chore: import upstream snapshot with attribution

2026-07-13 12:39:17 +08:00
commit 4ed4e9ff99
1368 changed files with 334957 additions and 0 deletions
@@ -0,0 +1,146 @@
+"""
+Answer questions over a synthetic dataroom.
+"""
+
+import argparse
+import asyncio
+import sys
+from pathlib import Path
+from textwrap import dedent
+
+from agents import Runner, RunResultStreaming, TResponseInputItem
+from agents.run import RunConfig
+from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
+from agents.sandbox.capabilities import Shell
+from agents.sandbox.entries import File, LocalDir
+
+if __package__ is None or __package__ == "":
+    sys.path.insert(0, str(Path(__file__).resolve().parents[4]))
+
+from examples.sandbox.tutorials.misc import (
+    DEFAULT_SANDBOX_IMAGE,
+    create_sandbox_client_and_session,
+    load_env_defaults,
+    print_event,
+    run_interactive_loop,
+)
+
+DEMO_DIR = Path(__file__).resolve().parent
+DATAROOM_DATA_DIR = DEMO_DIR.parent / "data" / "dataroom"
+DEFAULT_QUESTION = (
+    "How did revenue, gross margin, operating income, and operating cash flow change in "
+    "FY2025 versus FY2024, and which segment contributed the most revenue?"
+)
+AGENTS_MD = dedent(
+    """\
+    # AGENTS.md
+
+    Answer the user's financial question using only the synthetic 10-K packet in `data/`.
+
+    ## Evidence & citations
+
+    - Cite every material claim with markdown links in these formats (no bare links):
+      - `[1](data/source-file.txt:line:14)` for text sources
+      - `[2](data/source-file.pdf:page:1)` for PDF sources (each synthetic PDF is one page)
+    - Use `rg` and `sed` to find and quote exact evidence; do not use `data/setup.py`.
+
+    Keep the final answer direct and finance-oriented.
+    """
+)
+
+
+async def print_streamed_result(result: RunResultStreaming) -> list[TResponseInputItem]:
+    async for event in result.stream_events():
+        print_event(event)
+    print_event(str(result.final_output).strip())
+    return result.to_input_list()
+
+
+async def main(
+    model: str, question: str, use_docker: bool, image: str, no_interactive: bool
+) -> None:
+    if not (DATAROOM_DATA_DIR / "10-k-mdna-overview.txt").exists():
+        raise SystemExit(
+            "Run `uv run python examples/sandbox/tutorials/data/dataroom/setup.py` "
+            "before starting this demo."
+        )
+
+    manifest = Manifest(
+        entries={
+            "AGENTS.md": File(content=AGENTS_MD.encode("utf-8")),
+            "data": LocalDir(src=DATAROOM_DATA_DIR),
+        }
+    )
+    agent = SandboxAgent(
+        name="Dataroom Analyst",
+        model=model,
+        instructions=AGENTS_MD,
+        capabilities=[Shell()],
+    )
+
+    client, sandbox = await create_sandbox_client_and_session(
+        manifest=manifest,
+        use_docker=use_docker,
+        image=image,
+    )
+    try:
+        async with sandbox:
+
+            async def run_turn(
+                conversation: list[TResponseInputItem],
+            ) -> list[TResponseInputItem]:
+                result = Runner.run_streamed(
+                    agent,
+                    conversation,
+                    max_turns=20,
+                    run_config=RunConfig(
+                        sandbox=SandboxRunConfig(session=sandbox),
+                        tracing_disabled=True,
+                        workflow_name="Dataroom Q&A example",
+                    ),
+                )
+                return await print_streamed_result(result)
+
+            conversation: list[TResponseInputItem] = [{"role": "user", "content": question}]
+            conversation = await run_turn(conversation)
+            await run_interactive_loop(
+                conversation=conversation,
+                no_interactive=no_interactive,
+                run_turn=run_turn,
+            )
+    finally:
+        await client.delete(sandbox)
+
+
+if __name__ == "__main__":
+    load_env_defaults(DEMO_DIR / ".env")
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model",
+        default="gpt-5.4-mini",
+        help="Model name to use.",
+    )
+    parser.add_argument(
+        "--question",
+        default=DEFAULT_QUESTION,
+        help="Prompt to send to the agent.",
+    )
+    parser.add_argument(
+        "--docker",
+        action="store_true",
+        help="Run this example in Docker instead of Unix-local.",
+    )
+    parser.add_argument(
+        "--image",
+        default=DEFAULT_SANDBOX_IMAGE,
+        help="Docker image to use when --docker is set.",
+    )
+    parser.add_argument(
+        "--no-interactive",
+        action="store_true",
+        help="Run the scripted turn and skip follow-up terminal input.",
+    )
+    args = parser.parse_args()
+
+    asyncio.run(main(args.model, args.question, args.docker, args.image, args.no_interactive))