diff --git a/README.md b/README.md index 8296d2f2..b4926652 100644 --- a/README.md +++ b/README.md @@ -82,61 +82,31 @@ async def main_async(): ```python import os -import time import openai from dotenv import load_dotenv from parea import Parea -from parea.helpers import to_date_and_time_string, gen_trace_id -from parea.parea_logger import parea_logger -from parea.schemas.models import TraceLog, LLMInputs load_dotenv() openai.api_key = os.getenv("OPENAI_API_KEY") + p = Parea(api_key=os.getenv("PAREA_API_KEY")) -# define your OpenAI call as you would normally x = "Golang" y = "Fiber" -inputs = {"x": x, "y": y} -messages = [ - {"role": "user", "content": f"Write a hello world program using {x} and the {y} framework."}, -] +messages = [{ + "role": "user", + "content": f"Write a hello world program using {x} and the {y} framework." +}] model = "gpt-3.5-turbo" -model_params = { - "temperature": 0.7, - "top_p": 1.0, -} -model_config = {"model": model, "messages": messages, "model_params": model_params} -start_time = time.time() -completion = openai.ChatCompletion.create(model=model, messages=messages, **model_params) -output = completion.choices[0].message["content"] -end_time = time.time() - -# the TraceLog schema -log_request = TraceLog( - trace_id=gen_trace_id(), - start_timestamp=to_date_and_time_string(start_time), - end_timestamp=to_date_and_time_string(end_time), - status="success", - trace_name="Test Log", - inputs=inputs, - configuration=LLMInputs(**model_config), - output=output, - input_tokens=completion.usage["prompt_tokens"], - output_tokens=completion.usage["completion_tokens"], - total_tokens=completion.usage["total_tokens"], -) +temperature = 0.0 +# define your OpenAI call as you would normally and we'll automatically log the results def main(): - parea_logger.record_log(data=log_request) - - -async def main_async(): - await parea_logger.arecord_log(data=log_request) + openai.ChatCompletion.create(model=model, temperature=temperature, messages=messages).choices[0].message["content"] ``` ### Open source community features diff --git a/parea/client.py b/parea/client.py index ecb5d24e..2772ff3d 100644 --- a/parea/client.py +++ b/parea/client.py @@ -1,10 +1,10 @@ import asyncio import time -from uuid import uuid4 from attrs import asdict, define, field from parea.api_client import HTTPClient +from parea.helpers import gen_trace_id from parea.parea_logger import parea_logger from parea.schemas.models import Completion, CompletionResponse, FeedbackRequest, UseDeployedPrompt, UseDeployedPromptResponse from parea.utils.trace_utils import default_logger, get_current_trace_id, trace_data @@ -24,7 +24,7 @@ def __attrs_post_init__(self): parea_logger.set_client(self._client) def completion(self, data: Completion) -> CompletionResponse: - inference_id = str(uuid4()) + inference_id = gen_trace_id() data.inference_id = inference_id r = self._client.request( "POST", @@ -37,7 +37,7 @@ def completion(self, data: Completion) -> CompletionResponse: return CompletionResponse(**r.json()) async def acompletion(self, data: Completion) -> CompletionResponse: - inference_id = str(uuid4()) + inference_id = gen_trace_id() data.inference_id = inference_id r = await self._client.request_async( "POST", diff --git a/parea/cookbook/tracing_with_Parea_sdk.ipynb b/parea/cookbook/tracing_with_Parea_sdk.ipynb index 50b7513a..4b58a9e2 100644 --- a/parea/cookbook/tracing_with_Parea_sdk.ipynb +++ b/parea/cookbook/tracing_with_Parea_sdk.ipynb @@ -114,7 +114,7 @@ "def argument_generator(query: str, additional_description: str = \"\") -> str:\n", " return p.completion(\n", " Completion(\n", - " deployment_id=\"p-RG8d9rfJc_0cctwfpb_n6\",\n", + " deployment_id=\"p-XOh3kp8B0nIE82WgioPnr\",\n", " llm_inputs={\n", " \"additional_description\": additional_description,\n", " \"date\": f\"{datetime.now()}\",\n", @@ -127,21 +127,21 @@ "def critic(argument: str) -> str:\n", " return p.completion(\n", " Completion(\n", - " deployment_id=\"p-fXgZytT3dJjXD_71TDR4s\",\n", + " deployment_id=\"p-PSOwRyIPaQRq4xQW3MbpV\",\n", " llm_inputs={\"argument\": argument},\n", " )\n", " ).content\n", "\n", "\n", - "def refiner(query: str, additional_description: str, current_arg: str, criticism: str) -> str:\n", + "def refiner(query: str, additional_description: str, argument: str, criticism: str) -> str:\n", " return p.completion(\n", " Completion(\n", - " deployment_id=\"p--G2s9okMTvBEh3d8YqLY2\",\n", + " deployment_id=\"p-bJ3-UKh9-ixapZafaRBsj\",\n", " llm_inputs={\n", " \"additional_description\": additional_description,\n", " \"date\": f\"{datetime.now()}\",\n", " \"query\": query,\n", - " \"current_arg\": current_arg,\n", + " \"argument\": argument,\n", " \"criticism\": criticism,\n", " },\n", " )\n", @@ -285,15 +285,15 @@ "\n", "\n", "# let's return the full CompletionResponse to see what other information is returned\n", - "def refiner2(query: str, additional_description: str, current_arg: str, criticism: str) -> CompletionResponse:\n", + "def refiner2(query: str, additional_description: str, argument: str, criticism: str) -> CompletionResponse:\n", " return p.completion(\n", " Completion(\n", - " deployment_id=\"p-8Er1Xo0GDGF2xtpmMOpbn\",\n", + " deployment_id=\"p-bJ3-UKh9-ixapZafaRBsj\",\n", " llm_inputs={\n", " \"additional_description\": additional_description,\n", " \"date\": f\"{datetime.now()}\",\n", " \"query\": query,\n", - " \"current_arg\": current_arg,\n", + " \"argument\": argument,\n", " \"criticism\": criticism,\n", " },\n", " )\n", @@ -337,15 +337,19 @@ }, "outputs": [], "source": [ + "from typing import Tuple\n", + "\n", + "\n", "# 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", + " metadata={\"source\": \"python-sdk\", \"deployed\": \"True\"},\n", ")\n", - "def argument_chain3(query: str, additional_description: str = \"\") -> CompletionResponse:\n", + "def argument_chain_tags_metadata(query: str, additional_description: str = \"\") -> Tuple[CompletionResponse, str]:\n", + " trace_id = get_current_trace_id() # get parent's trace_id\n", " argument = argument_generator(query, additional_description)\n", " criticism = critic(argument)\n", - " return refiner2(query, additional_description, argument, criticism)" + " return refiner2(query, additional_description, argument, criticism), trace_id" ] }, { @@ -353,19 +357,17 @@ "source": [ "import json, attrs\n", "\n", - "result = argument_chain3(\n", + "result2, trace_id = argument_chain_tags_metadata(\n", " \"Whether coffee is good for you.\",\n", " additional_description=\"Provide a concise, few sentence argument on why coffee is good for you.\",\n", ")\n", - "\n", - "print(json.dumps(attrs.asdict(result), indent=4))\n", + "print(json.dumps(attrs.asdict(result2), indent=2))\n", "\n", "p.record_feedback(\n", " FeedbackRequest(\n", - " # we can access the inference_id (same as trace_id) from the CompletionResponse object directly\n", - " trace_id=result.inference_id,\n", - " score=0.5,\n", - " target=\"Coffee is nice. Full stop.\",\n", + " trace_id=trace_id,\n", + " score=0.7, # 0.0 (bad) to 1.0 (good)\n", + " target=\"Coffee is wonderful. End of story.\",\n", " )\n", ")" ], diff --git a/parea/cookbook/tracing_with_agent.py b/parea/cookbook/tracing_with_agent.py index 3d005fcd..1e44d34b 100644 --- a/parea/cookbook/tracing_with_agent.py +++ b/parea/cookbook/tracing_with_agent.py @@ -1,15 +1,17 @@ import os import random +import time from dotenv import load_dotenv from parea import Parea +from parea.helpers import to_date_and_time_string from parea.schemas.models import Completion, CompletionResponse, FeedbackRequest, LLMInputs, Message, ModelParams, Role from parea.utils.trace_utils import get_current_trace_id, trace load_dotenv() -p = Parea(api_key=os.getenv("PAREA_API_KEY")) +p = Parea(api_key=os.getenv("DEV_API_KEY")) # Parea SDK makes it easy to use different LLMs with the same apis structure and standardized request/response schemas. LLM_OPTIONS = [("gpt-3.5-turbo", "openai"), ("gpt-4", "openai"), ("claude-instant-1", "anthropic"), ("claude-2", "anthropic")] @@ -84,7 +86,7 @@ def generate_tasks(main_objective: str, expounded_initial_task: list[dict[str, s return new_tasks_list -@trace +@trace(name=f"run_agent-{to_date_and_time_string(time.time())}") # You can provide a custom name other than the function name def run_agent(main_objective: str, initial_task: str = "") -> tuple[list[dict[str, str]], str]: trace_id = get_current_trace_id() generated_tasks = [] diff --git a/parea/cookbook/tracing_with_deployed_prompt.py b/parea/cookbook/tracing_with_deployed_prompt.py index 058bd04f..b1822074 100644 --- a/parea/cookbook/tracing_with_deployed_prompt.py +++ b/parea/cookbook/tracing_with_deployed_prompt.py @@ -1,8 +1,10 @@ from typing import Tuple +import json import os from datetime import datetime +from attrs import asdict from dotenv import load_dotenv from parea import Parea @@ -11,13 +13,13 @@ load_dotenv() -p = Parea(api_key=os.getenv("PAREA_API_KEY")) +p = Parea(api_key=os.getenv("DEV_API_KEY")) def deployed_argument_generator(query: str, additional_description: str = "") -> str: return p.completion( Completion( - deployment_id="p-RG8d9rfJc_0cctwfpb_n6", + deployment_id="p-XOh3kp8B0nIE82WgioPnr", llm_inputs={ "additional_description": additional_description, "date": f"{datetime.now()}", @@ -30,7 +32,7 @@ def deployed_argument_generator(query: str, additional_description: str = "") -> def deployed_critic(argument: str) -> str: return p.completion( Completion( - deployment_id="p-fXgZytT3dJjXD_71TDR4s", + deployment_id="p-PSOwRyIPaQRq4xQW3MbpV", llm_inputs={"argument": argument}, ) ).content @@ -39,12 +41,12 @@ def deployed_critic(argument: str) -> str: def deployed_refiner(query: str, additional_description: str, current_arg: str, criticism: str) -> str: return p.completion( Completion( - deployment_id="p--G2s9okMTvBEh3d8YqLY2", + deployment_id="p-bJ3-UKh9-ixapZafaRBsj", llm_inputs={ "additional_description": additional_description, "date": f"{datetime.now()}", "query": query, - "current_arg": current_arg, + "argument": current_arg, "criticism": criticism, }, ) @@ -54,12 +56,12 @@ def deployed_refiner(query: str, additional_description: str, current_arg: str, def deployed_refiner2(query: str, additional_description: str, current_arg: str, criticism: str) -> CompletionResponse: return p.completion( Completion( - deployment_id="p--G2s9okMTvBEh3d8YqLY2", + deployment_id="p-bJ3-UKh9-ixapZafaRBsj", llm_inputs={ "additional_description": additional_description, "date": f"{datetime.now()}", "query": query, - "current_arg": current_arg, + "argument": current_arg, "criticism": criticism, }, ) @@ -73,22 +75,15 @@ def deployed_argument_chain(query: str, additional_description: str = "") -> str return deployed_refiner(query, additional_description, argument, criticism) -@trace -def deployed_argument_chain2(query: str, additional_description: str = "") -> Tuple[str, str]: - trace_id = get_current_trace_id() - argument = deployed_argument_generator(query, additional_description) - criticism = deployed_critic(argument) - return deployed_refiner(query, additional_description, argument, criticism), trace_id - - @trace( tags=["cookbook-example-deployed", "feedback_tracked-deployed"], - metadata={"source": "python-sdk", "deployed": True}, + metadata={"source": "python-sdk", "deployed": "True"}, ) -def deployed_argument_chain_tags_metadata(query: str, additional_description: str = "") -> CompletionResponse: +def deployed_argument_chain_tags_metadata(query: str, additional_description: str = "") -> tuple[CompletionResponse, str]: + trace_id = get_current_trace_id() # get parent's trace_id argument = deployed_argument_generator(query, additional_description) criticism = deployed_critic(argument) - return deployed_refiner2(query, additional_description, argument, criticism) + return deployed_refiner2(query, additional_description, argument, criticism), trace_id if __name__ == "__main__": @@ -98,27 +93,14 @@ def deployed_argument_chain_tags_metadata(query: str, additional_description: st ) print(result1) - result2, trace_id2 = deployed_argument_chain2( - "Whether wine is good for you.", - additional_description="Provide a concise, few sentence argument on why wine is good for you.", - ) - print(result2) - p.record_feedback( - FeedbackRequest( - trace_id=trace_id2, - score=0.0, # 0.0 (bad) to 1.0 (good) - target="Moonshine is wonderful.", - ) - ) - - result3 = deployed_argument_chain_tags_metadata( + result2, trace_id = deployed_argument_chain_tags_metadata( "Whether coffee is good for you.", additional_description="Provide a concise, few sentence argument on why coffee is good for you.", ) - print(result3.content) + print(json.dumps(asdict(result2), indent=2)) p.record_feedback( FeedbackRequest( - trace_id=result3.inference_id, + trace_id=trace_id, score=0.7, # 0.0 (bad) to 1.0 (good) target="Coffee is wonderful. End of story.", ) diff --git a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py index 6d9683ae..4fc914e4 100644 --- a/parea/cookbook/tracing_with_open_ai_endpoint_directly.py +++ b/parea/cookbook/tracing_with_open_ai_endpoint_directly.py @@ -12,77 +12,75 @@ openai.api_key = os.getenv("OPENAI_API_KEY") -p = Parea(api_key=os.getenv("PAREA_API_KEY")) +p = Parea(api_key=os.getenv("DEV_API_KEY")) + + +def call_llm(data: list[dict], model: str = "gpt-3.5-turbo", temperature: float = 0.0) -> str: + return openai.ChatCompletion.create(model=model, temperature=temperature, messages=data).choices[0].message["content"] @trace -def argument_chain(query: str, additional_description: str = "") -> str: - trace_id = get_current_trace_id() - argument = ( - openai.ChatCompletion.create( - model="gpt-3.5-turbo-0613", - temperature=0.0, - messages=[ - { - "role": "system", - "content": f"""You are a debater making an argument on a topic. {additional_description}. -The current time is {datetime.now()}""", - }, - {"role": "user", "content": f"The discussion topic is {query}"}, - ], - ) - .choices[0] - .message["content"] +def argumentor(query: str, additional_description: str = "") -> str: + return call_llm( + [ + { + "role": "system", + "content": f"""You are a debater making an argument on a topic. {additional_description}. + The current time is {datetime.now()}""", + }, + {"role": "user", "content": f"The discussion topic is {query}"}, + ] ) - criticism = ( - openai.ChatCompletion.create( - model="gpt-3.5-turbo-0613", - temperature=0.0, - 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": argument}, - ], - ) - .choices[0] - .message["content"] + +@trace +def critic(argument: str) -> str: + return call_llm( + [ + { + "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": argument}, + ] ) - refined_argument = ( - openai.ChatCompletion.create( - model="gpt-3.5-turbo-0613", - temperature=0.0, - messages=[ - { - "role": "system", - "content": f"""You are a debater making an argument on a topic. {additional_description}. -The current time is {datetime.now()}""", - }, - {"role": "user", "content": f"""The discussion topic is {query}"""}, - {"role": "assistant", "content": argument}, - {"role": "user", "content": criticism}, - { - "role": "system", - "content": f"Please generate a new argument that incorporates the feedback from the user.", - }, - ], - ) - .choices[0] - .message["content"] + +@trace +def refiner(query: str, additional_description: str, argument: str, criticism: str) -> str: + return call_llm( + [ + { + "role": "system", + "content": f"""You are a debater making an argument on a topic. {additional_description}. + The current time is {datetime.now()}""", + }, + {"role": "user", "content": f"""The discussion topic is {query}"""}, + {"role": "assistant", "content": argument}, + {"role": "user", "content": criticism}, + { + "role": "system", + "content": f"Please generate a new argument that incorporates the feedback from the user.", + }, + ], ) + +@trace +def argument_chain(query: str, additional_description: str = "") -> tuple[str, str]: + trace_id = get_current_trace_id() + argument = argumentor(query, additional_description) + criticism = critic(argument) + refined_argument = refiner(query, additional_description, argument, criticism) return refined_argument, trace_id if __name__ == "__main__": result, trace_id = argument_chain( - "Whether sparkling water is good for you.", - additional_description="Provide a concise, few sentence argument on why sparkling water is good for you.", + "Whether sparkling wine is good for you.", + additional_description="Provide a concise, few sentence argument on why sparkling wine is good for you.", ) print(result) p.record_feedback( diff --git a/parea/cookbook/tracing_with_openai_with_functions.py b/parea/cookbook/tracing_with_openai_with_functions.py new file mode 100644 index 00000000..7702aaf4 --- /dev/null +++ b/parea/cookbook/tracing_with_openai_with_functions.py @@ -0,0 +1,188 @@ +import json +import os + +import openai +import requests +from dotenv import load_dotenv + +from parea import Parea +from parea.schemas.models import FeedbackRequest +from parea.utils.trace_utils import get_current_trace_id, trace + +load_dotenv() + +API_KEY = os.getenv("GOOGLE_PLACES_API_KEY") +PLACES_URL = "https://maps.googleapis.com/maps/api/place/nearbysearch/json" +openai.api_key = os.getenv("OPENAI_API_KEY") + +p = Parea(api_key=os.getenv("DEV_API_KEY")) + +functions = [ + { + "name": "call_google_places_api", + "description": f""" + This function calls the Google Places API to find the top places of a specified type near + a specific location. It can be used when a user expresses a need (e.g., feeling hungry or tired) or wants to + find a certain type of place (e.g., restaurant or hotel). + """, + "parameters": {"type": "object", "properties": {"place_type": {"type": "string", "description": "The type of place to search for."}}}, + "result": {"type": "array", "items": {"type": "string"}}, + } +] + + +@trace(tags=["tool-db-get-user"]) +def fetch_customer_profile(user_id): + # You can replace this with a real API call in the production code + if user_id == "user1234": + return { + "name": "John Doe", + "location": { + "latitude": 37.7955, + "longitude": -122.4026, + }, + "preferences": { + "food": ["Italian", "Sushi"], + "activities": ["Hiking", "Reading"], + }, + "behavioral_metrics": { + "app_usage": {"daily": 2, "weekly": 14}, # hours # hours + "favourite_post_categories": ["Nature", "Food", "Books"], + "active_time": "Evening", + }, + "recent_searches": ["Italian restaurants nearby", "Book clubs"], + "recent_interactions": ["Liked a post about 'Best Pizzas in New York'", "Commented on a post about 'Central Park Trails'"], + "user_rank": "Gold", # based on some internal ranking system + } + else: + return None + + +@trace(tags=["tool-api-places_id"]) +def get_place_details(place_id, api_key): + URL = f"https://maps.googleapis.com/maps/api/place/details/json?place_id={place_id}&key={api_key}" + response = requests.get(URL) + if response.status_code == 200: + result = json.loads(response.content)["result"] + return result + else: + print(f"Google Place Details API request failed with status code {response.status_code}") + print(f"Response content: {response.content}") + return None + + +@trace(tags=["tool-api-nearbysearch"]) +def call_google_places_api(user_id, place_type, food_preference=None): + try: + # Fetch customer profile + customer_profile = fetch_customer_profile(user_id) + if customer_profile is None: + return "I couldn't find your profile. Could you please verify your user ID?" + + # Get location from customer profile + lat = customer_profile["location"]["latitude"] + lng = customer_profile["location"]["longitude"] + + LOCATION = f"{lat},{lng}" + RADIUS = 500 # search within a radius of 500 meters + TYPE = place_type + + # If the place_type is restaurant and food_preference is not None, include it in the API request + if place_type == "restaurant" and food_preference: + URL = f"{PLACES_URL}?location={LOCATION}&radius={RADIUS}&type={TYPE}&keyword={food_preference}&key={API_KEY}" + else: + URL = f"{PLACES_URL}?location={LOCATION}&radius={RADIUS}&type={TYPE}&key={API_KEY}" + + response = requests.get(URL) + if response.status_code == 200: + results = json.loads(response.content)["results"] + places = [] + for place in results[:2]: # limit to top 2 results + place_id = place.get("place_id") + place_details = get_place_details(place_id, API_KEY) # Get the details of the place + + place_name = place_details.get("name", "N/A") + place_types = next( + (t for t in place_details.get("types", []) if t not in ["food", "point_of_interest"]), "N/A" + ) # Get the first type of the place, excluding "food" and "point_of_interest" + place_rating = place_details.get("rating", "N/A") # Get the rating of the place + total_ratings = place_details.get("user_ratings_total", "N/A") # Get the total number of ratings + place_address = place_details.get("vicinity", "N/A") # Get the vicinity of the place + + if "," in place_address: # If the address contains a comma + street_address = place_address.split(",")[0] # Split by comma and keep only the first part + else: + street_address = place_address + + # Prepare the output string for this place + place_info = f"{place_name} is a {place_types} located at {street_address}. It has a rating of {place_rating} based on {total_ratings} user reviews." + + places.append(place_info) + + return places + else: + print(f"Google Places API request failed with status code {response.status_code}") + print(f"Response content: {response.content}") # print out the response content for debugging + return [] + except Exception as e: + print(f"Error during the Google Places API call: {e}") + return [] + + +@trace +def provide_user_specific_recommendations(user_input, user_id, functions) -> tuple[str, str]: + trace_id = get_current_trace_id() + customer_profile = fetch_customer_profile(user_id) + if customer_profile is None: + return "I couldn't find your profile. Could you please verify your user ID?", trace_id + + customer_profile_str = json.dumps(customer_profile) + + food_preference = customer_profile.get("preferences", {}).get("food", [])[0] if customer_profile.get("preferences", {}).get("food") else None + + response = openai.ChatCompletion.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "system", + "content": f"You are a sophisticated AI assistant, " + f"a specialist in user intent detection and interpretation. " + f"Your task is to perceive and respond to the user's needs, even when they're expressed " + f"in an indirect or direct manner. You excel in recognizing subtle cues: for example, " + f"if a user states they are 'hungry', you should assume they are seeking nearby dining " + f"options such as a restaurant or a cafe. If they indicate feeling 'tired', 'weary', " + f"or mention a long journey, interpret this as a request for accommodation options like " + f"hotels or guest houses. However, remember to navigate the fine line of interpretation " + f"and assumption: if a user's intent is unclear or can be interpreted in multiple ways, " + f"do not hesitate to politely ask for additional clarification. Make sure to tailor your " + f"responses to the user based on their preferences and past experiences which can " + f"be found here {customer_profile_str}", + }, + {"role": "user", "content": user_input}, + ], + temperature=0, + functions=functions, + ) + + if "message" in response.choices[0] and "function_call" in response.choices[0]["message"]: + function_call = response.choices[0]["message"]["function_call"] + if function_call["name"] == "call_google_places_api": + place_type = json.loads(function_call["arguments"])["place_type"] + places = call_google_places_api(user_id, place_type, food_preference) + if places: # If the list of places is not empty + return f"Here are some places you might be interested in: {' '.join(places)}", trace_id + else: + return "I couldn't find any places of interest nearby.", trace_id + + return "I am sorry, but I could not understand your request.", trace_id + + +if __name__ == "__main__": + result, trace_id = provide_user_specific_recommendations("I'm hungry", "user1234", functions) + print(result) + p.record_feedback( + FeedbackRequest( + trace_id=trace_id, + score=0.7, # 0.0 (bad) to 1.0 (good) + ) + ) diff --git a/parea/cookbook/tracing_without_deployed_prompt.py b/parea/cookbook/tracing_without_deployed_prompt.py index 9d302c86..13de7564 100644 --- a/parea/cookbook/tracing_without_deployed_prompt.py +++ b/parea/cookbook/tracing_without_deployed_prompt.py @@ -9,10 +9,10 @@ load_dotenv() -p = Parea(api_key=os.getenv("PAREA_API_KEY")) +p = Parea(api_key=os.getenv("DEV_API_KEY")) -@trace +@trace # <--- If you want to log the inputs to the LLM call you can optionally add a trace decorator here def call_llm( data: list[dict], model: str = "gpt-3.5-turbo", @@ -31,7 +31,6 @@ def call_llm( ) -@trace def argument_generator(query: str, additional_description: str = "") -> str: return call_llm( [ @@ -44,7 +43,6 @@ def argument_generator(query: str, additional_description: str = "") -> str: ).content -@trace def critic(argument: str) -> str: return call_llm( [ @@ -59,7 +57,6 @@ def critic(argument: str) -> str: ).content -@trace def refiner(query: str, additional_description: str, current_arg: str, criticism: str) -> str: return call_llm( [ @@ -93,7 +90,6 @@ def argument_chain2(query: str, additional_description: str = "") -> tuple[str, return refiner(query, additional_description, argument, criticism), trace_id -@trace def refiner2(query: str, additional_description: str, current_arg: str, criticism: str) -> CompletionResponse: return call_llm( [ diff --git a/parea/utils/trace_utils.py b/parea/utils/trace_utils.py index 2ffee83b..840c19c9 100644 --- a/parea/utils/trace_utils.py +++ b/parea/utils/trace_utils.py @@ -1,4 +1,4 @@ -from typing import Any, Optional, Tuple +from typing import Any, List, Optional, Tuple, Union import contextvars import inspect @@ -8,11 +8,10 @@ import time from collections import ChainMap from functools import wraps -from uuid import uuid4 from attrs import asdict -from parea.helpers import to_date_and_time_string +from parea.helpers import gen_trace_id, to_date_and_time_string from parea.parea_logger import parea_logger from parea.schemas.models import CompletionResponse, TraceLog @@ -57,9 +56,9 @@ def trace( target: Optional[str] = None, end_user_identifier: Optional[str] = None, ): - def init_trace(func_name, args, kwargs, func) -> Tuple[str, float]: + def init_trace(func_name, args, kwargs, func) -> tuple[str, float]: start_time = time.time() - trace_id = str(uuid4()) + trace_id = gen_trace_id() trace_context.get().append(trace_id) sig = inspect.signature(func) @@ -95,9 +94,10 @@ def decorator(func): @wraps(func) async def async_wrapper(*args, **kwargs): trace_id, start_time = init_trace(func.__name__, args, kwargs, func) + output_as_list = check_multiple_return_values(func) try: result = await func(*args, **kwargs) - output = asdict(result) if isinstance(result, CompletionResponse) else result + output = make_output(result, output_as_list) trace_data.get()[trace_id].output = json.dumps(output) except Exception as e: logger.exception(f"Error occurred in function {func.__name__}, {e}") @@ -111,9 +111,10 @@ async def async_wrapper(*args, **kwargs): @wraps(func) def wrapper(*args, **kwargs): trace_id, start_time = init_trace(func.__name__, args, kwargs, func) + output_as_list = check_multiple_return_values(func) try: result = func(*args, **kwargs) - output = asdict(result) if isinstance(result, CompletionResponse) else result + output = make_output(result, output_as_list) trace_data.get()[trace_id].output = json.dumps(output) except Exception as e: logger.exception(f"Error occurred in function {func.__name__}, {e}") @@ -143,3 +144,20 @@ def default_logger(trace_id: str): kwargs={"data": trace_data.get()[trace_id]}, ) logging_thread.start() + + +def check_multiple_return_values(func) -> bool: + specs = inspect.getfullargspec(func) + try: + r = specs.annotations.get("return", None) + if r and r.__origin__ == tuple: + return len(r.__args__) > 1 + except Exception: + return False + + +def make_output(result, islist) -> Union[list[Any], Any]: + if islist: + return [asdict(r) if isinstance(r, CompletionResponse) else r for r in result] + else: + return asdict(result) if isinstance(result, CompletionResponse) else result diff --git a/poetry.lock b/poetry.lock index 9cd6de5d..7c0fc773 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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-version = "0.2.7a0" +version = "0.2.7" description = "Parea python sdk" readme = "README.md" authors = ["joel-parea-ai "]