Refactor Hint Retrieval

experiments/generic/run_generic_agent.py

@@ -0,0 +1,65 @@
+import argparse
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import argparse
+import logging
+
+from agentlab.agents.generic_agent.tmlr_config import get_base_agent
+from agentlab.experiments.study import Study
+from bgym import DEFAULT_BENCHMARKS
+
+logging.getLogger().setLevel(logging.WARNING)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--benchmark", required=True)
+    parser.add_argument("--llm-config", required=True)
+    parser.add_argument("--relaunch", action="store_true")
+    parser.add_argument("--n-jobs", type=int, default=5)
+    parser.add_argument("--n-relaunch", type=int, default=3)
+    parser.add_argument("--parallel-backend", type=str, default="ray")
+    parser.add_argument("--reproducibility-mode", action="store_true")
+
+    args = parser.parse_args()
+
+    # instantiate agent
+    agent_args = [get_base_agent(args.llm_config)]
+    benchmark = DEFAULT_BENCHMARKS[args.benchmark]()
+
+    ##################### Shuffle env args list, pick subset
+    import numpy as np
+    rng = np.random.default_rng(42)
+    rng.shuffle(benchmark.env_args_list)
+    benchmark.env_args_list = benchmark.env_args_list[:33]
+    #####################
+
+    # for env_args in benchmark.env_args_list:
+        # env_args.max_steps = 100
+
+    if args.relaunch:
+        #  relaunch an existing study
+        study = Study.load_most_recent(contains=None)
+        study.find_incomplete(include_errors=True)
+
+    else:
+        study = Study(
+            agent_args,
+            benchmark,
+            logging_level=logging.WARNING,
+            logging_level_stdout=logging.WARNING,
+        )
+
+    study.run(
+        n_jobs=args.n_jobs,
+        parallel_backend="ray",
+        strict_reproducibility=args.reproducibility_mode,
+        n_relaunch=args.n_relaunch,
+    )
+
+
+if __name__ == "__main__":
+    main()

experiments/generic/run_generic_agent.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+BENCHMARK="workarena_l1"
+
+LLM_CONFIG="azure/gpt-5-mini-2025-08-07"
+# PARALLEL_BACKEND="sequential"
+PARALLEL_BACKEND="ray"
+
+N_JOBS=5
+N_RELAUNCH=3
+
+python experiments/generic/run_generic_agent.py \
+    --benchmark $BENCHMARK \
+    --llm-config $LLM_CONFIG \
+    --parallel-backend $PARALLEL_BACKEND \
+    --n-jobs $N_JOBS \
+    --n-relaunch $N_RELAUNCH

experiments/hinter/run_hinter_agent.py

@@ -0,0 +1,84 @@
+
+from dotenv import load_dotenv
+import argparse
+
+load_dotenv()
+
+import logging
+import argparse
+
+from agentlab.agents.generic_agent_hinter.generic_agent import GenericAgentArgs
+from agentlab.agents.generic_agent_hinter.agent_configs import CHAT_MODEL_ARGS_DICT, FLAGS_GPT_4o
+from bgym import DEFAULT_BENCHMARKS
+from agentlab.experiments.study import Study
+
+logging.getLogger().setLevel(logging.WARNING)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--benchmark", required=True)
+    parser.add_argument("--llm-config", required=True)
+    parser.add_argument("--relaunch", action="store_true")
+    parser.add_argument("--n-jobs", type=int, default=6)
+    parser.add_argument("--parallel-backend", type=str, default="ray")
+    parser.add_argument("--reproducibility-mode", action="store_true")
+    # hint flags
+    parser.add_argument("--hint-type", type=str, default="docs")
+    parser.add_argument("--hint-index-type", type=str, default="sparse")
+    parser.add_argument("--hint-query-type", type=str, default="direct")
+    parser.add_argument("--hint-index-path", type=str, default="indexes/servicenow-docs-bm25")
+    parser.add_argument("--hint-retriever-path", type=str, default="google/embeddinggemma-300m")
+    parser.add_argument("--hint-num-results", type=int, default=5)
+    parser.add_argument("--debug", action="store_true")
+    args = parser.parse_args()
+
+    flags = FLAGS_GPT_4o
+    flags.use_task_hint = True
+    flags.hint_type = args.hint_type
+    flags.hint_index_type = args.hint_index_type
+    flags.hint_query_type = args.hint_query_type
+    flags.hint_index_path = args.hint_index_path
+    flags.hint_retriever_path = args.hint_retriever_path
+    flags.hint_num_results = args.hint_num_results
+
+    # instantiate agent
+    agent_args = [GenericAgentArgs(
+        chat_model_args=CHAT_MODEL_ARGS_DICT[args.llm_config],
+        flags=flags,
+    )]
+
+    benchmark = DEFAULT_BENCHMARKS[args.benchmark]()
+
+    if args.debug:
+        # shuffle env_args_list and 
+        import numpy as np
+        rng = np.random.default_rng(42)
+        rng.shuffle(benchmark.env_args_list)
+        benchmark.env_args_list = benchmark.env_args_list[:6]
+
+
+    if args.relaunch:
+        #  relaunch an existing study
+        study = Study.load_most_recent(contains=None)
+        study.find_incomplete(include_errors=True)
+
+    else:
+        study = Study(
+            agent_args,
+            benchmark,
+            logging_level=logging.WARNING,
+            logging_level_stdout=logging.WARNING,
+        )
+
+    study.run(
+        n_jobs=args.n_jobs,
+        parallel_backend=args.parallel_backend,
+        strict_reproducibility=args.reproducibility_mode,
+        n_relaunch=3,
+    )
+
+
+
+if __name__ == "__main__":
+    main()

experiments/hinter/run_hinter_agent.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+BENCHMARK="workarena_l1"
+
+LLM_CONFIG="azure/gpt-5-mini-2025-08-07"
+# PARALLEL_BACKEND="sequential"
+PARALLEL_BACKEND="ray"
+
+HINT_TYPE="docs"    # human, llm, docs
+HINT_INDEX_TYPE="sparse" # sparse, dense
+HINT_QUERY_TYPE="goal" # goal, llm
+HINT_NUM_RESULTS=3
+
+HINT_INDEX_PATH="indexes/servicenow-docs-bm25"
+# HINT_INDEX_PATH="indexes/servicenow-docs-embeddinggemma-300m"
+HINT_RETRIEVER_PATH="google/embeddinggemma-300m"
+
+N_JOBS=6
+
+python experiments/hinter/run_hinter_agent.py \
+    --benchmark $BENCHMARK \
+    --llm-config $LLM_CONFIG \
+    --parallel-backend $PARALLEL_BACKEND \
+    --n-jobs $N_JOBS \
+    --hint-type $HINT_TYPE \
+    --hint-index-type $HINT_INDEX_TYPE \
+    --hint-query-type $HINT_QUERY_TYPE \
+    --hint-index-path $HINT_INDEX_PATH \
+    --hint-retriever-path $HINT_RETRIEVER_PATH \
+    --hint-num-results $HINT_NUM_RESULTS