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(>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp 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(>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.21,<0.22)", "urllib3 (<2.0.0)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=1.0.11)", "tokenizers (>=0.21,<0.22)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] @@ -10231,12 +10135,12 @@ tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,< tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] tiktoken = ["blobfile", "tiktoken"] -timm = ["timm (<=0.9.16)"] -tokenizers = ["tokenizers (>=0.20,<0.21)"] +timm = ["timm (<=1.0.11)"] +tokenizers = ["tokenizers (>=0.21,<0.22)"] torch = ["accelerate (>=0.26.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.24.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.21,<0.22)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -10316,13 +10220,13 @@ typing-extensions = ">=3.7.4.3" [[package]] name = "types-python-dateutil" -version = "2.9.0.20241003" +version = "2.9.0.20241206" description = "Typing stubs for python-dateutil" optional = false python-versions = ">=3.8" files = [ - {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"}, - {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"}, + {file = "types_python_dateutil-2.9.0.20241206-py3-none-any.whl", hash = "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"}, + {file = "types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb"}, ] [[package]] @@ -11127,4 +11031,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "~3.10.0" -content-hash = "8dc834d92843e3ee72f6b225fd3b2f99fe59dd400a6435af1275f28a37e79ab8" +content-hash = "6002767ff283a9a59c1bdbb1b9c907f1825296cd0599993c11706c84b9010e90" diff --git a/prediction_market_agent/agents/microchain_agent/messages_functions.py b/prediction_market_agent/agents/microchain_agent/messages_functions.py index 28d8078b..b73ccf85 100644 --- a/prediction_market_agent/agents/microchain_agent/messages_functions.py +++ b/prediction_market_agent/agents/microchain_agent/messages_functions.py @@ -1,28 +1,59 @@ from microchain import Function +from prediction_market_agent_tooling.gtypes import xdai_type +from prediction_market_agent_tooling.tools.contract import ContractOnGnosisChain +from prediction_market_agent_tooling.tools.web3_utils import send_xdai_to, xdai_to_wei +from web3 import Web3 +from prediction_market_agent.agents.microchain_agent.microchain_agent_keys import ( + MicrochainAgentKeys, +) +from prediction_market_agent.agents.microchain_agent.utils import compress_message -class SendMessage(Function): +TRANSACTION_MESSAGE_FEE = xdai_type(0.01) + + +class BroadcastPublicMessageToHumans(Function): @property def description(self) -> str: - return f"Use {SendMessage.__class__} to send a message to everyone." + return f"""Use {BroadcastPublicMessageToHumans.__name__} to send a message that humans can see. Use this to communicate with users that send you messages.""" @property def example_args(self) -> list[str]: return ["Hello!"] - def __call__( - self, - message: str, - ) -> str: - # TODO: Complete the logic. - return "Message sent." + def __call__(self, message: str) -> str: + # TODO: Implement as needed in https://github.com/gnosis/prediction-market-agent/issues/570. + print(message) + return f"Message broadcasted to humans." + + +class SendPaidMessageToAnotherAgent(Function): + @property + def description(self) -> str: + return f"""Use {SendPaidMessageToAnotherAgent.__name__} to send a message to an another agent, given his wallet address. +Fee for sending the message is {TRANSACTION_MESSAGE_FEE} xDai.""" + + @property + def example_args(self) -> list[str]: + return ["0x123", "Hello!"] + + def __call__(self, address: str, message: str) -> str: + keys = MicrochainAgentKeys() + send_xdai_to( + web3=ContractOnGnosisChain.get_web3(), + from_private_key=keys.bet_from_private_key, + to_address=Web3.to_checksum_address(address), + value=xdai_to_wei(keys.cap_sending_xdai(TRANSACTION_MESSAGE_FEE)), + data_text=compress_message(message), + ) + return "Message sent to the agent." class ReceiveMessage(Function): @property def description(self) -> str: # TODO: Add number of unseen messages to the description. - return f"Use {ReceiveMessage.__class__} to receive last unseen message from the users." + return f"Use {ReceiveMessage.__name__} to receive last unseen message from the users." @property def example_args(self) -> list[str]: @@ -36,6 +67,7 @@ def __call__(self) -> str: MESSAGES_FUNCTIONS: list[type[Function]] = [ - SendMessage, + BroadcastPublicMessageToHumans, + SendPaidMessageToAnotherAgent, ReceiveMessage, ] diff --git a/prediction_market_agent/agents/microchain_agent/microchain_agent_keys.py b/prediction_market_agent/agents/microchain_agent/microchain_agent_keys.py index b74bd147..b18ea5fe 100644 --- a/prediction_market_agent/agents/microchain_agent/microchain_agent_keys.py +++ b/prediction_market_agent/agents/microchain_agent/microchain_agent_keys.py @@ -1,6 +1,19 @@ +from prediction_market_agent_tooling.gtypes import xDai +from prediction_market_agent_tooling.loggers import logger +from prediction_market_agent_tooling.markets.omen.omen import OMEN_TINY_BET_AMOUNT + from prediction_market_agent.utils import APIKeys class MicrochainAgentKeys(APIKeys): # Double check to make sure you want to actually post on public social media. ENABLE_SOCIAL_MEDIA: bool = False + # Double check to not spend big money during testing. + SENDING_XDAI_CAP: float | None = OMEN_TINY_BET_AMOUNT + + def cap_sending_xdai(self, amount: xDai) -> xDai: + if self.SENDING_XDAI_CAP is None: + return amount + amount = xDai(min(amount, self.SENDING_XDAI_CAP)) + logger.warning(f"Caping sending xDai value to {amount}.") + return amount diff --git a/prediction_market_agent/agents/microchain_agent/prompts.py b/prediction_market_agent/agents/microchain_agent/prompts.py index acf80f0c..0959506a 100644 --- a/prediction_market_agent/agents/microchain_agent/prompts.py +++ b/prediction_market_agent/agents/microchain_agent/prompts.py @@ -170,7 +170,6 @@ def from_system_prompt_choice( include_job_functions = True elif system_prompt_choice == SystemPromptChoice.DARE_YOU_GET_MY_RESOURCES_AGENT: - include_sending_functions = True include_messages_functions = True return FunctionsConfig( diff --git a/prediction_market_agent/agents/microchain_agent/sending_functions.py b/prediction_market_agent/agents/microchain_agent/sending_functions.py index 48c886ef..83ab0575 100644 --- a/prediction_market_agent/agents/microchain_agent/sending_functions.py +++ b/prediction_market_agent/agents/microchain_agent/sending_functions.py @@ -4,7 +4,9 @@ from prediction_market_agent_tooling.tools.web3_utils import send_xdai_to, xdai_to_wei from web3 import Web3 -from prediction_market_agent.utils import APIKeys +from prediction_market_agent.agents.microchain_agent.microchain_agent_keys import ( + MicrochainAgentKeys, +) class SendXDAI(Function): @@ -21,14 +23,14 @@ def __call__( address: str, amount: float, ) -> str: - keys = APIKeys() + keys = MicrochainAgentKeys() web3 = ContractOnGnosisChain.get_web3() address_checksum = Web3.to_checksum_address(address) send_xdai_to( web3, keys.bet_from_private_key, address_checksum, - xdai_to_wei(xdai_type(amount)), + xdai_to_wei(keys.cap_sending_xdai(xdai_type(amount))), ) return f"Sent {amount} xDAI to {address_checksum}." diff --git a/prediction_market_agent/agents/microchain_agent/utils.py b/prediction_market_agent/agents/microchain_agent/utils.py index 38502afc..305384e9 100644 --- a/prediction_market_agent/agents/microchain_agent/utils.py +++ b/prediction_market_agent/agents/microchain_agent/utils.py @@ -1,4 +1,5 @@ import typing as t +import zlib import pandas as pd from microchain import Agent @@ -140,3 +141,12 @@ def get_function_useage_from_history( data={"Usage Count": list(function_useage.values())}, index=function_names, ) + + +def compress_message(message: str) -> bytes: + """Used to reduce size of the message before sending it to reduce gas costs.""" + return zlib.compress(message.encode(), level=zlib.Z_BEST_COMPRESSION) + + +def decompress_message(message: bytes) -> str: + return zlib.decompress(message).decode() diff --git a/pyproject.toml b/pyproject.toml index a079909c..bb6a4d5f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ poetry = "^1.7.1" poetry-plugin-export = "^1.6.0" functions-framework = "^3.5.0" cron-validator = "^1.0.8" -prediction-market-agent-tooling = {version = "0.57.5.dev218", extras = ["langchain", "google"]} +prediction-market-agent-tooling = { version = "^0.57.6", extras = ["langchain", "google"] } pydantic-settings = "^2.1.0" autoflake = "^2.2.1" isort = "^5.13.2" diff --git a/tests/agents/microchain/test_utils.py b/tests/agents/microchain/test_utils.py new file mode 100644 index 00000000..8712f7b8 --- /dev/null +++ b/tests/agents/microchain/test_utils.py @@ -0,0 +1,10 @@ +from prediction_market_agent.agents.microchain_agent.utils import ( + compress_message, + decompress_message, +) + + +def test_message_compression() -> None: + message = "Hello!" + encoded = compress_message(message) + assert message == decompress_message(encoded)