From 6915fad08a5a140398c055129f4a57e259d27b69 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Wed, 20 Sep 2023 14:50:07 +0200 Subject: [PATCH 01/34] feat: Duplicated prediction_request.py tool and added print statement --- tools/prediction_sum_url_content.py | 277 ++++++++++++++++++++++++++++ 1 file changed, 277 insertions(+) create mode 100644 tools/prediction_sum_url_content.py diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py new file mode 100644 index 00000000..6e8bd74f --- /dev/null +++ b/tools/prediction_sum_url_content.py @@ -0,0 +1,277 @@ +# -*- 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.""" + +import json +from concurrent.futures import Future, ThreadPoolExecutor +from typing import Any, Dict, Generator, List, Optional, Tuple + +import openai +import requests +from bs4 import BeautifulSoup +from googleapiclient.discovery import build + + +NUM_URLS_EXTRACT = 5 +DEFAULT_OPENAI_SETTINGS = { + "max_tokens": 500, + "temperature": 0.7, +} +ALLOWED_TOOLS = [ + "prediction-offline-sum-url-content", + "prediction-online-sum-url-content", +] +TOOL_TO_ENGINE = { + "prediction-offline-sum-url-content": "gpt-3.5-turbo", + "prediction-online-sum-url-content": "gpt-3.5-turbo", +} + +PREDICTION_PROMPT = """ +You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation +for a given event. You are provided with an input under the label "USER_PROMPT". You must follow the instructions +under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". + +INSTRUCTIONS +* Read the input under the label "USER_PROMPT" delimited by three backticks. +* The "USER_PROMPT" specifies an event. +* The event will only have two possible outcomes: either the event will happen or the event will not happen. +* If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". +* You must provide a probability estimation of the event happening, based on your training data. +* You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION" delimited by three backticks. +* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data. +* If an item in "ADDITIONAL_INFORMATION" is not relevant, you must ignore that item for the estimation. +* 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 item in the JSON must have a value between 0 and 1. + - "p_yes": Estimated probability that the event in the "USER_PROMPT" occurs. + - "p_no": Estimated probability that the event in the "USER_PROMPT" does not occur. + - "confidence": A value between 0 and 1 indicating the confidence in the prediction. 0 indicates lowest + confidence value; 1 maximum confidence value. + - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction. + 0 indicates lowest utility; 1 maximum utility. +* The sum of "p_yes" and "p_no" must equal 1. +* Output only the JSON object. Do not include any other contents in your response. +""" + +URL_QUERY_PROMPT = """ +You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation +for a given event. You are provided with an input under the label "USER_PROMPT". You must follow the instructions +under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". + +INSTRUCTIONS +* Read the input under the label "USER_PROMPT" delimited by three backticks. +* The "USER_PROMPT" specifies an event. +* The event will only have two possible outcomes: either the event will happen or the event will not happen. +* If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". +* 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} +``` + +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": An array of strings of size between 1 and 5. Each string must be a search engine query that can help obtain relevant information to estimate + the probability that the event in "USER_PROMPT" occurs. You must provide original information in each query, and they should not overlap + or lead to obtain the same set of results. +* Output only the JSON object. Do not include any other contents in your response. +""" + + +def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]: + 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 get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List[str]: + """Get URLs from search engine queries""" + results = [] + for query in queries: + for url in search_google( + query=query, + api_key=api_key, + engine=engine, + num=3, # Number of returned results + ): + results.append(url) + unique_results = list(set(results)) + return unique_results + + +def extract_text( + html: str, + num_words: int = 300, # TODO: summerise using GPT instead of limit +) -> str: + """Extract text from a single HTML document""" + soup = BeautifulSoup(html, "html.parser") + for script in soup(["script", "style"]): + script.extract() + 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 = "\n".join(chunk for chunk in chunks if chunk) + return text[:num_words] + + +def process_in_batches( + urls: List[str], window: int = 5, timeout: int = 10 +) -> Generator[None, None, List[Tuple[Future, str]]]: + """Iter URLs in batches.""" + with ThreadPoolExecutor() as executor: + for i in range(0, len(urls), window): + batch = urls[i : i + window] + futures = [(executor.submit(requests.get, url, timeout=timeout), url) for url in batch] + yield futures + +def extract_texts(urls: List[str], num_words: int = 300) -> List[str]: + """Extract texts from URLs""" + max_allowed = 5 + extracted_texts = [] + count = 0 + stop = False + for batch in process_in_batches(urls=urls): + for future, url in batch: + try: + result = future.result() + if result.status_code != 200: + continue + extracted_texts.append(extract_text(html=result.text, num_words=num_words)) + count += 1 + if count >= max_allowed: + stop = True + break + except requests.exceptions.ReadTimeout: + print(f"Request timed out: {url}.") + except Exception as e: + print(f"An error occurred: {e}") + if stop: + break + return extracted_texts + + +def fetch_additional_information( + prompt: str, + engine: str, + temperature: float, + max_tokens: int, + google_api_key: str, + google_engine: str, +) -> str: + """Fetch additional information.""" + url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt) + moderation_result = openai.Moderation.create(url_query_prompt) + if moderation_result["results"][0]["flagged"]: + return "" + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": url_query_prompt}, + ] + response = openai.ChatCompletion.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=90, + request_timeout=90, + stop=None, + ) + json_data = json.loads(response.choices[0].message.content) + print(f">>>>>>>>>>>>>>>>>>>>>>> json_data: {json_data}") + urls = get_urls_from_queries( + json_data["queries"], + api_key=google_api_key, + engine=google_engine, + ) + texts = extract_texts(urls) + return "\n".join(["- " + text for text in texts]) + + +def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: + """Run the task""" + tool = kwargs["tool"] + prompt = kwargs["prompt"] + max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_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.") + + engine = TOOL_TO_ENGINE[tool] + additional_information = ( + fetch_additional_information( + prompt=prompt, + engine=engine, + temperature=temperature, + max_tokens=max_tokens, + google_api_key=kwargs["api_keys"]["google_api_key"], + google_engine=kwargs["api_keys"]["google_engine_id"], + ) + if tool == "prediction-online" + else "" + ) + prediction_prompt = PREDICTION_PROMPT.format( + user_prompt=prompt, additional_information=additional_information + ) + moderation_result = openai.Moderation.create(prediction_prompt) + if moderation_result["results"][0]["flagged"]: + return "Moderation flagged the prompt as in violation of terms.", None + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prediction_prompt}, + ] + response = openai.ChatCompletion.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=150, + request_timeout=150, + stop=None, + ) + return response.choices[0].message.content, None From 6ce967933017a0450be7c18b4cfbb3a5339598de Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Thu, 21 Sep 2023 12:58:20 +0200 Subject: [PATCH 02/34] feat: Added print statements to follow script steps. --- .gitignore | 2 ++ tools/prediction_sum_url_content.py | 51 ++++++++++++++++++++++++++--- 2 files changed, 49 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 7c27f657..3c3690c4 100644 --- a/.gitignore +++ b/.gitignore @@ -28,6 +28,8 @@ packages/valory/protocols/ipfs packages/valory/protocols/ledger_api packages/valory/protocols/tendermint +*test_mech.py + .idea **/__pycache__/ *.DS_Store diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 6e8bd74f..3a457d24 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -32,7 +32,7 @@ NUM_URLS_EXTRACT = 5 DEFAULT_OPENAI_SETTINGS = { "max_tokens": 500, - "temperature": 0.7, + "temperature": 0, } ALLOWED_TOOLS = [ "prediction-offline-sum-url-content", @@ -220,28 +220,64 @@ def fetch_additional_information( stop=None, ) json_data = json.loads(response.choices[0].message.content) - print(f">>>>>>>>>>>>>>>>>>>>>>> json_data: {json_data}") + print(f"json_data: {json_data}") urls = get_urls_from_queries( json_data["queries"], api_key=google_api_key, engine=google_engine, ) + print(f"urls: {urls}\n") texts = extract_texts(urls) - return "\n".join(["- " + text for text in texts]) + additional_informations = "\n".join(["- " + text for text in texts]) + print(f"additional_informations: {additional_informations}\n") + return additional_informations + +# # To be adjusted +# def get_website_summary(engine, temperature, max_tokens, prompt) -> Tuple[str, str]: +# """Get SME title and introduction""" +# market_question = SME_GENERATION_MARKET_PROMPT.format(question=prompt) +# system_prompt = SME_GENERATION_SYSTEM_PROMPT + +# messages = [ +# {"role": "system", "content": system_prompt}, +# {"role": "user", "content": market_question}, +# ] +# response = openai.ChatCompletion.create( +# model=engine, +# messages=messages, +# temperature=temperature, +# max_tokens=max_tokens, +# n=1, +# timeout=150, +# request_timeout=150, +# stop=None, +# ) +# generated_sme_roles = response.choices[0].message.content +# sme = json.loads(generated_sme_roles)[0] +# return sme["sme"], sme["sme_introduction"] def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: """Run the task""" + print("Starting...") + tool = kwargs["tool"] prompt = kwargs["prompt"] max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) + + print(f"Tool: {tool}") + print(f"Prompt: {prompt}") + print(f"Max tokens: {max_tokens}") + print(f"Temperature: {temperature}") openai.api_key = kwargs["api_keys"]["openai"] if tool not in ALLOWED_TOOLS: raise ValueError(f"Tool {tool} is not supported.") engine = TOOL_TO_ENGINE[tool] + print(f"Engine: {engine}") + additional_information = ( fetch_additional_information( prompt=prompt, @@ -251,19 +287,25 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: google_api_key=kwargs["api_keys"]["google_api_key"], google_engine=kwargs["api_keys"]["google_engine_id"], ) - if tool == "prediction-online" + if tool == "prediction-online-sum-url-content" else "" ) prediction_prompt = PREDICTION_PROMPT.format( user_prompt=prompt, additional_information=additional_information ) + print(f"prediction_prompt: {prediction_prompt}\n") + moderation_result = openai.Moderation.create(prediction_prompt) + print(f"moderation_result: {moderation_result}\n") + if moderation_result["results"][0]["flagged"]: return "Moderation flagged the prompt as in violation of terms.", None messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prediction_prompt}, ] + print(f"messages: {messages}") + response = openai.ChatCompletion.create( model=engine, messages=messages, @@ -274,4 +316,5 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: request_timeout=150, stop=None, ) + print(f"response: {response}") return response.choices[0].message.content, None From f2548d6b593a0e783328986c1fdb4fcae738f5a4 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Thu, 21 Sep 2023 16:06:34 +0200 Subject: [PATCH 03/34] feat: Added an openai api request that extracts the most relevant information from each website in order to estimate the prediction market question later. This strategy replaced the strategy where only the first 300 characters from each website's html text were extracted and used for prediction. --- tools/prediction_sum_url_content.py | 123 +++++++++++++++++++++------- 1 file changed, 92 insertions(+), 31 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 3a457d24..83e441ab 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -32,7 +32,7 @@ NUM_URLS_EXTRACT = 5 DEFAULT_OPENAI_SETTINGS = { "max_tokens": 500, - "temperature": 0, + "temperature": 0.2, } ALLOWED_TOOLS = [ "prediction-offline-sum-url-content", @@ -108,9 +108,38 @@ - "queries": An array of strings of size between 1 and 5. Each string must be a search engine query that can help obtain relevant information to estimate the probability that the event in "USER_PROMPT" occurs. You must provide original information in each query, and they should not overlap or lead to obtain the same set of results. -* Output only the JSON object. Do not include any other contents in your response. +* Output only the JSON object. Do not include any other contents in your response. """ +SUMMARY_SYSTEM_PROMPT = """ +You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation +for a given event. You are provided with input under the label "USER_PROMPT" and "WEBSITE_TEXT". You must follow the instructions +under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". + +INSTRUCTIONS +* Read the input under the label "USER_PROMPT" and "WEBSITE_TEXT", each delimited by three backticks. +* You must extract the content inside "WEBSITE_TEXT" that can be used to estimate the outcome of the event described inside "USER_PROMPT". +* You must provide your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response except for those extracted from "WEBSITE_TEXT". + +USER_PROMPT: +``` +{user_prompt} +``` + +WEBSITE_TEXT: +``` +{website_text} +``` + +OUTPUT_FORMAT +* Your output response must be only one string containing the most relevant statements, separated by a ".". +* Provide only the extracted, relevant information for estimating the outcome of the event. +* Do not include any headers or introductory phrases. +* Your response must not exceed 100 words. +* If the content in "WEBSITE_TEXT" is not relevant for estimating the outcome of the event described in "USER_PROMPT", your response must be an empty string. + +""" def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]: service = build("customsearch", "v1", developerKey=api_key) @@ -141,9 +170,39 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List return unique_results +def get_website_summary( + text: str, + prompt: str, + engine: str, + temperature: float, + max_tokens: int, +) -> str: + """Get text summary from a website""" + user_prompt_summary = SUMMARY_SYSTEM_PROMPT.format(user_prompt=prompt, website_text=text) + + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": user_prompt_summary}, + ] + response = openai.ChatCompletion.create( + model=engine, + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + n=1, + timeout=150, + request_timeout=150, + stop=None, + ) + return response.choices[0].message.content + + def extract_text( html: str, - num_words: int = 300, # TODO: summerise using GPT instead of limit + prompt: str, + engine: str, + temperature: float, + max_tokens: int, ) -> str: """Extract text from a single HTML document""" soup = BeautifulSoup(html, "html.parser") @@ -153,7 +212,14 @@ def extract_text( lines = (line.strip() for line in text.splitlines()) chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) text = "\n".join(chunk for chunk in chunks if chunk) - return text[:num_words] + text_summary = get_website_summary( + text=text, + prompt=prompt, + engine=engine, + temperature=temperature, + max_tokens=max_tokens, + ) + return text_summary def process_in_batches( @@ -166,7 +232,13 @@ def process_in_batches( futures = [(executor.submit(requests.get, url, timeout=timeout), url) for url in batch] yield futures -def extract_texts(urls: List[str], num_words: int = 300) -> List[str]: +def extract_texts( + urls: List[str], + prompt: str, + engine: str, + temperature: float, + max_tokens: int, +) -> List[str]: """Extract texts from URLs""" max_allowed = 5 extracted_texts = [] @@ -178,7 +250,14 @@ def extract_texts(urls: List[str], num_words: int = 300) -> List[str]: result = future.result() if result.status_code != 200: continue - extracted_texts.append(extract_text(html=result.text, num_words=num_words)) + extracted_text = extract_text( + html=result.text, + prompt=prompt, + engine=engine, + temperature=temperature, + max_tokens=max_tokens, + ) + extracted_texts.append(extracted_text) count += 1 if count >= max_allowed: stop = True @@ -227,35 +306,17 @@ def fetch_additional_information( engine=google_engine, ) print(f"urls: {urls}\n") - texts = extract_texts(urls) + texts = extract_texts( + urls=urls, + prompt=prompt, + engine=engine, + temperature=temperature, + max_tokens=max_tokens, + ) additional_informations = "\n".join(["- " + text for text in texts]) print(f"additional_informations: {additional_informations}\n") return additional_informations -# # To be adjusted -# def get_website_summary(engine, temperature, max_tokens, prompt) -> Tuple[str, str]: -# """Get SME title and introduction""" -# market_question = SME_GENERATION_MARKET_PROMPT.format(question=prompt) -# system_prompt = SME_GENERATION_SYSTEM_PROMPT - -# messages = [ -# {"role": "system", "content": system_prompt}, -# {"role": "user", "content": market_question}, -# ] -# response = openai.ChatCompletion.create( -# model=engine, -# messages=messages, -# temperature=temperature, -# max_tokens=max_tokens, -# n=1, -# timeout=150, -# request_timeout=150, -# stop=None, -# ) -# generated_sme_roles = response.choices[0].message.content -# sme = json.loads(generated_sme_roles)[0] -# return sme["sme"], sme["sme_introduction"] - def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: """Run the task""" From 97ffbd1e5e1a55ada35aace6495bff229f14134c Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Fri, 22 Sep 2023 16:00:41 +0200 Subject: [PATCH 04/34] feat: Added preprocessing website content with BERT language model to extract only the relevant sections. --- poetry.lock | 1235 +++++++++++++++++++++++---- pyproject.toml | 4 + tools/prediction_sum_url_content.py | 278 ++++-- 3 files changed, 1263 insertions(+), 254 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7720ec82..4f63f68b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "aiohttp" version = "3.7.4.post0" description = "Async http client/server framework (asyncio)" -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -62,7 +61,6 @@ speedups = ["aiodns", "brotlipy", "cchardet"] name = "annotated-types" version = "0.5.0" description = "Reusable constraint types to use with typing.Annotated" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -74,7 +72,6 @@ files = [ name = "anthropic" version = "0.3.11" description = "Client library for the anthropic API" -category = "main" optional = false python-versions = ">=3.7,<4.0" files = [ @@ -94,7 +91,6 @@ typing-extensions = ">=4.5,<5" name = "anyio" version = "3.7.1" description = "High level compatibility layer for multiple asynchronous event loop implementations" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -116,7 +112,6 @@ trio = ["trio (<0.22)"] name = "asn1crypto" version = "1.4.0" description = "Fast ASN.1 parser and serializer with definitions for private keys, public keys, certificates, CRL, OCSP, CMS, PKCS#3, PKCS#7, PKCS#8, PKCS#12, PKCS#5, X.509 and TSP" -category = "main" optional = false python-versions = "*" files = [ @@ -128,7 +123,6 @@ files = [ name = "async-timeout" version = "3.0.1" description = "Timeout context manager for asyncio programs" -category = "main" optional = false python-versions = ">=3.5.3" files = [ @@ -140,7 +134,6 @@ files = [ name = "attrs" version = "23.1.0" description = "Classes Without Boilerplate" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -159,7 +152,6 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte name = "backoff" version = "2.2.1" description = "Function decoration for backoff and retry" -category = "main" optional = false python-versions = ">=3.7,<4.0" files = [ @@ -171,7 +163,6 @@ files = [ name = "base58" version = "2.1.1" description = "Base58 and Base58Check implementation." 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"pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] +[[package]] +name = "wasabi" +version = "1.1.2" +description = "A lightweight console printing and formatting toolkit" +optional = false +python-versions = ">=3.6" +files = [ + {file = "wasabi-1.1.2-py3-none-any.whl", hash = "sha256:0a3f933c4bf0ed3f93071132c1b87549733256d6c8de6473c5f7ed2e171b5cf9"}, + {file = "wasabi-1.1.2.tar.gz", hash = "sha256:1aaef3aceaa32edb9c91330d29d3936c0c39fdb965743549c173cb54b16c30b5"}, +] + +[package.dependencies] +colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python_version >= \"3.7\""} + [[package]] name = "watchdog" version = "3.0.0" description = "Filesystem events monitoring" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -3994,7 +4910,6 @@ watchmedo = ["PyYAML (>=3.10)"] 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 = [ @@ -4009,7 +4924,6 @@ 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 = [ @@ -4089,7 +5003,6 @@ files = [ name = "werkzeug" version = "2.0.3" description = "The comprehensive WSGI web application library." -category = "main" optional = false python-versions = ">=3.6" files = [ @@ -4104,7 +5017,6 @@ watchdog = ["watchdog"] name = "wheel" version = "0.41.1" description = "A built-package format for Python" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4119,7 +5031,6 @@ 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 = [ @@ -4206,4 +5117,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "bffbe49c38b1c849d82d88cbb6c747dba2afcdbfd36c34f7593355b953a1c7c2" +content-hash = "5b8bc5de0335e9daaf6340318a98a0d3cad96b667d7e2523dce0f14c61c83013" diff --git a/pyproject.toml b/pyproject.toml index da17c959..55b615e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,10 @@ eth-utils = "==2.2.0" eth-abi = "==4.0.0" pycryptodome = "==3.18.0" anthropic = "^0.3.11" +gensim = "^4.3.2" +sentence-transformers = "^2.2.2" +spacy = "^3.6.1" +tqdm = "^4.66.1" [tool.poetry.group.dev.dependencies.tomte] version = "==0.2.12" diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 83e441ab..86df8885 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -20,14 +20,22 @@ """This module implements a Mech tool for binary predictions.""" import json +import re +from datetime import datetime from concurrent.futures import Future, ThreadPoolExecutor from typing import Any, Dict, Generator, List, Optional, Tuple +from tqdm import tqdm import openai import requests from bs4 import BeautifulSoup from googleapiclient.discovery import build +from sentence_transformers import SentenceTransformer, util +from transformers import AutoTokenizer, AutoModel, BertForPreTraining, BertForMaskedLM + +import spacy +import torch NUM_URLS_EXTRACT = 5 DEFAULT_OPENAI_SETTINGS = { @@ -41,6 +49,7 @@ TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-3.5-turbo", "prediction-online-sum-url-content": "gpt-3.5-turbo", + # "prediction-online-sum-url-content": "gpt-4", } PREDICTION_PROMPT = """ @@ -56,7 +65,10 @@ * You must provide a probability estimation of the event happening, based on your training data. * You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION" delimited by three backticks. * You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data. -* If an item in "ADDITIONAL_INFORMATION" is not relevant, you must ignore that item for the estimation. +* Given today's date {today_date} you should use predominantly the more recent information in "ADDITIONAL_INFORMATION" to make your probability estimation. +* You must pay very close attention to the specific wording of the question in "USER_PROMPT" +* If a date is provided in the USER_PROMPT for the event to have occured, you must also consider in your estimation, given today's date {today_date}, how likely it is that the event will occur before or on that provided date. +* If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. * You must provide your response in the format specified under "OUTPUT_FORMAT". * Do not include any other contents in your response. @@ -81,7 +93,7 @@ - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction. 0 indicates lowest utility; 1 maximum utility. * The sum of "p_yes" and "p_no" must equal 1. -* Output only the JSON object. Do not include any other contents in your response. +* Output only the JSON object first and a short explanation (max. 3 sentences) what led you to the estimation after that. Do not include any other contents in your response. """ URL_QUERY_PROMPT = """ @@ -90,7 +102,7 @@ under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". INSTRUCTIONS -* Read the input under the label "USER_PROMPT" delimited by three backticks. +* Read the input under the label "USER_PROMPT", delimited by three backticks, carefully. * The "USER_PROMPT" specifies an event. * The event will only have two possible outcomes: either the event will happen or the event will not happen. * If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". @@ -105,42 +117,12 @@ 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": An array of strings of size between 1 and 5. Each string must be a search engine query that can help obtain relevant information to estimate - the probability that the event in "USER_PROMPT" occurs. You must provide original information in each query, and they should not overlap - or lead to obtain the same set of results. + - "queries": An array of strings of size between 1 and 5. Each string must be a search engine query that has a high chance to yield search engine results that + help obtain relevant information to estimate the probability that the event specified in "USER_PROMPT" occurs. You must provide original information in each query, + and the queries should not overlap or lead to obtain the same set of results. * Output only the JSON object. Do not include any other contents in your response. """ -SUMMARY_SYSTEM_PROMPT = """ -You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation -for a given event. You are provided with input under the label "USER_PROMPT" and "WEBSITE_TEXT". You must follow the instructions -under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". - -INSTRUCTIONS -* Read the input under the label "USER_PROMPT" and "WEBSITE_TEXT", each delimited by three backticks. -* You must extract the content inside "WEBSITE_TEXT" that can be used to estimate the outcome of the event described inside "USER_PROMPT". -* You must provide your response in the format specified under "OUTPUT_FORMAT". -* Do not include any other contents in your response except for those extracted from "WEBSITE_TEXT". - -USER_PROMPT: -``` -{user_prompt} -``` - -WEBSITE_TEXT: -``` -{website_text} -``` - -OUTPUT_FORMAT -* Your output response must be only one string containing the most relevant statements, separated by a ".". -* Provide only the extracted, relevant information for estimating the outcome of the event. -* Do not include any headers or introductory phrases. -* Your response must not exceed 100 words. -* If the content in "WEBSITE_TEXT" is not relevant for estimating the outcome of the event described in "USER_PROMPT", your response must be an empty string. - -""" - def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]: service = build("customsearch", "v1", developerKey=api_key) search = ( @@ -163,63 +145,171 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List query=query, api_key=api_key, engine=engine, - num=3, # Number of returned results + num=3, # Number of returned urls per query ): results.append(url) unique_results = list(set(results)) + + # Remove urls that are pdfs + unique_results = [url for url in unique_results if not url.endswith(".pdf")] return unique_results -def get_website_summary( - text: str, - prompt: str, - engine: str, - temperature: float, - max_tokens: int, -) -> str: - """Get text summary from a website""" - user_prompt_summary = SUMMARY_SYSTEM_PROMPT.format(user_prompt=prompt, website_text=text) +def get_website_summary(text: str, prompt: str, model, tokenizer, nlp, max_words: int = 150) -> str: + """Get text summary from a website""" + # Check for empty inputs + if not prompt or not text: + return "" - messages = [ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": user_prompt_summary}, + # Calculate the BERT embedding for the prompt + with torch.no_grad(): + question_tokens = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True) + question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) + + # Sentence splitting and NER + doc = nlp(text) + sentences = [sent.text for sent in doc.sents if len(sent.text.split()) >= 5] + entities = [ent.text for ent in doc.ents] + + # Crop the sentences list to the first 300 sentences to reduce the time taken for the similarity calculations. + sentences = sentences[:300] + + + + # Similarity calculations and sentence ranking + similarities = [] + for sentence in tqdm(sentences, desc="Calculating Similarities for Sentences"): + with torch.no_grad(): + sentence_tokens = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True) + sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) + similarity = torch.cosine_similarity(question_embedding, sentence_embedding).item() + if any(entity in sentence for entity in entities): + similarity += 0.05 # Give a slight boost for sentences with entities + similarities.append(similarity) + + # Extract the top relevant sentences + relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.7] + + # Print each sentence in relevant_sentences in a new line along with its similarity score > 0.7 + for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + if sim > 0.7: + print(f"{sim} : {sent}") + + # Join the top 4 relevant sentences + output = ' '.join(relevant_sentences[:4]) + output_words = output.split(' ') + if len(output_words) > max_words: + output = ' '.join(output_words[:max_words]) + + return output + + +def get_date(soup): + # Get the updated or release date of the website. + # The following are some of the possible values for the "name" attribute: + 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', + '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' ] - response = openai.ChatCompletion.create( - model=engine, - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - n=1, - timeout=150, - request_timeout=150, - stop=None, - ) - return response.choices[0].message.content + + 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' + ] + + release_date = "unknown" + modified_date = "unknown" + + # First, try to find an update or modified date + 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", "") + + # 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", "") + + 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 {release_date}, Modified date {modified_date}" def extract_text( html: str, prompt: str, - engine: str, - temperature: float, - max_tokens: int, + model, + tokenizer, + nlp, ) -> str: """Extract text from a single HTML document""" + # Remove HTML tags and extract text soup = BeautifulSoup(html, "html.parser") - for script in soup(["script", "style"]): + + # Get the date of the website + date = get_date(soup) + + # Get the main element of the website + main_element = soup.find("main") + if main_element: + soup = main_element + + for script in soup(["script", "style", "header", "footer", "aside", "nav", "form", "button", "iframe"]): script.extract() 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 = "\n".join(chunk for chunk in chunks if chunk) + text = ". ".join(chunk for chunk in chunks if chunk) + text = re.sub(r"\.{2,}", ".", text) # Use regex to replace multiple "."s with a single ".". + print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< TEXT: \n{text}") + text_summary = get_website_summary( text=text, prompt=prompt, - engine=engine, - temperature=temperature, - max_tokens=max_tokens, + model=model, + tokenizer=tokenizer, + nlp=nlp, ) - return text_summary + return f"{date}:\n{text_summary}" def process_in_batches( @@ -232,20 +322,26 @@ def process_in_batches( futures = [(executor.submit(requests.get, url, timeout=timeout), url) for url in batch] yield futures + def extract_texts( urls: List[str], prompt: str, - engine: str, - temperature: float, - max_tokens: int, ) -> List[str]: """Extract texts from URLs""" - max_allowed = 5 + max_allowed = 45 extracted_texts = [] count = 0 stop = False - for batch in process_in_batches(urls=urls): - for future, url in batch: + + # BERT Initialization + model = AutoModel.from_pretrained("bert-base-uncased") + tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + + # Spacy Initialization for NER and sentence splitting + nlp = spacy.load("en_core_web_sm") + + for batch in tqdm(process_in_batches(urls=urls), desc="Processing Batches"): + for future, url in tqdm(batch, desc="Processing URLs"): try: result = future.result() if result.status_code != 200: @@ -253,11 +349,11 @@ def extract_texts( extracted_text = extract_text( html=result.text, prompt=prompt, - engine=engine, - temperature=temperature, - max_tokens=max_tokens, + model=model, + tokenizer=tokenizer, + nlp=nlp, ) - extracted_texts.append(extracted_text) + extracted_texts.append(f"{url}\n{extracted_text}") count += 1 if count >= max_allowed: stop = True @@ -265,7 +361,7 @@ def extract_texts( except requests.exceptions.ReadTimeout: print(f"Request timed out: {url}.") except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") if stop: break return extracted_texts @@ -289,15 +385,16 @@ def fetch_additional_information( {"role": "user", "content": url_query_prompt}, ] response = openai.ChatCompletion.create( - model=engine, + model="gpt-3.5-turbo", messages=messages, - temperature=temperature, + temperature=0.7, max_tokens=max_tokens, n=1, timeout=90, request_timeout=90, stop=None, ) + json_data = json.loads(response.choices[0].message.content) print(f"json_data: {json_data}") urls = get_urls_from_queries( @@ -305,16 +402,13 @@ def fetch_additional_information( api_key=google_api_key, engine=google_engine, ) - print(f"urls: {urls}\n") + print(f"urls: {urls}") texts = extract_texts( urls=urls, prompt=prompt, - engine=engine, - temperature=temperature, - max_tokens=max_tokens, ) - additional_informations = "\n".join(["- " + text for text in texts]) - print(f"additional_informations: {additional_informations}\n") + additional_informations = "\n\n".join(["- " + text for text in texts]) + # print(f"additional_informations: {additional_informations}") return additional_informations @@ -351,21 +445,21 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: if tool == "prediction-online-sum-url-content" else "" ) + + # Get today's date and generate the prediction prompt + today_date = datetime.today().strftime('%Y-%m-%d') prediction_prompt = PREDICTION_PROMPT.format( - user_prompt=prompt, additional_information=additional_information + user_prompt=prompt, additional_information=additional_information, today_date=today_date, ) print(f"prediction_prompt: {prediction_prompt}\n") moderation_result = openai.Moderation.create(prediction_prompt) - print(f"moderation_result: {moderation_result}\n") - if moderation_result["results"][0]["flagged"]: return "Moderation flagged the prompt as in violation of terms.", None messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prediction_prompt}, ] - print(f"messages: {messages}") response = openai.ChatCompletion.create( model=engine, From 3c6a285c52bac0da07a1506ce6bfc825f3c3fa7e Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Fri, 22 Sep 2023 18:35:46 +0200 Subject: [PATCH 05/34] chore: Reduce GPU usage, added batched processing, reduced size of soup file --- tools/prediction_sum_url_content.py | 75 ++++++++++++++++++++--------- 1 file changed, 53 insertions(+), 22 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 86df8885..026fe4c1 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -93,7 +93,7 @@ - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction. 0 indicates lowest utility; 1 maximum utility. * The sum of "p_yes" and "p_no" must equal 1. -* Output only the JSON object first and a short explanation (max. 3 sentences) what led you to the estimation after that. Do not include any other contents in your response. +* Output only the JSON object first and a short explanation (max. 3 sentences) what specificly were the most relevant information that led to your estimation. Do not include any other contents in your response. """ URL_QUERY_PROMPT = """ @@ -118,7 +118,7 @@ * 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": An array of strings of size between 1 and 5. Each string must be a search engine query that has a high chance to yield search engine results that - help obtain relevant information to estimate the probability that the event specified in "USER_PROMPT" occurs. You must provide original information in each query, + help obtain contemporary and relevant information for you to estimate the probability that the event specified in "USER_PROMPT" occurs. You must provide original information in each query, and the queries should not overlap or lead to obtain the same set of results. * Output only the JSON object. Do not include any other contents in your response. """ @@ -174,29 +174,40 @@ def get_website_summary(text: str, prompt: str, model, tokenizer, nlp, max_words # Crop the sentences list to the first 300 sentences to reduce the time taken for the similarity calculations. sentences = sentences[:300] - - # Similarity calculations and sentence ranking similarities = [] - for sentence in tqdm(sentences, desc="Calculating Similarities for Sentences"): + + # Batch the sentences to reduce the time taken for the similarity calculations + batch_size = 32 + for i in range(0, len(sentences), batch_size): + batch = sentences[i:i+batch_size] with torch.no_grad(): - sentence_tokens = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True) + sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) - similarity = torch.cosine_similarity(question_embedding, sentence_embedding).item() - if any(entity in sentence for entity in entities): - similarity += 0.05 # Give a slight boost for sentences with entities - similarities.append(similarity) - + similarity = torch.cosine_similarity(question_embedding.repeat(len(batch), 1), sentence_embedding).tolist() + + for j, sent in enumerate(batch): + if any(entity in sent for entity in entities): + similarity[j] += 0.05 + similarities.extend(similarity) + + # Free up GPU memory + del question_embedding, sentence_embedding + torch.cuda.empty_cache() + # Extract the top relevant sentences - relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.7] + relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.9] # Print each sentence in relevant_sentences in a new line along with its similarity score > 0.7 for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): if sim > 0.7: - print(f"{sim} : {sent}") + print(f"{sim} : {sent}\n") + if len(relevant_sentences) == 0: + return "" + # Join the top 4 relevant sentences - output = ' '.join(relevant_sentences[:4]) + output = ' '.join(relevant_sentences[:4]) output_words = output.split(' ') if len(output_words) > max_words: output = ' '.join(output_words[:max_words]) @@ -284,23 +295,35 @@ def extract_text( """Extract text from a single HTML document""" # Remove HTML tags and extract text soup = BeautifulSoup(html, "html.parser") - + # Get the date of the website date = get_date(soup) # Get the main element of the website - main_element = soup.find("main") - if main_element: - soup = main_element + # main_element = soup.find("main") + # if main_element: + # soup = main_element - for script in soup(["script", "style", "header", "footer", "aside", "nav", "form", "button", "iframe"]): + for script in soup(["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"]): script.extract() + + # print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< SOUP 1: \n{soup}") + + # for tag in soup.find_all(): + # if tag.name not in ['p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'article', 'main', 'blockquote', 'ul', 'ol', 'li', 'strong', 'b', 'em', 'i', 'q', 'a', 'span', 'pre', 'code', 'time', 'abbr', 'section', 'div', 'figure', 'figcaption', 'mark']: + # tag.extract() + + # print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< SOUP 2: \n{soup}") + + + + 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) # Use regex to replace multiple "."s with a single ".". - print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< TEXT: \n{text}") + # print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< TEXT: \n{text}") text_summary = get_website_summary( text=text, @@ -309,7 +332,7 @@ def extract_text( tokenizer=tokenizer, nlp=nlp, ) - return f"{date}:\n{text_summary}" + return f"{date}:\n{text_summary}" if text_summary else "" def process_in_batches( @@ -353,7 +376,8 @@ def extract_texts( tokenizer=tokenizer, nlp=nlp, ) - extracted_texts.append(f"{url}\n{extracted_text}") + if extracted_text: + extracted_texts.append(f"{url}\n{extracted_text}") count += 1 if count >= max_allowed: stop = True @@ -433,6 +457,13 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: engine = TOOL_TO_ENGINE[tool] print(f"Engine: {engine}") + # Event question is the text between the first pair of double quotes in the prompt + event_question = re.search(r"\"(.+?)\"", prompt).group(1) + print(f"event_question: {event_question}") + + # Make an openai request to get similar formulations of the event question and store them in a list + similar_formulations = [] + additional_information = ( fetch_additional_information( prompt=prompt, From 06dde74f50fdb0694cfb43a3a4fdc965b312e2c7 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sat, 23 Sep 2023 11:18:52 +0200 Subject: [PATCH 06/34] feat: Changed prompts to give more concise and clear instructions. GPT-4 is able to give p_no = 1 when specified event deadline exceeded and no information indicating for p_yes are found. --- tools/prediction_sum_url_content.py | 113 ++++++++++++++-------------- 1 file changed, 57 insertions(+), 56 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 026fe4c1..c9556518 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -48,27 +48,32 @@ ] TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-3.5-turbo", - "prediction-online-sum-url-content": "gpt-3.5-turbo", - # "prediction-online-sum-url-content": "gpt-4", + # "prediction-online-sum-url-content": "gpt-3.5-turbo", + # "prediction-online-sum-url-content": "gpt-3.5-turbo-16k", + "prediction-online-sum-url-content": "gpt-4", } PREDICTION_PROMPT = """ -You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation -for a given event. You are provided with an input under the label "USER_PROMPT". You must follow the instructions -under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". - -INSTRUCTIONS -* Read the input under the label "USER_PROMPT" delimited by three backticks. -* The "USER_PROMPT" specifies an event. -* The event will only have two possible outcomes: either the event will happen or the event will not happen. -* If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". -* You must provide a probability estimation of the event happening, based on your training data. -* You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION" delimited by three backticks. -* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data. -* Given today's date {today_date} you should use predominantly the more recent information in "ADDITIONAL_INFORMATION" to make your probability estimation. -* You must pay very close attention to the specific wording of the question in "USER_PROMPT" -* If a date is provided in the USER_PROMPT for the event to have occured, you must also consider in your estimation, given today's date {today_date}, how likely it is that the event will occur before or on that provided date. +You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', +which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event +will either occur or not, given the conditions. Find the 'event question' enclosed in double quotes as a part of +the user's prompt under 'USER_PROMPT'. The user's prompt also contains a more elaborate description of the task. +You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", delimited by three backticks. +You must adhere to the 'INSTRUCTIONS'. + + +INSTRUCTIONS: +* Carefully read the user's prompt under 'USER_PROMPT', enclosed by triple backticks. +* If the 'event question' has more than two outcomes, respond with "Error" and ignore further instructions. +* Based on your training data, provide a probability estimation of the event specified in the 'event question' occuring, considering all conditions provided. +* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data to make the probability estimation. +* Prioritize recent information in "ADDITIONAL_INFORMATION" based on the current time {timestamp}. +* You must pay very close attention to the specific wording of the 'event question' in "USER_PROMPT". +* If a date is provided in the 'event question' specifying when the event has to have occured, you must consider in your estimation, given the current time {timestamp}, how likely it is that the event will occur within the remaining timespan to that provided date. * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. +* If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. So make a prediction that takes into account that you don't have up-to-date information. +* Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before or on the date specified in the 'event question'. +* If the 'event question' is formulated in a way that an event must have happend by or before a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the remaining time is 0, decrease the probability to 0. * You must provide your response in the format specified under "OUTPUT_FORMAT". * Do not include any other contents in your response. @@ -82,45 +87,41 @@ {additional_information} ``` -OUTPUT_FORMAT +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 item in the JSON must have a value between 0 and 1. - - "p_yes": Estimated probability that the event in the "USER_PROMPT" occurs. - - "p_no": Estimated probability that the event in the "USER_PROMPT" does not occur. - - "confidence": A value between 0 and 1 indicating the confidence in the prediction. 0 indicates lowest - confidence value; 1 maximum confidence value. - - "info_utility": Utility of the information provided in "ADDITIONAL_INFORMATION" to help you make the prediction. - 0 indicates lowest utility; 1 maximum utility. +* 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 specified in the 'event question' occurs, considering all conditions provided. + - "p_no": Estimated probability that the 'event question' does not occur, considering all conditions provided. + - "confidence": Indicating the confidence in the estimated probabilities you provided ranging from 0 (lowest confidence) to 1 (maximum confidence). + - "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 first and a short explanation (max. 3 sentences) what specificly were the most relevant information that led to your estimation. Do not include any other contents in your response. +* Output only the JSON object in your response. """ URL_QUERY_PROMPT = """ -You are an LLM inside a multi-agent system that takes in a prompt of a user requesting a probability estimation -for a given event. You are provided with an input under the label "USER_PROMPT". You must follow the instructions -under the label "INSTRUCTIONS". You must provide your response in the format specified under "OUTPUT_FORMAT". - -INSTRUCTIONS -* Read the input under the label "USER_PROMPT", delimited by three backticks, carefully. -* The "USER_PROMPT" specifies an event. -* The event will only have two possible outcomes: either the event will happen or the event will not happen. -* If the event has more than two possible outcomes, you must ignore the rest of the instructions and output the response "Error". +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 1-5 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: ``` -{user_prompt} +{event_question} ``` -OUTPUT_FORMAT +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": An array of strings of size between 1 and 5. Each string must be a search engine query that has a high chance to yield search engine results that - help obtain contemporary and relevant information for you to estimate the probability that the event specified in "USER_PROMPT" occurs. You must provide original information in each query, - and the queries should not overlap or lead to obtain the same set of results. -* Output only the JSON object. Do not include any other contents in your response. + - "queries": A 1-5 item array of the generated search engine queries. +* Include only the JSON object in your output. """ def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[str]: @@ -155,15 +156,15 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List return unique_results -def get_website_summary(text: str, prompt: str, model, tokenizer, nlp, max_words: int = 150) -> str: +def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, max_words: int = 150) -> str: """Get text summary from a website""" # Check for empty inputs - if not prompt or not text: + if not event_question or not text: return "" # Calculate the BERT embedding for the prompt with torch.no_grad(): - question_tokens = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True) + question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) # Sentence splitting and NER @@ -227,7 +228,7 @@ def get_date(soup): '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', + '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', @@ -287,7 +288,7 @@ def get_date(soup): def extract_text( html: str, - prompt: str, + event_question: str, model, tokenizer, nlp, @@ -327,7 +328,7 @@ def extract_text( text_summary = get_website_summary( text=text, - prompt=prompt, + event_question=event_question, model=model, tokenizer=tokenizer, nlp=nlp, @@ -348,7 +349,7 @@ def process_in_batches( def extract_texts( urls: List[str], - prompt: str, + event_question: str, ) -> List[str]: """Extract texts from URLs""" max_allowed = 45 @@ -371,7 +372,7 @@ def extract_texts( continue extracted_text = extract_text( html=result.text, - prompt=prompt, + event_question=event_question, model=model, tokenizer=tokenizer, nlp=nlp, @@ -392,7 +393,7 @@ def extract_texts( def fetch_additional_information( - prompt: str, + event_question: str, engine: str, temperature: float, max_tokens: int, @@ -400,7 +401,7 @@ def fetch_additional_information( google_engine: str, ) -> str: """Fetch additional information.""" - url_query_prompt = URL_QUERY_PROMPT.format(user_prompt=prompt) + url_query_prompt = URL_QUERY_PROMPT.format(event_question=event_question) moderation_result = openai.Moderation.create(url_query_prompt) if moderation_result["results"][0]["flagged"]: return "" @@ -429,7 +430,7 @@ def fetch_additional_information( print(f"urls: {urls}") texts = extract_texts( urls=urls, - prompt=prompt, + event_question=event_question, ) additional_informations = "\n\n".join(["- " + text for text in texts]) # print(f"additional_informations: {additional_informations}") @@ -466,7 +467,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: additional_information = ( fetch_additional_information( - prompt=prompt, + event_question=event_question, engine=engine, temperature=temperature, max_tokens=max_tokens, @@ -478,9 +479,9 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: ) # Get today's date and generate the prediction prompt - today_date = datetime.today().strftime('%Y-%m-%d') + timestamp = datetime.now().strftime('%Y-%m-%d') prediction_prompt = PREDICTION_PROMPT.format( - user_prompt=prompt, additional_information=additional_information, today_date=today_date, + user_prompt=prompt, additional_information=additional_information, timestamp=timestamp, ) print(f"prediction_prompt: {prediction_prompt}\n") From 7c73baf6aed7e6477ac1b9c17bef004597d1c969 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sat, 23 Sep 2023 11:40:29 +0200 Subject: [PATCH 07/34] feat: Adjusted prompt instructions. GPT-4 now decreases probability of the event happening by a specific deadline only if it assumes that it has access to up-to-date information. --- tools/prediction_sum_url_content.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index c9556518..10030ab4 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -73,7 +73,7 @@ * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. So make a prediction that takes into account that you don't have up-to-date information. * Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before or on the date specified in the 'event question'. -* If the 'event question' is formulated in a way that an event must have happend by or before a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the remaining time is 0, decrease the probability to 0. +* If the 'event question' is formulated in a way that an event must have happend by a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the remaining time is 0, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. * You must provide your response in the format specified under "OUTPUT_FORMAT". * Do not include any other contents in your response. @@ -197,7 +197,7 @@ def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, m torch.cuda.empty_cache() # Extract the top relevant sentences - relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.9] + relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.7] # Print each sentence in relevant_sentences in a new line along with its similarity score > 0.7 for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): From 5fa51f9b5a154ba50d87100ca95e1cb89e8d8ca5 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sat, 23 Sep 2023 13:42:36 +0200 Subject: [PATCH 08/34] feat: Adjusted prompt instructions - Additional information is up-to-date information queried from a search engine; Must pay attention on keyword BEFORE in 'event question'. --- tools/prediction_sum_url_content.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 10030ab4..48058ead 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -53,12 +53,16 @@ "prediction-online-sum-url-content": "gpt-4", } +# * If the 'event question' is formulated in a way that an event must have happend before a specific date, consider the deadline of the event being 23:59:59 of the day before that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. +# * If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have access to up-to-date information. / that you have been provided with the most up-to-date information that can be found on the internet. + + PREDICTION_PROMPT = """ You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event will either occur or not, given the conditions. Find the 'event question' enclosed in double quotes as a part of the user's prompt under 'USER_PROMPT'. The user's prompt also contains a more elaborate description of the task. -You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", delimited by three backticks. +You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", delimited by three backticks. This information results from a search engine query that has been done a few seconds ago with the aim to get up-to-date information that could be relevant estimating the 'event question'. You must adhere to the 'INSTRUCTIONS'. @@ -71,9 +75,11 @@ * You must pay very close attention to the specific wording of the 'event question' in "USER_PROMPT". * If a date is provided in the 'event question' specifying when the event has to have occured, you must consider in your estimation, given the current time {timestamp}, how likely it is that the event will occur within the remaining timespan to that provided date. * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. -* If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. So make a prediction that takes into account that you don't have up-to-date information. -* Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before or on the date specified in the 'event question'. -* If the 'event question' is formulated in a way that an event must have happend by a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the remaining time is 0, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. +* If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. +* Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. +* 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 up-to-date information that can be found on the internet. +* If the 'event question' is formulated in a way that an event must have happend BY a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. +* If the 'event question' is formulated in a way that an event must have happend BEFORE a specific date, consider the deadline of the event being 23:59:59 of the day before. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. * You must provide your response in the format specified under "OUTPUT_FORMAT". * Do not include any other contents in your response. @@ -479,7 +485,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: ) # Get today's date and generate the prediction prompt - timestamp = datetime.now().strftime('%Y-%m-%d') + timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') prediction_prompt = PREDICTION_PROMPT.format( user_prompt=prompt, additional_information=additional_information, timestamp=timestamp, ) From 63c67b8b89ecb8324d3c8cbeb1fd4ffc192ac148 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sat, 23 Sep 2023 17:21:44 +0200 Subject: [PATCH 09/34] chore: Removed duplicated sentences before processing; Adjusted prompt instructions to also pay attention on the keyword ON in 'event question'. --- tools/prediction_sum_url_content.py | 27 +++++++++++++++++++++------ 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 48058ead..cd7d8f2f 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -21,6 +21,7 @@ import json import re +from collections import Counter from datetime import datetime from concurrent.futures import Future, ThreadPoolExecutor from typing import Any, Dict, Generator, List, Optional, Tuple @@ -48,9 +49,9 @@ ] TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-3.5-turbo", - # "prediction-online-sum-url-content": "gpt-3.5-turbo", + "prediction-online-sum-url-content": "gpt-3.5-turbo", # "prediction-online-sum-url-content": "gpt-3.5-turbo-16k", - "prediction-online-sum-url-content": "gpt-4", + # "prediction-online-sum-url-content": "gpt-4", } # * If the 'event question' is formulated in a way that an event must have happend before a specific date, consider the deadline of the event being 23:59:59 of the day before that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. @@ -78,7 +79,7 @@ * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. * Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. * 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 up-to-date information that can be found on the internet. -* If the 'event question' is formulated in a way that an event must have happend BY a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. +* If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. * If the 'event question' is formulated in a way that an event must have happend BEFORE a specific date, consider the deadline of the event being 23:59:59 of the day before. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. * You must provide your response in the format specified under "OUTPUT_FORMAT". * Do not include any other contents in your response. @@ -168,14 +169,26 @@ def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, m if not event_question or not text: return "" - # Calculate the BERT embedding for the prompt + # Calculate the BERT embedding for the event_question to use in similarity computation with torch.no_grad(): question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) - # Sentence splitting and NER + # Apply spaCy's NLP pipeline to the text to prepare for sentence extraction doc = nlp(text) - sentences = [sent.text for sent in doc.sents if len(sent.text.split()) >= 5] + + # Extract sentences in text that have more than 5 words and are not duplicates. + seen = set() + sentences = [sent.text for sent in doc.sents if len(sent.text.split()) >= 5 and not (sent.text in seen or seen.add(sent.text))] + + counter = Counter(sentences) + + duplicates = {k: v for k, v in counter.items() if v > 1} + num_duplicates = sum(duplicates.values()) - len(duplicates) + + print(f"Number of duplicate sentences: {num_duplicates}") + + # Named entity recognition (NER) entities = [ent.text for ent in doc.ents] # Crop the sentences list to the first 300 sentences to reduce the time taken for the similarity calculations. @@ -375,6 +388,7 @@ def extract_texts( try: result = future.result() if result.status_code != 200: + del result continue extracted_text = extract_text( html=result.text, @@ -383,6 +397,7 @@ def extract_texts( tokenizer=tokenizer, nlp=nlp, ) + del result if extracted_text: extracted_texts.append(f"{url}\n{extracted_text}") count += 1 From 452fbc01ec8005b577b588034e59452b4b8e4fd0 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sun, 24 Sep 2023 04:00:59 +0200 Subject: [PATCH 10/34] feat: Added funcionality for better extraction of isolated but relevant dates and surrounding context --- tools/prediction_sum_url_content.py | 157 +++++++++++++++++++++++----- 1 file changed, 130 insertions(+), 27 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index cd7d8f2f..84930ac2 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -23,6 +23,7 @@ import re from collections import Counter from datetime import datetime +from dateutil import parser from concurrent.futures import Future, ThreadPoolExecutor from typing import Any, Dict, Generator, List, Optional, Tuple from tqdm import tqdm @@ -49,9 +50,9 @@ ] TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-3.5-turbo", - "prediction-online-sum-url-content": "gpt-3.5-turbo", + # "prediction-online-sum-url-content": "gpt-3.5-turbo", # "prediction-online-sum-url-content": "gpt-3.5-turbo-16k", - # "prediction-online-sum-url-content": "gpt-4", + "prediction-online-sum-url-content": "gpt-4", } # * If the 'event question' is formulated in a way that an event must have happend before a specific date, consider the deadline of the event being 23:59:59 of the day before that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. @@ -163,8 +164,99 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List return unique_results +def standardize_date(date_text): + try: + standardize_date = None + # Create regex to check if month or day appears in date_text + month_re = 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_re = re.compile(r'\b\d{1,2}\b') + + # Forcing parser.parse to use dayfirst=True for European date format + parsed_date = parser.parse(date_text) + + # Check if year, month, and day are in the original date_text as paser.parse() will fill in the current day, month or year if either is not provided + month_exists = month_re.search(date_text) is not None + day_exists = day_re.search(date_text) is not None + year_exists = str(parsed_date.year) in date_text + + 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_dates(doc_text, target_date_ydm, len_sentence_threshold, max_words=50): + """Get context around a target date with a maximum word limit. + + Parameters: + - doc_text: spaCy doc_text object + - target_date_ydm: The target date as a string + - len_sentence_threshold: Minimum number of words in a sentence to be considered as contextful + - max_words: Maximum number of words in the context + + Returns: + - context: Sentences surrounding the target date + """ + contexts_list = [] + target_date_dm = target_date_ydm[5:] # Get the day and month of the target date + + for ent in doc_text.ents: + if ent.label_ == 'DATE': + standardized_date = standardize_date(ent.text) + if standardized_date is None: + continue + # print(f"Comparing standardized date {standardized_date} with target date {target_date_ydm}") + if standardized_date == target_date_ydm or standardized_date == target_date_dm: + 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()) + if context_words < len_sentence_threshold: + start_token = sentence.start + end_token = sentence.end + while context_words < max_words: + # Adding context to the start + 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 + + if context_words >= max_words: + break + + # Adding context to the end + 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 + + if context_words >= max_words: + break + + # Conditions to break if max_words is not 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 + print(f"Successfully extracted context for isolated date {target_date_ydm}: {context}\n") + contexts_list.append(context) + + return contexts_list + def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, max_words: int = 150) -> str: - """Get text summary from a website""" + """Get text summary from a website""" + len_sentence_threshold = 5 + num_sentences_threshold = 300 + event_question_date = None + event_date_sentences = [] + # Check for empty inputs if not event_question or not text: return "" @@ -173,54 +265,65 @@ def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, m with torch.no_grad(): question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) - - # Apply spaCy's NLP pipeline to the text to prepare for sentence extraction - doc = nlp(text) - # Extract sentences in text that have more than 5 words and are not duplicates. - seen = set() - sentences = [sent.text for sent in doc.sents if len(sent.text.split()) >= 5 and not (sent.text in seen or seen.add(sent.text))] - - counter = Counter(sentences) + # Apply spaCy's NLP pipeline to the text to prepare for sentence extraction + doc_text = nlp(text) + doc_question = nlp(event_question) - duplicates = {k: v for k, v in counter.items() if v > 1} - num_duplicates = sum(duplicates.values()) - len(duplicates) + # Extract the date from the event question + for ent in doc_question.ents: + if ent.label_ == 'DATE': + print(f"Found date {ent.text} in text.") + event_date_ydm = standardize_date(ent.text) - print(f"Number of duplicate sentences: {num_duplicates}") + + # Find sentences in the text that contain the event date but are too short to contain relevant information and get the context around them. + if event_date_ydm is not None: + event_date_sentences.extend(get_context_around_isolated_dates(doc_text, event_date_ydm, len_sentence_threshold, max_words=50)) + - # Named entity recognition (NER) - entities = [ent.text for ent in doc.ents] + print(f"Event date sentences: {event_date_sentences}") + + + # Extract sentences in text that have more or equal number of words than the sentence threshold and are not duplicates. + seen = set() + sentences = [sent.text for sent in doc_text.sents if len(sent.text.split()) >= len_sentence_threshold and not (sent.text in seen or seen.add(sent.text))] + sentences.extend(event_date_sentences) - # Crop the sentences list to the first 300 sentences to reduce the time taken for the similarity calculations. - sentences = sentences[:300] + + # Crop the sentences list to reduce the time taken for the similarity calculations. + sentences = sentences[:num_sentences_threshold] # Similarity calculations and sentence ranking similarities = [] # Batch the sentences to reduce the time taken for the similarity calculations + start_time = datetime.now() batch_size = 32 - for i in range(0, len(sentences), batch_size): + for i in tqdm(range(0, len(sentences), batch_size), desc="Calculating sentence similarities"): batch = sentences[i:i+batch_size] with torch.no_grad(): sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) similarity = torch.cosine_similarity(question_embedding.repeat(len(batch), 1), sentence_embedding).tolist() - for j, sent in enumerate(batch): - if any(entity in sent for entity in entities): - similarity[j] += 0.05 + # for j, sent in enumerate(batch): + # if any(entity in sent for entity in entities): + # similarity[j] += 0.05 similarities.extend(similarity) + end_time = datetime.now() + print(f"Batch size: {batch_size}:\nTime taken to calculate sentence similarities: {end_time - start_time}\n") # Free up GPU memory del question_embedding, sentence_embedding torch.cuda.empty_cache() # Extract the top relevant sentences - relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.7] + relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.8] # Print each sentence in relevant_sentences in a new line along with its similarity score > 0.7 for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - if sim > 0.7: + if sim < 0.5: print(f"{sim} : {sent}\n") if len(relevant_sentences) == 0: @@ -312,7 +415,7 @@ def extract_text( tokenizer, nlp, ) -> str: - """Extract text from a single HTML document""" + """Extract text from a single HTML doc_textument""" # Remove HTML tags and extract text soup = BeautifulSoup(html, "html.parser") @@ -377,8 +480,8 @@ def extract_texts( stop = False # BERT Initialization - model = AutoModel.from_pretrained("bert-base-uncased") - tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") + model = AutoModel.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") + tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") # Spacy Initialization for NER and sentence splitting nlp = spacy.load("en_core_web_sm") From 7880ffcc98fe9162dd1253fc3872801277dc2ed3 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sun, 24 Sep 2023 19:42:16 +0200 Subject: [PATCH 11/34] chore: Formatted code and added comments --- tools/prediction_sum_url_content.py | 697 +++++++++++++++++++--------- 1 file changed, 482 insertions(+), 215 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 84930ac2..f4d159af 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -19,26 +19,24 @@ """This module implements a Mech tool for binary predictions.""" +from typing import Any, Dict, Generator, List, Optional, Tuple +from datetime import datetime import json import re -from collections import Counter -from datetime import datetime -from dateutil import parser from concurrent.futures import Future, ThreadPoolExecutor -from typing import Any, Dict, Generator, List, Optional, Tuple -from tqdm import tqdm -import openai -import requests from bs4 import BeautifulSoup from googleapiclient.discovery import build +import openai +import requests +import spacy +import torch +from dateutil import parser +from tqdm import tqdm from sentence_transformers import SentenceTransformer, util from transformers import AutoTokenizer, AutoModel, BertForPreTraining, BertForMaskedLM -import spacy -import torch - NUM_URLS_EXTRACT = 5 DEFAULT_OPENAI_SETTINGS = { "max_tokens": 500, @@ -50,15 +48,11 @@ ] TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-3.5-turbo", - # "prediction-online-sum-url-content": "gpt-3.5-turbo", + "prediction-online-sum-url-content": "gpt-3.5-turbo", # "prediction-online-sum-url-content": "gpt-3.5-turbo-16k", - "prediction-online-sum-url-content": "gpt-4", + # "prediction-online-sum-url-content": "gpt-4", } -# * If the 'event question' is formulated in a way that an event must have happend before a specific date, consider the deadline of the event being 23:59:59 of the day before that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. -# * If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have access to up-to-date information. / that you have been provided with the most up-to-date information that can be found on the internet. - - PREDICTION_PROMPT = """ You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event @@ -79,9 +73,8 @@ * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. * Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. -* 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 up-to-date information that can be found on the internet. +* If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have been provided with up-to-date information that can be found on the internet. * If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. -* If the 'event question' is formulated in a way that an event must have happend BEFORE a specific date, consider the deadline of the event being 23:59:59 of the day before. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. * You must provide your response in the format specified under "OUTPUT_FORMAT". * Do not include any other contents in your response. @@ -115,7 +108,7 @@ 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 1-5 unique search queries likely to yield relevant and contemporary information for assessing the event's likelihood under the given conditions. +* 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. @@ -132,7 +125,63 @@ * 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" +] + + 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() @@ -146,39 +195,77 @@ def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[s return [result["link"] for result in search["items"]] -def get_urls_from_queries(queries: List[str], api_key: str, engine: str) -> List[str]: - """Get URLs from search engine queries""" - results = [] +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): Search engine to be used. + 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 URLs. + """ + + results = set() + max_num = 10 + + if num > max_num: + raise ValueError(f"The maximum number of URLs per query is {max_num}.") + for query in queries: - for url in search_google( + fetched_urls = search_google( query=query, api_key=api_key, engine=engine, - num=3, # Number of returned urls per query - ): - results.append(url) - unique_results = list(set(results)) - - # Remove urls that are pdfs - unique_results = [url for url in unique_results if not url.endswith(".pdf")] - return unique_results + num=max_num # Limit the number of returned URLs per query + ) + + # Add only unique URLs up to 'num' per query + 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 + + 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. + + Returns: + str: The standardized date string if possible, otherwise None. + """ + try: - standardize_date = None - # Create regex to check if month or day appears in date_text - month_re = 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_re = re.compile(r'\b\d{1,2}\b') + # 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') - # Forcing parser.parse to use dayfirst=True for European date format + # Parse date_text using dateutil parser parsed_date = parser.parse(date_text) - # Check if year, month, and day are in the original date_text as paser.parse() will fill in the current day, month or year if either is not provided - month_exists = month_re.search(date_text) is not None - day_exists = day_re.search(date_text) is not None + # 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: @@ -189,35 +276,59 @@ def standardize_date(date_text): return None -def get_context_around_isolated_dates(doc_text, target_date_ydm, len_sentence_threshold, max_words=50): - """Get context around a target date with a maximum word limit. - - Parameters: - - doc_text: spaCy doc_text object - - target_date_ydm: The target date as a string - - len_sentence_threshold: Minimum number of words in a sentence to be considered as contextful - - max_words: Maximum number of words in the context - +def get_context_around_isolated_dates( + doc_text, target_date_ydm, len_sentence_threshold, max_words=50 +): + """ + Extract context around isolated dates within the text. + + Args: + doc_text (spaCy Doc): Document text as a spaCy Doc object. + target_date_ydm (str): Target date in year-day-month format. + len_sentence_threshold (int): Minimum number of words required for a sentence to be considered contextful. + max_words (int, optional): Maximum number of words to include in the context. Defaults to 50. + + Raises: + ValueError: If max_words is less than len_sentence_threshold or greater than 300. + Returns: - - context: Sentences surrounding the target date - """ + list: List of sentences surrounding the target date. + """ + + # Check max_words value constraints + if max_words < 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_words > 300: + raise ValueError( + f"The maximum number of words must be less than or equal to 300." + ) + contexts_list = [] - target_date_dm = target_date_ydm[5:] # Get the day and month of the target date + target_date_dm = target_date_ydm[5:] + len_doc_text = len(doc_text) for ent in doc_text.ents: if ent.label_ == 'DATE': standardized_date = standardize_date(ent.text) if standardized_date is None: continue - # print(f"Comparing standardized date {standardized_date} with target date {target_date_ydm}") + + # Check if the entity matches the target date if standardized_date == target_date_ydm or standardized_date == target_date_dm: - sentence = next(sent for sent in doc_text.sents if sent.start <= ent.start and sent.end >= ent.end) + 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 = sentence.start - end_token = sentence.end + start_token, end_token = sentence.start, sentence.end while context_words < max_words: - # Adding context to the start + # 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 @@ -225,112 +336,184 @@ def get_context_around_isolated_dates(doc_text, target_date_ydm, len_sentence_th context_words += len(doc_text[new_start:start_token].text.split()) start_token = new_start + # Break if max_words is reached if context_words >= max_words: break - # Adding context to the end + # 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: + while new_end < len_doc_text and doc_text[new_end].sent == sentence.sent: new_end += 1 - - if new_end < len(doc_text): + if new_end < len_doc_text: context_words += len(doc_text[end_token:new_end].text.split()) end_token = new_end + # Break if max_words is reached if context_words >= max_words: break - # Conditions to break if max_words is not reached - if new_end == len(doc_text) and start_token <= 0: + # Break if max_words 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 + context = doc_text[max(0, start_token):min(len_doc_text, end_token)].text print(f"Successfully extracted context for isolated date {target_date_ydm}: {context}\n") contexts_list.append(context) return contexts_list -def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, max_words: int = 150) -> str: - """Get text summary from a website""" + +def get_sentence_embeddings_and_similarities( + sentences: List[str], + question_embedding: torch.Tensor, + model, + tokenizer, + batch_size: int = 32 +) -> Tuple[List[torch.Tensor], List[float]]: + """ + Calculate the sentence embeddings and similarities. + + Args: + sentences (List[str]): List of sentences to compare. + question_embedding (torch.Tensor): Tensor of the question embedding. + model: The BERT model for text embeddings. + tokenizer: The tokenizer for the BERT model. + batch_size (int, optional): Number of sentences to process in each batch. Defaults to 32. + + Raises: + ValueError: If batch_size is less than 1. + + Returns: + Tuple[List[torch.Tensor], List[float]]: List of sentence embeddings and their similarities. + """ + + if batch_size < 1: + raise ValueError("Batch size must be at least 1.") + + similarities = [] + + # Repeat the question embedding tensor to match the batch size + question_embedding_repeated = question_embedding.repeat(batch_size, 1) + + # Batch the sentences for efficient processing + sentence_batches = [sentences[i:i + batch_size] for i in range(0, len(sentences), batch_size)] + + for batch in tqdm(sentence_batches, desc="Calculating sentence similarities"): + # Adjust the repeated question embedding if the batch size changes + actual_batch_size = len(batch) + if actual_batch_size != batch_size: + question_embedding_repeated = question_embedding.repeat(actual_batch_size, 1) + + with torch.no_grad(): + # Tokenize and preprocess sentence batch + sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) + # Compute sentence embeddings + sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) + # Compute cosine similarities + similarity = torch.cosine_similarity(question_embedding_repeated, sentence_embedding).tolist() + + similarities.extend(similarity) + + # Free up GPU memory + del question_embedding, sentence_embedding + + return similarities + + +def get_website_summary( + text: str, + event_question: str, + model, + tokenizer, + nlp, + max_words: int = 130 +) -> str: + """ + Generate a summary of a website's text based on a given event question. + + Args: + text (str): The website text to summarize. + event_question (str): The question to focus the summary on. + model: The BERT model for text embeddings. + tokenizer: The tokenizer for the BERT model. + nlp: The spaCy NLP model. + max_words (int, optional): Maximum number of words for the output summary. Defaults to 130. + + Raises: + ValueError: If max_words is less than 1 or greater than 300. + + Returns: + str: The generated summary. + """ + + if max_words < 1: + raise ValueError("The maximum number of words must be at least 1.") + if max_words > 300: + raise ValueError("The maximum number of words must be less than or equal to 300.") + + # Constants for sentence length and number thresholds len_sentence_threshold = 5 - num_sentences_threshold = 300 + num_sentences_threshold = 100 + + # Initialize variables event_question_date = None event_date_sentences = [] - # Check for empty inputs + # Validate inputs if not event_question or not text: return "" - # Calculate the BERT embedding for the event_question to use in similarity computation + # Calculate the BERT embedding for the event question with torch.no_grad(): question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) - # Apply spaCy's NLP pipeline to the text to prepare for sentence extraction + # Apply NLP pipeline to text and event question doc_text = nlp(text) doc_question = nlp(event_question) - # Extract the date from the event question + # Extract the date from the event question if present for ent in doc_question.ents: if ent.label_ == 'DATE': print(f"Found date {ent.text} in text.") event_date_ydm = standardize_date(ent.text) - # Find sentences in the text that contain the event date but are too short to contain relevant information and get the context around them. + # Extract contextual sentences around isolated dates if event_date_ydm is not None: - event_date_sentences.extend(get_context_around_isolated_dates(doc_text, event_date_ydm, len_sentence_threshold, max_words=50)) - + event_date_sentences.extend( + get_context_around_isolated_dates(doc_text, event_date_ydm, len_sentence_threshold, max_words=50) + ) - print(f"Event date sentences: {event_date_sentences}") - - - # Extract sentences in text that have more or equal number of words than the sentence threshold and are not duplicates. seen = set() - sentences = [sent.text for sent in doc_text.sents if len(sent.text.split()) >= len_sentence_threshold and not (sent.text in seen or seen.add(sent.text))] + sentences = [] + + # Extract unique and sufficiently long 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) - - # Crop the sentences list to reduce the time taken for the similarity calculations. + # Limit the number of sentences for performance sentences = sentences[:num_sentences_threshold] - # Similarity calculations and sentence ranking - similarities = [] - - # Batch the sentences to reduce the time taken for the similarity calculations - start_time = datetime.now() - batch_size = 32 - for i in tqdm(range(0, len(sentences), batch_size), desc="Calculating sentence similarities"): - batch = sentences[i:i+batch_size] - with torch.no_grad(): - sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) - sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) - similarity = torch.cosine_similarity(question_embedding.repeat(len(batch), 1), sentence_embedding).tolist() - - # for j, sent in enumerate(batch): - # if any(entity in sent for entity in entities): - # similarity[j] += 0.05 - similarities.extend(similarity) - end_time = datetime.now() - print(f"Batch size: {batch_size}:\nTime taken to calculate sentence similarities: {end_time - start_time}\n") - - # Free up GPU memory - del question_embedding, sentence_embedding - torch.cuda.empty_cache() - - # Extract the top relevant sentences - relevant_sentences = [sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.8] + # Calculate sentence similarities + similarities = get_sentence_embeddings_and_similarities( + sentences, question_embedding, model, tokenizer, batch_size=32 + ) - # Print each sentence in relevant_sentences in a new line along with its similarity score > 0.7 - for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - if sim < 0.5: - print(f"{sim} : {sent}\n") + # Extract top relevant sentences + relevant_sentences = [ + sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.9 + ] - if len(relevant_sentences) == 0: + if not relevant_sentences: return "" - # Join the top 4 relevant sentences - output = ' '.join(relevant_sentences[:4]) + # Truncate summary to fit max_words limit + output = ' '.join(relevant_sentences[:10]) output_words = output.split(' ') if len(output_words) > max_words: output = ' '.join(output_words[:max_words]) @@ -339,72 +522,39 @@ def get_website_summary(text: str, event_question: str, model, tokenizer, nlp, m def get_date(soup): - # Get the updated or release date of the website. - # The following are some of the possible values for the "name" attribute: - 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' - ] - + """ + Retrieves the release and modification dates from the soup object containing the text of the website. + + 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" - # First, try to find an update or modified date - for name in update_date_names: + # 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: + 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 {release_date}, Modified date {modified_date}" @@ -415,39 +565,45 @@ def extract_text( tokenizer, nlp, ) -> str: - """Extract text from a single HTML doc_textument""" - # Remove HTML tags and extract text - soup = BeautifulSoup(html, "html.parser") + """ + Extract relevant information from HTML string. - # Get the date of the website - date = get_date(soup) + Args: + html (str): The HTML content to extract text from. + event_question (str): Event question for context. + model: Pre-trained model for text summarization. + tokenizer: Tokenizer for the pre-trained model. + nlp: NLP object for additional text processing. - # Get the main element of the website - # main_element = soup.find("main") - # if main_element: - # soup = main_element + Raises: + ValueError: If the HTML content is empty. - for script in soup(["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"]): - script.extract() - - # print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< SOUP 1: \n{soup}") - - # for tag in soup.find_all(): - # if tag.name not in ['p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'article', 'main', 'blockquote', 'ul', 'ol', 'li', 'strong', 'b', 'em', 'i', 'q', 'a', 'span', 'pre', 'code', 'time', 'abbr', 'section', 'div', 'figure', 'figcaption', 'mark']: - # tag.extract() + Returns: + str: Summarized text with the date. + """ - # print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< SOUP 2: \n{soup}") - + if not html: + raise ValueError("HTML content cannot be 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 date from the HTML") + # Remove unnecessary tags to clean up text + for script in soup(HTML_TAGS_TO_REMOVE): + script.extract() + + # 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) # Use regex to replace multiple "."s with a single ".". - # print(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>< TEXT: \n{text}") + text = re.sub(r"\.{2,}", ".", text) + # Get summarized text text_summary = get_website_summary( text=text, event_question=event_question, @@ -455,17 +611,48 @@ def extract_text( tokenizer=tokenizer, nlp=nlp, ) - return f"{date}:\n{text_summary}" if text_summary else "" + + if not text_summary: + return "" + + return f"{date}:\n{text_summary}" def process_in_batches( - urls: List[str], window: int = 5, timeout: int = 10 + urls: List[str], batch_size: int = 15, timeout: int = 10 ) -> Generator[None, None, List[Tuple[Future, str]]]: - """Iter URLs in batches.""" + """ + 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.") + + # Using ThreadPoolExecutor to execute requests in parallel with ThreadPoolExecutor() as executor: - for i in range(0, len(urls), window): - batch = urls[i : i + window] - futures = [(executor.submit(requests.get, url, timeout=timeout), url) for url in batch] + # Loop through the URLs in windows of size 'window' + for i in range(0, len(urls), batch_size): + batch = urls[i : i + batch_size] + + # Submit the batch of URLs for processing + futures = [ + (executor.submit(requests.get, url, timeout=timeout), url) for url in batch + ] yield futures @@ -473,26 +660,42 @@ def extract_texts( urls: List[str], event_question: str, ) -> List[str]: - """Extract texts from URLs""" + """ + Extract texts from a list of URLs using BERT and Spacy. + + Parameters: + urls (List[str]): List of URLs to extract text from. + event_question (str): Event-related question for text extraction. + + Returns: + List[str]: List of extracted texts. + """ + + # Maximum number of allowed extractions max_allowed = 45 + + # Initialize empty list for storing extracted texts extracted_texts = [] + + # Initialize count and stop flag count = 0 stop = False - - # BERT Initialization + + # Initialize BERT and Spacy models model = AutoModel.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") - - # Spacy Initialization for NER and sentence splitting nlp = spacy.load("en_core_web_sm") - for batch in tqdm(process_in_batches(urls=urls), desc="Processing Batches"): + # Process URLs in batches + for batch in process_in_batches(urls=urls): for future, url in tqdm(batch, desc="Processing URLs"): 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, event_question=event_question, @@ -500,19 +703,30 @@ def extract_texts( tokenizer=tokenizer, nlp=nlp, ) + + # 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}") count += 1 + + # Break if the maximum number of extractions is reached if count >= max_allowed: stop = True break + except requests.exceptions.ReadTimeout: print(f"Request timed out: {url}.") + except Exception as e: print(f"An error occurred: {e}") + + # Break if the maximum number of extractions is reached if stop: break + return extracted_texts @@ -524,15 +738,37 @@ def fetch_additional_information( google_api_key: str, google_engine: str, ) -> str: - """Fetch additional information.""" + + """ + Fetch additional information based on an event question. + + Args: + event_question (str): The question related to the event. + engine (str): The engine to be used for fetching information. + temperature (float): The temperature parameter for the engine. + max_tokens (int): The maximum number of tokens for the engine's response. + google_api_key (str): The API key for the Google service. + google_engine (str): The Google engine to be used. + + Returns: + str: The additional information fetched. + """ + + # 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 "" + 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="gpt-3.5-turbo", messages=messages, @@ -544,25 +780,45 @@ def fetch_additional_information( stop=None, ) + # Parse the response content json_data = json.loads(response.choices[0].message.content) print(f"json_data: {json_data}") + + # Get URLs from queries urls = get_urls_from_queries( json_data["queries"], api_key=google_api_key, engine=google_engine, ) - print(f"urls: {urls}") + + # Extract texts from URLs texts = extract_texts( urls=urls, event_question=event_question, ) + + # Join the texts and return additional_informations = "\n\n".join(["- " + text for text in texts]) - # print(f"additional_informations: {additional_informations}") + return additional_informations def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: - """Run the task""" + """ + Run the task with the given parameters. + + 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. + """ + print("Starting...") tool = kwargs["tool"] @@ -570,25 +826,31 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - print(f"Tool: {tool}") - print(f"Prompt: {prompt}") - print(f"Max tokens: {max_tokens}") - print(f"Temperature: {temperature}") + if not tool or not prompt: + raise ValueError("Both 'mech tool' and 'prompt' must be provided.") + + # Print the settings + print(f"MECH TOOL: {tool}") + print(f"PROMPT: {prompt}") + print(f"MAX OPENAI RETURN TOKENS: {max_tokens}") + print(f"LLM TEMPERATURE: {temperature}") openai.api_key = kwargs["api_keys"]["openai"] + if tool not in ALLOWED_TOOLS: - raise ValueError(f"Tool {tool} is not supported.") + raise ValueError(f"TOOL {tool} is not supported.") + # Get the LLM engine to be used engine = TOOL_TO_ENGINE[tool] - print(f"Engine: {engine}") + print(f"ENGINE: {engine}") - # Event question is the text between the first pair of double quotes in the prompt + # Extract the event question from the prompt event_question = re.search(r"\"(.+?)\"", prompt).group(1) - print(f"event_question: {event_question}") - - # Make an openai request to get similar formulations of the event question and store them in a list - similar_formulations = [] + if not event_question: + raise ValueError("No event question found in prompt.") + print(f"EVENT_QUESTION: {event_question}") + # Fetch additional information additional_information = ( fetch_additional_information( event_question=event_question, @@ -602,21 +864,25 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: else "" ) - # Get today's date and generate the prediction prompt + # Generate the prediction prompt timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') prediction_prompt = PREDICTION_PROMPT.format( user_prompt=prompt, additional_information=additional_information, timestamp=timestamp, ) - print(f"prediction_prompt: {prediction_prompt}\n") + print(f"PREDICTION 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, @@ -627,5 +893,6 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: request_timeout=150, stop=None, ) - print(f"response: {response}") + print(f"RESPONSE: {response}") + return response.choices[0].message.content, None From ccd7c7e5d15f9c375bc5594ffdb758b6625464cc Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Mon, 25 Sep 2023 02:07:53 +0200 Subject: [PATCH 12/34] feat: Improved url requests, minor improvements in different code parts. --- poetry.lock | 47 +++++++++++- pyproject.toml | 1 + tools/prediction_sum_url_content.py | 109 +++++++++++++++++++++++----- 3 files changed, 139 insertions(+), 18 deletions(-) diff --git a/poetry.lock b/poetry.lock index 4f63f68b..7b082bd4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4356,6 +4356,51 @@ files = [ {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"}, ] +[[package]] +name = "tiktoken" +version = "0.5.1" +description = "tiktoken is a fast BPE tokeniser for use with 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a/pyproject.toml b/pyproject.toml index 55b615e3..d57e063f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,7 @@ gensim = "^4.3.2" sentence-transformers = "^2.2.2" spacy = "^3.6.1" tqdm = "^4.66.1" +tiktoken = "^0.5.1" [tool.poetry.group.dev.dependencies.tomte] version = "==0.2.12" diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index f4d159af..0ee929da 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -23,19 +23,24 @@ from datetime import datetime import json import re -from concurrent.futures import Future, ThreadPoolExecutor +import traceback +from concurrent.futures import Future, ThreadPoolExecutor, as_completed from bs4 import BeautifulSoup from googleapiclient.discovery import build import openai import requests +from requests import Session +from requests.adapters import HTTPAdapter import spacy import torch from dateutil import parser from tqdm import tqdm from sentence_transformers import SentenceTransformer, util +# from tiktoken import Tokenizer, encoding_for_model from transformers import AutoTokenizer, AutoModel, BertForPreTraining, BertForMaskedLM +from urllib3.util.retry import Retry NUM_URLS_EXTRACT = 5 DEFAULT_OPENAI_SETTINGS = { @@ -181,7 +186,6 @@ 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() @@ -195,6 +199,27 @@ def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[s return [result["link"] for result in search["items"]] +# def truncate_text_to_tokens(text, max_tokens): +# """Get the token encoding that corresponds to the GPT-4 model""" +# enc = encoding_for_model("cl100k_base") + +# # Initialize tokenizer and token list +# tokenizer = Tokenizer(encoders=[enc]) +# token_list = [] + +# # Tokenize the text and add tokens to the token list +# for token, _ in tokenizer.count_tokens(text): +# token_list.append(token) + +# # Truncate the token list to 'max_tokens' +# truncated_tokens = token_list[:max_tokens] + +# # Reconstruct the truncated text +# truncated_text = ''.join(truncated_tokens) + +# return truncated_text + + 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. @@ -209,7 +234,7 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in ValueError: If the number of URLs per query exceeds the maximum allowed. Returns: - List[str]: Unique list of URLs, omitting PDF URLs. + List[str]: Unique list of URLs, omitting PDF and download-related URLs. """ results = set() @@ -225,16 +250,15 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in engine=engine, num=max_num # Limit the number of returned URLs per query ) - - # Add only unique URLs up to 'num' 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"): + if "download" not in url.lower() and url not in results and not url.endswith(".pdf"): results.add(url) count += 1 if count >= num: break - return list(results) @@ -395,9 +419,21 @@ def get_sentence_embeddings_and_similarities( # Repeat the question embedding tensor to match the batch size question_embedding_repeated = question_embedding.repeat(batch_size, 1) + # Print the number of sentences + # print(f"Number of sentences: {len(sentences)}") + + # Print the number of batches + # print(f"Number of batches to create: {len(sentences) // batch_size + 1}") + # Batch the sentences for efficient processing sentence_batches = [sentences[i:i + batch_size] for i in range(0, len(sentences), batch_size)] + # print(f"Number of batches created: {len(sentence_batches)}") + # Number of sentences in each batch + # print(f"Number of sentences in all batches except the last: {len(sentence_batches[:-1])}") + + # print(f"Number of sentences in the last batch: {len(sentence_batches[-1])}") + for batch in tqdm(sentence_batches, desc="Calculating sentence similarities"): # Adjust the repeated question embedding if the batch size changes actual_batch_size = len(batch) @@ -426,7 +462,7 @@ def get_website_summary( model, tokenizer, nlp, - max_words: int = 130 + max_words: int = 120 ) -> str: """ Generate a summary of a website's text based on a given event question. @@ -437,7 +473,7 @@ def get_website_summary( model: The BERT model for text embeddings. tokenizer: The tokenizer for the BERT model. nlp: The spaCy NLP model. - max_words (int, optional): Maximum number of words for the output summary. Defaults to 130. + max_words (int, optional): Maximum number of words for the output summary. Defaults to 120. Raises: ValueError: If max_words is less than 1 or greater than 300. @@ -468,6 +504,9 @@ def get_website_summary( question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) + # Truncate text to stay within nlp character limit of 1,000,000 + text = text[:1000000] + # Apply NLP pipeline to text and event question doc_text = nlp(text) doc_question = nlp(event_question) @@ -475,10 +514,8 @@ def get_website_summary( # Extract the date from the event question if present for ent in doc_question.ents: if ent.label_ == 'DATE': - print(f"Found date {ent.text} in text.") event_date_ydm = standardize_date(ent.text) - # Extract contextual sentences around isolated dates if event_date_ydm is not None: event_date_sentences.extend( @@ -501,7 +538,7 @@ def get_website_summary( # Calculate sentence similarities similarities = get_sentence_embeddings_and_similarities( - sentences, question_embedding, model, tokenizer, batch_size=32 + sentences, question_embedding, model, tokenizer, batch_size=16 ) # Extract top relevant sentences @@ -509,6 +546,12 @@ def get_website_summary( sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.9 ] + # Print sentences and similarities if similarity is greater than 0.9 + for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + if sim > 0.9: + print(f"Sentence: {sent}\nSimilarity: {sim}\n") + print() + if not relevant_sentences: return "" @@ -619,7 +662,7 @@ def extract_text( def process_in_batches( - urls: List[str], batch_size: int = 15, timeout: int = 10 + urls: List[str], batch_size: int = 5, timeout: int = 10 ) -> Generator[None, None, List[Tuple[Future, str]]]: """ Process URLs in batches using a generator and thread pool executor. @@ -643,15 +686,32 @@ def process_in_batches( if timeout <= 0: raise ValueError("The 'timeout' must be greater than zero.") + session = Session() + + # Set up retry logic + retries = Retry( + total=5, + backoff_factor=0.1, + status_forcelist=[500, 502, 503, 504] + ) + session.mount('http://', HTTPAdapter(max_retries=retries)) + session.mount('https://', HTTPAdapter(max_retries=retries)) + + # 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 windows of size 'window' + # 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 = [ - (executor.submit(requests.get, url, timeout=timeout), url) for url in batch + (executor.submit(session.get, url, headers=headers, timeout=timeout), url) for url in batch ] yield futures @@ -691,7 +751,9 @@ def extract_texts( for future, url in tqdm(batch, desc="Processing URLs"): try: result = future.result() + print(f"\n{result.status_code}: status code for {url}\n") if result.status_code != 200: + # print(f"result.status_code: {result.status_code}") del result continue @@ -703,6 +765,7 @@ def extract_texts( tokenizer=tokenizer, nlp=nlp, ) + # print(f"extracted_text: {extracted_text}") # Delete the result object to free memory del result @@ -711,22 +774,32 @@ def extract_texts( if extracted_text: extracted_texts.append(f"{url}\n{extracted_text}") count += 1 + # print(f"extracted_texts: {extracted_texts}\n") + print(f"count: {count}\n") # Break if the maximum number of extractions is reached if count >= max_allowed: stop = True + print(f"Maximum number of extractions reached: {max_allowed}.") break except requests.exceptions.ReadTimeout: print(f"Request timed out: {url}.") + 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: + print(f"Maximum number of extractions reached: {max_allowed}.") break + # print(f"\nbatch: {batch}\n") + return extracted_texts @@ -772,7 +845,7 @@ def fetch_additional_information( response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, - temperature=0.7, + temperature=0, max_tokens=max_tokens, n=1, timeout=90, @@ -820,7 +893,8 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: """ print("Starting...") - + print() + tool = kwargs["tool"] prompt = kwargs["prompt"] max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) @@ -849,6 +923,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: if not event_question: raise ValueError("No event question found in prompt.") print(f"EVENT_QUESTION: {event_question}") + print() # Fetch additional information additional_information = ( From db52be5e35f9d7d444b1a007266dce47d4145e86 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Mon, 25 Sep 2023 12:55:01 +0200 Subject: [PATCH 13/34] feat: Added token truncation before passing prompt to openai chat completion; added minor changes accounting for not exceeding string limits --- tools/prediction_sum_url_content.py | 202 +++++++++++++++++----------- 1 file changed, 127 insertions(+), 75 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 0ee929da..f611b429 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -21,6 +21,7 @@ from typing import Any, Dict, Generator, List, Optional, Tuple from datetime import datetime +import time import json import re import traceback @@ -33,20 +34,22 @@ from requests import Session from requests.adapters import HTTPAdapter import spacy +import tiktoken import torch from dateutil import parser from tqdm import tqdm from sentence_transformers import SentenceTransformer, util -# from tiktoken import Tokenizer, encoding_for_model from transformers import AutoTokenizer, AutoModel, BertForPreTraining, BertForMaskedLM from urllib3.util.retry import Retry NUM_URLS_EXTRACT = 5 +MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 DEFAULT_OPENAI_SETTINGS = { - "max_tokens": 500, + "max_tokens": 200, "temperature": 0.2, } + ALLOWED_TOOLS = [ "prediction-offline-sum-url-content", "prediction-online-sum-url-content", @@ -199,25 +202,57 @@ def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[s return [result["link"] for result in search["items"]] -# def truncate_text_to_tokens(text, max_tokens): -# """Get the token encoding that corresponds to the GPT-4 model""" -# enc = encoding_for_model("cl100k_base") +def truncate_additional_information( + additional_informations: str, + max_tokens: int, + prompt: str, + enc: tiktoken.Encoding, + safety_factor: float = 1.05, +) -> str: + """ + Truncates additional information string to a specified number of tokens using tiktoken encoding. -# # Initialize tokenizer and token list -# tokenizer = Tokenizer(encoders=[enc]) -# token_list = [] - -# # Tokenize the text and add tokens to the token list -# for token, _ in tokenizer.count_tokens(text): -# token_list.append(token) + Parameters: + additional_informations (str): The additional information string to be truncated. + max_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 truncation. Defaults to 1.05. -# # Truncate the token list to 'max_tokens' -# truncated_tokens = token_list[:max_tokens] - -# # Reconstruct the truncated text -# truncated_text = ''.join(truncated_tokens) - -# return truncated_text + Returns: + - str: The truncated additional information string. + """ + + # Encode the strings into tokens + additional_information_token_enc = enc.encode(additional_informations) + user_prompt_tokens_token_enc = enc.encode(prompt) + prediction_prompt_tokens_token_enc = enc.encode(PREDICTION_PROMPT) + + print("Max Total Tokens:", MAX_TOTAL_TOKENS_CHAT_COMPLETION) + print("Number of tokens in additional informations:", len(additional_information_token_enc)) + print("Number of tokens in user prompt:", len(user_prompt_tokens_token_enc)) + print("Number of tokens in prediction prompt:", len(prediction_prompt_tokens_token_enc)) + print("Number of tokens reserved for chat completion output:", max_tokens) + + # Calculate the rough token sum of final prediction prompt + prompt_token_sum = len(additional_information_token_enc) + len(user_prompt_tokens_token_enc) + len(prediction_prompt_tokens_token_enc) + max_tokens + print(f"Total number of tokens in prompt: {prompt_token_sum}") + prompt_token_sum_safety_factor = prompt_token_sum * safety_factor + print(f"Total number of tokens in prompt with safety factor: {prompt_token_sum_safety_factor}") + + if prompt_token_sum_safety_factor > MAX_TOTAL_TOKENS_CHAT_COMPLETION: + num_tokens_to_truncate = prompt_token_sum_safety_factor - MAX_TOTAL_TOKENS_CHAT_COMPLETION + print(f"Truncating additional information by {num_tokens_to_truncate} tokens.") + + # Truncate the additional informations tokens + truncated_additional_informations_token = additional_information_token_enc[:-int(num_tokens_to_truncate)] + print(f"Number of tokens in truncated additional informations: {len(truncated_additional_informations_token)}") + + # Decode the truncated tokens back into text + truncated_additional_informations_string = enc.decode(truncated_additional_informations_token) + return truncated_additional_informations_string + else: + return additional_informations def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: int = 3) -> List[str]: @@ -254,11 +289,16 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in # Add only unique URLs up to 'num' per query, omitting PDF and 'download' URLs count = 0 for url in fetched_urls: - if "download" not in url.lower() and url not in results and not url.endswith(".pdf"): - results.add(url) - count += 1 - if count >= num: - break + results.add(url) + count += 1 + if count >= num: + break + + # if "download" not in url.lower() and url not in results and not url.endswith(".pdf"): + # results.add(url) + # count += 1 + # if count >= num: + # break return list(results) @@ -301,7 +341,7 @@ def standardize_date(date_text): def get_context_around_isolated_dates( - doc_text, target_date_ydm, len_sentence_threshold, max_words=50 + doc_text, target_date_ydm, len_sentence_threshold, max_context=50 ): """ Extract context around isolated dates within the text. @@ -310,21 +350,21 @@ def get_context_around_isolated_dates( doc_text (spaCy Doc): Document text as a spaCy Doc object. target_date_ydm (str): Target date in year-day-month format. len_sentence_threshold (int): Minimum number of words required for a sentence to be considered contextful. - max_words (int, optional): Maximum number of words to include in the context. Defaults to 50. + max_context (int, optional): Maximum number of words to include in the context. Defaults to 50. Raises: - ValueError: If max_words is less than len_sentence_threshold or greater than 300. + ValueError: If max_context is less than len_sentence_threshold or greater than 300. Returns: list: List of sentences surrounding the target date. """ - # Check max_words value constraints - if max_words < len_sentence_threshold: + # 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_words > 300: + if max_context > 300: raise ValueError( f"The maximum number of words must be less than or equal to 300." ) @@ -351,7 +391,7 @@ def get_context_around_isolated_dates( # 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_words: + 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: @@ -360,8 +400,8 @@ def get_context_around_isolated_dates( context_words += len(doc_text[new_start:start_token].text.split()) start_token = new_start - # Break if max_words is reached - if context_words >= max_words: + # Break if max_context is reached + if context_words >= max_context: break # Extend the context from the end of the sentence @@ -372,11 +412,11 @@ def get_context_around_isolated_dates( context_words += len(doc_text[end_token:new_end].text.split()) end_token = new_end - # Break if max_words is reached - if context_words >= max_words: + # Break if max_context is reached + if context_words >= max_context: break - # Break if max_words cannot be reached + # Break if max_context cannot be reached if new_end == len_doc_text and start_token <= 0: break @@ -439,19 +479,18 @@ def get_sentence_embeddings_and_similarities( actual_batch_size = len(batch) if actual_batch_size != batch_size: question_embedding_repeated = question_embedding.repeat(actual_batch_size, 1) - - with torch.no_grad(): - # Tokenize and preprocess sentence batch - sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) - # Compute sentence embeddings - sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) - # Compute cosine similarities - similarity = torch.cosine_similarity(question_embedding_repeated, sentence_embedding).tolist() - - similarities.extend(similarity) - - # Free up GPU memory - del question_embedding, sentence_embedding + try: + with torch.no_grad(): + # Tokenize and preprocess sentence batch + sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) + # Compute sentence embeddings + sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) + # Compute cosine similarities + similarity = torch.cosine_similarity(question_embedding_repeated, sentence_embedding).tolist() + similarities.extend(similarity) + finally: + # Free up GPU memory + del sentence_tokens, sentence_embedding, similarity return similarities @@ -462,7 +501,7 @@ def get_website_summary( model, tokenizer, nlp, - max_words: int = 120 + max_words: int ) -> str: """ Generate a summary of a website's text based on a given event question. @@ -473,7 +512,7 @@ def get_website_summary( model: The BERT model for text embeddings. tokenizer: The tokenizer for the BERT model. nlp: The spaCy NLP model. - max_words (int, optional): Maximum number of words for the output summary. Defaults to 120. + max_words (int, optional): Maximum number of words for the output summary. Defaults to 200. Raises: ValueError: If max_words is less than 1 or greater than 300. @@ -482,17 +521,9 @@ def get_website_summary( str: The generated summary. """ - if max_words < 1: - raise ValueError("The maximum number of words must be at least 1.") - if max_words > 300: - raise ValueError("The maximum number of words must be less than or equal to 300.") - # Constants for sentence length and number thresholds len_sentence_threshold = 5 num_sentences_threshold = 100 - - # Initialize variables - event_question_date = None event_date_sentences = [] # Validate inputs @@ -519,7 +550,7 @@ def get_website_summary( # Extract contextual sentences around isolated dates if event_date_ydm is not None: event_date_sentences.extend( - get_context_around_isolated_dates(doc_text, event_date_ydm, len_sentence_threshold, max_words=50) + get_context_around_isolated_dates(doc_text, event_date_ydm, len_sentence_threshold, max_context=50) ) seen = set() @@ -541,16 +572,16 @@ def get_website_summary( sentences, question_embedding, model, tokenizer, batch_size=16 ) - # Extract top relevant sentences + # Extract top relevant sentences relevant_sentences = [ sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.9 ] - # Print sentences and similarities if similarity is greater than 0.9 - for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - if sim > 0.9: - print(f"Sentence: {sent}\nSimilarity: {sim}\n") - print() + # # Print sentences and similarities if similarity is greater than 0.9 + # for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + # if sim > 0.9: + # print(f"Similarity: {sim}\nSentence: {sent}\n") + # print() if not relevant_sentences: return "" @@ -607,6 +638,7 @@ def extract_text( model, tokenizer, nlp, + max_words: int, ) -> str: """ Extract relevant information from HTML string. @@ -626,7 +658,7 @@ def extract_text( """ if not html: - raise ValueError("HTML content cannot be empty") + raise ValueError("HTML is empty.") soup = BeautifulSoup(html, "html.parser") @@ -653,6 +685,7 @@ def extract_text( model=model, tokenizer=tokenizer, nlp=nlp, + max_words=max_words, ) if not text_summary: @@ -690,7 +723,7 @@ def process_in_batches( # Set up retry logic retries = Retry( - total=5, + total=2, backoff_factor=0.1, status_forcelist=[500, 502, 503, 504] ) @@ -732,7 +765,12 @@ def extract_texts( """ # Maximum number of allowed extractions - max_allowed = 45 + max_allowed = 25 + + # Maximum number of words for each extraction + # ~ 2642 tokens free for additional information ~ 1981 words + # split by number of URLs + max_words = 1981 // len(urls) # Initialize empty list for storing extracted texts extracted_texts = [] @@ -751,9 +789,10 @@ def extract_texts( for future, url in tqdm(batch, desc="Processing URLs"): try: result = future.result() - print(f"\n{result.status_code}: status code for {url}\n") + print(f"\nURL: {url}") + print(f"Status code: {result.status_code}") + print(f"Content type: {result.headers.get('content-type')}\n") if result.status_code != 200: - # print(f"result.status_code: {result.status_code}") del result continue @@ -764,6 +803,7 @@ def extract_texts( model=model, tokenizer=tokenizer, nlp=nlp, + max_words=max_words, ) # print(f"extracted_text: {extracted_text}") @@ -798,8 +838,6 @@ def extract_texts( print(f"Maximum number of extractions reached: {max_allowed}.") break - # print(f"\nbatch: {batch}\n") - return extracted_texts @@ -845,8 +883,8 @@ def fetch_additional_information( response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, - temperature=0, - max_tokens=max_tokens, + temperature=0, # Override the default temperature parameter set for the engine + max_tokens=500, # Override the default max_tokens parameter set for the engine n=1, timeout=90, request_timeout=90, @@ -863,6 +901,8 @@ def fetch_additional_information( api_key=google_api_key, engine=google_engine, ) + for url in urls: + print(f"url: {url}") # Extract texts from URLs texts = extract_texts( @@ -939,6 +979,18 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: else "" ) + start_time = time.time() + # # Truncate additional information to stay within the chat completion token limit of 4096 + enc = tiktoken.get_encoding("cl100k_base") # Get the tiktoken base encoding + additional_information = truncate_additional_information( + additional_information, + max_tokens, + prompt=prompt, + enc=enc, + ) + end_time = time.time() + print(f"Time taken to truncate additional information: {end_time - start_time} seconds.") + # Generate the prediction prompt timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') prediction_prompt = PREDICTION_PROMPT.format( From f0ec8e993a86bf0adca60c5f1555b3c61cdafe43 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Mon, 25 Sep 2023 13:21:43 +0200 Subject: [PATCH 14/34] feat: Added conten type check before url request; removed retry logic for better efficiency --- tools/prediction_sum_url_content.py | 48 +++++++++-------------------- 1 file changed, 14 insertions(+), 34 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index f611b429..4334c4db 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -41,7 +41,6 @@ from tqdm import tqdm from sentence_transformers import SentenceTransformer, util from transformers import AutoTokenizer, AutoModel, BertForPreTraining, BertForMaskedLM -from urllib3.util.retry import Retry NUM_URLS_EXTRACT = 5 MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 @@ -228,27 +227,14 @@ def truncate_additional_information( user_prompt_tokens_token_enc = enc.encode(prompt) prediction_prompt_tokens_token_enc = enc.encode(PREDICTION_PROMPT) - print("Max Total Tokens:", MAX_TOTAL_TOKENS_CHAT_COMPLETION) - print("Number of tokens in additional informations:", len(additional_information_token_enc)) - print("Number of tokens in user prompt:", len(user_prompt_tokens_token_enc)) - print("Number of tokens in prediction prompt:", len(prediction_prompt_tokens_token_enc)) - print("Number of tokens reserved for chat completion output:", max_tokens) - # Calculate the rough token sum of final prediction prompt prompt_token_sum = len(additional_information_token_enc) + len(user_prompt_tokens_token_enc) + len(prediction_prompt_tokens_token_enc) + max_tokens - print(f"Total number of tokens in prompt: {prompt_token_sum}") prompt_token_sum_safety_factor = prompt_token_sum * safety_factor - print(f"Total number of tokens in prompt with safety factor: {prompt_token_sum_safety_factor}") + # Truncate the additional information string if the token sum exceeds the maximum allowed if prompt_token_sum_safety_factor > MAX_TOTAL_TOKENS_CHAT_COMPLETION: num_tokens_to_truncate = prompt_token_sum_safety_factor - MAX_TOTAL_TOKENS_CHAT_COMPLETION - print(f"Truncating additional information by {num_tokens_to_truncate} tokens.") - - # Truncate the additional informations tokens truncated_additional_informations_token = additional_information_token_enc[:-int(num_tokens_to_truncate)] - print(f"Number of tokens in truncated additional informations: {len(truncated_additional_informations_token)}") - - # Decode the truncated tokens back into text truncated_additional_informations_string = enc.decode(truncated_additional_informations_token) return truncated_additional_informations_string else: @@ -721,15 +707,6 @@ def process_in_batches( session = Session() - # Set up retry logic - retries = Retry( - total=2, - backoff_factor=0.1, - status_forcelist=[500, 502, 503, 504] - ) - session.mount('http://', HTTPAdapter(max_retries=retries)) - session.mount('https://', HTTPAdapter(max_retries=retries)) - # User-Agent headers headers = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) Gecko/20100101 Firefox/117.0' @@ -743,9 +720,18 @@ def process_in_batches( batch = urls[i : i + batch_size] # Submit the batch of URLs for processing - futures = [ - (executor.submit(session.get, url, headers=headers, timeout=timeout), url) for url in batch - ] + futures = [] + for url in batch: + # Submit a HEAD request to the url to check the Content-Type + head_future = executor.submit(session.head, url, headers=headers, timeout=timeout) + head_response = head_future.result() + if 'text/html' not in head_response.headers.get('Content-Type', ''): + print(f"Aborting, {url} is not an HTML page.") + continue + else: + # Submit a GET request to the url + futures.append((executor.submit(session.get, url, headers=headers, timeout=timeout), url)) + yield futures @@ -789,9 +775,6 @@ def extract_texts( for future, url in tqdm(batch, desc="Processing URLs"): try: result = future.result() - print(f"\nURL: {url}") - print(f"Status code: {result.status_code}") - print(f"Content type: {result.headers.get('content-type')}\n") if result.status_code != 200: del result continue @@ -979,8 +962,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: else "" ) - start_time = time.time() - # # Truncate additional information to stay within the chat completion token limit of 4096 + # Truncate additional information to stay within the chat completion token limit of 4096 enc = tiktoken.get_encoding("cl100k_base") # Get the tiktoken base encoding additional_information = truncate_additional_information( additional_information, @@ -988,8 +970,6 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: prompt=prompt, enc=enc, ) - end_time = time.time() - print(f"Time taken to truncate additional information: {end_time - start_time} seconds.") # Generate the prediction prompt timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') From 11292d63f9c06778d1ef331e8de183b11662c9c4 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Mon, 25 Sep 2023 15:05:05 +0200 Subject: [PATCH 15/34] chore: Allowed for redirects for header request; Removed print statements --- tools/prediction_sum_url_content.py | 53 ++++++++++++----------------- 1 file changed, 21 insertions(+), 32 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 4334c4db..42edaf48 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -25,22 +25,20 @@ import json import re import traceback -from concurrent.futures import Future, ThreadPoolExecutor, as_completed +from concurrent.futures import Future, ThreadPoolExecutor from bs4 import BeautifulSoup from googleapiclient.discovery import build import openai import requests from requests import Session -from requests.adapters import HTTPAdapter import spacy import tiktoken import torch from dateutil import parser from tqdm import tqdm -from sentence_transformers import SentenceTransformer, util -from transformers import AutoTokenizer, AutoModel, BertForPreTraining, BertForMaskedLM +from transformers import AutoTokenizer, AutoModel NUM_URLS_EXTRACT = 5 MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 @@ -407,7 +405,6 @@ def get_context_around_isolated_dates( break context = doc_text[max(0, start_token):min(len_doc_text, end_token)].text - print(f"Successfully extracted context for isolated date {target_date_ydm}: {context}\n") contexts_list.append(context) return contexts_list @@ -445,21 +442,9 @@ def get_sentence_embeddings_and_similarities( # Repeat the question embedding tensor to match the batch size question_embedding_repeated = question_embedding.repeat(batch_size, 1) - # Print the number of sentences - # print(f"Number of sentences: {len(sentences)}") - - # Print the number of batches - # print(f"Number of batches to create: {len(sentences) // batch_size + 1}") - # Batch the sentences for efficient processing sentence_batches = [sentences[i:i + batch_size] for i in range(0, len(sentences), batch_size)] - # print(f"Number of batches created: {len(sentence_batches)}") - # Number of sentences in each batch - # print(f"Number of sentences in all batches except the last: {len(sentence_batches[:-1])}") - - # print(f"Number of sentences in the last batch: {len(sentence_batches[-1])}") - for batch in tqdm(sentence_batches, desc="Calculating sentence similarities"): # Adjust the repeated question embedding if the batch size changes actual_batch_size = len(batch) @@ -562,12 +547,11 @@ def get_website_summary( relevant_sentences = [ sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.9 ] - - # # Print sentences and similarities if similarity is greater than 0.9 - # for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - # if sim > 0.9: - # print(f"Similarity: {sim}\nSentence: {sent}\n") - # print() + + # Print similarity scores along with the sentences + for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + print(f"{sim:.4f}: {sent}") + print() if not relevant_sentences: return "" @@ -706,6 +690,7 @@ def process_in_batches( raise ValueError("The 'timeout' must be greater than zero.") session = Session() + session.max_redirects = 3 # User-Agent headers headers = { @@ -721,12 +706,16 @@ def process_in_batches( # Submit the batch of URLs for processing futures = [] + for url in batch: # Submit a HEAD request to the url to check the Content-Type - head_future = executor.submit(session.head, url, headers=headers, timeout=timeout) + head_future = executor.submit(session.head, url, headers=headers, timeout=timeout, allow_redirects=True) head_response = head_future.result() + + print(f"Content-Type: {head_response.headers.get('Content-Type')}") if 'text/html' not in head_response.headers.get('Content-Type', ''): - print(f"Aborting, {url} is not an HTML page.") + print(f"\nAborting, {url} is not an HTML page.") + print(head_response.headers) continue else: # Submit a GET request to the url @@ -788,7 +777,6 @@ def extract_texts( nlp=nlp, max_words=max_words, ) - # print(f"extracted_text: {extracted_text}") # Delete the result object to free memory del result @@ -797,13 +785,10 @@ def extract_texts( if extracted_text: extracted_texts.append(f"{url}\n{extracted_text}") count += 1 - # print(f"extracted_texts: {extracted_texts}\n") - print(f"count: {count}\n") # Break if the maximum number of extractions is reached if count >= max_allowed: stop = True - print(f"Maximum number of extractions reached: {max_allowed}.") break except requests.exceptions.ReadTimeout: @@ -818,7 +803,6 @@ def extract_texts( # Break if the maximum number of extractions is reached if stop: - print(f"Maximum number of extractions reached: {max_allowed}.") break return extracted_texts @@ -876,7 +860,11 @@ def fetch_additional_information( # Parse the response content json_data = json.loads(response.choices[0].message.content) - print(f"json_data: {json_data}") + # 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( @@ -884,6 +872,7 @@ def fetch_additional_information( api_key=google_api_key, engine=google_engine, ) + print("\nURLS:") for url in urls: print(f"url: {url}") @@ -976,7 +965,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: prediction_prompt = PREDICTION_PROMPT.format( user_prompt=prompt, additional_information=additional_information, timestamp=timestamp, ) - print(f"PREDICTION PROMPT: {prediction_prompt}\n") + print(f"\nPREDICTION PROMPT: {prediction_prompt}\n") # Perform moderation moderation_result = openai.Moderation.create(prediction_prompt) From fcf3116c4239b2c46a7d2e85d3b0e9b746edcd18 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Mon, 25 Sep 2023 21:39:05 +0200 Subject: [PATCH 16/34] chore: minor changes to print statements, variable names. --- tools/prediction_sum_url_content.py | 73 +++++++++++++++++------------ 1 file changed, 43 insertions(+), 30 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 42edaf48..40182b66 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -21,10 +21,8 @@ from typing import Any, Dict, Generator, List, Optional, Tuple from datetime import datetime -import time import json import re -import traceback from concurrent.futures import Future, ThreadPoolExecutor from bs4 import BeautifulSoup @@ -257,24 +255,28 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in """ results = set() - max_num = 10 + max_num_fetch = 10 - if num > max_num: - raise ValueError(f"The maximum number of URLs per query is {max_num}.") + 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 # Limit the number of returned URLs per query + 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: + # print(f"URL: {url}") results.add(url) count += 1 + # print(f"Count: {count}") if count >= num: break @@ -283,6 +285,9 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in # count += 1 # if count >= num: # break + print("get_urls_from_queries result:") + for url in results: + print(url) return list(results) @@ -483,7 +488,7 @@ def get_website_summary( model: The BERT model for text embeddings. tokenizer: The tokenizer for the BERT model. nlp: The spaCy NLP model. - max_words (int, optional): Maximum number of words for the output summary. Defaults to 200. + max_words (int): Maximum number of words for the output summary. Raises: ValueError: If max_words is less than 1 or greater than 300. @@ -549,9 +554,9 @@ def get_website_summary( ] # Print similarity scores along with the sentences - for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - print(f"{sim:.4f}: {sent}") - print() + # for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + # print(f"{sim:.4f}: {sent}") + # print() if not relevant_sentences: return "" @@ -690,7 +695,7 @@ def process_in_batches( raise ValueError("The 'timeout' must be greater than zero.") session = Session() - session.max_redirects = 3 + session.max_redirects = 5 # User-Agent headers headers = { @@ -706,20 +711,22 @@ def process_in_batches( # Submit the batch of URLs for processing futures = [] - for url in batch: - # Submit a HEAD request to the url to check the Content-Type - head_future = executor.submit(session.head, url, headers=headers, timeout=timeout, allow_redirects=True) - head_response = head_future.result() - - print(f"Content-Type: {head_response.headers.get('Content-Type')}") - if 'text/html' not in head_response.headers.get('Content-Type', ''): - print(f"\nAborting, {url} is not an HTML page.") - print(head_response.headers) - continue - else: - # Submit a GET request to the url - futures.append((executor.submit(session.get, url, headers=headers, timeout=timeout), url)) + try: + # Submit a HEAD request to the url to check the Content-Type + head_future = executor.submit(session.head, url, headers=headers, timeout=timeout, allow_redirects=True) + head_response = head_future.result() + + print(f"Content-Type: {head_response.headers.get('Content-Type')}") + if 'text/html' not in head_response.headers.get('Content-Type', ''): + print(f"\nAborting, {url} is not an HTML page.") + print(head_response.headers) + 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 @@ -746,6 +753,11 @@ def extract_texts( # ~ 2642 tokens free for additional information ~ 1981 words # split by number of URLs max_words = 1981 // len(urls) + # print(f"Max allowed extractions: {max_allowed}") + # print(f"Max words per extraction: {max_words}") + # print("URLS:") + # for url in urls: + # print(f"url: {url}") # Initialize empty list for storing extracted texts extracted_texts = [] @@ -783,11 +795,12 @@ def extract_texts( # 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: + print(f"Maximum number of extractions reached: {max_allowed}.") stop = True break @@ -799,10 +812,10 @@ def extract_texts( 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: + print(f"Maximum number of extractions reached: {max_allowed}.") break return extracted_texts @@ -850,7 +863,7 @@ def fetch_additional_information( response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, - temperature=0, # Override the default temperature parameter set for the engine + temperature=0.7, # Override the default temperature parameter set for the engine max_tokens=500, # Override the default max_tokens parameter set for the engine n=1, timeout=90, @@ -872,9 +885,9 @@ def fetch_additional_information( api_key=google_api_key, engine=google_engine, ) - print("\nURLS:") - for url in urls: - print(f"url: {url}") + # print("\nFetch additional information URLS:") + # for url in urls: + # print(f"url: {url}") # Extract texts from URLs texts = extract_texts( From bf68bbb48be74f3d78195f03b34ee3861b3c449b Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Mon, 25 Sep 2023 23:41:38 +0200 Subject: [PATCH 17/34] feat: Changed sentence transformers model and used dot score for calculating similarities --- tools/prediction_sum_url_content.py | 57 ++++++++++++++++++++++------- 1 file changed, 43 insertions(+), 14 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 40182b66..d8a6a3a2 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -33,10 +33,12 @@ import spacy import tiktoken import torch +import traceback from dateutil import parser from tqdm import tqdm -from transformers import AutoTokenizer, AutoModel +from transformers import AutoTokenizer, AutoModel, AutoModelForQuestionAnswering, pipeline +from sentence_transformers import SentenceTransformer, util NUM_URLS_EXTRACT = 5 MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 @@ -509,8 +511,7 @@ def get_website_summary( # Calculate the BERT embedding for the event question with torch.no_grad(): question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) - question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) - + #question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) # Truncate text to stay within nlp character limit of 1,000,000 text = text[:1000000] @@ -518,6 +519,8 @@ def get_website_summary( doc_text = nlp(text) doc_question = nlp(event_question) + query_emb = model.encode(event_question) + # Extract the date from the event question if present for ent in doc_question.ents: if ent.label_ == 'DATE': @@ -543,10 +546,31 @@ def get_website_summary( # Limit the number of sentences for performance sentences = sentences[:num_sentences_threshold] - # Calculate sentence similarities - similarities = get_sentence_embeddings_and_similarities( - sentences, question_embedding, model, tokenizer, batch_size=16 - ) + # # Calculate sentence similarities + # similarities = get_sentence_embeddings_and_similarities( + # sentences, question_embedding, model, tokenizer, batch_size=16 + # ) + + + sent_emb = model.encode(sentences) + similarities = util.dot_score(query_emb, sent_emb)[0].cpu().tolist() + + + + # similarities = [] + # print("Now initialize pipeline()") + # nlp_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer) + + # for sent in sentences: + # QA_input = { + # "question": event_question, + # "context": text + # } + # result = nlp_pipeline(QA_input, padding=True, truncation=True) + # print(f"Score: {result['score']}") + # print(f"Sentence: {sent}\n") + # similarities.append(result['score']) + # Extract top relevant sentences relevant_sentences = [ @@ -554,9 +578,11 @@ def get_website_summary( ] # Print similarity scores along with the sentences - # for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - # print(f"{sim:.4f}: {sent}") - # print() + + for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + if sim > 0.6: + print(f"{sim:.4f}: {sent}") + print() if not relevant_sentences: return "" @@ -632,6 +658,7 @@ def extract_text( str: Summarized text with the date. """ + print(f"Started extract_text function") if not html: raise ValueError("HTML is empty.") @@ -767,8 +794,8 @@ def extract_texts( stop = False # Initialize BERT and Spacy models - model = AutoModel.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") - tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english") + model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-tas-b') + tokenizer = AutoTokenizer.from_pretrained("csarron/bert-base-uncased-squad-v1") nlp = spacy.load("en_core_web_sm") # Process URLs in batches @@ -779,7 +806,7 @@ def extract_texts( if result.status_code != 200: del result continue - + print("Request successful.") # Extract relevant information for the event question extracted_text = extract_text( html=result.text, @@ -812,6 +839,7 @@ def extract_texts( 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: @@ -863,7 +891,7 @@ def fetch_additional_information( response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, - temperature=0.7, # Override the default temperature parameter set for the engine + temperature=1, # Override the default temperature parameter set for the engine max_tokens=500, # Override the default max_tokens parameter set for the engine n=1, timeout=90, @@ -872,6 +900,7 @@ def fetch_additional_information( ) # 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") From 0f20bb2971abba4cf24a055e32f4cdde2966c630 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Tue, 26 Sep 2023 02:58:04 +0200 Subject: [PATCH 18/34] feat: Changed to sentence transformer model multi-qa-distilbert-cos-v1 for better performance --- tools/prediction_sum_url_content.py | 131 ++++++---------------------- 1 file changed, 25 insertions(+), 106 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index d8a6a3a2..870d253f 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -37,7 +37,6 @@ from dateutil import parser from tqdm import tqdm -from transformers import AutoTokenizer, AutoModel, AutoModelForQuestionAnswering, pipeline from sentence_transformers import SentenceTransformer, util NUM_URLS_EXTRACT = 5 @@ -417,67 +416,10 @@ def get_context_around_isolated_dates( return contexts_list -def get_sentence_embeddings_and_similarities( - sentences: List[str], - question_embedding: torch.Tensor, - model, - tokenizer, - batch_size: int = 32 -) -> Tuple[List[torch.Tensor], List[float]]: - """ - Calculate the sentence embeddings and similarities. - - Args: - sentences (List[str]): List of sentences to compare. - question_embedding (torch.Tensor): Tensor of the question embedding. - model: The BERT model for text embeddings. - tokenizer: The tokenizer for the BERT model. - batch_size (int, optional): Number of sentences to process in each batch. Defaults to 32. - - Raises: - ValueError: If batch_size is less than 1. - - Returns: - Tuple[List[torch.Tensor], List[float]]: List of sentence embeddings and their similarities. - """ - - if batch_size < 1: - raise ValueError("Batch size must be at least 1.") - - similarities = [] - - # Repeat the question embedding tensor to match the batch size - question_embedding_repeated = question_embedding.repeat(batch_size, 1) - - # Batch the sentences for efficient processing - sentence_batches = [sentences[i:i + batch_size] for i in range(0, len(sentences), batch_size)] - - for batch in tqdm(sentence_batches, desc="Calculating sentence similarities"): - # Adjust the repeated question embedding if the batch size changes - actual_batch_size = len(batch) - if actual_batch_size != batch_size: - question_embedding_repeated = question_embedding.repeat(actual_batch_size, 1) - try: - with torch.no_grad(): - # Tokenize and preprocess sentence batch - sentence_tokens = tokenizer(batch, return_tensors="pt", padding=True, truncation=True) - # Compute sentence embeddings - sentence_embedding = model(**sentence_tokens).last_hidden_state.mean(dim=1) - # Compute cosine similarities - similarity = torch.cosine_similarity(question_embedding_repeated, sentence_embedding).tolist() - similarities.extend(similarity) - finally: - # Free up GPU memory - del sentence_tokens, sentence_embedding, similarity - - return similarities - - def get_website_summary( text: str, event_question: str, model, - tokenizer, nlp, max_words: int ) -> str: @@ -501,17 +443,13 @@ def get_website_summary( # Constants for sentence length and number thresholds len_sentence_threshold = 5 - num_sentences_threshold = 100 + num_sentences_threshold = 1000 event_date_sentences = [] # Validate inputs if not event_question or not text: return "" - # Calculate the BERT embedding for the event question - with torch.no_grad(): - question_tokens = tokenizer(event_question, return_tensors="pt", padding=True, truncation=True) - #question_embedding = model(**question_tokens).last_hidden_state.mean(dim=1) # Truncate text to stay within nlp character limit of 1,000,000 text = text[:1000000] @@ -519,8 +457,6 @@ def get_website_summary( doc_text = nlp(text) doc_question = nlp(event_question) - query_emb = model.encode(event_question) - # Extract the date from the event question if present for ent in doc_question.ents: if ent.label_ == 'DATE': @@ -533,9 +469,9 @@ def get_website_summary( ) seen = set() - sentences = [] + sentences = [] - # Extract unique and sufficiently long sentences + # 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: @@ -543,44 +479,36 @@ def get_website_summary( seen.add(sentence_text) sentences.extend(event_date_sentences) - # Limit the number of sentences for performance + if not sentences: + return "" + + # Limit the number of sentences for performance optimization sentences = sentences[:num_sentences_threshold] - - # # Calculate sentence similarities - # similarities = get_sentence_embeddings_and_similarities( - # sentences, question_embedding, model, tokenizer, batch_size=16 - # ) - - + + print(f"Number of sentences: {len(sentences)}") + + # Encode event question and sentences + query_emb = model.encode(event_question) sent_emb = model.encode(sentences) - similarities = util.dot_score(query_emb, sent_emb)[0].cpu().tolist() - + print(f"Query embedding shape: {query_emb.shape}") + print(f"Sentence embedding shape: {sent_emb.shape}") - - # similarities = [] - # print("Now initialize pipeline()") - # nlp_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer) - - # for sent in sentences: - # QA_input = { - # "question": event_question, - # "context": text - # } - # result = nlp_pipeline(QA_input, padding=True, truncation=True) - # print(f"Score: {result['score']}") - # print(f"Sentence: {sent}\n") - # similarities.append(result['score']) + # Check for empty embeddings + if not query_emb.size or not sent_emb.size: + print(f"Sentences: {sentences}") + print(f"Query embedding: {query_emb}") + print(f"Sentence embedding: {sent_emb}") + 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 > 0.9 + sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.4 ] # Print similarity scores along with the sentences - for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - if sim > 0.6: + if sim > 0.4: print(f"{sim:.4f}: {sent}") print() @@ -588,7 +516,7 @@ def get_website_summary( return "" # Truncate summary to fit max_words limit - output = ' '.join(relevant_sentences[:10]) + output = ' '.join(relevant_sentences[:20]) output_words = output.split(' ') if len(output_words) > max_words: output = ' '.join(output_words[:max_words]) @@ -637,7 +565,6 @@ def extract_text( html: str, event_question: str, model, - tokenizer, nlp, max_words: int, ) -> str: @@ -685,7 +612,6 @@ def extract_text( text=text, event_question=event_question, model=model, - tokenizer=tokenizer, nlp=nlp, max_words=max_words, ) @@ -780,11 +706,6 @@ def extract_texts( # ~ 2642 tokens free for additional information ~ 1981 words # split by number of URLs max_words = 1981 // len(urls) - # print(f"Max allowed extractions: {max_allowed}") - # print(f"Max words per extraction: {max_words}") - # print("URLS:") - # for url in urls: - # print(f"url: {url}") # Initialize empty list for storing extracted texts extracted_texts = [] @@ -794,8 +715,7 @@ def extract_texts( stop = False # Initialize BERT and Spacy models - model = SentenceTransformer('sentence-transformers/msmarco-distilbert-base-tas-b') - tokenizer = AutoTokenizer.from_pretrained("csarron/bert-base-uncased-squad-v1") + model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1') nlp = spacy.load("en_core_web_sm") # Process URLs in batches @@ -812,7 +732,6 @@ def extract_texts( html=result.text, event_question=event_question, model=model, - tokenizer=tokenizer, nlp=nlp, max_words=max_words, ) @@ -822,7 +741,7 @@ def extract_texts( # Append the extracted text if available and increment the count if extracted_text: - extracted_texts.append(extracted_text) + extracted_texts.append(f"{url}\n{extracted_text}") count += 1 # Break if the maximum number of extractions is reached From 3f001c99989e0b0f865303eb7055a0154a195083 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Tue, 26 Sep 2023 16:18:13 +0200 Subject: [PATCH 19/34] chore: Added helper functions and did minor changes in variable and parameter names. --- tools/prediction_sum_url_content.py | 374 ++++++++++++++++------------ 1 file changed, 210 insertions(+), 164 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 870d253f..af646417 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -19,6 +19,7 @@ """This module implements a Mech tool for binary predictions.""" +import time from typing import Any, Dict, Generator, List, Optional, Tuple from datetime import datetime import json @@ -41,8 +42,9 @@ NUM_URLS_EXTRACT = 5 MAX_TOTAL_TOKENS_CHAT_COMPLETION = 4096 +WORDS_PER_TOKEN_FACTOR = 0.75 DEFAULT_OPENAI_SETTINGS = { - "max_tokens": 200, + "max_compl_tokens": 200, "temperature": 0.2, } @@ -179,7 +181,7 @@ "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" + "object", "param", "embed", "link" ] @@ -198,44 +200,89 @@ def search_google(query: str, api_key: str, engine: str, num: int = 3) -> List[s return [result["link"] for result in search["items"]] -def truncate_additional_information( - additional_informations: str, - max_tokens: int, +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. - Parameters: + Args: additional_informations (str): The additional information string to be truncated. - max_tokens (int): The maximum number of chat completion output tokens. - prompt (str): The user prompt containing the event question. + max_add_tokens (int): The maximum number of tokens allowed for the additional information string. enc (tiktoken.Encoding): The tiktoken encoding to be used. - safety_factor (float, optional): The safety factor to be used for truncation. Defaults to 1.05. Returns: - str: The truncated additional information string. """ - # Encode the strings into tokens - additional_information_token_enc = enc.encode(additional_informations) - user_prompt_tokens_token_enc = enc.encode(prompt) - prediction_prompt_tokens_token_enc = enc.encode(PREDICTION_PROMPT) - - # Calculate the rough token sum of final prediction prompt - prompt_token_sum = len(additional_information_token_enc) + len(user_prompt_tokens_token_enc) + len(prediction_prompt_tokens_token_enc) + max_tokens - prompt_token_sum_safety_factor = prompt_token_sum * safety_factor - - # Truncate the additional information string if the token sum exceeds the maximum allowed - if prompt_token_sum_safety_factor > MAX_TOTAL_TOKENS_CHAT_COMPLETION: - num_tokens_to_truncate = prompt_token_sum_safety_factor - MAX_TOTAL_TOKENS_CHAT_COMPLETION - truncated_additional_informations_token = additional_information_token_enc[:-int(num_tokens_to_truncate)] - truncated_additional_informations_string = enc.decode(truncated_additional_informations_token) - return truncated_additional_informations_string - else: + # 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]: @@ -245,7 +292,7 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in Args: queries (List[str]): List of search engine queries. api_key (str): API key for the search engine. - engine (str): Search engine to be used. + engine (str): Custom google search engine ID. num (int, optional): Number of returned URLs per query. Defaults to 3. Raises: @@ -271,24 +318,17 @@ def get_urls_from_queries(queries: List[str], api_key: str, engine: str, num: in # Add only unique URLs up to 'num' per query, omitting PDF and 'download' URLs count = 0 - - for url in fetched_urls: - # print(f"URL: {url}") - results.add(url) - count += 1 - # print(f"Count: {count}") - if count >= num: - break - - # if "download" not in url.lower() and url not in results and not url.endswith(".pdf"): - # results.add(url) - # count += 1 - # if count >= num: - # break + 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) @@ -298,6 +338,9 @@ def standardize_date(date_text): 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. @@ -330,20 +373,20 @@ def standardize_date(date_text): return None -def get_context_around_isolated_dates( - doc_text, target_date_ydm, len_sentence_threshold, max_context=50 +def get_context_around_isolated_event_date( + doc_text, event_date_ymd, len_sentence_threshold, max_context=50 ): """ - Extract context around isolated dates within the text. + Extract sentences around isolated dates within the text. Args: doc_text (spaCy Doc): Document text as a spaCy Doc object. - target_date_ydm (str): Target date in year-day-month format. + 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 max_context is less than len_sentence_threshold or greater than 300. + ValueError: If maximum context is less than threshold or greater than 100. Returns: list: List of sentences surrounding the target date. @@ -354,23 +397,25 @@ def get_context_around_isolated_dates( 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 > 300: + if max_context > 100: raise ValueError( f"The maximum number of words must be less than or equal to 300." ) - + contexts_list = [] - target_date_dm = target_date_ydm[5:] 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 == target_date_ydm or standardized_date == target_date_dm: + 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 @@ -416,60 +461,41 @@ def get_context_around_isolated_dates( return contexts_list -def get_website_summary( +def extract_relevant_information( text: str, - event_question: str, + query_emb, + event_date: str, model, nlp, max_words: int ) -> str: """ - Generate a summary of a website's text based on a given event question. + Extract relevant information from website text based on a given event question. Args: - text (str): The website text to summarize. - event_question (str): The question to focus the summary on. + 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. - tokenizer: The tokenizer for the BERT model. nlp: The spaCy NLP model. - max_words (int): Maximum number of words for the output summary. - - Raises: - ValueError: If max_words is less than 1 or greater than 300. + max_words (int): Maximum number of words allowed for output. Returns: - str: The generated summary. + 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 = [] - - # Validate inputs - if not event_question or not text: - return "" + seen = set() - # Truncate text to stay within nlp character limit of 1,000,000 - text = text[:1000000] + # Truncate text for performance optimization + text = text[:50000] - # Apply NLP pipeline to text and event question + # Apply NLP pipeline to text doc_text = nlp(text) - doc_question = nlp(event_question) - - # Extract the date from the event question if present - for ent in doc_question.ents: - if ent.label_ == 'DATE': - event_date_ydm = standardize_date(ent.text) - - # Extract contextual sentences around isolated dates - if event_date_ydm is not None: - event_date_sentences.extend( - get_context_around_isolated_dates(doc_text, event_date_ydm, len_sentence_threshold, max_context=50) - ) - - seen = set() - sentences = [] # Extract unique sentences for sent in doc_text.sents: @@ -479,26 +505,21 @@ def get_website_summary( 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] - - print(f"Number of sentences: {len(sentences)}") - - # Encode event question and sentences - query_emb = model.encode(event_question) + + # Encode event question calculate similarity scores sent_emb = model.encode(sentences) - print(f"Query embedding shape: {query_emb.shape}") - print(f"Sentence embedding shape: {sent_emb.shape}") - - # Check for empty embeddings - if not query_emb.size or not sent_emb.size: - print(f"Sentences: {sentences}") - print(f"Query embedding: {query_emb}") - print(f"Sentence embedding: {sent_emb}") - similarities = util.dot_score(query_emb, sent_emb)[0].cpu().tolist() # Extract top relevant sentences @@ -506,16 +527,16 @@ def get_website_summary( sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.4 ] - # Print similarity scores along with the sentences - for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - if sim > 0.4: - print(f"{sim:.4f}: {sent}") - print() + # # Print similarity scores along with the sentences + # for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): + # if sim > 0.4: + # print(f"{sim:.4f}: {sent}") + # print() if not relevant_sentences: return "" - # Truncate summary to fit max_words limit + # Truncate text to fit max_words limit output = ' '.join(relevant_sentences[:20]) output_words = output.split(' ') if len(output_words) > max_words: @@ -526,7 +547,7 @@ def get_website_summary( def get_date(soup): """ - Retrieves the release and modification dates from the soup object containing the text of the website. + Retrieves the release and modification dates from the soup object containing the HTML tree. Args: soup (BeautifulSoup): The BeautifulSoup object for the webpage. @@ -563,7 +584,8 @@ def get_date(soup): def extract_text( html: str, - event_question: str, + query_emb, + event_date: str, model, nlp, max_words: int, @@ -574,27 +596,31 @@ def extract_text( Args: html (str): The HTML content to extract text from. event_question (str): Event question for context. - model: Pre-trained model for text summarization. - tokenizer: Tokenizer for the pre-trained model. + 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: Summarized text with the date. + str: Relevant website information with release date. """ - print(f"Started extract_text function") if not html: raise ValueError("HTML is empty.") + + # print(f"HTML:\n{html}") + print() soup = BeautifulSoup(html, "html.parser") # Get the date of the website date = get_date(soup) if date is None: - raise ValueError("Could not extract date from the HTML") + raise ValueError("Could not extract release or update date from HTML.") # Remove unnecessary tags to clean up text for script in soup(HTML_TAGS_TO_REMOVE): @@ -608,18 +634,19 @@ def extract_text( text = re.sub(r"\.{2,}", ".", text) # Get summarized text - text_summary = get_website_summary( + relevant_text = extract_relevant_information( text=text, - event_question=event_question, + query_emb=query_emb, + event_date=event_date, model=model, nlp=nlp, max_words=max_words, ) - if not text_summary: + if not relevant_text: return "" - return f"{date}:\n{text_summary}" + return f"{date}:\n{relevant_text}" def process_in_batches( @@ -666,11 +693,9 @@ def process_in_batches( futures = [] for url in batch: try: - # Submit a HEAD request to the url to check the Content-Type + # 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() - - print(f"Content-Type: {head_response.headers.get('Content-Type')}") if 'text/html' not in head_response.headers.get('Content-Type', ''): print(f"\nAborting, {url} is not an HTML page.") print(head_response.headers) @@ -687,25 +712,26 @@ def process_in_batches( def extract_texts( urls: List[str], event_question: str, + max_words_per_url: int, ) -> List[str]: """ Extract texts from a list of URLs using BERT and Spacy. - Parameters: - urls (List[str]): List of URLs to extract text from. - event_question (str): Event-related question for text extraction. + 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. + List[str]: List of extracted texts. """ # Maximum number of allowed extractions max_allowed = 25 - - # Maximum number of words for each extraction - # ~ 2642 tokens free for additional information ~ 1981 words - # split by number of URLs - max_words = 1981 // len(urls) # Initialize empty list for storing extracted texts extracted_texts = [] @@ -714,10 +740,21 @@ def extract_texts( count = 0 stop = False - # Initialize BERT and Spacy models + # Initialize Sentence Transformer and Spacy models model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1') nlp = spacy.load("en_core_web_sm") + + # Process the event question with spacy + doc_question = nlp(event_question) + event_date = extract_event_date(doc_question) + + # Create sentence embeddings for the 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}") + start_time = time.time() # Process URLs in batches for batch in process_in_batches(urls=urls): for future, url in tqdm(batch, desc="Processing URLs"): @@ -726,14 +763,14 @@ def extract_texts( if result.status_code != 200: del result continue - print("Request successful.") # Extract relevant information for the event question extracted_text = extract_text( html=result.text, - event_question=event_question, + query_emb=query_emb, + event_date=event_date, model=model, nlp=nlp, - max_words=max_words, + max_words=max_words_per_url, ) # Delete the result object to free memory @@ -741,17 +778,14 @@ def extract_texts( # Append the extracted text if available and increment the count if extracted_text: - extracted_texts.append(f"{url}\n{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: - print(f"Maximum number of extractions reached: {max_allowed}.") stop = True break - - except requests.exceptions.ReadTimeout: - print(f"Request timed out: {url}.") except requests.exceptions.Timeout: print(f"Request for {url} timed out.") @@ -762,34 +796,40 @@ def extract_texts( # Break if the maximum number of extractions is reached if stop: - print(f"Maximum number of extractions reached: {max_allowed}.") break + + end_time = time.time() + print(f"Time elapsed: {end_time - start_time:.4f} seconds") + return extracted_texts def fetch_additional_information( event_question: str, - engine: str, - temperature: float, - max_tokens: int, + max_add_words: int, google_api_key: str, google_engine: str, + engine: str = "gpt-3.5-turbo", + temperature: float = 1.0, + max_compl_tokens: int = 500, ) -> str: """ - Fetch additional information based on an event question. + 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. - engine (str): The engine to be used for fetching information. - temperature (float): The temperature parameter for the engine. - max_tokens (int): The maximum number of tokens for the engine's response. + 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 additional information fetched. + str: The relevant information fetched from all the URLs concatenated. """ # Create URL query prompt @@ -808,10 +848,10 @@ def fetch_additional_information( # Fetch queries from the OpenAI engine response = openai.ChatCompletion.create( - model="gpt-3.5-turbo", + model=engine, messages=messages, - temperature=1, # Override the default temperature parameter set for the engine - max_tokens=500, # Override the default max_tokens parameter set for the engine + 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, @@ -833,14 +873,15 @@ def fetch_additional_information( api_key=google_api_key, engine=google_engine, ) - # print("\nFetch additional information URLS:") - # for url in urls: - # print(f"url: {url}") + + # 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, ) # Join the texts and return @@ -851,7 +892,7 @@ def fetch_additional_information( def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: """ - Run the task with the given parameters. + Run the task with the given arguments. Args: kwargs (Dict): Keyword arguments that specify settings and API keys. @@ -865,12 +906,9 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: Tuple[str, Optional[Dict[str, Any]]]: The generated content and any additional data. """ - print("Starting...") - print() - tool = kwargs["tool"] prompt = kwargs["prompt"] - max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"]) + max_compl_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"]) temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) if not tool or not prompt: @@ -879,7 +917,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: # Print the settings print(f"MECH TOOL: {tool}") print(f"PROMPT: {prompt}") - print(f"MAX OPENAI RETURN TOKENS: {max_tokens}") + print(f"MAX OPENAI RETURN TOKENS: {max_compl_tokens}") print(f"LLM TEMPERATURE: {temperature}") openai.api_key = kwargs["api_keys"]["openai"] @@ -898,13 +936,25 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: 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_tokens=max_tokens, + max_compl_tokens=max_compl_tokens, + max_add_words=max_add_words, google_api_key=kwargs["api_keys"]["google_api_key"], google_engine=kwargs["api_keys"]["google_engine_id"], ) @@ -913,12 +963,8 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: ) # Truncate additional information to stay within the chat completion token limit of 4096 - enc = tiktoken.get_encoding("cl100k_base") # Get the tiktoken base encoding additional_information = truncate_additional_information( - additional_information, - max_tokens, - prompt=prompt, - enc=enc, + additional_information, max_add_tokens, enc=enc, ) # Generate the prediction prompt @@ -944,7 +990,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: model=engine, messages=messages, temperature=temperature, - max_tokens=max_tokens, + max_tokens=max_compl_tokens, n=1, timeout=150, request_timeout=150, From f2e45acb8cbbbd6d8ca787a71805635009116606 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Wed, 27 Sep 2023 00:49:22 +0200 Subject: [PATCH 20/34] feat: Changed prediction prompt formulation; Minor changes to variables and return values --- tools/prediction_sum_url_content.py | 121 ++++++++++++++++++---------- 1 file changed, 77 insertions(+), 44 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index af646417..403a60b3 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -21,12 +21,12 @@ import time from typing import Any, Dict, Generator, List, Optional, Tuple -from datetime import datetime +from datetime import datetime, timezone import json import re from concurrent.futures import Future, ThreadPoolExecutor -from bs4 import BeautifulSoup +from bs4 import BeautifulSoup, NavigableString from googleapiclient.discovery import build import openai import requests @@ -54,35 +54,67 @@ ] TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-3.5-turbo", - "prediction-online-sum-url-content": "gpt-3.5-turbo", + # "prediction-online-sum-url-content": "gpt-3.5-turbo", # "prediction-online-sum-url-content": "gpt-3.5-turbo-16k", - # "prediction-online-sum-url-content": "gpt-4", + "prediction-online-sum-url-content": "gpt-4", } + +# You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', +# which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event +# will either occur or not, given the conditions. Find the 'event question' enclosed in double quotes as a part of +# the user's prompt under 'USER_PROMPT'. The user's prompt also contains a more elaborate description of the task. +# You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", delimited by three backticks. This information results from a search engine query that has been done a few seconds ago with the aim to get up-to-date information that could be relevant estimating the 'event question'. +# You must adhere to the 'INSTRUCTIONS'. + +# * Carefully read the user's prompt under 'USER_PROMPT', enclosed by triple backticks. +# * If the 'event question' has more than two outcomes, respond with "Error" and ignore further instructions. +# * Based on your training data, provide a probability estimation of the event specified in the 'event question' occuring, considering all conditions provided. +# * You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data to make the probability estimation. +# * Prioritize recent information in "ADDITIONAL_INFORMATION" based on the current time {timestamp}. +# * You must pay very close attention to the specific wording of the 'event question' in "USER_PROMPT". +# * If a date is provided in the 'event question' specifying when the event has to have occured, you must consider in your estimation, given the current time {timestamp}, how likely it is that the event will occur within the remaining timespan to that provided date. +# * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. +# * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. +# * Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. +# * If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have been provided with up-to-date information that can be found on the internet. +# * If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. +# * You must provide your response in the format specified under "OUTPUT_FORMAT". +# * Do not include any other contents in your response. + + +# * If a deadline date is specified in the 'event question', factor it into your probability estimate, assessing the likelihood of the event occurring within the timespan from the current time {timestamp} leading up to that date. +# * For events with a deadline specified as BY or ON a particular date, treat 23:59:59 of that date as the cutoff. If no supporting information is found as the deadline nears, lower the probability estimate accordingly. For reference, the current time is {timestamp}. + + PREDICTION_PROMPT = """ -You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', -which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event -will either occur or not, given the conditions. Find the 'event question' enclosed in double quotes as a part of -the user's prompt under 'USER_PROMPT'. The user's prompt also contains a more elaborate description of the task. -You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", delimited by three backticks. This information results from a search engine query that has been done a few seconds ago with the aim to get up-to-date information that could be relevant estimating the 'event question'. -You must adhere to the 'INSTRUCTIONS'. +You are a Large Language Model (LLM) operating within a multi-agent system. Your primary task is to precisely estimate the probability of +an event occurring by or on a specific date, as specified in the 'event question' within 'USER_PROMPT'. The 'event question' has only two outcomes: the event either +occurs or it does not. Conditions for the event, often in the form of a specific deadline date, are crucial for your probability assessment. + +You receive a list of information in the "ADDITIONAL_INFORMATION" section. Each entry in this list comes with timestamps in parenthesis, showing its initial +release and last modification date. This information was obtained from a search engine query conducted a few seconds prior to your task, intended +to be as current as possible for aiding in your evaluation. + +Note: Take extra care when interpreting dates. If a date is specified in the 'event question', it usually serves as a deadline for the event to occur. +Do not mix this up with the timestamps in "ADDITIONAL_INFORMATION," as those are meant to indicate the recency of that specific information. +Mistaking these could lead to inaccurate probability assessments with significant financial consequences. +Strictly adhere to the 'INSTRUCTIONS' for a trustworthy and accurate probability estimation. INSTRUCTIONS: -* Carefully read the user's prompt under 'USER_PROMPT', enclosed by triple backticks. -* If the 'event question' has more than two outcomes, respond with "Error" and ignore further instructions. -* Based on your training data, provide a probability estimation of the event specified in the 'event question' occuring, considering all conditions provided. -* You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data to make the probability estimation. -* Prioritize recent information in "ADDITIONAL_INFORMATION" based on the current time {timestamp}. -* You must pay very close attention to the specific wording of the 'event question' in "USER_PROMPT". -* If a date is provided in the 'event question' specifying when the event has to have occured, you must consider in your estimation, given the current time {timestamp}, how likely it is that the event will occur within the remaining timespan to that provided date. -* If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. -* If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. -* Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. -* If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have been provided with up-to-date information that can be found on the internet. -* If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. -* You must provide your response in the format specified under "OUTPUT_FORMAT". -* Do not include any other contents in your response. +* Thoroughly scrutinize the 'event question' contained within 'USER_PROMPT'. +* If the 'event question' permits more than two outcomes, return "Error" and discontinue further processing. +* Utilize your training data to formulate a probability estimate for the event specified in the 'event question', incorporating all stipulated conditions. +* Supplement your estimate with information from "ADDITIONAL_INFORMATION", paying special attention to the timestamps to gauge recency. +* Prioritize the specific phrasing in the 'event question' as it holds crucial information, especially when a deadline date is involved. + +* Disregard any irrelevant items in "ADDITIONAL_INFORMATION". +* In case of information gaps in "ADDITIONAL_INFORMATION". be cognizant of your training data's limitations. Make an estimate acknowledging the absence of current data. +* Your probability estimate should consider not only the event's occurrence but also its likelihood of happening before, on, or by the date in the 'event question'. +* If "ADDITIONAL_INFORMATION" contains relevant data, assume it is current and factor it into your estimate. +* For events with a deadline specified as BY or ON a particular date, treat 23:59:59 of that date as the cutoff. If this is the case and no or only vague supporting information for the event occurring within the deadline is found as the deadline nears, lower the probability estimate exponentially. For reference, the current time is {timestamp}. +* Adhere to the "OUTPUT_FORMAT" for your response, and refrain from including extraneous content. USER_PROMPT: ``` @@ -95,15 +127,16 @@ ``` 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 specified in the 'event question' occurs, considering all conditions provided. - - "p_no": Estimated probability that the 'event question' does not occur, considering all conditions provided. - - "confidence": Indicating the confidence in the estimated probabilities you provided ranging from 0 (lowest confidence) to 1 (maximum confidence). - - "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. +* Your response should consist solely of a single JSON object, compatible with Python's "json.loads()" function. +* The JSON object must include four numerical fields: "p_yes," "p_no," "confidence," and "info_utility," each with values between 0 and 1. + - "p_yes": The estimated likelihood of the event in the 'event question' taking place, given all specified conditions. + - "p_no": The estimated likelihood of the event in the 'event question' not occurring, considering all conditions. + - "confidence": A measure of your assurance in the provided estimates, ranging from 0 for lowest to 1 for highest confidence. + - "info_utility": A value indicating the usefulness of "ADDITIONAL_INFORMATION" in informing your estimate, ranging from 0 for no utility to 1 for maximum utility. +* Ensure that the sum of "p_yes" and "p_no" equals 1. +* Exclude any content other than this JSON object in your output, except for max three sentences explaining your reasoning. """ +# , except for max three sentences explaining your reasoning. URL_QUERY_PROMPT = """ You are a Large Language Model in a multi-agent system. Your task is to formulate search engine queries based on @@ -579,7 +612,7 @@ def get_date(soup): if time_tag: release_date = time_tag.get("datetime", "") - return f"Release date {release_date}, Modified date {modified_date}" + return f"({release_date}, {modified_date})" def extract_text( @@ -612,9 +645,6 @@ def extract_text( if not html: raise ValueError("HTML is empty.") - # print(f"HTML:\n{html}") - print() - soup = BeautifulSoup(html, "html.parser") # Get the date of the website @@ -623,8 +653,8 @@ def extract_text( raise ValueError("Could not extract release or update date from HTML.") # Remove unnecessary tags to clean up text - for script in soup(HTML_TAGS_TO_REMOVE): - script.extract() + for element in soup(HTML_TAGS_TO_REMOVE): + element.replace_with(NavigableString(' ')) # Extract and clean text text = soup.get_text() @@ -646,11 +676,11 @@ def extract_text( if not relevant_text: return "" - return f"{date}:\n{relevant_text}" + return f"{date}: {relevant_text}" def process_in_batches( - urls: List[str], batch_size: int = 5, timeout: int = 10 + 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. @@ -697,8 +727,6 @@ def process_in_batches( 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', ''): - print(f"\nAborting, {url} is not an HTML page.") - print(head_response.headers) continue else: # Submit a GET request to the url @@ -758,6 +786,7 @@ def extract_texts( # 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: @@ -967,10 +996,14 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: 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-%d %H:%M:%S %Z%z') + # Generate the prediction prompt - timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + prediction_prompt = PREDICTION_PROMPT.format( - user_prompt=prompt, additional_information=additional_information, timestamp=timestamp, + user_prompt=prompt, additional_information=additional_information, timestamp=formatted_time_utc, ) print(f"\nPREDICTION PROMPT: {prediction_prompt}\n") From c802085a815aa217c4ada0c422fccb3835ddf800 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Wed, 27 Sep 2023 10:38:12 +0200 Subject: [PATCH 21/34] chore: [unfinished] changed prediction prompt and date representations. --- tools/prediction_sum_url_content.py | 114 +++++++++++++++------------- 1 file changed, 63 insertions(+), 51 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 403a60b3..16bf42b4 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -21,7 +21,7 @@ import time from typing import Any, Dict, Generator, List, Optional, Tuple -from datetime import datetime, timezone +from datetime import datetime, time, timezone import json import re from concurrent.futures import Future, ThreadPoolExecutor @@ -59,7 +59,7 @@ "prediction-online-sum-url-content": "gpt-4", } - +# OLD: # You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', # which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event # will either occur or not, given the conditions. Find the 'event question' enclosed in double quotes as a part of @@ -78,70 +78,74 @@ # * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. # * Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. # * If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have been provided with up-to-date information that can be found on the internet. -# * If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the deadline of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the deadline, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the deadline, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. +# * If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the timepoint of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the timepoint, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the timepoint, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. # * You must provide your response in the format specified under "OUTPUT_FORMAT". # * Do not include any other contents in your response. +# _______________________________________________________ + +# * If a timepoint date is specified in the 'event question', factor it into your probability estimate, assessing the likelihood of the event occurring within the timespan from the current time {timestamp} leading up to that date. +# * For events with a timepoint specified as BY or ON a particular date, treat 23:59:59 of that date as the cutoff. If no supporting information is found as the timepoint nears, lower the probability estimate accordingly. For reference, the current time is {timestamp}. +# * Prioritize the specific phrasing in the 'event question' as it holds crucial information, especially when a timepoint date is involved. +# * Your probability estimate should consider not only the event's occurrence but also its likelihood of happening before, on, or by the date in the 'event question'. +# no or only vague supporting information for the event occurring within the timepoint is found as the timepoint nears, lower the probability estimate exponentially. For reference, the current time is {timestamp}. -# * If a deadline date is specified in the 'event question', factor it into your probability estimate, assessing the likelihood of the event occurring within the timespan from the current time {timestamp} leading up to that date. -# * For events with a deadline specified as BY or ON a particular date, treat 23:59:59 of that date as the cutoff. If no supporting information is found as the deadline nears, lower the probability estimate accordingly. For reference, the current time is {timestamp}. +# Conditions for the event, often in the form of a specific timepoint date, are crucial for your probability assessment. PREDICTION_PROMPT = """ -You are a Large Language Model (LLM) operating within a multi-agent system. Your primary task is to precisely estimate the probability of -an event occurring by or on a specific date, as specified in the 'event question' within 'USER_PROMPT'. The 'event question' has only two outcomes: the event either -occurs or it does not. Conditions for the event, often in the form of a specific deadline date, are crucial for your probability assessment. +You are a Large Language Model (LLM) operating within a multi-agent system. Your primary task is to precisely estimate the probability of the event occurring \ +given the timepoint `{timepoint}`as specified in the following event question: "{event_question}". The event question has only two outcomes: the event either \ +occurs before the timepoint or it does not occur before the timepoint. -You receive a list of information in the "ADDITIONAL_INFORMATION" section. Each entry in this list comes with timestamps in parenthesis, showing its initial -release and last modification date. This information was obtained from a search engine query conducted a few seconds prior to your task, intended -to be as current as possible for aiding in your evaluation. +You receive a list of information in the "ADDITIONAL_INFORMATION" section. Each entry in this list comes with timestamps in parenthesis, showing its initial \ +release and last modification date. This information was obtained from a search engine query conducted a few seconds prior to your task, intended \ +to be as current as possible for aiding in your probability estimation. -Note: Take extra care when interpreting dates. If a date is specified in the 'event question', it usually serves as a deadline for the event to occur. -Do not mix this up with the timestamps in "ADDITIONAL_INFORMATION," as those are meant to indicate the recency of that specific information. +Note: Take extra care when interpreting dates. The date in the event question serves as the timepoint for the event to occur and is crucial for your probability assessment. \ +Do not mix this up with the timestamps in "ADDITIONAL_INFORMATION," as those are meant to indicate the recency of that specific information. \ Mistaking these could lead to inaccurate probability assessments with significant financial consequences. Strictly adhere to the 'INSTRUCTIONS' for a trustworthy and accurate probability estimation. INSTRUCTIONS: -* Thoroughly scrutinize the 'event question' contained within 'USER_PROMPT'. -* If the 'event question' permits more than two outcomes, return "Error" and discontinue further processing. -* Utilize your training data to formulate a probability estimate for the event specified in the 'event question', incorporating all stipulated conditions. +* Thoroughly scrutinize the event question: "{event_question}". +* If the event question permits more than two outcomes, return "Error" and discontinue further processing. +* Utilize your training data to formulate a probability estimate for the event occuring by or on the timepoint specified in the event question. * Supplement your estimate with information from "ADDITIONAL_INFORMATION", paying special attention to the timestamps to gauge recency. -* Prioritize the specific phrasing in the 'event question' as it holds crucial information, especially when a deadline date is involved. - +* If there exist any information in "ADDITIONAL_INFORMATION" that is related to the event question you can assume that you have been provided with most of the relevant information that can currently `{timestamp}` be found on the internet about that topic. * Disregard any irrelevant items in "ADDITIONAL_INFORMATION". * In case of information gaps in "ADDITIONAL_INFORMATION". be cognizant of your training data's limitations. Make an estimate acknowledging the absence of current data. -* Your probability estimate should consider not only the event's occurrence but also its likelihood of happening before, on, or by the date in the 'event question'. -* If "ADDITIONAL_INFORMATION" contains relevant data, assume it is current and factor it into your estimate. -* For events with a deadline specified as BY or ON a particular date, treat 23:59:59 of that date as the cutoff. If this is the case and no or only vague supporting information for the event occurring within the deadline is found as the deadline nears, lower the probability estimate exponentially. For reference, the current time is {timestamp}. +* It could be the case that there exist lots of supporting information for the event occurring some time but without specifying a date. Be aware that your task is to estimate the probability of the event occurring BY OR ON the timepoint `{timepoint}`. Always be aware of the current time `{timestamp}` and the remaining time until the timepoint. +* For the timepoint, treat 23:59:59 of the date within the event question as the cutoff. +* Be neutral and unbiased in your probability estimation and ____________________________________________________-?????? * Adhere to the "OUTPUT_FORMAT" for your response, and refrain from including extraneous content. -USER_PROMPT: -``` -{user_prompt} -``` - ADDITIONAL_INFORMATION: ``` {additional_information} ``` +USER_PROMPT: +{user_prompt} + + OUTPUT_FORMAT: * Your response should consist solely of a single JSON object, compatible with Python's "json.loads()" function. * The JSON object must include four numerical fields: "p_yes," "p_no," "confidence," and "info_utility," each with values between 0 and 1. - - "p_yes": The estimated likelihood of the event in the 'event question' taking place, given all specified conditions. - - "p_no": The estimated likelihood of the event in the 'event question' not occurring, considering all conditions. + - "p_yes": The estimated probability of the event in the 'event question' taking place by or on the given date. + - "p_no": The estimated likelihood of the event in the 'event question' not occurring by or on the given date. - "confidence": A measure of your assurance in the provided estimates, ranging from 0 for lowest to 1 for highest confidence. - "info_utility": A value indicating the usefulness of "ADDITIONAL_INFORMATION" in informing your estimate, ranging from 0 for no utility to 1 for maximum utility. * Ensure that the sum of "p_yes" and "p_no" equals 1. -* Exclude any content other than this JSON object in your output, except for max three sentences explaining your reasoning. +* Exclude any content other than this JSON object in your output. """ # , except for max three sentences explaining your reasoning. 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' +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: @@ -741,6 +745,7 @@ 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. @@ -768,21 +773,19 @@ def extract_texts( count = 0 stop = False - # Initialize Sentence Transformer and Spacy models - model = SentenceTransformer('sentence-transformers/multi-qa-distilbert-cos-v1') - nlp = spacy.load("en_core_web_sm") - # Process the event question with spacy doc_question = nlp(event_question) event_date = extract_event_date(doc_question) - # Create sentence embeddings for the event question with Sentence Transformer + # 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}") - start_time = time.time() # Process URLs in batches for batch in process_in_batches(urls=urls): for future, url in tqdm(batch, desc="Processing URLs"): @@ -827,10 +830,6 @@ def extract_texts( if stop: break - end_time = time.time() - print(f"Time elapsed: {end_time - start_time:.4f} seconds") - - return extracted_texts @@ -839,6 +838,7 @@ def fetch_additional_information( 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, @@ -911,6 +911,7 @@ def fetch_additional_information( urls=urls, event_question=event_question, max_words_per_url=max_words_per_url, + nlp=nlp, ) # Join the texts and return @@ -940,8 +941,11 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: max_compl_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_compl_tokens"]) temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"]) - if not tool or not prompt: - raise ValueError("Both 'mech tool' and 'prompt' must be provided.") + + 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}") @@ -949,11 +953,9 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: print(f"MAX OPENAI RETURN TOKENS: {max_compl_tokens}") print(f"LLM TEMPERATURE: {temperature}") - openai.api_key = kwargs["api_keys"]["openai"] + # Load the spacy model + nlp = spacy.load("en_core_web_sm") - if tool not in ALLOWED_TOOLS: - raise ValueError(f"TOOL {tool} is not supported.") - # Get the LLM engine to be used engine = TOOL_TO_ENGINE[tool] print(f"ENGINE: {engine}") @@ -983,6 +985,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: 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"], @@ -998,12 +1001,21 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: # Get the current utc timestamp current_time_utc = datetime.now(timezone.utc) - formatted_time_utc = current_time_utc.strftime('%Y-%m-%d %H:%M:%S %Z%z') + formatted_time_utc = current_time_utc.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-6] + "Z" - # Generate the prediction prompt + # 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( - user_prompt=prompt, additional_information=additional_information, timestamp=formatted_time_utc, + event_question=event_question, + timepoint=formatted_event_date, + additional_information=additional_information, + timestamp=formatted_time_utc, ) print(f"\nPREDICTION PROMPT: {prediction_prompt}\n") From 01ef9207d5dca6caad1f88b97842961c4bddc130 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Wed, 27 Sep 2023 15:42:33 +0200 Subject: [PATCH 22/34] fead: added new instructions regarding deadline. Not effectively considered by gpt-4 yet. --- tools/prediction_sum_url_content.py | 76 ++++++++++++++++------------- 1 file changed, 41 insertions(+), 35 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 16bf42b4..b05a2bb9 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -45,7 +45,7 @@ WORDS_PER_TOKEN_FACTOR = 0.75 DEFAULT_OPENAI_SETTINGS = { "max_compl_tokens": 200, - "temperature": 0.2, + "temperature": 0, } ALLOWED_TOOLS = [ @@ -73,6 +73,7 @@ # * You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data to make the probability estimation. # * Prioritize recent information in "ADDITIONAL_INFORMATION" based on the current time {timestamp}. # * You must pay very close attention to the specific wording of the 'event question' in "USER_PROMPT". + # * If a date is provided in the 'event question' specifying when the event has to have occured, you must consider in your estimation, given the current time {timestamp}, how likely it is that the event will occur within the remaining timespan to that provided date. # * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. # * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. @@ -81,6 +82,13 @@ # * If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the timepoint of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the timepoint, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the timepoint, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. # * You must provide your response in the format specified under "OUTPUT_FORMAT". # * Do not include any other contents in your response. +# * If the current time has exceeded the deadline, decrease the probability to 0. Do this only, if there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question'. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. + + + +# * If both the "ADDITIONAL_INFORMATION" and your training data are not sufficient, produce an estimate and adjust the "confidence" field accordingly. +# * If the current time is about to exceed the deadline or has already exceeded the deadline, lower the probability estimate exponentially. For reference, the current time is {timestamp}. + # _______________________________________________________ @@ -94,51 +102,48 @@ PREDICTION_PROMPT = """ -You are a Large Language Model (LLM) operating within a multi-agent system. Your primary task is to precisely estimate the probability of the event occurring \ -given the timepoint `{timepoint}`as specified in the following event question: "{event_question}". The event question has only two outcomes: the event either \ -occurs before the timepoint or it does not occur before the timepoint. +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'. -You receive a list of information in the "ADDITIONAL_INFORMATION" section. Each entry in this list comes with timestamps in parenthesis, showing its initial \ -release and last modification date. This information was obtained from a search engine query conducted a few seconds prior to your task, intended \ -to be as current as possible for aiding in your probability estimation. -Note: Take extra care when interpreting dates. The date in the event question serves as the timepoint for the event to occur and is crucial for your probability assessment. \ -Do not mix this up with the timestamps in "ADDITIONAL_INFORMATION," as those are meant to indicate the recency of that specific information. \ -Mistaking these could lead to inaccurate probability assessments with significant financial consequences. +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. +* 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. +* You must provide your response in the format specified under "OUTPUT_FORMAT". +* Do not include any other contents in your response. -Strictly adhere to the 'INSTRUCTIONS' for a trustworthy and accurate probability estimation. -INSTRUCTIONS: -* Thoroughly scrutinize the event question: "{event_question}". -* If the event question permits more than two outcomes, return "Error" and discontinue further processing. -* Utilize your training data to formulate a probability estimate for the event occuring by or on the timepoint specified in the event question. -* Supplement your estimate with information from "ADDITIONAL_INFORMATION", paying special attention to the timestamps to gauge recency. -* If there exist any information in "ADDITIONAL_INFORMATION" that is related to the event question you can assume that you have been provided with most of the relevant information that can currently `{timestamp}` be found on the internet about that topic. -* Disregard any irrelevant items in "ADDITIONAL_INFORMATION". -* In case of information gaps in "ADDITIONAL_INFORMATION". be cognizant of your training data's limitations. Make an estimate acknowledging the absence of current data. -* It could be the case that there exist lots of supporting information for the event occurring some time but without specifying a date. Be aware that your task is to estimate the probability of the event occurring BY OR ON the timepoint `{timepoint}`. Always be aware of the current time `{timestamp}` and the remaining time until the timepoint. -* For the timepoint, treat 23:59:59 of the date within the event question as the cutoff. -* Be neutral and unbiased in your probability estimation and ____________________________________________________-?????? -* Adhere to the "OUTPUT_FORMAT" for your response, and refrain from including extraneous content. +USER_PROMPT: +``` +{user_prompt} +``` ADDITIONAL_INFORMATION: ``` {additional_information} ``` -USER_PROMPT: -{user_prompt} - - OUTPUT_FORMAT: -* Your response should consist solely of a single JSON object, compatible with Python's "json.loads()" function. -* The JSON object must include four numerical fields: "p_yes," "p_no," "confidence," and "info_utility," each with values between 0 and 1. - - "p_yes": The estimated probability of the event in the 'event question' taking place by or on the given date. - - "p_no": The estimated likelihood of the event in the 'event question' not occurring by or on the given date. - - "confidence": A measure of your assurance in the provided estimates, ranging from 0 for lowest to 1 for highest confidence. - - "info_utility": A value indicating the usefulness of "ADDITIONAL_INFORMATION" in informing your estimate, ranging from 0 for no utility to 1 for maximum utility. -* Ensure that the sum of "p_yes" and "p_no" equals 1. -* Exclude any content other than this JSON object in your output. +* 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. """ # , except for max three sentences explaining your reasoning. @@ -1013,6 +1018,7 @@ def run(**kwargs) -> Tuple[str, Optional[Dict[str, Any]]]: # 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, From f8d3e708ec41680c2a4587a87e87ebb07cfbb6a1 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Wed, 27 Sep 2023 17:00:09 +0200 Subject: [PATCH 23/34] feat: Reasonable estimations by GPT-4 for event questions specifying an event happening BY a certain date. Consideres if the event has already happened. --- tools/prediction_sum_url_content.py | 44 ++--------------------------- 1 file changed, 3 insertions(+), 41 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index b05a2bb9..29bed794 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -59,47 +59,6 @@ "prediction-online-sum-url-content": "gpt-4", } -# OLD: -# You are an LLM inside a multi-agent system. Your task is to estimate the probability of a user's 'event question', -# which specifies an event in the physical world and any accompanying conditions to be met for the 'event question' to be true. The 'event question' allows only two outcomes: the event -# will either occur or not, given the conditions. Find the 'event question' enclosed in double quotes as a part of -# the user's prompt under 'USER_PROMPT'. The user's prompt also contains a more elaborate description of the task. -# You are provided an itemized list of information under the label "ADDITIONAL_INFORMATION", delimited by three backticks. This information results from a search engine query that has been done a few seconds ago with the aim to get up-to-date information that could be relevant estimating the 'event question'. -# You must adhere to the 'INSTRUCTIONS'. - -# * Carefully read the user's prompt under 'USER_PROMPT', enclosed by triple backticks. -# * If the 'event question' has more than two outcomes, respond with "Error" and ignore further instructions. -# * Based on your training data, provide a probability estimation of the event specified in the 'event question' occuring, considering all conditions provided. -# * You can use any item in "ADDITIONAL_INFORMATION" in addition to your training data to make the probability estimation. -# * Prioritize recent information in "ADDITIONAL_INFORMATION" based on the current time {timestamp}. -# * You must pay very close attention to the specific wording of the 'event question' in "USER_PROMPT". - -# * If a date is provided in the 'event question' specifying when the event has to have occured, you must consider in your estimation, given the current time {timestamp}, how likely it is that the event will occur within the remaining timespan to that provided date. -# * If an item in "ADDITIONAL_INFORMATION" is not relevant for the estimation, you must ignore that item. -# * If there is insufficient information in "ADDITIONAL_INFORMATION", be aware of the limitations of your training data especially when relying on it for predicting events that require up-to-date information. In this case make a prediction that takes into account that you don't have up-to-date information. -# * Your pobability estimation must not only take into account if the specified event happens or not, but also if the event is likely to happen before, by or on the date specified in the 'event question'. -# * If there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question' you can assume that you have been provided with up-to-date information that can be found on the internet. -# * If the 'event question' is formulated in a way that an event must have happend BY or ON a specific date, consider the timepoint of the event being 23:59:59 of that date. Decrease the probability of the event specified in the 'event question' happening the closer the current time {timestamp} is to the timepoint, if you could not find information that the event could happen within the remaining time. If the current time has exceeded the timepoint, decrease the probability to 0. Do this only if you have been provided with input under ADDITIONAL_INFORMATION that indicates that you have access to information that is up-to-date. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. -# * You must provide your response in the format specified under "OUTPUT_FORMAT". -# * Do not include any other contents in your response. -# * If the current time has exceeded the deadline, decrease the probability to 0. Do this only, if there exist any information in "ADDITIONAL_INFORMATION" that is related to the 'event question'. If you have not been provided with such information, do not decrease the probability, but rather make a prediction that takes into account that you don't have up-to-date information. - - - -# * If both the "ADDITIONAL_INFORMATION" and your training data are not sufficient, produce an estimate and adjust the "confidence" field accordingly. -# * If the current time is about to exceed the deadline or has already exceeded the deadline, lower the probability estimate exponentially. For reference, the current time is {timestamp}. - - -# _______________________________________________________ - -# * If a timepoint date is specified in the 'event question', factor it into your probability estimate, assessing the likelihood of the event occurring within the timespan from the current time {timestamp} leading up to that date. -# * For events with a timepoint specified as BY or ON a particular date, treat 23:59:59 of that date as the cutoff. If no supporting information is found as the timepoint nears, lower the probability estimate accordingly. For reference, the current time is {timestamp}. -# * Prioritize the specific phrasing in the 'event question' as it holds crucial information, especially when a timepoint date is involved. -# * Your probability estimate should consider not only the event's occurrence but also its likelihood of happening before, on, or by the date in the 'event question'. -# no or only vague supporting information for the event occurring within the timepoint is found as the timepoint nears, lower the probability estimate exponentially. For reference, the current time is {timestamp}. - -# Conditions for the event, often in the form of a specific timepoint date, are crucial for your probability assessment. - PREDICTION_PROMPT = """ INTRODUCTION: @@ -114,6 +73,7 @@ * 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. @@ -121,6 +81,8 @@ * 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 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. From 29a4afd0c82a58ece0383afe59845707a32b4e93 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Wed, 27 Sep 2023 17:29:09 +0200 Subject: [PATCH 24/34] chore: Minor change to the prediction prompt --- tools/prediction_sum_url_content.py | 19 ++++--------------- 1 file changed, 4 insertions(+), 15 deletions(-) diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 29bed794..00bba663 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -19,9 +19,8 @@ """This module implements a Mech tool for binary predictions.""" -import time from typing import Any, Dict, Generator, List, Optional, Tuple -from datetime import datetime, time, timezone +from datetime import datetime, timezone import json import re from concurrent.futures import Future, ThreadPoolExecutor @@ -33,7 +32,6 @@ from requests import Session import spacy import tiktoken -import torch import traceback from dateutil import parser @@ -53,9 +51,7 @@ "prediction-online-sum-url-content", ] TOOL_TO_ENGINE = { - "prediction-offline-sum-url-content": "gpt-3.5-turbo", - # "prediction-online-sum-url-content": "gpt-3.5-turbo", - # "prediction-online-sum-url-content": "gpt-3.5-turbo-16k", + "prediction-offline-sum-url-content": "gpt-4", "prediction-online-sum-url-content": "gpt-4", } @@ -82,7 +78,7 @@ * 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 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. +* 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. @@ -105,9 +101,8 @@ - "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. +* Output only the JSON object in your response. Do not include any other contents in your response. """ -# , except for max three sentences explaining your reasoning. URL_QUERY_PROMPT = """ You are a Large Language Model in a multi-agent system. Your task is to formulate search engine queries based on \ @@ -531,12 +526,6 @@ def extract_relevant_information( sent for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True) if sim > 0.4 ] - # # Print similarity scores along with the sentences - # for sent, sim in sorted(zip(sentences, similarities), key=lambda x: x[1], reverse=True): - # if sim > 0.4: - # print(f"{sim:.4f}: {sent}") - # print() - if not relevant_sentences: return "" From 6df7711df9f0fc4767022921690cf6cce26c7d0a Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Fri, 29 Sep 2023 12:47:29 +0200 Subject: [PATCH 25/34] chore: Removed and updated packages --- poetry.lock | 1449 ++++++++++++++++++++++++------------------------ pyproject.toml | 1 - 2 files changed, 737 insertions(+), 713 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7b082bd4..8a339a67 100644 --- a/poetry.lock +++ b/poetry.lock @@ -349,39 +349,45 @@ files = [ [[package]] name = "blis" -version = "0.7.10" +version 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[[package]] name = "typer" @@ -4818,13 +4793,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6. 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python-dateutil = "^2.8.2" spacy = "^3.6.1" +# torch = [ +# { version = "^2.0.1", markers = "sys_platform == 'darwin'" }, +# { url = "https://download.pytorch.org/whl/cpu", markers = "sys_platform == 'linux'" } +# ] sentence-transformers = "^2.2.2" - [tool.poetry.group.dev.dependencies.tomte] version = "==0.2.12" extras = [ "cli", "tests",] diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index 00bba663..d3314859 100644 --- a/tools/prediction_sum_url_content.py +++ b/tools/prediction_sum_url_content.py @@ -52,7 +52,7 @@ ] TOOL_TO_ENGINE = { "prediction-offline-sum-url-content": "gpt-4", - "prediction-online-sum-url-content": "gpt-4", + "prediction-online-sum-url-content": "gpt-3.5-turbo", } From 3d6c1c7dd5cc6a4693ecde33ef499725918770ed Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sat, 30 Sep 2023 17:11:41 +0200 Subject: [PATCH 29/34] chore: Added marker for torch version --- pyproject.toml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index efef9aae..19bea5f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,10 +57,10 @@ tqdm = "^4.66.1" tiktoken = "^0.5.1" python-dateutil = "^2.8.2" spacy = "^3.6.1" -# torch = [ -# { version = "^2.0.1", markers = "sys_platform == 'darwin'" }, -# { url = "https://download.pytorch.org/whl/cpu", markers = "sys_platform == 'linux'" } -# ] +torch = [ + { version = "^2.0.1", markers = "sys_platform == 'darwin'" }, + { url = "https://download.pytorch.org/whl/cpu", markers = "sys_platform == 'linux'" } +] sentence-transformers = "^2.2.2" From 0305d61001a4f95f9771b889fdc947aa1c754d62 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Sat, 30 Sep 2023 18:20:50 +0200 Subject: [PATCH 30/34] feat: Added download link for torch and torchvision for cpu only version --- poetry.lock | 96 +++++++++++++++++++++++------ pyproject.toml | 8 ++- tools/prediction_sum_url_content.py | 4 +- 3 files changed, 84 insertions(+), 24 deletions(-) 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b/pyproject.toml @@ -58,8 +58,12 @@ tiktoken = "^0.5.1" python-dateutil = "^2.8.2" spacy = "^3.6.1" torch = [ - { version = "^2.0.1", markers = "sys_platform == 'darwin'" }, - { url = "https://download.pytorch.org/whl/cpu", markers = "sys_platform == 'linux'" } + { url = "https://download.pytorch.org/whl/cpu/torch-2.0.1%2Bcpu-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux'" }, + { url = "https://download.pytorch.org/whl/cpu/torch-2.0.1-cp311-none-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin'" } +] +torchvision = [ + { url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.2%2Bcpu-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux'" }, + { url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.2-cp311-cp311-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin'"} ] sentence-transformers = "^2.2.2" diff --git a/tools/prediction_sum_url_content.py b/tools/prediction_sum_url_content.py index d3314859..63b12dfe 100644 --- 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00:00:00 2001 From: Jannik Hehemann Date: Sat, 30 Sep 2023 20:18:17 +0200 Subject: [PATCH 32/34] chore: Update poetry --- pyproject.toml | 22 ++++++++++++++-------- tox.ini | 7 +++++++ 2 files changed, 21 insertions(+), 8 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3fc461a1..5ae8155b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,16 +57,22 @@ tqdm = "^4.66.1" tiktoken = "^0.5.1" python-dateutil = "^2.8.2" spacy = "^3.6.1" -torch = [ - { url = "https://download.pytorch.org/whl/cpu/torch-2.0.1%2Bcpu-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux'" }, - { url = "https://download.pytorch.org/whl/cpu/torch-2.0.1-cp311-none-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin'" } -] -torchvision = [ - { url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.2%2Bcpu-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux'" }, - { url = 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+++ b/tox.ini @@ -55,6 +55,13 @@ deps = pycryptodome==3.18.0 anthropic==0.3.11 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 + torch[{'url': 'https://download.pytorch.org/whl/cpu/torch-2.0.1%2Bcpu-cp310-cp310-linux_x86_64.whl', 'markers': "sys_platform == 'linux'"}, {'url': 'https://download.pytorch.org/whl/cpu/torch-2.0.1-cp311-none-macosx_11_0_arm64.whl', 'markers': "sys_platform == 'darwin'"}] + torchvision[{'url': 'https://download.pytorch.org/whl/cpu/torchvision-0.15.2%2Bcpu-cp310-cp310-linux_x86_64.whl', 'markers': "sys_platform == 'linux'"}, {'url': 'https://download.pytorch.org/whl/cpu/torchvision-0.15.2-cp311-cp311-macosx_11_0_arm64.whl', 'markers': "sys_platform == 'darwin'"}] [testenv] basepython = python3 From c9c27799f0f6bcea8dddb61031200538e526e80e Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Fri, 6 Oct 2023 10:05:51 +0200 Subject: [PATCH 33/34] chore: Update poetry --- poetry.lock | 1208 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"https://download.pytorch.org/whl/cpu/torch-2.0.1%2Bcpu-cp310-cp310-linux_x86_64.whl" -markers = "sys_platform == 'linux'" - -[[tool.poetry.dependencies.torch]] -url = "https://download.pytorch.org/whl/cpu/torch-2.0.1-cp311-none-macosx_11_0_arm64.whl" -markers = "sys_platform == 'darwin'" - -[[tool.poetry.dependencies.torchvision]] -url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.2%2Bcpu-cp310-cp310-linux_x86_64.whl" -markers = "sys_platform == 'linux'" - -[[tool.poetry.dependencies.torchvision]] -url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.2-cp311-cp311-macosx_11_0_arm64.whl" -markers = "sys_platform == 'darwin'" - [tool.poetry.group.dev.dependencies.tomte] version = "==0.2.12" extras = [ "cli", "tests",] From ada65bb0fe0ad10dd0e65a0a85c1c73645825799 Mon Sep 17 00:00:00 2001 From: Jannik Hehemann Date: Fri, 6 Oct 2023 10:42:07 +0200 Subject: [PATCH 34/34] chore: update poetry --- poetry.lock | 193 +++++++++++++++++++++------------------------------- 1 file changed, 79 insertions(+), 114 deletions(-) diff --git a/poetry.lock b/poetry.lock index c809d104..27d632ed 100644 --- a/poetry.lock +++ b/poetry.lock @@ -112,7 +112,6 @@ speedups = ["Brotli", "aiodns", "cchardet"] name = "aiosignal" version = "1.3.1" description = "aiosignal: a list of registered asynchronous callbacks" -category = "main" optional = false python-versions = ">=3.7" files = [ @@ -673,17 +672,6 @@ files = [ [package.dependencies] pycparser = "*" -[[package]] -name = "chardet" -version = "4.0.0" -description = "Universal encoding detector for Python 2 and 3" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" -files = [ - {file = "chardet-4.0.0-py2.py3-none-any.whl", hash = "sha256:f864054d66fd9118f2e67044ac8981a54775ec5b67aed0441892edb553d21da5"}, - {file = "chardet-4.0.0.tar.gz", hash = "sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa"}, -] - [[package]] name = "charset-normalizer" version = "3.3.0" @@ 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