diff --git a/README.md b/README.md index 92b05fd..faee7cb 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,12 @@ Before running the Q&A bot, ensure the following environment variables are set: ```bash OPENAI_API_KEY COHERE_API_KEY -SLACK_APP_TOKEN -SLACK_BOT_TOKEN -SLACK_SIGNING_SECRET +SLACK_EN_APP_TOKEN +SLACK_EN_BOT_TOKEN +SLACK_EN_SIGNING_SECRET +SLACK_JA_APP_TOKEN +SLACK_JA_BOT_TOKEN +SLACK_JA_SIGNING_SECRET WANDB_API_KEY DISCORD_BOT_TOKEN COHERE_API_KEY @@ -66,7 +69,8 @@ Once these environment variables are set, you can start the Q&A bot application ```bash (poetry run uvicorn wandbot.api.app:app --host="0.0.0.0" --port=8000 > api.log 2>&1) & \ -(poetry run python -m wandbot.apps.slack > slack_app.log 2>&1) & \ +(poetry run python -m wandbot.apps.slack -l en > slack_en_app.log 2>&1) & \ +(poetry run python -m wandbot.apps.slack -l ja > slack_ja_app.log 2>&1) & \ (poetry run python -m wandbot.apps.discord > discord_app.log 2>&1) ``` diff --git a/data/prompts/chat_prompt.json b/data/prompts/chat_prompt.json index b0ec8ee..9d2b3cd 100644 --- a/data/prompts/chat_prompt.json +++ b/data/prompts/chat_prompt.json @@ -1,4 +1,4 @@ { - "system_template": "You are wandbot, a developer assistant designed to guide users with tasks related to Weight & Biases, its sdk `wandb` and its visualization library `weave`. As a trustworthy expert, you must provide helpful answers to queries only using the document excerpts and code examples in the provided context and not prior knowledge.\n\nHere are your guidelines:\n1. Provide clear and concise explanations, along with relevant code snippets, to help users understand and instrument various functionalities of wandb efficiently.\n2. Only generate code that is directly derived from the provided context excerpts and ensure that the code is accurate and runnable.\n3. Do not generate code from prior knowledge or create any methods, functions and classes that is not found in the provided context.\n4. Always cite the sources from the provided context in your response.\n5. Where the provided context is insufficient and you are uncertain about the response, respond with \"Hmm, I'm not sure.\" and direct the user to the Weights & Biases [support](support@wandb.com) or [community forums](http://wandb.me/community)\n6. For questions unrelated to wandb, Weights & Biases or weave, kindly remind the user of your specialization.\n7. Always respond in concise fully formatted Markdown with the necessary code and links.\n8. For best user experience, always respond in the user's language. For instance, if the query is in Japanese, you should respond in Japanese\n\nHere are some examples:\n\n\n\nWeights & Biases allows logging of audio data arrays or files for playback in W&B. \nYou can use the `wandb.Audio()` to create audio instances and log them to W&B using `wandb.log()`.\nSource: 28-pl\n\n# Log an audio array or file\nwandb.log({{\"my whale song\": wandb.Audio(array_or_path, caption=\"montery whale 0034\", sample_rate=32)}})\nSource: 29-pl\n\n# Log multiple audio files\n# Log audio within a W&B Table\nmy_table = wandb.Table(columns=[\"audio\", \"spectrogram\", \"bird_class\", \"prediction\"])\nfor (audio_arr, spec, label) in my_data:\n pred = model(audio)\n audio = wandb.Audio(audio_arr, sample_rate=32)\n img = wandb.Image(spec)\n my_table.add_data(audio, img, label, pred)\n\n# Log the Table to wandb\nwandb.log({{\"validation_samples\" : my_table}})\nSource: 30-pl\n\n\n\nHow do I log audio using wandb?\n\n\n\nUse `wandb.Audio()` to log audio arrays and files for playback in W&B.\nHere is an example that illustrates the steps to log audio.\n\n```\n# import libraries\nimport wandb\n\n# create your audio instance\naudio = wandb.Audio(data_or_path=\"path/to/audio.wav\", sample_rate=44100, caption=\"My audio clip\")\n\n# log your audio to w&b\nwandb.log({{\"audio\": audio}})\n```\n\nYou can also log audio within a W&B Table. Please refer to the [documentation](30-pl) for more details.\n\nSources: \n - 28-pl\n - 29-pl\n - 30-pl\n\n\n\n\n\nExtensionArray.repeat(repeats, axis=None) is a method to repeat elements of an ExtensionArray.\nSource: 0-pl\nParameters include repeats (int or array of ints) and axis (0 or ‘index’, 1 or ‘columns’), with axis=0 being the default.\nSource: 1-pl\n\n\n\nHow to eat vegetables using pandas?\n\n\nYour question doesn't pertain to wandb. I'm here to assist with wandb-related queries. Please ask a wandb-specific question\n\nSources:\n\n\n", - "human_template": "\n{context_str}\n\n\n{query_str}\n\n\n" + "system_template": "You are wandbot, a developer assistant designed to guide users with tasks related to Weight & Biases, its sdk `wandb` and its visualization library `weave`. As a trustworthy expert, you must provide helpful answers to queries only using the document excerpts and code examples in the provided context and not prior knowledge.\n\nHere are your guidelines:\n1. Provide clear and concise explanations, along with relevant code snippets, to help users understand and instrument various functionalities of wandb efficiently.\n2. Only generate code that is directly derived from the provided context excerpts and ensure that the code is accurate and runnable.\n3. Do not generate code from prior knowledge or create any methods, functions and classes that is not found in the provided context.\n4. Always cite the sources from the provided context in your response.\n5. Where the provided context is insufficient and you are uncertain about the response, respond with \"Hmm, I'm not sure.\" and direct the user to the Weights & Biases [support](support@wandb.com) or [community forums](http://wandb.me/community)\n6. For questions unrelated to wandb, Weights & Biases or weave, kindly remind the user of your specialization.\n7. Always respond in concise fully formatted Markdown with the necessary code and links.\n8. For best user experience, always respond in the user's language. For instance, if the query is in Japanese, you should respond in Japanese\n\nHere are some examples:\n\n\n\nWeights & Biases allows logging of audio data arrays or files for playback in W&B. \nYou can use the `wandb.Audio()` to create audio instances and log them to W&B using `wandb.log()`.\nSource: 28-pl\n\n# Log an audio array or file\nwandb.log({{\"my whale song\": wandb.Audio(array_or_path, caption=\"montery whale 0034\", sample_rate=32)}})\nSource: 29-pl\n\n# Log multiple audio files\n# Log audio within a W&B Table\nmy_table = wandb.Table(columns=[\"audio\", \"spectrogram\", \"bird_class\", \"prediction\"])\nfor (audio_arr, spec, label) in my_data:\n pred = model(audio)\n audio = wandb.Audio(audio_arr, sample_rate=32)\n img = wandb.Image(spec)\n my_table.add_data(audio, img, label, pred)\n\n# Log the Table to wandb\nwandb.log({{\"validation_samples\" : my_table}})\nSource: 30-pl\n\n\n\nHow do I log audio using wandb?\n\n\n\nUse `wandb.Audio()` to log audio arrays and files for playback in W&B.\nHere is an example that illustrates the steps to log audio.\n\n```\n# import libraries\nimport wandb\n\n# create your audio instance\naudio = wandb.Audio(data_or_path=\"path/to/audio.wav\", sample_rate=44100, caption=\"My audio clip\")\n\n# log your audio to w&b\nwandb.log({{\"audio\": audio}})\n```\n\nYou can also log audio within a W&B Table. Please refer to the [documentation](30-pl) for more details.\n\nSources: \n - 28-pl\n - 29-pl\n - 30-pl\n\n\n\n\n\nExtensionArray.repeat(repeats, axis=None) is a method to repeat elements of an ExtensionArray.\nSource: 0-pl\nParameters include repeats (int or array of ints) and axis (0 or ‘index’, 1 or ‘columns’), with axis=0 being the default.\nSource: 1-pl\n\n\n\nHow to eat vegetables using pandas?\n\n\nYour question doesn't pertain to wandb. I'm here to assist with wandb-related queries. 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-12,11 +12,10 @@ include = ["src/**/*", "LICENSE", "README.md"] [tool.poetry.dependencies] python = ">=3.10.0,<3.11" numpy = "^1.26.1" -wandb = "^0.15.12" -openai = "^0.28.1" +wandb = "^0.16.0" tiktoken = "^0.5.1" -pandas = "^2.0.3" -unstructured = "^0.10.26" +pandas = "^2.1.2" +unstructured = "^0.10.28" pydantic-settings = "^2.0.3" gitpython = "^3.1.40" giturlparse = "^0.12.0" @@ -26,15 +25,16 @@ faiss-cpu = "^1.7.4" slack-bolt = "^1.18.0" slack-sdk = "^3.21.3" discord = "^2.3.2" -markdown = "^3.5" -fastapi = "^0.103.1" -langdetect = "^1.0.9" -llama-index = "^0.8.42" +markdown = "^3.5.1" +fastapi = "^0.104.1" +llama-index = "^0.9.4" tree-sitter-languages = "^1.7.1" cohere = "^4.32" markdownify = "^0.11.6" -uvicorn = "^0.23.2" -zenpy = { version = "^2.0.41", optional = true } +uvicorn = "^0.24.0" +zenpy = "^2.0.41" +openai = "^1.3.2" +weave = "^0.30.0" [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/run.sh b/run.sh index 746ac5c..7c03138 100644 --- a/run.sh +++ b/run.sh @@ -1,3 +1,4 @@ (uvicorn wandbot.api.app:app --host="0.0.0.0" --port=8000) & \ -(python -m wandbot.apps.slack) & \ +(python -m wandbot.apps.slack -l en) & \ +(python -m wandbot.apps.slack -l ja) & \ (python -m wandbot.apps.discord) diff --git a/src/wandbot/api/app.py b/src/wandbot/api/app.py index bbb797f..7ee83f3 100644 --- a/src/wandbot/api/app.py +++ b/src/wandbot/api/app.py @@ -29,12 +29,12 @@ """ import asyncio -from datetime import datetime +from datetime import datetime, timezone import pandas as pd -import wandb from fastapi import FastAPI, Response, status +import wandb from wandbot.api.schemas import ( APICreateChatThreadRequest, APIFeedbackRequest, @@ -57,9 +57,9 @@ Base.metadata.create_all(bind=engine) chat: Chat | None = None -app = FastAPI(name="wandbot", version="0.2.0") +app = FastAPI(name="wandbot", version="1.0.0") db_client: DatabaseClient | None = None -last_backup = datetime.now() +last_backup = datetime.now().astimezone(timezone.utc) async def backup_db(): @@ -79,7 +79,7 @@ async def backup_db(): chat_table = pd.DataFrame( [chat_thread for chat_thread in chat_threads] ) - last_backup = datetime.now() + last_backup = datetime.now().astimezone(timezone.utc) logger.info(f"Backing up database to Table at {last_backup}") wandb.log( {"question_answers_db": wandb.Table(dataframe=chat_table)} @@ -178,7 +178,9 @@ async def query( """ result = chat( ChatRequest( - question=request.question, chat_history=request.chat_history + question=request.question, + chat_history=request.chat_history, + language=request.language, ), ) result = APIQueryResponse(**result.model_dump()) diff --git a/src/wandbot/api/client.py b/src/wandbot/api/client.py index 18e7d15..1b42e58 100644 --- a/src/wandbot/api/client.py +++ b/src/wandbot/api/client.py @@ -9,7 +9,6 @@ """ import json -import uuid from datetime import datetime from typing import List, Optional from urllib.parse import urljoin @@ -115,7 +114,7 @@ def _create_question_answer( with requests.Session() as session: with session.post( self.chat_question_answer_endpoint, - json=json.loads(request.json()), + json=json.loads(request.model_dump_json()), ) as response: if response.status_code == 201: return APIQuestionAnswerResponse(**response.json()) @@ -138,6 +137,7 @@ def create_question_answer( time_taken: float | None = None, start_time: datetime | None = None, end_time: datetime | None = None, + language: str | None = None, ) -> APIQuestionAnswerResponse | None: """Creates a question answer in the API. @@ -158,6 +158,7 @@ def create_question_answer( time_taken: The time taken. start_time: The start time. end_time: The end time. + language: The language of the question answer. Returns: The response from the API. @@ -179,6 +180,7 @@ def create_question_answer( time_taken=time_taken, start_time=start_time, end_time=end_time, + language=language, ) response = self._create_question_answer(request) return response @@ -196,7 +198,7 @@ def _create_feedback( """ with requests.Session() as session: with session.post( - self.feedback_endpoint, json=request.dict() + self.feedback_endpoint, json=request.model_dump() ) as response: if response.status_code == 201: return APIFeedbackResponse(**response.json()) @@ -232,7 +234,7 @@ def _query(self, request: APIQueryRequest) -> APIQueryResponse | None: The response from the API, or None if the status code is not 200. """ with requests.Session() as session: - payload = json.loads(request.json()) + payload = json.loads(request.model_dump_json()) with session.post(self.query_endpoint, json=payload) as response: if response.status_code == 200: return APIQueryResponse(**response.json()) @@ -333,7 +335,7 @@ async def _create_question_answer( async with aiohttp.ClientSession() as session: async with session.post( self.chat_question_answer_endpoint, - json=json.loads(request.json()), + json=json.loads(request.model_dump_json()), ) as response: if response.status == 201: response = await response.json() @@ -357,6 +359,7 @@ async def create_question_answer( time_taken: float | None = None, start_time: datetime | None = None, end_time: datetime | None = None, + language: str | None = None, ) -> APIQuestionAnswerResponse | None: """Creates a question answer in the API. @@ -377,6 +380,7 @@ async def create_question_answer( time_taken: The time taken. start_time: The start time. end_time: The end time. + language: The language of the question answer. Returns: The response from the API, or None if the status code is not 201. @@ -398,6 +402,7 @@ async def create_question_answer( time_taken=time_taken, start_time=start_time, end_time=end_time, + language=language, ) response = await self._create_question_answer(request) return response @@ -415,7 +420,8 @@ async def _create_feedback( """ async with aiohttp.ClientSession() as session: async with session.post( - self.feedback_endpoint, json=json.loads(request.json()) + self.feedback_endpoint, + json=json.loads(request.model_dump_json()), ) as response: if response.status == 201: response = await response.json() @@ -453,7 +459,7 @@ async def _query(self, request: APIQueryRequest) -> APIQueryResponse | None: """ async with aiohttp.ClientSession() as session: async with session.post( - self.query_endpoint, json=json.loads(request.json()) + self.query_endpoint, json=json.loads(request.model_dump_json()) ) as response: if response.status == 200: response = await response.json() @@ -465,12 +471,14 @@ async def query( self, question: str, chat_history: List[QuestionAnswer] = None, + language: str | None = None, ) -> APIQueryResponse: """Queries the API. Args: question: The question to query. chat_history: The chat history. + language: The language of the question. Returns: The response from the API. @@ -478,80 +486,8 @@ async def query( request = APIQueryRequest( question=question, chat_history=chat_history, + language=language, ) response = await self._query(request) return response - - -if __name__ == "__main__": - import asyncio - - async def run(): - from wandbot.ingestion.utils import Timer - - with Timer() as timer: - api_client = AsyncAPIClient(url="http://localhost:8000") - - application = "test" - # thread_id = str(uuid.uuid4()) - thread_id = "300d9a8c-ea55-4bb1-94e6-d3e3ed2df8bd" - chat_history = await api_client.get_chat_history( - application=application, thread_id=thread_id - ) - - if not chat_history: - print("No chat history found") - else: - print( - json.dumps( - [json.loads(item.json()) for item in chat_history], - indent=2, - ) - ) - # chat_history = [(item.question, item.answer) for item in chat_history] - - # query the api and get the chat response - question = "Hi @wandbot, How about openai?" - chat_response = await api_client.query( - question=question, chat_history=chat_history - ) - # save the chat response to the database - question_answer_id = str(uuid.uuid4()) - - await api_client.create_question_answer( - question_answer_id=question_answer_id, - thread_id=thread_id, - **chat_response.dict(), - ) - - # get the chat history again - chat_history = await api_client.get_chat_history( - application=application, thread_id=thread_id - ) - print( - json.dumps( - [json.loads(item.json()) for item in chat_history], indent=2 - ) - ) - - # add feedback - feedback_id = str(uuid.uuid4()) - await api_client.create_feedback( - feedback_id=feedback_id, - question_answer_id=question_answer_id, - rating=1, - ) - - # get the chat history again - chat_history = await api_client.get_chat_history( - application=application, thread_id=thread_id - ) - print( - json.dumps( - [json.loads(item.json()) for item in chat_history], indent=2 - ) - ) - print(timer.start, timer.start, timer.elapsed) - - asyncio.run(run()) diff --git a/src/wandbot/api/schemas.py b/src/wandbot/api/schemas.py index 13a67c1..f1289c6 100644 --- a/src/wandbot/api/schemas.py +++ b/src/wandbot/api/schemas.py @@ -16,7 +16,7 @@ APIFeedbackResponse: Response schema for feedback. """ -from wandbot.chat.schemas import ChatRepsonse, ChatRequest +from wandbot.chat.schemas import ChatRequest, ChatResponse from wandbot.database.schemas import ( ChatThread, ChatThreadCreate, @@ -51,7 +51,7 @@ class APIQueryRequest(ChatRequest): pass -class APIQueryResponse(ChatRepsonse): +class APIQueryResponse(ChatResponse): pass diff --git a/src/wandbot/apps/ZD/ZDWandbot.py b/src/wandbot/apps/ZD/ZDWandbot.py deleted file mode 100644 index 88cc695..0000000 --- a/src/wandbot/apps/ZD/ZDWandbot.py +++ /dev/null @@ -1,115 +0,0 @@ -import asyncio -from zenpy import Zenpy -from zenpy.lib.api_objects import Comment -from zenpy.lib.api_objects import Ticket -import wandb -from wandbot.chat.config import ChatConfig -from wandbot.chat.schemas import ChatRepsonse, ChatRequest -import os -from datetime import datetime - -from functools import partial -from wandbot.api.client import APIClient -from wandbot.api.schemas import APIQueryResponse -from wandbot.utils import get_logger -from wandbot.apps.ZD.config import ZDAppConfig -import pandas as pd - -logger = get_logger(__name__) -config = ZDAppConfig() - -class ZendeskAIResponseSystem: - - def __init__(self): - userCreds = { - 'email' : config.ZENDESK_EMAIL, - 'password' : config.ZENDESK_PASSWORD, - 'subdomain': config.ZENDESK_SUBDOMAIN - } - self.zenpy_client = Zenpy(**userCreds) - self.api_client = APIClient(url=config.WANDBOT_API_URL) - - def create_new_ticket(self, questionText): - self.zenpy_client.tickets.create(Ticket(subject="WandbotTest4", description=questionText, status = 'new', priority = 'low', tags=["botTest","forum"])) - - def fetch_new_tickets(self): - new_tickets = self.zenpy_client.search(type='ticket', status='new', group_id=config.ZDGROUPID) - # Filtering based on specific requirements - filtered_tickets = [ticket for ticket in new_tickets if 'forum' in ticket.tags] - # filtered_tickets = [ticket for ticket in new_tickets if 'bottest' in ticket.tags] # for testing purposes only - filtered_ticketsNotAnswered = [ticket for ticket in filtered_tickets if 'answered_by_bot' not in ticket.tags] # for testing purposes only - - return filtered_ticketsNotAnswered - - def extract_question(self, ticket): - description = ticket.description - # Preprocessing - question = description.lower().replace('\n', ' ').replace('\r', '') - question = question.replace('[discourse post]','') - question = question[:4095] - - return question - - async def generate_response(self, question, ticket_id): - try: - chat_history = [] - - response = self.api_client.query(question=question, chat_history=[]) - if response == None: - raise Exception("Recieved no response") - - except Exception as e: - logger.error(f"Error: {e}") - response = 'Something went wrong!' - return response - - return response.answer - - #TODO: add the necessary format we want to depending on ticket type - def format_response(self, response): - response = str(response) - max_length = 2000 - if len(response) > max_length: - response = response[:max_length] + '...' - return response+"\n\n-WandBot 🤖" - - def update_ticket(self, ticket, response): - try: - comment = Comment(body=response) - ticket.comment = Comment(body=response, public=False) - - ticket.status="open" - ticket.tags.append('answered_by_bot') - self.zenpy_client.tickets.update(ticket) - except Exception as e: - logger.error(f"Error: {e}") - - #TODO add feedback gathering - def gather_feedback(self, ticket): - try: - ticket.comment = Comment(body="How did we do?", public=False) - self.zenpy_client.tickets.update(ticket) - except Exception as e: - logger.error(f"Error: {e}") - - async def run(self): - # test tickets - # self.create_new_ticket("How Do I start a run?") - self.create_new_ticket("Is there a way to programatically list all projects for a given entity?") - while True: - await asyncio.sleep(120) - - new_tickets = self.fetch_new_tickets() - logger.info(f"New unanswered Tickets: {len(new_tickets)}") - for ticket in new_tickets: - question = self.extract_question(ticket) - response = await self.generate_response(question, ticket) - - formatted_response = self.format_response(response) - self.update_ticket(ticket, formatted_response) - # self.gather_feedback(ticket) - -if __name__ == "__main__": - - zd = ZendeskAIResponseSystem() - asyncio.run(zd.run()) \ No newline at end of file diff --git a/src/wandbot/apps/ZD/config.py b/src/wandbot/apps/ZD/config.py deleted file mode 100644 index a69497b..0000000 --- a/src/wandbot/apps/ZD/config.py +++ /dev/null @@ -1,19 +0,0 @@ -from pydantic import AnyHttpUrl, Field -from pydantic_settings import BaseSettings - -ZDGROUPID = "360016040851" - -class ZDAppConfig(BaseSettings): - ZENDESK_EMAIL: str = Field(..., env="ZENDESK_EMAIL"), - ZENDESK_PASSWORD: str = Field(..., env="ZENDESK_PASSWORD"), - ZENDESK_SUBDOMAIN: str = Field(..., env="ZENDESK_SUBDOMAIN"), - - WANDB_API_KEY: str = Field(..., env="WANDB_API_KEY") - ZDGROUPID: str = ZDGROUPID - WANDBOT_API_URL: AnyHttpUrl = Field(..., env="WANDBOT_API_URL") - include_sources: bool = True - - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - extra = "allow" \ No newline at end of file diff --git a/src/wandbot/apps/discord/__main__.py b/src/wandbot/apps/discord/__main__.py index 66e974a..9f8b431 100644 --- a/src/wandbot/apps/discord/__main__.py +++ b/src/wandbot/apps/discord/__main__.py @@ -10,7 +10,6 @@ import uuid import discord -import langdetect from discord.ext import commands from wandbot.api.client import AsyncAPIClient @@ -44,7 +43,6 @@ async def on_message(message: discord.Message): if message.author == bot.user: return if bot.user is not None and bot.user.mentioned_in(message): - lang_code = langdetect.detect(message.clean_content) mention = f"<@{message.author.id}>" thread = None is_following = None @@ -72,37 +70,23 @@ async def on_message(message: discord.Message): else: chat_history = None if not chat_history: - if lang_code == "ja": - await thread.send( - f"🤖 {mention}: {config.JA_INTRO_MESSAGE}", - mention_author=True, - ) - else: - await thread.send( - f"🤖 Hi {mention}: {config.EN_INTRO_MESSAGE}", - mention_author=True, - ) + await thread.send( + config.INTRO_MESSAGE.format(mention=mention), + mention_author=True, + ) response = await api_client.query( question=str(message.clean_content), chat_history=chat_history, + language=config.bot_language, ) if response is None: - if lang_code == "ja": - await thread.send( - f"🤖 {mention}: {config.JA_ERROR_MESSAGE}", - mention_author=True, - ) - else: - await thread.send( - f"🤖 {mention}: {config.EN_ERROR_MESSAGE}", - mention_author=True, - ) + await thread.send( + config.ERROR_MESSAGE.format(mention=mention), + mention_author=True, + ) return - if lang_code == "ja": - outro_message = config.JA_OUTRO_MESSAGE - else: - outro_message = config.EN_OUTRO_MESSAGE + outro_message = config.OUTRO_MESSAGE sent_message = None if len(response.answer) > 2000: answer_chunks = [] @@ -117,7 +101,6 @@ async def on_message(message: discord.Message): config, response_copy, outro_message, - lang_code, ), ) else: @@ -126,7 +109,6 @@ async def on_message(message: discord.Message): config, response_copy, "", - lang_code, is_last=False, ), ) @@ -136,13 +118,13 @@ async def on_message(message: discord.Message): config, response, outro_message, - lang_code, ), ) if sent_message is not None: await api_client.create_question_answer( thread_id=str(thread.id), question_answer_id=str(sent_message.id), + language=config.bot_language, **response.model_dump(), ) # # Add reactions for feedback @@ -150,8 +132,10 @@ async def on_message(message: discord.Message): await sent_message.add_reaction("👎") # # Wait for reactions - def check(reaction, user): - return user == message.author and str(reaction.emoji) in [ + def check(user_reaction, author): + return author == message.author and str( + user_reaction.emoji + ) in [ "👍", "👎", ] diff --git a/src/wandbot/apps/discord/config.py b/src/wandbot/apps/discord/config.py index a6c837c..2ade184 100644 --- a/src/wandbot/apps/discord/config.py +++ b/src/wandbot/apps/discord/config.py @@ -16,10 +16,11 @@ """ from pydantic import AnyHttpUrl, Field -from pydantic_settings import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict EN_INTRO_MESSAGE = ( - f"Please note that **wandbot is currently in alpha testing** and will experience frequent updates.\n\n" + "🤖 Hi {mention}: \n\n" + f"Please note that **wandbot** will experience frequent updates.\n\n" f"Please do not share any private or sensitive information in your query at this time.\n\n" f"Please note that overly long messages (>1024 words) will be truncated!\n\nGenerating response...\n\n" ) @@ -30,21 +31,7 @@ f" Was this response helpful? Please react below to let us know" ) -EN_ERROR_MESSAGE = ( - "Oops!, Something went wrong. Please retry again in some time" -) - -JA_INTRO_MESSAGE = ( - "Wandbotは現在アルファテスト中ですので、頻繁にアップデートされます。" - "ご利用の際にはプライバシーに関わる情報は入力されないようお願いします。返答を生成しています・・・" -) - -JA_OUTRO_MESSAGE = ( - ":robot_face: この答えが十分でなかった場合には、質問を少し変えて試してみると結果が良くなることがあるので、お試しください。もしくは、" - "#support チャンネルにいるwandbチームに質問してください。この答えは役に立ったでしょうか？下のボタンでお知らせ下さい。" -) - -JA_ERROR_MESSAGE = "「おっと、問題が発生しました。しばらくしてからもう一度お試しください。」" +EN_ERROR_MESSAGE = "Oops!, Sorry 🤖 {mention}: Something went wrong. Please retry again in some time" class DiscordAppConfig(BaseSettings): @@ -53,17 +40,13 @@ class DiscordAppConfig(BaseSettings): PROD_DISCORD_CHANNEL_ID: int = 1090739438310654023 TEST_DISCORD_CHANNEL_ID: int = 1088892013321142484 DISCORD_BOT_TOKEN: str = Field(..., env="DISCORD_BOT_TOKEN") - WANDB_API_KEY: str = Field(..., env="WANDB_API_KEY") - EN_INTRO_MESSAGE: str = Field(EN_INTRO_MESSAGE) - EN_OUTRO_MESSAGE: str = Field(EN_OUTRO_MESSAGE) - EN_ERROR_MESSAGE: str = Field(EN_ERROR_MESSAGE) - JA_INTRO_MESSAGE: str = Field(JA_INTRO_MESSAGE) - JA_OUTRO_MESSAGE: str = Field(JA_OUTRO_MESSAGE) - JA_ERROR_MESSAGE: str = Field(JA_ERROR_MESSAGE) + INTRO_MESSAGE: str = Field(EN_INTRO_MESSAGE) + OUTRO_MESSAGE: str = Field(EN_OUTRO_MESSAGE) + ERROR_MESSAGE: str = Field(EN_ERROR_MESSAGE) WANDBOT_API_URL: AnyHttpUrl = Field(..., env="WANDBOT_API_URL") include_sources: bool = True + bot_language: str = "en" - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - extra = "allow" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) diff --git a/src/wandbot/apps/slack/__main__.py b/src/wandbot/apps/slack/__main__.py index 607e6df..880d739 100644 --- a/src/wandbot/apps/slack/__main__.py +++ b/src/wandbot/apps/slack/__main__.py @@ -8,35 +8,58 @@ It also communicates with an external API for processing queries and storing chat history and feedback. """ - +import argparse +import asyncio import logging from functools import partial -import langdetect -from slack_bolt import App -from slack_bolt.adapter.socket_mode import SocketModeHandler +from slack_bolt.adapter.socket_mode.async_handler import AsyncSocketModeHandler +from slack_bolt.async_app import AsyncApp +from slack_sdk.web import SlackResponse -from wandbot.api.client import APIClient -from wandbot.apps.slack.config import SlackAppConfig +from wandbot.api.client import AsyncAPIClient +from wandbot.apps.slack.config import SlackAppEnConfig, SlackAppJaConfig from wandbot.apps.utils import format_response from wandbot.utils import get_logger logger = get_logger(__name__) -config = SlackAppConfig() -app = App(token=config.SLACK_APP_TOKEN) -api_client = APIClient(url=config.WANDBOT_API_URL) +parser = argparse.ArgumentParser() + +parser.add_argument( + "-l", + "--language", + default="en", + help="Language of the bot", + type=str, + choices=["en", "ja"], +) + +args = parser.parse_args() + +if args.language == "ja": + config = SlackAppJaConfig() +else: + config = SlackAppEnConfig() -def send_message(say: callable, message: str, thread: str = None) -> None: +app = AsyncApp(token=config.SLACK_APP_TOKEN) +api_client = AsyncAPIClient(url=config.WANDBOT_API_URL) + + +async def send_message( + say: callable, message: str, thread: str = None +) -> SlackResponse: if thread is not None: - return say(text=message, thread_ts=thread) + return await say(text=message, thread_ts=thread) else: - return say(text=message) + return await say(text=message) @app.event("app_mention") -def command_handler(body: dict, say: callable, logger: logging.Logger) -> None: +async def command_handler( + body: dict, say: callable, logger: logging.Logger +) -> None: """ Handles the command when the app is mentioned in a message. @@ -50,54 +73,47 @@ def command_handler(body: dict, say: callable, logger: logging.Logger) -> None: """ try: query = body["event"].get("text") - lang_code = langdetect.detect(query) user = body["event"].get("user") thread_id = body["event"].get("thread_ts", None) or body["event"].get( "ts", None ) say = partial(say, token=config.SLACK_BOT_TOKEN) - chat_history = api_client.get_chat_history( + chat_history = await api_client.get_chat_history( application=config.APPLICATION, thread_id=thread_id ) if not chat_history: # send out the intro message - if lang_code == "ja": - send_message( - say=say, - message=f"こんにちは <@{user}>:\n\n{config.JA_INTRO_MESSAGE}", - thread=thread_id, - ) - else: - send_message( - say=say, - message=f"Hi <@{user}>:\n\n{config.EN_INTRO_MESSAGE}", - thread=thread_id, - ) + await send_message( + say=say, + message=config.INTRO_MESSAGE.format(user=user), + thread=thread_id, + ) # process the query through the api - api_response = api_client.query( - question=query, chat_history=chat_history + api_response = await api_client.query( + question=query, + chat_history=chat_history, + language=config.bot_language, + ) + response = format_response( + config, + api_response, + config.OUTRO_MESSAGE, ) - if lang_code == "ja": - response = format_response( - config, api_response, config.JA_OUTRO_MESSAGE, lang_code - ) - else: - response = format_response( - config, api_response, config.EN_OUTRO_MESSAGE - ) # send the response - sent_message = send_message(say=say, message=response, thread=thread_id) + sent_message = await send_message( + say=say, message=response, thread=thread_id + ) - app.client.reactions_add( + await app.client.reactions_add( channel=body["event"]["channel"], timestamp=sent_message["ts"], name="thumbsup", token=config.SLACK_BOT_TOKEN, ) - app.client.reactions_add( + await app.client.reactions_add( channel=body["event"]["channel"], timestamp=sent_message["ts"], name="thumbsdown", @@ -105,9 +121,10 @@ def command_handler(body: dict, say: callable, logger: logging.Logger) -> None: ) # save the question answer to the database - api_client.create_question_answer( + await api_client.create_question_answer( thread_id=thread_id, question_answer_id=sent_message["ts"], + language=config.bot_language, **api_response.model_dump(), ) @@ -134,18 +151,18 @@ def parse_reaction(reaction: str) -> int: @app.event("reaction_added") -def handle_reaction_added(event: dict, say: callable) -> None: +async def handle_reaction_added(event: dict) -> None: """ Handles the event when a reaction is added to a message. Args: event (dict): The event details. - say (callable): The function to send a message. + """ channel_id = event["item"]["channel"] message_ts = event["item"]["ts"] - conversation = app.client.conversations_replies( + conversation = await app.client.conversations_replies( channel=channel_id, ts=message_ts, inclusive=True, @@ -159,13 +176,17 @@ def handle_reaction_added(event: dict, say: callable) -> None: thread_ts = messages[0].get("thread_ts") if thread_ts: rating = parse_reaction(event["reaction"]) - api_client.create_feedback( + await api_client.create_feedback( feedback_id=event["event_ts"], question_answer_id=message_ts, rating=rating, ) +async def main(): + handler = AsyncSocketModeHandler(app, config.SLACK_APP_TOKEN) + await handler.start_async() + + if __name__ == "__main__": - handler = SocketModeHandler(app, config.SLACK_APP_TOKEN) - handler.start() + asyncio.run(main()) diff --git a/src/wandbot/apps/slack/config.py b/src/wandbot/apps/slack/config.py index 26350af..310e900 100644 --- a/src/wandbot/apps/slack/config.py +++ b/src/wandbot/apps/slack/config.py @@ -2,21 +2,22 @@ This module uses the Pydantic library to define the configuration settings for the Slack application. These settings include tokens, secrets, API keys, and messages for the application. -The settings are loaded from an environment file and can be accessed as properties of the `SlackAppConfig` class. +The settings are loaded from an environment file and can be accessed as properties of the `SlackAppEnConfig` class. Typical usage example: - from .config import SlackAppConfig + from .config import SlackAppEnConfig - config = SlackAppConfig() + config = SlackAppEnConfig() token = config.SLACK_APP_TOKEN """ from pydantic import AnyHttpUrl, Field -from pydantic_settings import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict EN_INTRO_MESSAGE = ( - f"Please note that *wandbot is currently in alpha testing* and will experience frequent updates.\n\n" + "Hi <@{user}>:\n\n" + f"Please note that *wandbot* and will experience frequent updates.\n\n" f"Please do not share any private or sensitive information in your query at this time.\n\n" f"Please note that overly long messages (>1024 words) will be truncated!\n\nGenerating response...\n\n" ) @@ -31,7 +32,13 @@ "Oops!, Something went wrong. Please retry again in some time" ) +EN_FALLBACK_WARNING_MESSAGE = ( + "*Warning: Falling back to {model}*, These results may nor be as good as " + "*gpt-4*\n\n" +) + JA_INTRO_MESSAGE = ( + "こんにちは <@{user}>:\n\n" "Wandbotは現在アルファテスト中ですので、頻繁にアップデートされます。" "ご利用の際にはプライバシーに関わる情報は入力されないようお願いします。返答を生成しています・・・" ) @@ -43,23 +50,46 @@ JA_ERROR_MESSAGE = "「おっと、問題が発生しました。しばらくしてからもう一度お試しください。」" +JA_FALLBACK_WARNING_MESSAGE = ( + "*警告: {model}* にフォールバックします。これらの結果は *gpt-4* ほど良くない可能性があります*\n\n" +) + + +class SlackAppEnConfig(BaseSettings): + APPLICATION: str = Field("Slack") + SLACK_APP_TOKEN: str = Field(..., validation_alias="SLACK_EN_APP_TOKEN") + SLACK_BOT_TOKEN: str = Field(..., validation_alias="SLACK_EN_BOT_TOKEN") + SLACK_SIGNING_SECRET: str = Field( + ..., validation_alias="SLACK_EN_SIGNING_SECRET" + ) + INTRO_MESSAGE: str = Field(EN_INTRO_MESSAGE) + OUTRO_MESSAGE: str = Field(EN_OUTRO_MESSAGE) + ERROR_MESSAGE: str = Field(EN_ERROR_MESSAGE) + WARNING_MESSAGE: str = Field(EN_FALLBACK_WARNING_MESSAGE) + WANDBOT_API_URL: AnyHttpUrl = Field(..., validation_alias="WANDBOT_API_URL") + include_sources: bool = True + bot_language: str = "en" + + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) + -class SlackAppConfig(BaseSettings): +class SlackAppJaConfig(BaseSettings): APPLICATION: str = Field("Slack") - SLACK_APP_TOKEN: str = Field(..., env="SLACK_APP_TOKEN") - SLACK_BOT_TOKEN: str = Field(..., env="SLACK_BOT_TOKEN") - SLACK_SIGNING_SECRET: str = Field(..., env="SLACK_SIGNING_SECRET") - WANDB_API_KEY: str = Field(..., env="WANDB_API_KEY") - EN_INTRO_MESSAGE: str = Field(EN_INTRO_MESSAGE) - EN_OUTRO_MESSAGE: str = Field(EN_OUTRO_MESSAGE) - EN_ERROR_MESSAGE: str = Field(EN_ERROR_MESSAGE) - JA_INTRO_MESSAGE: str = Field(JA_INTRO_MESSAGE) - JA_OUTRO_MESSAGE: str = Field(JA_OUTRO_MESSAGE) - JA_ERROR_MESSAGE: str = Field(JA_ERROR_MESSAGE) - WANDBOT_API_URL: AnyHttpUrl = Field(..., env="WANDBOT_API_URL") + SLACK_APP_TOKEN: str = Field(..., validation_alias="SLACK_JA_APP_TOKEN") + SLACK_BOT_TOKEN: str = Field(..., validation_alias="SLACK_JA_BOT_TOKEN") + SLACK_SIGNING_SECRET: str = Field( + ..., validation_alias="SLACK_JA_SIGNING_SECRET" + ) + INTRO_MESSAGE: str = Field(JA_INTRO_MESSAGE) + OUTRO_MESSAGE: str = Field(JA_OUTRO_MESSAGE) + ERROR_MESSAGE: str = Field(JA_ERROR_MESSAGE) + WARNING_MESSAGE: str = Field(JA_FALLBACK_WARNING_MESSAGE) + WANDBOT_API_URL: AnyHttpUrl = Field(..., validation_alias="WANDBOT_API_URL") include_sources: bool = True + bot_language: str = "ja" - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - extra = "allow" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) diff --git a/src/wandbot/apps/utils.py b/src/wandbot/apps/utils.py index 111ce70..e46ba85 100644 --- a/src/wandbot/apps/utils.py +++ b/src/wandbot/apps/utils.py @@ -36,16 +36,14 @@ def format_response( config: BaseSettings, response: APIQueryResponse | None, outro_message: str = "", - lang: str = "en", is_last: bool = True, ) -> str: """Formats the response from the API query. Args: - :param config: The config object for the app. + config: The config object for the app. response: The response from the API query. outro_message: The outro message to append to the formatted response. - lang: The language of the response. is_last: Whether the response is the last in a series. Returns: @@ -55,13 +53,9 @@ def format_response( if response is not None: result = response.answer if "gpt-4" not in response.model: - if lang == "ja": - warning_message = f"*警告: {response.model}* にフォールバックします。これらの結果は *gpt-4* ほど良くない可能性があります*" - else: - warning_message = ( - f"*Warning: Falling back to {response.model}*, These results may nor be as good as " - f"*gpt-4*\n\n" - ) + warning_message = config.WARNING_MESSAGE.format( + model=response.model + ) result = warning_message + response.answer if config.include_sources and response.sources and is_last: @@ -74,7 +68,7 @@ def format_response( ) if len(sources_list) > 0: items = min(len(sources_list), 3) - if lang == "ja": + if config.bot_language == "ja": result = ( f"{result}\n\n*参考文献*\n\n>" + "\n> ".join(sources_list[:items]) diff --git a/src/wandbot/evaluation/__init__.py b/src/wandbot/apps/zendesk/__init__.py similarity index 100% rename from src/wandbot/evaluation/__init__.py rename to src/wandbot/apps/zendesk/__init__.py diff --git a/src/wandbot/apps/zendesk/__main__.py b/src/wandbot/apps/zendesk/__main__.py new file mode 100644 index 0000000..eeb7cc5 --- /dev/null +++ b/src/wandbot/apps/zendesk/__main__.py @@ -0,0 +1,279 @@ +"""This module provides the main entry point for the Zendesk AI Response System. + +The Zendesk AI Response System is responsible for creating and updating tickets in the Zendesk system. It uses the +Zenpy client for interacting with the Zendesk API and the APIClient for interacting with the WandBot API. + +The system performs the following steps: +1. Creates a new ticket with a predefined question. +2. Enters a loop where it fetches new tickets every 120 seconds. +3. For each new ticket, it extracts the question, generates a response, formats the response, and updates the ticket + with the response. + +This module contains the following classes: +- ZendeskAIResponseSystem: Handles the interaction with the Zendesk system. + +This module contains the following functions: +- extract_question(ticket): Extracts the question from a given ticket. +- format_response(response): Formats the response to be sent as a ticket comment. + +This module is meant to be run as a script and not imported as a module. When run as a script, it initializes a +ZendeskAIResponseSystem object and runs it in an event loop. + +""" +import asyncio +from typing import List + +from zenpy import Zenpy +from zenpy.lib.api_objects import Comment, Ticket + +from wandbot.api.client import AsyncAPIClient +from wandbot.apps.zendesk.config import ZendeskAppConfig +from wandbot.utils import get_logger + +logger = get_logger(__name__) +config = ZendeskAppConfig() + + +def extract_question(ticket: Ticket) -> str: + """Extracts the question from a given ticket. + + This function performs the following steps: + 1. Extracts the description from the ticket. + 2. Converts the description to lower case and replaces newline and carriage return characters with a space. + 3. Removes the string "[discourse post]" from the description. + 4. Truncates the description to a maximum length of 4095 characters. + + Args: + ticket (Ticket): The ticket object from which the question is to be extracted. + + Returns: + str: The extracted question from the ticket's description. + """ + + description = ticket.description + # Convert the description to lower case and replace newline and carriage return characters with a space + question = description.lower().replace("\n", " ").replace("\r", "") + # Remove the string "[discourse post]" from the description + question = question.replace("[discourse post]", "") + # Truncate the description to a maximum length of 4095 characters + question = question[:4095] + + return question + + +# TODO: add the necessary format we want to depending on ticket type +def format_response(response: str) -> str: + """Formats the response to be sent as a ticket comment. + + This function performs the following steps: + 1. Converts the response to a string. + 2. Truncates the response to a maximum length of 2000 characters. + 3. Appends an ellipsis (...) if the response was truncated. + 4. Appends a signature at the end of the response. + + Args: + response (str): The response to be formatted. + + Returns: + str: The formatted response. + """ + + response = str(response) + max_length = 2000 + # Truncate the response to a maximum length of 2000 characters + if len(response) > max_length: + response = response[:max_length] + "..." + # Append a signature at the end of the response + return response + "\n\n-WandBot 🤖" + + +class ZendeskAIResponseSystem: + """Handles the interaction with the Zendesk system. + + This class is responsible for creating and updating tickets in the Zendesk system. It uses the Zenpy client for + interacting with the Zendesk API and the AsyncAPIClient for interacting with the WandBot API. + + Attributes: + zenpy_client (Zenpy): The client for interacting with the Zendesk API. + api_client (AsyncAPIClient): The client for interacting with the WandBot API. + """ + + def __init__(self) -> None: + """Initializes the ZendeskAIResponseSystem with the necessary clients. + + The Zenpy client is initialized with the user credentials from the configuration. The AsyncAPIClient is + initialized with the WandBot API URL from the configuration. + """ + + user_creds = { + "email": config.ZENDESK_EMAIL, + "password": config.ZENDESK_PASSWORD, + "subdomain": config.ZENDESK_SUBDOMAIN, + } + self.zenpy_client = Zenpy(**user_creds) + self.api_client = AsyncAPIClient(url=config.WANDBOT_API_URL) + + def create_new_ticket(self, question_text: str) -> None: + """Creates a new ticket in the Zendesk system. + + This method uses the Zenpy client to create a new ticket in the Zendesk system. The ticket is created with a + predefined subject, status, priority, and tags. The description of the ticket is set to the provided question + text. + + Args: + question_text (str): The text to be used as the description of the ticket. + + Returns: + None + """ + + self.zenpy_client.tickets.create( + Ticket( + subject="WandbotTest4", + description=question_text, + status="new", + priority="low", + tags=["botTest", "forum"], + ) + ) + + def fetch_new_tickets(self) -> List[Ticket]: + """Fetches new tickets from the Zendesk system. + + This method uses the Zenpy client to fetch new tickets from the Zendesk system. It filters the fetched + tickets based on specific requirements. The tickets are filtered if they have the tag "forum" and do not have + the tag "answered_by_bot". + + Returns: + list: A list of filtered tickets that are new and have not been answered by the bot. + """ + + new_tickets = self.zenpy_client.search( + type="ticket", status="new", group_id=config.ZDGROUPID + ) + # Filtering based on specific requirements + filtered_tickets = [ + ticket for ticket in new_tickets if "forum" in ticket.tags + ] + # filtered_tickets = [ticket for ticket in new_tickets if 'bottest' in ticket.tags] + # for testing purposes only + filtered_tickets_not_answered = [ + ticket + for ticket in filtered_tickets + if "answered_by_bot" not in ticket.tags + ] # for testing purposes only + + return filtered_tickets_not_answered + + async def generate_response(self, question: str) -> str: + """Generates a response to a given question. + + This method uses the APIClient to query the WandBot API with the provided question and an empty chat history. + If the API returns a response, it is returned as the answer. If the API does not return a response, + an exception is raised and a default error message is returned. + + Args: + question (str): The question to be answered. + + Returns: + str: The answer to the question, or an error message if something went wrong. + """ + + try: + response = await self.api_client.query( + question=question, chat_history=[], language=config.bot_language + ) + if response is None: + raise Exception("Received no response") + + except Exception as e: + logger.error(f"Error: {e}") + response = "Something went wrong!" + return response + + return response.answer + + def update_ticket(self, ticket: Ticket, response: str) -> None: + """Updates a ticket in the Zendesk system with a response. + + This method uses the Zenpy client to update a ticket in the Zendesk system. The ticket's comment is updated + with the provided response, its status is set to "open", and the tag "answered_by_bot" is added. If an error + occurs during the update, it is logged and the method continues. + + Args: + ticket (Ticket): The ticket to be updated. + response (str): The response to be added as a comment to the ticket. + + Returns: + None + """ + + try: + ticket.comment = Comment(body=response, public=False) + ticket.status = "open" + ticket.tags.append("answered_by_bot") + self.zenpy_client.tickets.update(ticket) + except Exception as e: + logger.error(f"Error: {e}") + + # TODO add feedback gathering + def gather_feedback(self, ticket: Ticket) -> None: + """Gathers feedback for a given ticket. + + This method uses the Zenpy client to update a ticket in the Zendesk system. The ticket's comment is updated + with a feedback question. If an error occurs during the update, it is logged and the method continues. + + Args: + ticket (Ticket): The ticket for which feedback is to be gathered. + + Returns: + None + """ + + try: + ticket.comment = Comment(body="How did we do?", public=False) + self.zenpy_client.tickets.update(ticket) + except Exception as e: + logger.error(f"Error: {e}") + + async def run(self) -> None: + """Runs the Zendesk AI Response System. + + This method performs the following steps: + 1. Creates a new ticket with a predefined question. + 2. Enters a loop where it fetches new tickets every 120 seconds. + 3. For each new ticket, it extracts the question, generates a response, formats the response, + and updates the ticket with the response. + + This method is asynchronous and should be run in an event loop. + + Returns: + None + """ + + # Create a new ticket with a predefined question + self.create_new_ticket( + "Is there a way to programmatically list all projects for a given entity?" + ) + + # Enter a loop where it fetches new tickets every 120 seconds + while True: + await asyncio.sleep(120) + + # Fetch new tickets + new_tickets = self.fetch_new_tickets() + logger.info(f"New unanswered Tickets: {len(new_tickets)}") + + # For each new ticket, extract the question, generate a response, format the response, + # and update the ticket with the response + for ticket in new_tickets: + question = extract_question(ticket) + response = await self.generate_response(question) + + formatted_response = format_response(response) + self.update_ticket(ticket, formatted_response) + + +if __name__ == "__main__": + zd = ZendeskAIResponseSystem() + asyncio.run(zd.run()) diff --git a/src/wandbot/apps/zendesk/config.py b/src/wandbot/apps/zendesk/config.py new file mode 100644 index 0000000..0ca60c4 --- /dev/null +++ b/src/wandbot/apps/zendesk/config.py @@ -0,0 +1,18 @@ +from pydantic import AnyHttpUrl, Field +from pydantic_settings import BaseSettings, SettingsConfigDict + +ZDGROUPID = "360016040851" + + +class ZendeskAppConfig(BaseSettings): + ZENDESK_EMAIL: str = (Field(..., env="ZENDESK_EMAIL"),) + ZENDESK_PASSWORD: str = (Field(..., env="ZENDESK_PASSWORD"),) + ZENDESK_SUBDOMAIN: str = (Field(..., env="ZENDESK_SUBDOMAIN"),) + ZDGROUPID: str = ZDGROUPID + WANDBOT_API_URL: AnyHttpUrl = Field(..., env="WANDBOT_API_URL") + include_sources: bool = True + bot_language: str = "en" + + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) diff --git a/src/wandbot/chat/chat.py b/src/wandbot/chat/chat.py index 9464cbc..d0cc0a8 100644 --- a/src/wandbot/chat/chat.py +++ b/src/wandbot/chat/chat.py @@ -29,7 +29,6 @@ from typing import Any, Dict, List, Optional import tiktoken -import wandb from llama_index import StorageContext, load_index_from_storage from llama_index.callbacks import ( CallbackManager, @@ -40,12 +39,21 @@ from llama_index.indices.postprocessor import CohereRerank from llama_index.llms import ChatMessage, MessageRole from llama_index.vector_stores import FaissVectorStore +from llama_index.vector_stores.simple import DEFAULT_VECTOR_STORE, NAMESPACE_SEP +from llama_index.vector_stores.types import DEFAULT_PERSIST_FNAME +from weave.monitoring import StreamTable +import wandb from wandbot.chat.config import ChatConfig from wandbot.chat.prompts import load_chat_prompt -from wandbot.chat.schemas import ChatRepsonse, ChatRequest +from wandbot.chat.schemas import ChatRequest, ChatResponse from wandbot.database.schemas import QuestionAnswer -from wandbot.utils import Timer, get_logger, load_service_context +from wandbot.utils import ( + LanguageFilterPostprocessor, + Timer, + get_logger, + load_service_context, +) logger = get_logger(__name__) @@ -55,15 +63,16 @@ def get_chat_history( ) -> Optional[List[ChatMessage]]: """Generates a list of chat messages from a given chat history. - This function takes a list of QuestionAnswer objects and transforms them into a list of ChatMessage objects. - Each QuestionAnswer object is split into two ChatMessage objects: one for the user's question and one for the assistant's answer. - If the chat history is empty or None, the function returns None. + This function takes a list of QuestionAnswer objects and transforms them into a list of ChatMessage objects. Each + QuestionAnswer object is split into two ChatMessage objects: one for the user's question and one for the + assistant's answer. If the chat history is empty or None, the function returns None. - Args: - chat_history: A list of QuestionAnswer objects representing the history of a chat. Each QuestionAnswer object contains a question from the user and an answer from the assistant. + Args: chat_history: A list of QuestionAnswer objects representing the history of a chat. Each QuestionAnswer + object contains a question from the user and an answer from the assistant. - Returns: - A list of ChatMessage objects representing the chat history. Each ChatMessage object has a role (either 'USER' or 'ASSISTANT') and content (the question or answer text). If the chat history is empty or None, the function returns None. + Returns: A list of ChatMessage objects representing the chat history. Each ChatMessage object has a role (either + 'USER' or 'ASSISTANT') and content (the question or answer text). If the chat history is empty or None, + the function returns None. """ if not chat_history: return None @@ -95,8 +104,6 @@ class Chat: token_counter: An instance of TokenCountingHandler for counting tokens. callback_manager: An instance of CallbackManager for managing callbacks. qa_prompt: A string representing the chat prompt. - chat_engine: An instance of ChatEngine for generating chat responses. - fallback_chat_engine: An instance of ChatEngine for fallback chat responses. """ def __init__(self, config: ChatConfig): @@ -111,6 +118,10 @@ def __init__(self, config: ChatConfig): entity=self.config.wandb_entity, job_type="chat", ) + self.run._label(repo="wandbot") + self.chat_table = StreamTable( + f"{self.config.wandb_entity}/{self.config.wandb_project}/chat_logs" + ) self.tokenizer = tiktoken.get_encoding("cl100k_base") self.storage_context = self.load_storage_context_from_artifact( @@ -127,14 +138,6 @@ def __init__(self, config: ChatConfig): ) self.qa_prompt = load_chat_prompt(self.config.chat_prompt) - self.chat_engine = self._load_chat_engine( - self.config.chat_model_name, - max_retries=self.config.max_retries, - ) - self.fallback_chat_engine = self._load_chat_engine( - self.config.fallback_model_name, - max_retries=self.config.max_fallback_retries, - ) def load_storage_context_from_artifact( self, artifact_url: str @@ -149,14 +152,19 @@ def load_storage_context_from_artifact( """ artifact = self.run.use_artifact(artifact_url) artifact_dir = artifact.download() + index_path = f"{artifact_dir}/{DEFAULT_VECTOR_STORE}{NAMESPACE_SEP}{DEFAULT_PERSIST_FNAME}" storage_context = StorageContext.from_defaults( - vector_store=FaissVectorStore.from_persist_dir(artifact_dir), + vector_store=FaissVectorStore.from_persist_path(index_path), persist_dir=artifact_dir, ) return storage_context def _load_chat_engine( - self, model_name: str, max_retries: int + self, + model_name: str, + max_retries: int, + has_chat_history: bool = False, + language: str = "en", ) -> BaseChatEngine: """Loads the chat engine with the given model name and maximum retries. @@ -171,17 +179,23 @@ def _load_chat_engine( model_name, temperature=self.config.chat_temperature, max_retries=max_retries, - embeddings_cache=self.config.embeddings_cache, + embeddings_cache=str(self.config.embeddings_cache), callback_manager=self.callback_manager, ) + chat_engine = self.index.as_chat_engine( - chat_mode=ChatMode.CONDENSE_QUESTION, - similarity_top_k=25, + chat_mode=ChatMode.CONDENSE_QUESTION + if has_chat_history + else ChatMode.CONTEXT, + similarity_top_k=50, response_mode="compact", service_context=service_context, text_qa_template=self.qa_prompt, node_postprocessors=[ - CohereRerank(top_n=15, model="rerank-english-v2.0") + LanguageFilterPostprocessor(languages=[language, "python"]), + CohereRerank(top_n=5, model="rerank-english-v2.0") + if language == "en" + else CohereRerank(top_n=5, model="rerank-multilingual-v2.0"), ], storage_context=self.storage_context, ) @@ -242,19 +256,29 @@ def format_response(self, result: Dict[str, Any]) -> Dict[str, Any]: return response def get_answer( - self, query: str, chat_history: Optional[List[ChatMessage]] = None + self, + query: str, + chat_history: Optional[List[ChatMessage]] = None, + language: str = "en", ) -> Dict[str, Any]: """Gets the answer for the given query and chat history. Args: query: A string representing the query. chat_history: A list of ChatMessage representing the chat history. + language: A string representing the language of the query. Returns: A formatted response dictionary. """ try: - response = self.chat_engine.chat( + chat_engine = self._load_chat_engine( + self.config.chat_model_name, + max_retries=self.config.max_retries, + language=language, + has_chat_history=bool(chat_history), + ) + response = chat_engine.chat( message=query, chat_history=chat_history ) result = { @@ -268,8 +292,15 @@ def get_answer( f"Falling back to {self.config.fallback_model_name} model" ) try: - response = self.fallback_chat_engine.chat( - message=query, chat_history=chat_history + fallback_chat_engine = self._load_chat_engine( + self.config.fallback_model_name, + max_retries=self.config.max_fallback_retries, + language=language, + has_chat_history=bool(chat_history), + ) + response = fallback_chat_engine.chat( + message=query, + chat_history=chat_history, ) result = { "answer": response.response, @@ -289,7 +320,7 @@ def get_answer( } return self.format_response(result) - def __call__(self, chat_request: ChatRequest) -> ChatRepsonse: + def __call__(self, chat_request: ChatRequest) -> ChatResponse: """Handles the chat request and returns the chat response. Args: @@ -308,8 +339,9 @@ def __call__(self, chat_request: ChatRequest) -> ChatRepsonse: } else: result = self.get_answer( - query, + query=query, chat_history=get_chat_history(chat_request.chat_history), + language=chat_request.language, ) usage_stats = { "total_tokens": self.token_counter.total_llm_token_count, @@ -331,7 +363,8 @@ def __call__(self, chat_request: ChatRequest) -> ChatRepsonse: ) self.run.log(usage_stats) result["system_prompt"] = self.qa_prompt.message_templates[0].content - return ChatRepsonse(**result) + self.chat_table.log(result) + return ChatResponse(**result) def main(): diff --git a/src/wandbot/chat/config.py b/src/wandbot/chat/config.py index 6cb262d..6a85162 100644 --- a/src/wandbot/chat/config.py +++ b/src/wandbot/chat/config.py @@ -15,17 +15,21 @@ import pathlib from pydantic import Field -from pydantic_settings import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict class ChatConfig(BaseSettings): - chat_model_name: str = "gpt-4-0613" + chat_model_name: str = "gpt-4-1106-preview" max_retries: int = 2 fallback_model_name: str = "gpt-3.5-turbo-16k-0613" max_fallback_retries: int = 6 chat_temperature: float = 0.1 chat_prompt: pathlib.Path = pathlib.Path("data/prompts/chat_prompt.json") - index_artifact: str = "wandbot/wandbot-dev/wandbot_index:latest" + index_artifact: str = Field( + "wandbot/wandbot-dev/wandbot_index:latest", + env="WANDB_INDEX_ARTIFACT", + validation_alias="wandb_index_artifact", + ) embeddings_cache: pathlib.Path = Field( pathlib.Path("data/cache/embeddings"), env="EMBEDDINGS_CACHE_PATH" ) @@ -34,3 +38,7 @@ class ChatConfig(BaseSettings): wandb_entity: str | None = Field("wandbot", env="WANDB_ENTITY") include_sources: bool = True query_tokens_threshold: int = 1024 + + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) diff --git a/src/wandbot/chat/schemas.py b/src/wandbot/chat/schemas.py index ef91f11..21867f4 100644 --- a/src/wandbot/chat/schemas.py +++ b/src/wandbot/chat/schemas.py @@ -9,7 +9,7 @@ chat_thread = ChatThread(thread_id="123", application="app1") chat_request = ChatRequest(question="What is the weather?", chat_history=None) - chat_response = ChatRepsonse(system_prompt="Weather is sunny", question="What is the weather?", + chat_response = ChatResponse(system_prompt="Weather is sunny", question="What is the weather?", answer="It's sunny", model="model1", sources="source1", source_documents="doc1", total_tokens=10, prompt_tokens=2, completion_tokens=8, time_taken=1.0, @@ -44,9 +44,10 @@ class Config: class ChatRequest(BaseModel): question: str chat_history: List[QuestionAnswer] | None = None + language: str = "en" -class ChatRepsonse(BaseModel): +class ChatResponse(BaseModel): system_prompt: str question: str answer: str diff --git a/src/wandbot/database/client.py b/src/wandbot/database/client.py index 3226a2e..2cdd0b0 100644 --- a/src/wandbot/database/client.py +++ b/src/wandbot/database/client.py @@ -1,8 +1,10 @@ """This module provides a Database and DatabaseClient class for managing database operations. -The Database class provides a connection to the database and manages the session. It also provides methods for getting and setting the current session object and the name of the database. +The Database class provides a connection to the database and manages the session. It also provides methods for +getting and setting the current session object and the name of the database. -The DatabaseClient class uses an instance of the Database class to perform operations such as getting and creating chat threads, question answers, and feedback from the database. +The DatabaseClient class uses an instance of the Database class to perform operations such as getting and creating +chat threads, question answers, and feedback from the database. Typical usage example: @@ -10,11 +12,11 @@ chat_thread = db_client.get_chat_thread(application='app1', thread_id='123') question_answer = db_client.create_question_answer(question_answer=QuestionAnswerCreateSchema()) """ - from typing import Any, List from sqlalchemy.future import create_engine from sqlalchemy.orm import sessionmaker + from wandbot.database.config import DataBaseConfig from wandbot.database.models import ChatThread as ChatThreadModel from wandbot.database.models import FeedBack as FeedBackModel @@ -24,6 +26,9 @@ from wandbot.database.schemas import ( QuestionAnswerCreate as QuestionAnswerCreateSchema, ) +from wandbot.utils import get_logger + +logger = get_logger(__name__) class Database: @@ -148,6 +153,7 @@ def create_chat_thread( self.database.refresh(chat_thread) except Exception as e: + logger.error(f"Create chat thread failed with error: {e}") self.database.rollback() return chat_thread @@ -187,13 +193,14 @@ def create_question_answer( """ try: question_answer: QuestionAnswerModel = QuestionAnswerModel( - **question_answer.dict() + **question_answer.model_dump() ) self.database.add(question_answer) self.database.flush() self.database.commit() self.database.refresh(question_answer) except Exception as e: + logger.error(f"Create question answer failed with error: {e}") self.database.rollback() return question_answer @@ -224,12 +231,13 @@ def create_feedback(self, feedback: FeedbackSchema) -> FeedBackModel: """ if feedback.rating: try: - feedback: FeedBackModel = FeedBackModel(**feedback.dict()) + feedback: FeedBackModel = FeedBackModel(**feedback.model_dump()) self.database.add(feedback) self.database.flush() self.database.commit() self.database.refresh(feedback) except Exception as e: + logger.error(f"Create feedback failed with error: {e}") self.database.rollback() return feedback @@ -245,9 +253,7 @@ def get_all_question_answers( Returns: A list of question answer dictionaries if found, None otherwise. """ - question_answers: List[dict[str, Any]] | None = self.database.query( - QuestionAnswerModel - ) + question_answers = self.database.query(QuestionAnswerModel) if time is not None: question_answers = question_answers.filter( QuestionAnswerModel.end_time >= time @@ -255,7 +261,7 @@ def get_all_question_answers( question_answers = question_answers.all() if question_answers is not None: question_answers = [ - QuestionAnswerCreateSchema.from_orm( + QuestionAnswerCreateSchema.model_validate( question_answer ).model_dump() for question_answer in question_answers diff --git a/src/wandbot/database/config.py b/src/wandbot/database/config.py index d5c75cb..67c7cbd 100644 --- a/src/wandbot/database/config.py +++ b/src/wandbot/database/config.py @@ -1,6 +1,7 @@ """This module provides a DataBaseConfig class for managing database configuration. -The DataBaseConfig class uses the BaseSettings class from pydantic_settings to define and manage the database configuration settings. It includes the SQLAlchemy database URL and connection arguments. +The DataBaseConfig class uses the BaseSettings class from pydantic_settings to define and manage the database +configuration settings. It includes the SQLAlchemy database URL and connection arguments. Typical usage example: diff --git a/src/wandbot/database/database.py b/src/wandbot/database/database.py index d8aeec7..0ed0a93 100644 --- a/src/wandbot/database/database.py +++ b/src/wandbot/database/database.py @@ -1,6 +1,8 @@ """This module provides the setup for the SQLAlchemy database engine and session. -It imports the create_engine and sessionmaker modules from SQLAlchemy, and the DataBaseConfig class from the config module. It then creates an instance of DataBaseConfig, sets up the engine with the SQLAlchemy database URL and connection arguments, and creates a sessionmaker bound to this engine. +It imports the create_engine and sessionmaker modules from SQLAlchemy, and the DataBaseConfig class from the config +module. It then creates an instance of DataBaseConfig, sets up the engine with the SQLAlchemy database URL and +connection arguments, and creates a sessionmaker bound to this engine. Typical usage example: diff --git a/src/wandbot/database/models.py b/src/wandbot/database/models.py index 1586397..04803a8 100644 --- a/src/wandbot/database/models.py +++ b/src/wandbot/database/models.py @@ -1,6 +1,7 @@ """This module defines the SQLAlchemy models for the ChatThread, QuestionAnswer, and FeedBack tables. -Each class represents a table in the database and includes columns and relationships. The Base class is a declarative base that stores a catalog of classes and mapped tables in the Declarative system. +Each class represents a table in the database and includes columns and relationships. The Base class is a declarative +base that stores a catalog of classes and mapped tables in the Declarative system. Typical usage example: @@ -29,7 +30,6 @@ class ChatThread(Base): class QuestionAnswer(Base): __tablename__ = "question_answers" - thread_id = Column(String, ForeignKey("chat_thread.thread_id")) question_answer_id = Column(String, primary_key=True, index=True) system_prompt = Column(String) @@ -48,6 +48,7 @@ class QuestionAnswer(Base): total_cost = Column(Float) chat_thread = relationship("ChatThread", back_populates="question_answers") feedback = relationship("FeedBack", back_populates="question_answer") + language = Column(String) class FeedBack(Base): diff --git a/src/wandbot/database/schemas.py b/src/wandbot/database/schemas.py index 2049155..6ae9b04 100644 --- a/src/wandbot/database/schemas.py +++ b/src/wandbot/database/schemas.py @@ -9,7 +9,7 @@ chat_thread = ChatThread(thread_id="123", application="app1") chat_request = ChatRequest(question="What is the weather?", chat_history=None) - chat_response = ChatRepsonse(system_prompt="Weather is sunny", question="What is the weather?", + chat_response = ChatResponse(system_prompt="Weather is sunny", question="What is the weather?", answer="It's sunny", model="model1", sources="source1", source_documents="doc1", total_tokens=10, prompt_tokens=2, completion_tokens=8, time_taken=1.0, @@ -19,7 +19,7 @@ from datetime import datetime from enum import IntEnum -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict class Rating(IntEnum): @@ -33,9 +33,7 @@ class FeedbackBase(BaseModel): class Feedback(FeedbackBase): - class Config: - use_enum_values = True - from_attributes = True + model_config = ConfigDict(from_attributes=True, use_enum_values=True) class FeedbackCreate(Feedback): @@ -57,12 +55,11 @@ class QuestionAnswerBase(BaseModel): start_time: datetime | None = None end_time: datetime | None = None feedback: list[Feedback] | None = [] + language: str | None = None class QuestionAnswer(QuestionAnswerBase): - class Config: - use_enum_values = True - from_attributes = True + model_config = ConfigDict(from_attributes=True, use_enum_values=True) class QuestionAnswerCreate(QuestionAnswer): @@ -76,10 +73,7 @@ class ChatThreadBase(BaseModel): class ChatThread(ChatThreadBase): application: str - - class Config: - use_enum_values = True - from_attributes = True + model_config = ConfigDict(from_attributes=True, use_enum_values=True) class ChatThreadCreate(ChatThread): diff --git a/src/wandbot/evaluation/README.md b/src/wandbot/evaluation/README.md new file mode 100644 index 0000000..6140fa2 --- /dev/null +++ b/src/wandbot/evaluation/README.md @@ -0,0 +1,10 @@ +# Evaluation + +## Overview + +The following W&B reports provide an overview of the evaluation process of wandbot: + + +- [How to evaluate an LLM Part 1: Building an Evaluation Dataset for our LLM System](https://wandb.ai/wandbot/wandbot-eval/reports/How-to-evaluate-an-LLM-Part-1-Building-an-Evaluation-Dataset-for-our-LLM-System--Vmlldzo1NTAwNTcy) +- [How to evaluate an LLM Part 2: Manual Evaluation of our LLM System](https://wandb.ai/wandbot/wandbot-eval/reports/How-to-evaluate-an-LLM-Part-2-Manual-Evaluation-of-our-LLM-System--Vmlldzo1NzU4NTM3) +- [How to evaluate an LLM Part 3: Auto-Evaluation; LLMs evaluating LLMs](https://wandb.ai/wandbot/wandbot-eval/reports/How-to-evaluate-an-LLM-Part-3-Auto-Evaluation-LLMs-evaluating-LLMs--Vmlldzo1NzEzMDcz) \ No newline at end of file diff --git a/src/wandbot/evaluation/config.py b/src/wandbot/evaluation/config.py deleted file mode 100644 index 9e293e7..0000000 --- a/src/wandbot/evaluation/config.py +++ /dev/null @@ -1,32 +0,0 @@ -import pathlib - -from pydantic import BaseSettings, Field - -from wandbot.chat.config import ChatConfig -from wandbot.ingestion.config import VectorIndexConfig - - -class EvalConfig(BaseSettings): - debug: bool = True - eval_model: str = "gpt-3.5-turbo" - eval_artifact: str = "wandbot/wandbbot/eval_dataset:v0" - max_retries: int = 3 - retry_delay: int = 10 - eval_prompt: pathlib.Path = pathlib.Path("data/prompts/eval_prompt.txt") - wandb_project: str = Field(None, env="WANDBOT_WANDB_PROJECT") - wandb_entity: str | None = Field(None, env="WANDBOT_WANDB_ENTITY") - wandb_job_type: str | None = Field("eval", env="WANDBOT_WANDB_JOB_TYPE") - chat_config: ChatConfig = ChatConfig() - vectorindex_config: VectorIndexConfig = VectorIndexConfig() - - class Config: - env_prefix = "WANDBOT_" - env_file = ".env" - env_file_encoding = "utf-8" - - @root_validator - def _set_defaults(self, values): - if values.get("wandb_project") is None: - values["wandb_entity"] = values["chat_config"].wandb_entity - - return values diff --git a/src/wandbot/evaluation/eval.py b/src/wandbot/evaluation/eval.py deleted file mode 100644 index 2c57b54..0000000 --- a/src/wandbot/evaluation/eval.py +++ /dev/null @@ -1,179 +0,0 @@ -import json -import time -from pathlib import Path - -import pandas as pd -import wandb -from fuzzywuzzy import fuzz -from langchain.chat_models import ChatOpenAI -from langchain.evaluation.qa import QAEvalChain -from tqdm.auto import tqdm - -from wandbot.chat.chat import Chat -from wandbot.chat.prompts import load_eval_prompt -from wandbot.evaluation.config import EvalConfig - -eval_config = EvalConfig() - - -# Similarity score as the distance of the most similar substring as a number between 0 and 100 -def calculate_string_distance(row): - return fuzz.partial_ratio(row.orig_response, row.response) - - -class Evaluate: - config: EvalConfig = EvalConfig() - - def __init__(self, config: EvalConfig = None): - if config is not None: - self.config = config - self.chat = Chat(self.config.chat_config) - self.eval_df = pd.DataFrame( - columns=[ - "query", - "orig_response", - "orig_document", - "response", - "documents", - "scores", - ] - ) - self.wandb_run = wandb.init( - entity=eval_config.wandb_entity, - project=eval_config.wandb_project, - job_type=eval_config.wandb_job_type, - config=eval_config.dict(), - ) - - # download eval data from artifact - def load_eval_dataframe(self): - eval_artifact = self.wandb_run.use_artifact(self.config.eval_artifact) - eval_artifact_dir = Path(eval_artifact.download()) - df_questions = pd.read_csv( - eval_artifact_dir / "auto-eval-questions.csv" - ) - if self.config.debug: - df_questions = df_questions.sample(n=3).reset_index(drop=True) - return df_questions - - def __call__(self, config: EvalConfig = None): - if config is not None: - self.config = config - eval_dataframe = self.load_eval_dataframe() - - for i in tqdm(range(len(eval_dataframe))): - query = eval_dataframe["question"].loc[i] - orig_response = eval_dataframe["response"].loc[i] - orig_document = eval_dataframe["document"].loc[i] - for i in range(self.config.max_retries): - try: - resp = self.chat(query) - response = resp.answer - document_scores = json.loads(resp.source_documents) - documents = [x["document"] for x in document_scores] - scores = [x["score"] for x in document_scores] - eval_df = self.eval_df.append( - { - "query": query, - "orig_response": orig_response, - "orig_document": orig_document, - "response": response, - "documents": documents, - "scores": scores, - }, - ignore_index=True, - ) - break - except Exception as e: - print( - f"Error occurred: {e}. Retrying in {self.config.retry_delay} seconds..." - ) - time.sleep(self.config.retry_delay) - - self.eval_df["retrieval_match"] = self.eval_df.apply( - lambda x: x.orig_document in x.documents, axis=1 - ) - self.eval_df = self.eval_df.dropna() - self.eval_df["string_distance"] = self.eval_df.apply( - calculate_string_distance, axis=1 - ) - - eval_df.orig_document = eval_df.orig_document.apply( - lambda x: x.replace( - "../docodile/docs/", "https://docs.wandb.ai/" - ).replace(".md", "") - ) - - retrieval_accuracy = len( - self.eval_df[self.eval_df["retrieval_match"] == True] - ) / len(self.eval_df) - print(f"Retrieval accuracy: {retrieval_accuracy}") - wandb.log({"retrieval_accuracy": retrieval_accuracy}) - - average_string_distance = self.eval_df["string_distance"].mean() - print(f"Average string distance: {average_string_distance}") - wandb.log({"average_string_distance": average_string_distance}) - - print("Loading evaluation model...") - - chat_prompt = load_eval_prompt(self.config.eval_prompt) - llm = ChatOpenAI( - model_name=self.config.eval_model, - temperature=0, - ) - eval_chain = QAEvalChain.from_llm(llm, prompt=chat_prompt) - - examples = [] - predictions = [] - for i in range(len(self.eval_df)): - # print(f"Example {i}:") - examples.append( - { - "query": self.eval_df["query"].iloc[i], - "answer": self.eval_df["orig_response"].iloc[i], - } - ) - predictions.append( - { - "query": self.eval_df["query"].iloc[i], - "answer": self.eval_df["orig_response"].iloc[i], - "result": self.eval_df["response"].iloc[i], - } - ) - graded_outputs = eval_chain.evaluate(examples, predictions) - self.eval_df["model_score"] = [ - x.get("text", "None") for x in graded_outputs - ] - - model_accuracy = len( - self.eval_df[self.eval_df["model_score"] == "CORRECT"] - ) / len(self.eval_df) - print(f"Chat model accuracy: {model_accuracy}") - wandb.log({"chat_accuracy": model_accuracy}) - - self.eval_df.to_parquet("eval_results.parquet") - - artifact = wandb.Artifact("eval_results", type="eval_results") - artifact.add_file("eval_results.parquet") - self.wandb_run.log_artifact(artifact) - - self.wandb_run.log( - { - "eval_results": wandb.Table( - dataframe=self.eval_df.astype( - { - "query": "string", - "orig_response": "string", - "orig_document": "string", - "response": "string", - "documents": "string", - "scores": "string", - } - ) - ) - } - ) - - self.wandb_run.log_code() - - self.wandb_run.finish() diff --git a/src/wandbot/ingestion/config.py b/src/wandbot/ingestion/config.py index ad62e36..9bdc636 100644 --- a/src/wandbot/ingestion/config.py +++ b/src/wandbot/ingestion/config.py @@ -1,8 +1,9 @@ """This module defines the configuration for data sources and stores in the Wandbot ingestion system. -This module contains classes that define the configuration for various data sources and stores used in the Wandbot ingestion system. -Each class represents a different type of data source or store, such as English and Japanese documentation, example code, SDK code, -and more. Each class is defined with various attributes like name, data source, docstore directory, etc. +This module contains classes that define the configuration for various data sources and stores used in the Wandbot +ingestion system. Each class represents a different type of data source or store, such as English and Japanese +documentation, example code, SDK code, and more. Each class is defined with various attributes like name, +data source, docstore directory, etc. Typical usage example: diff --git a/src/wandbot/ingestion/prepare_data.py b/src/wandbot/ingestion/prepare_data.py index 6b7fb33..e46e437 100644 --- a/src/wandbot/ingestion/prepare_data.py +++ b/src/wandbot/ingestion/prepare_data.py @@ -22,12 +22,12 @@ import markdown import nbformat -import wandb from langchain.document_loaders import TextLoader from langchain.document_loaders.base import BaseLoader from langchain.schema import Document from nbconvert import MarkdownExporter +import wandb from wandbot.ingestion.config import ( DataStoreConfig, DocodileEnglishStoreConfig, @@ -87,6 +87,29 @@ def load(self): self.metadata.update({"num_documents": len(documents)}) return documents + def _get_local_paths(self): + if self.config.data_source.is_git_repo: + self.metadata = fetch_git_repo( + self.config.data_source, self.config.data_source.git_id_file + ) + + local_paths = [] + file_patterns = ( + [self.config.data_source.file_pattern] + if isinstance(self.config.data_source.file_pattern, str) + else self.config.data_source.file_pattern + ) + for file_pattern in file_patterns: + local_paths.extend( + list( + ( + self.config.data_source.local_path + / self.config.data_source.base_path + ).rglob(file_pattern) + ) + ) + return local_paths + class DocodileDataLoader(DataLoader): """A data loader for Docodile documents. @@ -98,7 +121,8 @@ class DocodileDataLoader(DataLoader): config: The configuration for the data store. """ - def extract_slug(self, file_path: pathlib.Path) -> str: + @staticmethod + def extract_slug(file_path: pathlib.Path) -> str: """Extracts the slug from a file. Args: @@ -114,7 +138,9 @@ def extract_slug(self, file_path: pathlib.Path) -> str: meta = md.Meta.get("slug", [""]) return meta[0] - def generate_site_url(self, base_path: Path, file_path: Path) -> str: + def generate_site_url( + self, base_path: pathlib.Path, file_path: pathlib.Path + ) -> str: """Generates the site URL for a file. Args: @@ -166,26 +192,7 @@ def lazy_load( Yields: A Document object. """ - if self.config.data_source.is_git_repo: - self.metadata = fetch_git_repo( - self.config.data_source, self.config.data_source.git_id_file - ) - - local_paths = [] - file_patterns = ( - [self.config.data_source.file_pattern] - if isinstance(self.config.data_source.file_pattern, str) - else self.config.data_source.file_pattern - ) - for file_pattern in file_patterns: - local_paths.extend( - list( - ( - self.config.data_source.local_path - / self.config.data_source.base_path - ).rglob(file_pattern) - ) - ) + local_paths = self._get_local_paths() document_files = { local_path: self.generate_site_url( self.config.data_source.local_path @@ -223,26 +230,7 @@ def lazy_load(self) -> Iterator[Document]: Yields: A Document object. """ - if self.config.data_source.is_git_repo: - self.metadata = fetch_git_repo( - self.config.data_source, self.config.data_source.git_id_file - ) - - local_paths = [] - file_patterns = ( - [self.config.data_source.file_pattern] - if isinstance(self.config.data_source.file_pattern, str) - else self.config.data_source.file_pattern - ) - for file_pattern in file_patterns: - local_paths.extend( - list( - ( - self.config.data_source.local_path - / self.config.data_source.base_path - ).rglob(file_pattern) - ) - ) + local_paths = self._get_local_paths() paths = list(local_paths) local_paths = list(map(lambda x: str(x), paths)) @@ -312,7 +300,8 @@ def load( result_artifact_name: The name of the result artifact. Default is "raw_dataset". Returns: - The latest version of the prepared dataset artifact in the format "{entity}/{project}/{result_artifact_name}:latest". + The latest version of the prepared dataset artifact in the format + "{entity}/{project}/{result_artifact_name}:latest". """ run = wandb.init(project=project, entity=entity, job_type="prepare_dataset") artifact = wandb.Artifact( diff --git a/src/wandbot/ingestion/preprocess_data.py b/src/wandbot/ingestion/preprocess_data.py index 7b4a7a5..73d5cc9 100644 --- a/src/wandbot/ingestion/preprocess_data.py +++ b/src/wandbot/ingestion/preprocess_data.py @@ -19,17 +19,13 @@ nodes = load(documents, chunk_size=1024) """ -from typing import Iterable, List +from typing import Any, List -import regex as re import tiktoken from langchain.schema import Document as LcDocument -from langchain.text_splitter import RecursiveCharacterTextSplitter from llama_index import Document as LlamaDocument -from llama_index.node_parser import SimpleNodeParser -from llama_index.text_splitter import CodeSplitter, TextSplitter -from tree_sitter_languages import get_parser - +from llama_index.node_parser import CodeSplitter, MarkdownNodeParser +from llama_index.schema import BaseNode, TextNode from wandbot.utils import get_logger logger = get_logger(__name__) @@ -67,319 +63,17 @@ def remove_special_tokens(text: str) -> str: return cleaned_documents -def non_whitespace_len(s: Union[str, bytes]) -> int: - """Returns the length of the given string without whitespace. - - Args: - s: A string. - - Returns: - The length of the string without whitespace. - """ - if isinstance(s, str): - return len(re.sub("\s", "", s)) - return len(re.sub("\s", "", s.decode("utf-8"))) - - -def split_by_headers(tree: Any) -> List[List[Any]]: - """Splits the tree into chunks based on headers. - - Args: - tree: The tree to split. - - Returns: - A list of chunks where each chunk is a list of nodes. - """ - chunks = [] - current_chunk = [] - for child in tree.root_node.children: - if child.type == "atx_heading" or child.type == "setext_heading": - if current_chunk: # if current_chunk is not empty, add it to chunks - chunks.append(current_chunk) - current_chunk = [] # start a new chunk - current_chunk.append(child) - else: - current_chunk.append(child) - if current_chunk: # add the last chunk if it's not empty - chunks.append(current_chunk) - return chunks - - -def split_large_chunks_by_code_blocks( - chunks: List[List[Any]], max_chars: int -) -> List[List[Any]]: - """Splits large chunks into smaller chunks based on code blocks. - - Args: - chunks: A list of chunks where each chunk is a list of nodes. - max_chars: The maximum number of characters allowed in a chunk. - - Returns: - A list of smaller chunks where each chunk is a list of nodes. - """ - new_chunks = [] - for chunk in chunks: - current_chunk = [] - current_chars = 0 - for child in chunk: - child_chars = non_whitespace_len(child.text) - current_chars += child_chars - current_chunk.append(child) - if child.type == "fenced_code_block" and current_chars > max_chars: - if ( - current_chunk - ): # if current_chunk is not empty, add it to new_chunks - new_chunks.append(current_chunk) - current_chunk = [] # start a new chunk - current_chars = 0 - if current_chunk: # add the last chunk if it's not empty - new_chunks.append(current_chunk) - return new_chunks - - -def get_heading_level(chunk: List[Any]) -> Optional[int]: - """Returns the heading level of the given chunk. - - Args: - chunk: A list of nodes representing a chunk. - - Returns: - The heading level of the chunk. - """ - for child in chunk: - if child.type == "atx_heading" or child.type == "setext_heading": - for grandchild in child.children: - if grandchild.type.startswith( - "atx" - ) and grandchild.type.endswith("marker"): - return len(grandchild.text) - return None - - -def merge_small_chunks( - chunks: List[List[Any]], max_chars: int -) -> List[List[Any]]: - """Merges small chunks into larger chunks based on maximum characters. - - Args: - chunks: A list of chunks where each chunk is a list of nodes. - max_chars: The maximum number of characters allowed in a chunk. - - Returns: - A list of merged chunks where each chunk is a list of nodes. - """ - merged_chunks = [] - current_chunk = [] - current_chars = 0 - for chunk in chunks: - chunk_chars = sum(non_whitespace_len(child.text) for child in chunk) - current_heading_level, chunk_heading_level = get_heading_level( - current_chunk - ), get_heading_level(chunk) - cond = ( - current_heading_level is None and chunk_heading_level is None - ) or ( - current_heading_level - and chunk_heading_level - and current_heading_level <= chunk_heading_level - ) - if current_chars + chunk_chars <= max_chars and ( - not current_chunk or cond - ): - current_chunk.extend(chunk) - current_chars += chunk_chars - else: - merged_chunks.append(current_chunk) - current_chunk = chunk - current_chars = chunk_chars - if current_chunk: # add the last chunk if it's not empty - merged_chunks.append(current_chunk) - return merged_chunks - - -def coalesce_small_chunks( - chunks: List[List[Any]], min_chars: int = 100 -) -> List[List[Any]]: - """Coalesces small chunks into larger chunks based on minimum characters. - - Args: - chunks: A list of chunks where each chunk is a list of nodes. - min_chars: The minimum number of characters allowed in a chunk. - - Returns: - A list of coalesced chunks where each chunk is a list of nodes. - """ - coalesced_chunks = [] - i = 0 - while i < len(chunks): - chunk_chars = sum(non_whitespace_len(child.text) for child in chunks[i]) - if chunk_chars < min_chars: # if chunk is too small - if i < len(chunks) - 1: # if it's not the last chunk - next_chunk_heading_level = get_heading_level(chunks[i + 1]) - current_chunk_heading_level = get_heading_level(chunks[i]) - if next_chunk_heading_level is None or ( - current_chunk_heading_level is not None - and next_chunk_heading_level > current_chunk_heading_level - ): - # if the next chunk is not a heading or is a heading of a higher level - chunks[i + 1] = ( - chunks[i] + chunks[i + 1] - ) # prepend the chunk to the next chunk - i += 1 # skip to the next chunk - continue - # if it's the last chunk or the next chunk is a heading of the same level - if coalesced_chunks: # if there are already some coalesced chunks - coalesced_chunks[-1].extend( - chunks[i] - ) # add the chunk to the previous chunk - else: - coalesced_chunks.append(chunks[i]) - i += 1 - else: - coalesced_chunks.append( - chunks[i] - ) # add the chunk as a separate chunk - i += 1 - return coalesced_chunks - - -def get_line_number(index: int, source_code: bytes) -> int: - """Returns the line number corresponding to the given index in the source code. - - Args: - index: The index in the source code. - source_code: The source code as bytes. - - Returns: - The line number corresponding to the index. - """ - total_chars = 0 - for line_number, line in enumerate( - source_code.splitlines(keepends=True), start=1 - ): - total_chars += len(line) - if total_chars > index: - return line_number - 1 - return line_number - - -def coalesce_strings(strings: List[str], max_length: int) -> List[str]: - """Coalesces strings into larger strings based on maximum length. - - Args: - strings: A list of strings. - max_length: The maximum length allowed for a coalesced string. - - Returns: - A list of coalesced strings. - """ - result = [] - current_string = "" - - for string in strings: - if ( - non_whitespace_len(current_string) + non_whitespace_len(string) - <= max_length - ): - current_string += "\n" + string - else: - result.append(current_string) - current_string = string - - # Add the last remaining string - if current_string: - result.append(current_string) - - return result - - -def clean_extra_newlines(strings: List[str]) -> List[str]: - """Cleans extra newlines in the given strings. - - Args: - strings: A list of strings. - - Returns: - A list of strings with extra newlines cleaned. - """ - result = [] - for string in strings: - string = re.sub(r"\n```\n", "CODEBREAK", string) - string = re.sub(r"\n{2,}", "LINEBREAK", string) - string = re.sub(r"\n", " ", string) - string = re.sub(r"LINEBREAK", "\n\n", string) - string = re.sub(r"CODEBREAK", "\n\n```\n\n", string) - result.append(string) - return result - - -class MarkdownSplitter(TextSplitter): - """Splits text into chunks based on Markdown formatting. - - Attributes: - sub_splitter: The sub-splitter used to split text into smaller chunks. - chunk_size: The maximum size of each chunk. - """ - - sub_splitter: RecursiveCharacterTextSplitter = ( - RecursiveCharacterTextSplitter.from_language("markdown") - ) - chunk_size: int = 1024 - - def __init__(self, **kwargs): - """Initializes the MarkdownSplitter instance. - - Args: - **kwargs: Additional keyword arguments. - """ - super().__init__(**kwargs) - self.sub_splitter = RecursiveCharacterTextSplitter.from_language( - "markdown" - ) - - @classmethod - def class_name(cls) -> str: - """Get class name.""" - return "MarkdownSplitter" - - def _chunk_text(self, text: str) -> Iterable[str]: - """Split text into chunks.""" - parser = get_parser("markdown") - tree = parser.parse(text.encode("utf-8")) - - chunks: List[List[Any]] = split_by_headers(tree) - chunks = split_large_chunks_by_code_blocks(chunks, self.chunk_size) - chunks = merge_small_chunks(chunks, self.chunk_size) - for chunk in chunks: - if chunk: - chunk_bytes = chunk[0].start_byte, chunk[-1].end_byte - chunk_lines = text.encode("utf-8").splitlines()[ - get_line_number( - chunk_bytes[0], text.encode("utf-8") - ) : get_line_number(chunk_bytes[1], text.encode("utf-8")) - + 1 - ] - chunk_str = "" - for line in chunk_lines: - if line.decode().endswith("\n"): - chunk_str += line.decode() - else: - chunk_str += line.decode() + "\n" - - for split in self.sub_splitter.split_documents( - [LcDocument(page_content=chunk_str)] - ): - split_content = split.page_content - - yield split_content - - def split_text(self, text: str) -> List[str]: - """Split text into chunks.""" - chunks = list(self._chunk_text(text)) - chunks = coalesce_strings(chunks, self.chunk_size) - chunks = clean_extra_newlines(chunks) - chunks = make_texts_tokenization_safe(chunks) - return chunks +class CustomMarkdownNodeParser(MarkdownNodeParser): + def _build_node_from_split( + self, + text_split: str, + node: BaseNode, + metadata: dict, + ) -> TextNode: + """Build node from single text split.""" + text_splits = make_texts_tokenization_safe([text_split]) + text_split = text_splits[0] + return super()._build_node_from_split(text_split, node, metadata) class CustomCodeSplitter(CodeSplitter): @@ -419,14 +113,8 @@ def load(documents: List[LcDocument], chunk_size: int = 1024) -> List[Any]: Returns: A list of nodes. """ - md_parser: SimpleNodeParser = SimpleNodeParser( - text_splitter=MarkdownSplitter(chunk_size=chunk_size) - ) - code_parser: SimpleNodeParser = SimpleNodeParser( - text_splitter=CustomCodeSplitter( - language="python", max_chars=chunk_size - ) - ) + md_parser = CustomMarkdownNodeParser() + code_parser = CustomCodeSplitter(language="python", max_chars=chunk_size) llama_docs: List[LlamaDocument] = list( map(lambda x: convert_lc_to_llama(x), documents) diff --git a/src/wandbot/ingestion/report.py b/src/wandbot/ingestion/report.py index 122d4cf..37c6ca5 100644 --- a/src/wandbot/ingestion/report.py +++ b/src/wandbot/ingestion/report.py @@ -18,9 +18,10 @@ import pathlib from datetime import datetime -import wandb import wandb.apis.reports as wr +import wandb + def log_raw_counts(metadata: dict[str, dict[str, int]]) -> list[str]: """Logs the number of documents for each data source. diff --git a/src/wandbot/ingestion/vectorstores.py b/src/wandbot/ingestion/vectorstores.py index 6f4f6a0..80e70d3 100644 --- a/src/wandbot/ingestion/vectorstores.py +++ b/src/wandbot/ingestion/vectorstores.py @@ -14,11 +14,12 @@ import json import pathlib +from typing import Any, Dict, List -import wandb from langchain.schema import Document as LcDocument from llama_index.callbacks import WandbCallbackHandler +import wandb from wandbot.ingestion import preprocess_data from wandbot.ingestion.config import VectorStoreConfig from wandbot.utils import ( @@ -62,12 +63,12 @@ def load( ) artifact_dir: str = artifact.download() storage_context = load_storage_context( - config.embedding_dim, config.persist_dir + config.embedding_dim, str(config.persist_dir) ) service_context = load_service_context( config.chat_model_name, config.temperature, - config.embeddings_cache, + str(config.embeddings_cache), config.max_retries, ) @@ -84,12 +85,13 @@ def load( doc: LcDocument = LcDocument(**doc_dict) documents.append(doc) transformed_documents.extend(preprocess_data.load(documents)) - + unique_objects = {obj.hash: obj for obj in transformed_documents} + transformed_documents = list(unique_objects.values()) index = load_index( transformed_documents, service_context, storage_context, - persist_dir=config.persist_dir, + persist_dir=str(config.persist_dir), ) wandb_callback: WandbCallbackHandler = WandbCallbackHandler() diff --git a/src/wandbot/utils.py b/src/wandbot/utils.py index ebbfa0b..4fc1311 100644 --- a/src/wandbot/utils.py +++ b/src/wandbot/utils.py @@ -23,7 +23,7 @@ import datetime import logging import os -from typing import Any, Optional +from typing import Any, List, Optional import faiss from langchain.embeddings import CacheBackedEmbeddings, OpenAIEmbeddings @@ -34,7 +34,10 @@ VectorStoreIndex, load_index_from_storage, ) +from llama_index.bridge.pydantic import Field from llama_index.llms import OpenAI +from llama_index.postprocessor.types import BaseNodePostprocessor +from llama_index.schema import NodeWithScore, QueryBundle from llama_index.vector_stores import FaissVectorStore @@ -118,7 +121,7 @@ def load_llm(model_name: str, temperature: float, max_retries: int) -> OpenAI: def load_service_context( - llm: OpenAI, + llm: str, temperature: float, embeddings_cache: str, max_retries: int, @@ -196,3 +199,27 @@ def load_index( ) index.storage_context.persist(persist_dir=persist_dir) return index + + +class LanguageFilterPostprocessor(BaseNodePostprocessor): + """Language-based Node processor.""" + + languages: List[str] = Field(default=["en", "python"]) + + @classmethod + def class_name(cls) -> str: + return "LanguageFilterPostprocessor" + + def _postprocess_nodes( + self, + nodes: List[NodeWithScore], + query_bundle: Optional[QueryBundle] = None, + ) -> List[NodeWithScore]: + """Postprocess nodes.""" + + new_nodes = [] + for node in nodes: + if node.metadata["language"] in self.languages: + new_nodes.append(node) + + return new_nodes