diff --git a/parea/__init__.py b/parea/__init__.py index 72d4a2e2..32997f23 100644 --- a/parea/__init__.py +++ b/parea/__init__.py @@ -11,6 +11,7 @@ from importlib import metadata as importlib_metadata +import parea.wrapper # noqa: F401 from parea.client import Parea diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb index 6cf1eb64..12167fe5 100644 --- a/parea/cookbook/tracing_with_Parea_sdk.ipynb +++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb @@ -26,8 +26,8 @@ "\n", "## Prerequisites\n", "\n", - "First, install the Parea-ai SDK package. If you have an account with Parea, your LLM API Keys will be automatically used, so you won't need to redefine them here. \n", - "All you need is your Parea API key. Follow the instructions in the [docs](https://docs.parea.ai/api-reference/authentication) to get your api keys. " + "First, install the Parea-ai SDK package. If you have an account with Parea, your LLM API Keys will be automatically used, so you won't need to redefine them here.\n", + "All you need is your Parea API key. Follow the instructions in the [docs](https://docs.parea.ai/api-reference/authentication) to get your api keys." ] }, { @@ -113,50 +113,50 @@ "# We pass the deployment_id and the required inputs to the completion function along with the trace_id\n", "@trace\n", "def argument_generator(query: str, additional_description: str = \"\") -> str:\n", - " return p.completion(\n", - " Completion(\n", - " deployment_id=\"p-Ar-Oi14-nBxHUiradyql9\",\n", - " llm_inputs={\n", - " \"additional_description\": additional_description,\n", - " \"date\": f\"{datetime.now()}\",\n", - " \"query\": query,\n", - " },\n", - " )\n", - " ).content\n", + " return p.completion(\n", + " Completion(\n", + " deployment_id=\"p-Ar-Oi14-nBxHUiradyql9\",\n", + " llm_inputs={\n", + " \"additional_description\": additional_description,\n", + " \"date\": f\"{datetime.now()}\",\n", + " \"query\": query,\n", + " },\n", + " )\n", + " ).content\n", "\n", "\n", "@trace\n", "def critic(argument: str) -> str:\n", - " return p.completion(\n", - " Completion(\n", - " deployment_id=\"p-W2yPy93tAczYrxkipjli6\",\n", - " llm_inputs={\"argument\": argument},\n", - " )\n", - " ).content\n", + " return p.completion(\n", + " Completion(\n", + " deployment_id=\"p-W2yPy93tAczYrxkipjli6\",\n", + " llm_inputs={\"argument\": argument},\n", + " )\n", + " ).content\n", "\n", "\n", "@trace\n", "def refiner(query: str, additional_description: str, current_arg: str, criticism: str) -> str:\n", - " return p.completion(\n", - " Completion(\n", - " deployment_id=\"p-8Er1Xo0GDGF2xtpmMOpbn\",\n", - " llm_inputs={\n", - " \"additional_description\": additional_description,\n", - " \"date\": f\"{datetime.now()}\",\n", - " \"query\": query,\n", - " \"current_arg\": current_arg,\n", - " \"criticism\": criticism,\n", - " },\n", - " )\n", - " ).content\n", + " return p.completion(\n", + " Completion(\n", + " deployment_id=\"p-8Er1Xo0GDGF2xtpmMOpbn\",\n", + " llm_inputs={\n", + " \"additional_description\": additional_description,\n", + " \"date\": f\"{datetime.now()}\",\n", + " \"query\": query,\n", + " \"current_arg\": current_arg,\n", + " \"criticism\": criticism,\n", + " },\n", + " )\n", + " ).content\n", "\n", "\n", "# This is the parent function which orchestrates the chaining. We'll define our trace_id and trace_name here\n", "@trace\n", "def argument_chain(query: str, additional_description: str = \"\") -> str:\n", - " argument = argument_generator(query, additional_description)\n", - " criticism = critic(argument)\n", - " return refiner(query, additional_description, argument, criticism)" + " argument = argument_generator(query, additional_description)\n", + " criticism = critic(argument)\n", + " return refiner(query, additional_description, argument, criticism)" ] }, { @@ -184,8 +184,8 @@ "outputs": [], "source": [ "result = argument_chain(\n", - " \"Whether moonshine is good for you.\",\n", - " additional_description=\"Provide a concise, few sentence argument on why moonshine is good for you.\",\n", + " \"Whether moonshine is good for you.\",\n", + " additional_description=\"Provide a concise, few sentence argument on why moonshine is good for you.\",\n", ")\n", "print(result)" ] @@ -222,10 +222,10 @@ "\n", "@trace\n", "def argument_chain2(query: str, additional_description: str = \"\") -> tuple[str, str]:\n", - " trace_id = get_current_trace_id()\n", - " argument = argument_generator(query, additional_description)\n", - " criticism = critic(argument)\n", - " return refiner(query, additional_description, argument, criticism), trace_id" + " trace_id = get_current_trace_id()\n", + " argument = argument_generator(query, additional_description)\n", + " criticism = critic(argument)\n", + " return refiner(query, additional_description, argument, criticism), trace_id" ] }, { @@ -239,8 +239,8 @@ "outputs": [], "source": [ "result, trace_id = argument_chain2(\n", - " \"Whether moonshine is good for you.\",\n", - " additional_description=\"Provide a concise, few sentence argument on why moonshine is good for you.\",\n", + " \"Whether moonshine is good for you.\",\n", + " additional_description=\"Provide a concise, few sentence argument on why moonshine is good for you.\",\n", ")\n", "print(result)" ] @@ -292,7 +292,7 @@ "One way to make your application traces more useful or actionable is to tag or add metadata to the logs. The completion function accepts additional properties such as:\n", "\n", "- tags: List[str]\n", - "- metadata: Dict[str, str] - arbitrary key-value metadata \n", + "- metadata: Dict[str, str] - arbitrary key-value metadata\n", "- target: str - a gold standard/expected output\n", "- end_user_identifier: str - unique identifier for your end user\n", "\n", @@ -308,18 +308,18 @@ "# let's return the full CompletionResponse to see what other information is returned\n", "@trace\n", "def refiner2(query: str, additional_description: str, current_arg: str, criticism: str) -> CompletionResponse:\n", - " return p.completion(\n", - " Completion(\n", - " deployment_id=\"p-8Er1Xo0GDGF2xtpmMOpbn\",\n", - " llm_inputs={\n", - " \"additional_description\": additional_description,\n", - " \"date\": f\"{datetime.now()}\",\n", - " \"query\": query,\n", - " \"current_arg\": current_arg,\n", - " \"criticism\": criticism,\n", - " },\n", - " )\n", - " )" + " return p.completion(\n", + " Completion(\n", + " deployment_id=\"p-8Er1Xo0GDGF2xtpmMOpbn\",\n", + " llm_inputs={\n", + " \"additional_description\": additional_description,\n", + " \"date\": f\"{datetime.now()}\",\n", + " \"query\": query,\n", + " \"current_arg\": current_arg,\n", + " \"criticism\": criticism,\n", + " },\n", + " )\n", + " )" ], "metadata": { "id": "cXUHZpZbegIn", @@ -348,13 +348,13 @@ "source": [ "# you can also add metadata and tags via the decorator\n", "@trace(\n", - " tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n", - " metadata={\"source\": \"python-sdk\", \"deployed\": True},\n", + " tags=[\"cookbook-example-deployed\", \"feedback_tracked-deployed\"],\n", + " metadata={\"source\": \"python-sdk\", \"deployed\": True},\n", ")\n", "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n", - " argument = argument_generator(query, additional_description)\n", - " criticism = critic(argument)\n", - " return refiner2(query, additional_description, argument, criticism)" + " argument = argument_generator(query, additional_description)\n", + " criticism = critic(argument)\n", + " return refiner2(query, additional_description, argument, criticism)" ] }, { @@ -363,16 +363,16 @@ "import json, attrs\n", "\n", "result = argument_chain3(\n", - " \"Whether moonshine is good for you.\",\n", - " additional_description=\"Provide a concise, few sentence argument on why sunshine is good for you.\",\n", + " \"Whether moonshine is good for you.\",\n", + " additional_description=\"Provide a concise, few sentence argument on why sunshine is good for you.\",\n", ")\n", "\n", "p.record_feedback(\n", - " FeedbackRequest(\n", - " trace_id=result.trace_id,\n", - " score=0.5,\n", - " target=\"Moonshine is nice. Full stop.\",\n", - " )\n", + " FeedbackRequest(\n", + " trace_id=result.trace_id,\n", + " score=0.5,\n", + " target=\"Moonshine is nice. Full stop.\",\n", + " )\n", ")\n", "print(json.dumps(attrs.asdict(result), indent=4))" ], @@ -413,7 +413,7 @@ "## Recap\n", "You made an example LLM application in this walkthrough and instrumented it using Parea's SDK.\n", "\n", - "You also added tags and metadata and even logged feedback to the logs. The SDK integrates wonderfully with your deployed prompts on Parea, keeping your code flexible and lightweight. Now you can iterate, debug, and monitor your application with ease." + "You also added tags and metadata and even logged feedback to the logs. The SDK integrates wonderfully with your deployed prompts on Parea, keeping your code flexible and lightweight. Now you can iterate, debug, and monitor your application with ease.\n" ] } ], diff --git a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py index 9cf0de19..83811d20 100644 --- a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py +++ b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py @@ -1 +1,104 @@ -# TBU +import os +from datetime import datetime + +import openai +from dotenv import load_dotenv + +from parea import Parea + +load_dotenv() + +openai.api_key = os.getenv("OPENAI_API_KEY") + +p = Parea(api_key=os.getenv("PAREA_API_KEY")) + + +def argument_generator(query: str, additional_description: str = "", date=datetime.now()) -> str: + return ( + openai.ChatCompletion.create( + model="gpt-3.5-turbo-0613", + messages=[ + { + "role": "system", + "content": f"""You are a debater making an argument on a topic. +{additional_description}. +The current time is {date}""", + }, + {"role": "user", "content": f"""The discussion topic is {query}"""}, + ], + temperature=0.0, + ) + .choices[0] + .message["content"] + ) + + +def critic(argument: str) -> str: + return ( + openai.ChatCompletion.create( + model="gpt-3.5-turbo-0613", + messages=[ + { + "role": "system", + "content": f"""You are a critic. +What unresolved questions or criticism do you have after reading the following argument? +Provide a concise summary of your feedback.""", + }, + {"role": "user", "content": f"""{argument}"""}, + ], + temperature=0.0, + ) + .choices[0] + .message["content"] + ) + + +def refiner(query: str, additional_description: str, current_arg: str, criticism: str, date=datetime.now()) -> str: + return ( + openai.ChatCompletion.create( + model="gpt-3.5-turbo-0613", + messages=[ + { + "role": "system", + "content": f"""You are a debater making an argument on a topic. +{additional_description}. +The current time is {date}""", + }, + {"role": "user", "content": f"""The discussion topic is {query}"""}, + {"role": "assistant", "content": f"""{current_arg}"""}, + {"role": "user", "content": f"""{criticism}"""}, + {"role": "system", "content": f"""Please generate a new argument that incorporates the feedback from the user."""}, + ], + temperature=0.0, + ) + .choices[0] + .message["content"] + ) + + +def argument_chain(query: str, additional_description: str = "") -> str: + argument = argument_generator(query, additional_description) + criticism = critic(argument) + return refiner(query, additional_description, argument, criticism) + + +if __name__ == "__main__": + result = argument_chain( + "Whether caffeine is good for you.", + additional_description="Provide a concise, few sentence argument on why caffeine is good for you.", + ) + print(result) + + from parea.schemas.models import FeedbackRequest + from parea.utils.trace_utils import get_current_trace_id + + p = Parea(api_key=os.getenv("PAREA_API_KEY")) + + trace_id = get_current_trace_id() + print(f"trace_id: {trace_id}") + p.record_feedback( + FeedbackRequest( + trace_id=trace_id, + score=0.7, # 0.0 (bad) to 1.0 (good) + ) + ) diff --git a/parea/schemas/models.py b/parea/schemas/models.py index 93279780..ded3f502 100644 --- a/parea/schemas/models.py +++ b/parea/schemas/models.py @@ -21,7 +21,7 @@ class Message: @define class ModelParams: - temp: float = 0.5 + temp: float = 1.0 top_p: float = 1.0 frequency_penalty: float = 0.0 presence_penalty: float = 0.0 diff --git a/parea/utils/trace_utils.py b/parea/utils/trace_utils.py index 4b37336a..14135bae 100644 --- a/parea/utils/trace_utils.py +++ b/parea/utils/trace_utils.py @@ -84,11 +84,7 @@ def init_trace(func_name, args, kwargs, func): def cleanup_trace(trace_id): end_time = time.time() trace_data.get()[trace_id].end_timestamp = to_date_and_time_string(end_time) - logging_thread = threading.Thread( - target=parea_logger.record_log, - kwargs={"data": trace_data.get()[trace_id]}, - ) - logging_thread.start() + default_logger(trace_id) trace_context.get().pop() def decorator(func): @@ -135,3 +131,11 @@ def wrapper(*args, **kwargs): return decorator(func) return decorator + + +def default_logger(trace_id: str): + logging_thread = threading.Thread( + target=parea_logger.record_log, + kwargs={"data": trace_data.get()[trace_id]}, + ) + logging_thread.start() diff --git a/parea/wrapper/__init__.py b/parea/wrapper/__init__.py new file mode 100644 index 00000000..eb6d319b --- /dev/null +++ b/parea/wrapper/__init__.py @@ -0,0 +1,17 @@ +from parea.utils.trace_utils import default_logger +from parea.wrapper.openai import OpenAIWrapper + +_initialized_parea_wrapper = False + + +def init(): + global _initialized_parea_wrapper + if _initialized_parea_wrapper: + return + + OpenAIWrapper.init(default_logger) + + _initialized_parea_wrapper = True + + +init() diff --git a/parea/wrapper/openai.py b/parea/wrapper/openai.py new file mode 100644 index 00000000..d5157923 --- /dev/null +++ b/parea/wrapper/openai.py @@ -0,0 +1,122 @@ +from typing import Any, Callable, Dict, Optional, Sequence + +import json + +import openai + +from ..schemas.models import LLMInputs, ModelParams +from ..utils.trace_utils import trace_data +from .wrapper import Wrapper + +MODEL_COST_MAPPING: Dict[str, float] = { + "gpt-4": 0.03, + "gpt-4-0314": 0.03, + "gpt-4-0613": 0.03, + "gpt-4-completion": 0.06, + "gpt-4-0314-completion": 0.06, + "gpt-4-0613-completion": 0.06, + "gpt-4-32k": 0.06, + "gpt-4-32k-0314": 0.06, + "gpt-4-32k-0613": 0.06, + "gpt-4-32k-completion": 0.12, + "gpt-4-32k-0314-completion": 0.12, + "gpt-4-32k-0613-completion": 0.12, + "gpt-3.5-turbo": 0.0015, + "gpt-3.5-turbo-0301": 0.0015, + "gpt-3.5-turbo-0613": 0.0015, + "gpt-3.5-turbo-16k": 0.003, + "gpt-3.5-turbo-16k-0613": 0.003, + "gpt-3.5-turbo-completion": 0.002, + "gpt-3.5-turbo-0301-completion": 0.002, + "gpt-3.5-turbo-0613-completion": 0.004, + "gpt-3.5-turbo-16k-completion": 0.004, + "gpt-3.5-turbo-16k-0613-completion": 0.004, + "text-ada-001": 0.0004, + "ada": 0.0004, + "text-babbage-001": 0.0005, + "babbage": 0.0005, + "text-curie-001": 0.002, + "curie": 0.002, + "text-davinci-003": 0.02, + "text-davinci-002": 0.02, + "code-davinci-002": 0.02, +} + + +class OpenAIWrapper: + original_methods = {"ChatCompletion.create": openai.ChatCompletion.create, "ChatCompletion.acreate": openai.ChatCompletion.acreate} + + @staticmethod + def resolver(trace_id: str, _args: Sequence[Any], kwargs: Dict[str, Any], response: Optional[Any]): + if response: + usage = response["usage"] + output = OpenAIWrapper._get_output(response) + else: + output = None + usage = {} + + model = kwargs.get("model", None) + + llm_inputs = LLMInputs( + model=model, + provider="openai", + messages=kwargs.get("messages", None), + functions=kwargs.get("functions", None), + function_call=kwargs.get("function_call", None), + model_params=ModelParams( + temp=kwargs.get("temperature", 1.0), + max_length=kwargs.get("max_tokens", None), + top_p=kwargs.get("top_p", 1.0), + frequency_penalty=kwargs.get("frequency_penalty", 0.0), + presence_penalty=kwargs.get("presence_penalty", 0.0), + ), + ) + + model_rate = OpenAIWrapper.get_model_cost(model) + model_completion_rate = OpenAIWrapper.get_model_cost(model, is_completion=True) + completion_cost = model_completion_rate * (usage.get("completion_tokens", 0) / 1000) + prompt_cost = model_rate * (usage.get("prompt_tokens", 0) / 1000) + total_cost = sum([prompt_cost, completion_cost]) + + trace_data.get()[trace_id].configuration = llm_inputs + trace_data.get()[trace_id].input_tokens = usage.get("prompt_tokens", 0) + trace_data.get()[trace_id].output_tokens = usage.get("completion_tokens", 0) + trace_data.get()[trace_id].total_tokens = usage.get("total_tokens", 0) + trace_data.get()[trace_id].cost = total_cost + trace_data.get()[trace_id].output = output + + @staticmethod + def init(log: Callable): + Wrapper(resolver=OpenAIWrapper.resolver, log=log, module=openai, func_names=list(OpenAIWrapper.original_methods.keys())) + + @staticmethod + def _get_output(result) -> str: + response_message = result.choices[0].message + if response_message.get("function_call", None): + completion = OpenAIWrapper._format_function_call(response_message) + else: + completion = response_message.content.strip() + return completion + + @staticmethod + def _format_function_call(response_message) -> str: + function_name = response_message["function_call"]["name"] + if isinstance(response_message["function_call"]["arguments"], openai.openai_object.OpenAIObject): + function_args = dict(response_message["function_call"]["arguments"]) + else: + function_args = json.loads(response_message["function_call"]["arguments"]) + return f'```{json.dumps({"name": function_name, "arguments": function_args}, indent=4)}```' + + @staticmethod + def get_model_cost(model_name: str, is_completion: bool = False) -> float: + model_name = model_name.lower() + + if model_name.startswith("gpt-4") and is_completion: + model_name += "-completion" + + cost = MODEL_COST_MAPPING.get(model_name, None) + if cost is None: + msg = f"Unknown model: {model_name}. " f"Please provide a valid OpenAI model name. " f"Known models are: {', '.join(MODEL_COST_MAPPING.keys())}" + raise ValueError(msg) + + return cost diff --git a/parea/wrapper/wrapper.py b/parea/wrapper/wrapper.py new file mode 100644 index 00000000..1f68358e --- /dev/null +++ b/parea/wrapper/wrapper.py @@ -0,0 +1,121 @@ +from typing import Any, Callable, List, Tuple + +import functools +import inspect +import time +from uuid import uuid4 + +from parea.schemas.models import TraceLog +from parea.utils.trace_utils import default_logger, to_date_and_time_string, trace_context, trace_data + + +class Wrapper: + def __init__(self, module: Any, func_names: List[str], resolver: Callable, log: Callable = default_logger) -> None: + self.resolver = resolver + self.log = log + self.wrap_functions(module, func_names) + + def wrap_functions(self, module: Any, func_names: List[str]): + for func_name in func_names: + func_name_parts = func_name.split(".") + original = functools.reduce(getattr, func_name_parts, module) + setattr(module if len(func_name_parts) == 1 else functools.reduce(getattr, func_name_parts[:-1], module), func_name_parts[-1], self._wrapped_func(original)) + + def _wrapped_func(self, original_func: Callable) -> Callable: + unwrapped_func = original_func + while hasattr(original_func, "__wrapped__"): + unwrapped_func = original_func.__wrapped__ + return self._get_decorator(unwrapped_func, original_func) + + def _get_decorator(self, unwrapped_func: Callable, original_func: Callable): + if inspect.iscoroutinefunction(unwrapped_func): + return self.async_decorator(original_func) + else: + return self.sync_decorator(original_func) + + def _init_trace(self) -> Tuple[str, float]: + start_time = time.time() + trace_id = str(uuid4()) + trace_context.get().append(trace_id) + + trace_data.get()[trace_id] = TraceLog( + trace_id=trace_id, + start_timestamp=to_date_and_time_string(start_time), + trace_name="LLM", + end_user_identifier=None, + metadata=None, + target=None, + tags=None, + inputs={}, + ) + + parent_trace_id = trace_context.get()[-2] if len(trace_context.get()) > 1 else None + if not parent_trace_id: + # we don't have a parent trace id, so we need to create one + parent_trace_id = str(uuid4()) + trace_context.get().insert(0, parent_trace_id) + trace_data.get()[parent_trace_id] = TraceLog( + trace_id=parent_trace_id, + start_timestamp=to_date_and_time_string(start_time), + end_user_identifier=None, + metadata=None, + target=None, + tags=None, + inputs={}, + ) + trace_data.get()[parent_trace_id].children.append(trace_id) + self.log(parent_trace_id) + + return trace_id, start_time + + def async_decorator(self, orig_func: Callable) -> Callable: + async def wrapper(*args, **kwargs): + trace_id, start_time = self._init_trace() + response = None + try: + response = await orig_func(*args, **kwargs) + return response + except Exception as e: + self._handle_error(trace_id, e) + raise + finally: + self._cleanup_trace(trace_id, start_time, response, args, kwargs) + + return wrapper + + def sync_decorator(self, orig_func: Callable) -> Callable: + def wrapper(*args, **kwargs): + trace_id, start_time = self._init_trace() + response = None + error = None + try: + response = orig_func(*args, **kwargs) + return response + except Exception as e: + error = str(e) + raise + finally: + self._cleanup_trace(trace_id, start_time, error, response, args, kwargs) + + return wrapper + + def _cleanup_trace(self, trace_id: str, start_time: float, error: str, response: Any, args, kwargs): + end_time = time.time() + trace_data.get()[trace_id].end_timestamp = to_date_and_time_string(end_time) + trace_data.get()[trace_id].latency = end_time - start_time + + if error: + trace_data.get()[trace_id].error = error + trace_data.get()[trace_id].status = "error" + else: + trace_data.get()[trace_id].status = "success" + + self.resolver(trace_id, args, kwargs, response) + + self.log(trace_id) + trace_context.get().pop() + + @staticmethod + def _handle_error(trace_id: str, e: Exception): + trace_data.get()[trace_id].error = str(e) + trace_data.get()[trace_id].status = "error" diff --git a/poetry.lock b/poetry.lock index e805402a..88d58689 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,127 @@ # This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +[[package]] +name = "aiohttp" +version = "3.8.5" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a94159871304770da4dd371f4291b20cac04e8c94f11bdea1c3478e557fbe0d8"}, + {file = "aiohttp-3.8.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:13bf85afc99ce6f9ee3567b04501f18f9f8dbbb2ea11ed1a2e079670403a7c84"}, + {file = "aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2ce2ac5708501afc4847221a521f7e4b245abf5178cf5ddae9d5b3856ddb2f3a"}, + {file = "aiohttp-3.8.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", 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