diff --git a/.github/workflows/daily_run_simple_bot.yaml b/.github/workflows/daily_run_simple_bot.yaml index 23fe53f..e744fd3 100644 --- a/.github/workflows/daily_run_simple_bot.yaml +++ b/.github/workflows/daily_run_simple_bot.yaml @@ -1,6 +1,7 @@ name: Daily Build Simple Bot on: + workflow_dispatch: schedule: - cron: "0 15 * * *" # every day, 11:00 EST @@ -38,4 +39,4 @@ jobs: env: METACULUS_TOKEN: ${{ secrets.METACULUS_TOKEN }} # replace this with the name of the variable under which you stored your own Metaculus token PERPLEXITY_API_KEY: ${{ secrets.PERPLEXITY_API_KEY }} - OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} \ No newline at end of file + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} diff --git a/.github/workflows/pr_check.yaml b/.github/workflows/pr_check.yaml deleted file mode 100644 index 68e52e9..0000000 --- a/.github/workflows/pr_check.yaml +++ /dev/null @@ -1,36 +0,0 @@ -name: PR Check - -on: - pull_request: - branches: [main] - -jobs: - pr_check: - runs-on: ubuntu-latest - steps: - - name: Check out repository - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: "3.11" - - name: Install poetry - uses: snok/install-poetry@v1 - with: - virtualenvs-create: true - virtualenvs-in-project: true - installer-parallel: true - - name: Load cached venv - id: cached-poetry-dependencies - uses: actions/cache@v2 - with: - path: .venv - key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }} - - name: Install dependencies - run: poetry install --no-interaction --no-root - - name: Run simple bot - run: | - poetry run python simple-forecast-bot.py - env: - METACULUS_TOKEN: ${{ secrets.METACULUS_TOKEN }} - PERPLEXITY_API_KEY: ${{ secrets.PERPLEXITY_API_KEY }} - OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} \ No newline at end of file diff --git a/README.md b/README.md index 6ee67d0..a8dec1e 100644 --- a/README.md +++ b/README.md @@ -5,9 +5,7 @@ This repository contains a simple bot meant to get you started with creating you ## Getting started -### Setting up the repository -Fork this repository (click the "Fork" button in the top right corner). You don't have to fork this repository. But if you do, you can make use of the github actions to run your own bot automatically. - +### Run the bot locally Clone the repository. Find your terminal and run the following commands: ```bash git clone https://github.com/Metaculus/metac-bot @@ -30,15 +28,11 @@ to install all required dependencies. Running the bot requires various environment variables. If you run the bot locally, the easiest way to set them is to create a file called `.env` in the root directory of the repository and add the variables in the following format: ```bash METACULUS_TOKEN=1234567890 # register your bot to get a here: https://www.metaculus.com/aib/ -OPENAI_API_KEY=1234567890 PERPLEXITY_API_KEY=1234567890 # optional, if you want to use perplexity.ai -ANTHROPIC_API_KEY=1234567890 ``` #### Github Actions -If you want to automate running the bot using github actions, you have to set the environment variables in the github repository settings. -Go to (Settings -> Secrets and variables -> Actions). Set API keys as secrets and the tournament id and API base URL as variables. - -For this simple bot, `TOURNAMENT_ID` and `API_BASE_URL` are simply hard-coded in the script and can be changed in the code itself. +If you want to automate running the bot using github actions, you have to fork the repository and then set the environment variables in the github repository settings. +Go to (Settings -> Secrets and variables -> Actions) and set API keys as secrets. ## Running the bot @@ -54,6 +48,4 @@ Github can automatically run code in a repository. To that end, you need to fork Automation is handled in the `.github/workflows/` folder. -The `pr_check.yaml` file is responsible for triggering a test run every time a pull request is made to the main branch. This is useful for development and testing. - The `daily_run_simple_bot.yaml` file runs the simple bot every day (note that since `submit_predictions` is set to `False` in the script by default, no predictions will actually be posted). The `daily_run_simple_bot.yaml` file contains various comments and explanations. You should be able to simply copy this file and modify it to run your own bot. diff --git a/poetry.lock b/poetry.lock index 796c9d0..5757c5f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,1865 +1,143 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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description = "" authors = ["Vasile Popescu "] @@ -9,11 +9,8 @@ package-mode = false [tool.poetry.dependencies] python = "^3.11" python-decouple = "^3.8" -guidance = "^0.1.15" -openai = "^1.35.3" -llama-index-llms-openai = "^0.1.23" -llama-index = "^0.10.50" -llama-index-llms-anthropic = "^0.1.14" +requests = "^2.32.3" + [build-system] requires = ["poetry-core"] diff --git a/simple-forecast-bot.py b/simple-forecast-bot.py index 2804cfc..1fad36f 100755 --- a/simple-forecast-bot.py +++ b/simple-forecast-bot.py @@ -1,4 +1,3 @@ - #!/usr/bin/env python import json @@ -8,22 +7,131 @@ import datetime import re -from llama_index.core.llms import ChatMessage, MessageRole -from llama_index.core import Settings -from llama_index.llms.anthropic import Anthropic -from llama_index.llms.openai import OpenAI -# Note: To understand this code, it may be easiest to start with the `main()` -# function and read the code that is called from there. +def main(): + """ + Main function to run the forecasting bot. This function accesses the questions + for a given tournament, fetches information about them, and then uses an LLM + to generate a forecast. + + Installing dependencies + ---------------------- + Install poetry: https://python-poetry.org/docs/#installing-with-pipx. + Then run `poetry install` in your terminal. + + Environment variables + ---------------------- + + You need to have a .env file with all required environment variables. + + Alternatively, if you're running the bot via github actions, you can set + the environment variables in the repository settings. + (Settings -> Secrets and variables -> Actions). Set API keys as secrets and + things like the tournament id as variables. + + When using an .env file, the environment variables should be specified in the following format: + METACULUS_TOKEN=1234567890 + + The following environment variables are important: + - METACULUS_TOKEN: your metaculus API token (go to https://www.metaculus.com/aib/ to get one) + - PERPLEXITY_API_KEY: your perplexity API key, if you are using perplexity (i.e. use_perplexity is set to Tru ) + + Running the bot + ---------------------- + Run this bot using `poetry run python simple-forecast-bot.py`. + By default, the bot will not submit any predictions. You need set the "submit_predictions" flag to True in the code. + """ + + # define bot parameters + use_perplexity = False + submit_predictions = False + metac_token = config("METACULUS_TOKEN") + metac_base_url = "https://www.metaculus.com/api2" + tournament_id = 32506 + llm_model_name = config("LLM_MODEL_NAME", default="gpt-4o") + + all_questions = [] + offset = 0 + + # get all questions in the tournament and add them to the all_questions list + while True: + questions = list_questions( + metac_base_url, metac_token, tournament_id, offset=offset + ) + if len(questions) < 1: + break # break the while loop if there are no more questions to process + offset += len(questions) # update the offset for the next batch of questions + all_questions.extend(questions) + + for question in all_questions: + print("Forecasting ", question["id"], question["question"]["title"]) + + # get news info from perplexity if the user wants to use it + news_summary = ( + call_perplexity(question["question"]["title"]) if use_perplexity else None + ) + + prompt = build_prompt( + question["question"]["title"], + question["question"]["description"], + question["question"].get("resolution_criteria", ""), + question["question"].get("fine_print", ""), + news_summary, + ) + + print( + f"\n\n*****\nPrompt for question {question['id']}/{question['question']['title']}:\n{prompt} \n\n\n\n" + ) + + llm_output = call_llm_model(llm_model_name, prompt) + + llm_prediction = process_forecast_probabilty(llm_output) + print(f"\n\n*****\nLLM prediction: {llm_prediction}\n*****\n") + rationale = llm_output + + if llm_prediction is not None and submit_predictions: + # post prediction + post_url = f"{metac_base_url}/questions/{question['id']}/predict/" + response = requests.post( + post_url, + json={"prediction": float(llm_prediction) / 100}, + headers={"Authorization": f"Token {metac_token}"}, + ) + + if not response.ok: + raise Exception(response.text) + + # post comment with rationale + rationale = ( + rationale + + "\n\n" + + "Used the following information from perplexity:\n\n" + + news_summary + ) + comment_url = f"{metac_base_url}/comments/" # this is the url for the comments endpoint + response = requests.post( + comment_url, + json={ + "comment_text": rationale, + "submit_type": "N", # submit this as a private note + "include_latest_prediction": True, + "question": question["id"], + }, + headers={ + "Authorization": f"Token {metac_token}" + }, # your token is used to authenticate the request + ) + + print(f"\n\n*****\nPosted prediction for {question['id']}\n*****\n") def build_prompt( - title: str, - description: str, - resolution_criteria: str, - fine_print: str, - news_info: str | None = None - ): + title: str, + description: str, + resolution_criteria: str, + fine_print: str, + news_info: str | None = None, +): """ Function to build the prompt using various arguments. """ @@ -50,7 +158,6 @@ def build_prompt( """ - prompt += f""" Today is {datetime.datetime.now().strftime("%Y-%m-%d")}. @@ -65,6 +172,7 @@ def build_prompt( return prompt + def process_forecast_probabilty(forecast_text: str): """ Extract the forecast probability from the forecast text and clamp it between 1 and 99. @@ -73,13 +181,15 @@ def process_forecast_probabilty(forecast_text: str): if matches: # Return the last number found before a '%' number = int(matches[-1]) - number = min(99, max(1, number)) # clamp the number between 1 and 99 + number = min(99, max(1, number)) # clamp the number between 1 and 99 return number else: return None -def list_questions(base_url: str, metac_token: str, tournament_id: int, offset=0, count=10): +def list_questions( + base_url: str, metac_token: str, tournament_id: int, offset=0, count=10 +): """ List questions from a specific tournament. This uses the questions endpoint and queries it for questions belonging to a specific tournament. @@ -106,22 +216,20 @@ def list_questions(base_url: str, metac_token: str, tournament_id: int, offset=0 """ # a set of parameters to pass to the questions endpoint url_qparams = { - "limit": count, # the number of questions to return - "offset": offset, # pagination offset + "limit": count, # the number of questions to return + "offset": offset, # pagination offset "has_group": "false", - "order_by": "-activity", # order by activity (most recent questions first) - "forecast_type": "binary", # only binary questions are returned - "project": tournament_id, # only questions in the specified tournament are returned - "status": "open", # only open questions are returned - "format": "json", # return results in json format - "type": "forecast", # only forecast questions are returned - "include_description": "true", # include the description in the results + "order_by": "-activity", # order by activity (most recent questions first) + "forecast_type": "binary", # only binary questions are returned + "project": tournament_id, # only questions in the specified tournament are returned + "status": "open", # only open questions are returned + "format": "json", # return results in json format + "type": "forecast", # only forecast questions are returned + "include_description": "true", # include the description in the results } - url = f"{base_url}/questions/" # url for the questions endpoint + url = f"{base_url}/questions/" # url for the questions endpoint response = requests.get( - url, - headers={"Authorization": f"Token {metac_token}"}, - params=url_qparams + url, headers={"Authorization": f"Token {metac_token}"}, params=url_qparams ) # you can verify what this is doing by looking at # https://www.metaculus.com/api2/questions/?format=json&has_group=false&limit=5&offset=0&order_by=-activity&project=3294&status=open&type=forecast @@ -129,7 +237,9 @@ def list_questions(base_url: str, metac_token: str, tournament_id: int, offset=0 # base_url/questions/, then a "?"" before the first url param and then a "&" # between additional parameters - response.raise_for_status() + if not response.ok: + raise Exception(response.text) + data = json.loads(response.content) return data["results"] @@ -160,7 +270,7 @@ def call_perplexity(query): "model": "llama-3.1-sonar-large-128k-chat", "messages": [ { - "role": "system", # this is a system prompt designed to guide the perplexity assistant + "role": "system", # this is a system prompt designed to guide the perplexity assistant "content": """ You are an assistant to a superforecaster. The superforecaster will give you a question they intend to forecast on. @@ -169,13 +279,16 @@ def call_perplexity(query): """, }, { - "role": "user", # this is the actual prompt we ask the perplexity assistant to answer + "role": "user", # this is the actual prompt we ask the perplexity assistant to answer "content": query, }, ], } response = requests.post(url=url, json=payload, headers=headers) - response.raise_for_status() + + if not response.ok: + raise Exception(response.text) + content = response.json()["choices"][0]["message"]["content"] print( f"\n\nCalled perplexity with:\n----\n{json.dumps(payload)}\n---\n, and got\n:", @@ -184,173 +297,48 @@ def call_perplexity(query): return content -def get_model(model_name: str): - """ - Get the appropriate language model based on the provided model name. - This uses the classes provided by the llama-index library. - - Parameters: - ----------- - model_name : str - The name of the model to instantiate. Supported values are: - "gpt-4o", "gpt-3.5-turbo", "anthropic", "o1-preview" - - Returns: - -------- - Union[OpenAI, Anthropic, None] - An instance of the specified model, or None if the model name is not recognized. - - Note: - ----- - This function relies on environment variables for API keys. These should be - stored in a file called ".env", which will be accessed using the - `config` function from the decouple library. - """ - - match model_name: - case "gpt-4o": - return OpenAI( - api_key=config("OPENAI_API_KEY", default=""), - model=model_name - ) - case "gpt-3.5-turbo": - return OpenAI( - api_key=config("OPENAI_API_KEY", default=""), - model=model_name - ) - case "anthropic": - tokenizer = Anthropic().tokenizer - Settings.tokenizer = tokenizer - return Anthropic( - api_key=config("ANTHROPIC_API_KEY", default=""), - model="claude-3-5-sonnet-20240620", - ) - case "o1-preview": - return OpenAI( - api_key=config("OPENAI_API_KEY", default=""), - model=model_name - ) - - return None - - -def main(): - """ - Main function to run the forecasting bot. This function accesses the questions - for a given tournament, fetches information about them, and then uses an LLM - to generate a forecast. - - Parameters: - ----------- - None. All relevant parameters are devined via environment variables or - directly in the code. +def call_llm_model(model_name: str, prompt: str): + extra_headers = {} + extra_params = {} + proxy_base_url = "https://www.metaculus.com" + #proxy_base_url = "http://localhost:3000" - Installing dependencies - ---------------------- - Install poetry: https://python-poetry.org/docs/#installing-with-pipx. - Then run `poetry install` in your terminal. + def get_content_openai(x): + return x["choices"][0]["message"]["content"] - Environment variables - ---------------------- - - You need to have a .env file with all required environment variables. - - Alternatively, if you're running the bot via github actions, you can set - the environment variables in the repository settings. - (Settings -> Secrets and variables -> Actions). Set API keys as secrets and - things like the tournament id as variables. - - When using an .env file, the environment variables should be specified in the following format: - METACULUS_TOKEN=1234567890 - - The following environment variables are important: - - METACULUS_TOKEN: your metaculus API token (go to https://www.metaculus.com/aib/ to get one) - - OPENAI_API_KEY: your openai API key - - ANTHROPIC_API_KEY: your anthropic API key - - PERPLEXITY_API_KEY: your perplexity API key - - Running the bot - ---------------------- - Run this bot using `poetry run python simple-forecast-bot.py`. - By default, the bot will not submit any predictions. You need to change that - setting in the code. - """ - - # define bot parameters - use_perplexity = False - submit_predictions = False - metac_token = config("METACULUS_TOKEN") - metac_base_url = "https://www.metaculus.com/api2" - tournament_id = 32506 - llm_model_name = "gpt-4o" + def get_content_anthropic(x): + return x["content"][0]["text"] - all_questions = [] - offset = 0 - - # get all questions in the tournament and add them to the all_questions list - while True: - questions = list_questions( - metac_base_url, metac_token, tournament_id, offset=offset - ) - if len(questions) < 1: - break # break the while loop if there are no more questions to process - offset += len(questions) # update the offset for the next batch of questions - all_questions.extend(questions) - - for question in all_questions: - print("Forecasting ", question["id"], question["question"]["title"]) - - # get news info from perplexity if the user wants to use it - news_summary = call_perplexity(question["question"]["title"]) if use_perplexity else None - - prompt = build_prompt( - question["question"]["title"], - question["question"]["description"], - question["question"].get("resolution_criteria", ""), - question["question"].get("fine_print", ""), - news_summary, - ) - - print( - f"\n\n*****\nPrompt for question {question['id']}/{question['question']['title']}:\n{prompt} \n\n\n\n" - ) + if model_name in ["gpt-4o", "gpt-3.5-turbo"]: + url = f"{proxy_base_url}/proxy/openai/v1/chat/completions/" + get_content = get_content_openai + elif model_name == "claude-3-5-sonnet-20240620": + extra_headers = {"anthropic-version": "2023-06-01"} + # modify this as you see fit + extra_params = {"max_tokens": 4096} + url = f"{proxy_base_url}/proxy/anthropic/v1/messages/" + get_content = get_content_anthropic + else: + raise ValueError(f"Model {model_name} not supported") - # get the language model to be used based on the name of the model - llm_model = get_model(llm_model_name) - # make a call to the language model - response = llm_model.chat( - messages=[ChatMessage(role=MessageRole.USER, content=prompt)] - ) - llm_prediction = process_forecast_probabilty(response.message.content) - rationale = response.message.content + headers = { + "Authorization": f"Token {config('METACULUS_TOKEN')}", + "Content-Type": "application/json", + **extra_headers, + } + data = { + "model": model_name, + "messages": [{"role": "user", "content": prompt}], + **extra_params, + } - if llm_prediction is not None and submit_predictions: + response = requests.post(url, headers=headers, json=data) + if not response.ok: + raise Exception(response.text) - # post prediction - post_url = f"{metac_base_url}/questions/{question['id']}/predict/" - response = requests.post( - post_url, - json={"prediction": float(llm_prediction) / 100}, - headers={"Authorization": f"Token {metac_token}"}, - ) - response.raise_for_status() - - # post comment with rationale - rationale = rationale + "\n\n" + "Used the following information from perplexity:\n\n" + news_summary - comment_url = f"{metac_base_url}/comments/" # this is the url for the comments endpoint - response = requests.post( - comment_url, - json={ - "comment_text": rationale, - "submit_type": "N", # submit this as a private note - "include_latest_prediction": True, - "question": question["id"], - }, - headers={"Authorization": f"Token {metac_token}"}, # your token is used to authenticate the request - ) + return get_content(response.json()) - print(f"Posted prediction for {question['id']}") if __name__ == "__main__": main()