From d07d61a2bcbfc97a3aa677692b127ab1aa1eb44c Mon Sep 17 00:00:00 2001 From: Peter Jung Date: Thu, 6 Jun 2024 14:09:06 +0200 Subject: [PATCH] First commit (#1) --- .env.example | 3 + .github/actions/python_prepare/action.yaml | 15 + .github/workflows/python_ci.yaml | 18 + README.md | 123 +- mypy.ini | 36 + poetry.lock | 7374 ++++++++++++++++++++ pyproject.toml | 23 + trader/app.py | 38 + trader/benchmark.py | 117 + trader/main.py | 73 + trader/prediction.py | 103 + 11 files changed, 7921 insertions(+), 2 deletions(-) create mode 100644 .env.example create mode 100644 .github/actions/python_prepare/action.yaml create mode 100644 .github/workflows/python_ci.yaml create mode 100644 mypy.ini create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 trader/app.py create mode 100644 trader/benchmark.py create mode 100644 trader/main.py create mode 100644 trader/prediction.py diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..cd336b6 --- /dev/null +++ b/.env.example @@ -0,0 +1,3 @@ +OPENAI_API_KEY= +TAVILY_API_KEY= +BET_FROM_PRIVATE_KEY= diff --git a/.github/actions/python_prepare/action.yaml b/.github/actions/python_prepare/action.yaml new file mode 100644 index 0000000..da07bc8 --- /dev/null +++ b/.github/actions/python_prepare/action.yaml @@ -0,0 +1,15 @@ +name: "Prepare Python environment" +description: "Set up Python and install dependencies" +runs: + using: "composite" + steps: + - name: Set up Python 3.10 + uses: actions/setup-python@v2 + with: + python-version: 3.10.14 + - name: Install Poetry + shell: bash + run: curl -sSL https://install.python-poetry.org | python3 - + - name: Install dependencies + shell: bash + run: poetry install diff --git a/.github/workflows/python_ci.yaml b/.github/workflows/python_ci.yaml new file mode 100644 index 0000000..7e876ab --- /dev/null +++ b/.github/workflows/python_ci.yaml @@ -0,0 +1,18 @@ +name: Python CI + +on: + pull_request: + push: + branches: [main] + workflow_dispatch: + +jobs: + mypy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + with: + submodules: true + - uses: ./.github/actions/python_prepare + - name: Run mypy + run: poetry run mypy diff --git a/README.md b/README.md index ff9462f..11d52cc 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,121 @@ -# gnosis-labs-zuberlin2024 -Repository for the hackathon run by Gnosis Labs at ZuBerlin 2024. +# Gnosis Labs ZuBerlin 2024 + +Welcome to the Gnosis AI ZuBerlin 2024 Hackathon repo! Here you will find all you need to build a tool for AI Agents that can make predictions on outcomes of future events. + +[Presentation available here.](https://docs.google.com/presentation/d/1gajA3m5p_X4R4oyNc80p5_uSYZz0z2R-YKxm0RQnz_4/edit?usp=sharing) + +Follow the instructions below to get started. + +## Support + +Contact us at https://t.me/+Fb0trLKZdMw2MTQ8. + +## Setup + +Install the project dependencies with `poetry`, using Python 3.10 (you can use [pyenv](https://github.com/pyenv/pyenv) to manage multiple Python versions): + +```bash +python3.10 -m pip install poetry +python3.10 -m poetry install +python3.10 -m poetry shell +``` + +Copy `.env.example` to `.env` and fill in the values: + +### OpenAI API key + +We will provide you with OpenAI key that's allowed to use gpt-3.5-turbo and embedding models, contact us on the TG group above. + +However, everyone is welcome to use arbitrary LLM if wanted. + +### Tavily API key + +Create a free acount on https://tavily.com and get the key there. + +Again, everyone is welcome to use arbitrary search engines, combine them, or even do a totally different approaches! + +### Private key on Gnosis Chain + +Use your existing or create a new wallet on Gnosis Chain. + +By default the script will do only very tiny bets (0.00001 xDai per market), but of course, you can contact us on the TG group above with your public key to get some free xDai. + +## Task + +Your task is to modify `predict` function in `trader/prediction.py` by any means necessary. + +Goal of the `predict` function is, given an `question` about the future, answer it with either `True` (the answer is `yes`), `False` (if the answer is `no`) or `None` (if the prediction failed). + +All the questions are guaranteed to be about the future and to be in a binary yes/no format. + +You can play with the prompts, different approaches, different LLMs, search engines, or anything you can think of. + +The code can be messy, the only thing we ask you is for it to be reproducible on our machines, and to help with that, there is `mypy` as the only check of CI pipeline on Github. + +A few ideas to jump start your experiments: + +On the research side: + +- Scrape multiple search engines +- Scrape different kinds of sources depending on question type +- Trying different methods for extracting valuable information from each site +- Handling cases where two sources contain conflicting information + +On the prediction side: + +- Have an ensemble of agents making predictions, and taking an average or other aggregation method +- Currently the LLM returns a float, and this is converted to a binary Yes/No answer by thresholding at 0.5. Experiment with having the LLM return different kinds of answers (e.g. categorical) + +### Testing your experiments + +Run + +```bash +PYTHONPATH=. streamlit run trader/app.py +``` + +to start a Streamlit application where you can give your prediction method either question [from the Omen market](https://aiomen.eth.limo/), or write your own. + +Run + +```bash +python trader/benchmark.py --n N +``` + +where `N` is number of markets to do a prediction on. The benchmark script will run + +1. Random agent (coin flip between yes and no answers) +2. Question-only agent (only LLM call, without any information from internet) +3. `prediction.py/predict`-based agent + +on `N` open markets from https://manifold.markets. + +The idea is that markets on Manifold are mostly answered by real people, so the closer your agent is to their predictions, the better. However, it isn't always the case. + +Bear in mind your LLM credits, Tavily credits or any other paid 3rd provider credits when running the benchmark, as it answers many markets in a single run, which can be very costly. + +Run + +```bash +python trader/main.py +``` + +the script will place bets on random 10 markets from https://aiomen.eth.limo, these won't be used for the final evaluation, but you can double-check that all works as expected. + +### Submission + +1. Run `python trader/main.py --final`, it will place bets on all markets that will be used for the evaluation. You can run the script multiple times, but we will always look only at the latest bet on the market from your public key. If you get no markets found error, either we didn't open them yet, or they are already closed and it's too late for the submission. +2. Once you are happy with your agent's predictions, open a PR against this repository with your implementation and public key used for placing bets. This is your submission. +3. Make sure the CI pipeline is all green. + +### Evaluation + +1. Quantitative + 1. We will create N markets from the address `0xa7E93F5A0e718bDDC654e525ea668c64Fd572882` by the end of the June, and they will be resolved in roughly two weeks after the creation. + 2. We will measure the accuracy of your agent's answers (by the last bet on each market). + +2. Qualitative + 1. We will look into implementation and judge the creativity of the improvements. + +3. Cheating + 1. For example, sometimes, the exactly same markets can be found on other prediction market platforms. If we see in the code that the prediction isn’t doing anything practical, we will disqualify it. That being said, it's okay to look at other markets if they are not about the same question, for example, given the evaluation question `Will GNO hit $1000 by the end of 2025?` it's okay to use markets such as `Will GNO hit $500 by the mid of 2025?` as a guidance, but it's not okay to look at the market `Will GNO hit $1000 by the end of 2025?` and copy-paste current probabilities. diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 0000000..42bb821 --- /dev/null +++ b/mypy.ini @@ -0,0 +1,36 @@ +[mypy] +python_version = 3.10 +files = trader/ +plugins = pydantic.mypy +warn_redundant_casts = True +warn_unused_ignores = True +disallow_any_generics = True +warn_return_any = True +check_untyped_defs = True +show_error_codes = True +strict_equality = True +explicit_package_bases = True +show_traceback = True +disallow_incomplete_defs = True +disallow_untyped_defs = True +ignore_missing_imports = True +# Exclude submodules that are themselves not type-checked +exclude = prediction_market_agent/tools/mech/mech/ + +# See https://github.com/python/mypy/issues/3905#issuecomment-421065323 +# We don't want to ignore all missing imports as we want to catch those in our own code +# But for certain libraries they don't have a stub file, so we only enforce import checking for our own libraries. +# Another alternative would be to list out every single dependency that does not have a stub. +[mypy-prediction_market_agent.*] +ignore_missing_imports = False +[mypy-scripts.*] +ignore_missing_imports = False +[mypy-tests.*] +ignore_missing_imports = False + +[pydantic-mypy] +# See https://pydantic-docs.helpmanual.io/mypy_plugin/ +init_forbid_extra = True +init_typed = True +warn_required_dynamic_aliases = True +warn_untyped_fields = True diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..9c469c3 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,7374 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + 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--- /dev/null +++ b/pyproject.toml @@ -0,0 +1,23 @@ +[tool.poetry] +name = "trader" +version = "0.1.0" +description = "" +authors = ["Gnosis Labs "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "~3.10.0" +prediction-market-agent-tooling = { version = "^0.34.0" } +prediction-prophet = { git = "https://github.com/polywrap/predictionprophet.git", branch = "main" } +pydantic-settings = "^2.1.0" + +[tool.poetry.group.dev.dependencies] +pytest = "^8.2.1" +mypy = "^1.8.0" +black = "^23.12.1" +autoflake = "^2.2.1" +isort = "^5.13.2" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/trader/app.py b/trader/app.py new file mode 100644 index 0000000..bec06ec --- /dev/null +++ b/trader/app.py @@ -0,0 +1,38 @@ +import streamlit as st +from dotenv import load_dotenv +from prediction_market_agent_tooling.markets.agent_market import SortBy +from prediction_market_agent_tooling.markets.markets import ( + MarketType, + get_binary_markets, +) + +from trader.prediction import predict + +# Load the environment variables. +load_dotenv() + +# Get some open markets from Omen. +markets = get_binary_markets(42, MarketType.OMEN, sort_by=SortBy.CLOSING_SOONEST) + +# Either select a market or provide a custom question. +custom_question_input = st.checkbox("Provide a custom question", value=False) +question = ( + st.text_input("Question") + if custom_question_input + else st.selectbox("Select a question", [m.question for m in markets]) +) +if not question: + st.warning("Please enter a question.") + st.stop() + +# Get the prediction. +prediction = predict(question) + +# Display the prediction. +if prediction is None: + st.error("The agent failed to generate a prediction") + st.stop() + +st.write( + f"Prediction for the question: {'Yes, it will happen.' if question else 'No, it will not.'}", +) diff --git a/trader/benchmark.py b/trader/benchmark.py new file mode 100644 index 0000000..e877887 --- /dev/null +++ b/trader/benchmark.py @@ -0,0 +1,117 @@ +import typer +from dotenv import load_dotenv +from prediction_market_agent_tooling.benchmark.agents import ( + AbstractBenchmarkedAgent, + RandomAgent, +) +from prediction_market_agent_tooling.benchmark.benchmark import Benchmarker +from prediction_market_agent_tooling.benchmark.utils import ( + OutcomePrediction, + Prediction, +) +from prediction_market_agent_tooling.gtypes import Probability +from prediction_market_agent_tooling.loggers import logger +from prediction_market_agent_tooling.markets.markets import ( + FilterBy, + MarketType, + SortBy, + get_binary_markets, +) +from prediction_prophet.benchmark.agents import _make_prediction + +from trader.prediction import DEFAULT_MODEL, predict + + +def main( + n: int = 10, + output: str = "./benchmark_report.md", +) -> None: + # Load environment variables + load_dotenv() + + # Get n markets from https://manifold.markets, where real humans are trading. + markets = get_binary_markets( + n, MarketType.MANIFOLD, filter_by=FilterBy.OPEN, sort_by=SortBy.CLOSING_SOONEST + ) + markets_deduplicated = list(({m.question: m for m in markets}.values())) + if len(markets) != len(markets_deduplicated): + logger.info( + f"Warning: Deduplicated markets from {len(markets)} to {len(markets_deduplicated)}." + ) + + logger.info(f"Found {len(markets_deduplicated)} markets.") + + benchmarker = Benchmarker( + markets=markets_deduplicated, + agents=[ + RandomAgent(agent_name="random"), + QuestionOnlyAgent(), + ParticipantPredictionAgent(), + ], + ) + + benchmarker.run_agents() + md = benchmarker.generate_markdown_report() + + with open(output, "w") as f: + logger.info(f"Writing benchmark report to: {output}") + f.write(md) + + +class QuestionOnlyAgent(AbstractBenchmarkedAgent): + def __init__( + self, + model: str = DEFAULT_MODEL, + agent_name: str = "question-only", + max_workers: int = 1, + ): + super().__init__(agent_name=agent_name, max_workers=max_workers) + self.model: str = model + + def predict(self, market_question: str) -> Prediction: + try: + return _make_prediction( + market_question=market_question, + additional_information="", + engine=self.model, + temperature=0.0, + ) + except Exception as e: + logger.info(f"Error in QuestionOnlyAgent's predict: {e}") + return Prediction() + + +class ParticipantPredictionAgent(AbstractBenchmarkedAgent): + def __init__( + self, + model: str = DEFAULT_MODEL, + agent_name: str = "your-agent", + max_workers: int = 1, + ): + super().__init__(agent_name=agent_name, max_workers=max_workers) + self.model: str = model + + def predict(self, market_question: str) -> Prediction: + try: + prediction = predict(market_question) + return ( + Prediction( + outcome_prediction=OutcomePrediction( + decision=prediction, + p_yes=Probability( + 1.0 if prediction else 0.0 + ), # Hardcoded to 1.0 or 0.0 based on the bool value, because the aim of this hackathon is to predict the outcome of the market, not the probability of the outcome. + confidence=1.0, + info_utility=1.0, + ) + ) + if prediction is not None + else Prediction() + ) + except Exception as e: + logger.info(f"Error in prediction's predict: {e}") + return Prediction() + + +if __name__ == "__main__": + typer.run(main) diff --git a/trader/main.py b/trader/main.py new file mode 100644 index 0000000..fed0574 --- /dev/null +++ b/trader/main.py @@ -0,0 +1,73 @@ +import random + +import pandas as pd +import typer +from dotenv import load_dotenv +from prediction_market_agent_tooling.gtypes import HexAddress, HexStr +from prediction_market_agent_tooling.loggers import logger +from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket +from prediction_market_agent_tooling.markets.omen.omen_subgraph_handler import ( + OmenSubgraphHandler, +) +from prediction_market_agent_tooling.tools.utils import utcnow + +from trader.prediction import predict + +MARKET_CREATOR_USED_FOR_EVALUATION = HexAddress( + HexStr("0xa7E93F5A0e718bDDC654e525ea668c64Fd572882") +) + + +def main(final: bool = False) -> None: + # Load the environment variables. + load_dotenv() + + # Get all markets created by the specified creator. + markets = OmenSubgraphHandler().get_omen_binary_markets( + limit=None, + opened_after=utcnow(), + # If `final` is True, get all markets created by the specified creator that will be used for the evaluation of the winner. + creator=MARKET_CREATOR_USED_FOR_EVALUATION if final else None, + ) + + if not markets: + logger.error("No markets found, please try again later.") + return + + # If this isn't for the final evaluation, just bet on random 10 markets. + if not final: + markets = random.sample(markets, k=min(10, len(markets))) + + results: dict[str, list[str | bool | None]] = { + "market_id": [], + "question": [], + "prediction": [], + } + + for market_idx, market in enumerate(markets): + # AgentMarket class contains the logic to interact with the market. + agent_market = OmenAgentMarket.from_data_model(market) + + # Get the prediction. + prediction = predict(agent_market.question) + + # Place a bet. + if prediction is not None: + agent_market.place_bet( + prediction, + amount=agent_market.get_tiny_bet_amount(), # Just 0.00001 xDai. + ) + + results["market_id"].append(market.id) + results["question"].append(agent_market.question) + results["prediction"].append(prediction) + + logger.info( + f"[{market_idx + 1} / {len(markets)}] Placed {prediction} bet on market {market.url}." + ) + + pd.DataFrame.from_dict(results).to_csv("results.csv", index=False) + + +if __name__ == "__main__": + typer.run(main) diff --git a/trader/prediction.py b/trader/prediction.py new file mode 100644 index 0000000..42055e9 --- /dev/null +++ b/trader/prediction.py @@ -0,0 +1,103 @@ +import streamlit as st +from langchain.text_splitter import RecursiveCharacterTextSplitter +from prediction_prophet.benchmark.agents import _make_prediction +from prediction_prophet.functions.create_embeddings_from_results import ( + create_embeddings_from_results, +) +from prediction_prophet.functions.generate_subqueries import generate_subqueries +from prediction_prophet.functions.prepare_report import prepare_report +from prediction_prophet.functions.scrape_results import scrape_results +from prediction_prophet.functions.search import search + +DEFAULT_MODEL = "gpt-3.5-turbo-0125" + + +def research( + goal: str, + scrape_content_split_chunk_size: int = 800, + scrape_content_split_chunk_overlap: int = 225, + top_k_per_query: int = 8, +) -> str: + with st.status("Generating subqueries"): + # Generate subqueries out of the original question, to have more chances of finding relevant information. + queries = generate_subqueries(query=goal, limit=5, model=DEFAULT_MODEL) + st.write(f"Generated subqueries:" + "\n- " + "\n- ".join(queries)) + + with st.status("Searching the web"): + # For each subquery, do a search using Tavily. + search_results = [r for _, r in search(queries)] + + if not search_results: + raise ValueError(f"No search results found for the goal {goal}.") + + st.write( + f"Found the following relevant results" + + "\n- " + + "\n- ".join(set(result.url for result in search_results)) + ) + + with st.status(f"Scraping web results"): + # Scrap content of each page. + scraped = [ + result + for result in scrape_results(search_results) + if result.content.strip() + ] + st.write(f"Scraped content from {len(scraped)} websites") + + text_splitter = RecursiveCharacterTextSplitter( + separators=["\n\n", "\n", ". ", " "], + chunk_size=scrape_content_split_chunk_size, + chunk_overlap=scrape_content_split_chunk_overlap, + ) + + with st.status(f"Performing similarity searches"): + # Chunk scraped contents into chunks and create embeddings for them using OpenAI. + collection = create_embeddings_from_results(scraped, text_splitter) + st.write("Created embeddings") + + vector_result_texts: list[str] = [] + + # For each subquery, do a similarity search against the chunks and collect the most relevant ones. + for query in queries: + top_k_per_query_results = collection.similarity_search( + query, k=top_k_per_query + ) + vector_result_texts += [ + result.page_content + for result in top_k_per_query_results + if result.page_content not in vector_result_texts + ] + st.write(f"Similarity searched for: {query}") + + st.write(f"Found {len(vector_result_texts)} relevant information chunks") + + with st.status(f"Preparing report"): + # Prepare a report based on all relevant collected information. + report = prepare_report(goal, vector_result_texts, model=DEFAULT_MODEL) + st.markdown(report) + + return report + + +def predict(question: str) -> bool | None: + """ + Customize this function to make a prediction about the question. + + You can keep using `research` and `_make_prediction` functions and just improve them, or create something new entirely. + """ + # Create a report about the question. + report = research(goal=question) + # Make an informed prediction based on the report. + prediction = _make_prediction( + market_question=question, + additional_information=report, + engine=DEFAULT_MODEL, + temperature=0.0, + ) + # Answer yes if p_yes > 0.5, no otherwise. + return ( + prediction.outcome_prediction.p_yes > 0.5 + if prediction.outcome_prediction + else None + )