From 2cff00a1bb921d6487dd62397e02ed0082db8010 Mon Sep 17 00:00:00 2001 From: Peter Jung Date: Tue, 7 May 2024 08:55:20 +0200 Subject: [PATCH] Add OFV market resolver --- .gitignore | 4 +- .../customs/ofv_market_resolver/benchmark.py | 133 + .../ofv_market_resolver.py | 178 + poetry.lock | 2893 ++++++++++++----- pyproject.toml | 84 +- 5 files changed, 2492 insertions(+), 800 deletions(-) create mode 100644 packages/kongzii/customs/ofv_market_resolver/benchmark.py create mode 100644 packages/kongzii/customs/ofv_market_resolver/ofv_market_resolver.py diff --git a/.gitignore b/.gitignore index 7ce2b2c6..b782804d 100644 --- a/.gitignore +++ b/.gitignore @@ -54,4 +54,6 @@ backup_mech/ /packages/valory/skills/termination_abci/ /pip /tool_test.py -.venv \ No newline at end of file +.venv +log +.benchmark-cache \ No newline at end of file diff --git a/packages/kongzii/customs/ofv_market_resolver/benchmark.py b/packages/kongzii/customs/ofv_market_resolver/benchmark.py new file mode 100644 index 00000000..d85f480f --- /dev/null +++ b/packages/kongzii/customs/ofv_market_resolver/benchmark.py @@ -0,0 +1,133 @@ +import typer +import pandas as pd +from packages.kongzii.customs.ofv_market_resolver.ofv_market_resolver import ( + run as ofv_run, +) +from packages.napthaai.customs.resolve_market_reasoning.resolve_market_reasoning import ( + Results, + run as original_run, +) +from pydantic import SecretStr, ValidationError +from joblib import Memory + +# File cache to not re-run the same questions. +MEMORY = Memory(".benchmark-cache", verbose=0) +APP = typer.Typer() + +ofv_run_cached = MEMORY.cache(ofv_run) + + +@MEMORY.cache +def run_original_resolver_cached( + question: str, + openai_api_key: SecretStr, + google_api_key: SecretStr, + google_engine_id: SecretStr, +) -> bool | None: + try: + dump = original_run( + api_keys={ + "openai": openai_api_key.get_secret_value(), + "google_api_key": google_api_key.get_secret_value(), + "google_engine_id": google_engine_id.get_secret_value(), + }, + tool="resolve-market-reasoning-gpt-4", + prompt=question, + )[0] + return Results.model_validate_json(dump).has_occurred + except ValueError: + return None + + +@APP.command() +def full( + data_path: str, + openai_api_key: str, + serper_api_key: str, + google_api_key: str, + google_engine_id: str, +) -> None: + """ + Will run the prediction market resolver on all provided data and compare the results. + + Expects a tsv file with columns: + - question + - resolution (YES/NO, as currently resolved on Omen) + - my_resolution (YES/NO, as resolved manually by you, used as ground truth) + + Example command: + + ``` + python packages/kongzii/customs/ofv_market_resolver/benchmark.py full markets.tsv {openai api key} {serper api key} {google api key} {google engine id} + ``` + """ + df = pd.read_csv(data_path, sep="\t") + + # Run the resolution on all the data. + df["ofv_resolution"] = df["question"].apply( + lambda q: ofv_run_cached( + q, + openai_api_key=SecretStr(openai_api_key), + serper_api_key=SecretStr(serper_api_key), + ) + ) + df["new_original_resolution"] = df["question"].apply( + lambda q: run_original_resolver_cached( + q, + openai_api_key=SecretStr(openai_api_key), + google_api_key=SecretStr(google_api_key), + google_engine_id=SecretStr(google_engine_id), + ) + ) + # Normalise boolean to YES/NO/None. + df["ofv_resolution"] = df["ofv_resolution"].apply( + lambda r: "None" if r is None else "YES" if r else "NO" + ) + df["new_original_resolution"] = df["new_original_resolution"].apply( + lambda r: "None" if r is None else "YES" if r else "NO" + ) + # Save all the predictions and separatelly these that are incorrect. + df.to_csv("markets_resolved.tsv", sep="\t", index=False) + df[df["ofv_resolution"] != df["my_resolution"]].to_csv( + "markets_resolved_incorretly_by_ofv.tsv", sep="\t", index=False + ) + + # Calculate the accuracy. + accuracy_current = sum(df["resolution"] == df["my_resolution"]) / len(df) + accuracy_new_original = sum( + df["new_original_resolution"] == df["my_resolution"] + ) / len(df) + accuracy_ofv = sum(df["ofv_resolution"] == df["my_resolution"]) / len(df) + print( + f""" +Current accuracy: {accuracy_current*100:.2f}% +Original's new run accuracy: {accuracy_new_original * 100:.2f} +OFV's accuracy: {accuracy_ofv*100:.2f}% +""" + ) + + +@APP.command() +def single( + question: str, + openai_api_key: str, + serper_api_key: str, +) -> None: + """ + Will run the prediction market resolver and print the result on a single question. + + Example command: + + ``` + python packages/kongzii/customs/ofv_market_resolver/benchmark.py single "Will McDonald's successfully buy back all its Israeli restaurants by 12 April 2024?" {openai api key} {serper api key} + ``` + """ + ofv_run( + question, + openai_api_key=SecretStr(openai_api_key), + serper_api_key=SecretStr(serper_api_key), + ) + + +if __name__ == "__main__": + APP() diff --git a/packages/kongzii/customs/ofv_market_resolver/ofv_market_resolver.py b/packages/kongzii/customs/ofv_market_resolver/ofv_market_resolver.py new file mode 100644 index 00000000..442d0ad1 --- /dev/null +++ b/packages/kongzii/customs/ofv_market_resolver/ofv_market_resolver.py @@ -0,0 +1,178 @@ +from factcheck import FactCheck +from factcheck.utils.multimodal import modal_normalization +from langchain_openai import ChatOpenAI +from typing import Annotated +from pydantic import SecretStr, BaseModel, BeforeValidator + +DEFAULT_OPENAI_MODEL = "gpt-4-0125-preview" + +Factuality = Annotated[ + bool | None, + BeforeValidator(lambda v: None if v in ("Nothing to check.", "non-factual") else v), +] + + +class FactCheckClaimDetails(BaseModel): + claim: str + factuality: Factuality + correction: str | None + reference_url: str + + +class FactCheckResult(BaseModel): + factuality: Factuality + claims_details: list[FactCheckClaimDetails] | None + + +def factcheck( + statement: str, + model: str = DEFAULT_OPENAI_MODEL, + openai_api_key: SecretStr | None = None, + serper_api_key: SecretStr | None = None, +) -> FactCheckResult: + api_config = { + "OPENAI_API_KEY": openai_api_key.get_secret_value(), + "SERPER_API_KEY": serper_api_key.get_secret_value(), + } + factcheck = FactCheck( + default_model=model, + api_config=api_config, + retriever="serper", + num_seed_retries=5, + ) + content = modal_normalization("string", statement) + res = factcheck.check_response(content) + + return FactCheckResult.model_validate(res) + + +def rewrite_as_sentence( + question: str, + model: str = DEFAULT_OPENAI_MODEL, + openai_api_key: SecretStr | None = None, +) -> str: + """ + Rewrites the question into a sentence, example: + + `Will former Trump Organization CFO Allen Weisselberg be sentenced to jail by 15 April 2024?` + -> + `Former Trump Organization CFO Allen Weisselberg was sentenced to jail by 15 April 2024.` + """ + llm = ChatOpenAI( + model=model, temperature=0.0, api_key=openai_api_key.get_secret_value() + ) + + prompt = f""" +Rewrite the question into a simple annoucment sentence stating a fact or prediction like it is already known. +Make future tense into past tense. +For future questions that ask if something will happen "by" some date, rewrite it to "before" that date or any time sooner. +For future questions that ask if something will happen "on" some date, rewrite it to "on" that date. +If the question is both "on" and "by" some date, rewrite it as "before or any time sooner than" that date. +If the question is about exact date, keep it exact. +If the question is about a date range, keep it a range. +Always keep the same meaning. +Never negate the sentence into opposite meaning of the question. + +Question: {question} +Sentence: +""" + completion = str(llm.invoke(prompt, max_tokens=512).content) + + return completion + + +# TODO: This could be imported from prediction-market-agent-tooling, but given the conflict in the langchain versions, +# it would require changes in other mechs of this repository. +def is_predictable_binary( + question: str, + model: str = DEFAULT_OPENAI_MODEL, + openai_api_key: SecretStr | None = None, +) -> str: + """ + Evaluate if the question is actually answerable. + """ + llm = ChatOpenAI( + model=model, temperature=0.0, api_key=openai_api_key.get_secret_value() + ) + + prompt = f"""Main signs about a fully qualified question (sometimes referred to as a "market"): +- The market's question needs to be specific, without use of pronouns. +- The market's question needs to have a clear future event. +- The market's question needs to have a clear time frame. +- The event in the market's question doesn't have to be ultra-specific, it will be decided by a crowd later on. +- If the market's question contains date, but without an year, it's okay. +- If the market's question contains year, but without an exact date, it's okay. +- The market's question can not be about itself or refer to itself. +- The answer is probably Google-able, after the event happened. +- The potential asnwer can be only "Yes" or "No". + +Follow a chain of thought to evaluate if the question is fully qualified: + +First, write the parts of the following question: + +"{question}" + +Then, write down what is the future event of the question, what it refers to and when that event will happen if the question contains it. + +Then, explain why do you think it is or isn't fully qualified. + +Finally, write your final decision, write `decision: ` followed by either "yes it is fully qualified" or "no it isn't fully qualified" about the question. Don't write anything else after that. You must include "yes" or "no". +""" + completion = str(llm.invoke(prompt, max_tokens=512).content) + + try: + decision = completion.lower().rsplit("decision", 1)[1] + except IndexError as e: + raise ValueError( + f"Invalid completion in is_predictable for `{question}`: {completion}" + ) from e + + if "yes" in decision: + is_predictable = True + elif "no" in decision: + is_predictable = False + else: + raise ValueError( + f"Invalid completion in is_predictable for `{question}`: {completion}" + ) + + return is_predictable + + +def run( + market_question: str, + openai_api_key: SecretStr | None = None, + serper_api_key: SecretStr | None = None, +) -> bool | None: + """ + Run the prediction market resolver based on Open Fact Verifier. + + Returns: + - None if can't decide + - True if the answer for the question is "Yes" + - False if the answer for the question is "No" + """ + # Check if the question is reasonable to look for an answer. + is_answerable = is_predictable_binary( + market_question, openai_api_key=openai_api_key + ) + if not is_answerable: + print( + f"Question `{market_question}` is not answerable, skipping fact checking." + ) + return None + + # Rewrite the question (which was about a future) into a sentence (which is about the past). + market_sentence = rewrite_as_sentence( + market_question, openai_api_key=openai_api_key + ) + print(f"Question `{market_question}` rewritten into `{market_sentence}`.") + # Fact-check the sentence. + factresult = factcheck( + market_sentence, openai_api_key=openai_api_key, serper_api_key=serper_api_key + ) + print( + f"Fact check result for `{market_sentence}` is `{factresult.factuality}`, because {factresult.claims_details}." + ) + + return factresult.factuality diff --git a/poetry.lock b/poetry.lock index 1b1c0a14..ddaa67ec 100644 --- a/poetry.lock +++ b/poetry.lock @@ -123,13 +123,13 @@ files = [ [[package]] name = "anthropic" -version = "0.21.3" +version = "0.23.1" description = "The official Python library for the anthropic API" optional = false python-versions = ">=3.7" files = [ - 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"pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + [metadata] lock-version = "2.0" -python-versions = "^3.10" -content-hash = "7ad0eac0f7eb1e326e68d7acdf47a8060a45e61d4ce805facaefa6ef3fe07f5f" +python-versions = ">=3.10,<3.12" +content-hash = "9f209bc22c5efc6b6ae4e96a1d9b6b7a641a797f2ef74dd6f08188142459a3fb" diff --git a/pyproject.toml b/pyproject.toml index c0627ff1..c4e65f5d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,23 +15,23 @@ classifiers = [ "Environment :: Console", "Environment :: Web Environment", "Dev include = "packages" [tool.poetry.dependencies] -python = "^3.10" -open-autonomy = "==0.14.10" -openai = "==1.11.0" -requests = "==2.28.1" -mech-client = "==0.2.5" -py-multibase = "==1.0.3" -py-multicodec = "==0.2.1" -grpcio = "==1.53.0" +python = ">=3.10,<3.12" +open-autonomy = { git = "https://github.com/kongzii/open-autonomy.git", rev = "13344d6551222224492024623cd10aa79ad1a13e" } +openai = "^1.11.0" +requests = "^2.28.1" +mech-client = "^0.2.5" +py-multibase = "^1.0.3" +py-multicodec = "^0.2.1" +grpcio = "^1.53.0" asn1crypto = "<1.5.0,>=1.4.0" -open-aea-ledger-ethereum = "==1.50.0" -open-aea-ledger-cosmos = "==1.50.0" +open-aea-ledger-ethereum = "^1.50.0" +open-aea-ledger-cosmos = "^1.50.0" protobuf = "<4.25.0,>=4.21.6" -hypothesis = "==6.21.6" -open-aea-test-autonomy = "==0.14.10" +hypothesis = "^6.21.6" +open-aea-test-autonomy = "^0.14.10" web3 = "<7,>=6.0.0" -ipfshttpclient = "==0.8.0a2" -open-aea-cli-ipfs = "==1.50.0" +ipfshttpclient = "^0.8.0a2" +open-aea-cli-ipfs = "^1.50.0" pytest-asyncio = "*" aiohttp = "<4.0.0,>=3.8.5" certifi = "*" @@ -40,34 +40,38 @@ ecdsa = ">=0.15" eth_typing = "*" hexbytes = "*" packaging = "*" -py-ecc = "==6.0.0" -pytz = "==2022.2.1" +py-ecc = "^6.0.0" +pytz = "^2022.2.1" typing_extensions = ">=3.10.0.2" websocket_client = "<1,>=0.32.0" -toml = "==0.10.2" -googlesearch-python = "==1.2.3" -beautifulsoup4 = "==4.12.2" -google-api-python-client = "==2.95.0" -eth-utils = "==2.2.0" -eth-abi = "==4.0.0" -pycryptodome = "==3.18.0" -anthropic = "0.21.3" -langchain = "==0.0.303" -scikit-learn = "==1.3.1" -pytest = "==7.2.1" +toml = "^0.10.2" +googlesearch-python = "^1.2.3" +beautifulsoup4 = "^4.12.2" +google-api-python-client = "^2.95.0" +eth-utils = "^2.2.0" +eth-abi = "^4.0.0" +pycryptodome = "^3.18.0" +anthropic = "^0.23.1" +langchain = "^0.0.303" +scikit-learn = "^1.3.1" +pytest = "^7.2.1" jsonschema = "<4.4.0,>=4.3.0" -spacy = "==3.7.2" -pandas = "==2.1.1" -tiktoken = "==0.5.1" -python-dateutil = "==2.8.2" -tomte = {version = "==0.2.15", extras = ["cli", "tests"]} -markdownify = "==0.11.6" -tavily-python = "==0.3.0" -chromadb = "==0.4.13" -readability-lxml = "0.8.1" -docstring-parser = "0.15" -faiss-cpu = "1.7.4" +spacy = "^3.7.2" +pandas = "^2.1.1" +tiktoken = "^0.6.0" +python-dateutil = "^2.8.2" +tomte = {git = "https://github.com/kongzii/tomte.git", rev = "883b89dc469d1f7c14ec981eea7789fc939a1102", extras = ["cli", "tests"]} +markdownify = "^0.11.6" +tavily-python = "^0.3.0" +chromadb = "^0.4.13" +readability-lxml = "^0.8.1" +docstring-parser = "^0.15" +faiss-cpu = "^1.7.4" pypdf2 = "^3.0.1" -moviepy = "1.0.3" -replicate = "0.15.7" +moviepy = "^1.0.3" +replicate = "^0.15.7" lxml = {extras = ["html-clean"], version = "^5.1.0"} +openfactverification = { git = "https://github.com/kongzii/OpenFactVerification.git", rev = "dd3a8a49a42694a614ac9f54981df194f6831507" } +langchain-openai = { version = "^0.0.6" } +en_core_web_sm = { url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl" } +sentence-transformers = "^2.1.0" \ No newline at end of file