Added has_question_event_happened_in_the_past

prediction_market_agent/agents/known_outcome_agent/deploy.py

@@ -1,6 +1,7 @@
 import getpass
 from decimal import Decimal
 
+from dotenv import load_dotenv
 from prediction_market_agent_tooling.config import APIKeys
 from prediction_market_agent_tooling.deploy.agent import DeployableAgent
 from prediction_market_agent_tooling.deploy.constants import OWNER_KEY
@@ -17,6 +18,7 @@
 from prediction_market_agent.agents.known_outcome_agent.known_outcome_agent import (
     Result,
     get_known_outcome,
+    has_question_event_happened_in_the_past,
 )
 
 
@@ -25,7 +27,7 @@ def market_is_saturated(market: AgentMarket) -> bool:
 
 
 class DeployableKnownOutcomeAgent(DeployableAgent):
-    model = "gpt-4-1106-preview"
+    model = "gpt-3.5-turbo"
 
     def load(self) -> None:
         self.markets_with_known_outcomes: dict[str, Result] = {}
@@ -37,6 +39,13 @@ def pick_markets(self, markets: list[AgentMarket]) -> list[AgentMarket]:
             # been correctly bet on, and therefore the value of betting on them
             # is low.
             if not market_is_saturated(market=market):
+
+                if not has_question_event_happened_in_the_past(model=self.model,
+                                                                        question=market.question):
+                    # event happened
+                    continue
+
+
                 answer = get_known_outcome(
                     model=self.model,
                     question=market.question,
@@ -62,22 +71,29 @@ def calculate_bet_amount(self, answer: bool, market: AgentMarket) -> BetAmount:
 
 if __name__ == "__main__":
     agent = DeployableKnownOutcomeAgent()
-    agent.deploy_gcp(
-        repository=f"git+{get_current_git_url()}@{get_current_git_commit_sha()}",
+    load_dotenv()
+    agent.deploy_local(
         market_type=MarketType.OMEN,
-        labels={OWNER_KEY: getpass.getuser()},
-        secrets={
-            "TAVILY_API_KEY": "GNOSIS_AI_TAVILY_API_KEY:latest",
-        },
-        memory=1024,
-        api_keys=APIKeys(
-            BET_FROM_ADDRESS=verify_address(
-                "0xb611A9f02B318339049264c7a66ac3401281cc3c"
-            ),
-            BET_FROM_PRIVATE_KEY=private_key_type("EVAN_OMEN_BETTER_0_PKEY:latest"),
-            OPENAI_API_KEY=SecretStr("EVAN_OPENAI_API_KEY:latest"),
-            MANIFOLD_API_KEY=None,
-        ),
-        cron_schedule="0 */12 * * *",
+        sleep_time=10,
         timeout=540,
+        place_bet=False
     )
+    # agent.deploy_gcp(
+    #     repository=f"git+{get_current_git_url()}@{get_current_git_commit_sha()}",
+    #     market_type=MarketType.OMEN,
+    #     labels={OWNER_KEY: getpass.getuser()},
+    #     secrets={
+    #         "TAVILY_API_KEY": "GNOSIS_AI_TAVILY_API_KEY:latest",
+    #     },
+    #     memory=1024,
+    #     api_keys=APIKeys(
+    #         BET_FROM_ADDRESS=verify_address(
+    #             "0xb611A9f02B318339049264c7a66ac3401281cc3c"
+    #         ),
+    #         BET_FROM_PRIVATE_KEY=private_key_type("EVAN_OMEN_BETTER_0_PKEY:latest"),
+    #         OPENAI_API_KEY=SecretStr("EVAN_OPENAI_API_KEY:latest"),
+    #         MANIFOLD_API_KEY=None,
+    #     ),
+    #     cron_schedule="0 */12 * * *",
+    #     timeout=540,
+    # )

prediction_market_agent/agents/known_outcome_agent/known_outcome_agent.py

@@ -1,14 +1,16 @@
 import json
 import typing as t
-from datetime import datetime
+from datetime import datetime, timezone
 from enum import Enum
 
 from langchain.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel
 
+
 from prediction_market_agent.tools.web_scrape.basic_summary import _summary
 from prediction_market_agent.tools.web_scrape.markdown import web_scrape
+from prediction_market_agent_tooling.tools.utils import utcnow
 from prediction_market_agent.tools.web_search.tavily import web_search
 
 
@@ -42,6 +44,22 @@ def has_known_outcome(self) -> bool:
         return self.result is not Result.UNKNOWN
 
 
+HAS_QUESTION_HAPPENED_IN_THE_PAST_PROMPT = """
+The current date is {date_str}. Your goal is to assert if a QUESTION references an event that is already finished (according to the current date and time) or if it will still take place in a later date. 
+
+For example, you should return 1 if given the event "Will Bitcoin have reached the price of $100 by 30 March 2023?", since the event ends on a data prior to the current date.
+
+Your answer MUST be an integer and follow the logic below:
+- If the event is already finished, return 1
+- If the event has not yet finished, return 0
+- If you are not sure, return -1
+
+Answer with the single prompt only, and nothing else.
+
+[QUESTION]
+"{question}"
+"""
+
 GENERATE_SEARCH_QUERY_PROMPT = """
 The current date is {date_str}. You are trying to determine whether the answer
 to the following question has a definite answer. Generate a web search query
@@ -137,6 +155,28 @@ def summarize_if_required(content: str, model: str, question: str) -> str:
     else:
         return content
 
+def has_question_event_happened_in_the_past(model: str, question: str) -> bool:
+    """ Asks the model if the event referenced by the question has finished (given the
+    current date) (returning 1), if the event has not yet finished (returning 0) or
+     if it cannot be sure (returning -1)."""
+    date_str = utcnow().strftime("%Y-%m-%d %H:%M:%S %Z")
+    llm = ChatOpenAI(model=model, temperature=0.0)
+    prompt = ChatPromptTemplate.from_template(
+                template=HAS_QUESTION_HAPPENED_IN_THE_PAST_PROMPT
+            ).format_messages(
+                date_str=date_str,
+                question=question,
+            )
+    answer = str(llm.invoke(prompt).content)
+    try:
+        parsed_answer = int(answer)
+        if parsed_answer == 1:
+            return True
+    except Exception as e:
+        print ("Exception occured, cannot assert if title happened in the past. ", e)
+
+    return False
+
 
 def get_known_outcome(model: str, question: str, max_tries: int) -> Answer:
     """