PageIndex/examples/agentic_vectorless_rag_demo.py

"""
PageIndex x OpenAI Agents Demo

Demonstrates how to use PageIndexClient with the OpenAI Agents SDK
to build a document QA agent with 3 tools:
  - get_document()
  - get_document_structure()
  - get_page_content()

Requirements:
    pip install openai-agents

Steps:
  1 — Index PDF and inspect tree structure
  2 — Inspect document metadata
  3 — Ask a question (agent auto-calls tools)
"""
import os
import sys
import json
import asyncio
import concurrent.futures
import requests

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from agents import Agent, Runner, function_tool, set_tracing_disabled
from agents.model_settings import ModelSettings
from agents.stream_events import RawResponsesStreamEvent, RunItemStreamEvent
from openai.types.responses import ResponseTextDeltaEvent, ResponseReasoningSummaryTextDeltaEvent

from pageindex import PageIndexClient
import pageindex.utils as utils

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")

AGENT_SYSTEM_PROMPT = """
You are PageIndex, a document QA assistant.
TOOL USE:
- Call get_document() first to confirm status and page/line count.
- Call get_document_structure() to find relevant page ranges (use node summaries and start_index/end_index).
- Call get_page_content(pages="5-7") with tight ranges. Never fetch the whole doc.
- When calling tool call, output one short sentence explaining reason.
ANSWERING: Answer based only on tool output. Be concise.
"""


def query_agent(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.
    Tool calls are always printed; verbose=True also prints arguments and output previews.
    """

    @function_tool
    def get_document() -> str:
        """Get document metadata: status, page count, name, and description."""
        return client.get_document(doc_id)

    @function_tool
    def get_document_structure() -> str:
        """Get the document's full tree structure (without text) to find relevant sections."""
        return client.get_document_structure(doc_id)

    @function_tool
    def get_page_content(pages: str) -> str:
        """
        Get the text content of specific pages or line numbers.
        Use tight ranges: e.g. '5-7' for pages 5 to 7, '3,8' for pages 3 and 8, '12' for page 12.
        For Markdown documents, use line numbers from the structure's line_num field.
        """
        return client.get_page_content(doc_id, pages)

    agent = Agent(
        name="PageIndex",
        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():
        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):
                    if current_stream_kind != "reasoning":
                        if current_stream_kind is not None:
                            print()
                        print("\n[reasoning]: ", end="", flush=True)
                    delta = event.data.delta
                    print(delta, end="", flush=True)
                    current_stream_kind = "reasoning"
                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 == "tool_call_item":
                    if current_stream_kind is not None:
                        print()
                    raw = item.raw_item
                    args = getattr(raw, "arguments", "{}")
                    args_str = f"({args})" if verbose else ""
                    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"\n[tool call output]: {preview}", flush=True)
                    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()
        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
            return pool.submit(asyncio.run, _run()).result()
    except RuntimeError:
        return asyncio.run(_run())


if __name__ == "__main__":

    set_tracing_disabled(True)

    # 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)
        with requests.get(PDF_URL, stream=True, timeout=30) as r:
            r.raise_for_status()
            with open(PDF_PATH, "wb") as f:
                for chunk in r.iter_content(chunk_size=8192):
                    if chunk:
                        f.write(chunk)
        print("Download complete.\n")

    # Setup
    client = PageIndexClient(workspace=WORKSPACE)

    # 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() if doc.get('doc_name') == os.path.basename(PDF_PATH)),
        None,
    )
    if doc_id:
        print(f"\nLoaded cached doc_id: {doc_id}")
    else:
        doc_id = client.index(PDF_PATH)
        print(f"\nIndexed. doc_id: {doc_id}")
    print("\nTree Structure (top-level sections):")
    structure = json.loads(client.get_document_structure(doc_id))
    utils.print_tree(structure)

    # Step 2: Document Metadata
    print("\n" + "=" * 60)
    print("Step 2: Document Metadata (get_document)")
    print("=" * 60)
    doc_metadata = client.get_document(doc_id)
    print(f"\n{doc_metadata}")

    # 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}'")
    query_agent(client, doc_id, question, verbose=True)