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+name = "weasel" +version = "0.3.2" +description = "Weasel: A small and easy workflow system" +optional = false +python-versions = ">=3.6" +files = [ + {file = "weasel-0.3.2-py3-none-any.whl", hash = "sha256:f2921a7fa78ad69dcd7068fa40a43786f3acf7edd6c35b44763e9163e045b664"}, + {file = "weasel-0.3.2.tar.gz", hash = "sha256:96c244ec5ada220f6a1d0af082edf97ef77c11e8eb1d2fbdcd895ad4d102ce19"}, +] + +[package.dependencies] +cloudpathlib = ">=0.7.0,<0.16.0" +confection = ">=0.0.4,<0.2.0" +packaging = ">=20.0" +pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0" +requests = ">=2.13.0,<3.0.0" +smart-open = ">=5.2.1,<7.0.0" +srsly = ">=2.4.3,<3.0.0" +typer = ">=0.3.0,<0.10.0" +wasabi = ">=0.9.1,<1.2.0" + [[package]] name = "websocket-client" version = "0.59.0" description = "WebSocket client for Python with low level API options" -category = "main" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -4527,7 +5473,6 @@ six = "*" name = "websockets" version = "11.0.3" description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4607,7 +5552,6 @@ files = [ name = "werkzeug" version = "2.0.3" description = "The comprehensive WSGI web application library." -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -4622,7 +5566,6 @@ watchdog = ["watchdog"] name = "wheel" version = "0.41.2" description = "A built-package format for Python" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4637,7 +5580,6 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"] name = "yarl" version = "1.9.2" description = "Yet another URL library" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4724,4 +5666,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "45a984dcf58caf4287eed6cdc758e0bfade36c621ab2e3cae048f447f47c2658" +content-hash = "909e851a755103b6765a829343eca4435cc309e660dce38ed7e3de8dd3914591" diff --git a/pyproject.toml b/pyproject.toml index caa1ce6b..21e00ecf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,11 @@ anthropic = "==0.3.11" langchain = "==0.0.303" scikit-learn = "==1.3.1" pytest = "==7.2.1" +tqdm = "^4.66.1" +tiktoken = "^0.5.1" +python-dateutil = "^2.8.2" +spacy = "^3.6.1" +sentence-transformers = "^2.2.2" jsonschema = ">=4.16.0,<=4.19.0" [tool.poetry.group.dev.dependencies.tomte] diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py new file mode 100644 index 00000000..63b12dfe --- /dev/null +++ b/tools/prediction_sum_url_content.py @@ -0,0 +1,1003 @@ +# -*- coding: utf-8 -*- +# ------------------------------------------------------------------------------ +# +# Copyright 2023 Valory AG +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# ------------------------------------------------------------------------------ + +"""This module implements a Mech tool for binary predictions.""" + +from typing import Any, Dict, Generator, List, Optional, Tuple +from datetime import datetime, timezone +import json +import re +from concurrent.futures import Future, ThreadPoolExecutor + +from bs4 import BeautifulSoup, NavigableString +from googleapiclient.discovery import build +import openai +import requests +from requests import Session +import spacy +import tiktoken +import traceback + +from dateutil import parser +from tqdm import tqdm +from sentence_transformers import SentenceTransformer, util + +NUM_URLS_EXTRACT = 5 +MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 # Set the limit for cost efficiency +WORDS_PER_TOKEN_FACTOR = 0.75 +DEFAULT_OPENAI_SETTINGS = { + "max_compl_tokens": 200, + "temperature": 0, +} + +ALLOWED_TOOLS = [ + "prediction-offline-sum-url-content", + "prediction-online-sum-url-content", +] +TOOL_TO_ENGINE = { + "prediction-offline-sum-url-content": "gpt-4", + "prediction-online-sum-url-content": "gpt-4", +} + + +PREDICTION_PROMPT = """ +INTRODUCTION: +You are a Large Language Model (LLM) within a multi-agent system. Your primary task is to accurately estimate the probability of a specified event occurring by a specified deadline, \ +detailed in the 'event question' found in 'USER_PROMPT'. This 'event question' should ideally have only two possible outcomes: the event will either occur or not \ +by the deadline specified in the question, which is 23:59:59 of the date provided. It is critical that you incorporate this deadline date in your \ +probability estimation. You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", which is \ +sourced from a Google search engine query performed a few seconds ago and is meant to assist you in your estimation. You must adhere to the following 'INSTRUCTIONS'. + + +INSTRUCTIONS: +* Examine the user's input labeled 'USER_PROMPT'. Focus on the part enclosed in double quotes, which contains the 'event question'. +* If the 'event question' implies more than two outcomes, output the response "Error" and halt further processing. +* Utilize your training data to generate a probability estimation for the event specified in the 'event question' occurring by the given deadline of 23:59:59 on the specified date. +* Also rely on your training data to analyze what information would be relevant to make a probability estimation for the event specified in the 'event question' occurring by the given deadline. +* Examine the itemized list under "ADDITIONAL_INFORMATION". This data is sourced from a Google search engine query done a few seconds ago. +* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data to make the probability estimation. +* If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have been provided with the most current and relevant information available on the internet, regardless of its age. Still pay close attention on the release and modification timestamps for each information item provided in parentheses right before it as well as the current time {timestamp}. Even if "ADDITIONAL_INFORMATION" contains the most recent and relevant information about the topic in the event question, it does not imply that it is relevant to make a prediction about the event question. +* If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question', but does not clearly state that the event has already happened, you can assume that the event has not happened by now `{timestamp}`. +* Given the importance of the deadline for the 'event question,' recent information generally holds more weight for your probability estimation. However, do not disregard older information that provides foundational or contextual relevance to the event in question. Use your judgment to weigh the importance of recency against the relevance of older data. +* Factor the deadline into your probability estimation. It determines the timeframe by which the event must occur for the 'event question' to be answered affirmatively. For reference, the current time is `{timestamp}`. +* Decrease the probability estimation of the event occurring drastically the closer the current time `{timestamp}` is to the deadline, if you have not found information clearly indicating that the event will happen within the remaining time. +* If the event question is formulated too vaguely or if the information under "ADDITIONAL_INFORMATION" contradict each other, decrease the confidence value in your probability estimation accordingly. +* If the information in "ADDITIONAL_INFORMATION" indicate without a doubt that the event has already happened, set the probability estimation to a very high score. If not, make a probability estimation based on the information provided as well as your training data for context and background information. +* You must provide your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response. + + +USER_PROMPT: +``` +{user_prompt} +``` + +ADDITIONAL_INFORMATION: +``` +{additional_information} +``` + +OUTPUT_FORMAT: +* Your output response must be only a single JSON object to be parsed by Python's "json.loads()". +* The JSON must contain four fields: "p_yes", "p_no", "confidence", and "info_utility", each ranging from 0 to 1. + - "p_yes": Estimated probability that the event occurs within the deadline. + - "p_no": Estimated probability that the 'event question' does not occur within the deadline. + - "confidence": Indicating the confidence in the estimated probabilities you provided ranging from 0 (lowest confidence) to 1 (maximum confidence). Confidence can be calculated based on the quality and quantity of data used for the estimation. + - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction ranging from 0 (lowest utility) to 1 (maximum utility). +* The sum of "p_yes" and "p_no" must equal 1. +* Output only the JSON object in your response. Do not include any other contents in your response. +""" + +URL_QUERY_PROMPT = """ +You are a Large Language Model in a multi-agent system. Your task is to formulate search engine queries based on \ +a user's 'event question', which specifies an event and any accompanying conditions. The 'event question' allows \ +only two outcomes: the event will either occur or not, given the conditions. Find the 'event question' under 'USER_PROMPT' \ +and adhere to the 'INSTRUCTIONS'. + +INSTRUCTIONS: +* Carefully read the 'event question' under 'USER_PROMPT', enclosed by triple backticks. +* If the 'event question' has more than two outcomes, respond with "Error" and ignore further instructions. +* Create a list of 1-4 unique search queries likely to yield relevant and contemporary information for assessing the event's likelihood under the given conditions. +* Each query must be unique, and they should not overlap or yield the same set of results. +* You must provide your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response. + +USER_PROMPT: +``` +{event_question} +``` + +OUTPUT_FORMAT: +* Your output response must be only a single JSON object to be parsed by Python's "json.loads()". +* The JSON must contain two fields: "queries", and "urls". + - "queries": A 1-5 item array of the generated search engine queries. +* Include only the JSON object in your output. +""" + +# Global constants for possible attribute names for release and update dates +RELEASE_DATE_NAMES = [ + 'date', 'pubdate', 'publishdate', 'OriginalPublicationDate', + 'article:published_time', 'sailthru.date', 'article.published', + 'published-date', 'og:published_time', 'publication_date', + 'publishedDate', 'dc.date', 'DC.date', 'article:published', + 'article_date_original', 'cXenseParse:recs:publishtime', 'DATE_PUBLISHED', + 'pub-date', 'pub_date', 'datePublished', 'date_published', + 'time_published', 'article:published_date', 'parsely-pub-date', + 'publish-date', 'pubdatetime', 'published_time', 'publishedtime', + 'article_date', 'created_date', 'published_at', 'lastPublishedDate' + 'og:published_time', 'og:release_date', 'article:published_time', + 'og:publication_date', 'og:pubdate', 'article:publication_date', + 'product:availability_starts', 'product:release_date', 'event:start_date', + 'event:release_date', 'og:time_published', 'og:start_date', 'og:created', + 'og:creation_date', 'og:launch_date', 'og:first_published', + 'og:original_publication_date', 'article:published', 'article:pub_date', + 'news:published_time', 'news:publication_date', 'blog:published_time', + 'blog:publication_date', 'report:published_time', 'report:publication_date', + 'webpage:published_time', 'webpage:publication_date', 'post:published_time', + 'post:publication_date', 'item:published_time', 'item:publication_date' +] + +UPDATE_DATE_NAMES = [ + 'lastmod', 'lastmodified', 'last-modified', 'updated', + 'dateModified', 'article:modified_time', 'modified_date', + 'article:modified', 'og:updated_time', 'mod_date', + 'modifiedDate', 'lastModifiedDate', 'lastUpdate', 'last_updated', + 'LastUpdated', 'UpdateDate', 'updated_date', 'revision_date', + 'sentry:revision', 'article:modified_date', 'date_updated', + 'time_updated', 'lastUpdatedDate', 'last-update-date', 'lastupdate', + 'dateLastModified', 'article:update_time', 'modified_time', + 'last_modified_date', 'date_last_modified', + 'og:updated_time', 'og:modified_time', 'article:modified_time', + 'og:modification_date', 'og:mod_time', 'article:modification_date', + 'product:availability_ends', 'product:modified_date', 'event:end_date', + 'event:updated_date', 'og:time_modified', 'og:end_date', 'og:last_modified', + 'og:modification_date', 'og:revision_date', 'og:last_updated', + 'og:most_recent_update', 'article:updated', 'article:mod_date', + 'news:updated_time', 'news:modification_date', 'blog:updated_time', + 'blog:modification_date', 'report:updated_time', 'report:modification_date', + 'webpage:updated_time', 'webpage:modification_date', 'post:updated_time', + 'post:modification_date', 'item:updated_time', 'item:modification_date' +] + +# Global constant for HTML tags to remove +HTML_TAGS_TO_REMOVE = [ + "script", "style", "header", "footer", "aside", "nav", "form", "button", + "iframe", "input", "textarea", "select", "option", "label", "fieldset", + "legend", "img", "audio", "video", "source", "track", "canvas", "svg", + "object", "param", "embed", "link" +] + + +def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]: + """Search Google using a custom search engine.""" + service = build("customsearch", "v1", developerKey=api_key) + search = ( + service.cse() + .list( + q=query, + cx=engine, + num=num, + ) + .execute() + ) + return [result["link"] for result in search["items"]] + + +def extract_event_date(doc_question) -> str: + ''' + Extracts the event date from the event question if present. + + Args: + doc_question (spaCy Doc): Document text as a spaCy Doc object. + + Returns: + str: The event date in year-month-day format if present, otherwise None. + ''' + + event_date_ymd = None + + # Extract the date from the event question if present + for ent in doc_question.ents: + if ent.label_ == 'DATE': + event_date_ymd = standardize_date(ent.text) + + # If event date not formatted as YMD or not found, return None + if not datetime.strptime(event_date_ymd, '%Y-%m-%d') or event_date_ymd is None: + return None + else: + return event_date_ymd + + + +def get_max_tokens_for_additional_information( + max_compl_tokens: int, + prompt: str, + enc: tiktoken.Encoding, + safety_factor: float = 1.05, +) -> int: + """ + Calculates the estimated maximum number of tokens that can be consumed by the additional information string. + + Args: + max_compl_tokens (int): The maximum number of chat completion output tokens. + prompt (str): The user prompt containing the event question. + enc (tiktoken.Encoding): The tiktoken encoding to be used. + safety_factor (float, optional): The safety factor to be used for prompt variations and message headers. Defaults to 1.05. + + Returns: + int: The estimated number of tokens that can be consumed by the additional information string. + """ + + # Encode the strings into tokens + user_prompt_enc = enc.encode(prompt) + prediction_prompt_enc = enc.encode(PREDICTION_PROMPT) + + # Calculate token sum of thus far allocated tokens for the final prediction prompt + token_sum = len(user_prompt_enc) + len(prediction_prompt_enc) + max_compl_tokens + token_sum_safety = token_sum * safety_factor + + return int(MAX_TOTAL_TOKENS_CHAT_COMPLETION - token_sum_safety) + + +def truncate_additional_information( + additional_informations: str, + max_add_tokens: int, + enc: tiktoken.Encoding, +) -> str: + """ + Truncates additional information string to a specified number of tokens using tiktoken encoding. + + Args: + additional_informations (str): The additional information string to be truncated. + max_add_tokens (int): The maximum number of tokens allowed for the additional information string. + enc (tiktoken.Encoding): The tiktoken encoding to be used. + + Returns: + - str: The truncated additional information string. + """ + + # Encode the string into tokens + add_enc = enc.encode(additional_informations) + len_add_enc = len(add_enc) + + # Truncate additional information string if token sum exceeds maximum allowed + if len_add_enc <= max_add_tokens: + return additional_informations + else: + add_trunc_enc = add_enc[:-int(len_add_enc - max_add_tokens)] + return enc.decode(add_trunc_enc) + + +def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: int = 3) -> List[str]: + """ + Fetch unique URLs from search engine queries, limiting the number of URLs per query. + + Args: + queries (List[str]): List of search engine queries. + api_key (str): API key for the search engine. + engine (str): Custom google search engine ID. + num (int, optional): Number of returned URLs per query. Defaults to 3. + + Raises: + ValueError: If the number of URLs per query exceeds the maximum allowed. + + Returns: + List[str]: Unique list of URLs, omitting PDF and download-related URLs. + """ + + results = set() + max_num_fetch = 10 + + if num > max_num_fetch: + raise ValueError(f"The maximum number of URLs per query is {max_num_fetch}.") + + for query in queries: + fetched_urls = search_google( + query=query, + api_key=api_key, + engine=engine, + num=max_num_fetch # Limit the number of returned URLs per query + ) + + # Add only unique URLs up to 'num' per query, omitting PDF and 'download' URLs + count = 0 + for url in fetched_urls: + if url not in results and not url.endswith(".pdf"): + results.add(url) + count += 1 + if count >= num: + break + + print("get_urls_from_queries result:") + for url in results: + print(url) + + return list(results) + + +def standardize_date(date_text): + """ + Standardizes a given date string to the format 'YYYY-MM-DD' or 'MM-DD' if possible. + + Args: + date_text (str): The date string to be standardized. + + Raises: + ValueError: If the date string cannot be parsed. + + Returns: + str: The standardized date string if possible, otherwise None. + """ + + try: + # Compile regex patterns for month and day + month_regex = re.compile( + r'\b(?:Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)\b', + re.IGNORECASE + ) + day_regex = re.compile(r'\b\d{1,2}\b') + + # Parse date_text using dateutil parser + parsed_date = parser.parse(date_text) + + # Check if year, month, and day are in the original date_text + month_exists = month_regex.search(date_text) is not None + day_exists = day_regex.search(date_text) is not None + year_exists = str(parsed_date.year) in date_text + + # Format the parsed date accordingly + if year_exists and month_exists and day_exists: + return parsed_date.strftime("%Y-%m-%d") + elif month_exists and day_exists: + return parsed_date.strftime("%m-%d") + else: + return None + except Exception as e: + return None + + +def get_context_around_isolated_event_date( + doc_text, event_date_ymd, len_sentence_threshold, max_context=50 +): + """ + Extract sentences around isolated dates within the text. + + Args: + doc_text (spaCy Doc): Document text as a spaCy Doc object. + event_date_ymd (str): Event date in year-day-month format. + len_sentence_threshold (int): Minimum number of words required for a sentence to be considered contextful. + max_context (int, optional): Maximum number of words to include in the context. Defaults to 50. + + Raises: + ValueError: If maximum context is less than threshold or greater than 100. + + Returns: + list: List of sentences surrounding the target date. + """ + + # Check max_context value constraints + if max_context < len_sentence_threshold: + raise ValueError( + f"The maximum number of words must be greater than or equal to the minimum number of words ({len_sentence_threshold}) required for a sentence to be considered contextful." + ) + if max_context > 100: + raise ValueError( + f"The maximum number of words must be less than or equal to 300." + ) + + contexts_list = [] + len_doc_text = len(doc_text) + + # Extract the month and day from the event date + event_date_md = event_date_ymd[5:] + + for ent in doc_text.ents: + if ent.label_ == 'DATE': + standardized_date = standardize_date(ent.text) + if standardized_date is None: + continue + + # Check if the entity matches the target date + if standardized_date == event_date_ymd or standardized_date == event_date_md: + sentence = next( + sent for sent in doc_text.sents + if sent.start <= ent.start and sent.end >= ent.end + ) + + context_words = len(sentence.text.split()) + + # Extend the context if the sentence is too short + if context_words < len_sentence_threshold: + start_token, end_token = sentence.start, sentence.end + while context_words < max_context: + # Extend the context from the start of the sentence + new_start = start_token - 1 + while new_start >= 0 and doc_text[new_start].is_sent_start is None: + new_start -= 1 + if new_start >= 0: + context_words += len(doc_text[new_start:start_token].text.split()) + start_token = new_start + + # Break if max_context is reached + if context_words >= max_context: + break + + # Extend the context from the end of the sentence + new_end = end_token + 1 + while new_end < len_doc_text and doc_text[new_end].sent == sentence.sent: + new_end += 1 + if new_end < len_doc_text: + context_words += len(doc_text[end_token:new_end].text.split()) + end_token = new_end + + # Break if max_context is reached + if context_words >= max_context: + break + + # Break if max_context cannot be reached + if new_end == len_doc_text and start_token <= 0: + break + + context = doc_text[max(0, start_token):min(len_doc_text, end_token)].text + contexts_list.append(context) + + return contexts_list + + +def extract_relevant_information( + text: str, + query_emb, + event_date: str, + model, + nlp, + max_words: int +) -> str: + """ + Extract relevant information from website text based on a given event question. + + Args: + text (str): The website text to extract information from. + event_question (str): The question to find relevant information to. + event_date (str): Event date in year-day-month format. + model: The BERT model for text embeddings. + nlp: The spaCy NLP model. + max_words (int): Maximum number of words allowed for output. + + Returns: + str: The relevant sentences extracted from the website text. + """ + + # Constants for sentence length and number thresholds + len_sentence_threshold = 5 + num_sentences_threshold = 1000 + sentences = [] + event_date_sentences = [] + seen = set() + + # Truncate text for performance optimization + text = text[:50000] + + # Apply NLP pipeline to text + doc_text = nlp(text) + + # Extract unique sentences + for sent in doc_text.sents: + sentence_text = sent.text + if len(sentence_text.split()) >= len_sentence_threshold and sentence_text not in seen: + sentences.append(sentence_text) + seen.add(sentence_text) + sentences.extend(event_date_sentences) + + # Extract contextual sentences around event date occurences within too short sentences + event_date_sentences.extend( + get_context_around_isolated_event_date( + doc_text, event_date, len_sentence_threshold, max_context=50 + ) + ) + + if not sentences: + return "" + + # Limit the number of sentences for performance optimization + sentences = sentences[:num_sentences_threshold] + + # Encode event question calculate similarity scores + sent_emb = model.encode(sentences) + similarities = util.dot_score(query_emb, sent_emb)[0].cpu().tolist() + + # Extract top relevant sentences + relevant_sentences = [ + sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.4 + ] + + if not relevant_sentences: + return "" + + # Truncate text to fit max_words limit + output = ' '.join(relevant_sentences[:20]) + output_words = output.split(' ') + if len(output_words) > max_words: + output = ' '.join(output_words[:max_words]) + + return output + + +def get_date(soup): + """ + Retrieves the release and modification dates from the soup object containing the HTML tree. + + Args: + soup (BeautifulSoup): The BeautifulSoup object for the webpage. + + Returns: + str: A string representing the release and modification dates. + """ + + release_date = "unknown" + modified_date = "unknown" + + # Search for an update or modified date in the meta tags + for name in UPDATE_DATE_NAMES: + meta_tag = soup.find("meta", {"name": name}) or soup.find("meta", {"property": name}) + if meta_tag: + modified_date = meta_tag.get("content", "") + break + + # If not found, then look for release or publication date + for name in RELEASE_DATE_NAMES: + meta_tag = soup.find("meta", {"name": name}) or soup.find("meta", {"property": name}) + if meta_tag: + release_date = meta_tag.get("content", "") + break + + # Fallback to using the first time tag if neither release nor modified dates are found + if release_date == "unknown" and modified_date == "unknown": + time_tag = soup.find("time") + if time_tag: + release_date = time_tag.get("datetime", "") + + return f"({release_date}, {modified_date})" + + +def extract_text( + html: str, + query_emb, + event_date: str, + model, + nlp, + max_words: int, +) -> str: + """ + Extract relevant information from HTML string. + + Args: + html (str): The HTML content to extract text from. + event_question (str): Event question for context. + event_date (str): Event date in year-month-day format. + model: Pre-trained model for sentence transformer. + nlp: NLP object for additional text processing. + max_words (int): Maximum number of words for the output summary. + + Raises: + ValueError: If the HTML content is empty. + ValueError: If the release or update date could not be extracted from the HTML. + + Returns: + str: Relevant website information with release date. + """ + + if not html: + raise ValueError("HTML is empty.") + + soup = BeautifulSoup(html, "html.parser") + + # Get the date of the website + date = get_date(soup) + if date is None: + raise ValueError("Could not extract release or update date from HTML.") + + # Remove unnecessary tags to clean up text + for element in soup(HTML_TAGS_TO_REMOVE): + element.replace_with(NavigableString(' ')) + + # Extract and clean text + text = soup.get_text() + lines = (line.strip() for line in text.splitlines()) + chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) + text = ". ".join(chunk for chunk in chunks if chunk) + text = re.sub(r"\.{2,}", ".", text) + + # Get summarized text + relevant_text = extract_relevant_information( + text=text, + query_emb=query_emb, + event_date=event_date, + model=model, + nlp=nlp, + max_words=max_words, + ) + + if not relevant_text: + return "" + + return f"{date}: {relevant_text}" + + +def process_in_batches( + urls: List[str], batch_size: int = 15, timeout: int = 10 +) -> Generator[None, None, List[Tuple[Future, str]]]: + """ + Process URLs in batches using a generator and thread pool executor. + + Args: + urls (List[str]): List of URLs to process. + batch_size (int, optional): Size of the processing batch_size. Default is 5. + timeout (int, optional): Timeout for each request in seconds. Default is 10. + + Raises: + ValueError: If the batch_size is less than or equal to zero. + ValueError: If the timeout is less than or equal to zero. + + Yields: + List[Tuple[Future, str]]: List containing Future objects and URLs for each batch. + """ + + if batch_size <= 0: + raise ValueError("The 'batch_size' size must be greater than zero.") + + if timeout <= 0: + raise ValueError("The 'timeout' must be greater than zero.") + + session = Session() + session.max_redirects = 5 + + # User-Agent headers + headers = { + 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) Gecko/20100101 Firefox/117.0' + } + session.headers.update(headers) + + # Using ThreadPoolExecutor to execute requests in parallel + with ThreadPoolExecutor() as executor: + # Loop through the URLs in batch_size of size 'batch_size' + for i in range(0, len(urls), batch_size): + batch = urls[i : i + batch_size] + + # Submit the batch of URLs for processing + futures = [] + for url in batch: + try: + # Submit a HEAD request to the url and check Content-Type + head_future = executor.submit(session.head, url, headers=headers, timeout=timeout, allow_redirects=True) + head_response = head_future.result() + if 'text/html' not in head_response.headers.get('Content-Type', ''): + continue + else: + # Submit a GET request to the url + futures.append((executor.submit(session.get, url, headers=headers, timeout=timeout), url)) + except Exception as e: + print(f"An error occurred: {e}") + + yield futures + + +def extract_texts( + urls: List[str], + event_question: str, + max_words_per_url: int, + nlp, +) -> List[str]: + """ + Extract texts from a list of URLs using BERT and Spacy. + + Args: + urls (List[str]): List of URLs to extract text from. + event_question (str): Event-related question for text extraction. + max_words_per_url (int): Maximum number of words allowed to extract for each URL. + + Raises: + ValueError: If the event date could not be extracted from the event question. + Timeout: If the request timed out. + + Returns: + List[str]: List of extracted texts. + """ + + # Maximum number of allowed extractions + max_allowed = 25 + + # Initialize empty list for storing extracted texts + extracted_texts = [] + + # Initialize count and stop flag + count = 0 + stop = False + + # Process the event question with spacy + doc_question = nlp(event_question) + event_date = extract_event_date(doc_question) + + # Initialize Sentence Transformer model + model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1') + + # Create sentence embeddings for event question with Sentence Transformer + query_emb = model.encode(event_question) + + if event_date is None: + raise ValueError(f"Could not extract precise event date from event question: {event_question}") + + # Process URLs in batches + for batch in process_in_batches(urls=urls): + for future, url in tqdm(batch, desc="Processing URLs"): + print(f"Processing {url}") + try: + result = future.result() + if result.status_code != 200: + del result + continue + # Extract relevant information for the event question + extracted_text = extract_text( + html=result.text, + query_emb=query_emb, + event_date=event_date, + model=model, + nlp=nlp, + max_words=max_words_per_url, + ) + + # Delete the result object to free memory + del result + + # Append the extracted text if available and increment the count + if extracted_text: + # extracted_texts.append(f"{url}\n{extracted_text}") + extracted_texts.append(extracted_text) + count += 1 + + # Break if the maximum number of extractions is reached + if count >= max_allowed: + stop = True + break + + except requests.exceptions.Timeout: + print(f"Request for {url} timed out.") + + except Exception as e: + print(f"An error occurred: {e}") + traceback.print_exc() # Print stack trace for debugging + + # Break if the maximum number of extractions is reached + if stop: + break + + return extracted_texts + + +def fetch_additional_information( + event_question: str, + max_add_words: int, + google_api_key: str, + google_engine: str, + nlp, + engine: str = "gpt-3.5-turbo", + temperature: float = 1.0, + max_compl_tokens: int = 500, +) -> str: + + """ + Get urls from a web search and extract relevant information based on an event question. + + Args: + event_question (str): The question related to the event. + max_add_words (int): The maximum number of words allowed for the additional information. + google_api_key (str): The API key for the Google service. + google_engine (str): The Google engine to be used. + temperature (float): The temperature parameter for the engine. + engine (str): The openai engine. Defaults to "gpt-3.5-turbo". + temperature (float): The temperature parameter for the engine. Defaults to 1.0. + max_compl_tokens (int): The maximum number of tokens for the engine's response. + + Returns: + str: The relevant information fetched from all the URLs concatenated. + """ + + # Create URL query prompt + url_query_prompt = URL_QUERY_PROMPT.format(event_question=event_question) + + # Perform moderation check + moderation_result = openai.Moderation.create(url_query_prompt) + if moderation_result["results"][0]["flagged"]: + return "Moderation flagged the prompt as in violation of terms.", None + + # Create messages for the OpenAI engine + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": url_query_prompt}, + ] + + # Fetch queries from the OpenAI engine + response = openai.ChatCompletion.create( + model=engine, + messages=messages, + temperature=temperature, # Override the default temperature parameter set for the engine + max_tokens=max_compl_tokens, # Override the default max_compl_tokens parameter set for the engine + n=1, + timeout=90, + request_timeout=90, + stop=None, + ) + + # Parse the response content + print(f"RESPONSE: {response}") + json_data = json.loads(response.choices[0].message.content) + # Print queries each on a new line + print("QUERIES:\n") + for query in json_data["queries"]: + print(f"query: {query}\n") + + + # Get URLs from queries + urls = get_urls_from_queries( + json_data["queries"], + api_key=google_api_key, + engine=google_engine, + ) + + # Get max number of words per URL + max_words_per_url = max_add_words // len(urls) if len(urls) > 0 else 0 + + # Extract texts from URLs + texts = extract_texts( + urls=urls, + event_question=event_question, + max_words_per_url=max_words_per_url, + nlp=nlp, + ) + + # Join the texts and return + additional_informations = "\n\n".join(["- " + text for text in texts]) + + return additional_informations + + +def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: + """ + Run the task with the given arguments. + + Args: + kwargs (Dict): Keyword arguments that specify settings and API keys. + + Raises: + ValueError: If the tool or prompt is not provided. + ValueError: If the tool is not supported. + ValueError: If the event question is not found in the prompt. + + Returns: + Tuple[str, Optional[Dict[str, Any]]]: The generated content and any additional data. + """ + + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_compl_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"]) + temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + + + openai.api_key = kwargs["api_keys"]["openai"] + if tool not in ALLOWED_TOOLS: + raise ValueError(f"TOOL {tool} is not supported.") + + + # Print the settings + print(f"MECH TOOL: {tool}") + print(f"PROMPT: {prompt}") + print(f"MAX OPENAI RETURN TOKENS: {max_compl_tokens}") + print(f"LLM TEMPERATURE: {temperature}") + + # Load the spacy model + nlp = spacy.load("en_core_web_sm") + + # Get the LLM engine to be used + engine = TOOL_TO_ENGINE[tool] + print(f"ENGINE: {engine}") + + # Extract the event question from the prompt + event_question = re.search(r"\"(.+?)\"", prompt).group(1) + if not event_question: + raise ValueError("No event question found in prompt.") + print(f"EVENT_QUESTION: {event_question}") + print() + + # Get the tiktoken base encoding + enc = tiktoken.get_encoding("cl100k_base") + + # Calculate the maximum number of tokens and words that can be consumed by the additional information string + max_add_tokens = get_max_tokens_for_additional_information( + max_compl_tokens=max_compl_tokens, + prompt=prompt, + enc=enc, + ) + max_add_words = int(max_add_tokens * 0.75) + + # Fetch additional information + additional_information = ( + fetch_additional_information( + event_question=event_question, + engine=engine, + temperature=temperature, + max_compl_tokens=max_compl_tokens, + nlp=nlp, + max_add_words=max_add_words, + google_api_key=kwargs["api_keys"]["google_api_key"], + google_engine=kwargs["api_keys"]["google_engine_id"], + ) + if tool == "prediction-online-sum-url-content" + else "" + ) + + # Truncate additional information to stay within the chat completion token limit of 4096 + additional_information = truncate_additional_information( + additional_information, max_add_tokens, enc=enc, + ) + + # Get the current utc timestamp + current_time_utc = datetime.now(timezone.utc) + formatted_time_utc = current_time_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" + + # Extract event date and format it to ISO 8601 with UTC timezone and 23:59:59 time + doc_question = nlp(event_question) + raw_event_date = extract_event_date(doc_question) + parsed_event_date = datetime.strptime(raw_event_date, "%Y-%m-%d") + final_event_date = parsed_event_date.replace(hour=23, minute=59, second=59, microsecond=0, tzinfo=timezone.utc) + formatted_event_date = final_event_date.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" + + # Generate the prediction prompt + prediction_prompt = PREDICTION_PROMPT.format( + event_question=event_question, + user_prompt=prompt, + timepoint=formatted_event_date, + additional_information=additional_information, + timestamp=formatted_time_utc, + ) + print(f"\nPREDICTION PROMPT: {prediction_prompt}\n") + + # Perform moderation + moderation_result = openai.Moderation.create(prediction_prompt) + if moderation_result["results"][0]["flagged"]: + return "Moderation flagged the prompt as in violation of terms.", None + + # Create messages for the OpenAI engine + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prediction_prompt}, + ] + + # Generate the response + response = openai.ChatCompletion.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_compl_tokens, + n=1, + timeout=150, + request_timeout=150, + stop=None, + ) + print(f"RESPONSE: {response}") + + return response.choices[0].message.content, None