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Initial LangGraph Setup for Market Data Ingestion
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Linked challenge: LangChain Geopolitical Market Watch with OpenAI o3 and LangGraph
Format
Code-aware
Lines
9
Sections
1
Linked challenge
LangChain Geopolitical Market Watch with OpenAI o3 and LangGraph
Prompt source
Original prompt text with formatting preserved for inspection.
9 lines
1 sections
No variables
1 code block
Design the initial LangGraph structure. Define a 'DataIngestor' agent that uses OpenAI o3 to parse raw market data and news feeds. Create a graph node for this agent. Your initial code should set up the LangGraph state and define the entry point. ```python
from typing import TypedDict, List
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langgraph.graph import StateGraph, END class AgentState(TypedDict): market_data: List[dict] news_feed: List[dict] analysis_report: str anomalies: List[dict] def data_ingestor_node(state: AgentState): # Use OpenAI o3 to parse and structure raw data llm = ChatOpenAI(model="openai/o3") # Example model ID, replace with actual OpenAI o3 ID # ... (implementation for parsing data using LLM) return {'market_data': [...], 'news_feed': [...]} # Placeholder workflow = StateGraph(AgentState)
workflow.add_node("data_ingestor", data_ingestor_node)
workflow.set_entry_point("data_ingestor")
# ... further graph definition ... app = workflow.compile()
```Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Tune next
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Verify after
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.