File size: 3,146 Bytes
b2c86fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import json
import os
from datetime import datetime
from pathlib import Path
from biomni.agent import A1
PROJECT_ROOT = Path(__file__).resolve().parent
RESULTS_DIR = PROJECT_ROOT / "paper_repro_foxj1" / "results"
MCP_CONFIG_PATH = PROJECT_ROOT / "mcp_config_shim.yaml"
QUERY = """Use the processed GEO files in
paper_repro_foxj1/data
Only do these four things:
1. data loading and schema inspection
2. reproduce the core differential expression findings from resistant vs sensitive comparisons
3. run GSEA / pathway enrichment for the LuCaP35CR resistant signature
4. partially reproduce the FOXJ1 overexpression model from the processed differential expression file
Do not perform external cohort analysis, DepMap analysis, or LP-WGS analysis.
Save all intermediate and final outputs under
paper_repro_foxj1/results
In the final answer, summarize results by these four sections and include output file paths."""
def save_run_outputs(log_entries: list[str], answer: str) -> dict[str, str]:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
run_dir = RESULTS_DIR / f"main_run_{timestamp}"
run_dir.mkdir(parents=True, exist_ok=True)
log_txt_path = run_dir / "main_execution_log.txt"
log_json_path = run_dir / "main_execution_log.json"
answer_path = run_dir / "main_final_answer.txt"
metadata_path = run_dir / "main_run_metadata.json"
log_txt_path.write_text("\n\n".join(str(entry) for entry in log_entries), encoding="utf-8")
log_json_path.write_text(
json.dumps({"log_entries": log_entries}, ensure_ascii=False, indent=2),
encoding="utf-8",
)
answer_path.write_text(answer, encoding="utf-8")
metadata_path.write_text(
json.dumps(
{
"timestamp": timestamp,
"mcp_config_path": str(MCP_CONFIG_PATH),
"results_dir": str(RESULTS_DIR),
"run_dir": str(run_dir),
"query": QUERY,
"log_entry_count": len(log_entries),
"log_txt_path": str(log_txt_path),
"log_json_path": str(log_json_path),
"answer_path": str(answer_path),
},
ensure_ascii=False,
indent=2,
),
encoding="utf-8",
)
return {
"run_dir": str(run_dir),
"log_txt": str(log_txt_path),
"log_json": str(log_json_path),
"answer_txt": str(answer_path),
"metadata_json": str(metadata_path),
}
agent = A1(
path="./data",
llm="deepseek-chat",
source="Custom",
base_url="https://api.deepseek.com/v1",
api_key=os.getenv("DEEPSEEK_API_KEY", "EMPTY"),
expected_data_lake_files=[],
rewrite_user_query=True,
dynamic_mcp_registration=True,
mcp_server_top_k=20,
mcp_tool_top_k=12,
)
# 这里传你那份包含大量 MCP servers 的总配置
agent.attach_mcp_catalog(MCP_CONFIG_PATH)
log, answer = agent.go(QUERY)
artifact_paths = save_run_outputs(log, answer)
print("\n===== SAVED ARTIFACTS =====")
for name, path in artifact_paths.items():
print(f"{name}: {path}")
print("\n===== FINAL ANSWER =====\n")
print(answer)
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