reasoning_tokens"] > 0 + assert response.metrics["sync non-stream"]["reasoning_tokens"] > 0 + assert response.metrics["async stream"]["reasoning_tokens"] > 0 + assert response.metrics["sync stream"]["reasoning_tokens"] > 0 + print(f"All Max Tokens Usage Reached Tests Passed for {provider} - {model}") diff --git a/libs/core/tests/integration_tests/test_langchain_agents.py b/libs/core/tests/integration_tests/test_langchain_agents.py new file mode 100644 index 00000000..da3562b5 --- /dev/null +++ b/libs/core/tests/integration_tests/test_langchain_agents.py @@ -0,0 +1,93 @@ +import pytest +from langchain import hub +from langchain.agents import AgentExecutor, create_openai_tools_agent +from langchain.agents.openai_functions_agent.base import create_openai_functions_agent +from langchain.tools import tool +from langchain_core.prompts import ChatPromptTemplate +from llmstudio.langchain import ChatLLMstudio +from llmstudio.providers import LLM + + +@tool +def power_disco_ball(power: bool) -> bool: + """Powers the spinning disco ball.""" + print(f"Disco ball is {'spinning!' if power else 'stopped.'}") + return True + + +@tool +def start_music(energetic: bool, loud: bool, bpm: int) -> str: + """Play some music matching the specified parameters.""" + print(f"Starting music! {energetic=} {loud=}, {bpm=}") + return "Never gonna give you up." + + +@tool +def dim_lights(brightness: float) -> bool: + """Dim the lights.""" + print(f"Lights are now set to {brightness:.0%}") + return True + + +def assistant_functions(chat_llm: ChatLLMstudio, question: str) -> str: + tools = [power_disco_ball, start_music, dim_lights] + + prompt = hub.pull("hwchase17/openai-functions-agent") + + agent = create_openai_functions_agent(llm=chat_llm, tools=tools, prompt=prompt) + + agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, return_intermediate_steps=True + ) + + response = agent_executor.invoke({"input": question}) + + return response + + +@pytest.mark.parametrize( + "provider, model", + [ + ("openai", "gpt-4o-mini"), + ], +) +def test_function_calling(provider, model): + llm = LLM(provider=provider) + chat_llm = ChatLLMstudio(llm=llm, model=model, parameters={"temperature": 0}) + response = assistant_functions(chat_llm, "Turn this into a party!") + assert len(response["intermediate_steps"]) > 0 + + +def assistant_tools(chat_llm: ChatLLMstudio, question: str) -> str: + tools = [power_disco_ball, start_music, dim_lights] + + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a helpful assistant"), + ("human", "{input}"), + ("placeholder", "{agent_scratchpad}"), + ] + ) + + agent = create_openai_tools_agent(chat_llm, tools, prompt) + + agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, return_intermediate_steps=True + ) + + response = agent_executor.invoke({"input": question}) + + return response + + +@pytest.mark.parametrize( + "provider, model", + [ + ("openai", "gpt-4o-mini"), + ], +) +def test_tool_calling(provider, model): + llm = LLM(provider=provider) + chat_llm = ChatLLMstudio(llm=llm, model=model, parameters={"temperature": 0}) + response = assistant_tools(chat_llm, "Turn this into a party!") + assert len(response["intermediate_steps"]) > 0 diff --git a/libs/core/tests/unit_tests/conftest.py b/libs/core/tests/unit_tests/conftest.py index 1d1939a0..0a5feb41 100644 --- a/libs/core/tests/unit_tests/conftest.py +++ b/libs/core/tests/unit_tests/conftest.py @@ -12,7 +12,7 @@ async def aparse_response(self, response, **kwargs): def parse_response(self, response, **kwargs): return response - def output_to_string(self, output): + def _output_to_string(self, output): # Handle string inputs if isinstance(output, str): return output diff --git a/libs/core/tests/unit_tests/test_provider.py b/libs/core/tests/unit_tests/test_provider.py index 42396c99..4baa5b3a 100644 --- a/libs/core/tests/unit_tests/test_provider.py +++ b/libs/core/tests/unit_tests/test_provider.py @@ -10,7 +10,7 @@ def test_chat_response_non_stream(mock_provider): mock_provider.validate_request = MagicMock() mock_provider.validate_model = MagicMock() mock_provider.generate_client = MagicMock(return_value="mock_response") - mock_provider.handle_response = MagicMock(return_value="final_response") + mock_provider._handle_response = MagicMock(return_value="final_response") response = mock_provider.chat(chat_input="Hello", model="test_model") @@ -23,7 +23,7 @@ def test_chat_streaming_response(mock_provider): mock_provider.validate_request = MagicMock() mock_provider.validate_model = MagicMock() mock_provider.generate_client = MagicMock(return_value="mock_response_stream") - mock_provider.handle_response = MagicMock( + mock_provider._handle_response = MagicMock( return_value=iter(["streamed_response_1", "streamed_response_2"]) ) @@ -73,7 +73,7 @@ def test_join_chunks_finish_reason_stop(mock_provider): ], }, ] - response, output_string = mock_provider.join_chunks(chunks) + response, output_string = mock_provider._join_chunks(chunks) assert output_string == "Hello, world!" assert response.choices[0].message.content == "Hello, world!" @@ -121,7 +121,7 @@ def test_join_chunks_finish_reason_function_call(mock_provider): ], }, ] - response, output_string = mock_provider.join_chunks(chunks) + response, output_string = mock_provider._join_chunks(chunks) assert output_string == "arg1arg2" assert response.choices[0].message.function_call.arguments == "arg1arg2" @@ -181,7 +181,7 @@ def test_join_chunks_tool_calls(mock_provider): }, ] - response, output_string = mock_provider.join_chunks(chunks) + response, output_string = mock_provider._join_chunks(chunks) assert output_string == "['search_tool', '{\"query\": \"weather details\"}']" @@ -197,7 +197,7 @@ def test_join_chunks_tool_calls(mock_provider): def test_input_to_string_with_string(mock_provider): input_data = "Hello, world!" - assert mock_provider.input_to_string(input_data) == "Hello, world!" + assert mock_provider._input_to_string(input_data) == "Hello, world!" def test_input_to_string_with_list_of_text_messages(mock_provider): @@ -205,7 +205,7 @@ def test_input_to_string_with_list_of_text_messages(mock_provider): {"content": "Hello"}, {"content": " world!"}, ] - assert mock_provider.input_to_string(input_data) == "Hello world!" + assert mock_provider._input_to_string(input_data) == "Hello world!" def test_input_to_string_with_list_of_text_and_url(mock_provider): @@ -223,7 +223,7 @@ def test_input_to_string_with_list_of_text_and_url(mock_provider): {"role": "user", "content": [{"type": "text", "text": " world!"}]}, ] expected_output = "Hello http://example.com/image.jpg world!" - assert mock_provider.input_to_string(input_data) == expected_output + assert mock_provider._input_to_string(input_data) == expected_output def test_input_to_string_with_mixed_roles_and_missing_content(mock_provider): @@ -241,7 +241,7 @@ def test_input_to_string_with_mixed_roles_and_missing_content(mock_provider): }, ] expected_output = "Admin text;User texthttp://example.com/another.jpg" - assert mock_provider.input_to_string(input_data) == expected_output + assert mock_provider._input_to_string(input_data) == expected_output def test_input_to_string_with_missing_content_key(mock_provider): @@ -250,9 +250,9 @@ def test_input_to_string_with_missing_content_key(mock_provider): {"role": "user", "content": [{"type": "text", "text": "Hello again"}]}, ] expected_output = "Hello again" - assert mock_provider.input_to_string(input_data) == expected_output + assert mock_provider._input_to_string(input_data) == expected_output def test_input_to_string_with_empty_list(mock_provider): input_data = [] - assert mock_provider.input_to_string(input_data) == "" + assert mock_provider._input_to_string(input_data) == "" diff --git a/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py b/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py index fb54d602..de01f3ff 100644 --- a/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py +++ b/libs/core/tests/unit_tests/test_provider_costs_and_metrics.py @@ -3,7 +3,7 @@ def test_calculate_metrics(mock_provider): - metrics = mock_provider.calculate_metrics( + metrics = mock_provider._calculate_metrics( input="Hello", output="Hello World", model="test_model", @@ -12,6 +12,7 @@ def test_calculate_metrics(mock_provider): first_token_time=0.5, token_times=(0.1,), token_count=2, + is_stream=True, ) assert metrics["input_tokens"] == 1 @@ -28,7 +29,7 @@ def test_calculate_metrics(mock_provider): def test_calculate_metrics_single_token(mock_provider): - metrics = mock_provider.calculate_metrics( + metrics = mock_provider._calculate_metrics( input="Hello", output="World", model="test_model", @@ -37,6 +38,7 @@ def test_calculate_metrics_single_token(mock_provider): first_token_time=0.5, token_times=(), token_count=1, + is_stream=True, ) assert metrics["input_tokens"] == 1 @@ -53,7 +55,7 @@ def test_calculate_cost_fixed_cost(mock_provider): fixed_cost = 0.02 token_count = 100 expected_cost = token_count * fixed_cost - assert mock_provider.calculate_cost(token_count, fixed_cost) == expected_cost + assert mock_provider._calculate_cost(token_count, fixed_cost) == expected_cost def test_calculate_cost_variable_cost(mock_provider): @@ -68,7 +70,7 @@ def test_calculate_cost_variable_cost(mock_provider): variable_cost = [cost_range_1, cost_range_2] token_count = 75 expected_cost = token_count * 0.02 - assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost def test_calculate_cost_variable_cost_higher_range(mock_provider): @@ -87,7 +89,7 @@ def test_calculate_cost_variable_cost_higher_range(mock_provider): variable_cost = [cost_range_1, cost_range_2, cost_range_3] token_count = 150 expected_cost = token_count * 0.03 - assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost def test_calculate_cost_variable_cost_no_matching_range(mock_provider): @@ -106,7 +108,7 @@ def test_calculate_cost_variable_cost_no_matching_range(mock_provider): variable_cost = [cost_range_1, cost_range_2, cost_range_3] token_count = 200 expected_cost = 0 - assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost def test_calculate_cost_variable_cost_no_matching_range_inferior(mock_provider): @@ -125,4 +127,4 @@ def test_calculate_cost_variable_cost_no_matching_range_inferior(mock_provider): variable_cost = [cost_range_1, cost_range_2, cost_range_3] token_count = 5 expected_cost = 0 - assert mock_provider.calculate_cost(token_count, variable_cost) == expected_cost + assert mock_provider._calculate_cost(token_count, variable_cost) == expected_cost diff --git a/libs/core/tests/unit_tests/test_provider_handle_response.py b/libs/core/tests/unit_tests/test_provider_handle_response.py index ac04e32b..b9531be2 100644 --- a/libs/core/tests/unit_tests/test_provider_handle_response.py +++ b/libs/core/tests/unit_tests/test_provider_handle_response.py @@ -1,7 +1,13 @@ from unittest.mock import MagicMock import pytest -from llmstudio_core.providers.provider import ChatCompletion, ChatCompletionChunk, time +from llmstudio_core.providers.provider import ( + ChatCompletion, + ChatCompletionChunk, + ChatCompletionChunkLLMstudio, + ChatCompletionLLMstudio, + time, +) @pytest.mark.asyncio @@ -25,7 +31,7 @@ async def mock_aparse_response(*args, **kwargs): yield response_chunk mock_provider.aparse_response = mock_aparse_response - mock_provider.join_chunks = MagicMock( + mock_provider._join_chunks = MagicMock( return_value=( ChatCompletion( id="id", @@ -37,15 +43,15 @@ async def mock_aparse_response(*args, **kwargs): "Non-streamed response", ) ) - mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + mock_provider._calculate_metrics = MagicMock(return_value=None) response = [] - async for chunk in mock_provider.ahandle_response( + async for chunk in mock_provider._ahandle_response( request, mock_aparse_response(), start_time ): response.append(chunk) - assert isinstance(response[0], ChatCompletion) + assert isinstance(response[0], ChatCompletionLLMstudio) assert response[0].choices == [] assert response[0].chat_output == "Non-streamed response" @@ -73,7 +79,7 @@ async def mock_aparse_response(*args, **kwargs): yield response_chunk mock_provider.aparse_response = mock_aparse_response - mock_provider.join_chunks = MagicMock( + mock_provider._join_chunks = MagicMock( return_value=( ChatCompletion( id="id", @@ -85,15 +91,15 @@ async def mock_aparse_response(*args, **kwargs): "Streamed response", ) ) - mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + mock_provider._calculate_metrics = MagicMock(return_value=None) response = [] - async for chunk in mock_provider.ahandle_response( + async for chunk in mock_provider._ahandle_response( request, mock_aparse_response(), start_time ): response.append(chunk) - assert isinstance(response[0], ChatCompletionChunk) + assert isinstance(response[0], ChatCompletionChunkLLMstudio) assert response[0].chat_output_stream == "Streamed response" @@ -120,7 +126,7 @@ async def mock_aparse_response(*args, **kwargs): yield response_chunk mock_provider.aparse_response = mock_aparse_response - mock_provider.join_chunks = MagicMock( + mock_provider._join_chunks = MagicMock( return_value=( ChatCompletion( id="id", @@ -132,16 +138,16 @@ async def mock_aparse_response(*args, **kwargs): "Streamed response", ) ) - mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + mock_provider._calculate_metrics = MagicMock(return_value=None) response = [] - async for chunk in mock_provider.ahandle_response( + async for chunk in mock_provider._ahandle_response( request, mock_aparse_response(), start_time ): response.append(chunk) - assert isinstance(response[0], ChatCompletionChunk) - assert response[0].chat_output == "Streamed response" + assert isinstance(response[0], ChatCompletionChunkLLMstudio) + assert response[0].chat_output_stream == "Streamed response" def test_handle_response_non_streaming(mock_provider): @@ -164,7 +170,7 @@ def mock_parse_response(*args, **kwargs): yield response_chunk mock_provider.aparse_response = mock_parse_response - mock_provider.join_chunks = MagicMock( + mock_provider._join_chunks = MagicMock( return_value=( ChatCompletion( id="id", @@ -176,15 +182,15 @@ def mock_parse_response(*args, **kwargs): "Non-streamed response", ) ) - mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + mock_provider._calculate_metrics = MagicMock(return_value=None) response = [] - for chunk in mock_provider.handle_response( + for chunk in mock_provider._handle_response( request, mock_parse_response(), start_time ): response.append(chunk) - assert isinstance(response[0], ChatCompletion) + assert isinstance(response[0], ChatCompletionLLMstudio) assert response[0].choices == [] assert response[0].chat_output == "Non-streamed response" @@ -211,7 +217,7 @@ def mock_parse_response(*args, **kwargs): yield response_chunk mock_provider.aparse_response = mock_parse_response - mock_provider.join_chunks = MagicMock( + mock_provider._join_chunks = MagicMock( return_value=( ChatCompletion( id="id", @@ -223,15 +229,15 @@ def mock_parse_response(*args, **kwargs): "Streamed response", ) ) - mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + mock_provider._calculate_metrics = MagicMock(return_value=None) response = [] - for chunk in mock_provider.handle_response( + for chunk in mock_provider._handle_response( request, mock_parse_response(), start_time ): response.append(chunk) - assert isinstance(response[0], ChatCompletionChunk) + assert isinstance(response[0], ChatCompletionChunkLLMstudio) assert response[0].chat_output_stream == "Streamed response" @@ -257,7 +263,7 @@ def mock_parse_response(*args, **kwargs): yield response_chunk mock_provider.parse_response = mock_parse_response - mock_provider.join_chunks = MagicMock( + mock_provider._join_chunks = MagicMock( return_value=( ChatCompletion( id="id", @@ -265,17 +271,17 @@ def mock_parse_response(*args, **kwargs): created=0, model="test_model", object="chat.completion", + metrics=None, ), "Streamed response", ) ) - mock_provider.calculate_metrics = MagicMock(return_value={"input_tokens": 1}) + mock_provider._calculate_metrics = MagicMock(return_value=None) response = [] - for chunk in mock_provider.handle_response( + for chunk in mock_provider._handle_response( request, mock_parse_response(), start_time ): response.append(chunk) - assert isinstance(response[0], ChatCompletionChunk) - assert response[0].chat_output == "Streamed response" + assert response[0].chat_output_stream == "Streamed response" diff --git a/libs/llmstudio/llmstudio/langchain.py b/libs/llmstudio/llmstudio/langchain.py index 87180304..da1820a0 100644 --- a/libs/llmstudio/llmstudio/langchain.py +++ b/libs/llmstudio/llmstudio/langchain.py @@ -46,7 +46,7 @@ def _create_message_dicts( def _create_chat_result(self, response: Any) -> ChatResult: generations = [] if not isinstance(response, dict): - response = response.dict() + response = response.model_dump() for res in response["choices"]: message = convert_dict_to_message(res["message"]) generation_info = dict(finish_reason=res.get("finish_reason")) diff --git a/libs/proxy/llmstudio_proxy/server.py b/libs/proxy/llmstudio_proxy/server.py index da9f3291..2ac9ac4e 100644 --- a/libs/proxy/llmstudio_proxy/server.py +++ b/libs/proxy/llmstudio_proxy/server.py @@ -11,7 +11,7 @@ from llmstudio_core.providers.provider import provider_registry from llmstudio_proxy.config import ENGINE_HOST, ENGINE_PORT from llmstudio_proxy.utils import get_current_version -from pydantic import BaseModel +from pydantic import BaseModel, Field ENGINE_HEALTH_ENDPOINT = "/health" ENGINE_TITLE = "LLMstudio Proxy API" @@ -27,11 +27,13 @@ class CostRange(BaseModel): cost: float -class ModelConfig(BaseModel): +class ModelConfig(BaseModel): mode: str - max_tokens: int - input_token_cost: Union[float, List[CostRange]] - output_token_cost: Union[float, List[CostRange]] + max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens") + max_completion_tokens: Optional[int] = None + input_token_cost: Union[float, List["CostRange"]] + cached_token_cost: Optional[Union[float, List["CostRange"]]] = None + output_token_cost: Union[float, List["CostRange"]] class ProviderConfig(BaseModel): From c23dfae1e37c204c871aae7aac5f23fb8dcd13f9 Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 30 Jan 2025 15:10:04 +0000 Subject: [PATCH 15/38] chore: update poetry.lock --- libs/core/poetry.lock | 1171 +++++++++++++++++++++-------------------- 1 file changed, 597 insertions(+), 574 deletions(-) diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index 94272367..bea130b0 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -13,24 +13,24 @@ files = [ [[package]] name = "anyio" -version = "4.6.2.post1" +version = "4.8.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" files = [ - 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.../tests/integration_tests/test_cache_and_reasoning_costs.py | 0 .../tests/integration_tests/test_langchain_agents.py | 0 libs/llmstudio/tests/unit_tests/__init__.py | 0 libs/llmstudio/tests/unit_tests/conftest.py | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename libs/{core => llmstudio}/tests/integration_tests/test_cache_and_reasoning_costs.py (100%) rename libs/{core => llmstudio}/tests/integration_tests/test_langchain_agents.py (100%) delete mode 100644 libs/llmstudio/tests/unit_tests/__init__.py delete mode 100644 libs/llmstudio/tests/unit_tests/conftest.py diff --git a/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py b/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py similarity index 100% rename from libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py rename to libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py diff --git a/libs/core/tests/integration_tests/test_langchain_agents.py b/libs/llmstudio/tests/integration_tests/test_langchain_agents.py similarity index 100% rename from libs/core/tests/integration_tests/test_langchain_agents.py rename to libs/llmstudio/tests/integration_tests/test_langchain_agents.py diff --git a/libs/llmstudio/tests/unit_tests/__init__.py b/libs/llmstudio/tests/unit_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/libs/llmstudio/tests/unit_tests/conftest.py b/libs/llmstudio/tests/unit_tests/conftest.py deleted file mode 100644 index e69de29b..00000000 From 29e39d89ef7f7b0774042d71bd76ed9a213e50bd Mon Sep 17 00:00:00 2001 From: Diogo Goncalves Date: Thu, 30 Jan 2025 16:25:32 +0000 Subject: [PATCH 18/38] chore: format --- libs/proxy/llmstudio_proxy/server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/proxy/llmstudio_proxy/server.py b/libs/proxy/llmstudio_proxy/server.py index 2ac9ac4e..281e9d99 100644 --- a/libs/proxy/llmstudio_proxy/server.py +++ b/libs/proxy/llmstudio_proxy/server.py @@ -27,7 +27,7 @@ class CostRange(BaseModel): cost: float -class ModelConfig(BaseModel): +class ModelConfig(BaseModel): mode: str max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens") max_completion_tokens: Optional[int] = None From 39443df711db8dbaceca0f3692de4ea567e3d0e1 Mon Sep 17 00:00:00 2001 From: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> Date: Wed, 5 Feb 2025 09:20:08 +0000 Subject: [PATCH 19/38] feat: added support for o3-mini and updated o1-mini prices. also updated integration tests to support o3 (#202) --- examples/core.py | 4 +- libs/core/llmstudio_core/config.yaml | 12 +- .../test_cache_and_reasoning_costs.py | 192 ++++++++++++++++++ .../test_cache_and_reasoning_costs.py | 11 +- 4 files changed, 208 insertions(+), 11 deletions(-) create mode 100644 libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py diff --git a/examples/core.py b/examples/core.py index 255bcae4..e84ea89b 100644 --- a/examples/core.py +++ b/examples/core.py @@ -97,7 +97,7 @@ async def async_stream(): return latencies def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: int=1000): - if model == "o1-preview" or model == "o1-mini": + if model.startswith(('o1', 'o3')): chat_request = { "chat_input": chat_input, "model": model, @@ -132,7 +132,7 @@ def multiple_provider_runs(provider:str, model:str, num_runs:int, api_key:str, * # OpenAI multiple_provider_runs(provider="openai", model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) -multiple_provider_runs(provider="openai", model="o1-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) +multiple_provider_runs(provider="openai", model="o3-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) #multiple_provider_runs(provider="openai", model="o1-preview", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) diff --git a/libs/core/llmstudio_core/config.yaml b/libs/core/llmstudio_core/config.yaml index 86b84c96..b608bd01 100644 --- a/libs/core/llmstudio_core/config.yaml +++ b/libs/core/llmstudio_core/config.yaml @@ -213,9 +213,15 @@ providers: o1-mini: mode: chat max_completion_tokens: 128000 - input_token_cost: 0.000003 - cached_token_cost: 0.0000015 - output_token_cost: 0.000012 + input_token_cost: 0.0000011 + cached_token_cost: 0.00000055 + output_token_cost: 0.0000044 + o3-mini: + mode: chat + max_completion_tokens: 200000 + input_token_cost: 0.0000011 + cached_token_cost: 0.00000055 + output_token_cost: 0.0000044 gpt-4o-mini: mode: chat max_tokens: 128000 diff --git a/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py b/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py new file mode 100644 index 00000000..8dd3b18c --- /dev/null +++ b/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py @@ -0,0 +1,192 @@ +import asyncio +import os +from pprint import pprint + +import pytest +from dotenv import load_dotenv +from llmstudio_core.providers import LLMCore + +load_dotenv() + +# input prompt has to be >1024 tokens to auto cache on OpenAI +input_prompt = """ +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +What is Lorem Ipsum? json +Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. + +Why do we use it? +It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). + + +Where does it come from? +Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. + +After reading this answer just in one line, saying hello how are you in latin. +""" + + +def run_provider(provider, model, api_key, **kwargs): + print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") + llm = LLMCore(provider=provider, api_key=api_key, **kwargs) + + metrics = {} + + print("\nAsync Non-Stream") + + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) + + response_async = asyncio.run(llm.achat(**chat_request)) + pprint(response_async) + metrics["async non-stream"] = response_async.metrics + + print("\nAsync Stream") + + async def async_stream(): + chat_request = build_chat_request( + model, chat_input=input_prompt, is_stream=True + ) + + response_async = await llm.achat(**chat_request) + async for p in response_async: + if not p.metrics: + print("that: ", p.chat_output_stream) + if p.metrics: + pprint(p) + metrics["async stream"] = p.metrics + + asyncio.run(async_stream()) + + print("\nSync Non-Stream") + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) + + response_sync = llm.chat(**chat_request) + pprint(response_sync) + metrics["sync non-stream"] = response_sync.metrics + + print("\nSync Stream") + chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=True) + + response_sync_stream = llm.chat(**chat_request) + for p in response_sync_stream: + if p.metrics: + pprint(p) + metrics["sync stream"] = p.metrics + + print(f"\n\n###REPORT for <{provider}>, <{model}> ###") + return metrics + + +def build_chat_request( + model: str, chat_input: str, is_stream: bool, max_tokens: int = 1000 +): + if model.startswith(("o1", "o3")): + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": {"max_completion_tokens": max_tokens}, + } + else: + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": { + "temperature": 0, + "max_tokens": max_tokens, + "response_format": {"type": "json_object"}, + "functions": None, + }, + } + return chat_request + + +# Fixture for provider-model pairs +@pytest.fixture( + scope="module", params=[("openai", "gpt-4o-mini"), ("openai", "o1-mini")] +) +def provider_model(request): + return request.param + + +# Fixture for metrics, computes them once per provider-model pair +@pytest.fixture(scope="module") +def metrics(provider_model): + provider, model = provider_model + print(f"Running provider {provider} with model {model}") + return run_provider( + provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"] + ) + + +# Test cache metrics (runs for all models) +def test_metrics_cache(provider_model, metrics): + provider, model = provider_model + at_least_one_cached = False + for current_metrics in metrics.values(): + print(current_metrics) + assert current_metrics["input_tokens"] > current_metrics["cached_tokens"] + if current_metrics["cached_tokens"] > 0: + at_least_one_cached = True + assert at_least_one_cached == True + print(f"All Cache Tests Passed for {provider} - {model}") + + +# Test reasoning metrics (only runs for o1 and o3 family) +def test_metrics_reasoning(provider_model, metrics): + provider, model = provider_model + + if not model.startswith(("o1", "o3")): + pytest.skip(f"Reasoning metrics test not applicable for model {model}") + + for current_metrics in metrics.values(): + assert current_metrics["reasoning_tokens"] > 0 + assert current_metrics["reasoning_tokens"] < current_metrics["total_tokens"] + assert ( + current_metrics["input_tokens"] + + current_metrics["output_tokens"] + + current_metrics["reasoning_tokens"] + == current_metrics["total_tokens"] + ) + print(f"All Reasoning Tests Passed for {provider} - {model}") + + +def usage_when_max_tokens_reached(): + """ + Usefull to test handling of Usage in other finish_reason scenarios + """ + provider, model = ("openai", "o1-mini") + api_key = os.environ["OPENAI_API_KEY"] + + llm = LLMCore(provider=provider, api_key=api_key) + chat_request = build_chat_request( + model, chat_input=input_prompt, is_stream=False, max_tokens=7 + ) + response = asyncio.run(llm.achat(**chat_request)) + + assert response.metrics["async non-stream"]["reasoning_tokens"] > 0 + assert response.metrics["sync non-stream"]["reasoning_tokens"] > 0 + assert response.metrics["async stream"]["reasoning_tokens"] > 0 + assert response.metrics["sync stream"]["reasoning_tokens"] > 0 + print(f"All Max Tokens Usage Reached Tests Passed for {provider} - {model}") diff --git a/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py b/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py index 4a910814..50f2777b 100644 --- a/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py +++ b/libs/llmstudio/tests/integration_tests/test_cache_and_reasoning_costs.py @@ -98,7 +98,7 @@ async def async_stream(): def build_chat_request( model: str, chat_input: str, is_stream: bool, max_tokens: int = 1000 ): - if model == "o1-preview" or model == "o1-mini": + if model.startswith(("o1", "o3")): chat_request = { "chat_input": chat_input, "model": model, @@ -124,7 +124,7 @@ def build_chat_request( # Fixture for provider-model pairs @pytest.fixture( - scope="module", params=[("openai", "gpt-4o-mini"), ("openai", "o1-mini")] + scope="module", params=[("openai", "gpt-4o-mini"), ("openai", "o3-mini")] ) def provider_model(request): return request.param @@ -153,12 +153,11 @@ def test_metrics_cache(provider_model, metrics): print(f"All Cache Tests Passed for {provider} - {model}") -# Test reasoning metrics (only runs for o1-mini and o1-preview) +# Test reasoning metrics (only runs for o1 and o3 family) def test_metrics_reasoning(provider_model, metrics): provider, model = provider_model - # Skip test if model is not o1-mini or o1-preview - if model not in ["o1-mini", "o1-preview"]: + if not model.startswith(("o1", "o3")): pytest.skip(f"Reasoning metrics test not applicable for model {model}") for current_metrics in metrics.values(): @@ -177,7 +176,7 @@ def usage_when_max_tokens_reached(): """ Usefull to test handling of Usage in other finish_reason scenarios """ - provider, model = ("openai", "o1-mini") + provider, model = ("openai", "o3-mini") api_key = os.environ["OPENAI_API_KEY"] llm = LLMCore(provider=provider, api_key=api_key) From 9610bcb690c69d9f7d6cdb200c7e5d4b4e6f8782 Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 15:46:29 +0000 Subject: [PATCH 20/38] chore: removed duplicated code; removed duplicated integration tests --- .../test_cache_and_reasoning_costs.py | 192 ------------------ libs/proxy/llmstudio_proxy/server.py | 29 +-- 2 files changed, 3 insertions(+), 218 deletions(-) delete mode 100644 libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py diff --git a/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py b/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py deleted file mode 100644 index 8dd3b18c..00000000 --- a/libs/core/tests/integration_tests/test_cache_and_reasoning_costs.py +++ /dev/null @@ -1,192 +0,0 @@ -import asyncio -import os -from pprint import pprint - -import pytest -from dotenv import load_dotenv -from llmstudio_core.providers import LLMCore - -load_dotenv() - -# input prompt has to be >1024 tokens to auto cache on OpenAI -input_prompt = """ -What is Lorem Ipsum? json -Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. - -Why do we use it? -It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). - - -Where does it come from? -Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. - -What is Lorem Ipsum? json -Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. - -Why do we use it? -It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). - - -Where does it come from? -Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. - -What is Lorem Ipsum? json -Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. - -Why do we use it? -It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like). - - -Where does it come from? -Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. - -After reading this answer just in one line, saying hello how are you in latin. -""" - - -def run_provider(provider, model, api_key, **kwargs): - print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") - llm = LLMCore(provider=provider, api_key=api_key, **kwargs) - - metrics = {} - - print("\nAsync Non-Stream") - - chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) - - response_async = asyncio.run(llm.achat(**chat_request)) - pprint(response_async) - metrics["async non-stream"] = response_async.metrics - - print("\nAsync Stream") - - async def async_stream(): - chat_request = build_chat_request( - model, chat_input=input_prompt, is_stream=True - ) - - response_async = await llm.achat(**chat_request) - async for p in response_async: - if not p.metrics: - print("that: ", p.chat_output_stream) - if p.metrics: - pprint(p) - metrics["async stream"] = p.metrics - - asyncio.run(async_stream()) - - print("\nSync Non-Stream") - chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=False) - - response_sync = llm.chat(**chat_request) - pprint(response_sync) - metrics["sync non-stream"] = response_sync.metrics - - print("\nSync Stream") - chat_request = build_chat_request(model, chat_input=input_prompt, is_stream=True) - - response_sync_stream = llm.chat(**chat_request) - for p in response_sync_stream: - if p.metrics: - pprint(p) - metrics["sync stream"] = p.metrics - - print(f"\n\n###REPORT for <{provider}>, <{model}> ###") - return metrics - - -def build_chat_request( - model: str, chat_input: str, is_stream: bool, max_tokens: int = 1000 -): - if model.startswith(("o1", "o3")): - chat_request = { - "chat_input": chat_input, - "model": model, - "is_stream": is_stream, - "retries": 0, - "parameters": {"max_completion_tokens": max_tokens}, - } - else: - chat_request = { - "chat_input": chat_input, - "model": model, - "is_stream": is_stream, - "retries": 0, - "parameters": { - "temperature": 0, - "max_tokens": max_tokens, - "response_format": {"type": "json_object"}, - "functions": None, - }, - } - return chat_request - - -# Fixture for provider-model pairs -@pytest.fixture( - scope="module", params=[("openai", "gpt-4o-mini"), ("openai", "o1-mini")] -) -def provider_model(request): - return request.param - - -# Fixture for metrics, computes them once per provider-model pair -@pytest.fixture(scope="module") -def metrics(provider_model): - provider, model = provider_model - print(f"Running provider {provider} with model {model}") - return run_provider( - provider=provider, model=model, api_key=os.environ["OPENAI_API_KEY"] - ) - - -# Test cache metrics (runs for all models) -def test_metrics_cache(provider_model, metrics): - provider, model = provider_model - at_least_one_cached = False - for current_metrics in metrics.values(): - print(current_metrics) - assert current_metrics["input_tokens"] > current_metrics["cached_tokens"] - if current_metrics["cached_tokens"] > 0: - at_least_one_cached = True - assert at_least_one_cached == True - print(f"All Cache Tests Passed for {provider} - {model}") - - -# Test reasoning metrics (only runs for o1 and o3 family) -def test_metrics_reasoning(provider_model, metrics): - provider, model = provider_model - - if not model.startswith(("o1", "o3")): - pytest.skip(f"Reasoning metrics test not applicable for model {model}") - - for current_metrics in metrics.values(): - assert current_metrics["reasoning_tokens"] > 0 - assert current_metrics["reasoning_tokens"] < current_metrics["total_tokens"] - assert ( - current_metrics["input_tokens"] - + current_metrics["output_tokens"] - + current_metrics["reasoning_tokens"] - == current_metrics["total_tokens"] - ) - print(f"All Reasoning Tests Passed for {provider} - {model}") - - -def usage_when_max_tokens_reached(): - """ - Usefull to test handling of Usage in other finish_reason scenarios - """ - provider, model = ("openai", "o1-mini") - api_key = os.environ["OPENAI_API_KEY"] - - llm = LLMCore(provider=provider, api_key=api_key) - chat_request = build_chat_request( - model, chat_input=input_prompt, is_stream=False, max_tokens=7 - ) - response = asyncio.run(llm.achat(**chat_request)) - - assert response.metrics["async non-stream"]["reasoning_tokens"] > 0 - assert response.metrics["sync non-stream"]["reasoning_tokens"] > 0 - assert response.metrics["async stream"]["reasoning_tokens"] > 0 - assert response.metrics["sync stream"]["reasoning_tokens"] > 0 - print(f"All Max Tokens Usage Reached Tests Passed for {provider} - {model}") diff --git a/libs/proxy/llmstudio_proxy/server.py b/libs/proxy/llmstudio_proxy/server.py index 281e9d99..13ee7a5a 100644 --- a/libs/proxy/llmstudio_proxy/server.py +++ b/libs/proxy/llmstudio_proxy/server.py @@ -1,6 +1,6 @@ import json from threading import Event, Thread -from typing import Any, Dict, List, Optional, Union +from typing import Dict, Optional import requests import uvicorn @@ -9,9 +9,10 @@ from fastapi.responses import StreamingResponse from llmstudio_core.providers import _load_providers_config from llmstudio_core.providers.provider import provider_registry +from llmstudio_core.utils import ProviderConfig from llmstudio_proxy.config import ENGINE_HOST, ENGINE_PORT from llmstudio_proxy.utils import get_current_version -from pydantic import BaseModel, Field +from pydantic import BaseModel ENGINE_HEALTH_ENDPOINT = "/health" ENGINE_TITLE = "LLMstudio Proxy API" @@ -22,30 +23,6 @@ _proxy_server_started = False -class CostRange(BaseModel): - range: List[Optional[int]] - cost: float - - -class ModelConfig(BaseModel): - mode: str - max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens") - max_completion_tokens: Optional[int] = None - input_token_cost: Union[float, List["CostRange"]] - cached_token_cost: Optional[Union[float, List["CostRange"]]] = None - output_token_cost: Union[float, List["CostRange"]] - - -class ProviderConfig(BaseModel): - id: str - name: str - chat: bool - embed: bool - keys: Optional[List[str]] = None - models: Optional[Dict[str, ModelConfig]] = None - parameters: Optional[Dict[str, Any]] = None - - class ProxyConfig(BaseModel): providers: Dict[str, ProviderConfig] From 7b9a86651408f735db2ac3175dc85adc3e96ab54 Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:08:49 +0000 Subject: [PATCH 21/38] chore: updated github actions to run integration tests --- .github/workflows/tests.yml | 60 ++++++++++++++++++++++++++++++++++++- 1 file changed, 59 insertions(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index abdcdde1..ad0fd8da 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -10,7 +10,7 @@ on: - reopened jobs: - tests: + unit-tests: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 @@ -25,6 +25,16 @@ jobs: - name: Install Poetry run: | curl -sSL https://install.python-poetry.org | python3 - + + # Cache Poetry Dependencies + - name: Cache Poetry dependencies + uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: poetry-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }} + restore-keys: | + poetry-${{ runner.os }}- + # Install lib and dev dependencies - name: Install llmstudio-core working-directory: ./libs/core @@ -39,3 +49,51 @@ jobs: echo ${{ env.POETRY_ENV }} source ${{ env.POETRY_ENV }}/bin/activate poetry run pytest libs/core + + integration-tests: + needs: unit-tests + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + # Set up Python environment + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ">=3.9 <3.13" + + # Install Poetry + - name: Install Poetry + run: | + curl -sSL https://install.python-poetry.org | python3 - + + # Cache Poetry Dependencies - allows reusing the unit test cache if possible + - name: Cache Poetry dependencies + uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: poetry-integration-${{ runner.os }}-${{ hashFiles('libs/llmstudio/poetry.lock') }} + restore-keys: | + poetry-integration-${{ runner.os }}- + poetry-unit-${{ runner.os }}- + + # Install llmstudio-core dependencies + - name: Install llmstudio-core + working-directory: ./libs/core + run: | + poetry install + POETRY_ENV=$(poetry env info --path) + echo $POETRY_ENV + echo "POETRY_ENV=$POETRY_ENV" >> $GITHUB_ENV + + # Install llmstudio + - name: Install llmstudio + working-directory: ./libs/llmstudio + run: | + poetry install + + # Run Integration Tests + - name: Run Integration Tests + run: | + source ${{ env.POETRY_ENV }}/bin/activate + poetry run pytest libs/llmstudio/tests/integration_tests \ No newline at end of file From c231e91f7a3332a9d826a93136488054551ce12f Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:20:16 +0000 Subject: [PATCH 22/38] chore: fixing github actions --- .github/workflows/tests.yml | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index ad0fd8da..fd86ce02 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -4,6 +4,7 @@ on: pull_request: branches: - main + - develop types: - opened - synchronize @@ -67,16 +68,6 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - - # Cache Poetry Dependencies - allows reusing the unit test cache if possible - - name: Cache Poetry dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pypoetry - key: poetry-integration-${{ runner.os }}-${{ hashFiles('libs/llmstudio/poetry.lock') }} - restore-keys: | - poetry-integration-${{ runner.os }}- - poetry-unit-${{ runner.os }}- - # Install llmstudio-core dependencies - name: Install llmstudio-core working-directory: ./libs/core From 0503810854b1e06323fccef8975a1891f4da5ba2 Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:23:46 +0000 Subject: [PATCH 23/38] chore: fixing github actions again --- .github/workflows/tests.yml | 9 --------- 1 file changed, 9 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index fd86ce02..d76f5220 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -68,15 +68,6 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - - # Install llmstudio-core dependencies - - name: Install llmstudio-core - working-directory: ./libs/core - run: | - poetry install - POETRY_ENV=$(poetry env info --path) - echo $POETRY_ENV - echo "POETRY_ENV=$POETRY_ENV" >> $GITHUB_ENV - # Install llmstudio - name: Install llmstudio working-directory: ./libs/llmstudio From 30e4fa6ce4834da498b85010a0c90d19cce11604 Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:29:10 +0000 Subject: [PATCH 24/38] chore: fixing github actions again-x2 --- .github/workflows/tests.yml | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d76f5220..3965eb3c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -41,14 +41,14 @@ jobs: working-directory: ./libs/core run: | poetry install - POETRY_ENV=$(poetry env info --path) - echo $POETRY_ENV - echo "POETRY_ENV=$POETRY_ENV" >> $GITHUB_ENV + UNIT_ENV=$(poetry env info --path) + echo $UNIT_ENV + echo "UNIT_ENV=$UNIT_ENV" >> $GITHUB_ENV - name: Run unit tests run: | - echo ${{ env.POETRY_ENV }} - source ${{ env.POETRY_ENV }}/bin/activate + echo ${{ env.UNIT_ENV }} + source ${{ env.UNIT_ENV }}/bin/activate poetry run pytest libs/core integration-tests: @@ -68,6 +68,15 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - + # Install llmstudio-core dependencies + - name: Install llmstudio-core + working-directory: ./libs/core + run: | + poetry install + INTEGRATION_ENV=$(poetry env info --path) + echo $INTEGRATION_ENV + echo "INTEGRATION_ENV=$INTEGRATION_ENV" >> $GITHUB_ENV + # Install llmstudio - name: Install llmstudio working-directory: ./libs/llmstudio @@ -77,5 +86,5 @@ jobs: # Run Integration Tests - name: Run Integration Tests run: | - source ${{ env.POETRY_ENV }}/bin/activate + source ${{ env.INTEGRATION_ENV }}/bin/activate poetry run pytest libs/llmstudio/tests/integration_tests \ No newline at end of file From 91562b3eb8a89ffd635b4449e5fb5fd89c5e5cd2 Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:33:04 +0000 Subject: [PATCH 25/38] chore: fixing github actions again-x2 --- .github/workflows/tests.yml | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 3965eb3c..f0ac59a6 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -68,20 +68,14 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - - # Install llmstudio-core dependencies - - name: Install llmstudio-core - working-directory: ./libs/core - run: | - poetry install - INTEGRATION_ENV=$(poetry env info --path) - echo $INTEGRATION_ENV - echo "INTEGRATION_ENV=$INTEGRATION_ENV" >> $GITHUB_ENV - # Install llmstudio - name: Install llmstudio working-directory: ./libs/llmstudio run: | poetry install + INTEGRATION_ENV=$(poetry env info --path) + echo $INTEGRATION_ENV + echo "INTEGRATION_ENV=$INTEGRATION_ENV" >> $GITHUB_ENV # Run Integration Tests - name: Run Integration Tests From 545d9909ad9d55e31348dc27b3f47034ededfa6c Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:38:26 +0000 Subject: [PATCH 26/38] chore: added cache of dependencies to integration-tests in githubaction --- .github/workflows/tests.yml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index f0ac59a6..5580c627 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -68,6 +68,16 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - + # Cache Poetry Dependencies - leverages cached dependencies from Unit Tests job when available + - name: Cache Poetry dependencies (Integration Tests) + uses: actions/cache@v3 + with: + path: ~/.cache/pypoetry + key: poetry-integration-${{ runner.os }}-${{ hashFiles('libs/llmstudio/poetry.lock') }} + restore-keys: | + poetry-integration-${{ runner.os }}- + poetry-unit-${{ runner.os }}- + # Install llmstudio - name: Install llmstudio working-directory: ./libs/llmstudio From ddc250f30ef8ab640397f0eae752f18a6e2829f5 Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Mon, 10 Feb 2025 16:52:09 +0000 Subject: [PATCH 27/38] chore: updated integration-tests action to inject github secrets into env --- .github/workflows/tests.yml | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 5580c627..c8c34c39 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -68,7 +68,16 @@ jobs: run: | curl -sSL https://install.python-poetry.org | python3 - - # Cache Poetry Dependencies - leverages cached dependencies from Unit Tests job when available + # Inject Secrets as Environment Variables + - name: Set up environment variables + run: | + echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> $GITHUB_ENV + echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" >> $GITHUB_ENV + echo "BEDROCK_ACCESS_KEY=${{ secrets.BEDROCK_ACCESS_KEY }}" >> $GITHUB_ENV + echo "BEDROCK_SECRET_KEY=${{ secrets.BEDROCK_SECRET_KEY }}" >> $GITHUB_ENV + echo "BEDROCK_REGION=${{ secrets.BEDROCK_REGION }}" >> $GITHUB_ENV + + # Cache Poetry Dependencies - name: Cache Poetry dependencies (Integration Tests) uses: actions/cache@v3 with: @@ -76,7 +85,6 @@ jobs: key: poetry-integration-${{ runner.os }}-${{ hashFiles('libs/llmstudio/poetry.lock') }} restore-keys: | poetry-integration-${{ runner.os }}- - poetry-unit-${{ runner.os }}- # Install llmstudio - name: Install llmstudio From 83d7b55027e895db061413dbc290e647623da894 Mon Sep 17 00:00:00 2001 From: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> Date: Wed, 12 Feb 2025 09:30:08 +0000 Subject: [PATCH 28/38] Feat/bedrock support for Nova models through the ConverseAPI (#207) * feat: added support for bedrock nova models * feat: tokens are now read from usage if available to ensure accuracy * chore: removed duplicated integration tests folder in wrong place * feat: refactored bedrock provider into being a single file instead of folder * chore: renamed bedrock to bedrock-converse in examples/core.py * chore: renamed bedrock in config.yaml --- examples/core.py | 23 ++++++---- libs/core/llmstudio_core/config.yaml | 18 +++++++- .../core/llmstudio_core/providers/__init__.py | 2 +- .../providers/bedrock/__init__.py | 0 .../providers/bedrock/provider.py | 43 ------------------- .../anthropic.py => bedrock_converse.py} | 29 ++++++++++--- .../core/llmstudio_core/providers/provider.py | 14 ++++-- 7 files changed, 68 insertions(+), 61 deletions(-) delete mode 100644 libs/core/llmstudio_core/providers/bedrock/__init__.py delete mode 100644 libs/core/llmstudio_core/providers/bedrock/provider.py rename libs/core/llmstudio_core/providers/{bedrock/anthropic.py => bedrock_converse.py} (92%) diff --git a/examples/core.py b/examples/core.py index e84ea89b..1eab6c81 100644 --- a/examples/core.py +++ b/examples/core.py @@ -8,7 +8,7 @@ from dotenv import load_dotenv load_dotenv() -def run_provider(provider, model, api_key, **kwargs): +def run_provider(provider, model, api_key=None, **kwargs): print(f"\n\n###RUNNING for <{provider}>, <{model}> ###") llm = LLMCore(provider=provider, api_key=api_key, **kwargs) @@ -107,6 +107,16 @@ def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: "max_completion_tokens": max_tokens } } + elif 'amazon.nova' in model or 'anthropic.claude' in model: + chat_request = { + "chat_input": chat_input, + "model": model, + "is_stream": is_stream, + "retries": 0, + "parameters": { + "maxTokens": max_tokens + } + } else: chat_request = { "chat_input": chat_input, @@ -150,10 +160,7 @@ def multiple_provider_runs(provider:str, model:str, num_runs:int, api_key:str, * multiple_provider_runs(provider="vertexai", model="gemini-1.5-flash", num_runs=1, api_key=os.environ["GOOGLE_API_KEY"]) -# provider = "vertexai" -# model = "gemini-1.5-pro-latest" -# for _ in range(1): -# latencies = run_provider(provider=provider, model=model, -# api_key=os.environ["GOOGLE_API_KEY"], -# ) -# pprint(latencies) + +# Bedrock +multiple_provider_runs(provider="bedrock", model="us.amazon.nova-lite-v1:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) +#multiple_provider_runs(provider="bedrock", model="anthropic.claude-3-5-sonnet-20241022-v2:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) diff --git a/libs/core/llmstudio_core/config.yaml b/libs/core/llmstudio_core/config.yaml index b608bd01..46813bd6 100644 --- a/libs/core/llmstudio_core/config.yaml +++ b/libs/core/llmstudio_core/config.yaml @@ -73,7 +73,7 @@ providers: step: 1 bedrock: id: bedrock - name: Bedrock + name: Bedrock ConverseAPI chat: true embed: true keys: @@ -126,6 +126,22 @@ providers: max_tokens: 100000 input_token_cost: 0.000008 output_token_cost: 0.000024 + us.amazon.nova-pro-v1:0: + mode: chat + max_tokens: 300000 + input_token_cost: 0.0000008 + output_token_cost: 0.0000016 + us.amazon.nova-lite-v1:0: + mode: chat + max_tokens: 300000 + input_token_cost: 0.00000006 + output_token_cost: 0.00000012 + us.amazon.nova-micro-v1:0: + mode: chat + max_tokens: 128000 + input_token_cost: 0.000000035 + output_token_cost: 0.00000007 + parameters: temperature: name: "Temperature" diff --git a/libs/core/llmstudio_core/providers/__init__.py b/libs/core/llmstudio_core/providers/__init__.py index 6aa6c4e7..330fe48e 100644 --- a/libs/core/llmstudio_core/providers/__init__.py +++ b/libs/core/llmstudio_core/providers/__init__.py @@ -2,7 +2,7 @@ from typing import Optional from llmstudio_core.providers.azure import AzureProvider -from llmstudio_core.providers.bedrock.provider import BedrockProvider +from llmstudio_core.providers.bedrock_converse import BedrockConverseProvider # from llmstudio_core.providers.ollama import OllamaProvider #TODO: adapt it from llmstudio_core.providers.openai import OpenAIProvider diff --git a/libs/core/llmstudio_core/providers/bedrock/__init__.py b/libs/core/llmstudio_core/providers/bedrock/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/libs/core/llmstudio_core/providers/bedrock/provider.py b/libs/core/llmstudio_core/providers/bedrock/provider.py deleted file mode 100644 index 7087dac2..00000000 --- a/libs/core/llmstudio_core/providers/bedrock/provider.py +++ /dev/null @@ -1,43 +0,0 @@ -from typing import Any, AsyncGenerator, Coroutine, Generator - -from llmstudio_core.providers.bedrock.anthropic import BedrockAnthropicProvider -from llmstudio_core.providers.provider import ChatRequest, ProviderCore, provider - - -@provider -class BedrockProvider(ProviderCore): - def __init__(self, config, **kwargs): - super().__init__(config, **kwargs) - self.kwargs = kwargs - self.selected_model = None - - def _get_provider(self, model): - if "anthropic." in model: - return BedrockAnthropicProvider(config=self.config, **self.kwargs) - - raise ValueError(f" provider is not yet supported.") - - @staticmethod - def _provider_config_name(): - return "bedrock" - - def validate_request(self, request: ChatRequest): - return ChatRequest(**request) - - async def agenerate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Any]: - self.selected_model = self._get_provider(request.model) - return await self.selected_model.agenerate_client(request) - - def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: - self.selected_model = self._get_provider(request.model) - return self.selected_model.generate_client(request=request) - - async def aparse_response( - self, response: Any, **kwargs - ) -> AsyncGenerator[Any, None]: - result = await self.selected_model.aparse_response(response=response, **kwargs) - for chunk in result: - yield chunk - - def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: - return self.selected_model.parse_response(response=response, **kwargs) diff --git a/libs/core/llmstudio_core/providers/bedrock/anthropic.py b/libs/core/llmstudio_core/providers/bedrock_converse.py similarity index 92% rename from libs/core/llmstudio_core/providers/bedrock/anthropic.py rename to libs/core/llmstudio_core/providers/bedrock_converse.py index f2b3cfc1..dc756e0a 100644 --- a/libs/core/llmstudio_core/providers/bedrock/anthropic.py +++ b/libs/core/llmstudio_core/providers/bedrock_converse.py @@ -23,6 +23,7 @@ ChoiceDelta, ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, + CompletionUsage, ) from pydantic import ValidationError @@ -30,7 +31,7 @@ @provider -class BedrockAnthropicProvider(ProviderCore): +class BedrockConverseProvider(ProviderCore): def __init__(self, config, **kwargs): super().__init__(config, **kwargs) self._client = boto3.client( @@ -46,17 +47,17 @@ def __init__(self, config, **kwargs): @staticmethod def _provider_config_name(): - return "bedrock-antropic" + return "bedrock" def validate_request(self, request: ChatRequest): return ChatRequest(**request) async def agenerate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Any]: - """Generate an AWS Bedrock client""" + """Generate an AWS Bedrock Converse client""" return self.generate_client(request=request) def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator]: - """Generate an AWS Bedrock client""" + """Generate an AWS Bedrock Converse client""" try: messages, system_prompt = self._process_messages(request.chat_input) tools = self._process_tools(request.parameters) @@ -83,7 +84,9 @@ def generate_client(self, request: ChatRequest) -> Coroutine[Any, Any, Generator async def aparse_response( self, response: Any, **kwargs ) -> AsyncGenerator[Any, None]: - return self.parse_response(response=response, **kwargs) + result = self.parse_response(response=response, **kwargs) + for chunk in result: + yield chunk def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: tool_name = None @@ -222,6 +225,22 @@ def parse_response(self, response: AsyncGenerator[Any, None], **kwargs) -> Any: ) yield final_chunk.model_dump() + elif chunk.get("metadata"): + usage = chunk["metadata"].get("usage") + final_stream_chunk = ChatCompletionChunk( + id=str(uuid.uuid4()), + choices=[], + created=int(time.time()), + model=kwargs.get("request").model, + object="chat.completion.chunk", + usage=CompletionUsage( + completion_tokens=usage["outputTokens"], + prompt_tokens=usage["inputTokens"], + total_tokens=usage["totalTokens"], + ), + ) + yield final_stream_chunk.model_dump() + @staticmethod def _process_messages( chat_input: Union[str, List[Dict[str, str]]] diff --git a/libs/core/llmstudio_core/providers/provider.py b/libs/core/llmstudio_core/providers/provider.py index 3cb9ad57..9646919f 100644 --- a/libs/core/llmstudio_core/providers/provider.py +++ b/libs/core/llmstudio_core/providers/provider.py @@ -808,6 +808,11 @@ def _calculate_metrics( output_tokens = len(self.tokenizer.encode(self._output_to_string(output))) total_tokens = input_tokens + output_tokens + if usage: + input_tokens = usage.get("prompt_tokens", input_tokens) + output_tokens = usage.get("completion_tokens", output_tokens) + total_tokens = usage.get("total_tokens", total_tokens) + # Cost calculations input_cost = self._calculate_cost(input_tokens, model_config.input_token_cost) output_cost = self._calculate_cost( @@ -823,9 +828,12 @@ def _calculate_metrics( ) total_cost_usd -= cached_savings - reasoning_tokens = usage.get("completion_tokens_details", {}).get( - "reasoning_tokens", None - ) + completion_tokens_details = usage.get("completion_tokens_details") + if completion_tokens_details: + reasoning_tokens = completion_tokens_details.get( + "reasoning_tokens", None + ) + if reasoning_tokens: total_tokens += reasoning_tokens reasoning_cost = self._calculate_cost( From 74e6b4f12294c3f1a26e82830ce0aacc7bfc3630 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 12 Feb 2025 09:40:44 +0000 Subject: [PATCH 29/38] [fix] bump prerelease version in pyproject.toml --- libs/core/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 0847b86b..883ce800 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-core" -version = "1.0.3a0" +version = "1.0.3a1" description = "LLMStudio core capabilities for routing llm calls for any vendor. No proxy server required. For that use llmstudio[proxy]" authors = ["Cláudio Lemos "] license = "MIT" From 7840ef426879db4e75973470e13f4be6872338ad Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 12 Feb 2025 09:47:03 +0000 Subject: [PATCH 30/38] [fix] bump prerelease version in pyproject.toml --- libs/llmstudio/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/llmstudio/pyproject.toml b/libs/llmstudio/pyproject.toml index f48197a9..2e8f5d36 100644 --- a/libs/llmstudio/pyproject.toml +++ b/libs/llmstudio/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio" -version = "1.0.2" +version = "1.0.3a0" description = "Prompt Perfection at Your Fingertips" authors = ["Cláudio Lemos "] license = "MIT" From 65d0f2215925d4e650aae2f955f790f6127ec6e6 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 12 Feb 2025 09:47:37 +0000 Subject: [PATCH 31/38] [fix] bump prerelease version in pyproject.toml --- libs/tracker/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/tracker/pyproject.toml b/libs/tracker/pyproject.toml index 395220df..8fdf0a43 100644 --- a/libs/tracker/pyproject.toml +++ b/libs/tracker/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-tracker" -version = "1.1.0a1" +version = "1.1.0a2" description = "LLMstudio Tracker is the module of LLMstudio that allows monitoring and logging your LLM calls. By leveraging LLMstudio Tracker, users can gain insights on model performance and streamline development workflows with actionable analytics." authors = ["Claudio Lemos "] readme = "README.md" From 162f1af0eb7e2bc5d3fdbea9e25c39e228934329 Mon Sep 17 00:00:00 2001 From: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> Date: Wed, 12 Feb 2025 09:49:17 +0000 Subject: [PATCH 32/38] Update pyproject.toml updated llmstudio-tracker version Signed-off-by: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> --- libs/llmstudio/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/llmstudio/pyproject.toml b/libs/llmstudio/pyproject.toml index 2e8f5d36..532a2429 100644 --- a/libs/llmstudio/pyproject.toml +++ b/libs/llmstudio/pyproject.toml @@ -21,7 +21,7 @@ langchain = "^0" langchain-experimental = "^0" llmstudio-core = "^1.0.0" -llmstudio-tracker = { version = "^1.0.1", optional = true } +llmstudio-tracker = { version = "^1.1.0", optional = true } llmstudio-proxy = { version = "^1.0.1", optional = true } [tool.poetry.extras] From 10a604df8aa7bd99b70ae7b7d5c0e2464db5908c Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 12 Feb 2025 09:51:07 +0000 Subject: [PATCH 33/38] [fix] bump prerelease version in pyproject.toml --- libs/proxy/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libs/proxy/pyproject.toml b/libs/proxy/pyproject.toml index e6dfb2d6..d6a6080a 100644 --- a/libs/proxy/pyproject.toml +++ b/libs/proxy/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "llmstudio-proxy" -version = "1.0.4" +version = "1.0.5a1" description = "LLMstudio Proxy is the module of LLMstudio that allows calling any LLM as a Service Provider in proxy server. By leveraging LLMstudio Proxy, you can have a centralized point for connecting to any provider running independently from your application." authors = ["Claudio Lemos "] readme = "README.md" From 92223ea2aa65b0726d944a04b24216e7551b08ef Mon Sep 17 00:00:00 2001 From: Miguel Neves Date: Wed, 12 Feb 2025 10:03:51 +0000 Subject: [PATCH 34/38] chore: updated llmstudio sdk poetry.lock --- libs/llmstudio/poetry.lock | 2695 ++++++++++++++++++++------------- libs/llmstudio/pyproject.toml | 4 +- 2 files changed, 1662 insertions(+), 1037 deletions(-) diff --git a/libs/llmstudio/poetry.lock b/libs/llmstudio/poetry.lock index 133edf8e..79aeb685 100644 --- a/libs/llmstudio/poetry.lock +++ b/libs/llmstudio/poetry.lock @@ -1,99 +1,104 @@ -# 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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+python-versions = ">=3.9,<3.13" +content-hash = "4fb80d2536264c16244f42b7badadcc3d5deac5de303eb2c45f050fa9e75301e" diff --git a/libs/llmstudio/pyproject.toml b/libs/llmstudio/pyproject.toml index 532a2429..87865c2c 100644 --- a/libs/llmstudio/pyproject.toml +++ b/libs/llmstudio/pyproject.toml @@ -14,14 +14,14 @@ keywords = ["ml", "ai", "llm", "llmops", "openai", "langchain", "chatgpt", "llms "Bug Tracker" = "https://github.com/tensoropsai/llmstudio/issues" [tool.poetry.dependencies] -python = "^3.9" +python = ">=3.9,<3.13" pydantic = "^2.5" python-dotenv = ">=0.4.0,<2.0.0" langchain = "^0" langchain-experimental = "^0" llmstudio-core = "^1.0.0" -llmstudio-tracker = { version = "^1.1.0", optional = true } +llmstudio-tracker = { version = "^1.1.0a0", optional = true } llmstudio-proxy = { version = "^1.0.1", optional = true } [tool.poetry.extras] From 2a9ae2523f916255ae45344a0f1db36a137a135f Mon Sep 17 00:00:00 2001 From: Miguel Neves <61327611+MiNeves00@users.noreply.github.com> Date: Mon, 3 Mar 2025 10:13:04 +0000 Subject: [PATCH 35/38] Feat/converse support images (#211) * feat: added converse-api support for images in input. started making an integration test for this. * chore: added integration test for converse image sending * chore: send images integration test now also tests for openai * chore: integration test of send_imgs added async testing * chore: updated examples core.py to also have send images * feat: bedrock image input is now same contract as openai * chore: ChatCompletionLLMstudio print now hides large image bytes for readability * chore: fixes in the pretty print of ChatCompletionLLMstudio * chore: small fix in examples/core.py * fix: test_send_imgs had bug on reading env * chore: made clean_print optional on chatcompletions; image from url is directly converted to bytes --- examples/core.py | 95 ++++++-- .../providers/bedrock_converse.py | 87 +++++++ .../providers/data_structures.py | 71 +++++- .../test_cache_and_reasoning_costs.py | 9 +- .../test_data/llmstudio-logo.jpeg | Bin 0 -> 34010 bytes .../tests/integration_tests/test_send_imgs.py | 218 ++++++++++++++++++ 6 files changed, 445 insertions(+), 35 deletions(-) create mode 100644 libs/llmstudio/tests/integration_tests/test_data/llmstudio-logo.jpeg create mode 100644 libs/llmstudio/tests/integration_tests/test_send_imgs.py diff --git a/examples/core.py b/examples/core.py index 1eab6c81..61c7debe 100644 --- a/examples/core.py +++ b/examples/core.py @@ -15,9 +15,9 @@ def run_provider(provider, model, api_key=None, **kwargs): latencies = {} print("\nAsync Non-Stream") - chat_request = build_chat_request(model, chat_input="Hello, my name is Jason Json", is_stream=False) + chat_request = build_chat_request(model, chat_input="Hello, my name is Jason", is_stream=False) string = """ -What is Lorem Ipsum? json +What is Lorem Ipsum? Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. Why do we use it? @@ -27,7 +27,7 @@ def run_provider(provider, model, api_key=None, **kwargs): Where does it come from? Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. -What is Lorem Ipsum? json +What is Lorem Ipsum? Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. Why do we use it? @@ -37,7 +37,7 @@ def run_provider(provider, model, api_key=None, **kwargs): Where does it come from? Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32. -What is Lorem Ipsum? json +What is Lorem Ipsum? Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. Why do we use it? @@ -58,7 +58,7 @@ def run_provider(provider, model, api_key=None, **kwargs): print("\nAsync Stream") async def async_stream(): - chat_request = build_chat_request(model, chat_input="Hello, my name is Tom Json", is_stream=True) + chat_request = build_chat_request(model, chat_input="Hello, my name is Tom", is_stream=True) response_async = await llm.achat(**chat_request) async for p in response_async: @@ -74,7 +74,7 @@ async def async_stream(): print("\nSync Non-Stream") - chat_request = build_chat_request(model, chat_input="Hello, my name is Alice Json", is_stream=False) + chat_request = build_chat_request(model, chat_input="Hello, my name is Alice", is_stream=False) response_sync = llm.chat(**chat_request) pprint(response_sync) @@ -82,7 +82,7 @@ async def async_stream(): print("\nSync Stream") - chat_request = build_chat_request(model, chat_input="Hello, my name is Mary Json", is_stream=True) + chat_request = build_chat_request(model, chat_input="Hello, my name is Mary", is_stream=True) response_sync_stream = llm.chat(**chat_request) for p in response_sync_stream: @@ -126,7 +126,6 @@ def build_chat_request(model: str, chat_input: str, is_stream: bool, max_tokens: "parameters": { "temperature": 0, "max_tokens": max_tokens, - "response_format": {"type": "json_object"}, "functions": None, } } @@ -138,29 +137,75 @@ def multiple_provider_runs(provider:str, model:str, num_runs:int, api_key:str, * latencies = run_provider(provider=provider, model=model, api_key=api_key, **kwargs) pprint(latencies) - +def run_chat_all_providers(): + # OpenAI + multiple_provider_runs(provider="openai", model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) + multiple_provider_runs(provider="openai", model="o3-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) + #multiple_provider_runs(provider="openai", model="o1-preview", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) + + + # Azure + multiple_provider_runs(provider="azure", model="gpt-4o-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + #multiple_provider_runs(provider="azure", model="gpt-4o", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + #multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + #multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -# OpenAI -multiple_provider_runs(provider="openai", model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) -multiple_provider_runs(provider="openai", model="o3-mini", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) -#multiple_provider_runs(provider="openai", model="o1-preview", api_key=os.environ["OPENAI_API_KEY"], num_runs=1) + #multiple_provider_runs(provider="anthropic", model="claude-3-opus-20240229", num_runs=1, api_key=os.environ["ANTHROPIC_API_KEY"]) -# Azure -multiple_provider_runs(provider="azure", model="gpt-4o-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -#multiple_provider_runs(provider="azure", model="gpt-4o", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -#multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -#multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + #multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + #multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -#multiple_provider_runs(provider="anthropic", model="claude-3-opus-20240229", num_runs=1, api_key=os.environ["ANTHROPIC_API_KEY"]) + multiple_provider_runs(provider="vertexai", model="gemini-1.5-flash", num_runs=1, api_key=os.environ["GOOGLE_API_KEY"]) -#multiple_provider_runs(provider="azure", model="o1-preview", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) -#multiple_provider_runs(provider="azure", model="o1-mini", num_runs=1, api_key=os.environ["AZURE_API_KEY"], api_version=os.environ["AZURE_API_VERSION"], api_endpoint=os.environ["AZURE_API_ENDPOINT"]) + # Bedrock + multiple_provider_runs(provider="bedrock", model="us.amazon.nova-lite-v1:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) + #multiple_provider_runs(provider="bedrock", model="anthropic.claude-3-5-sonnet-20241022-v2:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) +run_chat_all_providers() -multiple_provider_runs(provider="vertexai", model="gemini-1.5-flash", num_runs=1, api_key=os.environ["GOOGLE_API_KEY"]) -# Bedrock -multiple_provider_runs(provider="bedrock", model="us.amazon.nova-lite-v1:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) -#multiple_provider_runs(provider="bedrock", model="anthropic.claude-3-5-sonnet-20241022-v2:0", num_runs=1, api_key=None, region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) +import base64 + +def messages(img_path): + """ + Creates a message payload with both text and image. + Adapts format based on the provider. + """ + with open(img_path, "rb") as f: + image_bytes = f.read() + + base64_image = base64.b64encode(image_bytes).decode("utf-8") + return [ + { + "role": "user", + "content": [ + {"type": "text", "text": "What's in this image?"}, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}, + }, + { + "type": "image_url", + "image_url": {"url": "https://awsmp-logos.s3.amazonaws.com/seller-zx4pk43qpmxoa/53d235806f343cec94aac3c577d81c13.png"}, + }, + ], + } + ] + +def run_send_imgs(): + provider="bedrock" + model="us.amazon.nova-lite-v1:0" + chat_input=messages(img_path="./libs/llmstudio/tests/integration_tests/test_data/llmstudio-logo.jpeg") + chat_request = build_chat_request(model=model, chat_input=chat_input, is_stream=False) + llm = LLMCore(provider=provider, api_key=os.environ["OPENAI_API_KEY"], region=os.environ["BEDROCK_REGION"], secret_key=os.environ["BEDROCK_SECRET_KEY"], access_key=os.environ["BEDROCK_ACCESS_KEY"]) + response_sync = llm.chat(**chat_request) + #print(response_sync) + response_sync.clean_print() + + #for p in response_sync: + # if p.metrics: + # p.clean_print() + +run_send_imgs() \ No newline at end of file diff --git a/libs/core/llmstudio_core/providers/bedrock_converse.py b/libs/core/llmstudio_core/providers/bedrock_converse.py index dc756e0a..abc990f7 100644 --- a/libs/core/llmstudio_core/providers/bedrock_converse.py +++ b/libs/core/llmstudio_core/providers/bedrock_converse.py @@ -1,5 +1,7 @@ +import base64 import json import os +import re import time import uuid from typing import ( @@ -14,6 +16,7 @@ ) import boto3 +import requests from llmstudio_core.exceptions import ProviderError from llmstudio_core.providers.provider import ChatRequest, ProviderCore, provider from llmstudio_core.utils import OpenAIToolFunction @@ -276,6 +279,34 @@ def _process_messages( } ) messages.append(tool_use) + elif isinstance(message.get("content"), list): + converse_content_list = [] + for content in message.get("content"): + converse_content = {} + if content.get("type") == "text": + converse_content["text"] = content.get("text") + elif content.get("type") == "image_url": + image_url = content.get("image_url")["url"] + bytes_image = BedrockConverseProvider._get_image_bytes( + image_url + ) + format = ( + BedrockConverseProvider._get_img_format_from_bytes( + bytes_image + ) + ) + converse_content["image"] = { + "format": format, + "source": {"bytes": bytes_image}, + } + converse_content_list.append(converse_content) + + messages.append( + { + "role": message.get("role"), + "content": converse_content_list, + } + ) else: messages.append( { @@ -303,6 +334,62 @@ def _process_messages( return messages, system_prompt + @staticmethod + def _base64_to_bytes(image_url: str) -> bytes: + """ + Extracts and decodes Base64 image data from a 'data:image/...;base64,...' URL. + Returns the raw image bytes. + """ + if not image_url.startswith("data:image/"): + raise ValueError("Invalid Base64 image URL") + + base64_data = re.sub(r"^data:image/[^;]+;base64,", "", image_url) + + return base64.b64decode(base64_data) + + @staticmethod + def _get_img_format_from_bytes(image_bytes: bytes) -> str: + """ + Determines the image format from raw image bytes using file signatures (magic numbers). + """ + if image_bytes.startswith(b"\xFF\xD8\xFF"): + return "jpeg" + elif image_bytes.startswith(b"\x89PNG\r\n\x1A\n"): + return "png" + elif image_bytes.startswith(b"GIF87a") or image_bytes.startswith(b"GIF89a"): + return "gif" + elif ( + image_bytes.startswith(b"\x52\x49\x46\x46") and image_bytes[8:12] == b"WEBP" + ): + return "webp" + elif image_bytes.startswith(b"\x49\x49\x2A\x00") or image_bytes.startswith( + b"\x4D\x4D\x00\x2A" + ): + return "tiff" + else: + raise ValueError("Unknown image format") + + @staticmethod + def _get_image_bytes(image_url: str) -> bytes: + """ + Converts an image URL to a Base64-encoded string. + - If already in 'data:image/...;base64,...' format, it returns as-is. + - If it's a normal URL, downloads and encodes the image in Base64. + """ + if image_url.startswith("data:image/"): + return BedrockConverseProvider._base64_to_bytes(image_url) + + elif image_url.startswith(("http://", "https://")): + response = requests.get(image_url) + if response.status_code != 200: + raise ValueError(f"Failed to download image: {response.status_code}") + + image_bytes = response.content + return image_bytes + + else: + raise ValueError("Invalid image URL format") + @staticmethod def _process_tools(parameters: dict) -> Optional[Dict]: if parameters.get("tools") is None and parameters.get("functions") is None: diff --git a/libs/core/llmstudio_core/providers/data_structures.py b/libs/core/llmstudio_core/providers/data_structures.py index 374ad6eb..85c9482e 100644 --- a/libs/core/llmstudio_core/providers/data_structures.py +++ b/libs/core/llmstudio_core/providers/data_structures.py @@ -1,4 +1,5 @@ -from typing import Any, List, Optional +import copy +from typing import Any, List, Optional, Union from openai.types.chat import ChatCompletion, ChatCompletionChunk from pydantic import BaseModel @@ -90,8 +91,68 @@ def items(self): return self.model_dump().items() -class ChatCompletionLLMstudio(ChatCompletion): - chat_input: str +class ChatCompletionLLMstudioBase: + """ + Base class to share the methods between different ChatCompletionLLMstudio classes. + """ + + def clean_print(self): + """ + Custom representation of the class to prevent large fields from bloating the output. + Ensures missing fields are handled gracefully without errors. + """ + data = copy.deepcopy(self.model_dump()) + + def clean_large_fields(d): + """ + Recursively traverses the dictionary to replace large image Base64 data + with a placeholder while ensuring missing fields do not cause errors. + """ + for key, value in d.items(): + if isinstance(value, list): + for item in value: + if isinstance(item, dict): + # Handle image_url directly under chat_input or context + if "image_url" in item and isinstance( + item["image_url"], dict + ): + if "url" in item["image_url"] and isinstance( + item["image_url"]["url"], str + ): + if item["image_url"]["url"].startswith( + "data:image/" + ): + item["image_url"][ + "url" + ] = "