Improve summary (#10)

labs_api/config.py

@@ -1,23 +1,8 @@
-import typing as t
+from prediction_market_agent_tooling.config import APIKeys
 
-from prediction_market_agent_tooling.tools.utils import check_not_none
-from pydantic import SecretStr
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-
-class Config(BaseSettings):
-    model_config = SettingsConfigDict(
-        env_file=".env", env_file_encoding="utf-8", extra="ignore"
-    )
 
+class Config(APIKeys):
     HOST: str = "0.0.0.0"
     PORT: int = 8080
     WORKERS: int = 1
     RELOAD: bool = True
-    SQLALCHEMY_DB_URL: t.Optional[SecretStr] = None
-
-    @property
-    def sqlalchemy_db_url(self) -> SecretStr:
-        return check_not_none(
-            self.SQLALCHEMY_DB_URL, "SQLALCHEMY_DB_URL missing in the environment."
-        )

labs_api/insights.py

@@ -1,19 +1,36 @@
 import fastapi
+from langchain.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from loguru import logger
+from prediction_market_agent_tooling.config import APIKeys
 from prediction_market_agent_tooling.loggers import logger
 from prediction_market_agent_tooling.markets.omen.omen_subgraph_handler import (
     HexAddress,
+    OmenMarket,
     OmenSubgraphHandler,
 )
+from prediction_market_agent_tooling.tools.langfuse_ import (
+    get_langfuse_langchain_config,
+    observe,
+)
+from prediction_market_agent_tooling.tools.tavily_storage.tavily_models import (
+    TavilyResponse,
+    TavilyStorage,
+)
 from prediction_market_agent_tooling.tools.tavily_storage.tavily_storage import (
     TavilyStorage,
     tavily_search,
 )
-from prediction_market_agent_tooling.tools.utils import utcnow
+from prediction_market_agent_tooling.tools.utils import (
+    LLM_SUPER_LOW_TEMPERATURE,
+    utcnow,
+)
 
 from labs_api.insights_cache import MarketInsightsResponseCache
 from labs_api.models import MarketInsightsResponse
 
 
+# Don't observe the cached version, as it will always return the same thing that's already been observed.
 def market_insights_cached(
     market_id: HexAddress, cache: MarketInsightsResponseCache
 ) -> MarketInsightsResponse:
@@ -28,6 +45,7 @@ def market_insights_cached(
         return new
 
 
+@observe()
 def market_insights(market_id: HexAddress) -> MarketInsightsResponse:
     """Returns market insights for a given market on Omen."""
     try:
@@ -47,8 +65,58 @@ def market_insights(market_id: HexAddress) -> MarketInsightsResponse:
     except Exception as e:
         logger.error(f"Failed to get tavily_response for market `{market_id}`: {e}")
         tavily_response = None
+    try:
+        summary = (
+            tavily_response_to_summary(market, tavily_response)
+            if tavily_response is not None
+            else None
+        )
+    except Exception as e:
+        logger.warning(
+            f"Failed to generate short description for market `{market_id}`: {e}"
+        )
+        summary = None
     return MarketInsightsResponse.from_tavily_response(
         market_id=market_id,
         created_at=utcnow(),
+        summary=summary,
         tavily_response=tavily_response,
     )
+
+
+@observe()
+def tavily_response_to_summary(
+    market: OmenMarket, tavily_response: TavilyResponse
+) -> str:
+    contents = [result.content for result in tavily_response.results]
+
+    llm = ChatOpenAI(
+        model="gpt-4o-2024-08-06",
+        temperature=LLM_SUPER_LOW_TEMPERATURE,
+        api_key=APIKeys().openai_api_key_secretstr_v1,
+    )
+
+    prompt = ChatPromptTemplate.from_template(
+        template="""Based on the information provided, write a very brief, tweet-like summary about the current situation relevevant for the prediction market question.
+
+In the summary:
+- you should include the most important information that you think is relevant for the prediction market question
+- never answer the question, only provide the context for the reader
+- don't include any hashtags or links
+- always end up telling the user to do their own research to make their own decision, but in a more polite manner guiding them to do so before placing any bets in the prediction market
+
+Prediction Market question: {question}
+
+Information: {information}
+"""
+    )
+    messages = prompt.format_messages(
+        question=market.question_title, information=contents
+    )
+    completion = str(
+        llm.invoke(
+            messages, max_tokens=1024, config=get_langfuse_langchain_config()
+        ).content
+    )
+
+    return completion

labs_api/main.py

@@ -3,8 +3,8 @@
 
 import fastapi
 import uvicorn
-from config import Config
 from fastapi.middleware.cors import CORSMiddleware
+from prediction_market_agent_tooling.deploy.agent import initialize_langfuse
 from prediction_market_agent_tooling.gtypes import HexAddress
 from prediction_market_agent_tooling.loggers import logger
 
@@ -30,6 +30,9 @@ async def lifespan(app: fastapi.FastAPI) -> t.AsyncIterator[None]:
         # At end of the service.
         market_insights_cache.engine.dispose()
 
+    config = Config()
+    initialize_langfuse(config.default_enable_langfuse)
+
     app = fastapi.FastAPI(lifespan=lifespan)
     app.add_middleware(
         CORSMiddleware,

labs_api/models.py

@@ -32,12 +32,13 @@ def has_insights(self) -> bool:
     def from_tavily_response(
         market_id: HexAddress,
         created_at: datetime,
+        summary: str | None,
         tavily_response: t.Union[TavilyResponse, None],
     ) -> "MarketInsightsResponse":
         return MarketInsightsResponse(
             market_id=market_id,
             created_at=created_at,
-            summary=tavily_response.answer if tavily_response else None,
+            summary=summary,
             results=(
                 [
                     MarketInsightResult.from_tavily_result(result)