Sending messages from general agent

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,
 ]

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

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(

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}."
 

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()

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"

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)