forecasting-tools 0.2.85
AI forecasting and research tools to help humans reason about and forecast the future
License: MIT
Requires Python: <4.0,>=3.11
Keywords: ai, forecasting, research, metaculus, prediction, future, market

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# Quick Start
Install this package with `pip install forecasting-tools`

Demo website: https://forecasting-tools.streamlit.app/

Demo repo (get a Metaculus bot running in 30min): https://github.com/Metaculus/metac-bot-template

# Overview

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 and provides a number of utilities. You can forecast with a pre-existing bot or override the class to customize your own (without redoing all the aggregation/API code, etc)
- 🔌 **Metaculus API Wrapper:** for interacting with questions and tournaments
- 📊 **Benchmarking:** Randomly sample quality questions from Metaculus and run your bot against them so you can get an early sense of how your bot is doing by comparing to the community prediction and expected baseline scores.
- 🤖 **In-House Metaculus Bots**: You can see all the bots that Metaculus is running on their site in `run_bots.py`

Here are some other features of the project (not all are documented yet):
- **Smart Searcher:** A custom AI-powered internet-informed llm powered by Exa.ai and GPT. It is more configurable than Perplexity AI, allowing you to use any AI model, instruct the AI to decide on filters, get citations linking to exact paragraphs, etc.
- **Key Factor Analysis:** Key Factors Analysis for scoring, ranking, and prioritizing important variables in forecasting questions
- **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)
- **Monetary Cost Manager:** for tracking AI and API expenses
- **Prompt Optimizer:** for letting AI iterate through 100+ forecasting bot prompts
- **Question Decomposer/Operationalizer:** To turn a question or topic into relevant forecastable sub-questions
- **Other experimental tools:** See the demo site for other AI forecasting tools that this project supports (not all are documented). Also see the `scripts` folder for other common workflows and entry points into the code.

All the examples below are in a Jupyter Notebook called `README.ipynb` which you can run locally to test the package (make sure to run the first cell though).

If you decide you want to join the Metaculus AI Benchmarking Tournament, it is recommended that you start [here](https://github.com/Metaculus/metac-bot-template). This repo is easy to start with and has a 30min tutorial for how to set it up.

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 tool X for reason Y, though am busy and don't have time to elaborate" is helpful to know)

Note: This package is still in an experimental phase. The goal is to keep the package API fairly stable, though no guarantees are given at this phase especially with experimental tools. 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 most accurate bot based on testing. May be more expensive.
- **TemplateBot**: A simple bot 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, GeneralLlm

# 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=False,  # Whether to post the forecasts to Metaculus
    folder_to_save_reports_to="logs/forecasts/",  # Where to save detailed reports
    skip_previously_forecasted_questions=False,
    llms={ # LLM models to use for different tasks. Will use default llms if not specified. Requires the relevant provider environment variables to be set.
        "default": GeneralLlm(model="openrouter/google/gemini-2.5-pro", temperature=0),
        "summarizer": "openai/gpt-4o-mini",
    }
)

TOURNAMENT_ID = MetaculusApi.CURRENT_QUARTERLY_CUP_ID
reports = await bot.forecast_on_tournament(TOURNAMENT_ID)

# Print results (if the tournament is not active, no reports will be returned)
for report in reports:
    print(f"\nQuestion: {report.question.question_text}")
    print(f"Prediction: {report.prediction}")
```


    Question: Will a 2025 Major Atlantic Hurricane make landfall before September?
    Prediction: 0.35

    Question: Will China enact an export ban on a rare earth element to the United States before September 1, 2025?
    Prediction: 0.15

    Question: Will Israel strike the Iranian military in Iran again, before September 2025?
    Prediction: 0.28



### Forecasting Outside a Tournament


```python
from forecasting_tools import (
    TemplateBot,
    BinaryQuestion,
    MetaculusApi,
    DataOrganizer
)

# 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'
)

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}")
    shortened_explanation = report.explanation.replace('\n', ' ')[:100]
    print(f"Reasoning: {shortened_explanation}...")

# You can also save and load questions and reports
file_path = "temp/reports.json"
DataOrganizer.save_reports_to_file_path(reports, file_path) # This will overwrite the file if it already exists
loaded_reports = DataOrganizer.load_reports_from_file_path(file_path)
```

    Question: Will humans go extinct before 2100?
    Prediction: 0.03
    Reasoning:  # SUMMARY *Question*: Will humans go extinct before 2100? *Final Prediction*: 3.0% *Total Cost*: $0...
    Question: Will YouTube be blocked in Russia?
    Prediction: 0.7
    Reasoning:  # SUMMARY *Question*: Will YouTube be blocked in Russia? *Final Prediction*: 70.0% *Total Cost*: $0...


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)

## Customizing the Bot
### General Customization
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 interacting with the Metaculus API, implementing aggregation of predictions, creating benchmarking interfaces, etc. Below 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,
    MetaculusApi,
    GeneralLlm,
    PredictionExtractor
)
from forecasting_tools.util.misc import clean_indents

class MyCustomBot(TemplateBot):

    async def run_research(self, question: MetaculusQuestion) -> str:
        searcher = SmartSearcher(
            num_searches_to_run=2,
            num_sites_per_search=10
        )

        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 GeneralLlm(model="metaculus/gpt-4o", temperature=0).invoke(prompt)
        prediction = PredictionExtractor.extract_last_percentage_value(
            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]:
        ...

custom_bot = MyCustomBot()
question = MetaculusApi.get_question_by_url(
    "https://www.metaculus.com/questions/578/human-extinction-by-2100/"
)
report = await custom_bot.forecast_question(question)
print(f"Question: {report.question.question_text}")
print(f"Prediction: {report.prediction}")
print(f"Research: {report.research[:100]}...")
```

    Question: Will humans go extinct before 2100?
    Prediction: 0.05
    Research: # RESEARCH
    To analyze the question of whether humans will go extinct before 2100, we need to consider...


### Maintaining state with Notepad
If you want to maintain state between forecasts, you can use the `Notepad` object. This can be used to alternate forecasts between two different models, decide personalities for a bot up front, etc. There is a `Notepad` object made for every question forecasted.


```python
from forecasting_tools import (
    TemplateBot,
    BinaryQuestion,
    ReasonedPrediction,
    GeneralLlm,
    Notepad
)
import random

class NotepadBot(TemplateBot):

    async def _initialize_notepad(
        self, question: MetaculusQuestion
    ) -> Notepad:
        new_notepad = Notepad(question=question)
        random_personality = random.choice(["superforecaster", "financial analyst", "political advisor"])
        new_notepad.note_entries["personality"] = random_personality
        return new_notepad

    async def _run_forecast_on_binary(
        self, question: BinaryQuestion, research: str
    ) -> ReasonedPrediction[float]:
        notepad = await self._get_notepad(question)

        if notepad.total_predictions_attempted % 2 == 0:
            model = "metaculus/gpt-4o"
        else:
            model = "metaculus/claude-3-5-sonnet-20240620"

        personality = notepad.note_entries["personality"]
        prompt = f"You are a {personality}. Forecast this question: {question.question_text}. The last thing you write is your final answer as: 'Probability: ZZ%', 0-100"
        reasoning = await GeneralLlm(model=model, temperature=0).invoke(prompt)
        prediction = PredictionExtractor.extract_last_percentage_value(
            reasoning, max_prediction=1, min_prediction=0
        )
        return ReasonedPrediction(
            prediction_value=prediction, reasoning=reasoning
        )
```

## Join the Metaculus tournament using this package
You can create your own custom bot through the package in your own repo. An example can be found [at this repo](https://github.com/Metaculus/metac-bot-template) which you can fork and edit.

## 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.

# Important Utilities

## Benchmarking
Below is an example of how to run the benchmarker


```python
from forecasting_tools import Benchmarker, TemplateBot, BenchmarkForBot

class CustomBot(TemplateBot):
    ...

# Run benchmark on multiple bots
bots = [TemplateBot(), CustomBot()]  # Add your custom bots here
benchmarker = Benchmarker(
    forecast_bots=bots,
    number_of_questions_to_use=2,  # Recommended 100+ for meaningful results
    file_path_to_save_reports="benchmarks/",
        # It will create a file name for you if given a folder.
        # If a file name is given, and the file already exists, it will overwrite it.
    concurrent_question_batch_size=5,
)
benchmarks: list[BenchmarkForBot] = await benchmarker.run_benchmark()

# View results
for benchmark in benchmarks[:2]:
    print("--------------------------------")
    print(f"Bot: {benchmark.name}")
    print(f"Score: {benchmark.average_expected_baseline_score}") # Higher is better
    print(f"Num reports in benchmark: {len(benchmark.forecast_reports)}")
    print(f"Time: {benchmark.time_taken_in_minutes}min")
    print(f"Cost: ${benchmark.total_cost}")
```

    --------------------------------
    Bot: TemplateBot
    Score: 53.24105782939477
    Num reports in benchmark: 2
    Time: 0.23375582297643024min
    Cost: $0.03020605
    --------------------------------
    Bot: CustomBot
    Score: 53.24105782939476
    Num reports in benchmark: 2
    Time: 0.20734789768854778min
    Cost: $0.019155650000000003


The ideal number of questions to get a good sense of whether one bot is better than another can vary. 100+ should tell your something decent. See [this analysis](https://forum.effectivealtruism.org/posts/DzqSh7akX28JEHf9H/comparing-two-forecasters-in-an-ideal-world) for exploration of the numbers. With too few questions, the results could just be statistical noise, though how many questions you need depends highly on the difference in skill of your bot versions.

If you use the average expected baseline score, higher score is better. The scoring measures the expected value of your score without needing an actual resolution by assuming that the community prediction is the 'true probability'. Under this assumption, expected baseline scores are a proper score (see analysis in `scripts/simulate_a_tournament.ipynb`)

As of May 29, 2025 the benchmarker automatically selects a random set of questions from Metaculus that:
- Are binary questions (yes/no)
- Are currently open
- Opened within the last year
- Have at least 30 forecasters
- Have a community prediction
- Are not part of a group question

Note that sometimes there are not many questions matching these filters (e.g. at the beginning of a new year when a majority of open questions were just resolved). As of last edit there are plans to expand this to numeric and multiple choice, but right now it just benchmarks binary questions.

You can grab these questions without using the Benchmarker by running the below



```python
from forecasting_tools import MetaculusApi

questions = MetaculusApi.get_benchmark_questions(
    num_of_questions_to_return=100,
)
```

You can also save/load benchmarks to/from json


```python
from forecasting_tools import BenchmarkForBot

# Load
file_path = "benchmarks/benchmark.json"
benchmarks: list[BenchmarkForBot] = BenchmarkForBot.load_json_from_file_path(file_path)

# Save
new_benchmarks: list[BenchmarkForBot] = benchmarks
BenchmarkForBot.save_object_list_to_file_path(new_benchmarks, file_path) # Will overwrite the file if it already exists

# To/From Json String
single_benchmark = benchmarks[0]
json_object: dict = single_benchmark.to_json()
new_benchmark: BenchmarkForBot = BenchmarkForBot.from_json(json_object)
```

Once you have benchmark files in your project directory you can run `streamlit run forecasting_tools/benchmarking/benchmark_displayer.py` to get a UI with the benchmarks. You can also put `forecasting-tools.run_benchmark_streamlit_page()` into a new file, and run this file with streamlit to achieve the same results. This will allow you to see metrics side by side, explore code of past bots, see the actual bot responses, etc. It will pull in any files in your directory that contain "bench" in the name and are json. Results may take a while to load for large benchmark files.

![Benchmark Displayer Top](./docs/images/benchmark_top_screen.png)
![Benchmark Displayer Bottom](./docs/images/benchmark_bottom_screen.png)

## 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, ApiFilter, DataOrganizer
from datetime import datetime


# Get a question by post id
question = MetaculusApi.get_question_by_post_id(578)
print(f"Question found with url: {question.page_url}")

# Get a question by url
question = MetaculusApi.get_question_by_url("https://www.metaculus.com/questions/578/human-extinction-by-2100/")
print(f"Question found with url: {question.page_url}")

# Get all open questions from a tournament
questions = MetaculusApi.get_all_open_questions_from_tournament(
    tournament_id=MetaculusApi.CURRENT_QUARTERLY_CUP_ID
)
print(f"Num tournament questions: {len(questions)}")

# Get questions matching a filter
api_filter = ApiFilter(
    num_forecasters_gte=40,
    close_time_gt=datetime(2023, 12, 31),
    close_time_lt=datetime(2024, 12, 31),
    scheduled_resolve_time_lt=datetime(2024, 12, 31),
    allowed_types=["binary", "multiple_choice", "numeric", "date"],
    allowed_statuses=["resolved"],
)
questions = await MetaculusApi.get_questions_matching_filter(
    api_filter=api_filter,
    num_questions=50, # Remove this field to make it not error if you don't get 50 questions. However it will only go through one page of questions which may miss questions matching the ApiFilter since some filters are handled locally.
    randomly_sample=False
)
print(f"Num filtered questions: {len(questions)}")

# Load and save questions/reports
file_path = "temp/questions.json"
DataOrganizer.save_questions_to_file_path(questions, file_path) # Will overwrite the file if it already exists
questions = DataOrganizer.load_questions_from_file_path(file_path)

# Get benchmark questions
benchmark_questions = MetaculusApi.get_benchmark_questions(
    num_of_questions_to_return=20
)
print(f"Num benchmark questions: {len(benchmark_questions)}")

# Post a prediction
MetaculusApi.post_binary_question_prediction(
    question_id=578, # Note that the question ID is not always the same as the post ID
    prediction_in_decimal=0.012  # Must be between 0.01 and 0.99
)
print("Posted prediction")

# Post a comment
MetaculusApi.post_question_comment(
    post_id=578,
    comment_text="Here's example reasoning for testing... This will be a private comment..."
)
print("Posted comment")
```

    Question found with url: https://www.metaculus.com/questions/578
    Question found with url: https://www.metaculus.com/questions/578
    Num tournament questions: 11
    Num filtered questions: 50
    Num benchmark questions: 20
    Posted prediction
    Posted comment


# AI Research Tools/Agents

## 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

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,
    ScoredKeyFactor
)

# 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'
)

# Find key factors
key_factors = await KeyFactorsResearcher.find_and_sort_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...]

## General LLM
The `GeneralLlm` class is a wrapper around around litellm's acompletion function that adds some functionality like retry logic, calling the metaculus proxy, and cost callback handling. Litellm supports every model, most every parameter, and acts as one interface for every provider. See the litellm's acompletion function for a full list of parameters. Not all models will support all parameters. Additionally the Metaculus proxy doesn't support all models.


```python

result = await GeneralLlm(model="gpt-4o").invoke(prompt)
result = await GeneralLlm(model="claude-3-5-sonnet-20241022").invoke(prompt)
result = await GeneralLlm(model="metaculus/claude-3-5-sonnet-20241022").invoke(prompt) # Adding 'metaculus' Calls the Metaculus proxy
result = await GeneralLlm(model="gemini/gemini-pro").invoke(prompt)
result = await GeneralLlm(
    model="perplexity/sonar-pro",
    temperature=0.5,
    max_tokens=1000,
    top_p=0.95,
    frequency_penalty=0,
    presence_penalty=0,
    stop=["\n\n"],
    response_format=...
).invoke(prompt)
```

Additionally `GeneralLlm` provides some interesting structured response options. It will call the model a number of times until it gets the response type desired. The type validation works for any type including pydantic types like `list[BaseModel]` or nested types like `list[tuple[str,dict[int,float]]]`.


```python
from forecasting_tools import GeneralLlm
from pydantic import BaseModel

class President(BaseModel):
    name: str
    age_at_death: int
    biggest_accomplishment: str

model = GeneralLlm(model="gpt-4o")
pydantic_model_explanation = model.get_schema_format_instructions_for_pydantic_type(President)
prompt = clean_indents(f"""
    You are a historian helping with a presidential research project. Please answer the following question:
    Who is Abraham Lincoln?
    Please provide the information in the following format:
    {pydantic_model_explanation}
    """)

verified_president = await model.invoke_and_return_verified_type(
    prompt,
    President,
    allowed_invoke_tries_for_failed_output=2
)
regular_output = await model.invoke(prompt)

print(f"President is pydantic: {isinstance(verified_president, President)}")
print(f"President: {verified_president}")
print(f"Regular output: {regular_output}")

```

    President is pydantic: True
    President: name='Abraham Lincoln' age_at_death=56 biggest_accomplishment="Preserving the Union during the American Civil War and issuing the Emancipation Proclamation, which began the process of freedom for America's slaves."
    Regular output: ```json
    {
      "name": "Abraham Lincoln",
      "age_at_death": 56,
      "biggest_accomplishment": "Preserving the Union during the American Civil War and issuing the Emancipation Proclamation, which began the process of freedom for America's slaves."
    }
    ```


You can also do some basic code execution.


```python
from forecasting_tools import GeneralLlm, clean_indents

model = GeneralLlm(model="gpt-4o")

code = clean_indents("""
    Please run a fermi estimate for the number of books published worldwide each year.
    Generate only code and give your reasoning in comments. Do not include any other text, only code.
    Assign your answer to the variable 'final_result'.
    """)

result, code = await model.invoke_and_unsafely_run_and_return_generated_code(
    code,
    expected_output_type=float,
    allowed_invoke_tries_for_failed_output=2
)

print(f"Result: {result}")
print(f"Code:\n{code}")
```

    Result: 10010000.0
    Code:
    # Fermi estimate for the number of books published worldwide each year

    # Estimate the number of countries in the world
    num_countries = 200  # Rough estimate of the number of countries

    # Estimate the number of books published per country per year
    # Assume a small country might publish around 100 books per year
    # Assume a large country might publish around 100,000 books per year
    # Use an average of these estimates for a rough calculation
    avg_books_per_country = (100 + 100000) / 2

    # Calculate the total number of books published worldwide each year
    total_books_worldwide = num_countries * avg_books_per_country

    # Assign the result to the variable 'final_result'
    final_result = total_books_worldwide


## Monetary Cost Manager
The `MonetaryCostManager` 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. Any call made within the context of the manager will be logged in the manager. CostManagers are async safe and can nest inside of each other. Please note that cost is only tracked after a call finishes, so if you concurrently batch 1000 calls, they will all go through even if the cost is exceeded during the middle of the execution of the batch. Additionally not all models support cost tracking. You will get a logger warning if your model is not supported. Litellm models with weird non-token pricing structures may be inaccurate as well.


```python
from forecasting_tools import MonetaryCostManager
from forecasting_tools import (
    ExaSearcher, SmartSearcher, GeneralLlm
)

max_cost = 5.00

with MonetaryCostManager(max_cost) as cost_manager:
    prompt = "What is the weather in Tokyo?"
    result = await GeneralLlm(model="gpt-4o").invoke(prompt)
    result = await SmartSearcher(model="claude-3-5-sonnet-20241022").invoke(prompt)
    result = await ExaSearcher().invoke(prompt)
    # ... etc ...

    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.

## 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 it doesn't show up, press `ctrl+shift+P` and type in `devcontainers: build and reopen in remote 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 vsc