Finished trial task

backend/docs/docs/education_popes.md

@@ -0,0 +1,44 @@
+\# Popes Education and Secular Jobs
+
+
+
+\## Pope Francis (Jorge Mario Bergoglio)
+
+\- Education: Philosophy and Theology
+
+\- Secular jobs: Chemistry lab technician, literature teacher
+
+
+
+\## Pope Benedict XVI (Joseph Ratzinger)
+
+\- Education: Philosophy and Theology
+
+\- Secular jobs: None (mostly academic)
+
+
+
+\## Pope John Paul II (Karol Wojtyła)
+
+\- Education: Philosophy, Theology, Literature
+
+\- Secular jobs: Actor, poet
+
+
+
+\## Pope John Paul I (Albino Luciani)
+
+\- Education: Theology
+
+\- Secular jobs: None
+
+
+
+\## Pope Paul VI (Giovanni Battista Montini)
+
+\- Education: Theology, Canon Law
+
+\- Secular jobs: Journalist (early career)
+
+
+

backend/docs/docs/interrupt_example.md

@@ -0,0 +1,22 @@
+# LangGraph Interrupt Example
+
+LangGraph is a Python library for building stateful LLM applications
+using graphs or functional workflows.
+
+This document shows how interrupts can be implemented
+using both the Functional API and the Graph API.
+
+## Functional API
+
+The Functional API allows defining agent logic as a Python function.
+Interrupts are typically implemented using control flow statements.
+
+```python
+from langgraph.functional import agent
+
+def my_agent():
+    while True:
+        inp = input(">>> ")
+        if inp == "STOP":
+            return "Interrupted"
+        print(f"Echo: {inp}")

backend/examples/cli_research.py

@@ -1,42 +1,28 @@
+import os
 import argparse
-from langchain_core.messages import HumanMessage
 from agent.graph import graph
+from agent.state import OverallState
+from langchain_core.messages import HumanMessage
 
-
-def main() -> None:
-    """Run the research agent from the command line."""
-    parser = argparse.ArgumentParser(description="Run the LangGraph research agent")
-    parser.add_argument("question", help="Research question")
-    parser.add_argument(
-        "--initial-queries",
-        type=int,
-        default=3,
-        help="Number of initial search queries",
-    )
-    parser.add_argument(
-        "--max-loops",
-        type=int,
-        default=2,
-        help="Maximum number of research loops",
-    )
-    parser.add_argument(
-        "--reasoning-model",
-        default="gemini-2.5-pro-preview-05-06",
-        help="Model for the final answer",
-    )
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("question", nargs="?", default=None, help="Question to ask")
+    parser.add_argument("--dir", required=True, help="Directory for local Markdown sources")
+    parser.add_argument("--loops", type=int, default=3, help="Max research loops")
     args = parser.parse_args()
 
-    state = {
-        "messages": [HumanMessage(content=args.question)],
-        "initial_search_query_count": args.initial_queries,
-        "max_research_loops": args.max_loops,
-        "reasoning_model": args.reasoning_model,
-    }
+    state = OverallState(
+        messages=[HumanMessage(content=args.question or "")],
+        search_dir=args.dir,
+        max_research_loops=args.loops,
+        research_loop_count=0,
+        is_sufficient=False,
+    )
 
     result = graph.invoke(state)
-    messages = result.get("messages", [])
-    if messages:
-        print(messages[-1].content)
+
+    for msg in result["messages"]:
+        print("\n" + msg.content)
 
 
 if __name__ == "__main__":