From 4b32229b02cbbc1ea51977840da6885db09832e5 Mon Sep 17 00:00:00 2001 From: Ben Wilson <43449430+CodexVeritas@users.noreply.github.com> Date: Thu, 19 Dec 2024 15:03:00 -0700 Subject: [PATCH] Change readme to point to new code base (#15) --- README.md | 487 +----------------------------------------------------- 1 file changed, 3 insertions(+), 484 deletions(-) diff --git a/README.md b/README.md index 81cd243..d6cf18d 100644 --- a/README.md +++ b/README.md @@ -9,488 +9,7 @@ Last Update: Dec 17 2024 # Quick Install Install this package with `pip install forecasting-tools` -# Overview -Demo website: https://mokoresearch.streamlit.app/ +# Notice +This package's code has moved to https://github.com/Metaculus/forecasting-tools -This repository contains forecasting and research tools built with Python and Streamlit. The project aims to assist users in making predictions, conducting research, and analyzing data related to hard to answer questions (especially those from Metaculus). - -Here are the tools most likely to be useful to you: -- 🎯 **Forecasting Bot:** General Forecaster that integrates with the Metaculus AI benchmarking competition. You can forecast with a pre-existing bot or override the class to customize your own (without redoing all the API code, etc) -- 🔍 **Perplexity++ Smart Searcher:** An AI-powered internet-informed llm powered by Exa.ai. Its a better (but more expensive) alternative to Perplexity.ai that is configurable, more accurate, able to decide on filters, able to link to exact paragraphs, etc. -- 🔑 **Key Factor Analysis:** Key Factors Analysis for scoring, ranking, and prioritizing important variables in forecasting questions - - -Here are some other cool components and features of the project: -- **Base Rate Researcher:** for calculating event probabilities (still experimental) -- **Niche List Researcher:** for analyzing very specific lists of past events or items (still experimental) -- **Fermi Estimator:** for breaking down numerical estimates (still experimental) -- **Metaculus API Wrapper:** for interacting with questions and tournaments -- **Monetary Cost Manager:** for tracking AI and API expenses - -Join the [discord](https://discord.gg/Dtq4JNdXnw) for updates and to give feedback (btw feedback is very appreciated, even just a quick 'I did/didn't decide to use the tool for reason X' is helpful to know) - -Note: This package is still in a experimental phase. The goal is to keep the API fairly stable, though no guarantees are given at this phase. There will be special effort to keep the ForecastBot and TemplateBot APIs consistent. - -# Forecasting Bot Building - -## Using the Preexisting Bots - -The package comes with two major pre-built bots: -- **MainBot**: The more sophisticated bot that uses multiple research strategies and carefully structured prompts -- **TemplateBot**: A simpler bot that models the Metaculus templates that's cheaper, easier to start with, and faster to run. - -They both have roughly the same parameters. See below on how to use the TemplateBot to make forecasts. - -### Forecasting on a Tournament - -```python -from forecasting_tools import TemplateBot, MetaculusApi - -# Initialize the bot -bot = TemplateBot( - research_reports_per_question=3, # Number of separate research attempts per question - predictions_per_research_report=5, # Number of predictions to make per research report - publish_reports_to_metaculus=True, # Whether to post the forecasts to Metaculus - folder_to_save_reports_to="logs/forecasts/", # Where to save detailed reports - skip_previously_forecasted_questions=True -) - -# Run forecasts on Q4 2024 AI Tournament -TOURNAMENT_ID = MetaculusApi.AI_COMPETITION_ID_Q4 -reports = await bot.forecast_on_tournament(TOURNAMENT_ID) - -# Print results -for report in reports: - print(f"\nQuestion: {report.question.question_text}") - print(f"Prediction: {report.prediction}") -``` - -### Forecasting a Single Question - -```python -from forecasting_tools import TemplateBot, BinaryQuestion, QuestionState - -# Initialize the bot -bot = TemplateBot( - research_reports_per_question=3, - predictions_per_research_report=5, - publish_reports_to_metaculus=False, -) - -# Get and forecast a specific question -question1 = MetaculusApi.get_question_by_url( - "https://www.metaculus.com/questions/578/human-extinction-by-2100/" -) -question2 = BinaryQuestion( - question_text="Will YouTube be blocked in Russia?", - background_info="...", # Or 'None' - resolution_criteria="...", # Or 'None' - fine_print="...", # Or 'None' - id_of_question=0, # The ID and state only matters if using Metaculus API calls - question_state=QuestionState.OPEN -) - -reports = await bot.forecast_questions([question1, question2]) - -# Print results -for report in reports: - print(f"Question: {report.question.question_text}") - print(f"Prediction: {report.prediction}") - print("\nReasoning:") - print(report.explanation) -``` - -The bot will: -1. Research the question -2. Generate multiple independent predictions -3. Combine these predictions into a final forecast -4. Save detailed research and reasoning to the specified folder -5. Optionally post the forecast to Metaculus (if `publish_reports_to_metaculus=True`) - -Note: You'll need to have your environment variables set up (see the section below) - - -## Running your own bot - -### Join the tournament quick-start -The quickest way to join the Metaculus Benchmarking Tournament (or any other tournament) is to fork this repo, enable Github workflow/actions, and then set repository secrets. Ideally this takes less than 15min, and then you have a bot in the tournament! Later you can develop locally and then merge in changes to your fork. - -There is a prewritten workflow that will run the bot every 15min, pick up new questions, and forecast on them. Automation is handled in the `.github/workflows/` folder. The `hourly-run.yaml` file runs the bot every 15 min and will skip questions it has already forecasted on. - -1) **Fork the repository**: Click 'fork' in the right hand corner of the repo. -2) **Set secrets**: Go to `Settings -> Secrets and variables -> Actions -> New repository secret` and set API keys/Tokens as secrets. You will want to set your METACULUS_TOKEN. This will be used to post questions to Metaculus, and access the Metaculus OpenAI proxy (you should automatically be given some credits if you have a bot account). For additional environment variables you might want, see the section below. -3) **Enable Actions**: Go to 'Actions' then click 'Enable'. Then go to the 'Hourly Run' workflow, and click 'Enable'. To test if the workflow is working, click 'Run workflow', choose the main branch, then click the green 'Run workflow' button. This will check for new questions and forecast only on ones it has not yet successfully forecast on. - -The bot should just work as is at this point. You can disable the workflow by clicking `Actions > Hourly Run > Triple dots > disable workflow` - -### Local Development -See the 'Local Development' section later in this README. - -### Customizing the Bot -Generally all you have to do to make your own bot is inherit from the TemplateBot and override any combination of the 3 forecasting methods and the 1 research method. This saves you the headache of parsing the outputs, interacting with the Metaculus API, etc. Here is an example. It may also be helpful to look at the TemplateBot code (forecasting_tools/forecasting/forecast_bots/template_bot.py) for a more complete example. - -```python -from forecasting_tools import ( - TemplateBot, - MetaculusQuestion, - BinaryQuestion, - MultipleChoiceQuestion, - NumericQuestion, - ReasonedPrediction, - PredictedOptionList, - NumericDistribution, - SmartSearcher, - Gpt4oMetaculusProxy -) -from forecasting_tools.ai_models.ai_utils.ai_misc import clean_indents - -class MyCustomBot(TemplateBot): - async def run_research(self, question: MetaculusQuestion) -> str: - """Custom research method that focuses on recent events and expert opinions""" - searcher = SmartSearcher( - num_searches_to_run=3, - num_sites_per_search=5 - ) - - prompt = clean_indents( - f""" - Analyze this forecasting question: - 1. Filter for recent events in the past 6 months - 2. Don't include domains from youtube.com - 3. Look for current trends and data - 4. Find historical analogies and base rates - - Question: {question.question_text} - - Background Info: {question.background_info} - Resolution Criteria: {question.resolution_criteria} - """ - ) - - report = await searcher.invoke(prompt) - return report - - async def _run_forecast_on_binary( - self, question: BinaryQuestion, research: str - ) -> ReasonedPrediction[float]: - prompt = f"Please make a prediction on the following question: {question.question_text}. The last thing you write is your final answer as: 'Probability: ZZ%', 0-100" - reasoning = await Gpt4oMetaculusProxy.invoke(prompt) - prediction = self._extract_forecast_from_binary_rationale( - reasoning, max_prediction=1, min_prediction=0 - ) - return ReasonedPrediction( - prediction_value=prediction, reasoning=reasoning - ) - - async def _run_forecast_on_multiple_choice( - self, question: MultipleChoiceQuestion, research: str - ) -> ReasonedPrediction[PredictedOptionList]: - ... - - async def _run_forecast_on_numeric( - self, question: NumericQuestion, research: str - ) -> ReasonedPrediction[NumericDistribution]: - ... -``` - - -## Setting Environment Variables -Whether running locally or through Github actions, you will need to set environment variables. All environment variables you might want are in `.env.template`. Generally you only need the METACULUS_TOKEN if running the Template. Having an EXA_API_KEY (see www.exa.ai) or PERPLEXITY_API_KEY (see www.perplexity.ai) is needed for searching the web. Make sure to put these variables in your `.env` file if running locally and in the Github actions secrets if running on Github actions. Remember to set 'FILE_WRITING_ALLOWED' to true if you want to save results. - - -# Forecasting Tools Examples - -## Smart Searcher -The Smart Searcher acts like an LLM with internet access. It works a lot like Perplexity.ai API, except: -- It has clickable citations that highlights and links directly to the paragraph cited using text fragments -- You can ask the AI to use filters for domain, date, and keywords -- There are options for structured output (Pydantic objects, lists, dict, list\[dict\], etc.) -- Concurrent search execution for faster results -- Optional detailed works cited list - -```python -from forecasting_tools import SmartSearcher - -searcher = SmartSearcher( - temperature=0, - num_searches_to_run=2, - num_sites_per_search=10, # Results returned per search - include_works_cited_list=False # Add detailed citations at the end -) - -response = await searcher.invoke( - "What is the recent news for Apple?" -) - -print(response) -``` - -Example output: -> Recent news about Apple includes several significant developments: -> -> 1. **Expansion in India**: Apple is planning to open four more stores in India, with two in Delhi and Mumbai, and two in Bengaluru and Pune. This decision follows record revenues in India for the September 2024 quarter, driven by strong iPhone sales. Tim Cook, Apple's CEO, highlighted the enthusiasm and growth in the Indian market during the company's earnings call \[[1](https://telecomtalk.info/tim-cook-makes-major-announcement-for-apple-in-india/984260/#:~:text=This%20is%20not%20a%20new,first%20time%20Apple%20confirmed%20it.)\]\[[4](https://telecomtalk.info/tim-cook-makes-major-announcement-for-apple-in-india/984260/#:~:text=This%20is%20not%20a%20new,set%20an%20all%2Dtime%20revenue%20record.)\]\[[5](https://telecomtalk.info/tim-cook-makes-major-announcement-for-apple-in-india/984260/#:~:text=Previously%2C%20Diedre%20O%27Brien%2C%20Apple%27s%20senior,East%2C%20India%20and%20South%20Asia.)\]\[[8](https://telecomtalk.info/tim-cook-makes-major-announcement-for-apple-in-india/984260/#:~:text=At%20the%20company%27s%20earnings%20call,four%20new%20stores%20in%20India.)\]. -> -> 2. **Product Launches**: Apple is set to launch new iMac, Mac mini, and MacBook Pro models with M4 series chips on November 8, 2024. Additionally, the Vision Pro headset will be available in South Korea and the United Arab Emirates starting November 15, 2024. The second season of the Apple TV+ sci-fi series "Silo" will also premiere on November 15, 2024 \[[2](https://www.macrumors.com/2024/11/01/what-to-expect-from-apple-this-november/#:~:text=And%20the%20Vision%20Pro%20launches,the%20App%20Store%2C%20and%20more.)\]\[[12](https://www.macrumors.com/2024/11/01/what-to-expect-from-apple-this-november/#:~:text=As%20for%20hardware%2C%20the%20new,announcements%20in%20store%20this%20November.)\]. -> -> ... etc ... - -You can also use structured outputs by providing a Pydantic model (or any other simpler type hint) and using the schema formatting helper: - -```python -from pydantic import BaseModel, Field -from forecasting_tools import SmartSearcher - -class Company(BaseModel): - name: str = Field(description="Full company name") - market_cap: float = Field(description="Market capitalization in billions USD") - key_products: list[str] = Field(description="Main products or services") - relevance: str = Field(description="Why this company is relevant to the search") - -searcher = SmartSearcher(temperature=0, num_searches_to_run=4, num_sites_per_search=10) - -schema_instructions = searcher.get_schema_format_instructions_for_pydantic_type(Company) -prompt = f"""Find companies that are leading the development of autonomous vehicles. -Return as a list of companies with their details. Remember to give me a list of the schema provided. - -{schema_instructions}""" - -companies = await searcher.invoke_and_return_verified_type(prompt, list[Company]) - -for company in companies: - print(f"\n{company.name} (${company.market_cap}B)") - print(f"Relevance: {company.relevance}") - print("Key Products:") - for product in company.key_products: - print(f"- {product}") -``` - -The schema instructions will format the Pydantic model into clear instructions for the AI about the expected output format and field descriptions. - - -## Key Factors Researcher -The Key Factors Researcher helps identify and analyze key factors that should be considered for a forecasting question. As of last update, this is the most reliable of the tools, and gives something useful and accurate almost every time. It asks a lot of questions, turns search results into a long list of bullet points, rates each bullet point on ~8 criteria, and returns the top results. - -```python -from forecasting_tools import KeyFactorsResearcher, BinaryQuestion, QuestionState - -# Consider using MetaculusApi.get_question_by_id or MetaculusApi.get_question_by_url instead -question = BinaryQuestion( - question_text="Will YouTube be blocked in Russia?", - background_info="...", # Or 'None' - resolution_criteria="...", # Or 'None' - fine_print="...", # Or 'None' - id_of_question=0, # The ID and state only matters if using Metaculus API calls - question_state=QuestionState.OPEN -) - -# Find key factors -key_factors = await KeyFactorsResearcher.find_key_factors( - metaculus_question=question, - num_key_factors_to_return=5, # Number of final factors to return - num_questions_to_research_with=26 # Number of research questions to generate -) - -print(ScoredKeyFactor.turn_key_factors_into_markdown_list(key_factors)) -``` - -Example output: -> - The Russian authorities have slowed YouTube speeds to near unusable levels, indicating a potential groundwork for a future ban. [Source Published on 2024-09-12](https://meduza.io/en/feature/2024/09/12/the-russian-authorities-slowed-youtube-speeds-to-near-unusable-levels-so-why-are-kremlin-critics-getting-more-views#:~:text=Kolezev%20attributed%20this%20to%20the,suddenly%20stopped%20working%20in%20Russia.) -> - Russian lawmaker Alexander Khinshtein stated that YouTube speeds would be deliberately slowed by up to 70% due to Google's non-compliance with Russian demands, indicating escalating measures against YouTube. [Source Published on 2024-07-25](https://www.yahoo.com/news/russia-slow-youtube-speeds-google-180512830.html#:~:text=Russia%20will%20deliberately%20slow%20YouTube,forces%20and%20promoting%20extremist%20content.) -> - The press secretary of President Vladimir Putin, Dmitry Peskov, denied that the authorities intended to block YouTube, attributing access issues to outdated equipment due to sanctions. [Source Published on 2024-08-17](https://www.wsws.org/en/articles/2024/08/17/pbyj-a17.html#:~:text=%5BAP%20Photo%2FAP%20Photo%5D%20On%20July,two%20years%20due%20to%20sanctions.) -> - YouTube is currently the last Western social media platform still operational in Russia, with over 93 million users in the country. [Source Published on 2024-07-26](https://www.techradar.com/pro/vpn/youtube-is-getting-throttled-in-russia-heres-how-to-unblock-it#:~:text=If%20you%27re%20in%20Russia%20and,platform%20to%20work%20in%20Russia.) -> - Russian users reported mass YouTube outages amid growing official criticism, with reports of thousands of glitches in August 2024. [Source Published on 2024-08-09](https://www.aljazeera.com/news/2024/8/9/russian-users-report-mass-youtube-outage-amid-growing-official-criticism?traffic_source=rss#:~:text=Responding%20to%20this%2C%20a%20YouTube,reported%20about%20YouTube%20in%20Russia.) - - -The simplified pydantic structure of the scored key factors is: -```python -class ScoredKeyFactor(): - text: str - factor_type: KeyFactorType (Pro, Con, or Base_Rate) - citation: str - source_publish_date: datetime | None - url: str - score_card: ScoreCard - score: int - display_text: str -``` - -## Base Rate Researcher -The Base Rate Researcher helps calculate historical base rates for events. As of last update, it gives decent results around 50% of the time. It orchestrates the Niche List Researcher and the Fermi Estimator to find base rate. - -```python -from forecasting_tools import BaseRateResearcher - -# Initialize researcher -researcher = BaseRateResearcher( - "How often has Apple been successfully sued for patent violations?" -) - -# Get base rate analysis -report = await researcher.make_base_rate_report() - -print(f"Historical rate: {report.historical_rate:.2%}") -print(report.markdown_report) -``` - -## Niche List Researcher -The Niche List Researcher helps analyze specific lists of events or items. The researcher will: -1. Generate a comprehensive list of potential matches -2. Remove duplicates -3. Fact check each item against multiple criteria -4. Return only validated items (unless include_incorrect_items=True) - -```python -from forecasting_tools import NicheListResearcher - -researcher = NicheListResearcher( - type_of_thing_to_generate="Times Apple was successfully sued for patent violations between 2000-2024" -) - -fact_checked_items = await researcher.research_niche_reference_class( - return_invalid_items=False -) - -for item in fact_checked_items: - print(item) -``` - -The simplified pydantic structure of the fact checked items is: -```python -class FactCheckedItem(): - item_name: str - description: str - is_uncertain: bool | None = None - initial_citations: list[str] | None = None - fact_check: FactCheck - type_description: str - is_valid: bool - supporting_urls: list[str] - one_line_fact_check_summary: str - -class FactCheck(BaseModel): - criteria_assessments: list[CriteriaAssessment] - is_valid: bool - -class CriteriaAssessment(): - short_name: str - description: str - validity_assessment: str - is_valid_or_unknown: bool | None - citation_proving_assessment: str | None - url_proving_assessment: str | None: -``` - -## Fermi Estimator -The Fermi Estimator helps break down numerical estimates using Fermi estimation techniques. - -```python -from forecasting_tools import Estimator - -estimator = Estimator( - type_of_thing_to_estimate="books published worldwide each year", - previous_research=None # Optional: Pass in existing research -) - -size, explanation = await estimator.estimate_size() - -print(f"Estimate: {size:,}") -print(explanation) -``` - -Example output (Fake data with links not added): -> I estimate that there are 2,750,000 'books published worldwide each year'. -> -> **Facts**: -> - Traditional publishers release approximately 500,000 new titles annually in English-speaking countries [1] -> - China publishes around 450,000 new books annually [2] -> - The global book market was valued at $92.68 billion in 2023 [3] -> - Self-published titles have grown by 264% in the last 5 years [4] -> - Non-English language markets account for about 50% of global publishing [5] -> -> **Estimation Steps and Assumptions**: -> 1. Start with traditional English publishing: 500,000 titles -> 2. Add Chinese market: 500,000 + 450,000 = 950,000 -> 3. Account for other major languages (50% of market): 950,000 * 2 = 1,900,000 -> 4. Add self-published titles (estimated 45% of total): 1,900,000 * 1.45 = 2,755,000 -> -> **Background Research**: [Additional research details...] - -## Metaculus API -The Metaculus API wrapper helps interact with Metaculus questions and tournaments. Grabbing questions returns a pydantic object, and supports important information for Binary, Multiple Choice, Numeric,and Date questions. - -```python -from forecasting_tools import MetaculusApi - -question = MetaculusApi.get_question_by_id(11245) # US 2024 Election -question = MetaculusApi.get_question_by_url("https://www.metaculus.com/questions/11245/...") -questions = MetaculusApi.get_all_open_questions_from_tournament( - tournament_id=3672, # Q4 2024 Quarterly Cup -) -MetaculusApi.post_binary_question_prediction( - question_id=11245, - prediction_in_decimal=0.75 # Must be between 0.01 and 0.99 -) -MetaculusApi.post_question_comment( - question_id=11245, - comment_text="Here's my reasoning..." -) -benchmark_questions = MetaculusApi.get_benchmark_questions( - num_of_questions_to_return=20 -) -``` - -## Monetary Cost Manager -The Monetary Cost Manager helps to track AI and API costs. It tracks expenses and errors if it goes over the limit. Leave the limit empty to disable the limit. It shouldn't be trusted as an exact expense, but a good estimate of costs. See `forecasting_tools/ai_models/README.md` for more details, and some flaws it has. - -```python -from forecasting_tools import MonetaryCostManager - -max_cost = 5.00 - -with MonetaryCostManager(max_cost) as cost_manager: - results = await any_tool_that_uses_ai() - current_cost = cost_manager.current_usage - print(f"Current cost: ${current_cost:.2f}") -``` - -# Local Development - -## Environment Variables -The environment variables you need can be found in ```.env.template```. Copy this template as ```.env``` and fill it in. As of last update, you only strictly need OPENAI_API_KEY and EXA_API_KEY. - -## Docker Dev Container -Dev containers are reliable ways to make sure environments work on everyone's machine the first try and so you don't have to spend hours setting up your environment (especially if you have Docker already installed). If you would rather just use poetry, without the dev container, you can skip to "Alternatives to Docker". Otherwise, to get your development environment up and running, you need to have Docker Engine installed and running. Once you do, you can use the VSCode dev container pop-up to automatically set up everything for you. - -### Install Docker -For Windows and Mac, you will download Docker Desktop. For Linux, you will download Docker Engine. (NOTE: These instructions might be outdated). - -First download and setup Docker Engine using the instructions at the link below for your OS: - * Windows: [windows-install](https://docs.docker.com/desktop/install/windows-install/) - * Mac: [mac-install](https://docs.docker.com/desktop/install/mac-install/) - * Linux: [install](https://docs.docker.com/engine/install/) - * Do not install Docker Desktop for Linux, rather, select your Linux distribution on the left sidebar and follow the distribution specific instructions for Docker engine. Docker Desktop runs with a different environment in Linux. - * Remember to follow the post-installation steps for Linux: [linux-postinstall](https://docs.docker.com/engine/install/linux-postinstall/) - - -### Starting the container -Once Docker is installed, when you open up the project folder in VSCode, you will see a pop up noting that you have a setup for a dev container, and asking if you would like to open the folder in a container. You will want to click "open in container". This will automatically set up everything you need and bring you into the container. If the Docker process times out in the middle of installing python packages you can run the `.devcontiner/postinstall.sh` manually. You may need to have the VSCode Docker extension and/or devcontainer extension downloaded in order for the pop up to appear. - -Once you are in the container, poetry should have already installed a virtual environment. For VSCode features to use this environment, you will need to select the correct python interpreter. You can do this by pressing `Ctrl + Shift + P` and then typing `Python: Select Interpreter`. Then select the interpreter that starts with `.venv`. - -A number of vscode extensions are installed automatically (e.g. linting). You may need to wait a little while and then reload the window after all of these extensions are installed. You can install personal vscode extensions in the dev environment. - - -### Managing Docker -There are many ways to manager Docker containers, but generally if you download the vscode Docker extension, you will be able to stop/start/remove all containers and images. - - -### Alternatives to Docker -If you choose not to run Docker, you can use poetry to set up a local virtual environment. If you are on Ubuntu, you should be able to just read through and then run `.devcontainer/postinstall.sh`. If you aren't on Ubuntu, check out the links in the postinstall file for where install instructions for dependencies were originally found. You may also want to take a look at VSCode extensions that would be installed (see the list in the `.devcontainer/devcontainer.json` file) so that some VSCode workplace settings work out of the box (e.g. automatic Black Formatting). - -## Running the Front End -You can run any front end folder in the front_end directory by executing `streamlit run front_end/Home.py`. This will start a development server for you that you can run. - -## Testing -This repository uses pytest and pytest-xdist. xdist spreads out all the tests between multiple threads that are each run on a separate CPU in order to speed up execution. Currently its setup to create a thread per CPU. Configuration for this is in `pytest.ini`. The tests are gathered afresh from each thread, so any initialization done in imports, globals, or class variables are done for each thread. - -Tests are subdivided into folders based on cost and reliability, so do not run `pytest` without specifying which folder you want or else you will incur some large expenses from OpenAI. The more expensive tests run the tools all the way through, and on many example cases, and are not expected to get 100% success rates. +Join the [Discord](https://discord.gg/Dtq4JNdXnw) to give feedback, feature requests, and ideas