diff --git a/.github/workflows/daily_run_simple_bot.yaml b/.github/workflows/daily_run_simple_bot.yaml index e744fd3bc..17e89e0bd 100644 --- a/.github/workflows/daily_run_simple_bot.yaml +++ b/.github/workflows/daily_run_simple_bot.yaml @@ -3,12 +3,12 @@ name: Daily Build Simple Bot on: workflow_dispatch: schedule: - - cron: "0 15 * * *" # every day, 11:00 EST + - cron: "*/1 * * * *" # runs every 15 minutes. Make sure to skip already forecasted questions! # Daily job to run the simple forecast bot jobs: daily_build: - runs-on: ubuntu-latest #determines the machine that will run the job - keep as is + runs-on: ubuntu-latest # determines the machine that will run the job - keep as is steps: # sets up the steps that will be run in order # setup repository with all necessary dependencies - keep as is - name: Check out repository @@ -29,14 +29,13 @@ jobs: 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 # note that this will install dependencies for the main bot, which is a bit more than what's needed for the simple bot. But that shouldn't matter much. - # run your bot + run: poetry install --no-interaction --no-root - name: Run bot run: | - poetry run python simple-forecast-bot.py + poetry run python main.py # this reads the environment variables from the github repository. # Store under Settings --> Secrets and variables --> Actions 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 }} + ASKNEWS_CLIENT_ID: ${{ secrets.ASKNEWS_CLIENT_ID }} + ASKNEWS_SECRET: ${{ secrets.ASKNEWS_SECRET }} diff --git a/.gitignore b/.gitignore index e6033ab44..e497ef5eb 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,3 @@ .env - +.venv/ .vscode/ diff --git a/poetry.lock b/poetry.lock index 5757c5fdf..9e62a9e72 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,67 @@ -# This file is automatically @generated by Poetry 1.8.3 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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}\nLLM output:\n<{llm_output}>\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, -): - """ - Function to build the prompt using various arguments. - """ - - prompt = f""" -You are a professional forecaster interviewing for a job. - -Your interview question is: -{title} - -background: -{description} - -{resolution_criteria} - -{fine_print} - -""" - - if news_info: - prompt += f""" -Your research assistant says: -{news_info} - -""" - - prompt += f""" -Today is {datetime.datetime.now().strftime("%Y-%m-%d")}. - -Before answering you write: -(a) The time left until the outcome to the question is known. -(b) What the outcome would be if nothing changed. -(c) What you would forecast if there was only a quarter of the time left. -(d) What you would forecast if there was 4x the time left. - -You write your rationale and then the last thing you write is your final answer as: "Probability: ZZ%", 0-100 -""" - - return prompt - - -def process_forecast_probabilty(forecast_text: str): - """ - Extract the forecast probability from the forecast text and clamp it between 1 and 99. - """ - matches = re.findall(r"(\d+)%", forecast_text) - 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 - return number - else: - return None - - -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. - - Parameters: - ----------- - base_url : str - the base url of the metaculus API - metac_token : str - the token to use for authentication - tournament_id : int - the ID of the tournament to list questions from - offset : int, optional - the number of questions to skip. This is used for pagination. I.e. if - offset is 0 and count is 10 then the first 10 questions are returned. - If offset is 10 and count is 10 then the next 10 questions are returned. - count : int, optional - the number of questions to return - - Returns: - -------- - json - A list of JSON objects, each containing information for a single question - """ - # a set of parameters to pass to the questions endpoint - url_qparams = { - "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 - } - url = f"{base_url}/questions/" # url for the questions endpoint - response = requests.get( - 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 - # in the browser. The URL works as follows: - # base_url/questions/, then a "?"" before the first url param and then a "&" - # between additional parameters - - if not response.ok: - raise Exception(response.text) - - data = json.loads(response.content) - return data["results"] - - -def call_perplexity(query): - """ - Make a call to the perplexity API to obtain additional information. - - Parameters: - ----------- - query : str - The query to pass to the perplexity API. This is the question we want to - get information about. - - Returns: - -------- - str - The response from the perplexity API. - """ - url = "https://api.perplexity.ai/chat/completions" - api_key = config("PERPLEXITY_API_KEY", default="-") - headers = { - "accept": "application/json", - "authorization": f"Bearer {api_key}", - "content-type": "application/json", - } - payload = { - "model": "llama-3.1-sonar-large-128k-chat", - "messages": [ - { - "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. -To be a great assistant, you generate a concise but detailed rundown of the most relevant news, including if the question would resolve Yes or No based on current information. -You do not produce forecasts yourself. -""", - }, - { - "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) - - 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:", - content, - ) - return content - - -def call_llm_model(model_name: str, prompt: str): - extra_headers = {} - extra_params = {"stream": True} - proxy_base_url = "https://www.metaculus.com" - - def get_content_chunk_openai(data): - # data has the format: {"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-4o-mini", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]} - content = "" - if data.get("choices") and data["choices"] and data["choices"][0].get("delta"): - content = data["choices"][0]["delta"].get("content", "") - return content - - def get_content_chunk_anthropic(data): - # lines of format: - # {'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}} - # or {'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': '(a) The time'}} - content = "" - - if data.get("type", None) == "content_block_delta": - content = data["delta"]["text"] - - if data.get("type", None) == "content_block_start": - content = data["content_block"]["text"] - - return content - - if model_name in ["gpt-4o", "gpt-3.5-turbo"]: - url = f"{proxy_base_url}/proxy/openai/v1/chat/completions/" - get_content_chunk = get_content_chunk_openai - elif model_name == "claude-3-5-sonnet-20240620": - extra_headers = {"anthropic-version": "2023-06-01"} - extra_params["max_tokens"] = 4096 - url = f"{proxy_base_url}/proxy/anthropic/v1/messages/" - get_content_chunk = get_content_chunk_anthropic - else: - raise ValueError(f"Model {model_name} not supported") - - headers = { - "Authorization": f"Token {config('METACULUS_TOKEN')}", - "Content-Type": "application/json", - **extra_headers, - } - - data = { - "model": model_name, - "messages": [{"role": "user", "content": prompt}], - **extra_params, - } - - response = requests.post(url, headers=headers, json=data, stream=True) - - if not response.ok: - raise Exception(response.text) - - content = "" - for line in response.iter_lines(): - line = line.decode("utf-8") - if line.startswith("data: {"): - data = json.loads(line.split("data: ")[1]) - content += get_content_chunk(data) - - return content - - -if __name__ == "__main__": - main()