Information retrieval challenge (#4456)

* test: add information retrieval challenge b

* test: get information retrieval challenge be working.

* chore: clean up comments and imports.

* chore: fix incorrect import

* chore: clean up imports.

* fix: add web_selenium cmd. resolve missing loop cycle

* chore: remove commented code and unused imports.

* fix (4261): use 2 cycles instead of 3

* chore: fix mypy formatting

* chore: try 2 for mypy formatting

* chore: resolve flake8 issues

* chore: add docs

* chore: resolve linting flake8

* chore: correct formatting to black

* Update challenge_b.md

* refactored challenge

---------

Co-authored-by: PortlandKyGuy <kyleaaron1@gmail.com>

docs/challenges/information_retrieval/challenge_b.md

@@ -0,0 +1,22 @@
+# Information Retrieval Challenge B
+
+**Status**: Beaten
+
+**Command to try**:
+
+```
+pytest -s tests/integration/challenges/information_retrieval/test_information_retrieval_challenge_b.py
+```
+
+## Description
+
+The agent's goal is to find the names, affiliated university, and discovery of the individuals who won the nobel prize for physics in 2010.
+
+It should write the result in a file called 2010_nobel_prize_winners.txt.
+
+The agent should be able to beat this test consistently (this is the hardest part).
+
+## Objective
+
+The objective of this challenge is to test the agent's ability to retrieve multiple pieces of related information in a consistent way.
+The agent should not use google to perform the task, because it should already know the answer. This why the task fails after 2 cycles (1 cycle to retrieve information, 1 cycle to write the file)

mkdocs.yml

@@ -28,6 +28,7 @@ nav:
       - Information retrieval:
         - Introduction: challenges/information_retrieval/introduction.md
         - Information Retrieval Challenge A: challenges/information_retrieval/challenge_a.md
+        - Information Retrieval Challenge B: challenges/information_retrieval/challenge_b.md
   - Submit a Challenge: challenges/submit.md
   - Beat a Challenge: challenges/beat.md
 

tests/integration/agent_factory.py

@@ -202,3 +202,36 @@ def kubernetes_agent(memory_json_file, workspace: Workspace):
     )
 
     return agent
+
+
+@pytest.fixture
+def get_nobel_prize_agent(agent_test_config, memory_json_file, workspace: Workspace):
+    command_registry = CommandRegistry()
+    command_registry.import_commands("autogpt.commands.file_operations")
+    command_registry.import_commands("autogpt.app")
+    command_registry.import_commands("autogpt.commands.web_selenium")
+
+    ai_config = AIConfig(
+        ai_name="Get-PhysicsNobelPrize",
+        ai_role="An autonomous agent that specializes in physics history.",
+        ai_goals=[
+            "Write to file the winner's name(s), affiliated university, and discovery of the 2010 nobel prize in physics. Write your final answer to 2010_nobel_prize_winners.txt.",
+        ],
+    )
+    ai_config.command_registry = command_registry
+
+    system_prompt = ai_config.construct_full_prompt()
+    Config().set_continuous_mode(False)
+
+    agent = Agent(
+        ai_name="Get-PhysicsNobelPrize",
+        memory=memory_json_file,
+        command_registry=command_registry,
+        config=ai_config,
+        next_action_count=0,
+        system_prompt=system_prompt,
+        triggering_prompt=DEFAULT_TRIGGERING_PROMPT,
+        workspace_directory=workspace.root,
+    )
+
+    return agent

tests/integration/challenges/current_score.json

@@ -13,6 +13,10 @@
         "information_retrieval_challenge_a": {
             "max_level": 1,
             "max_level_beaten": 1
+        },
+        "information_retrieval_challenge_b": {
+            "max_level": 1,
+            "max_level_beaten": 1
         }
     },
     "kubernetes": {
@@ -35,4 +39,4 @@
             "max_level_beaten": 1
         }
     }
-}
+}

tests/integration/challenges/information_retrieval/test_information_retrieval_challenge_b.py

@@ -0,0 +1,51 @@
+import contextlib
+
+import pytest
+
+from autogpt.agent import Agent
+from autogpt.commands.file_operations import read_file
+from autogpt.config import Config
+from tests.integration.challenges.challenge_decorator.challenge_decorator import (
+    challenge,
+)
+from tests.integration.challenges.utils import run_interaction_loop
+from tests.utils import requires_api_key
+
+CYCLE_COUNT = 3
+
+
+@pytest.mark.vcr
+@requires_api_key("OPENAI_API_KEY")
+@challenge
+def test_information_retrieval_challenge_b(
+    get_nobel_prize_agent: Agent,
+    monkeypatch: pytest.MonkeyPatch,
+    patched_api_requestor: None,
+    level_to_run: int,
+    config: Config,
+) -> None:
+    """
+    Test the challenge_b function in a given agent by mocking user inputs and checking the output file content.
+
+    :param get_nobel_prize_agent: The agent to test.
+    :param monkeypatch: pytest's monkeypatch utility for modifying builtins.
+    :param patched_api_requestor: APIRequestor Patch to override the openai.api_requestor module for testing.
+    :param level_to_run: The level to run.
+    :param config: The config object.
+    """
+
+    with contextlib.suppress(SystemExit):
+        run_interaction_loop(monkeypatch, get_nobel_prize_agent, CYCLE_COUNT)
+
+    file_path = str(
+        get_nobel_prize_agent.workspace.get_path("2010_nobel_prize_winners.txt")
+    )
+    content = read_file(file_path, config)
+    assert "Andre Geim" in content, "Expected the file to contain Andre Geim"
+    assert (
+        "Konstantin Novoselov" in content
+    ), "Expected the file to contain Konstantin Novoselov"
+    assert (
+        "University of Manchester" in content
+    ), "Expected the file to contain University of Manchester"
+    assert "graphene" in content, "Expected the file to contain graphene"