Use submodules in CI

.github/workflows/python_ci.yaml

@@ -17,6 +17,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+        with:
+          submodules: true
       - uses: ./.github/actions/python_prepare
       - name: Run mypy
         run: poetry run mypy
@@ -25,6 +27,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+        with:
+          submodules: true
       - uses: ./.github/actions/python_prepare
       - name: Run pytest
         run: poetry run pytest
@@ -33,6 +37,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
+        with:
+          submodules: true
       - uses: ./.github/actions/python_prepare
       - name: Run pytest
         run: poetry run pytest

mypy.ini

@@ -14,7 +14,7 @@ show_traceback = True
 disallow_incomplete_defs = True
 disallow_untyped_defs = True
 ignore_missing_imports = True
-exclude = prediction_market_agent/agents/microchain_agent/mech/mech/
+exclude = prediction_market_agent/agents/microchain_agent/mech/mech/ # A submodule
 
 # 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

prediction_market_agent/agents/microchain_agent/functions.py

@@ -119,7 +119,10 @@ def description(self) -> str:
     def example_args(self) -> list[str]:
         return [get_example_market_id(self.market_type)]
 
-    def __call__(self, question: str) -> str:
+    def __call__(self, market_id: str) -> str:
+        question = self.market_type.market_class.get_binary_market(
+            id=market_id
+        ).question
         private_key = APIKeys().bet_from_private_key.get_secret_value()
         # 0.01 xDai is hardcoded cost for an interaction with the mech-client
         MECH_CALL_XDAI_LIMIT = 0.011
@@ -158,7 +161,10 @@ def description(self) -> str:
     def example_args(self) -> list[str]:
         return [get_example_market_id(self.market_type)]
 
-    def __call__(self, question: str) -> str:
+    def __call__(self, market_id: str) -> str:
+        question = self.market_type.market_class.get_binary_market(
+            id=market_id
+        ).question
         keys = APIKeys()
         openai_api_key = keys.openai_api_key.get_secret_value()
         tavily_api_key = keys.tavily_api_key.get_secret_value()

prediction_market_agent/agents/microchain_agent/microchain_agent.py

@@ -20,7 +20,7 @@
     temperature=0.7,
 )
 agent = Agent(llm=LLM(generator=generator), engine=engine)
-agent.prompt = f"""Act as a agent. You can use the following functions:
+agent.prompt = f"""Act as a agent to maximise your profit. You can use the following functions:
  
 {engine.help}