code_pipeline/data_construction/config_test.py

"""
测试配置：10 个用户试跑，验证 pipeline 是否正常工作
用法：LLM_CONFIG=config_test python -m phase_a_data_generation.step_a2_generate_sessions
或者直接把 config.py 替换为本文件内容
"""
import os
from pathlib import Path

# === Paths ===
PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
DATA_DIR = Path("/mnt/train-gui-agent/zhangzeyu/mem_memory/raw_data_construction/LongMemEval/data/custom_history_data")
OUTPUT_DIR = Path(__file__).resolve().parent / "output"
ERROR_DIR = OUTPUT_DIR / "errors"

# === LLM Configuration ===
LLM_PROVIDER = os.getenv("LLM_PROVIDER", "mify")
LLM_BASE_URL = os.getenv("LLM_BASE_URL", None)
LLM_API_KEY = os.getenv("LLM_API_KEY", None)

LLM_MODEL_GENERATION = os.getenv("LLM_MODEL_GENERATION", "gpt-5.5")
LLM_MODEL_PREDICTION = os.getenv("LLM_MODEL_PREDICTION", "gpt-5.5")
LLM_MODEL_TEACHER = os.getenv("LLM_MODEL_TEACHER", "gpt-5.5")

# === Concurrency ===
MAX_WORKERS = int(os.getenv("MAX_WORKERS", "4"))  # 试跑用 4 线程就够

# === Data Scale (试跑: 10 用户) ===
NUM_USERS = 10
QA_PER_USER_AVG = 6.0
TOTAL_QA_TARGET = 60             # 10 × 6
NUM_FILLERS = 200                # 试跑不需要太多 filler
FILLER_TO_EVIDENCE_RATIO = 4
SESSIONS_PER_USER_EVIDENCE = 5
SESSIONS_PER_USER_TANGENTIAL = 3

# === Query Type Distribution ===
QUERY_TYPES = [
    "single_hop",
    "knowledge_update",
    "multi_session_synthesis",
    "two_hop",
    "temp_reasoning_implicit",
    "temp_reasoning_explicit",
    "implicit_preference",
    "assistant_previnfo",
]

# 试跑: 每类均分，总和 = 60
QA_PER_TYPE = {
    "single_hop":              10,
    "assistant_previnfo":      10,
    "implicit_preference":     8,
    "two_hop":                 8,
    "multi_session_synthesis": 8,
    "knowledge_update":        6,
    "temp_reasoning_implicit": 5,
    "temp_reasoning_explicit": 5,
}
assert sum(QA_PER_TYPE.values()) == TOTAL_QA_TARGET, \
    f"QA_PER_TYPE sum {sum(QA_PER_TYPE.values())} != TOTAL_QA_TARGET {TOTAL_QA_TARGET}"
assert set(QA_PER_TYPE.keys()) == set(QUERY_TYPES), "QA_PER_TYPE keys must match QUERY_TYPES"

PER_USER_QA_CAP = int(os.getenv("PER_USER_QA_CAP", "8"))

# === Generation Parameters ===
PRED_TEMPERATURE = 0.7
PRED_NUM_SAMPLES = 3
TEACHER_NUM_VOTES = 3
GENERATION_TEMPERATURE = 0.9
MAX_RETRIES = 3

MAX_TOKENS_GENERATION = 8192
MAX_TOKENS_GENERATION_DUAL = 16384

# === Inline Check Pipeline ===
INLINE_CHECK = os.getenv("INLINE_CHECK", "1") == "1"

# === Filtering Thresholds ===
VOTE_AGREEMENT_THRESHOLD = 0.67
CONFIDENCE_THRESHOLD = 0.60
LOW_CONF_WEIGHT = 0.3

# === Retrieval ===
BM25_TOP_K = 10

# === Output Files ===
OUTPUT_A1 = OUTPUT_DIR / "a1_user_profiles.json"
OUTPUT_A1_STRICT = OUTPUT_DIR / "a1_user_profiles_strict.json"
OUTPUT_A2 = OUTPUT_DIR / "a2_user_sessions.json"
OUTPUT_A2_STRICT = OUTPUT_DIR / "a2_user_sessions_strict.json"
OUTPUT_A3 = OUTPUT_DIR / "a3_qa_pairs.json"
OUTPUT_A3_STRICT = OUTPUT_DIR / "a3_qa_pairs_strict.json"
OUTPUT_A4 = OUTPUT_DIR / "a4_filler_sessions.json"
OUTPUT_A5 = OUTPUT_DIR / "a5_user_data.json"
OUTPUT_B1 = OUTPUT_DIR / "b1_predictions.json"
OUTPUT_B2 = OUTPUT_DIR / "b2_ms_labels.json"
OUTPUT_B3 = OUTPUT_DIR / "b3_rewrite_queries.json"
OUTPUT_B4 = OUTPUT_DIR / "b4_validated.json"
OUTPUT_B5 = OUTPUT_DIR / "metamem_train.json"