Simplify agentic vectorless RAG demo (#191)

* Simplify and fix agentic RAG demo

* Show labeled reasoning output in RAG demo

* Comment out reasoning model settings by default

examples/agentic_vectorless_rag_demo.py

@@ -14,7 +14,6 @@ Steps:
   1 — Index PDF and inspect tree structure
   2 — Inspect document metadata
   3 — Ask a question (agent auto-calls tools)
-  4 — Reload from workspace and verify persistence
 """
 import os
 import sys
@@ -25,15 +24,17 @@ import requests
 
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-from agents import Agent, ItemHelpers, Runner, function_tool
+from agents import Agent, Runner, function_tool
+from agents.model_settings import ModelSettings
 from agents.stream_events import RawResponsesStreamEvent, RunItemStreamEvent
-from openai.types.responses import ResponseTextDeltaEvent, ResponseReasoningSummaryTextDeltaEvent  # noqa: F401
+from openai.types.responses import ResponseTextDeltaEvent, ResponseReasoningSummaryTextDeltaEvent
 
 from pageindex import PageIndexClient
 import pageindex.utils as utils
 
-_EXAMPLES_DIR = os.path.dirname(os.path.abspath(__file__))
 PDF_URL = "https://arxiv.org/pdf/2603.15031"
+
+_EXAMPLES_DIR = os.path.dirname(os.path.abspath(__file__))
 PDF_PATH = os.path.join(_EXAMPLES_DIR, "documents", "attention-residuals.pdf")
 WORKSPACE = os.path.join(_EXAMPLES_DIR, "workspace")
 
@@ -48,12 +49,7 @@ ANSWERING: Answer based only on tool output. Be concise.
 """
 
 
-def query_agent(
+def query_agent(client: PageIndexClient, doc_id: str, prompt: str, verbose: bool = False) -> str:
-    client: PageIndexClient,
-    doc_id: str,
-    prompt: str,
-    verbose: bool = False,
-) -> str:
     """Run a document QA agent using the OpenAI Agents SDK.
 
     Streams text output token-by-token and returns the full answer string.
@@ -84,42 +80,50 @@ def query_agent(
         instructions=AGENT_SYSTEM_PROMPT,
         tools=[get_document, get_document_structure, get_page_content],
         model=client.retrieve_model,
+        # model_settings=ModelSettings(reasoning={"effort": "low", "summary": "auto"}),  # Uncomment to enable reasoning
     )
 
     async def _run():
-        collected = []
-        streamed_this_turn = False
         streamed_run = Runner.run_streamed(agent, prompt)
+        current_stream_kind = None
         async for event in streamed_run.stream_events():
             if isinstance(event, RawResponsesStreamEvent):
                 if isinstance(event.data, ResponseReasoningSummaryTextDeltaEvent):
-                    print(event.data.delta, end="", flush=True)
+                    if current_stream_kind != "reasoning":
-                elif isinstance(event.data, ResponseTextDeltaEvent):
+                        if current_stream_kind is not None:
+                            print()
+                        print("\n[reasoning]: ", end="", flush=True)
                     delta = event.data.delta
                     print(delta, end="", flush=True)
-                    collected.append(delta)
+                    current_stream_kind = "reasoning"
-                    streamed_this_turn = True
+                elif isinstance(event.data, ResponseTextDeltaEvent):
+                    if current_stream_kind != "text":
+                        if current_stream_kind is not None:
+                            print()
+                        print("\n[text]: ", end="", flush=True)
+                    delta = event.data.delta
+                    print(delta, end="", flush=True)
+                    current_stream_kind = "text"
             elif isinstance(event, RunItemStreamEvent):
                 item = event.item
-                if item.type == "message_output_item":
+                if item.type == "tool_call_item":
-                    if not streamed_this_turn:
+                    if current_stream_kind is not None:
-                        text = ItemHelpers.text_message_output(item)
+                        print()
-                        if text:
-                            print(f"{text}")
-                    streamed_this_turn = False
-                    collected.clear()
-                elif item.type == "tool_call_item":
-                    if streamed_this_turn:
-                        print()  # end streaming line before tool call
                     raw = item.raw_item
                     args = getattr(raw, "arguments", "{}")
                     args_str = f"({args})" if verbose else ""
-                    print(f"[tool call]: {raw.name}{args_str}")
+                    print(f"\n[tool call]: {raw.name}{args_str}", flush=True)
+                    current_stream_kind = None
                 elif item.type == "tool_call_output_item" and verbose:
+                    if current_stream_kind is not None:
+                        print()
                     output = str(item.output)
                     preview = output[:200] + "..." if len(output) > 200 else output
-                    print(f"[tool output]: {preview}\n")
+                    print(f"\n[tool call output]: {preview}", flush=True)
-        return "".join(collected)
+                    current_stream_kind = None
+        if current_stream_kind is not None:
+            print()
+        return "" if not streamed_run.final_output else str(streamed_run.final_output)
 
     try:
         asyncio.get_running_loop()
@@ -129,7 +133,9 @@ def query_agent(
         return asyncio.run(_run())
 
 
-# ── Download PDF if needed ─────────────────────────────────────────────────────
+if __name__ == "__main__":
+
+    # Download PDF if needed
     if not os.path.exists(PDF_PATH):
         print(f"Downloading {PDF_URL} ...")
         os.makedirs(os.path.dirname(PDF_PATH), exist_ok=True)
@@ -141,15 +147,17 @@ if not os.path.exists(PDF_PATH):
                         f.write(chunk)
         print("Download complete.\n")
 
-# ── Setup ──────────────────────────────────────────────────────────────────────
+    # Setup
     client = PageIndexClient(workspace=WORKSPACE)
 
-# ── Step 1: Index + Tree ───────────────────────────────────────────────────────
+    # Step 1: Index + Tree
     print("=" * 60)
     print("Step 1: Indexing PDF and inspecting tree structure")
     print("=" * 60)
-doc_id = next((did for did, doc in client.documents.items()
+    doc_id = next(
-                if doc.get('doc_name') == os.path.basename(PDF_PATH)), None)
+        (did for did, doc in client.documents.items() if doc.get('doc_name') == os.path.basename(PDF_PATH)),
+        None,
+    )
     if doc_id:
         print(f"\nLoaded cached doc_id: {doc_id}")
     else:
@@ -159,16 +167,17 @@ print("\nTree Structure (top-level sections):")
     structure = json.loads(client.get_document_structure(doc_id))
     utils.print_tree(structure)
 
-# ── Step 2: Document Metadata ──────────────────────────────────────────────────
+    # Step 2: Document Metadata
     print("\n" + "=" * 60)
     print("Step 2: Document Metadata (get_document)")
     print("=" * 60)
-print(client.get_document(doc_id))
+    doc_metadata = client.get_document(doc_id)
+    print(f"\n{doc_metadata}")
 
-# ── Step 3: Agent Query ────────────────────────────────────────────────────────
+    # Step 3: Agent Query
     print("\n" + "=" * 60)
     print("Step 3: Agent Query (auto tool-use)")
     print("=" * 60)
     question = "Explain Attention Residuals in simple language."
-print(f"\nQuestion: '{question}'\n")
+    print(f"\nQuestion: '{question}'")
     query_agent(client, doc_id, question, verbose=True)